diff --git a/.github/workflows/build-docker.yml b/.github/workflows/build-docker.yml index d2869ed..d77958b 100644 --- a/.github/workflows/build-docker.yml +++ b/.github/workflows/build-docker.yml @@ -2,7 +2,7 @@ name: CD on: push: - branches: [ "main" ] + branches: [ "master" ] workflow_dispatch: # run manually jobs: diff --git a/.github/workflows/pypi.yml b/.github/workflows/pypi.yml deleted file mode 100644 index 248f4ef..0000000 --- a/.github/workflows/pypi.yml +++ /dev/null @@ -1,24 +0,0 @@ -name: PyPI CD - -on: - release: - types: [published] - workflow_dispatch: - -jobs: - pypi-publish: - name: upload release to PyPI - runs-on: ubuntu-latest - permissions: - # IMPORTANT: this permission is mandatory for Trusted Publishing - id-token: write - steps: - - uses: actions/checkout@v4 - - name: Set up Python - uses: actions/setup-python@v5 - - name: Build package - run: make build - - name: Publish package distributions to PyPI - uses: pypa/gh-action-pypi-publish@release/v1 - with: - verbose: true diff --git a/.github/workflows/test-eynollah.yml b/.github/workflows/test-eynollah.yml index 82de94d..b27586c 100644 --- a/.github/workflows/test-eynollah.yml +++ b/.github/workflows/test-eynollah.yml @@ -24,59 +24,36 @@ jobs: sudo rm -rf "$AGENT_TOOLSDIRECTORY" df -h - uses: actions/checkout@v4 - - # - name: Lint with ruff - # uses: astral-sh/ruff-action@v3 - # with: - # src: "./src" - - - name: Try to restore models_eynollah - uses: actions/cache/restore@v4 - id: all_model_cache + - uses: actions/cache@v4 + id: seg_model_cache with: path: models_eynollah - key: models_eynollah-${{ hashFiles('src/eynollah/model_zoo/default_specs.py') }} - + key: ${{ runner.os }}-models + - uses: actions/cache@v4 + id: bin_model_cache + with: + path: default-2021-03-09 + key: ${{ runner.os }}-modelbin - name: Download models - if: steps.all_model_cache.outputs.cache-hit != 'true' - run: | - make models - ls -la models_eynollah - - - uses: actions/cache/save@v4 - if: steps.all_model_cache.outputs.cache-hit != 'true' - with: - path: models_eynollah - key: models_eynollah-${{ hashFiles('src/eynollah/model_zoo/default_specs.py') }} - + if: steps.seg_model_cache.outputs.cache-hit != 'true' || steps.bin_model_cache.outputs.cache-hit != 'true' + run: make models - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@v5 with: python-version: ${{ matrix.python-version }} - - # - uses: actions/cache@v4 - # with: - # path: | - # path/to/dependencies - # some/other/dependencies - # key: ${{ runner.os }}-${{ hashFiles('**/lockfiles') }} - - name: Install dependencies run: | python -m pip install --upgrade pip make install-dev EXTRAS=OCR,plotting - make deps-test EXTRAS=OCR,plotting - + make deps-test - name: Test with pytest run: make coverage PYTEST_ARGS="-vv --junitxml=pytest.xml" - - name: Get coverage results run: | coverage report --format=markdown >> $GITHUB_STEP_SUMMARY coverage html coverage json coverage xml - - name: Store coverage results uses: actions/upload-artifact@v4 with: @@ -86,15 +63,12 @@ jobs: pytest.xml coverage.xml coverage.json - - name: Upload coverage results uses: codecov/codecov-action@v4 with: files: coverage.xml fail_ci_if_error: false - - name: Test standalone CLI run: make smoke-test - - name: Test OCR-D CLI run: make ocrd-test diff --git a/.gitignore b/.gitignore index 49835a7..5236dde 100644 --- a/.gitignore +++ b/.gitignore @@ -2,13 +2,6 @@ __pycache__ sbb_newspapers_org_image/pylint.log models_eynollah* -models_ocr* -models_layout* -default-2021-03-09 output.html /build /dist -*.tif -*.sw? -TAGS -uv.lock diff --git a/CHANGELOG.md b/CHANGELOG.md index 2848d21..f7ce6bb 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -5,147 +5,12 @@ Versioned according to [Semantic Versioning](http://semver.org/). ## Unreleased -Added: - - * "Model zoo", central place to describe and load models, #207 - * Training code for the CNN/RNN OCR model - -Changed: - - * Lint training code, #204 - * Update documentation: README, pyproject.toml metadata, guides in `docs/`, #209 - - -## [0.6.0] - 2025-10-17 - -Added: - - * `eynollah-training` CLI and docs for training the models, #187, #193, https://github.com/qurator-spk/sbb_pixelwise_segmentation/tree/unifying-training-models - -Fixed: - - * `join_polygons` always returning Polygon, not MultiPolygon, #203 - -## [0.6.0rc2] - 2025-10-14 - -Fixed: - - * Prevent OOM GPU error by avoiding loading the `region_fl` model, #199 - * XML output: encoding should be `utf-8`, not `utf8`, #196, #197 - -## [0.6.0rc1] - 2025-10-10 - -Fixed: - - * continue processing when no columns detected but text regions exist - * convert marginalia to main text if no main text is present - * reset deskewing angle to 0° when text covers <30% image area and detected angle >45° - * :fire: polygons: avoid invalid paths (use `Polygon.buffer()` instead of dilation etc.) - * `return_boxes_of_images_by_order_of_reading_new`: avoid Numpy.dtype mismatch, simplify - * `return_boxes_of_images_by_order_of_reading_new`: log any exceptions instead of ignoring - * `filter_contours_without_textline_inside`: avoid removing from duplicate lists twice - * `get_marginals`: exit early if no peaks found to avoid spurious overlap mask - * `get_smallest_skew`: after shifting search range of rotation angle, use overall best result - * Dockerfile: fix CUDA installation (cuDNN contested between Torch and TF due to extra OCR) - * OCR: re-instate missing methods and fix `utils_ocr` function calls - * mbreorder/enhancement CLIs: missing imports - * :fire: writer: `SeparatorRegion` needs `SeparatorRegionType` (not `ImageRegionType`), f458e3e - * tests: switch from `pytest-subtests` to `parametrize` so we can use `pytest-isolate` - (so CUDA memory gets freed between tests if running on GPU) - -Added: - * :fire: `layout` CLI: new option `--model_version` to override default choices - * test coverage for OCR options in `layout` - * test coverage for table detection in `layout` - * CI linting with ruff - -Changed: - - * polygons: slightly widen for regions and lines, increase for separators - * various refactorings, some code style and identifier improvements - * deskewing/multiprocessing: switch back to ProcessPoolExecutor (faster), - but use shared memory if necessary, and switch back from `loky` to stdlib, - and shutdown in `del()` instead of `atexit` - * :fire: OCR: switch CNN-RNN model to `20250930` version compatible with TF 2.12 on CPU, too - * OCR: allow running `-tr` without `-fl`, too - * :fire: writer: use `@type='heading'` instead of `'header'` for headings - * :fire: performance gains via refactoring (simplification, less copy-code, vectorization, - avoiding unused calculations, avoiding unnecessary 3-channel image operations) - * :fire: heuristic reading order detection: many improvements - - contour vs splitter box matching: - * contour must be contained in box exactly instead of heuristics - * make fallback center matching, center must be contained in box - - original vs deskewed contour matching: - * same min-area filter on both sides - * similar area score in addition to center proximity - * avoid duplicate and missing mappings by allowing N:M - matches and splitting+joining where necessary - * CI: update+improve model caching - - -## [0.5.0] - 2025-09-26 - -Fixed: - - * restoring the contour in the original image caused an error due to an empty tuple, #154 - * removed NumPy warnings calculating sigma, mean, (fixed issue #158) - * fixed bug in `separate_lines.py`, #124 - * Drop capitals are now handled separately from their corresponding textline - * Marginals are now divided into left and right. Their reading order is written first for left marginals, then for right marginals, and within each side from top to bottom - * Added a new page extraction model. Instead of bounding boxes, it outputs page contours in the XML file, improving results for skewed pages - * Improved reading order for cases where a textline is segmented into multiple smaller textlines - -Changed - - * CLIs: read only allowed filename suffixes (image or XML) with `--dir_in` - * CLIs: make all output option required, and `-i` / `-di` required but mutually exclusive - * ocr CLI: drop redundant `-brb` in favour of just `-dib` - * APIs: move all input/output path options from class (kwarg and attribute) ro `run` kwarg - * layout textlines: polygonal also without `-cl` - -Added: - - * `eynollah machine-based-reading-order` CLI to run reading order detection, #175 - * `eynollah enhancement` CLI to run image enhancement, #175 - * Improved models for page extraction and reading order detection, #175 - * For the lightweight version (layout and textline detection), thresholds are now assigned to the artificial class. Users can apply these thresholds to improve detection of isolated textlines and regions. To counteract the drawback of thresholding, the skeleton of the artificial class is used to keep lines as thin as possible (resolved issues #163 and #161) - * Added and integrated a trained CNN-RNN OCR models - * Added and integrated a trained TrOCR model - * Improved OCR detection to support vertical and curved textlines - * Introduced a new machine-based reading order model with rotation augmentation - * Optimized reading order speed by clustering text regions that belong to the same block, maintaining top-to-bottom order - * Implemented text merging across textlines based on hyphenation when a line ends with a hyphen - * Integrated image enhancement as a separate use case - * Added reading order functionality on the layout level as a separate use case - * CNN-RNN OCR models provide confidence scores for predictions - * Added OCR visualization: predicted OCR can be overlaid on an image of the same size as the input - * Introduced a threshold value for CNN-RNN OCR models, allowing users to filter out low-confidence textline predictions - * For OCR, users can specify a single model by name instead of always using the default model - * Under the OCR use case, if Ground Truth XMLs and images are available, textline image and corresponding text extraction can now be performed - -Merged PRs: - - * better machine based reading order + layout and textline + ocr by @vahidrezanezhad in https://github.com/qurator-spk/eynollah/pull/175 - * CI: pypi by @kba in https://github.com/qurator-spk/eynollah/pull/154 - * CI: Use most recent actions/setup-python@v5 by @kba in https://github.com/qurator-spk/eynollah/pull/157 - * update docker by @bertsky in https://github.com/qurator-spk/eynollah/pull/159 - * Ocrd fixes by @kba in https://github.com/qurator-spk/eynollah/pull/167 - * Updating readme for eynollah use cases cli by @kba in https://github.com/qurator-spk/eynollah/pull/166 - * OCR-D processor: expose reading_order_machine_based by @bertsky in https://github.com/qurator-spk/eynollah/pull/171 - * prepare release v0.5.0: fix logging by @bertsky in https://github.com/qurator-spk/eynollah/pull/180 - * mb_ro_on_layout: remove copy-pasta code not actually used by @kba in https://github.com/qurator-spk/eynollah/pull/181 - * prepare release v0.5.0: improve CLI docstring, refactor I/O path options from class to run kwargs, increase test coverage @bertsky in #182 - * prepare release v0.5.0: fix for OCR doit subtest by @bertsky in https://github.com/qurator-spk/eynollah/pull/183 - * Prepare release v0.5.0 by @kba in https://github.com/qurator-spk/eynollah/pull/178 - * updating eynollah README, how to use it for use cases by @vahidrezanezhad in https://github.com/qurator-spk/eynollah/pull/156 - * add feedback to command line interface by @michalbubula in https://github.com/qurator-spk/eynollah/pull/170 - ## [0.4.0] - 2025-04-07 Fixed: * allow empty imports for optional dependencies - * avoid Numpy warnings (empty slices etc.) + * avoid Numpy warnings (empty slices etc) * remove deprecated Numpy types * binarization CLI: make `dir_in` usable again @@ -318,12 +183,6 @@ Fixed: Initial release -[0.7.0]: ../../compare/v0.7.0...v0.6.0 -[0.6.0]: ../../compare/v0.6.0...v0.6.0rc2 -[0.6.0rc2]: ../../compare/v0.6.0rc2...v0.6.0rc1 -[0.6.0rc1]: ../../compare/v0.6.0rc1...v0.5.0 -[0.5.0]: ../../compare/v0.5.0...v0.4.0 -[0.4.0]: ../../compare/v0.4.0...v0.3.1 [0.3.1]: ../../compare/v0.3.1...v0.3.0 [0.3.0]: ../../compare/v0.3.0...v0.2.0 [0.2.0]: ../../compare/v0.2.0...v0.1.0 diff --git a/Dockerfile b/Dockerfile index a15776e..4785fc1 100644 --- a/Dockerfile +++ b/Dockerfile @@ -36,12 +36,8 @@ COPY . . COPY ocrd-tool.json . # prepackage ocrd-tool.json as ocrd-all-tool.json RUN ocrd ocrd-tool ocrd-tool.json dump-tools > $(dirname $(ocrd bashlib filename))/ocrd-all-tool.json -# prepackage ocrd-all-module-dir.json -RUN ocrd ocrd-tool ocrd-tool.json dump-module-dirs > $(dirname $(ocrd bashlib filename))/ocrd-all-module-dir.json # install everything and reduce image size RUN make install EXTRAS=OCR && rm -rf /build/eynollah -# fixup for broken cuDNN installation (Torch pulls in 8.5.0, which is incompatible with Tensorflow) -RUN pip install nvidia-cudnn-cu11==8.6.0.163 # smoke test RUN eynollah --help diff --git a/Makefile b/Makefile index c1458df..5f2bf34 100644 --- a/Makefile +++ b/Makefile @@ -3,22 +3,18 @@ PIP ?= pip3 EXTRAS ?= # DOCKER_BASE_IMAGE = artefakt.dev.sbb.berlin:5000/sbb/ocrd_core:v2.68.0 -DOCKER_BASE_IMAGE ?= docker.io/ocrd/core-cuda-tf2:latest -DOCKER_TAG ?= ocrd/eynollah -DOCKER ?= docker -WGET = wget -O +DOCKER_BASE_IMAGE = docker.io/ocrd/core-cuda-tf2:v3.3.0 +DOCKER_TAG = ocrd/eynollah #SEG_MODEL := https://qurator-data.de/eynollah/2021-04-25/models_eynollah.tar.gz #SEG_MODEL := https://qurator-data.de/eynollah/2022-04-05/models_eynollah_renamed.tar.gz -# SEG_MODEL := https://qurator-data.de/eynollah/2022-04-05/models_eynollah.tar.gz +SEG_MODEL := https://qurator-data.de/eynollah/2022-04-05/models_eynollah.tar.gz #SEG_MODEL := https://github.com/qurator-spk/eynollah/releases/download/v0.3.0/models_eynollah.tar.gz #SEG_MODEL := https://github.com/qurator-spk/eynollah/releases/download/v0.3.1/models_eynollah.tar.gz -#SEG_MODEL := https://zenodo.org/records/17194824/files/models_layout_v0_5_0.tar.gz?download=1 -EYNOLLAH_MODELS_URL := https://zenodo.org/records/17580627/files/models_all_v0_7_0.zip -EYNOLLAH_MODELS_ZIP = $(notdir $(EYNOLLAH_MODELS_URL)) -EYNOLLAH_MODELS_DIR = $(EYNOLLAH_MODELS_ZIP:%.zip=%) -PYTEST_ARGS ?= -vv --isolate +BIN_MODEL := https://github.com/qurator-spk/sbb_binarization/releases/download/v0.0.11/saved_model_2021_03_09.zip + +PYTEST_ARGS ?= -vv # BEGIN-EVAL makefile-parser --make-help Makefile @@ -31,8 +27,7 @@ help: @echo " install Install package with pip" @echo " install-dev Install editable with pip" @echo " deps-test Install test dependencies with pip" - @echo " models Download and extract models to $(CURDIR):" - @echo " $(EYNOLLAH_MODELS_DIR)" + @echo " models Download and extract models to $(CURDIR)/models_eynollah" @echo " smoke-test Run simple CLI check" @echo " ocrd-test Run OCR-D CLI check" @echo " test Run unit tests" @@ -41,22 +36,29 @@ help: @echo " EXTRAS comma-separated list of features (like 'OCR,plotting') for 'install' [$(EXTRAS)]" @echo " DOCKER_TAG Docker image tag for 'docker' [$(DOCKER_TAG)]" @echo " PYTEST_ARGS pytest args for 'test' (Set to '-s' to see log output during test execution, '-vv' to see individual tests. [$(PYTEST_ARGS)]" - @echo " ALL_MODELS URL of archive of all models [$(ALL_MODELS)]" + @echo " SEG_MODEL URL of 'models' archive to download for segmentation 'test' [$(SEG_MODEL)]" + @echo " BIN_MODEL URL of 'models' archive to download for binarization 'test' [$(BIN_MODEL)]" @echo "" # END-EVAL -# Download and extract models to $(PWD)/models_layout_v0_6_0 -models: $(EYNOLLAH_MODELS_DIR) -# do not download these files if we already have the directories -.INTERMEDIATE: $(EYNOLLAH_MODELS_ZIP) +# Download and extract models to $(PWD)/models_eynollah +models: models_eynollah default-2021-03-09 -$(EYNOLLAH_MODELS_ZIP): - $(WGET) $@ $(EYNOLLAH_MODELS_URL) +models_eynollah: models_eynollah.tar.gz + tar zxf models_eynollah.tar.gz -$(EYNOLLAH_MODELS_DIR): $(EYNOLLAH_MODELS_ZIP) - unzip $< +models_eynollah.tar.gz: + wget $(SEG_MODEL) + +default-2021-03-09: $(notdir $(BIN_MODEL)) + unzip $(notdir $(BIN_MODEL)) + mkdir $@ + mv $(basename $(notdir $(BIN_MODEL))) $@ + +$(notdir $(BIN_MODEL)): + wget $(BIN_MODEL) build: $(PIP) install build @@ -70,48 +72,41 @@ install: install-dev: $(PIP) install -e .$(and $(EXTRAS),[$(EXTRAS)]) -deps-test: +deps-test: models_eynollah $(PIP) install -r requirements-test.txt smoke-test: TMPDIR != mktemp -d -smoke-test: tests/resources/2files/kant_aufklaerung_1784_0020.tif +smoke-test: tests/resources/kant_aufklaerung_1784_0020.tif # layout analysis: - eynollah -m $(CURDIR) layout -i $< -o $(TMPDIR) + eynollah layout -i $< -o $(TMPDIR) -m $(CURDIR)/models_eynollah fgrep -q http://schema.primaresearch.org/PAGE/gts/pagecontent/2019-07-15 $(TMPDIR)/$(basename $( Document Layout Analysis with Deep Learning and Heuristics -> Document Layout Analysis, Binarization and OCR with Deep Learning and Heuristics - -[![Python Versions](https://img.shields.io/pypi/pyversions/eynollah.svg)](https://pypi.python.org/pypi/eynollah) [![PyPI Version](https://img.shields.io/pypi/v/eynollah)](https://pypi.org/project/eynollah/) [![GH Actions Test](https://github.com/qurator-spk/eynollah/actions/workflows/test-eynollah.yml/badge.svg)](https://github.com/qurator-spk/eynollah/actions/workflows/test-eynollah.yml) [![GH Actions Deploy](https://github.com/qurator-spk/eynollah/actions/workflows/build-docker.yml/badge.svg)](https://github.com/qurator-spk/eynollah/actions/workflows/build-docker.yml) @@ -12,22 +10,21 @@ ![](https://user-images.githubusercontent.com/952378/102350683-8a74db80-3fa5-11eb-8c7e-f743f7d6eae2.jpg) ## Features -* Document layout analysis using pixelwise segmentation models with support for 10 segmentation classes: +* Support for up to 10 segmentation classes: * background, [page border](https://ocr-d.de/en/gt-guidelines/trans/lyRand.html), [text region](https://ocr-d.de/en/gt-guidelines/trans/lytextregion.html#textregionen__textregion_), [text line](https://ocr-d.de/en/gt-guidelines/pagexml/pagecontent_xsd_Complex_Type_pc_TextLineType.html), [header](https://ocr-d.de/en/gt-guidelines/trans/lyUeberschrift.html), [image](https://ocr-d.de/en/gt-guidelines/trans/lyBildbereiche.html), [separator](https://ocr-d.de/en/gt-guidelines/trans/lySeparatoren.html), [marginalia](https://ocr-d.de/en/gt-guidelines/trans/lyMarginalie.html), [initial](https://ocr-d.de/en/gt-guidelines/trans/lyInitiale.html), [table](https://ocr-d.de/en/gt-guidelines/trans/lyTabellen.html) -* Textline segmentation to bounding boxes or polygons (contours) including for curved lines and vertical text -* Document image binarization with pixelwise segmentation or hybrid CNN-Transformer models -* Text recognition (OCR) with CNN-RNN or TrOCR models -* Detection of reading order (left-to-right or right-to-left) using heuristics or trainable models +* Support for various image optimization operations: + * cropping (border detection), binarization, deskewing, dewarping, scaling, enhancing, resizing +* Text line segmentation to bounding boxes or polygons (contours) including for curved lines and vertical text +* Detection of reading order (left-to-right or right-to-left) * Output in [PAGE-XML](https://github.com/PRImA-Research-Lab/PAGE-XML) * [OCR-D](https://github.com/qurator-spk/eynollah#use-as-ocr-d-processor) interface -:warning: Development is focused on achieving the best quality of results for a wide variety of historical -documents using a combination of multiple deep learning models and heuristics; therefore processing can be slow. +:warning: Development is currently focused on achieving the best possible quality of results for a wide variety of historical documents and therefore processing can be very slow. We aim to improve this, but contributions are welcome. ## Installation Python `3.8-3.11` with Tensorflow `<2.13` on Linux are currently supported. -For (limited) GPU support the CUDA toolkit needs to be installed. -A working config is CUDA `11.8` with cuDNN `8.6`. + +For (limited) GPU support the CUDA toolkit needs to be installed. You can either install from PyPI @@ -44,54 +41,19 @@ cd eynollah; pip install -e . Alternatively, you can run `make install` or `make install-dev` for editable installation. -To also install the dependencies for the OCR engines: - -``` -pip install "eynollah[OCR]" -# or -make install EXTRAS=OCR -``` - -### Docker - -Use - -``` -docker pull ghcr.io/qurator-spk/eynollah:latest -``` - -When using Eynollah with Docker, see [`docker.md`](https://github.com/qurator-spk/eynollah/tree/main/docs/docker.md). - ## Models +Pre-trained models can be downloaded from [qurator-data.de](https://qurator-data.de/eynollah/) or [huggingface](https://huggingface.co/SBB?search_models=eynollah). -Pretrained models can be downloaded from [Zenodo](https://zenodo.org/records/17194824) or [Hugging Face](https://huggingface.co/SBB?search_models=eynollah). +For documentation on methods and models, have a look at [`models.md`](https://github.com/qurator-spk/eynollah/tree/main/docs/models.md). -For model documentation and model cards, see [`models.md`](https://github.com/qurator-spk/eynollah/tree/main/docs/models.md). - -## Training - -To train your own model with Eynollah, see [`train.md`](https://github.com/qurator-spk/eynollah/tree/main/docs/train.md) and use the tools in the [`train`](https://github.com/qurator-spk/eynollah/tree/main/train) folder. +## Train +In case you want to train your own model with Eynollah, have a look at [`train.md`](https://github.com/qurator-spk/eynollah/tree/main/docs/train.md). ## Usage - -Eynollah supports five use cases: -1. [layout analysis (segmentation)](#layout-analysis), -2. [binarization](#binarization), -3. [image enhancement](#image-enhancement), -4. [text recognition (OCR)](#ocr), and -5. [reading order detection](#reading-order-detection). - -Some example outputs can be found in [`examples.md`](https://github.com/qurator-spk/eynollah/tree/main/docs/examples.md). - -### Layout Analysis - -The layout analysis module is responsible for detecting layout elements, identifying text lines, and determining reading -order using heuristic methods or a [pretrained model](https://github.com/qurator-spk/eynollah#machine-based-reading-order). - -The command-line interface for layout analysis can be called like this: +The command-line interface can be called like this: ```sh -eynollah layout \ +eynollah \ -i | -di \ -o \ -m \ @@ -100,102 +62,57 @@ eynollah layout \ The following options can be used to further configure the processing: -| option | description | -|-------------------|:--------------------------------------------------------------------------------------------| -| `-fl` | full layout analysis including all steps and segmentation classes (recommended) | -| `-tab` | apply table detection | -| `-ae` | apply enhancement (the resulting image is saved to the output directory) | -| `-as` | apply scaling | -| `-cl` | apply contour detection for curved text lines instead of bounding boxes | -| `-ib` | apply binarization (the resulting image is saved to the output directory) | -| `-ep` | enable plotting (MUST always be used with `-sl`, `-sd`, `-sa`, `-si` or `-ae`) | -| `-ho` | ignore headers for reading order dectection | -| `-si ` | save image regions detected to this directory | -| `-sd ` | save deskewed image to this directory | -| `-sl ` | save layout prediction as plot to this directory | -| `-sp ` | save cropped page image to this directory | -| `-sa ` | save all (plot, enhanced/binary image, layout) to this directory | -| `-thart` | threshold of artifical class in the case of textline detection. The default value is 0.1 | -| `-tharl` | threshold of artifical class in the case of layout detection. The default value is 0.1 | -| `-ncu` | upper limit of columns in document image | -| `-ncl` | lower limit of columns in document image | -| `-slro` | skip layout detection and reading order | -| `-romb` | apply machine based reading order detection | -| `-ipe` | ignore page extraction | +| option | description | +|-------------------|:-------------------------------------------------------------------------------| +| `-fl` | full layout analysis including all steps and segmentation classes | +| `-light` | lighter and faster but simpler method for main region detection and deskewing | +| `-tab` | apply table detection | +| `-ae` | apply enhancement (the resulting image is saved to the output directory) | +| `-as` | apply scaling | +| `-cl` | apply contour detection for curved text lines instead of bounding boxes | +| `-ib` | apply binarization (the resulting image is saved to the output directory) | +| `-ep` | enable plotting (MUST always be used with `-sl`, `-sd`, `-sa`, `-si` or `-ae`) | +| `-eoi` | extract only images to output directory (other processing will not be done) | +| `-ho` | ignore headers for reading order dectection | +| `-si ` | save image regions detected to this directory | +| `-sd ` | save deskewed image to this directory | +| `-sl ` | save layout prediction as plot to this directory | +| `-sp ` | save cropped page image to this directory | +| `-sa ` | save all (plot, enhanced/binary image, layout) to this directory | + +If no option is set, the tool performs layout detection of main regions (background, text, images, separators and marginals). +The best output quality is produced when RGB images are used as input rather than greyscale or binarized images. + +#### Use as OCR-D processor + +Eynollah ships with a CLI interface to be used as [OCR-D](https://ocr-d.de) [processor](https://ocr-d.de/en/spec/cli), +formally described in [`ocrd-tool.json`](https://github.com/qurator-spk/eynollah/tree/main/src/eynollah/ocrd-tool.json). + +In this case, the source image file group with (preferably) RGB images should be used as input like this: + + ocrd-eynollah-segment -I OCR-D-IMG -O OCR-D-SEG -P models 2022-04-05 -If no further option is set, the tool performs layout detection of main regions (background, text, images, separators -and marginals). -The best output quality is achieved when RGB images are used as input rather than greyscale or binarized images. - -Additional documentation can be found in [`usage.md`](https://github.com/qurator-spk/eynollah/tree/main/docs/usage.md). - -### Binarization - -The binarization module performs document image binarization using pretrained pixelwise segmentation models. - -The command-line interface for binarization can be called like this: - -```sh -eynollah binarization \ - -i | -di \ - -o \ - -m -``` - -### Image Enhancement -TODO - -### OCR - -The OCR module performs text recognition using either a CNN-RNN model or a Transformer model. - -The command-line interface for OCR can be called like this: - -```sh -eynollah ocr \ - -i | -di \ - -dx \ - -o \ - -m | --model_name -``` - -The following options can be used to further configure the ocr processing: - -| option | description | -|-------------------|:-------------------------------------------------------------------------------------------| -| `-dib` | directory of binarized images (file type must be '.png'), prediction with both RGB and bin | -| `-doit` | directory for output images rendered with the predicted text | -| `--model_name` | file path to use specific model for OCR | -| `-trocr` | use transformer ocr model (otherwise cnn_rnn model is used) | -| `-etit` | export textline images and text in xml to output dir (OCR training data) | -| `-nmtc` | cropped textline images will not be masked with textline contour | -| `-bs` | ocr inference batch size. Default batch size is 2 for trocr and 8 for cnn_rnn models | -| `-ds_pref` | add an abbrevation of dataset name to generated training data | -| `-min_conf` | minimum OCR confidence value. OCR with textline conf lower than this will be ignored | +If the input file group is PAGE-XML (from a previous OCR-D workflow step), Eynollah behaves as follows: +- existing regions are kept and ignored (i.e. in effect they might overlap segments from Eynollah results) +- existing annotation (and respective `AlternativeImage`s) are partially _ignored_: + - previous page frame detection (`cropped` images) + - previous derotation (`deskewed` images) + - previous thresholding (`binarized` images) +- if the page-level image nevertheless deviates from the original (`@imageFilename`) + (because some other preprocessing step was in effect like `denoised`), then + the output PAGE-XML will be based on that as new top-level (`@imageFilename`) -### Reading Order Detection -Reading order detection can be performed either as part of layout analysis based on image input, or, currently under -development, based on pre-existing layout analysis data in PAGE-XML format as input. + ocrd-eynollah-segment -I OCR-D-XYZ -O OCR-D-SEG -P models 2022-04-05 -The reading order detection module employs a pretrained model to identify the reading order from layouts represented in PAGE-XML files. +Still, in general, it makes more sense to add other workflow steps **after** Eynollah. -The command-line interface for machine based reading order can be called like this: - -```sh -eynollah machine-based-reading-order \ - -i | -di \ - -xml | -dx \ - -m \ - -o -``` - -## Use as OCR-D processor - -See [`ocrd.md`](https://github.com/qurator-spk/eynollah/tree/main/docs/ocrd.md). +#### Additional documentation +Please check the [wiki](https://github.com/qurator-spk/eynollah/wiki). ## How to cite +If you find this tool useful in your work, please consider citing our paper: ```bibtex @inproceedings{hip23rezanezhad, diff --git a/docs/docker.md b/docs/docker.md deleted file mode 100644 index 7965622..0000000 --- a/docs/docker.md +++ /dev/null @@ -1,43 +0,0 @@ -## Inference with Docker - - docker pull ghcr.io/qurator-spk/eynollah:latest - -### 1. ocrd resource manager -(just once, to get the models and install them into a named volume for later re-use) - - vol_models=ocrd-resources:/usr/local/share/ocrd-resources - docker run --rm -v $vol_models ocrd/eynollah ocrd resmgr download ocrd-eynollah-segment default - -Now, each time you want to use Eynollah, pass the same resources volume again. -Also, bind-mount some data directory, e.g. current working directory $PWD (/data is default working directory in the container). - -Either use standalone CLI (2) or OCR-D CLI (3): - -### 2. standalone CLI -(follow self-help, cf. readme) - - docker run --rm -v $vol_models -v $PWD:/data ocrd/eynollah eynollah binarization --help - docker run --rm -v $vol_models -v $PWD:/data ocrd/eynollah eynollah layout --help - docker run --rm -v $vol_models -v $PWD:/data ocrd/eynollah eynollah ocr --help - -### 3. OCR-D CLI -(follow self-help, cf. readme and https://ocr-d.de/en/spec/cli) - - docker run --rm -v $vol_models -v $PWD:/data ocrd/eynollah ocrd-eynollah-segment -h - docker run --rm -v $vol_models -v $PWD:/data ocrd/eynollah ocrd-sbb-binarize -h - -Alternatively, just "log in" to the container once and use the commands there: - - docker run --rm -v $vol_models -v $PWD:/data -it ocrd/eynollah bash - -## Training with Docker - -Build the Docker training image - - cd train - docker build -t model-training . - -Run the Docker training image - - cd train - docker run --gpus all -v $PWD:/entry_point_dir model-training diff --git a/docs/examples.md b/docs/examples.md deleted file mode 100644 index 24336b3..0000000 --- a/docs/examples.md +++ /dev/null @@ -1,18 +0,0 @@ -# Examples - -Example outputs of various Eynollah models - -# Binarisation - - - - -# Reading Order Detection - -Input Image -Output Image - -# OCR - -Input ImageOutput Image -Input ImageOutput Image diff --git a/docs/models.md b/docs/models.md index b858630..ac563b0 100644 --- a/docs/models.md +++ b/docs/models.md @@ -1,6 +1,5 @@ # Models documentation - -This suite of 15 models presents a document layout analysis (DLA) system for historical documents implemented by +This suite of 14 models presents a document layout analysis (DLA) system for historical documents implemented by pixel-wise segmentation using a combination of a ResNet50 encoder with various U-Net decoders. In addition, heuristic methods are applied to detect marginals and to determine the reading order of text regions. @@ -18,14 +17,12 @@ Two Arabic/Persian terms form the name of the model suite: عين الله, whic See the flowchart below for the different stages and how they interact: -eynollah_flowchart - +![](https://user-images.githubusercontent.com/952378/100619946-1936f680-331e-11eb-9297-6e8b4cab3c16.png) ## Models ### Image enhancement - Model card: [Image Enhancement](https://huggingface.co/SBB/eynollah-enhancement) This model addresses image resolution, specifically targeting documents with suboptimal resolution. In instances where @@ -33,14 +30,12 @@ the detection of document layout exhibits inadequate performance, the proposed e the quality and clarity of the images, thus facilitating enhanced visual interpretation and analysis. ### Page extraction / border detection - Model card: [Page Extraction/Border Detection](https://huggingface.co/SBB/eynollah-page-extraction) A problem that can negatively affect OCR are black margins around a page caused by document scanning. A deep learning model helps to crop to the page borders by using a pixel-wise segmentation method. ### Column classification - Model card: [Column Classification](https://huggingface.co/SBB/eynollah-column-classifier) This model is a trained classifier that recognizes the number of columns in a document by use of a training set with @@ -48,7 +43,6 @@ manual classification of all documents into six classes with either one, two, th respectively. ### Binarization - Model card: [Binarization](https://huggingface.co/SBB/eynollah-binarization) This model is designed to tackle the intricate task of document image binarization, which involves segmentation of the @@ -58,7 +52,6 @@ capability of the model enables improved accuracy and reliability in subsequent enhanced document understanding and interpretation. ### Main region detection - Model card: [Main Region Detection](https://huggingface.co/SBB/eynollah-main-regions) This model has employed a different set of labels, including an artificial class specifically designed to encompass the @@ -68,7 +61,6 @@ during the inference phase. By incorporating this methodology, improved efficien model's ability to accurately identify and classify text regions within documents. ### Main region detection (with scaling augmentation) - Model card: [Main Region Detection (with scaling augmentation)](https://huggingface.co/SBB/eynollah-main-regions-aug-scaling) Utilizing scaling augmentation, this model leverages the capability to effectively segment elements of extremely high or @@ -77,14 +69,12 @@ categorizing and isolating such elements, thereby enhancing its overall performa documents with varying scale characteristics. ### Main region detection (with rotation augmentation) - Model card: [Main Region Detection (with rotation augmentation)](https://huggingface.co/SBB/eynollah-main-regions-aug-rotation) This model takes advantage of rotation augmentation. This helps the tool to segment the vertical text regions in a robust way. ### Main region detection (ensembled) - Model card: [Main Region Detection (ensembled)](https://huggingface.co/SBB/eynollah-main-regions-ensembled) The robustness of this model is attained through an ensembling technique that combines the weights from various epochs. @@ -92,19 +82,16 @@ By employing this approach, the model achieves a high level of resilience and st strengths of multiple epochs to enhance its overall performance and deliver consistent and reliable results. ### Full region detection (1,2-column documents) - Model card: [Full Region Detection (1,2-column documents)](https://huggingface.co/SBB/eynollah-full-regions-1column) This model deals with documents comprising of one and two columns. ### Full region detection (3,n-column documents) - Model card: [Full Region Detection (3,n-column documents)](https://huggingface.co/SBB/eynollah-full-regions-3pluscolumn) This model is responsible for detecting headers and drop capitals in documents with three or more columns. ### Textline detection - Model card: [Textline Detection](https://huggingface.co/SBB/eynollah-textline) The method for textline detection combines deep learning and heuristics. In the deep learning part, an image-to-image @@ -119,7 +106,6 @@ segmentation is first deskewed and then the textlines are separated with the sam textline bounding boxes. Later, the strap is rotated back into its original orientation. ### Textline detection (light) - Model card: [Textline Detection Light (simpler but faster method)](https://huggingface.co/SBB/eynollah-textline_light) The method for textline detection combines deep learning and heuristics. In the deep learning part, an image-to-image @@ -133,7 +119,6 @@ enhancing the model's ability to accurately identify and delineate individual te eliminates the need for additional heuristics in extracting textline contours. ### Table detection - Model card: [Table Detection](https://huggingface.co/SBB/eynollah-tables) The objective of this model is to perform table segmentation in historical document images. Due to the pixel-wise @@ -143,84 +128,20 @@ effectively identify and delineate tables within the historical document images, enabling subsequent analysis and interpretation. ### Image detection - Model card: [Image Detection](https://huggingface.co/SBB/eynollah-image-extraction) This model is used for the task of illustration detection only. ### Reading order detection - Model card: [Reading Order Detection]() -The model extracts the reading order of text regions from the layout by classifying pairwise relationships between them. A sorting algorithm then determines the overall reading sequence. - -### OCR - -We have trained three OCR models: two CNN-RNN–based models and one transformer-based TrOCR model. The CNN-RNN models are generally faster and provide better results in most cases, though their performance decreases with heavily degraded images. The TrOCR model, on the other hand, is computationally expensive and slower during inference, but it can possibly produce better results on strongly degraded images. - -#### CNN-RNN model: model_eynollah_ocr_cnnrnn_20250805 - -This model is trained on data where most of the samples are in Fraktur german script. - -| Dataset | Input | CER | WER | -|-----------------------|:-------|:-----------|:----------| -| OCR-D-GT-Archiveform | BIN | 0.02147 | 0.05685 | -| OCR-D-GT-Archiveform | RGB | 0.01636 | 0.06285 | - -#### CNN-RNN model: model_eynollah_ocr_cnnrnn_20250904 (Default) - -Compared to the model_eynollah_ocr_cnnrnn_20250805 model, this model is trained on a larger proportion of Antiqua data and achieves superior performance. - -| Dataset | Input | CER | WER | -|-----------------------|:------------|:-----------|:----------| -| OCR-D-GT-Archiveform | BIN | 0.01635 | 0.05410 | -| OCR-D-GT-Archiveform | RGB | 0.01471 | 0.05813 | -| BLN600 | RGB | 0.04409 | 0.08879 | -| BLN600 | Enhanced | 0.03599 | 0.06244 | - - -#### Transformer OCR model: model_eynollah_ocr_trocr_20250919 - -This transformer OCR model is trained on the same data as model_eynollah_ocr_trocr_20250919. - -| Dataset | Input | CER | WER | -|-----------------------|:------------|:-----------|:----------| -| OCR-D-GT-Archiveform | BIN | 0.01841 | 0.05589 | -| OCR-D-GT-Archiveform | RGB | 0.01552 | 0.06177 | -| BLN600 | RGB | 0.06347 | 0.13853 | - -##### Qualitative evaluation of the models - -| | | | | -|:---:|:---:|:---:|:---:| -| Image | cnnrnn_20250805 | cnnrnn_20250904 | trocr_20250919 | - - - -| | | | | -|:---:|:---:|:---:|:---:| -| Image | cnnrnn_20250805 | cnnrnn_20250904 | trocr_20250919 | - - -| | | | | -|:---:|:---:|:---:|:---:| -| Image | cnnrnn_20250805 | cnnrnn_20250904 | trocr_20250919 | - - -| | | | | -|:---:|:---:|:---:|:---:| -| Image | cnnrnn_20250805 | cnnrnn_20250904 | trocr_20250919 | - - +TODO ## Heuristic methods - Additionally, some heuristic methods are employed to further improve the model predictions: - * After border detection, the largest contour is determined by a bounding box, and the image cropped to these coordinates. -* Unlike the non-light version, where the image is scaled up to help the model better detect the background spaces between text regions, the light version uses down-scaled images. In this case, introducing an artificial class along the boundaries of text regions and text lines has helped to isolate and separate the text regions more effectively. +* For text region detection, the image is scaled up to make it easier for the model to detect background space between text regions. * A minimum area is defined for text regions in relation to the overall image dimensions, so that very small regions that are noise can be filtered out. -* In the non-light version, deskewing is applied at the text-region level (since regions may have different degrees of skew) to improve text-line segmentation results. In contrast, the light version performs deskewing only at the page level to enhance margin detection and heuristic reading-order estimation. -* After deskewing, a calculation of the pixel distribution on the X-axis allows the separation of textlines (foreground) and background pixels (only in non-light version). -* Finally, using the derived coordinates, bounding boxes are determined for each textline (only in non-light version). -* As mentioned above, the reading order can be determined using a model; however, this approach is computationally expensive, time-consuming, and less accurate due to the limited amount of ground-truth data available for training. Therefore, our tool uses a heuristic reading-order detection method as the default. The heuristic approach relies on headers and separators to determine the reading order of text regions. +* Deskewing is applied on the text region level (due to regions having different degrees of skew) in order to improve the textline segmentation result. +* After deskewing, a calculation of the pixel distribution on the X-axis allows the separation of textlines (foreground) and background pixels. +* Finally, using the derived coordinates, bounding boxes are determined for each textline. diff --git a/docs/ocrd.md b/docs/ocrd.md deleted file mode 100644 index 9e7e268..0000000 --- a/docs/ocrd.md +++ /dev/null @@ -1,26 +0,0 @@ -## Use as OCR-D processor - -Eynollah ships with a CLI interface to be used as [OCR-D](https://ocr-d.de) [processor](https://ocr-d.de/en/spec/cli), -formally described in [`ocrd-tool.json`](https://github.com/qurator-spk/eynollah/tree/main/src/eynollah/ocrd-tool.json). - -When using Eynollah in OCR-D, the source image file group with (preferably) RGB images should be used as input like this: - - ocrd-eynollah-segment -I OCR-D-IMG -O OCR-D-SEG -P models eynollah_layout_v0_5_0 - -If the input file group is PAGE-XML (from a previous OCR-D workflow step), Eynollah behaves as follows: -- existing regions are kept and ignored (i.e. in effect they might overlap segments from Eynollah results) -- existing annotation (and respective `AlternativeImage`s) are partially _ignored_: - - previous page frame detection (`cropped` images) - - previous derotation (`deskewed` images) - - previous thresholding (`binarized` images) -- if the page-level image nevertheless deviates from the original (`@imageFilename`) - (because some other preprocessing step was in effect like `denoised`), then - the output PAGE-XML will be based on that as new top-level (`@imageFilename`) - - ocrd-eynollah-segment -I OCR-D-XYZ -O OCR-D-SEG -P models eynollah_layout_v0_5_0 - -In general, it makes more sense to add other workflow steps **after** Eynollah. - -There is also an OCR-D processor for binarization: - - ocrd-sbb-binarize -I OCR-D-IMG -O OCR-D-BIN -P models default-2021-03-09 diff --git a/docs/train.md b/docs/train.md index ffa39a9..9f44a63 100644 --- a/docs/train.md +++ b/docs/train.md @@ -1,93 +1,38 @@ -# Prerequisistes - -## 1. Install Eynollah with training dependencies - -Clone the repository and install eynollah along with the dependencies necessary for training: - -```sh -git clone https://github.com/qurator-spk/eynollah -cd eynollah -pip install '.[training]' -``` - -## 2. Pretrained encoder - -Download our pretrained weights and add them to a `train/pretrained_model` folder: - -```sh -cd train -wget -O pretrained_model.tar.gz https://zenodo.org/records/17243320/files/pretrained_model_v0_5_1.tar.gz?download=1 -tar xf pretrained_model.tar.gz -``` - -## 3. Example data - -### Binarization -A small sample of training data for binarization experiment can be found on [Zenodo](https://zenodo.org/records/17243320/files/training_data_sample_binarization_v0_5_1.tar.gz?download=1), -which contains `images` and `labels` folders. - -## 4. Helpful tools - -* [`pagexml2img`](https://github.com/qurator-spk/page2img) -> Tool to extract 2-D or 3-D RGB images from PAGE-XML data. In the former case, the output will be 1 2-D image array which each class has filled with a pixel value. In the case of a 3-D RGB image, -each class will be defined with a RGB value and beside images, a text file of classes will also be produced. -* [`cocoSegmentationToPng`](https://github.com/nightrome/cocostuffapi/blob/17acf33aef3c6cc2d6aca46dcf084266c2778cf0/PythonAPI/pycocotools/cocostuffhelper.py#L130) -> Convert COCO GT or results for a single image to a segmentation map and write it to disk. -* [`ocrd-segment-extract-pages`](https://github.com/OCR-D/ocrd_segment/blob/master/ocrd_segment/extract_pages.py) -> Extract region classes and their colours in mask (pseg) images. Allows the color map as free dict parameter, and comes with a default that mimics PageViewer's coloring for quick debugging; it also warns when regions do overlap. - # Training documentation +This aims to assist users in preparing training datasets, training models, and performing inference with trained models. +We cover various use cases including pixel-wise segmentation, image classification, image enhancement, and machine-based +reading order detection. For each use case, we provide guidance on how to generate the corresponding training dataset. -This document aims to assist users in preparing training datasets, training models, and -performing inference with trained models. We cover various use cases including -pixel-wise segmentation, image classification, image enhancement, and -machine-based reading order detection. For each use case, we provide guidance -on how to generate the corresponding training dataset. - -The following three tasks can all be accomplished using the code in the -[`train`](https://github.com/qurator-spk/eynollah/tree/main/train) directory: +The following three tasks can all be accomplished using the code in the +[`train`](https://github.com/qurator-spk/sbb_pixelwise_segmentation/tree/unifying-training-models) directory: * generate training dataset * train a model * inference with the trained model -## Training, evaluation and output - -The train and evaluation folders should contain subfolders of `images` and `labels`. - -The output folder should be an empty folder where the output model will be written to. - ## Generate training dataset +The script `generate_gt_for_training.py` is used for generating training datasets. As the results of the following +command demonstrates, the dataset generator provides three different commands: -The script `generate_gt_for_training.py` is used for generating training datasets. As the results of the following -command demonstrates, the dataset generator provides several subcommands: +`python generate_gt_for_training.py --help` -```sh -eynollah-training generate-gt --help -``` - -The three most important subcommands are: +These three commands are: * image-enhancement * machine-based-reading-order * pagexml2label ### image-enhancement - -Generating a training dataset for image enhancement is quite straightforward. All that is needed is a set of +Generating a training dataset for image enhancement is quite straightforward. All that is needed is a set of high-resolution images. The training dataset can then be generated using the following command: -```sh -eynollah-training image-enhancement \ - -dis "dir of high resolution images" \ - -dois "dir where degraded images will be written" \ - -dols "dir where the corresponding high resolution image will be written as label" \ - -scs "degrading scales json file" -``` +`python generate_gt_for_training.py image-enhancement -dis "dir of high resolution images" -dois "dir where degraded +images will be written" -dols "dir where the corresponding high resolution image will be written as label" -scs +"degrading scales json file"` -The scales JSON file is a dictionary with a key named `scales` and values representing scales smaller than 1. Images are -downscaled based on these scales and then upscaled again to their original size. This process causes the images to lose -resolution at different scales. The degraded images are used as input images, and the original high-resolution images +The scales JSON file is a dictionary with a key named 'scales' and values representing scales smaller than 1. Images are +downscaled based on these scales and then upscaled again to their original size. This process causes the images to lose +resolution at different scales. The degraded images are used as input images, and the original high-resolution images serve as labels. The enhancement model can be trained with this generated dataset. The scales JSON file looks like this: ```yaml @@ -97,40 +42,31 @@ serve as labels. The enhancement model can be trained with this generated datase ``` ### machine-based-reading-order - -For machine-based reading order, we aim to determine the reading priority between two sets of text regions. The model's -input is a three-channel image: the first and last channels contain information about each of the two text regions, -while the middle channel encodes prominent layout elements necessary for reading order, such as separators and headers. -To generate the training dataset, our script requires a page XML file that specifies the image layout with the correct +For machine-based reading order, we aim to determine the reading priority between two sets of text regions. The model's +input is a three-channel image: the first and last channels contain information about each of the two text regions, +while the middle channel encodes prominent layout elements necessary for reading order, such as separators and headers. +To generate the training dataset, our script requires a page XML file that specifies the image layout with the correct reading order. -For output images, it is necessary to specify the width and height. Additionally, a minimum text region size can be set -to filter out regions smaller than this minimum size. This minimum size is defined as the ratio of the text region area +For output images, it is necessary to specify the width and height. Additionally, a minimum text region size can be set +to filter out regions smaller than this minimum size. This minimum size is defined as the ratio of the text region area to the image area, with a default value of zero. To run the dataset generator, use the following command: -```shell -eynollah-training generate-gt machine-based-reading-order \ - -dx "dir of GT xml files" \ - -domi "dir where output images will be written" \ -"" -docl "dir where the labels will be written" \ - -ih "height" \ - -iw "width" \ - -min "min area ratio" -``` +`python generate_gt_for_training.py machine-based-reading-order -dx "dir of GT xml files" -domi "dir where output images +will be written" -docl "dir where the labels will be written" -ih "height" -iw "width" -min "min area ratio"` ### pagexml2label - pagexml2label is designed to generate labels from GT page XML files for various pixel-wise segmentation use cases, including 'layout,' 'textline,' 'printspace,' 'glyph,' and 'word' segmentation. -To train a pixel-wise segmentation model, we require images along with their corresponding labels. Our training script -expects a PNG image where each pixel corresponds to a label, represented by an integer. The background is always labeled -as zero, while other elements are assigned different integers. For instance, if we have ground truth data with four +To train a pixel-wise segmentation model, we require images along with their corresponding labels. Our training script +expects a PNG image where each pixel corresponds to a label, represented by an integer. The background is always labeled +as zero, while other elements are assigned different integers. For instance, if we have ground truth data with four elements including the background, the classes would be labeled as 0, 1, 2, and 3 respectively. -In binary segmentation scenarios such as textline or page extraction, the background is encoded as 0, and the desired +In binary segmentation scenarios such as textline or page extraction, the background is encoded as 0, and the desired element is automatically encoded as 1 in the PNG label. -To specify the desired use case and the elements to be extracted in the PNG labels, a custom JSON file can be passed. +To specify the desired use case and the elements to be extracted in the PNG labels, a custom JSON file can be passed. For example, in the case of 'textline' detection, the JSON file would resemble this: ```yaml @@ -164,35 +100,31 @@ A possible custom config json file for layout segmentation where the "printspace } ``` -For the layout use case, it is beneficial to first understand the structure of the page XML file and its elements. -In a given image, the annotations of elements are recorded in a page XML file, including their contours and classes. -For an image document, the known regions are 'textregion', 'separatorregion', 'imageregion', 'graphicregion', +For the layout use case, it is beneficial to first understand the structure of the page XML file and its elements. +In a given image, the annotations of elements are recorded in a page XML file, including their contours and classes. +For an image document, the known regions are 'textregion', 'separatorregion', 'imageregion', 'graphicregion', 'noiseregion', and 'tableregion'. -Text regions and graphic regions also have their own specific types. The known types for text regions are 'paragraph', -'header', 'heading', 'marginalia', 'drop-capital', 'footnote', 'footnote-continued', 'signature-mark', 'page-number', -and 'catch-word'. The known types for graphic regions are 'handwritten-annotation', 'decoration', 'stamp', and +Text regions and graphic regions also have their own specific types. The known types for text regions are 'paragraph', +'header', 'heading', 'marginalia', 'drop-capital', 'footnote', 'footnote-continued', 'signature-mark', 'page-number', +and 'catch-word'. The known types for graphic regions are 'handwritten-annotation', 'decoration', 'stamp', and 'signature'. -Since we don't know all types of text and graphic regions, unknown cases can arise. To handle these, we have defined -two additional types, "rest_as_paragraph" and "rest_as_decoration", to ensure that no unknown types are missed. +Since we don't know all types of text and graphic regions, unknown cases can arise. To handle these, we have defined +two additional types, "rest_as_paragraph" and "rest_as_decoration", to ensure that no unknown types are missed. This way, users can extract all known types from the labels and be confident that no unknown types are overlooked. -In the custom JSON file shown above, "header" and "heading" are extracted as the same class, while "marginalia" is shown -as a different class. All other text region types, including "drop-capital," are grouped into the same class. For the -graphic region, "stamp" has its own class, while all other types are classified together. "Image region" and "separator -region" are also present in the label. However, other regions like "noise region" and "table region" will not be +In the custom JSON file shown above, "header" and "heading" are extracted as the same class, while "marginalia" is shown +as a different class. All other text region types, including "drop-capital," are grouped into the same class. For the +graphic region, "stamp" has its own class, while all other types are classified together. "Image region" and "separator +region" are also present in the label. However, other regions like "noise region" and "table region" will not be included in the label PNG file, even if they have information in the page XML files, as we chose not to include them. -```sh -eynollah-training generate-gt pagexml2label \ - -dx "dir of GT xml files" \ - -do "dir where output label png files will be written" \ - -cfg "custom config json file" \ - -to "output type which has 2d and 3d. 2d is used for training and 3d is just to visualise the labels" -``` +`python generate_gt_for_training.py pagexml2label -dx "dir of GT xml files" -do "dir where output label png files will +be written" -cfg "custom config json file" -to "output type which has 2d and 3d. 2d is used for training and 3d is just +to visualise the labels" "` -We have also defined an artificial class that can be added to the boundary of text region types or text lines. This key -is called "artificial_class_on_boundary." If users want to apply this to certain text regions in the layout use case, +We have also defined an artificial class that can be added to the boundary of text region types or text lines. This key +is called "artificial_class_on_boundary." If users want to apply this to certain text regions in the layout use case, the example JSON config file should look like this: ```yaml @@ -215,13 +147,13 @@ the example JSON config file should look like this: } ``` -This implies that the artificial class label, denoted by 7, will be present on PNG files and will only be added to the +This implies that the artificial class label, denoted by 7, will be present on PNG files and will only be added to the elements labeled as "paragraph," "header," "heading," and "marginalia." -For "textline", "word", and "glyph", the artificial class on the boundaries will be activated only if the -"artificial_class_label" key is specified in the config file. Its value should be set as 2 since these elements -represent binary cases. For example, if the background and textline are denoted as 0 and 1 respectively, then the -artificial class should be assigned the value 2. The example JSON config file should look like this for "textline" use +For "textline", "word", and "glyph", the artificial class on the boundaries will be activated only if the +"artificial_class_label" key is specified in the config file. Its value should be set as 2 since these elements +represent binary cases. For example, if the background and textline are denoted as 0 and 1 respectively, then the +artificial class should be assigned the value 2. The example JSON config file should look like this for "textline" use case: ```yaml @@ -231,32 +163,25 @@ case: } ``` -If the coordinates of "PrintSpace" or "Border" are present in the page XML ground truth files, and the user wishes to -crop only the print space area, this can be achieved by activating the "-ps" argument. However, it should be noted that -in this scenario, since cropping will be applied to the label files, the directory of the original images must be -provided to ensure that they are cropped in sync with the labels. This ensures that the correct images and labels +If the coordinates of "PrintSpace" or "Border" are present in the page XML ground truth files, and the user wishes to +crop only the print space area, this can be achieved by activating the "-ps" argument. However, it should be noted that +in this scenario, since cropping will be applied to the label files, the directory of the original images must be +provided to ensure that they are cropped in sync with the labels. This ensures that the correct images and labels required for training are obtained. The command should resemble the following: -```sh -eynollah-training generate-gt pagexml2label \ - -dx "dir of GT xml files" \ - -do "dir where output label png files will be written" \ - -cfg "custom config json file" \ - -to "output type which has 2d and 3d. 2d is used for training and 3d is just to visualise the labels" \ - -ps \ - -di "dir where the org images are located" \ - -doi "dir where the cropped output images will be written" -``` +`python generate_gt_for_training.py pagexml2label -dx "dir of GT xml files" -do "dir where output label png files will +be written" -cfg "custom config json file" -to "output type which has 2d and 3d. 2d is used for training and 3d is just +to visualise the labels" -ps -di "dir where the org images are located" -doi "dir where the cropped output images will +be written" ` ## Train a model - ### classification -For the classification use case, we haven't provided a ground truth generator, as it's unnecessary. For classification, -all we require is a training directory with subdirectories, each containing images of its respective classes. We need -separate directories for training and evaluation, and the class names (subdirectories) must be consistent across both -directories. Additionally, the class names should be specified in the config JSON file, as shown in the following -example. If, for instance, we aim to classify "apple" and "orange," with a total of 2 classes, the +For the classification use case, we haven't provided a ground truth generator, as it's unnecessary. For classification, +all we require is a training directory with subdirectories, each containing images of its respective classes. We need +separate directories for training and evaluation, and the class names (subdirectories) must be consistent across both +directories. Additionally, the class names should be specified in the config JSON file, as shown in the following +example. If, for instance, we aim to classify "apple" and "orange," with a total of 2 classes, the "classification_classes_name" key in the config file should appear as follows: ```yaml @@ -281,7 +206,7 @@ example. If, for instance, we aim to classify "apple" and "orange," with a total The "dir_train" should be like this: -``` +``` . └── train # train directory ├── apple # directory of images for apple class @@ -290,7 +215,7 @@ The "dir_train" should be like this: And the "dir_eval" the same structure as train directory: -``` +``` . └── eval # evaluation directory ├── apple # directory of images for apple class @@ -300,13 +225,11 @@ And the "dir_eval" the same structure as train directory: The classification model can be trained using the following command line: -```sh -eynollah-training train with config_classification.json -``` +`python train.py with config_classification.json` -As evident in the example JSON file above, for classification, we utilize a "f1_threshold_classification" parameter. -This parameter is employed to gather all models with an evaluation f1 score surpassing this threshold. Subsequently, -an ensemble of these model weights is executed, and a model is saved in the output directory as "model_ens_avg". +As evident in the example JSON file above, for classification, we utilize a "f1_threshold_classification" parameter. +This parameter is employed to gather all models with an evaluation f1 score surpassing this threshold. Subsequently, +an ensemble of these model weights is executed, and a model is saved in the output directory as "model_ens_avg". Additionally, the weight of the best model based on the evaluation f1 score is saved as "model_best". ### reading order @@ -353,64 +276,58 @@ The classification model can be trained like the classification case command lin ### Segmentation (Textline, Binarization, Page extraction and layout) and enhancement #### Parameter configuration for segmentation or enhancement usecases - -The following parameter configuration can be applied to all segmentation use cases and enhancements. The augmentation, -its sub-parameters, and continued training are defined only for segmentation use cases and enhancements, not for +The following parameter configuration can be applied to all segmentation use cases and enhancements. The augmentation, +its sub-parameters, and continued training are defined only for segmentation use cases and enhancements, not for classification and machine-based reading order, as you can see in their example config files. -* `backbone_type`: For segmentation tasks (such as text line, binarization, and layout detection) and enhancement, we - offer two backbone options: a "nontransformer" and a "transformer" backbone. For the "transformer" backbone, we first - apply a CNN followed by a transformer. In contrast, the "nontransformer" backbone utilizes only a CNN ResNet-50. -* `task`: The task parameter can have values such as "segmentation", "enhancement", "classification", and "reading_order". -* `patches`: If you want to break input images into smaller patches (input size of the model) you need to set this -* parameter to `true`. In the case that the model should see the image once, like page extraction, patches should be - set to ``false``. -* `n_batch`: Number of batches at each iteration. -* `n_classes`: Number of classes. In the case of binary classification this should be 2. In the case of reading_order it - should set to 1. And for the case of layout detection just the unique number of classes should be given. -* `n_epochs`: Number of epochs. -* `input_height`: This indicates the height of model's input. -* `input_width`: This indicates the width of model's input. -* `weight_decay`: Weight decay of l2 regularization of model layers. -* `pretraining`: Set to `true` to load pretrained weights of ResNet50 encoder. The downloaded weights should be saved - in a folder named "pretrained_model" in the same directory of "train.py" script. -* `augmentation`: If you want to apply any kind of augmentation this parameter should first set to `true`. -* `flip_aug`: If `true`, different types of filp will be applied on image. Type of flips is given with "flip_index" parameter. -* `blur_aug`: If `true`, different types of blurring will be applied on image. Type of blurrings is given with "blur_k" parameter. -* `scaling`: If `true`, scaling will be applied on image. Scale of scaling is given with "scales" parameter. -* `degrading`: If `true`, degrading will be applied to the image. The amount of degrading is defined with "degrade_scales" parameter. -* `brightening`: If `true`, brightening will be applied to the image. The amount of brightening is defined with "brightness" parameter. -* `rotation_not_90`: If `true`, rotation (not 90 degree) will be applied on image. Rotation angles are given with "thetha" parameter. -* `rotation`: If `true`, 90 degree rotation will be applied on image. -* `binarization`: If `true`,Otsu thresholding will be applied to augment the input data with binarized images. -* `scaling_bluring`: If `true`, combination of scaling and blurring will be applied on image. -* `scaling_binarization`: If `true`, combination of scaling and binarization will be applied on image. -* `scaling_flip`: If `true`, combination of scaling and flip will be applied on image. -* `flip_index`: Type of flips. -* `blur_k`: Type of blurrings. -* `scales`: Scales of scaling. -* `brightness`: The amount of brightenings. -* `thetha`: Rotation angles. -* `degrade_scales`: The amount of degradings. -* `continue_training`: If `true`, it means that you have already trained a model and you would like to continue the - training. So it is needed to providethe dir of trained model with "dir_of_start_model" and index for naming - themodels. For example if you have already trained for 3 epochs then your lastindex is 2 and if you want to continue - from model_1.h5, you can set `index_start` to 3 to start naming model with index 3. -* `weighted_loss`: If `true`, this means that you want to apply weighted categorical_crossentropy as loss fucntion. Be carefull if you set to `true`the parameter "is_loss_soft_dice" should be ``false`` -* `data_is_provided`: If you have already provided the input data you can set this to `true`. Be sure that the train - and eval data are in"dir_output".Since when once we provide training data we resize and augmentthem and then wewrite - them in sub-directories train and eval in "dir_output". -* `dir_train`: This is the directory of "images" and "labels" (dir_train should include two subdirectories with names of images and labels ) for raw images and labels. Namely they are not prepared (not resized and not augmented) yet for training the model. When we run this tool these raw data will be transformed to suitable size needed for the model and they will be written in "dir_output" in train and eval directories. Each of train and eval include "images" and "labels" sub-directories. -* `index_start`: Starting index for saved models in the case that "continue_training" is `true`. -* `dir_of_start_model`: Directory containing pretrained model to continue training the model in the case that "continue_training" is `true`. -* `transformer_num_patches_xy`: Number of patches for vision transformer in x and y direction respectively. -* `transformer_patchsize_x`: Patch size of vision transformer patches in x direction. -* `transformer_patchsize_y`: Patch size of vision transformer patches in y direction. -* `transformer_projection_dim`: Transformer projection dimension. Default value is 64. -* `transformer_mlp_head_units`: Transformer Multilayer Perceptron (MLP) head units. Default value is [128, 64]. -* `transformer_layers`: transformer layers. Default value is 8. -* `transformer_num_heads`: Transformer number of heads. Default value is 4. -* `transformer_cnn_first`: We have two types of vision transformers. In one type, a CNN is applied first, followed by a transformer. In the other type, this order is reversed. If transformer_cnn_first is true, it means the CNN will be applied before the transformer. Default value is true. +* backbone_type: For segmentation tasks (such as text line, binarization, and layout detection) and enhancement, we +* offer two backbone options: a "nontransformer" and a "transformer" backbone. For the "transformer" backbone, we first +* apply a CNN followed by a transformer. In contrast, the "nontransformer" backbone utilizes only a CNN ResNet-50. +* task : The task parameter can have values such as "segmentation", "enhancement", "classification", and "reading_order". +* patches: If you want to break input images into smaller patches (input size of the model) you need to set this +* parameter to ``true``. In the case that the model should see the image once, like page extraction, patches should be +* set to ``false``. +* n_batch: Number of batches at each iteration. +* n_classes: Number of classes. In the case of binary classification this should be 2. In the case of reading_order it +* should set to 1. And for the case of layout detection just the unique number of classes should be given. +* n_epochs: Number of epochs. +* input_height: This indicates the height of model's input. +* input_width: This indicates the width of model's input. +* weight_decay: Weight decay of l2 regularization of model layers. +* pretraining: Set to ``true`` to load pretrained weights of ResNet50 encoder. The downloaded weights should be saved +* in a folder named "pretrained_model" in the same directory of "train.py" script. +* augmentation: If you want to apply any kind of augmentation this parameter should first set to ``true``. +* flip_aug: If ``true``, different types of filp will be applied on image. Type of flips is given with "flip_index" parameter. +* blur_aug: If ``true``, different types of blurring will be applied on image. Type of blurrings is given with "blur_k" parameter. +* scaling: If ``true``, scaling will be applied on image. Scale of scaling is given with "scales" parameter. +* degrading: If ``true``, degrading will be applied to the image. The amount of degrading is defined with "degrade_scales" parameter. +* brightening: If ``true``, brightening will be applied to the image. The amount of brightening is defined with "brightness" parameter. +* rotation_not_90: If ``true``, rotation (not 90 degree) will be applied on image. Rotation angles are given with "thetha" parameter. +* rotation: If ``true``, 90 degree rotation will be applied on image. +* binarization: If ``true``,Otsu thresholding will be applied to augment the input data with binarized images. +* scaling_bluring: If ``true``, combination of scaling and blurring will be applied on image. +* scaling_binarization: If ``true``, combination of scaling and binarization will be applied on image. +* scaling_flip: If ``true``, combination of scaling and flip will be applied on image. +* flip_index: Type of flips. +* blur_k: Type of blurrings. +* scales: Scales of scaling. +* brightness: The amount of brightenings. +* thetha: Rotation angles. +* degrade_scales: The amount of degradings. +* continue_training: If ``true``, it means that you have already trained a model and you would like to continue the training. So it is needed to provide the dir of trained model with "dir_of_start_model" and index for naming the models. For example if you have already trained for 3 epochs then your last index is 2 and if you want to continue from model_1.h5, you can set ``index_start`` to 3 to start naming model with index 3. +* weighted_loss: If ``true``, this means that you want to apply weighted categorical_crossentropy as loss fucntion. Be carefull if you set to ``true``the parameter "is_loss_soft_dice" should be ``false`` +* data_is_provided: If you have already provided the input data you can set this to ``true``. Be sure that the train and eval data are in "dir_output". Since when once we provide training data we resize and augment them and then we write them in sub-directories train and eval in "dir_output". +* dir_train: This is the directory of "images" and "labels" (dir_train should include two subdirectories with names of images and labels ) for raw images and labels. Namely they are not prepared (not resized and not augmented) yet for training the model. When we run this tool these raw data will be transformed to suitable size needed for the model and they will be written in "dir_output" in train and eval directories. Each of train and eval include "images" and "labels" sub-directories. +* index_start: Starting index for saved models in the case that "continue_training" is ``true``. +* dir_of_start_model: Directory containing pretrained model to continue training the model in the case that "continue_training" is ``true``. +* transformer_num_patches_xy: Number of patches for vision transformer in x and y direction respectively. +* transformer_patchsize_x: Patch size of vision transformer patches in x direction. +* transformer_patchsize_y: Patch size of vision transformer patches in y direction. +* transformer_projection_dim: Transformer projection dimension. Default value is 64. +* transformer_mlp_head_units: Transformer Multilayer Perceptron (MLP) head units. Default value is [128, 64]. +* transformer_layers: transformer layers. Default value is 8. +* transformer_num_heads: Transformer number of heads. Default value is 4. +* transformer_cnn_first: We have two types of vision transformers. In one type, a CNN is applied first, followed by a transformer. In the other type, this order is reversed. If transformer_cnn_first is true, it means the CNN will be applied before the transformer. Default value is true. In the case of segmentation and enhancement the train and evaluation directory should be as following. @@ -432,39 +349,12 @@ And the "dir_eval" the same structure as train directory: └── labels # directory of labels ``` -After configuring the JSON file for segmentation or enhancement, training can be initiated by running the following +After configuring the JSON file for segmentation or enhancement, training can be initiated by running the following command, similar to the process for classification and reading order: -``` -eynollah-training train with config_classification.json` -``` +`python train.py with config_classification.json` #### Binarization - -### Ground truth format - -Lables for each pixel are identified by a number. So if you have a -binary case, ``n_classes`` should be set to ``2`` and labels should -be ``0`` and ``1`` for each class and pixel. - -In the case of multiclass, just set ``n_classes`` to the number of classes -you have and the try to produce the labels by pixels set from ``0 , 1 ,2 .., n_classes-1``. -The labels format should be png. -Our lables are 3 channel png images but only information of first channel is used. -If you have an image label with height and width of 10, for a binary case the first channel should look like this: - - Label: [ [1, 0, 0, 1, 1, 0, 0, 1, 0, 0], - [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], - ..., - [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], - [0, 0, 0, 0, 0, 0, 0, 0, 0, 0] ] - - This means that you have an image by `10*10*3` and `pixel[0,0]` belongs - to class `1` and `pixel[0,1]` belongs to class `0`. - - A small sample of training data for binarization experiment can be found here, [Training data sample](https://qurator-data.de/~vahid.rezanezhad/binarization_training_data_sample/), which contains images and lables folders. - - An example config json file for binarization can be like this: ```yaml @@ -508,7 +398,7 @@ An example config json file for binarization can be like this: "thetha" : [10, -10], "continue_training": false, "index_start" : 0, - "dir_of_start_model" : " ", + "dir_of_start_model" : " ", "weighted_loss": false, "is_loss_soft_dice": false, "data_is_provided": false, @@ -553,7 +443,7 @@ An example config json file for binarization can be like this: "thetha" : [10, -10], "continue_training": false, "index_start" : 0, - "dir_of_start_model" : " ", + "dir_of_start_model" : " ", "weighted_loss": false, "is_loss_soft_dice": false, "data_is_provided": false, @@ -598,7 +488,7 @@ An example config json file for binarization can be like this: "thetha" : [10, -10], "continue_training": false, "index_start" : 0, - "dir_of_start_model" : " ", + "dir_of_start_model" : " ", "weighted_loss": false, "is_loss_soft_dice": false, "data_is_provided": false, @@ -608,7 +498,7 @@ An example config json file for binarization can be like this: } ``` -It's important to mention that the value of n_classes for enhancement should be 3, as the model's output is a 3-channel +It's important to mention that the value of n_classes for enhancement should be 3, as the model's output is a 3-channel image. #### Page extraction @@ -646,7 +536,7 @@ image. "thetha" : [10, -10], "continue_training": false, "index_start" : 0, - "dir_of_start_model" : " ", + "dir_of_start_model" : " ", "weighted_loss": false, "is_loss_soft_dice": false, "data_is_provided": false, @@ -656,11 +546,10 @@ image. } ``` -For page segmentation (or print space or border segmentation), the model needs to view the input image in its -entirety,hence the patches parameter should be set to false. +For page segmentation (or printspace or border segmentation), the model needs to view the input image in its entirety, +hence the patches parameter should be set to false. #### layout segmentation - An example config json file for layout segmentation with 5 classes (including background) can be like this: ```yaml @@ -704,7 +593,7 @@ An example config json file for layout segmentation with 5 classes (including ba "thetha" : [10, -10], "continue_training": false, "index_start" : 0, - "dir_of_start_model" : " ", + "dir_of_start_model" : " ", "weighted_loss": false, "is_loss_soft_dice": false, "data_is_provided": false, @@ -716,42 +605,28 @@ An example config json file for layout segmentation with 5 classes (including ba ## Inference with the trained model ### classification - -For conducting inference with a trained model, you simply need to execute the following command line, specifying the +For conducting inference with a trained model, you simply need to execute the following command line, specifying the directory of the model and the image on which to perform inference: -```sh -eynollah-training inference -m "model dir" -i "image" -``` +`python inference.py -m "model dir" -i "image" ` This will straightforwardly return the class of the image. ### machine based reading order +To infer the reading order using a reading order model, we need a page XML file containing layout information but +without the reading order. We simply need to provide the model directory, the XML file, and the output directory. +The new XML file with the added reading order will be written to the output directory with the same name. +We need to run: -To infer the reading order using a reading order model, we need a page XML file containing layout information but -without the reading order. We simply need to provide the model directory, the XML file, and the output directory. The -new XML file with the added reading order will be written to the output directory with the same name. We need to run: - -```sh -eynollah-training inference \ - -m "model dir" \ - -xml "page xml file" \ - -o "output dir to write new xml with reading order" -``` +`python inference.py -m "model dir" -xml "page xml file" -o "output dir to write new xml with reading order" ` ### Segmentation (Textline, Binarization, Page extraction and layout) and enhancement +For conducting inference with a trained model for segmentation and enhancement you need to run the following command +line: -For conducting inference with a trained model for segmentation and enhancement you need to run the following command line: - -```sh -eynollah-training inference \ - -m "model dir" \ - -i "image" \ - -p \ - -s "output image" -``` +`python inference.py -m "model dir" -i "image" -p -s "output image" ` Note that in the case of page extraction the -p flag is not needed. -For segmentation or binarization tasks, if a ground truth (GT) label is available, the IoU evaluation metric can be +For segmentation or binarization tasks, if a ground truth (GT) label is available, the IoU evaluation metric can be calculated for the output. To do this, you need to provide the GT label using the argument -gt. diff --git a/pyproject.toml b/pyproject.toml index e6821a5..4da39ef 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -11,18 +11,9 @@ description = "Document Layout Analysis" readme = "README.md" license.file = "LICENSE" requires-python = ">=3.8" -keywords = [ - "document layout analysis", - "image segmentation", - "binarization", - "optical character recognition" -] +keywords = ["document layout analysis", "image segmentation"] -dynamic = [ - "dependencies", - "optional-dependencies", - "version" -] +dynamic = ["dependencies", "version"] classifiers = [ "Development Status :: 4 - Beta", @@ -30,17 +21,16 @@ classifiers = [ "Intended Audience :: Science/Research", "License :: OSI Approved :: Apache Software License", "Programming Language :: Python :: 3", - "Programming Language :: Python :: 3.8", - "Programming Language :: Python :: 3.9", - "Programming Language :: Python :: 3.10", - "Programming Language :: Python :: 3.11", "Programming Language :: Python :: 3 :: Only", "Topic :: Scientific/Engineering :: Image Processing", ] +[project.optional-dependencies] +OCR = ["torch <= 2.0.1", "transformers <= 4.30.2"] +plotting = ["matplotlib"] + [project.scripts] eynollah = "eynollah.cli:main" -eynollah-training = "eynollah.training.cli:main" ocrd-eynollah-segment = "eynollah.ocrd_cli:main" ocrd-sbb-binarize = "eynollah.ocrd_cli_binarization:main" @@ -51,35 +41,13 @@ Repository = "https://github.com/qurator-spk/eynollah.git" [tool.setuptools.dynamic] dependencies = {file = ["requirements.txt"]} optional-dependencies.test = {file = ["requirements-test.txt"]} -optional-dependencies.OCR = {file = ["requirements-ocr.txt"]} -optional-dependencies.plotting = {file = ["requirements-plotting.txt"]} -optional-dependencies.training = {file = ["requirements-training.txt"]} [tool.setuptools.packages.find] where = ["src"] [tool.setuptools.package-data] -"*" = ["*.json", '*.yml', '*.xml', '*.xsd', '*.ttf'] +"*" = ["*.json", '*.yml', '*.xml', '*.xsd'] [tool.coverage.run] branch = true source = ["eynollah"] - -[tool.ruff] -line-length = 120 - -[tool.ruff.lint] -ignore = [ -# disable unused imports -"F401", -# disable import order -"E402", -# disable unused variables -"F841", -# disable bare except -"E722", -] - -[tool.ruff.format] -quote-style = "preserve" - diff --git a/requirements-ocr.txt b/requirements-ocr.txt deleted file mode 100644 index 8f3b062..0000000 --- a/requirements-ocr.txt +++ /dev/null @@ -1,2 +0,0 @@ -torch -transformers <= 4.30.2 diff --git a/requirements-plotting.txt b/requirements-plotting.txt deleted file mode 100644 index 6ccafc3..0000000 --- a/requirements-plotting.txt +++ /dev/null @@ -1 +0,0 @@ -matplotlib diff --git a/requirements-test.txt b/requirements-test.txt index 3ebcf71..cce9428 100644 --- a/requirements-test.txt +++ b/requirements-test.txt @@ -1,4 +1,4 @@ pytest -pytest-isolate +pytest-subtests coverage[toml] black diff --git a/requirements-training.txt b/requirements-training.txt deleted file mode 120000 index e1bc9c3..0000000 --- a/requirements-training.txt +++ /dev/null @@ -1 +0,0 @@ -train/requirements.txt \ No newline at end of file diff --git a/requirements.txt b/requirements.txt index bbacd48..9ed0584 100644 --- a/requirements.txt +++ b/requirements.txt @@ -4,6 +4,4 @@ numpy <1.24.0 scikit-learn >= 0.23.2 tensorflow < 2.13 numba <= 0.58.1 -scikit-image -biopython -tabulate +loky diff --git a/src/eynollah/Charis-Regular.ttf b/src/eynollah/Charis-Regular.ttf deleted file mode 100644 index a4e75a4..0000000 Binary files a/src/eynollah/Charis-Regular.ttf and /dev/null differ diff --git a/src/eynollah/cli.py b/src/eynollah/cli.py new file mode 100644 index 0000000..c189aca --- /dev/null +++ b/src/eynollah/cli.py @@ -0,0 +1,404 @@ +import sys +import click +from ocrd_utils import initLogging, getLevelName, getLogger +from eynollah.eynollah import Eynollah, Eynollah_ocr +from eynollah.sbb_binarize import SbbBinarizer + +@click.group() +def main(): + pass + +@main.command() +@click.option( + "--dir_xml", + "-dx", + help="directory of GT page-xml files", + type=click.Path(exists=True, file_okay=False), +) +@click.option( + "--dir_out_modal_image", + "-domi", + help="directory where ground truth images would be written", + type=click.Path(exists=True, file_okay=False), +) +@click.option( + "--dir_out_classes", + "-docl", + help="directory where ground truth classes would be written", + type=click.Path(exists=True, file_okay=False), +) +@click.option( + "--input_height", + "-ih", + help="input height", +) +@click.option( + "--input_width", + "-iw", + help="input width", +) +@click.option( + "--min_area_size", + "-min", + help="min area size of regions considered for reading order training.", +) +def machine_based_reading_order(dir_xml, dir_out_modal_image, dir_out_classes, input_height, input_width, min_area_size): + xml_files_ind = os.listdir(dir_xml) + +@main.command() +@click.option('--patches/--no-patches', default=True, help='by enabling this parameter you let the model to see the image in patches.') +@click.option('--model_dir', '-m', type=click.Path(exists=True, file_okay=False), required=True, help='directory containing models for prediction') +@click.argument('input_image', required=False) +@click.argument('output_image', required=False) +@click.option( + "--dir_in", + "-di", + help="directory of input images", + type=click.Path(exists=True, file_okay=False), +) +@click.option( + "--dir_out", + "-do", + help="directory for output images", + type=click.Path(exists=True, file_okay=False), +) +def binarization(patches, model_dir, input_image, output_image, dir_in, dir_out): + assert (dir_out is None) == (dir_in is None), "Options -di and -do are mutually dependent" + assert (input_image is None) == (output_image is None), "INPUT_IMAGE and OUTPUT_IMAGE are mutually dependent" + assert (dir_in is None) != (input_image is None), "Specify either -di and -do options, or INPUT_IMAGE and OUTPUT_IMAGE" + SbbBinarizer(model_dir).run(image_path=input_image, use_patches=patches, save=output_image, dir_in=dir_in, dir_out=dir_out) + + + + +@main.command() +@click.option( + "--image", + "-i", + help="image filename", + type=click.Path(exists=True, dir_okay=False), +) + +@click.option( + "--out", + "-o", + help="directory to write output xml data", + type=click.Path(exists=True, file_okay=False), + required=True, +) +@click.option( + "--overwrite", + "-O", + help="overwrite (instead of skipping) if output xml exists", + is_flag=True, +) +@click.option( + "--dir_in", + "-di", + help="directory of images", + type=click.Path(exists=True, file_okay=False), +) +@click.option( + "--model", + "-m", + help="directory of models", + type=click.Path(exists=True, file_okay=False), + required=True, +) +@click.option( + "--save_images", + "-si", + help="if a directory is given, images in documents will be cropped and saved there", + type=click.Path(exists=True, file_okay=False), +) +@click.option( + "--save_layout", + "-sl", + help="if a directory is given, plot of layout will be saved there", + type=click.Path(exists=True, file_okay=False), +) +@click.option( + "--save_deskewed", + "-sd", + help="if a directory is given, deskewed image will be saved there", + type=click.Path(exists=True, file_okay=False), +) +@click.option( + "--save_all", + "-sa", + help="if a directory is given, all plots needed for documentation will be saved there", + type=click.Path(exists=True, file_okay=False), +) +@click.option( + "--save_page", + "-sp", + help="if a directory is given, page crop of image will be saved there", + type=click.Path(exists=True, file_okay=False), +) +@click.option( + "--enable-plotting/--disable-plotting", + "-ep/-noep", + is_flag=True, + help="If set, will plot intermediary files and images", +) +@click.option( + "--extract_only_images/--disable-extracting_only_images", + "-eoi/-noeoi", + is_flag=True, + help="If a directory is given, only images in documents will be cropped and saved there and the other processing will not be done", +) +@click.option( + "--allow-enhancement/--no-allow-enhancement", + "-ae/-noae", + is_flag=True, + help="if this parameter set to true, this tool would check that input image need resizing and enhancement or not. If so output of resized and enhanced image and corresponding layout data will be written in out directory", +) +@click.option( + "--curved-line/--no-curvedline", + "-cl/-nocl", + is_flag=True, + help="if this parameter set to true, this tool will try to return contoure of textlines instead of rectangle bounding box of textline. This should be taken into account that with this option the tool need more time to do process.", +) +@click.option( + "--textline_light/--no-textline_light", + "-tll/-notll", + is_flag=True, + help="if this parameter set to true, this tool will try to return contoure of textlines instead of rectangle bounding box of textline with a faster method.", +) +@click.option( + "--full-layout/--no-full-layout", + "-fl/-nofl", + is_flag=True, + help="if this parameter set to true, this tool will try to return all elements of layout.", +) +@click.option( + "--tables/--no-tables", + "-tab/-notab", + is_flag=True, + help="if this parameter set to true, this tool will try to detect tables.", +) +@click.option( + "--right2left/--left2right", + "-r2l/-l2r", + is_flag=True, + help="if this parameter set to true, this tool will extract right-to-left reading order.", +) +@click.option( + "--input_binary/--input-RGB", + "-ib/-irgb", + is_flag=True, + help="in general, eynollah uses RGB as input but if the input document is strongly dark, bright or for any other reason you can turn binarized input on. This option does not mean that you have to provide a binary image, otherwise this means that the tool itself will binarized the RGB input document.", +) +@click.option( + "--allow_scaling/--no-allow-scaling", + "-as/-noas", + is_flag=True, + help="if this parameter set to true, this tool would check the scale and if needed it will scale it to perform better layout detection", +) +@click.option( + "--headers_off/--headers-on", + "-ho/-noho", + is_flag=True, + help="if this parameter set to true, this tool would ignore headers role in reading order", +) +@click.option( + "--light_version/--original", + "-light/-org", + is_flag=True, + help="if this parameter set to true, this tool would use lighter version", +) +@click.option( + "--ignore_page_extraction/--extract_page_included", + "-ipe/-epi", + is_flag=True, + help="if this parameter set to true, this tool would ignore page extraction", +) +@click.option( + "--reading_order_machine_based/--heuristic_reading_order", + "-romb/-hro", + is_flag=True, + help="if this parameter set to true, this tool would apply machine based reading order detection", +) +@click.option( + "--do_ocr", + "-ocr/-noocr", + is_flag=True, + help="if this parameter set to true, this tool will try to do ocr", +) +@click.option( + "--num_col_upper", + "-ncu", + help="lower limit of columns in document image", +) +@click.option( + "--num_col_lower", + "-ncl", + help="upper limit of columns in document image", +) +@click.option( + "--skip_layout_and_reading_order", + "-slro/-noslro", + is_flag=True, + help="if this parameter set to true, this tool will ignore layout detection and reading order. It means that textline detection will be done within printspace and contours of textline will be written in xml output file.", +) +@click.option( + "--log_level", + "-l", + type=click.Choice(['OFF', 'DEBUG', 'INFO', 'WARN', 'ERROR']), + help="Override log level globally to this", +) + +def layout(image, out, overwrite, dir_in, model, save_images, save_layout, save_deskewed, save_all, extract_only_images, save_page, enable_plotting, allow_enhancement, curved_line, textline_light, full_layout, tables, right2left, input_binary, allow_scaling, headers_off, light_version, reading_order_machine_based, do_ocr, num_col_upper, num_col_lower, skip_layout_and_reading_order, ignore_page_extraction, log_level): + initLogging() + if log_level: + getLogger('eynollah').setLevel(getLevelName(log_level)) + assert enable_plotting or not save_layout, "Plotting with -sl also requires -ep" + assert enable_plotting or not save_deskewed, "Plotting with -sd also requires -ep" + assert enable_plotting or not save_all, "Plotting with -sa also requires -ep" + assert enable_plotting or not save_page, "Plotting with -sp also requires -ep" + assert enable_plotting or not save_images, "Plotting with -si also requires -ep" + assert enable_plotting or not allow_enhancement, "Plotting with -ae also requires -ep" + assert not enable_plotting or save_layout or save_deskewed or save_all or save_page or save_images or allow_enhancement, \ + "Plotting with -ep also requires -sl, -sd, -sa, -sp, -si or -ae" + assert textline_light == light_version, "Both light textline detection -tll and light version -light must be set or unset equally" + assert not extract_only_images or not allow_enhancement, "Image extraction -eoi can not be set alongside allow_enhancement -ae" + assert not extract_only_images or not allow_scaling, "Image extraction -eoi can not be set alongside allow_scaling -as" + assert not extract_only_images or not light_version, "Image extraction -eoi can not be set alongside light_version -light" + assert not extract_only_images or not curved_line, "Image extraction -eoi can not be set alongside curved_line -cl" + assert not extract_only_images or not textline_light, "Image extraction -eoi can not be set alongside textline_light -tll" + assert not extract_only_images or not full_layout, "Image extraction -eoi can not be set alongside full_layout -fl" + assert not extract_only_images or not tables, "Image extraction -eoi can not be set alongside tables -tab" + assert not extract_only_images or not right2left, "Image extraction -eoi can not be set alongside right2left -r2l" + assert not extract_only_images or not headers_off, "Image extraction -eoi can not be set alongside headers_off -ho" + assert image or dir_in, "Either a single image -i or a dir_in -di is required" + eynollah = Eynollah( + model, + logger=getLogger('eynollah'), + dir_out=out, + dir_of_cropped_images=save_images, + extract_only_images=extract_only_images, + dir_of_layout=save_layout, + dir_of_deskewed=save_deskewed, + dir_of_all=save_all, + dir_save_page=save_page, + enable_plotting=enable_plotting, + allow_enhancement=allow_enhancement, + curved_line=curved_line, + textline_light=textline_light, + full_layout=full_layout, + tables=tables, + right2left=right2left, + input_binary=input_binary, + allow_scaling=allow_scaling, + headers_off=headers_off, + light_version=light_version, + ignore_page_extraction=ignore_page_extraction, + reading_order_machine_based=reading_order_machine_based, + do_ocr=do_ocr, + num_col_upper=num_col_upper, + num_col_lower=num_col_lower, + skip_layout_and_reading_order=skip_layout_and_reading_order, + ) + if dir_in: + eynollah.run(dir_in=dir_in, overwrite=overwrite) + else: + eynollah.run(image_filename=image, overwrite=overwrite) + + +@main.command() +@click.option( + "--dir_in", + "-di", + help="directory of images", + type=click.Path(exists=True, file_okay=False), +) +@click.option( + "--dir_in_bin", + "-dib", + help="directory of binarized images. This should be given if you want to do prediction based on both rgb and bin images. And all bin images are png files", + type=click.Path(exists=True, file_okay=False), +) +@click.option( + "--out", + "-o", + help="directory to write output xml data", + type=click.Path(exists=True, file_okay=False), + required=True, +) +@click.option( + "--dir_xmls", + "-dx", + help="directory of xmls", + type=click.Path(exists=True, file_okay=False), +) +@click.option( + "--dir_out_image_text", + "-doit", + help="directory of images with predicted text", + type=click.Path(exists=True, file_okay=False), +) +@click.option( + "--model", + "-m", + help="directory of models", + type=click.Path(exists=True, file_okay=False), + required=True, +) +@click.option( + "--tr_ocr", + "-trocr/-notrocr", + is_flag=True, + help="if this parameter set to true, transformer ocr will be applied, otherwise cnn_rnn model.", +) +@click.option( + "--export_textline_images_and_text", + "-etit/-noetit", + is_flag=True, + help="if this parameter set to true, images and text in xml will be exported into output dir. This files can be used for training a OCR engine.", +) +@click.option( + "--do_not_mask_with_textline_contour", + "-nmtc/-mtc", + is_flag=True, + help="if this parameter set to true, cropped textline images will not be masked with textline contour.", +) +@click.option( + "--draw_texts_on_image", + "-dtoi/-ndtoi", + is_flag=True, + help="if this parameter set to true, the predicted texts will be displayed on an image.", +) +@click.option( + "--prediction_with_both_of_rgb_and_bin", + "-brb/-nbrb", + is_flag=True, + help="If this parameter is set to True, the prediction will be performed using both RGB and binary images. However, this does not necessarily improve results; it may be beneficial for certain document images.", +) +@click.option( + "--log_level", + "-l", + type=click.Choice(['OFF', 'DEBUG', 'INFO', 'WARN', 'ERROR']), + help="Override log level globally to this", +) + +def ocr(dir_in, dir_in_bin, out, dir_xmls, dir_out_image_text, model, tr_ocr, export_textline_images_and_text, do_not_mask_with_textline_contour, draw_texts_on_image, prediction_with_both_of_rgb_and_bin, log_level): + initLogging() + if log_level: + getLogger('eynollah').setLevel(getLevelName(log_level)) + eynollah_ocr = Eynollah_ocr( + dir_xmls=dir_xmls, + dir_out_image_text=dir_out_image_text, + dir_in=dir_in, + dir_in_bin=dir_in_bin, + dir_out=out, + dir_models=model, + tr_ocr=tr_ocr, + export_textline_images_and_text=export_textline_images_and_text, + do_not_mask_with_textline_contour=do_not_mask_with_textline_contour, + draw_texts_on_image=draw_texts_on_image, + prediction_with_both_of_rgb_and_bin=prediction_with_both_of_rgb_and_bin, + ) + eynollah_ocr.run() + +if __name__ == "__main__": + main() diff --git a/src/eynollah/cli/__init__.py b/src/eynollah/cli/__init__.py deleted file mode 100644 index 05dafa1..0000000 --- a/src/eynollah/cli/__init__.py +++ /dev/null @@ -1,22 +0,0 @@ -# NOTE: For predictable order of imports of torch/shapely/tensorflow -# this must be the first import of the CLI! -from ..eynollah_imports import imported_libs - -from .cli_models import models_cli -from .cli_binarize import binarize_cli - -from .cli import main -from .cli_binarize import binarize_cli -from .cli_enhance import enhance_cli -from .cli_extract_images import extract_images_cli -from .cli_layout import layout_cli -from .cli_ocr import ocr_cli -from .cli_readingorder import readingorder_cli - -main.add_command(binarize_cli, 'binarization') -main.add_command(enhance_cli, 'enhancement') -main.add_command(layout_cli, 'layout') -main.add_command(readingorder_cli, 'machine-based-reading-order') -main.add_command(models_cli, 'models') -main.add_command(ocr_cli, 'ocr') -main.add_command(extract_images_cli, 'extract-images') diff --git a/src/eynollah/cli/cli.py b/src/eynollah/cli/cli.py deleted file mode 100644 index b374fa8..0000000 --- a/src/eynollah/cli/cli.py +++ /dev/null @@ -1,66 +0,0 @@ -from dataclasses import dataclass -import logging -import sys -import os -from typing import Union - -import click - -from ..model_zoo import EynollahModelZoo -from .cli_models import models_cli - -@dataclass() -class EynollahCliCtx: - """ - Holds options relevant for all eynollah subcommands - """ - model_zoo: EynollahModelZoo - log_level : Union[str, None] = 'INFO' - - -@click.group() -@click.option( - "--model-basedir", - "-m", - help="directory of models", - # NOTE: not mandatory to exist so --help for subcommands works but will log a warning - # and raise exception when trying to load models in the CLI - # type=click.Path(exists=True), - default=f'{os.getcwd()}/models_eynollah', -) -@click.option( - "--model-overrides", - "-mv", - help="override default versions of model categories, syntax is 'CATEGORY VARIANT PATH', e.g 'region light /path/to/model'. See eynollah list-models for the full list", - type=(str, str, str), - multiple=True, -) -@click.option( - "--log_level", - "-l", - type=click.Choice(['OFF', 'DEBUG', 'INFO', 'WARN', 'ERROR']), - help="Override log level globally to this", -) -@click.pass_context -def main(ctx, model_basedir, model_overrides, log_level): - """ - eynollah - Document Layout Analysis, Image Enhancement, OCR - """ - # Initialize logging - console_handler = logging.StreamHandler(sys.stderr) - console_handler.setLevel(logging.NOTSET) - formatter = logging.Formatter('%(asctime)s.%(msecs)03d %(levelname)s %(name)s - %(message)s', datefmt='%H:%M:%S') - console_handler.setFormatter(formatter) - logging.getLogger('eynollah').addHandler(console_handler) - logging.getLogger('eynollah').setLevel(log_level or logging.INFO) - # Initialize model zoo - model_zoo = EynollahModelZoo(basedir=model_basedir, model_overrides=model_overrides) - # Initialize CLI context - ctx.obj = EynollahCliCtx( - model_zoo=model_zoo, - log_level=log_level, - ) - - -if __name__ == "__main__": - main() diff --git a/src/eynollah/cli/cli_binarize.py b/src/eynollah/cli/cli_binarize.py deleted file mode 100644 index d4a6e31..0000000 --- a/src/eynollah/cli/cli_binarize.py +++ /dev/null @@ -1,44 +0,0 @@ -import click - -@click.command() -@click.option('--patches/--no-patches', default=True, help='by enabling this parameter you let the model to see the image in patches.') -@click.option( - "--input-image", "--image", - "-i", - help="input image filename", - type=click.Path(exists=True, dir_okay=False) -) -@click.option( - "--dir_in", - "-di", - help="directory of input images (instead of --image)", - type=click.Path(exists=True, file_okay=False), -) -@click.option( - "--output", - "-o", - help="output image (if using -i) or output image directory (if using -di)", - type=click.Path(file_okay=True, dir_okay=True), - required=True, -) -@click.pass_context -def binarize_cli( - ctx, - patches, - input_image, - dir_in, - output, -): - """ - Binarize images with a ML model - """ - from ..sbb_binarize import SbbBinarizer - assert bool(input_image) != bool(dir_in), "Either -i (single input) or -di (directory) must be provided, but not both." - binarizer = SbbBinarizer(model_zoo=ctx.obj.model_zoo) - binarizer.run( - image_path=input_image, - use_patches=patches, - output=output, - dir_in=dir_in - ) - diff --git a/src/eynollah/cli/cli_enhance.py b/src/eynollah/cli/cli_enhance.py deleted file mode 100644 index fa4158b..0000000 --- a/src/eynollah/cli/cli_enhance.py +++ /dev/null @@ -1,63 +0,0 @@ -import click - -@click.command() -@click.option( - "--image", - "-i", - help="input image filename", - type=click.Path(exists=True, dir_okay=False), -) -@click.option( - "--out", - "-o", - help="directory for output PAGE-XML files", - type=click.Path(exists=True, file_okay=False), - required=True, -) -@click.option( - "--overwrite", - "-O", - help="overwrite (instead of skipping) if output xml exists", - is_flag=True, -) -@click.option( - "--dir_in", - "-di", - help="directory of input images (instead of --image)", - type=click.Path(exists=True, file_okay=False), -) -@click.option( - "--num_col_upper", - "-ncu", - help="lower limit of columns in document image", -) -@click.option( - "--num_col_lower", - "-ncl", - help="upper limit of columns in document image", -) -@click.option( - "--save_org_scale/--no_save_org_scale", - "-sos/-nosos", - is_flag=True, - help="if this parameter set to true, this tool will save the enhanced image in org scale.", -) -@click.pass_context -def enhance_cli(ctx, image, out, overwrite, dir_in, num_col_upper, num_col_lower, save_org_scale): - """ - Enhance image - """ - assert bool(image) != bool(dir_in), "Either -i (single input) or -di (directory) must be provided, but not both." - from ..image_enhancer import Enhancer - enhancer = Enhancer( - model_zoo=ctx.obj.model_zoo, - num_col_upper=num_col_upper, - num_col_lower=num_col_lower, - save_org_scale=save_org_scale, - ) - enhancer.run(overwrite=overwrite, - dir_in=dir_in, - image_filename=image, - dir_out=out, - ) - diff --git a/src/eynollah/cli/cli_extract_images.py b/src/eynollah/cli/cli_extract_images.py deleted file mode 100644 index 0add5b5..0000000 --- a/src/eynollah/cli/cli_extract_images.py +++ /dev/null @@ -1,100 +0,0 @@ -import click - -@click.command() -@click.option( - "--image", - "-i", - help="input image filename", - type=click.Path(exists=True, dir_okay=False), -) - -@click.option( - "--out", - "-o", - help="directory for output PAGE-XML files", - type=click.Path(exists=True, file_okay=False), - required=True, -) -@click.option( - "--overwrite", - "-O", - help="overwrite (instead of skipping) if output xml exists", - is_flag=True, -) -@click.option( - "--dir_in", - "-di", - help="directory of input images (instead of --image)", - type=click.Path(exists=True, file_okay=False), -) -@click.option( - "--save_images", - "-si", - help="if a directory is given, images in documents will be cropped and saved there", - type=click.Path(exists=True, file_okay=False), -) -@click.option( - "--enable-plotting/--disable-plotting", - "-ep/-noep", - is_flag=True, - help="If set, will plot intermediary files and images", -) -@click.option( - "--input_binary/--input-RGB", - "-ib/-irgb", - is_flag=True, - help="In general, eynollah uses RGB as input but if the input document is very dark, very bright or for any other reason you can turn on input binarization. When this flag is set, eynollah will binarize the RGB input document, you should always provide RGB images to eynollah.", -) -@click.option( - "--ignore_page_extraction/--extract_page_included", - "-ipe/-epi", - is_flag=True, - help="if this parameter set to true, this tool would ignore page extraction", -) -@click.option( - "--num_col_upper", - "-ncu", - help="lower limit of columns in document image", -) -@click.option( - "--num_col_lower", - "-ncl", - help="upper limit of columns in document image", -) -@click.pass_context -def extract_images_cli( - ctx, - image, - out, - overwrite, - dir_in, - save_images, - enable_plotting, - input_binary, - num_col_upper, - num_col_lower, - ignore_page_extraction, -): - """ - Detect Layout (with optional image enhancement and reading order detection) - """ - assert enable_plotting or not save_images, "Plotting with -si also requires -ep" - assert not enable_plotting or save_images, "Plotting with -ep also requires -si" - assert bool(image) != bool(dir_in), "Either -i (single input) or -di (directory) must be provided, but not both." - - from ..extract_images import EynollahImageExtractor - extractor = EynollahImageExtractor( - model_zoo=ctx.obj.model_zoo, - enable_plotting=enable_plotting, - input_binary=input_binary, - ignore_page_extraction=ignore_page_extraction, - num_col_upper=num_col_upper, - num_col_lower=num_col_lower, - ) - extractor.run(overwrite=overwrite, - image_filename=image, - dir_in=dir_in, - dir_out=out, - dir_of_cropped_images=save_images, - ) - diff --git a/src/eynollah/cli/cli_layout.py b/src/eynollah/cli/cli_layout.py deleted file mode 100644 index df66993..0000000 --- a/src/eynollah/cli/cli_layout.py +++ /dev/null @@ -1,223 +0,0 @@ -import click - -@click.command() -@click.option( - "--image", - "-i", - help="input image filename", - type=click.Path(exists=True, dir_okay=False), -) - -@click.option( - "--out", - "-o", - help="directory for output PAGE-XML files", - type=click.Path(exists=True, file_okay=False), - required=True, -) -@click.option( - "--overwrite", - "-O", - help="overwrite (instead of skipping) if output xml exists", - is_flag=True, -) -@click.option( - "--dir_in", - "-di", - help="directory of input images (instead of --image)", - type=click.Path(exists=True, file_okay=False), -) -@click.option( - "--save_images", - "-si", - help="if a directory is given, images in documents will be cropped and saved there", - type=click.Path(exists=True, file_okay=False), -) -@click.option( - "--save_layout", - "-sl", - help="if a directory is given, plot of layout will be saved there", - type=click.Path(exists=True, file_okay=False), -) -@click.option( - "--save_deskewed", - "-sd", - help="if a directory is given, deskewed image will be saved there", - type=click.Path(exists=True, file_okay=False), -) -@click.option( - "--save_all", - "-sa", - help="if a directory is given, all plots needed for documentation will be saved there", - type=click.Path(exists=True, file_okay=False), -) -@click.option( - "--save_page", - "-sp", - help="if a directory is given, page crop of image will be saved there", - type=click.Path(exists=True, file_okay=False), -) -@click.option( - "--enable-plotting/--disable-plotting", - "-ep/-noep", - is_flag=True, - help="If set, will plot intermediary files and images", -) -@click.option( - "--allow-enhancement/--no-allow-enhancement", - "-ae/-noae", - is_flag=True, - help="if this parameter set to true, this tool would check that input image need resizing and enhancement or not. If so output of resized and enhanced image and corresponding layout data will be written in out directory", -) -@click.option( - "--curved-line/--no-curvedline", - "-cl/-nocl", - is_flag=True, - help="if this parameter set to true, this tool will try to return contoure of textlines instead of rectangle bounding box of textline. This should be taken into account that with this option the tool need more time to do process.", -) -@click.option( - "--full-layout/--no-full-layout", - "-fl/-nofl", - is_flag=True, - help="if this parameter set to true, this tool will try to return all elements of layout.", -) -@click.option( - "--tables/--no-tables", - "-tab/-notab", - is_flag=True, - help="if this parameter set to true, this tool will try to detect tables.", -) -@click.option( - "--right2left/--left2right", - "-r2l/-l2r", - is_flag=True, - help="if this parameter set to true, this tool will extract right-to-left reading order.", -) -@click.option( - "--input_binary/--input-RGB", - "-ib/-irgb", - is_flag=True, - help="In general, eynollah uses RGB as input but if the input document is very dark, very bright or for any other reason you can turn on input binarization. When this flag is set, eynollah will binarize the RGB input document, you should always provide RGB images to eynollah.", -) -@click.option( - "--allow_scaling/--no-allow-scaling", - "-as/-noas", - is_flag=True, - help="if this parameter set to true, this tool would check the scale and if needed it will scale it to perform better layout detection", -) -@click.option( - "--headers_off/--headers-on", - "-ho/-noho", - is_flag=True, - help="if this parameter set to true, this tool would ignore headers role in reading order", -) -@click.option( - "--ignore_page_extraction/--extract_page_included", - "-ipe/-epi", - is_flag=True, - help="if this parameter set to true, this tool would ignore page extraction", -) -@click.option( - "--reading_order_machine_based/--heuristic_reading_order", - "-romb/-hro", - is_flag=True, - help="if this parameter set to true, this tool would apply machine based reading order detection", -) -@click.option( - "--num_col_upper", - "-ncu", - help="lower limit of columns in document image", -) -@click.option( - "--num_col_lower", - "-ncl", - help="upper limit of columns in document image", -) -@click.option( - "--threshold_art_class_layout", - "-tharl", - help="threshold of artifical class in the case of layout detection. The default value is 0.1", -) -@click.option( - "--threshold_art_class_textline", - "-thart", - help="threshold of artifical class in the case of textline detection. The default value is 0.1", -) -@click.option( - "--skip_layout_and_reading_order", - "-slro/-noslro", - is_flag=True, - help="if this parameter set to true, this tool will ignore layout detection and reading order. It means that textline detection will be done within printspace and contours of textline will be written in xml output file.", -) -@click.pass_context -def layout_cli( - ctx, - image, - out, - overwrite, - dir_in, - save_images, - save_layout, - save_deskewed, - save_all, - save_page, - enable_plotting, - allow_enhancement, - curved_line, - full_layout, - tables, - right2left, - input_binary, - allow_scaling, - headers_off, - reading_order_machine_based, - num_col_upper, - num_col_lower, - threshold_art_class_textline, - threshold_art_class_layout, - skip_layout_and_reading_order, - ignore_page_extraction, -): - """ - Detect Layout (with optional image enhancement and reading order detection) - """ - from ..eynollah import Eynollah - assert enable_plotting or not save_layout, "Plotting with -sl also requires -ep" - assert enable_plotting or not save_deskewed, "Plotting with -sd also requires -ep" - assert enable_plotting or not save_all, "Plotting with -sa also requires -ep" - assert enable_plotting or not save_page, "Plotting with -sp also requires -ep" - assert enable_plotting or not save_images, "Plotting with -si also requires -ep" - assert enable_plotting or not allow_enhancement, "Plotting with -ae also requires -ep" - assert not enable_plotting or save_layout or save_deskewed or save_all or save_page or save_images or allow_enhancement, \ - "Plotting with -ep also requires -sl, -sd, -sa, -sp, -si or -ae" - assert bool(image) != bool(dir_in), "Either -i (single input) or -di (directory) must be provided, but not both." - eynollah = Eynollah( - model_zoo=ctx.obj.model_zoo, - enable_plotting=enable_plotting, - allow_enhancement=allow_enhancement, - curved_line=curved_line, - full_layout=full_layout, - tables=tables, - right2left=right2left, - input_binary=input_binary, - allow_scaling=allow_scaling, - headers_off=headers_off, - ignore_page_extraction=ignore_page_extraction, - reading_order_machine_based=reading_order_machine_based, - num_col_upper=num_col_upper, - num_col_lower=num_col_lower, - skip_layout_and_reading_order=skip_layout_and_reading_order, - threshold_art_class_textline=threshold_art_class_textline, - threshold_art_class_layout=threshold_art_class_layout, - ) - eynollah.run(overwrite=overwrite, - image_filename=image, - dir_in=dir_in, - dir_out=out, - dir_of_cropped_images=save_images, - dir_of_layout=save_layout, - dir_of_deskewed=save_deskewed, - dir_of_all=save_all, - dir_save_page=save_page, - ) - diff --git a/src/eynollah/cli/cli_models.py b/src/eynollah/cli/cli_models.py deleted file mode 100644 index f3de596..0000000 --- a/src/eynollah/cli/cli_models.py +++ /dev/null @@ -1,69 +0,0 @@ -from pathlib import Path -from typing import Set, Tuple -import click - -from eynollah.model_zoo.default_specs import MODELS_VERSION - -@click.group() -@click.pass_context -def models_cli( - ctx, -): - """ - Organize models for the various runners in eynollah. - """ - assert ctx.obj.model_zoo - - -@models_cli.command('list') -@click.pass_context -def list_models( - ctx, -): - """ - List all the models in the zoo - """ - print(f"Model basedir: {ctx.obj.model_zoo.model_basedir}") - print(f"Model overrides: {ctx.obj.model_zoo.model_overrides}") - print(ctx.obj.model_zoo) - - -@models_cli.command('package') -@click.option( - '--set-version', '-V', 'version', help="Version to use for packaging", default=MODELS_VERSION, show_default=True -) -@click.argument('output_dir') -@click.pass_context -def package( - ctx, - version, - output_dir, -): - """ - Generate shell code to copy all the models in the zoo into properly named folders in OUTPUT_DIR for distribution. - - eynollah models -m SRC package OUTPUT_DIR - - SRC should contain a directory "models_eynollah" containing all the models. - """ - mkdirs: Set[Path] = set([]) - copies: Set[Tuple[Path, Path]] = set([]) - for spec in ctx.obj.model_zoo.specs.specs: - # skip these as they are dependent on the ocr model - if spec.category in ('num_to_char', 'characters'): - continue - src: Path = ctx.obj.model_zoo.model_path(spec.category, spec.variant) - # Only copy the top-most directory relative to models_eynollah - while src.parent.name != 'models_eynollah': - src = src.parent - for dist in spec.dists: - dist_dir = Path(f"{output_dir}/models_{dist}_{version}/models_eynollah") - copies.add((src, dist_dir)) - mkdirs.add(dist_dir) - for dir in mkdirs: - print(f"mkdir -vp {dir}") - for (src, dst) in copies: - print(f"cp -vr {src} {dst}") - for dir in mkdirs: - zip_path = Path(f'../{dir.parent.name}.zip') - print(f"(cd {dir}/..; zip -vr {zip_path} models_eynollah)") diff --git a/src/eynollah/cli/cli_ocr.py b/src/eynollah/cli/cli_ocr.py deleted file mode 100644 index 406af61..0000000 --- a/src/eynollah/cli/cli_ocr.py +++ /dev/null @@ -1,103 +0,0 @@ -import click - -@click.command() -@click.option( - "--image", - "-i", - help="input image filename", - type=click.Path(exists=True, dir_okay=False), -) -@click.option( - "--dir_in", - "-di", - help="directory of input images (instead of --image)", - type=click.Path(exists=True, file_okay=False), -) -@click.option( - "--dir_in_bin", - "-dib", - help=("directory of binarized images (in addition to --dir_in for RGB images; filename stems must match the RGB image files, with '.png' \n Perform prediction using both RGB and binary images. (This does not necessarily improve results, however it may be beneficial for certain document images."), - type=click.Path(exists=True, file_okay=False), -) -@click.option( - "--dir_xmls", - "-dx", - help="directory of input PAGE-XML files (in addition to --dir_in; filename stems must match the image files, with '.xml' suffix).", - type=click.Path(exists=True, file_okay=False), - required=True, -) -@click.option( - "--out", - "-o", - help="directory for output PAGE-XML files", - type=click.Path(exists=True, file_okay=False), - required=True, -) -@click.option( - "--dir_out_image_text", - "-doit", - help="directory for output images, newly rendered with predicted text", - type=click.Path(exists=True, file_okay=False), -) -@click.option( - "--overwrite", - "-O", - help="overwrite (instead of skipping) if output xml exists", - is_flag=True, -) -@click.option( - "--tr_ocr", - "-trocr/-notrocr", - is_flag=True, - help="if this parameter set to true, transformer ocr will be applied, otherwise cnn_rnn model.", -) -@click.option( - "--do_not_mask_with_textline_contour", - "-nmtc/-mtc", - is_flag=True, - help="if this parameter set to true, cropped textline images will not be masked with textline contour.", -) -@click.option( - "--batch_size", - "-bs", - help="number of inference batch size. Default b_s for trocr and cnn_rnn models are 2 and 8 respectively", -) -@click.option( - "--min_conf_value_of_textline_text", - "-min_conf", - help="minimum OCR confidence value. Text lines with a confidence value lower than this threshold will not be included in the output XML file.", -) -@click.pass_context -def ocr_cli( - ctx, - image, - dir_in, - dir_in_bin, - dir_xmls, - out, - dir_out_image_text, - overwrite, - tr_ocr, - do_not_mask_with_textline_contour, - batch_size, - min_conf_value_of_textline_text, -): - """ - Recognize text with a CNN/RNN or transformer ML model. - """ - assert bool(image) ^ bool(dir_in), "Either -i (single image) or -di (directory) must be provided, but not both." - from ..eynollah_ocr import Eynollah_ocr - eynollah_ocr = Eynollah_ocr( - model_zoo=ctx.obj.model_zoo, - tr_ocr=tr_ocr, - do_not_mask_with_textline_contour=do_not_mask_with_textline_contour, - batch_size=batch_size, - min_conf_value_of_textline_text=min_conf_value_of_textline_text) - eynollah_ocr.run(overwrite=overwrite, - dir_in=dir_in, - dir_in_bin=dir_in_bin, - image_filename=image, - dir_xmls=dir_xmls, - dir_out_image_text=dir_out_image_text, - dir_out=out, - ) diff --git a/src/eynollah/cli/cli_readingorder.py b/src/eynollah/cli/cli_readingorder.py deleted file mode 100644 index 0f44b7f..0000000 --- a/src/eynollah/cli/cli_readingorder.py +++ /dev/null @@ -1,35 +0,0 @@ -import click - -@click.command() -@click.option( - "--input", - "-i", - help="PAGE-XML input filename", - type=click.Path(exists=True, dir_okay=False), -) -@click.option( - "--dir_in", - "-di", - help="directory of PAGE-XML input files (instead of --input)", - type=click.Path(exists=True, file_okay=False), -) -@click.option( - "--out", - "-o", - help="directory for output images", - type=click.Path(exists=True, file_okay=False), - required=True, -) -@click.pass_context -def readingorder_cli(ctx, input, dir_in, out): - """ - Generate ReadingOrder with a ML model - """ - from ..mb_ro_on_layout import machine_based_reading_order_on_layout - assert bool(input) != bool(dir_in), "Either -i (single input) or -di (directory) must be provided, but not both." - orderer = machine_based_reading_order_on_layout(model_zoo=ctx.obj.model_zoo) - orderer.run(xml_filename=input, - dir_in=dir_in, - dir_out=out, - ) - diff --git a/src/eynollah/extract_images.py b/src/eynollah/extract_images.py deleted file mode 100644 index 9942cf8..0000000 --- a/src/eynollah/extract_images.py +++ /dev/null @@ -1,281 +0,0 @@ -""" -extract images? -""" - -from concurrent.futures import ProcessPoolExecutor -import logging -from multiprocessing import cpu_count -import os -import time -from typing import Optional -from pathlib import Path -import tensorflow as tf -import numpy as np -import cv2 - -from eynollah.utils.contour import filter_contours_area_of_image, return_contours_of_image, return_contours_of_interested_region -from eynollah.utils.resize import resize_image - -from .model_zoo.model_zoo import EynollahModelZoo -from .eynollah import Eynollah -from .utils import box2rect, is_image_filename -from .plot import EynollahPlotter - -class EynollahImageExtractor(Eynollah): - - def __init__( - self, - *, - model_zoo: EynollahModelZoo, - enable_plotting : bool = False, - input_binary : bool = False, - ignore_page_extraction : bool = False, - num_col_upper : Optional[int] = None, - num_col_lower : Optional[int] = None, - full_layout : bool = False, - tables : bool = False, - curved_line : bool = False, - allow_enhancement : bool = False, - - ): - self.logger = logging.getLogger('eynollah.extract_images') - self.model_zoo = model_zoo - self.plotter = None - self.tables = tables - self.curved_line = curved_line - self.allow_enhancement = allow_enhancement - - self.enable_plotting = enable_plotting - # --input-binary sensible if image is very dark, if layout is not working. - self.input_binary = input_binary - self.ignore_page_extraction = ignore_page_extraction - self.full_layout = full_layout - if num_col_upper: - self.num_col_upper = int(num_col_upper) - else: - self.num_col_upper = num_col_upper - if num_col_lower: - self.num_col_lower = int(num_col_lower) - else: - self.num_col_lower = num_col_lower - - # for parallelization of CPU-intensive tasks: - self.executor = ProcessPoolExecutor(max_workers=cpu_count()) - - t_start = time.time() - - try: - for device in tf.config.list_physical_devices('GPU'): - tf.config.experimental.set_memory_growth(device, True) - except: - self.logger.warning("no GPU device available") - - self.logger.info("Loading models...") - self.setup_models() - self.logger.info(f"Model initialization complete ({time.time() - t_start:.1f}s)") - - def setup_models(self): - - loadable = [ - "col_classifier", - "binarization", - "page", - "extract_images", - ] - self.model_zoo.load_models(*loadable) - - def get_regions_light_v_extract_only_images(self,img, num_col_classifier): - self.logger.debug("enter get_regions_extract_images_only") - erosion_hurts = False - img_org = np.copy(img) - img_height_h = img_org.shape[0] - img_width_h = img_org.shape[1] - - if num_col_classifier == 1: - img_w_new = 700 - elif num_col_classifier == 2: - img_w_new = 900 - elif num_col_classifier == 3: - img_w_new = 1500 - elif num_col_classifier == 4: - img_w_new = 1800 - elif num_col_classifier == 5: - img_w_new = 2200 - elif num_col_classifier == 6: - img_w_new = 2500 - else: - raise ValueError("num_col_classifier must be in range 1..6") - img_h_new = int(img.shape[0] / float(img.shape[1]) * img_w_new) - img_resized = resize_image(img,img_h_new, img_w_new ) - - prediction_regions_org, _ = self.do_prediction_new_concept(True, img_resized, self.model_zoo.get("extract_images")) - - prediction_regions_org = resize_image(prediction_regions_org,img_height_h, img_width_h ) - image_page, page_coord, cont_page = self.extract_page() - - prediction_regions_org = prediction_regions_org[page_coord[0] : page_coord[1], page_coord[2] : page_coord[3]] - prediction_regions_org=prediction_regions_org[:,:,0] - - mask_lines_only = (prediction_regions_org[:,:] ==3)*1 - mask_texts_only = (prediction_regions_org[:,:] ==1)*1 - mask_images_only=(prediction_regions_org[:,:] ==2)*1 - - polygons_seplines, hir_seplines = return_contours_of_image(mask_lines_only) - polygons_seplines = filter_contours_area_of_image( - mask_lines_only, polygons_seplines, hir_seplines, max_area=1, min_area=0.00001, dilate=1) - - polygons_of_only_texts = return_contours_of_interested_region(mask_texts_only,1,0.00001) - polygons_of_only_lines = return_contours_of_interested_region(mask_lines_only,1,0.00001) - - text_regions_p_true = np.zeros(prediction_regions_org.shape) - text_regions_p_true = cv2.fillPoly(text_regions_p_true, pts = polygons_of_only_lines, color=(3,3,3)) - - text_regions_p_true[:,:][mask_images_only[:,:] == 1] = 2 - text_regions_p_true = cv2.fillPoly(text_regions_p_true, pts=polygons_of_only_texts, color=(1,1,1)) - - text_regions_p_true[text_regions_p_true.shape[0]-15:text_regions_p_true.shape[0], :] = 0 - text_regions_p_true[:, text_regions_p_true.shape[1]-15:text_regions_p_true.shape[1]] = 0 - - ##polygons_of_images = return_contours_of_interested_region(text_regions_p_true, 2, 0.0001) - polygons_of_images = return_contours_of_interested_region(text_regions_p_true, 2, 0.001) - - polygons_of_images_fin = [] - for ploy_img_ind in polygons_of_images: - box = _, _, w, h = cv2.boundingRect(ploy_img_ind) - if h < 150 or w < 150: - pass - else: - page_coord_img = box2rect(box) # type: ignore - polygons_of_images_fin.append(np.array([[page_coord_img[2], page_coord_img[0]], - [page_coord_img[3], page_coord_img[0]], - [page_coord_img[3], page_coord_img[1]], - [page_coord_img[2], page_coord_img[1]]])) - - self.logger.debug("exit get_regions_extract_images_only") - return (text_regions_p_true, - erosion_hurts, - polygons_seplines, - polygons_of_images_fin, - image_page, - page_coord, - cont_page) - - def run(self, - overwrite: bool = False, - image_filename: Optional[str] = None, - dir_in: Optional[str] = None, - dir_out: Optional[str] = None, - dir_of_cropped_images: Optional[str] = None, - dir_of_layout: Optional[str] = None, - dir_of_deskewed: Optional[str] = None, - dir_of_all: Optional[str] = None, - dir_save_page: Optional[str] = None, - ): - """ - Get image and scales, then extract the page of scanned image - """ - self.logger.debug("enter run") - t0_tot = time.time() - # Log enabled features directly - enabled_modes = [] - if self.full_layout: - enabled_modes.append("Full layout analysis") - if self.tables: - enabled_modes.append("Table detection") - if enabled_modes: - self.logger.info("Enabled modes: " + ", ".join(enabled_modes)) - if self.enable_plotting: - self.logger.info("Saving debug plots") - if dir_of_cropped_images: - self.logger.info(f"Saving cropped images to: {dir_of_cropped_images}") - if dir_of_layout: - self.logger.info(f"Saving layout plots to: {dir_of_layout}") - if dir_of_deskewed: - self.logger.info(f"Saving deskewed images to: {dir_of_deskewed}") - - if dir_in: - ls_imgs = [os.path.join(dir_in, image_filename) - for image_filename in filter(is_image_filename, - os.listdir(dir_in))] - elif image_filename: - ls_imgs = [image_filename] - else: - raise ValueError("run requires either a single image filename or a directory") - - for img_filename in ls_imgs: - self.logger.info(img_filename) - t0 = time.time() - - self.reset_file_name_dir(img_filename, dir_out) - if self.enable_plotting: - self.plotter = EynollahPlotter(dir_out=dir_out, - dir_of_all=dir_of_all, - dir_save_page=dir_save_page, - dir_of_deskewed=dir_of_deskewed, - dir_of_cropped_images=dir_of_cropped_images, - dir_of_layout=dir_of_layout, - image_filename_stem=Path(img_filename).stem) - #print("text region early -11 in %.1fs", time.time() - t0) - if os.path.exists(self.writer.output_filename): - if overwrite: - self.logger.warning("will overwrite existing output file '%s'", self.writer.output_filename) - else: - self.logger.warning("will skip input for existing output file '%s'", self.writer.output_filename) - continue - - pcgts = self.run_single() - self.logger.info("Job done in %.1fs", time.time() - t0) - self.writer.write_pagexml(pcgts) - - if dir_in: - self.logger.info("All jobs done in %.1fs", time.time() - t0_tot) - - def run_single(self): - t0 = time.time() - - self.logger.info(f"Processing file: {self.writer.image_filename}") - self.logger.info("Step 1/5: Image Enhancement") - - img_res, is_image_enhanced, num_col_classifier, _ = \ - self.run_enhancement() - - self.logger.info(f"Image: {self.image.shape[1]}x{self.image.shape[0]}, " - f"{self.dpi} DPI, {num_col_classifier} columns") - if is_image_enhanced: - self.logger.info("Enhancement applied") - - self.logger.info(f"Enhancement complete ({time.time() - t0:.1f}s)") - - - # Image Extraction Mode - self.logger.info("Step 2/5: Image Extraction Mode") - - _, _, _, polygons_of_images, \ - image_page, page_coord, cont_page = \ - self.get_regions_light_v_extract_only_images(img_res, num_col_classifier) - - pcgts = self.writer.build_pagexml_no_full_layout( - found_polygons_text_region=[], - page_coord=page_coord, - order_of_texts=[], - all_found_textline_polygons=[], - all_box_coord=[], - found_polygons_text_region_img=polygons_of_images, - found_polygons_marginals_left=[], - found_polygons_marginals_right=[], - all_found_textline_polygons_marginals_left=[], - all_found_textline_polygons_marginals_right=[], - all_box_coord_marginals_left=[], - all_box_coord_marginals_right=[], - slopes=[], - slopes_marginals_left=[], - slopes_marginals_right=[], - cont_page=cont_page, - polygons_seplines=[], - found_polygons_tables=[], - ) - if self.plotter: - self.plotter.write_images_into_directory(polygons_of_images, image_page) - - self.logger.info("Image extraction complete") - return pcgts diff --git a/src/eynollah/eynollah.py b/src/eynollah/eynollah.py index 9383c5e..022cf0a 100644 --- a/src/eynollah/eynollah.py +++ b/src/eynollah/eynollah.py @@ -1,47 +1,39 @@ -""" -document layout analysis (segmentation) with output in PAGE-XML -""" # pylint: disable=no-member,invalid-name,line-too-long,missing-function-docstring,missing-class-docstring,too-many-branches # pylint: disable=too-many-locals,wrong-import-position,too-many-lines,too-many-statements,chained-comparison,fixme,broad-except,c-extension-no-member # pylint: disable=too-many-public-methods,too-many-arguments,too-many-instance-attributes,too-many-public-methods, # pylint: disable=consider-using-enumerate -# FIXME: fix all of those... -# pyright: reportUnnecessaryTypeIgnoreComment=true -# pyright: reportPossiblyUnboundVariable=false -# pyright: reportOperatorIssue=false -# pyright: reportUnboundVariable=false -# pyright: reportArgumentType=false -# pyright: reportAttributeAccessIssue=false -# pyright: reportOptionalMemberAccess=false -# pyright: reportGeneralTypeIssues=false -# pyright: reportOptionalSubscript=false - -import logging -import sys +""" +document layout analysis (segmentation) with output in PAGE-XML +""" +from logging import Logger from difflib import SequenceMatcher as sq +from PIL import Image, ImageDraw, ImageFont import math import os +import sys import time from typing import Optional +import atexit +import warnings from functools import partial from pathlib import Path from multiprocessing import cpu_count import gc +import copy +import json -from concurrent.futures import ProcessPoolExecutor +from loky import ProcessPoolExecutor +import xml.etree.ElementTree as ET import cv2 import numpy as np -import shapely.affinity from scipy.signal import find_peaks from scipy.ndimage import gaussian_filter1d -from skimage.morphology import skeletonize -from ocrd_utils import tf_disable_interactive_logs -import statistics +from numba import cuda -tf_disable_interactive_logs() +from ocrd import OcrdPage +from ocrd_utils import getLogger, tf_disable_interactive_logs -import tensorflow as tf try: import torch except ImportError: @@ -50,53 +42,72 @@ try: import matplotlib.pyplot as plt except ImportError: plt = None +try: + from transformers import TrOCRProcessor, VisionEncoderDecoderModel +except ImportError: + TrOCRProcessor = VisionEncoderDecoderModel = None + +#os.environ['CUDA_VISIBLE_DEVICES'] = '-1' +tf_disable_interactive_logs() +import tensorflow as tf +from tensorflow.python.keras import backend as K +from tensorflow.keras.models import load_model +tf.get_logger().setLevel("ERROR") +warnings.filterwarnings("ignore") +# use tf1 compatibility for keras backend +from tensorflow.compat.v1.keras.backend import set_session +from tensorflow.keras import layers +from tensorflow.keras.layers import StringLookup -from .model_zoo import EynollahModelZoo from .utils.contour import ( filter_contours_area_of_image, filter_contours_area_of_image_tables, - find_center_of_contours, + find_contours_mean_y_diff, find_new_features_of_contours, find_features_of_contours, get_text_region_boxes_by_given_contours, + get_textregion_contours_in_org_image, get_textregion_contours_in_org_image_light, return_contours_of_image, return_contours_of_interested_region, + return_contours_of_interested_region_by_min_size, + return_contours_of_interested_textline, return_parent_contours, - dilate_textregion_contours, - dilate_textline_contours, - polygon2contour, - contour2polygon, - join_polygons, - make_intersection, ) from .utils.rotate import ( rotate_image, rotation_not_90_func, - rotation_not_90_func_full_layout, + rotation_not_90_func_full_layout ) from .utils.separate_lines import ( + textline_contours_postprocessing, + separate_lines_new2, return_deskew_slop, + do_work_of_slopes_new, do_work_of_slopes_new_curved, + do_work_of_slopes_new_light, +) +from .utils.drop_capitals import ( + adhere_drop_capital_region_into_corresponding_textline, + filter_small_drop_capitals_from_no_patch_layout ) from .utils.marginals import get_marginals from .utils.resize import resize_image -from .utils.shm import share_ndarray from .utils import ( - is_image_filename, - isNaN, + boosting_headers_by_longshot_region_segmentation, crop_image_inside_box, - box2rect, find_num_col, otsu_copy_binary, + put_drop_out_from_only_drop_model, putt_bb_of_drop_capitals_of_model_in_patches_in_layout, + check_any_text_region_in_model_one_is_main_or_header, check_any_text_region_in_model_one_is_main_or_header_light, small_textlines_to_parent_adherence2, order_of_regions, find_number_of_columns_in_document, return_boxes_of_images_by_order_of_reading_new ) -from .utils.pil_cv2 import pil2cv +from .utils.pil_cv2 import check_dpi, pil2cv from .utils.xml import order_and_id_of_texts from .plot import EynollahPlotter from .writer import EynollahXmlWriter @@ -113,47 +124,111 @@ patch_size = 1 num_patches =21*21#14*14#28*28#14*14#28*28 +class Patches(layers.Layer): + def __init__(self, **kwargs): + super(Patches, self).__init__() + self.patch_size = patch_size + + def call(self, images): + batch_size = tf.shape(images)[0] + patches = tf.image.extract_patches( + images=images, + sizes=[1, self.patch_size, self.patch_size, 1], + strides=[1, self.patch_size, self.patch_size, 1], + rates=[1, 1, 1, 1], + padding="VALID", + ) + patch_dims = patches.shape[-1] + patches = tf.reshape(patches, [batch_size, -1, patch_dims]) + return patches + def get_config(self): + + config = super().get_config().copy() + config.update({ + 'patch_size': self.patch_size, + }) + return config + + +class PatchEncoder(layers.Layer): + def __init__(self, **kwargs): + super(PatchEncoder, self).__init__() + self.num_patches = num_patches + self.projection = layers.Dense(units=projection_dim) + self.position_embedding = layers.Embedding( + input_dim=num_patches, output_dim=projection_dim + ) + + def call(self, patch): + positions = tf.range(start=0, limit=self.num_patches, delta=1) + encoded = self.projection(patch) + self.position_embedding(positions) + return encoded + def get_config(self): + + config = super().get_config().copy() + config.update({ + 'num_patches': self.num_patches, + 'projection': self.projection, + 'position_embedding': self.position_embedding, + }) + return config class Eynollah: def __init__( self, - *, - model_zoo: EynollahModelZoo, + dir_models : str, + dir_out : Optional[str] = None, + dir_of_cropped_images : Optional[str] = None, + extract_only_images : bool =False, + dir_of_layout : Optional[str] = None, + dir_of_deskewed : Optional[str] = None, + dir_of_all : Optional[str] = None, + dir_save_page : Optional[str] = None, enable_plotting : bool = False, allow_enhancement : bool = False, curved_line : bool = False, + textline_light : bool = False, full_layout : bool = False, tables : bool = False, right2left : bool = False, input_binary : bool = False, allow_scaling : bool = False, headers_off : bool = False, + light_version : bool = False, ignore_page_extraction : bool = False, reading_order_machine_based : bool = False, + do_ocr : bool = False, num_col_upper : Optional[int] = None, num_col_lower : Optional[int] = None, - threshold_art_class_layout: Optional[float] = None, - threshold_art_class_textline: Optional[float] = None, skip_layout_and_reading_order : bool = False, - logger : Optional[logging.Logger] = None, + logger : Optional[Logger] = None, ): - self.logger = logger or logging.getLogger('eynollah') - self.model_zoo = model_zoo - self.plotter = None - + if skip_layout_and_reading_order: + textline_light = True + self.light_version = light_version + self.dir_out = dir_out + self.dir_of_all = dir_of_all + self.dir_save_page = dir_save_page self.reading_order_machine_based = reading_order_machine_based + self.dir_of_deskewed = dir_of_deskewed + self.dir_of_deskewed = dir_of_deskewed + self.dir_of_cropped_images=dir_of_cropped_images + self.dir_of_layout=dir_of_layout self.enable_plotting = enable_plotting self.allow_enhancement = allow_enhancement self.curved_line = curved_line + self.textline_light = textline_light self.full_layout = full_layout self.tables = tables self.right2left = right2left - # --input-binary sensible if image is very dark, if layout is not working. self.input_binary = input_binary self.allow_scaling = allow_scaling self.headers_off = headers_off + self.light_version = light_version + self.extract_only_images = extract_only_images self.ignore_page_extraction = ignore_page_extraction self.skip_layout_and_reading_order = skip_layout_and_reading_order + self.ocr = do_ocr if num_col_upper: self.num_col_upper = int(num_col_upper) else: @@ -162,21 +237,62 @@ class Eynollah: self.num_col_lower = int(num_col_lower) else: self.num_col_lower = num_col_lower - + self.logger = logger if logger else getLogger('eynollah') # for parallelization of CPU-intensive tasks: - self.executor = ProcessPoolExecutor(max_workers=cpu_count()) - - if threshold_art_class_layout: - self.threshold_art_class_layout = float(threshold_art_class_layout) + self.executor = ProcessPoolExecutor(max_workers=cpu_count(), timeout=1200) + atexit.register(self.executor.shutdown) + self.dir_models = dir_models + self.model_dir_of_enhancement = dir_models + "/eynollah-enhancement_20210425" + self.model_dir_of_binarization = dir_models + "/eynollah-binarization_20210425" + self.model_dir_of_col_classifier = dir_models + "/eynollah-column-classifier_20210425" + self.model_region_dir_p = dir_models + "/eynollah-main-regions-aug-scaling_20210425" + self.model_region_dir_p2 = dir_models + "/eynollah-main-regions-aug-rotation_20210425" + #"/modelens_full_lay_1_3_031124" + #"/modelens_full_lay_13__3_19_241024" + #"/model_full_lay_13_241024" + #"/modelens_full_lay_13_17_231024" + #"/modelens_full_lay_1_2_221024" + #"/eynollah-full-regions-1column_20210425" + self.model_region_dir_fully_np = dir_models + "/modelens_full_lay_1__4_3_091124" + #self.model_region_dir_fully = dir_models + "/eynollah-full-regions-3+column_20210425" + self.model_page_dir = dir_models + "/eynollah-page-extraction_20210425" + self.model_region_dir_p_ens = dir_models + "/eynollah-main-regions-ensembled_20210425" + self.model_region_dir_p_ens_light = dir_models + "/eynollah-main-regions_20220314" + self.model_region_dir_p_ens_light_only_images_extraction = dir_models + "/eynollah-main-regions_20231127_672_org_ens_11_13_16_17_18" + self.model_reading_order_dir = dir_models + "/model_ens_reading_order_machine_based" + #"/modelens_12sp_elay_0_3_4__3_6_n" + #"/modelens_earlylayout_12spaltige_2_3_5_6_7_8" + #"/modelens_early12_sp_2_3_5_6_7_8_9_10_12_14_15_16_18" + #"/modelens_1_2_4_5_early_lay_1_2_spaltige" + #"/model_3_eraly_layout_no_patches_1_2_spaltige" + self.model_region_dir_p_1_2_sp_np = dir_models + "/modelens_e_l_all_sp_0_1_2_3_4_171024" + ##self.model_region_dir_fully_new = dir_models + "/model_2_full_layout_new_trans" + #"/modelens_full_lay_1_3_031124" + #"/modelens_full_lay_13__3_19_241024" + #"/model_full_lay_13_241024" + #"/modelens_full_lay_13_17_231024" + #"/modelens_full_lay_1_2_221024" + #"/modelens_full_layout_24_till_28" + #"/model_2_full_layout_new_trans" + self.model_region_dir_fully = dir_models + "/modelens_full_lay_1__4_3_091124" + if self.textline_light: + #"/modelens_textline_1_4_16092024" + #"/model_textline_ens_3_4_5_6_artificial" + #"/modelens_textline_1_3_4_20240915" + #"/model_textline_ens_3_4_5_6_artificial" + #"/modelens_textline_9_12_13_14_15" + #"/eynollah-textline_light_20210425" + self.model_textline_dir = dir_models + "/modelens_textline_0_1__2_4_16092024" else: - self.threshold_art_class_layout = 0.1 - - if threshold_art_class_textline: - self.threshold_art_class_textline = float(threshold_art_class_textline) - else: - self.threshold_art_class_textline = 0.1 - - t_start = time.time() + #"/eynollah-textline_20210425" + self.model_textline_dir = dir_models + "/modelens_textline_0_1__2_4_16092024" + if self.ocr: + self.model_ocr_dir = dir_models + "/trocr_model_ens_of_3_checkpoints_201124" + if self.tables: + if self.light_version: + self.model_table_dir = dir_models + "/modelens_table_0t4_201124" + else: + self.model_table_dir = dir_models + "/eynollah-tables_20210319" # #gpu_options = tf.compat.v1.GPUOptions(allow_growth=True) # #gpu_options = tf.compat.v1.GPUOptions(per_process_gpu_memory_fraction=7.7, allow_growth=True) @@ -191,60 +307,49 @@ class Eynollah: tf.config.experimental.set_memory_growth(device, True) except: self.logger.warning("no GPU device available") - - self.logger.info("Loading models...") - self.setup_models() - self.logger.info(f"Model initialization complete ({time.time() - t_start:.1f}s)") - def setup_models(self): - - # load models, depending on modes - # (note: loading too many models can cause OOM on GPU/CUDA, - # thus, we try set up the minimal configuration for the current mode) - loadable = [ - "col_classifier", - "binarization", - "page", - "region" - ] - loadable.append(("textline")) - loadable.append("region_1_2") - if self.full_layout: - loadable.append("region_fl_np") - #loadable.append("region_fl") - if self.reading_order_machine_based: - loadable.append("reading_order") - if self.tables: - loadable.append(("table")) - - self.model_zoo.load_models(*loadable) - - def __del__(self): - if hasattr(self, 'executor') and getattr(self, 'executor'): - assert self.executor - self.executor.shutdown() - self.executor = None - self.model_zoo.shutdown() - - @property - def device(self): - # TODO why here and why only for tr? - assert torch - if torch.cuda.is_available(): - self.logger.info("Using GPU acceleration") - return torch.device("cuda:0") - self.logger.info("Using CPU processing") - return torch.device("cpu") + self.model_page = self.our_load_model(self.model_page_dir) + self.model_classifier = self.our_load_model(self.model_dir_of_col_classifier) + self.model_bin = self.our_load_model(self.model_dir_of_binarization) + if self.extract_only_images: + self.model_region = self.our_load_model(self.model_region_dir_p_ens_light_only_images_extraction) + else: + self.model_textline = self.our_load_model(self.model_textline_dir) + if self.light_version: + self.model_region = self.our_load_model(self.model_region_dir_p_ens_light) + self.model_region_1_2 = self.our_load_model(self.model_region_dir_p_1_2_sp_np) + else: + self.model_region = self.our_load_model(self.model_region_dir_p_ens) + self.model_region_p2 = self.our_load_model(self.model_region_dir_p2) + self.model_enhancement = self.our_load_model(self.model_dir_of_enhancement) + ###self.model_region_fl_new = self.our_load_model(self.model_region_dir_fully_new) + self.model_region_fl_np = self.our_load_model(self.model_region_dir_fully_np) + self.model_region_fl = self.our_load_model(self.model_region_dir_fully) + if self.reading_order_machine_based: + self.model_reading_order = self.our_load_model(self.model_reading_order_dir) + if self.ocr: + self.model_ocr = VisionEncoderDecoderModel.from_pretrained(self.model_ocr_dir) + self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + #("microsoft/trocr-base-printed")#("microsoft/trocr-base-handwritten") + self.processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-handwritten") + if self.tables: + self.model_table = self.our_load_model(self.model_table_dir) def cache_images(self, image_filename=None, image_pil=None, dpi=None): ret = {} t_c0 = time.time() if image_filename: ret['img'] = cv2.imread(image_filename) - self.dpi = 100 + if self.light_version: + self.dpi = 100 + else: + self.dpi = check_dpi(image_filename) else: ret['img'] = pil2cv(image_pil) - self.dpi = 100 + if self.light_version: + self.dpi = 100 + else: + self.dpi = check_dpi(image_pil) ret['img_grayscale'] = cv2.cvtColor(ret['img'], cv2.COLOR_BGR2GRAY) for prefix in ('', '_grayscale'): ret[f'img{prefix}_uint8'] = ret[f'img{prefix}'].astype(np.uint8) @@ -252,13 +357,24 @@ class Eynollah: if dpi is not None: self.dpi = dpi - def reset_file_name_dir(self, image_filename, dir_out): + def reset_file_name_dir(self, image_filename): t_c = time.time() self.cache_images(image_filename=image_filename) + + self.plotter = None if not self.enable_plotting else EynollahPlotter( + dir_out=self.dir_out, + dir_of_all=self.dir_of_all, + dir_save_page=self.dir_save_page, + dir_of_deskewed=self.dir_of_deskewed, + dir_of_cropped_images=self.dir_of_cropped_images, + dir_of_layout=self.dir_of_layout, + image_filename_stem=Path(Path(image_filename).name).stem) + self.writer = EynollahXmlWriter( - dir_out=dir_out, + dir_out=self.dir_out, image_filename=image_filename, - curved_line=self.curved_line) + curved_line=self.curved_line, + textline_light = self.textline_light) def imread(self, grayscale=False, uint8=True): key = 'img' @@ -268,11 +384,14 @@ class Eynollah: key += '_uint8' return self._imgs[key].copy() + def isNaN(self, num): + return num != num + def predict_enhancement(self, img): self.logger.debug("enter predict_enhancement") - img_height_model = self.model_zoo.get("enhancement").layers[-1].output_shape[1] - img_width_model = self.model_zoo.get("enhancement").layers[-1].output_shape[2] + img_height_model = self.model_enhancement.layers[-1].output_shape[1] + img_width_model = self.model_enhancement.layers[-1].output_shape[2] if img.shape[0] < img_height_model: img = cv2.resize(img, (img.shape[1], img_width_model), interpolation=cv2.INTER_NEAREST) if img.shape[1] < img_width_model: @@ -313,7 +432,7 @@ class Eynollah: index_y_d = img_h - img_height_model img_patch = img[np.newaxis, index_y_d:index_y_u, index_x_d:index_x_u, :] - label_p_pred = self.model_zoo.get("enhancement").predict(img_patch, verbose=0) + label_p_pred = self.model_enhancement.predict(img_patch, verbose=0) seg = label_p_pred[0, :, :, :] * 255 if i == 0 and j == 0: @@ -437,6 +556,27 @@ class Eynollah: return img_new, num_column_is_classified + def calculate_width_height_by_columns_extract_only_images(self, img, num_col, width_early, label_p_pred): + self.logger.debug("enter calculate_width_height_by_columns") + if num_col == 1: + img_w_new = 700 + elif num_col == 2: + img_w_new = 900 + elif num_col == 3: + img_w_new = 1500 + elif num_col == 4: + img_w_new = 1800 + elif num_col == 5: + img_w_new = 2200 + elif num_col == 6: + img_w_new = 2500 + img_h_new = img_w_new * img.shape[0] // img.shape[1] + + img_new = resize_image(img, img_h_new, img_w_new) + num_column_is_classified = True + + return img_new, num_column_is_classified + def resize_image_with_column_classifier(self, is_image_enhanced, img_bin): self.logger.debug("enter resize_image_with_column_classifier") if self.input_binary: @@ -467,7 +607,7 @@ class Eynollah: img_in[0, :, :, 1] = img_1ch[:, :] img_in[0, :, :, 2] = img_1ch[:, :] - label_p_pred = self.model_zoo.get("col_classifier").predict(img_in, verbose=0) + label_p_pred = self.model_classifier.predict(img_in, verbose=0) num_col = np.argmax(label_p_pred[0]) + 1 self.logger.info("Found %s columns (%s)", num_col, label_p_pred) @@ -479,14 +619,14 @@ class Eynollah: return img, img_new, is_image_enhanced - def resize_and_enhance_image_with_column_classifier(self): + def resize_and_enhance_image_with_column_classifier(self, light_version): self.logger.debug("enter resize_and_enhance_image_with_column_classifier") dpi = self.dpi self.logger.info("Detected %s DPI", dpi) if self.input_binary: img = self.imread() - prediction_bin = self.do_prediction(True, img, self.model_zoo.get("binarization"), n_batch_inference=5) - prediction_bin = 255 * (prediction_bin[:,:,0] == 0) + prediction_bin = self.do_prediction(True, img, self.model_bin, n_batch_inference=5) + prediction_bin = 255 * (prediction_bin[:,:,0]==0) prediction_bin = np.repeat(prediction_bin[:, :, np.newaxis], 3, axis=2).astype(np.uint8) img= np.copy(prediction_bin) img_bin = prediction_bin @@ -525,9 +665,8 @@ class Eynollah: img_in[0, :, :, 1] = img_1ch[:, :] img_in[0, :, :, 2] = img_1ch[:, :] - label_p_pred = self.model_zoo.get("col_classifier").predict(img_in, verbose=0) + label_p_pred = self.model_classifier.predict(img_in, verbose=0) num_col = np.argmax(label_p_pred[0]) + 1 - elif (self.num_col_upper and self.num_col_lower) and (self.num_col_upper!=self.num_col_lower): if self.input_binary: img_in = np.copy(img) @@ -546,7 +685,7 @@ class Eynollah: img_in[0, :, :, 1] = img_1ch[:, :] img_in[0, :, :, 2] = img_1ch[:, :] - label_p_pred = self.model_zoo.get("col_classifier").predict(img_in, verbose=0) + label_p_pred = self.model_classifier.predict(img_in, verbose=0) num_col = np.argmax(label_p_pred[0]) + 1 if num_col > self.num_col_upper: @@ -560,25 +699,33 @@ class Eynollah: label_p_pred = [np.ones(6)] self.logger.info("Found %d columns (%s)", num_col, np.around(label_p_pred, decimals=5)) - if dpi < DPI_THRESHOLD: - if num_col in (1,2): - img_new, num_column_is_classified = self.calculate_width_height_by_columns_1_2( - img, num_col, width_early, label_p_pred) - else: - img_new, num_column_is_classified = self.calculate_width_height_by_columns( - img, num_col, width_early, label_p_pred) - image_res = np.copy(img_new) - is_image_enhanced = True - else: - if num_col in (1,2): - img_new, num_column_is_classified = self.calculate_width_height_by_columns_1_2( - img, num_col, width_early, label_p_pred) - image_res = np.copy(img_new) + if not self.extract_only_images: + if dpi < DPI_THRESHOLD: + if light_version and num_col in (1,2): + img_new, num_column_is_classified = self.calculate_width_height_by_columns_1_2( + img, num_col, width_early, label_p_pred) + else: + img_new, num_column_is_classified = self.calculate_width_height_by_columns( + img, num_col, width_early, label_p_pred) + if light_version: + image_res = np.copy(img_new) + else: + image_res = self.predict_enhancement(img_new) is_image_enhanced = True else: - num_column_is_classified = True - image_res = np.copy(img) - is_image_enhanced = False + if light_version and num_col in (1,2): + img_new, num_column_is_classified = self.calculate_width_height_by_columns_1_2( + img, num_col, width_early, label_p_pred) + image_res = np.copy(img_new) + is_image_enhanced = True + else: + num_column_is_classified = True + image_res = np.copy(img) + is_image_enhanced = False + else: + num_column_is_classified = True + image_res = np.copy(img) + is_image_enhanced = False self.logger.debug("exit resize_and_enhance_image_with_column_classifier") return is_image_enhanced, img, image_res, num_col, num_column_is_classified, img_bin @@ -593,8 +740,8 @@ class Eynollah: self.img_hight_int = int(self.image.shape[0] * scale) self.img_width_int = int(self.image.shape[1] * scale) - self.scale_y: float = self.img_hight_int / float(self.image.shape[0]) - self.scale_x: float = self.img_width_int / float(self.image.shape[1]) + self.scale_y = self.img_hight_int / float(self.image.shape[0]) + self.scale_x = self.img_width_int / float(self.image.shape[1]) self.image = resize_image(self.image, self.img_hight_int, self.img_width_int) @@ -637,11 +784,9 @@ class Eynollah: self, patches, img, model, n_batch_inference=1, marginal_of_patch_percent=0.1, thresholding_for_some_classes_in_light_version=False, - thresholding_for_artificial_class_in_light_version=False, - thresholding_for_fl_light_version=False, - threshold_art_class_textline=0.1): + thresholding_for_artificial_class_in_light_version=False): - self.logger.debug("enter do_prediction (patches=%d)", patches) + self.logger.debug("enter do_prediction") img_height_model = model.layers[-1].output_shape[1] img_width_model = model.layers[-1].output_shape[2] @@ -657,22 +802,10 @@ class Eynollah: if thresholding_for_artificial_class_in_light_version: seg_art = label_p_pred[0,:,:,2] - seg_art[seg_art0] =1 - - skeleton_art = skeletonize(seg_art) - skeleton_art = skeleton_art*1 - seg[skeleton_art==1]=2 - - if thresholding_for_fl_light_version: - seg_header = label_p_pred[0,:,:,2] - - seg_header[seg_header<0.2] = 0 - seg_header[seg_header>0] =1 - - seg[seg_header==1]=2 - + seg[seg_art==1]=2 seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2) prediction_true = resize_image(seg_color, img_h_page, img_w_page).astype(np.uint8) return prediction_true @@ -692,8 +825,10 @@ class Eynollah: img_w = img.shape[1] prediction_true = np.zeros((img_h, img_w, 3)) mask_true = np.zeros((img_h, img_w)) - nxf = math.ceil(img_w / float(width_mid)) - nyf = math.ceil(img_h / float(height_mid)) + nxf = img_w / float(width_mid) + nyf = img_h / float(height_mid) + nxf = int(nxf) + 1 if nxf > int(nxf) else int(nxf) + nyf = int(nyf) + 1 if nyf > int(nyf) else int(nyf) list_i_s = [] list_j_s = [] @@ -706,10 +841,18 @@ class Eynollah: img_patch = np.zeros((n_batch_inference, img_height_model, img_width_model, 3)) for i in range(nxf): for j in range(nyf): - index_x_d = i * width_mid - index_x_u = index_x_d + img_width_model - index_y_d = j * height_mid - index_y_u = index_y_d + img_height_model + if i == 0: + index_x_d = i * width_mid + index_x_u = index_x_d + img_width_model + else: + index_x_d = i * width_mid + index_x_u = index_x_d + img_width_model + if j == 0: + index_y_d = j * height_mid + index_y_u = index_y_d + img_height_model + else: + index_y_d = j * height_mid + index_y_u = index_y_d + img_height_model if index_x_u > img_w: index_x_u = img_w index_x_d = img_w - img_width_model @@ -753,17 +896,14 @@ class Eynollah: if thresholding_for_artificial_class_in_light_version: seg_art = label_p_pred[:,:,:,2] - seg_art[seg_art0] =1 - ##seg[seg_art==1]=2 + seg[seg_art==1]=2 indexer_inside_batch = 0 for i_batch, j_batch in zip(list_i_s, list_j_s): seg_in = seg[indexer_inside_batch] - - if thresholding_for_artificial_class_in_light_version: - seg_in_art = seg_art[indexer_inside_batch] index_y_u_in = list_y_u[indexer_inside_batch] index_y_d_in = list_y_d[indexer_inside_batch] @@ -777,107 +917,54 @@ class Eynollah: seg_in[0:-margin or None, 0:-margin or None, np.newaxis] - if thresholding_for_artificial_class_in_light_version: - prediction_true[index_y_d_in + 0:index_y_u_in - margin, - index_x_d_in + 0:index_x_u_in - margin, 1] = \ - seg_in_art[0:-margin or None, - 0:-margin or None] - elif i_batch == nxf - 1 and j_batch == nyf - 1: prediction_true[index_y_d_in + margin:index_y_u_in - 0, index_x_d_in + margin:index_x_u_in - 0] = \ seg_in[margin:, margin:, np.newaxis] - if thresholding_for_artificial_class_in_light_version: - prediction_true[index_y_d_in + margin:index_y_u_in - 0, - index_x_d_in + margin:index_x_u_in - 0, 1] = \ - seg_in_art[margin:, - margin:] - elif i_batch == 0 and j_batch == nyf - 1: prediction_true[index_y_d_in + margin:index_y_u_in - 0, index_x_d_in + 0:index_x_u_in - margin] = \ seg_in[margin:, 0:-margin or None, np.newaxis] - if thresholding_for_artificial_class_in_light_version: - prediction_true[index_y_d_in + margin:index_y_u_in - 0, - index_x_d_in + 0:index_x_u_in - margin, 1] = \ - seg_in_art[margin:, - 0:-margin or None] - elif i_batch == nxf - 1 and j_batch == 0: prediction_true[index_y_d_in + 0:index_y_u_in - margin, index_x_d_in + margin:index_x_u_in - 0] = \ seg_in[0:-margin or None, margin:, np.newaxis] - if thresholding_for_artificial_class_in_light_version: - prediction_true[index_y_d_in + 0:index_y_u_in - margin, - index_x_d_in + margin:index_x_u_in - 0, 1] = \ - seg_in_art[0:-margin or None, - margin:] - elif i_batch == 0 and j_batch != 0 and j_batch != nyf - 1: prediction_true[index_y_d_in + margin:index_y_u_in - margin, index_x_d_in + 0:index_x_u_in - margin] = \ seg_in[margin:-margin or None, 0:-margin or None, np.newaxis] - if thresholding_for_artificial_class_in_light_version: - prediction_true[index_y_d_in + margin:index_y_u_in - margin, - index_x_d_in + 0:index_x_u_in - margin, 1] = \ - seg_in_art[margin:-margin or None, - 0:-margin or None] - elif i_batch == nxf - 1 and j_batch != 0 and j_batch != nyf - 1: prediction_true[index_y_d_in + margin:index_y_u_in - margin, index_x_d_in + margin:index_x_u_in - 0] = \ seg_in[margin:-margin or None, margin:, np.newaxis] - if thresholding_for_artificial_class_in_light_version: - prediction_true[index_y_d_in + margin:index_y_u_in - margin, - index_x_d_in + margin:index_x_u_in - 0, 1] = \ - seg_in_art[margin:-margin or None, - margin:] - elif i_batch != 0 and i_batch != nxf - 1 and j_batch == 0: prediction_true[index_y_d_in + 0:index_y_u_in - margin, index_x_d_in + margin:index_x_u_in - margin] = \ seg_in[0:-margin or None, margin:-margin or None, np.newaxis] - if thresholding_for_artificial_class_in_light_version: - prediction_true[index_y_d_in + 0:index_y_u_in - margin, - index_x_d_in + margin:index_x_u_in - margin, 1] = \ - seg_in_art[0:-margin or None, - margin:-margin or None] - elif i_batch != 0 and i_batch != nxf - 1 and j_batch == nyf - 1: prediction_true[index_y_d_in + margin:index_y_u_in - 0, index_x_d_in + margin:index_x_u_in - margin] = \ seg_in[margin:, margin:-margin or None, np.newaxis] - if thresholding_for_artificial_class_in_light_version: - prediction_true[index_y_d_in + margin:index_y_u_in - 0, - index_x_d_in + margin:index_x_u_in - margin, 1] = \ - seg_in_art[margin:, - margin:-margin or None] - else: prediction_true[index_y_d_in + margin:index_y_u_in - margin, index_x_d_in + margin:index_x_u_in - margin] = \ seg_in[margin:-margin or None, margin:-margin or None, np.newaxis] - if thresholding_for_artificial_class_in_light_version: - prediction_true[index_y_d_in + margin:index_y_u_in - margin, - index_x_d_in + margin:index_x_u_in - margin, 1] = \ - seg_in_art[margin:-margin or None, - margin:-margin or None] indexer_inside_batch += 1 @@ -892,30 +979,45 @@ class Eynollah: img_patch[:] = 0 prediction_true = prediction_true.astype(np.uint8) - - if thresholding_for_artificial_class_in_light_version: - kernel_min = np.ones((3, 3), np.uint8) - prediction_true[:,:,0][prediction_true[:,:,0]==2] = 0 - - skeleton_art = skeletonize(prediction_true[:,:,1]) - skeleton_art = skeleton_art*1 - - skeleton_art = skeleton_art.astype('uint8') - - skeleton_art = cv2.dilate(skeleton_art, kernel_min, iterations=1) - - prediction_true[:,:,0][skeleton_art==1]=2 #del model gc.collect() return prediction_true - def do_prediction_new_concept( + def do_padding_with_scale(self, img, scale): + h_n = int(img.shape[0]*scale) + w_n = int(img.shape[1]*scale) + + channel0_avg = int( np.mean(img[:,:,0]) ) + channel1_avg = int( np.mean(img[:,:,1]) ) + channel2_avg = int( np.mean(img[:,:,2]) ) + + h_diff = img.shape[0] - h_n + w_diff = img.shape[1] - w_n + + h_start = int(0.5 * h_diff) + w_start = int(0.5 * w_diff) + + img_res = resize_image(img, h_n, w_n) + #label_res = resize_image(label, h_n, w_n) + + img_scaled_padded = np.copy(img) + + #label_scaled_padded = np.zeros(label.shape) + + img_scaled_padded[:,:,0] = channel0_avg + img_scaled_padded[:,:,1] = channel1_avg + img_scaled_padded[:,:,2] = channel2_avg + + img_scaled_padded[h_start:h_start+h_n, w_start:w_start+w_n,:] = img_res[:,:,:] + #label_scaled_padded[h_start:h_start+h_n, w_start:w_start+w_n,:] = label_res[:,:,:] + + return img_scaled_padded#, label_scaled_padded + + def do_prediction_new_concept_scatter_nd( self, patches, img, model, n_batch_inference=1, marginal_of_patch_percent=0.1, thresholding_for_some_classes_in_light_version=False, - thresholding_for_artificial_class_in_light_version=False, - threshold_art_class_textline=0.1, - threshold_art_class_layout=0.1): + thresholding_for_artificial_class_in_light_version=False): self.logger.debug("enter do_prediction_new_concept") img_height_model = model.layers[-1].output_shape[1] @@ -930,28 +1032,119 @@ class Eynollah: label_p_pred = model.predict(img[np.newaxis], verbose=0) seg = np.argmax(label_p_pred, axis=3)[0] + if thresholding_for_artificial_class_in_light_version: + #seg_text = label_p_pred[0,:,:,1] + #seg_text[seg_text<0.2] =0 + #seg_text[seg_text>0] =1 + #seg[seg_text==1]=1 + + seg_art = label_p_pred[0,:,:,4] + seg_art[seg_art<0.2] =0 + seg_art[seg_art>0] =1 + seg[seg_art==1]=4 + seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2) prediction_true = resize_image(seg_color, img_h_page, img_w_page).astype(np.uint8) - + return prediction_true + + if img.shape[0] < img_height_model: + img = resize_image(img, img_height_model, img.shape[1]) + if img.shape[1] < img_width_model: + img = resize_image(img, img.shape[0], img_width_model) + + self.logger.debug("Patch size: %sx%s", img_height_model, img_width_model) + ##margin = int(marginal_of_patch_percent * img_height_model) + #width_mid = img_width_model - 2 * margin + #height_mid = img_height_model - 2 * margin + img = img / 255.0 + img = img.astype(np.float16) + img_h = img.shape[0] + img_w = img.shape[1] + + stride_x = img_width_model - 100 + stride_y = img_height_model - 100 + + one_tensor = tf.ones_like(img) + img_patches, one_patches = tf.image.extract_patches( + images=[img, one_tensor], + sizes=[1, img_height_model, img_width_model, 1], + strides=[1, stride_y, stride_x, 1], + rates=[1, 1, 1, 1], + padding='SAME') + img_patches = tf.squeeze(img_patches) + one_patches = tf.squeeze(one_patches) + img_patches_resh = tf.reshape(img_patches, shape=(img_patches.shape[0] * img_patches.shape[1], + img_height_model, img_width_model, 3)) + pred_patches = model.predict(img_patches_resh, batch_size=n_batch_inference) + one_patches = tf.reshape(one_patches, shape=(img_patches.shape[0] * img_patches.shape[1], + img_height_model, img_width_model, 3)) + x = tf.range(img.shape[1]) + y = tf.range(img.shape[0]) + x, y = tf.meshgrid(x, y) + indices = tf.stack([y, x], axis=-1) + + indices_patches = tf.image.extract_patches( + images=tf.expand_dims(indices, axis=0), + sizes=[1, img_height_model, img_width_model, 1], + strides=[1, stride_y, stride_x, 1], + rates=[1, 1, 1, 1], + padding='SAME') + indices_patches = tf.squeeze(indices_patches) + indices_patches = tf.reshape(indices_patches, shape=(img_patches.shape[0] * img_patches.shape[1], + img_height_model, img_width_model, 2)) + margin_y = int( 0.5 * (img_height_model - stride_y) ) + margin_x = int( 0.5 * (img_width_model - stride_x) ) + + mask_margin = np.zeros((img_height_model, img_width_model)) + mask_margin[margin_y:img_height_model - margin_y, + margin_x:img_width_model - margin_x] = 1 + + indices_patches_array = indices_patches.numpy() + for i in range(indices_patches_array.shape[0]): + indices_patches_array[i,:,:,0] = indices_patches_array[i,:,:,0]*mask_margin + indices_patches_array[i,:,:,1] = indices_patches_array[i,:,:,1]*mask_margin + + reconstructed = tf.scatter_nd( + indices=indices_patches_array, + updates=pred_patches, + shape=(img.shape[0], img.shape[1], pred_patches.shape[-1])).numpy() + + prediction_true = np.argmax(reconstructed, axis=2).astype(np.uint8) + gc.collect() + return np.repeat(prediction_true[:, :, np.newaxis], 3, axis=2) + + def do_prediction_new_concept( + self, patches, img, model, + n_batch_inference=1, marginal_of_patch_percent=0.1, + thresholding_for_some_classes_in_light_version=False, + thresholding_for_artificial_class_in_light_version=False): + + self.logger.debug("enter do_prediction_new_concept") + img_height_model = model.layers[-1].output_shape[1] + img_width_model = model.layers[-1].output_shape[2] + + if not patches: + img_h_page = img.shape[0] + img_w_page = img.shape[1] + img = img / 255.0 + img = resize_image(img, img_height_model, img_width_model) + + label_p_pred = model.predict(img[np.newaxis], verbose=0) + seg = np.argmax(label_p_pred, axis=3)[0] + if thresholding_for_artificial_class_in_light_version: - kernel_min = np.ones((3, 3), np.uint8) + #seg_text = label_p_pred[0,:,:,1] + #seg_text[seg_text<0.2] =0 + #seg_text[seg_text>0] =1 + #seg[seg_text==1]=1 + seg_art = label_p_pred[0,:,:,4] - seg_art[seg_art0] =1 - #seg[seg_art==1]=4 - seg_art = resize_image(seg_art, img_h_page, img_w_page).astype(np.uint8) - - prediction_true[:,:,0][prediction_true[:,:,0]==4] = 0 - - skeleton_art = skeletonize(seg_art) - skeleton_art = skeleton_art*1 - - skeleton_art = skeleton_art.astype('uint8') - - skeleton_art = cv2.dilate(skeleton_art, kernel_min, iterations=1) - - prediction_true[:,:,0][skeleton_art==1] = 4 - + seg[seg_art==1]=4 + + seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2) + prediction_true = resize_image(seg_color, img_h_page, img_w_page).astype(np.uint8) return prediction_true , resize_image(label_p_pred[0, :, :, 1] , img_h_page, img_w_page) if img.shape[0] < img_height_model: @@ -1024,30 +1217,26 @@ class Eynollah: if thresholding_for_some_classes_in_light_version: seg_art = label_p_pred[:,:,:,4] - seg_art[seg_art0] =1 seg_line = label_p_pred[:,:,:,3] - seg_line[seg_line>0.4] =1#seg_line[seg_line>0.5] =1#seg_line[seg_line>0.1] =1 + seg_line[seg_line>0.1] =1 seg_line[seg_line<1] =0 - ##seg[seg_art==1]=4 - #seg[(seg_line==1) & (seg==0)]=3 + seg[seg_art==1]=4 + seg[(seg_line==1) & (seg==0)]=3 if thresholding_for_artificial_class_in_light_version: seg_art = label_p_pred[:,:,:,2] - seg_art[seg_art0] =1 - ##seg[seg_art==1]=2 + seg[seg_art==1]=2 indexer_inside_batch = 0 for i_batch, j_batch in zip(list_i_s, list_j_s): seg_in = seg[indexer_inside_batch] - - if (thresholding_for_artificial_class_in_light_version or - thresholding_for_some_classes_in_light_version): - seg_in_art = seg_art[indexer_inside_batch] index_y_u_in = list_y_u[indexer_inside_batch] index_y_d_in = list_y_d[indexer_inside_batch] @@ -1066,13 +1255,6 @@ class Eynollah: label_p_pred[0, 0:-margin or None, 0:-margin or None, 1] - if (thresholding_for_artificial_class_in_light_version or - thresholding_for_some_classes_in_light_version): - prediction_true[index_y_d_in + 0:index_y_u_in - margin, - index_x_d_in + 0:index_x_u_in - margin, 1] = \ - seg_in_art[0:-margin or None, - 0:-margin or None] - elif i_batch == nxf - 1 and j_batch == nyf - 1: prediction_true[index_y_d_in + margin:index_y_u_in - 0, index_x_d_in + margin:index_x_u_in - 0] = \ @@ -1084,13 +1266,6 @@ class Eynollah: label_p_pred[0, margin:, margin:, 1] - if (thresholding_for_artificial_class_in_light_version or - thresholding_for_some_classes_in_light_version): - prediction_true[index_y_d_in + margin:index_y_u_in - 0, - index_x_d_in + margin:index_x_u_in - 0, 1] = \ - seg_in_art[margin:, - margin:] - elif i_batch == 0 and j_batch == nyf - 1: prediction_true[index_y_d_in + margin:index_y_u_in - 0, index_x_d_in + 0:index_x_u_in - margin] = \ @@ -1102,14 +1277,6 @@ class Eynollah: label_p_pred[0, margin:, 0:-margin or None, 1] - - if (thresholding_for_artificial_class_in_light_version or - thresholding_for_some_classes_in_light_version): - prediction_true[index_y_d_in + margin:index_y_u_in - 0, - index_x_d_in + 0:index_x_u_in - margin, 1] = \ - seg_in_art[margin:, - 0:-margin or None] - elif i_batch == nxf - 1 and j_batch == 0: prediction_true[index_y_d_in + 0:index_y_u_in - margin, index_x_d_in + margin:index_x_u_in - 0] = \ @@ -1121,13 +1288,6 @@ class Eynollah: label_p_pred[0, 0:-margin or None, margin:, 1] - if (thresholding_for_artificial_class_in_light_version or - thresholding_for_some_classes_in_light_version): - prediction_true[index_y_d_in + 0:index_y_u_in - margin, - index_x_d_in + margin:index_x_u_in - 0, 1] = \ - seg_in_art[0:-margin or None, - margin:] - elif i_batch == 0 and j_batch != 0 and j_batch != nyf - 1: prediction_true[index_y_d_in + margin:index_y_u_in - margin, index_x_d_in + 0:index_x_u_in - margin] = \ @@ -1139,12 +1299,6 @@ class Eynollah: label_p_pred[0, margin:-margin or None, 0:-margin or None, 1] - if (thresholding_for_artificial_class_in_light_version or - thresholding_for_some_classes_in_light_version): - prediction_true[index_y_d_in + margin:index_y_u_in - margin, - index_x_d_in + 0:index_x_u_in - margin, 1] = \ - seg_in_art[margin:-margin or None, - 0:-margin or None] elif i_batch == nxf - 1 and j_batch != 0 and j_batch != nyf - 1: prediction_true[index_y_d_in + margin:index_y_u_in - margin, index_x_d_in + margin:index_x_u_in - 0] = \ @@ -1156,12 +1310,6 @@ class Eynollah: label_p_pred[0, margin:-margin or None, margin:, 1] - if (thresholding_for_artificial_class_in_light_version or - thresholding_for_some_classes_in_light_version): - prediction_true[index_y_d_in + margin:index_y_u_in - margin, - index_x_d_in + margin:index_x_u_in - 0, 1] = \ - seg_in_art[margin:-margin or None, - margin:] elif i_batch != 0 and i_batch != nxf - 1 and j_batch == 0: prediction_true[index_y_d_in + 0:index_y_u_in - margin, index_x_d_in + margin:index_x_u_in - margin] = \ @@ -1173,12 +1321,6 @@ class Eynollah: label_p_pred[0, 0:-margin or None, margin:-margin or None, 1] - if (thresholding_for_artificial_class_in_light_version or - thresholding_for_some_classes_in_light_version): - prediction_true[index_y_d_in + 0:index_y_u_in - margin, - index_x_d_in + margin:index_x_u_in - margin, 1] = \ - seg_in_art[0:-margin or None, - margin:-margin or None] elif i_batch != 0 and i_batch != nxf - 1 and j_batch == nyf - 1: prediction_true[index_y_d_in + margin:index_y_u_in - 0, index_x_d_in + margin:index_x_u_in - margin] = \ @@ -1190,12 +1332,6 @@ class Eynollah: label_p_pred[0, margin:, margin:-margin or None, 1] - if (thresholding_for_artificial_class_in_light_version or - thresholding_for_some_classes_in_light_version): - prediction_true[index_y_d_in + margin:index_y_u_in - 0, - index_x_d_in + margin:index_x_u_in - margin, 1] = \ - seg_in_art[margin:, - margin:-margin or None] else: prediction_true[index_y_d_in + margin:index_y_u_in - margin, index_x_d_in + margin:index_x_u_in - margin] = \ @@ -1207,12 +1343,6 @@ class Eynollah: label_p_pred[0, margin:-margin or None, margin:-margin or None, 1] - if (thresholding_for_artificial_class_in_light_version or - thresholding_for_some_classes_in_light_version): - prediction_true[index_y_d_in + margin:index_y_u_in - margin, - index_x_d_in + margin:index_x_u_in - margin, 1] = \ - seg_in_art[margin:-margin or None, - margin:-margin or None] indexer_inside_batch += 1 list_i_s = [] @@ -1226,32 +1356,6 @@ class Eynollah: img_patch[:] = 0 prediction_true = prediction_true.astype(np.uint8) - - if thresholding_for_artificial_class_in_light_version: - kernel_min = np.ones((3, 3), np.uint8) - prediction_true[:,:,0][prediction_true[:,:,0]==2] = 0 - - skeleton_art = skeletonize(prediction_true[:,:,1]) - skeleton_art = skeleton_art*1 - - skeleton_art = skeleton_art.astype('uint8') - - skeleton_art = cv2.dilate(skeleton_art, kernel_min, iterations=1) - - prediction_true[:,:,0][skeleton_art==1]=2 - - if thresholding_for_some_classes_in_light_version: - kernel_min = np.ones((3, 3), np.uint8) - prediction_true[:,:,0][prediction_true[:,:,0]==4] = 0 - - skeleton_art = skeletonize(prediction_true[:,:,1]) - skeleton_art = skeleton_art*1 - - skeleton_art = skeleton_art.astype('uint8') - - skeleton_art = cv2.dilate(skeleton_art, kernel_min, iterations=1) - - prediction_true[:,:,0][skeleton_art==1]=4 gc.collect() return prediction_true, confidence_matrix @@ -1259,11 +1363,11 @@ class Eynollah: self.logger.debug("enter extract_page") cont_page = [] if not self.ignore_page_extraction: - img = np.copy(self.image)#cv2.GaussianBlur(self.image, (5, 5), 0) - img_page_prediction = self.do_prediction(False, img, self.model_zoo.get("page")) + img = cv2.GaussianBlur(self.image, (5, 5), 0) + img_page_prediction = self.do_prediction(False, img, self.model_page) imgray = cv2.cvtColor(img_page_prediction, cv2.COLOR_BGR2GRAY) _, thresh = cv2.threshold(imgray, 0, 255, 0) - ##thresh = cv2.dilate(thresh, KERNEL, iterations=3) + thresh = cv2.dilate(thresh, KERNEL, iterations=3) contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) if len(contours)>0: @@ -1271,25 +1375,24 @@ class Eynollah: for j in range(len(contours))]) cnt = contours[np.argmax(cnt_size)] x, y, w, h = cv2.boundingRect(cnt) - #if x <= 30: - #w += x - #x = 0 - #if (self.image.shape[1] - (x + w)) <= 30: - #w = w + (self.image.shape[1] - (x + w)) - #if y <= 30: - #h = h + y - #y = 0 - #if (self.image.shape[0] - (y + h)) <= 30: - #h = h + (self.image.shape[0] - (y + h)) + if x <= 30: + w += x + x = 0 + if (self.image.shape[1] - (x + w)) <= 30: + w = w + (self.image.shape[1] - (x + w)) + if y <= 30: + h = h + y + y = 0 + if (self.image.shape[0] - (y + h)) <= 30: + h = h + (self.image.shape[0] - (y + h)) box = [x, y, w, h] else: box = [0, 0, img.shape[1], img.shape[0]] cropped_page, page_coord = crop_image_inside_box(box, self.image) - cont_page = [cnt] - #cont_page.append(np.array([[page_coord[2], page_coord[0]], - #[page_coord[3], page_coord[0]], - #[page_coord[3], page_coord[1]], - #[page_coord[2], page_coord[1]]])) + cont_page.append(np.array([[page_coord[2], page_coord[0]], + [page_coord[3], page_coord[0]], + [page_coord[3], page_coord[1]], + [page_coord[2], page_coord[1]]])) self.logger.debug("exit extract_page") else: box = [0, 0, self.image.shape[1], self.image.shape[0]] @@ -1308,7 +1411,7 @@ class Eynollah: else: img = self.imread() img = cv2.GaussianBlur(img, (5, 5), 0) - img_page_prediction = self.do_prediction(False, img, self.model_zoo.get("page")) + img_page_prediction = self.do_prediction(False, img, self.model_page) imgray = cv2.cvtColor(img_page_prediction, cv2.COLOR_BGR2GRAY) _, thresh = cv2.threshold(imgray, 0, 255, 0) @@ -1334,13 +1437,13 @@ class Eynollah: self.logger.debug("enter extract_text_regions") img_height_h = img.shape[0] img_width_h = img.shape[1] - model_region = self.model_zoo.get("region_fl") if patches else self.model_zoo.get("region_fl_np") + model_region = self.model_region_fl if patches else self.model_region_fl_np - thresholding_for_fl_light_version = True - if not patches: + if self.light_version: + pass + elif not patches: img = otsu_copy_binary(img).astype(np.uint8) prediction_regions = None - thresholding_for_fl_light_version = False elif cols: img = otsu_copy_binary(img).astype(np.uint8) if cols == 1: @@ -1356,10 +1459,7 @@ class Eynollah: else: img = resize_image(img, int(img_height_h * 2500 / float(img_width_h)), 2500).astype(np.uint8) - prediction_regions = self.do_prediction(patches, img, model_region, - marginal_of_patch_percent=0.1, - n_batch_inference=3, - thresholding_for_fl_light_version=thresholding_for_fl_light_version) + prediction_regions = self.do_prediction(patches, img, model_region, marginal_of_patch_percent=0.1, n_batch_inference=3) prediction_regions = resize_image(prediction_regions, img_height_h, img_width_h) self.logger.debug("exit extract_text_regions") return prediction_regions, prediction_regions @@ -1368,90 +1468,64 @@ class Eynollah: self.logger.debug("enter extract_text_regions") img_height_h = img.shape[0] img_width_h = img.shape[1] - model_region = self.model_zoo.get("region_fl") if patches else self.model_zoo.get("region_fl_np") + model_region = self.model_region_fl if patches else self.model_region_fl_np + + if not patches: + img = otsu_copy_binary(img) + img = img.astype(np.uint8) + prediction_regions2 = None + elif cols: + if cols == 1: + img_height_new = int(img_height_h * 0.7) + img_width_new = int(img_width_h * 0.7) + elif cols == 2: + img_height_new = int(img_height_h * 0.4) + img_width_new = int(img_width_h * 0.4) + else: + img_height_new = int(img_height_h * 0.3) + img_width_new = int(img_width_h * 0.3) + img2 = otsu_copy_binary(img) + img2 = img2.astype(np.uint8) + img2 = resize_image(img2, img_height_new, img_width_new) + prediction_regions2 = self.do_prediction(patches, img2, model_region, marginal_of_patch_percent=0.1) + prediction_regions2 = resize_image(prediction_regions2, img_height_h, img_width_h) + + img = otsu_copy_binary(img).astype(np.uint8) + if cols == 1: + img = resize_image(img, int(img_height_h * 0.5), int(img_width_h * 0.5)).astype(np.uint8) + elif cols == 2 and img_width_h >= 2000: + img = resize_image(img, int(img_height_h * 0.9), int(img_width_h * 0.9)).astype(np.uint8) + elif cols == 3 and ((self.scale_x == 1 and img_width_h > 3000) or + (self.scale_x != 1 and img_width_h > 2800)): + img = resize_image(img, 2800 * img_height_h // img_width_h, 2800).astype(np.uint8) + elif cols == 4 and ((self.scale_x == 1 and img_width_h > 4000) or + (self.scale_x != 1 and img_width_h > 3700)): + img = resize_image(img, 3700 * img_height_h // img_width_h, 3700).astype(np.uint8) + elif cols == 4: + img = resize_image(img, int(img_height_h * 0.9), int(img_width_h * 0.9)).astype(np.uint8) + elif cols == 5 and self.scale_x == 1 and img_width_h > 5000: + img = resize_image(img, int(img_height_h * 0.7), int(img_width_h * 0.7)).astype(np.uint8) + elif cols == 5: + img = resize_image(img, int(img_height_h * 0.9), int(img_width_h * 0.9)).astype(np.uint8) + elif img_width_h > 5600: + img = resize_image(img, 5600 * img_height_h // img_width_h, 5600).astype(np.uint8) + else: + img = resize_image(img, int(img_height_h * 0.9), int(img_width_h * 0.9)).astype(np.uint8) prediction_regions = self.do_prediction(patches, img, model_region, marginal_of_patch_percent=0.1) prediction_regions = resize_image(prediction_regions, img_height_h, img_width_h) self.logger.debug("exit extract_text_regions") - return prediction_regions, None - - def get_textlines_of_a_textregion_sorted(self, textlines_textregion, cx_textline, cy_textline, w_h_textline): - N = len(cy_textline) - if N==0: - return [] - - diff_cy = np.abs( np.diff(sorted(cy_textline)) ) - diff_cx = np.abs(np.diff(sorted(cx_textline)) ) + return prediction_regions, prediction_regions2 - - if len(diff_cy)>0: - mean_y_diff = np.mean(diff_cy) - mean_x_diff = np.mean(diff_cx) - count_hor = np.count_nonzero(np.array(w_h_textline) > 1) - count_ver = len(w_h_textline) - count_hor - - else: - mean_y_diff = 0 - mean_x_diff = 0 - count_hor = 1 - count_ver = 0 - - - if count_hor >= count_ver: - row_threshold = mean_y_diff / 1.5 if mean_y_diff > 0 else 10 - - indices_sorted_by_y = sorted(range(N), key=lambda i: cy_textline[i]) - - rows = [] - current_row = [indices_sorted_by_y[0]] - for i in range(1, N): - current_idx = indices_sorted_by_y[i] - prev_idx = current_row[0] - if abs(cy_textline[current_idx] - cy_textline[prev_idx]) <= row_threshold: - current_row.append(current_idx) - else: - rows.append(current_row) - current_row = [current_idx] - rows.append(current_row) - - sorted_textlines = [] - for row in rows: - row_sorted = sorted(row, key=lambda i: cx_textline[i]) - for idx in row_sorted: - sorted_textlines.append(textlines_textregion[idx]) - - else: - row_threshold = mean_x_diff / 1.5 if mean_x_diff > 0 else 10 - indices_sorted_by_x = sorted(range(N), key=lambda i: cx_textline[i]) - - rows = [] - current_row = [indices_sorted_by_x[0]] - - for i in range(1, N): - current_idy = indices_sorted_by_x[i] - prev_idy = current_row[0] - if abs(cx_textline[current_idy] - cx_textline[prev_idy] ) <= row_threshold: - current_row.append(current_idy) - else: - rows.append(current_row) - current_row = [current_idy] - rows.append(current_row) - - sorted_textlines = [] - for row in rows: - row_sorted = sorted(row , key=lambda i: cy_textline[i]) - for idy in row_sorted: - sorted_textlines.append(textlines_textregion[idy]) - - return sorted_textlines - - def get_slopes_and_deskew_new_light2(self, contours_par, textline_mask_tot, boxes, slope_deskew): + def get_slopes_and_deskew_new_light2(self, contours, contours_par, textline_mask_tot, image_page_rotated, boxes, slope_deskew): polygons_of_textlines = return_contours_of_interested_region(textline_mask_tot,1,0.00001) - cx_main_tot, cy_main_tot = find_center_of_contours(polygons_of_textlines) - w_h_textlines = [cv2.boundingRect(polygon)[2:] for polygon in polygons_of_textlines] + M_main_tot = [cv2.moments(polygons_of_textlines[j]) + for j in range(len(polygons_of_textlines))] + cx_main_tot = [(M_main_tot[j]["m10"] / (M_main_tot[j]["m00"] + 1e-32)) for j in range(len(M_main_tot))] + cy_main_tot = [(M_main_tot[j]["m01"] / (M_main_tot[j]["m00"] + 1e-32)) for j in range(len(M_main_tot))] - args_textlines = np.arange(len(polygons_of_textlines)) + args_textlines = np.array(range(len(polygons_of_textlines))) all_found_textline_polygons = [] slopes = [] all_box_coord =[] @@ -1462,36 +1536,54 @@ class Eynollah: results = np.array(results) indexes_in = args_textlines[results==1] textlines_ins = [polygons_of_textlines[ind] for ind in indexes_in] - cx_textline_in = [cx_main_tot[ind] for ind in indexes_in] - cy_textline_in = [cy_main_tot[ind] for ind in indexes_in] - w_h_textlines_in = [w_h_textlines[ind][0] / float(w_h_textlines[ind][1]) for ind in indexes_in] - textlines_ins = self.get_textlines_of_a_textregion_sorted(textlines_ins, - cx_textline_in, - cy_textline_in, - w_h_textlines_in) - - all_found_textline_polygons.append(textlines_ins)#[::-1]) + all_found_textline_polygons.append(textlines_ins[::-1]) slopes.append(slope_deskew) - crop_coor = box2rect(boxes[index]) + _, crop_coor = crop_image_inside_box(boxes[index],image_page_rotated) all_box_coord.append(crop_coor) - return (all_found_textline_polygons, - all_box_coord, - slopes) + return all_found_textline_polygons, boxes, contours, contours_par, all_box_coord, np.array(range(len(contours_par))), slopes - def get_slopes_and_deskew_new_curved(self, contours_par, textline_mask_tot, boxes, - mask_texts_only, num_col, scale_par, slope_deskew): - if not len(contours_par): - return [], [], [] + def get_slopes_and_deskew_new_light(self, contours, contours_par, textline_mask_tot, image_page_rotated, boxes, slope_deskew): + if not len(contours): + return [], [], [], [], [], [], [] + self.logger.debug("enter get_slopes_and_deskew_new_light") + results = self.executor.map(partial(do_work_of_slopes_new_light, + textline_mask_tot_ea=textline_mask_tot, + image_page_rotated=image_page_rotated, + slope_deskew=slope_deskew,textline_light=self.textline_light, + logger=self.logger,), + boxes, contours, contours_par, range(len(contours_par))) + #textline_polygons, boxes, text_regions, text_regions_par, box_coord, index_text_con, slopes = zip(*results) + self.logger.debug("exit get_slopes_and_deskew_new_light") + return tuple(zip(*results)) + + def get_slopes_and_deskew_new(self, contours, contours_par, textline_mask_tot, image_page_rotated, boxes, slope_deskew): + if not len(contours): + return [], [], [], [], [], [], [] + self.logger.debug("enter get_slopes_and_deskew_new") + results = self.executor.map(partial(do_work_of_slopes_new, + textline_mask_tot_ea=textline_mask_tot, + image_page_rotated=image_page_rotated, + slope_deskew=slope_deskew, + MAX_SLOPE=MAX_SLOPE, + KERNEL=KERNEL, + logger=self.logger, + plotter=self.plotter,), + boxes, contours, contours_par, range(len(contours_par))) + #textline_polygons, boxes, text_regions, text_regions_par, box_coord, index_text_con, slopes = zip(*results) + self.logger.debug("exit get_slopes_and_deskew_new") + return tuple(zip(*results)) + + def get_slopes_and_deskew_new_curved(self, contours, contours_par, textline_mask_tot, image_page_rotated, boxes, mask_texts_only, num_col, scale_par, slope_deskew): + if not len(contours): + return [], [], [], [], [], [], [] self.logger.debug("enter get_slopes_and_deskew_new_curved") - with share_ndarray(textline_mask_tot) as textline_mask_tot_shared: - with share_ndarray(mask_texts_only) as mask_texts_only_shared: - assert self.executor - results = self.executor.map(partial(do_work_of_slopes_new_curved, - textline_mask_tot_ea=textline_mask_tot_shared, - mask_texts_only=mask_texts_only_shared, + results = self.executor.map(partial(do_work_of_slopes_new_curved, + textline_mask_tot_ea=textline_mask_tot, + image_page_rotated=image_page_rotated, + mask_texts_only=mask_texts_only, num_col=num_col, scale_par=scale_par, slope_deskew=slope_deskew, @@ -1499,9 +1591,8 @@ class Eynollah: KERNEL=KERNEL, logger=self.logger, plotter=self.plotter,), - boxes, contours_par) - results = list(results) # exhaust prior to release - #textline_polygons, box_coord, slopes = zip(*results) + boxes, contours, contours_par, range(len(contours_par))) + #textline_polygons, boxes, text_regions, text_regions_par, box_coord, index_text_con, slopes = zip(*results) self.logger.debug("exit get_slopes_and_deskew_new_curved") return tuple(zip(*results)) @@ -1514,37 +1605,181 @@ class Eynollah: img_w = img_org.shape[1] img = resize_image(img_org, int(img_org.shape[0] * scaler_h), int(img_org.shape[1] * scaler_w)) - prediction_textline = self.do_prediction(use_patches, img, self.model_zoo.get("textline"), - marginal_of_patch_percent=0.15, - n_batch_inference=3, - threshold_art_class_textline=self.threshold_art_class_textline) + prediction_textline = self.do_prediction( + use_patches, img, self.model_textline, + marginal_of_patch_percent=0.15, n_batch_inference=3, + thresholding_for_artificial_class_in_light_version=self.textline_light) + #if not self.textline_light: + #if num_col_classifier==1: + #prediction_textline_nopatch = self.do_prediction(False, img, self.model_textline) + #prediction_textline[:,:][prediction_textline_nopatch[:,:]==0] = 0 prediction_textline = resize_image(prediction_textline, img_h, img_w) textline_mask_tot_ea_art = (prediction_textline[:,:]==2)*1 old_art = np.copy(textline_mask_tot_ea_art) - + if not self.textline_light: + textline_mask_tot_ea_art = textline_mask_tot_ea_art.astype('uint8') + #textline_mask_tot_ea_art = cv2.dilate(textline_mask_tot_ea_art, KERNEL, iterations=1) + prediction_textline[:,:][textline_mask_tot_ea_art[:,:]==1]=2 + textline_mask_tot_ea_lines = (prediction_textline[:,:]==1)*1 textline_mask_tot_ea_lines = textline_mask_tot_ea_lines.astype('uint8') + if not self.textline_light: + textline_mask_tot_ea_lines = cv2.dilate(textline_mask_tot_ea_lines, KERNEL, iterations=1) prediction_textline[:,:][textline_mask_tot_ea_lines[:,:]==1]=1 - - #cv2.imwrite('prediction_textline2.png', prediction_textline[:,:,0]) + if not self.textline_light: + prediction_textline[:,:][old_art[:,:]==1]=2 - prediction_textline_longshot = self.do_prediction(False, img, self.model_zoo.get("textline")) + prediction_textline_longshot = self.do_prediction(False, img, self.model_textline) prediction_textline_longshot_true_size = resize_image(prediction_textline_longshot, img_h, img_w) - - - #cv2.imwrite('prediction_textline.png', prediction_textline[:,:,0]) - #sys.exit() + self.logger.debug('exit textline_contours') return ((prediction_textline[:, :, 0]==1).astype(np.uint8), (prediction_textline_longshot_true_size[:, :, 0]==1).astype(np.uint8)) - + def do_work_of_slopes(self, q, poly, box_sub, boxes_per_process, textline_mask_tot, contours_per_process): + self.logger.debug('enter do_work_of_slopes') + slope_biggest = 0 + slopes_sub = [] + boxes_sub_new = [] + poly_sub = [] + for mv in range(len(boxes_per_process)): + crop_img, _ = crop_image_inside_box(boxes_per_process[mv], np.repeat(textline_mask_tot[:, :, np.newaxis], 3, axis=2)) + crop_img = crop_img[:, :, 0] + crop_img = cv2.erode(crop_img, KERNEL, iterations=2) + try: + textline_con, hierarchy = return_contours_of_image(crop_img) + textline_con_fil = filter_contours_area_of_image(crop_img, textline_con, hierarchy, max_area=1, min_area=0.0008) + y_diff_mean = find_contours_mean_y_diff(textline_con_fil) + sigma_des = max(1, int(y_diff_mean * (4.0 / 40.0))) + crop_img[crop_img > 0] = 1 + slope_corresponding_textregion = return_deskew_slop(crop_img, sigma_des, + map=self.executor.map, logger=self.logger, plotter=self.plotter) + except Exception as why: + self.logger.error(why) + slope_corresponding_textregion = MAX_SLOPE - def get_regions_light_v(self,img,is_image_enhanced, num_col_classifier): + if slope_corresponding_textregion == MAX_SLOPE: + slope_corresponding_textregion = slope_biggest + slopes_sub.append(slope_corresponding_textregion) + + cnt_clean_rot = textline_contours_postprocessing( + crop_img, slope_corresponding_textregion, contours_per_process[mv], boxes_per_process[mv]) + + poly_sub.append(cnt_clean_rot) + boxes_sub_new.append(boxes_per_process[mv]) + + q.put(slopes_sub) + poly.put(poly_sub) + box_sub.put(boxes_sub_new) + self.logger.debug('exit do_work_of_slopes') + + def get_regions_light_v_extract_only_images(self,img,is_image_enhanced, num_col_classifier): + self.logger.debug("enter get_regions_extract_images_only") + erosion_hurts = False + img_org = np.copy(img) + img_height_h = img_org.shape[0] + img_width_h = img_org.shape[1] + + if num_col_classifier == 1: + img_w_new = 700 + elif num_col_classifier == 2: + img_w_new = 900 + elif num_col_classifier == 3: + img_w_new = 1500 + elif num_col_classifier == 4: + img_w_new = 1800 + elif num_col_classifier == 5: + img_w_new = 2200 + elif num_col_classifier == 6: + img_w_new = 2500 + img_h_new = int(img.shape[0] / float(img.shape[1]) * img_w_new) + img_resized = resize_image(img,img_h_new, img_w_new ) + + prediction_regions_org, _ = self.do_prediction_new_concept(True, img_resized, self.model_region) + + prediction_regions_org = resize_image(prediction_regions_org,img_height_h, img_width_h ) + image_page, page_coord, cont_page = self.extract_page() + + prediction_regions_org = prediction_regions_org[page_coord[0] : page_coord[1], page_coord[2] : page_coord[3]] + prediction_regions_org=prediction_regions_org[:,:,0] + + mask_lines_only = (prediction_regions_org[:,:] ==3)*1 + mask_texts_only = (prediction_regions_org[:,:] ==1)*1 + mask_images_only=(prediction_regions_org[:,:] ==2)*1 + + polygons_lines_xml, hir_lines_xml = return_contours_of_image(mask_lines_only) + polygons_lines_xml = textline_con_fil = filter_contours_area_of_image( + mask_lines_only, polygons_lines_xml, hir_lines_xml, max_area=1, min_area=0.00001) + + polygons_of_only_texts = return_contours_of_interested_region(mask_texts_only,1,0.00001) + polygons_of_only_lines = return_contours_of_interested_region(mask_lines_only,1,0.00001) + + text_regions_p_true = np.zeros(prediction_regions_org.shape) + text_regions_p_true = cv2.fillPoly(text_regions_p_true, pts = polygons_of_only_lines, color=(3,3,3)) + + text_regions_p_true[:,:][mask_images_only[:,:] == 1] = 2 + text_regions_p_true = cv2.fillPoly(text_regions_p_true, pts=polygons_of_only_texts, color=(1,1,1)) + + text_regions_p_true[text_regions_p_true.shape[0]-15:text_regions_p_true.shape[0], :] = 0 + text_regions_p_true[:, text_regions_p_true.shape[1]-15:text_regions_p_true.shape[1]] = 0 + + ##polygons_of_images = return_contours_of_interested_region(text_regions_p_true, 2, 0.0001) + polygons_of_images = return_contours_of_interested_region(text_regions_p_true, 2, 0.001) + image_boundary_of_doc = np.zeros((text_regions_p_true.shape[0], text_regions_p_true.shape[1])) + + ###image_boundary_of_doc[:6, :] = 1 + ###image_boundary_of_doc[text_regions_p_true.shape[0]-6:text_regions_p_true.shape[0], :] = 1 + + ###image_boundary_of_doc[:, :6] = 1 + ###image_boundary_of_doc[:, text_regions_p_true.shape[1]-6:text_regions_p_true.shape[1]] = 1 + + polygons_of_images_fin = [] + for ploy_img_ind in polygons_of_images: + """ + test_poly_image = np.zeros((text_regions_p_true.shape[0], text_regions_p_true.shape[1])) + test_poly_image = cv2.fillPoly(test_poly_image, pts=[ploy_img_ind], color=(1,1,1)) + + test_poly_image = test_poly_image + image_boundary_of_doc + test_poly_image_intersected_area = ( test_poly_image[:,:]==2 )*1 + + test_poly_image_intersected_area = test_poly_image_intersected_area.sum() + + if test_poly_image_intersected_area==0: + ##polygons_of_images_fin.append(ploy_img_ind) + + box = cv2.boundingRect(ploy_img_ind) + _, page_coord_img = crop_image_inside_box(box, text_regions_p_true) + # cont_page.append(np.array([[page_coord[2], page_coord[0]], + # [page_coord[3], page_coord[0]], + # [page_coord[3], page_coord[1]], + # [page_coord[2], page_coord[1]]])) + polygons_of_images_fin.append(np.array([[page_coord_img[2], page_coord_img[0]], + [page_coord_img[3], page_coord_img[0]], + [page_coord_img[3], page_coord_img[1]], + [page_coord_img[2], page_coord_img[1]]]) ) + """ + box = x, y, w, h = cv2.boundingRect(ploy_img_ind) + if h < 150 or w < 150: + pass + else: + _, page_coord_img = crop_image_inside_box(box, text_regions_p_true) + # cont_page.append(np.array([[page_coord[2], page_coord[0]], + # [page_coord[3], page_coord[0]], + # [page_coord[3], page_coord[1]], + # [page_coord[2], page_coord[1]]])) + polygons_of_images_fin.append(np.array([[page_coord_img[2], page_coord_img[0]], + [page_coord_img[3], page_coord_img[0]], + [page_coord_img[3], page_coord_img[1]], + [page_coord_img[2], page_coord_img[1]]])) + + self.logger.debug("exit get_regions_extract_images_only") + return text_regions_p_true, erosion_hurts, polygons_lines_xml, polygons_of_images_fin, image_page, page_coord, cont_page + + def get_regions_light_v(self,img,is_image_enhanced, num_col_classifier, skip_layout_and_reading_order=False): self.logger.debug("enter get_regions_light_v") t_in = time.time() erosion_hurts = False @@ -1574,7 +1809,7 @@ class Eynollah: #if self.input_binary: #img_bin = np.copy(img_resized) ###if (not self.input_binary and self.full_layout) or (not self.input_binary and num_col_classifier >= 30): - ###prediction_bin = self.do_prediction(True, img_resized, self.model_zoo.get_model("binarization"), n_batch_inference=5) + ###prediction_bin = self.do_prediction(True, img_resized, self.model_bin, n_batch_inference=5) ####print("inside bin ", time.time()-t_bin) ###prediction_bin=prediction_bin[:,:,0] @@ -1588,257 +1823,637 @@ class Eynollah: ###img_bin = np.copy(prediction_bin) ###else: ###img_bin = np.copy(img_resized) - img_bin = np.copy(img_resized) + if self.ocr and not self.input_binary: + prediction_bin = self.do_prediction(True, img_resized, self.model_bin, n_batch_inference=5) + prediction_bin = 255 * (prediction_bin[:,:,0] == 0) + prediction_bin = np.repeat(prediction_bin[:, :, np.newaxis], 3, axis=2) + prediction_bin = prediction_bin.astype(np.uint16) + #img= np.copy(prediction_bin) + img_bin = np.copy(prediction_bin) + else: + img_bin = np.copy(img_resized) #print("inside 1 ", time.time()-t_in) ###textline_mask_tot_ea = self.run_textline(img_bin) - self.logger.debug("detecting textlines on %s with %d colors", - str(img_resized.shape), len(np.unique(img_resized))) + self.logger.debug("detecting textlines on %s with %d colors", str(img_resized.shape), len(np.unique(img_resized))) textline_mask_tot_ea = self.run_textline(img_resized, num_col_classifier) textline_mask_tot_ea = resize_image(textline_mask_tot_ea,img_height_h, img_width_h ) #print(self.image_org.shape) - #cv2.imwrite('textline.png', textline_mask_tot_ea) + #cv2.imwrite('out_13.png', self.image_page_org_size) #plt.imshwo(self.image_page_org_size) #plt.show() - if self.skip_layout_and_reading_order: - img_bin = resize_image(img_bin,img_height_h, img_width_h ) - self.logger.debug("exit get_regions_light_v") - return None, erosion_hurts, None, None, textline_mask_tot_ea, img_bin, None + if not skip_layout_and_reading_order: + #print("inside 2 ", time.time()-t_in) + if num_col_classifier == 1 or num_col_classifier == 2: + if self.image_org.shape[0]/self.image_org.shape[1] > 2.5: + self.logger.debug("resized to %dx%d for %d cols", + img_resized.shape[1], img_resized.shape[0], num_col_classifier) + prediction_regions_org, confidence_matrix = self.do_prediction_new_concept( + True, img_resized, self.model_region_1_2, n_batch_inference=1, + thresholding_for_some_classes_in_light_version=True) + else: + prediction_regions_org = np.zeros((self.image_org.shape[0], self.image_org.shape[1], 3)) + confidence_matrix = np.zeros((self.image_org.shape[0], self.image_org.shape[1])) + prediction_regions_page, confidence_matrix_page = self.do_prediction_new_concept( + False, self.image_page_org_size, self.model_region_1_2, n_batch_inference=1, + thresholding_for_artificial_class_in_light_version=True) + ys = slice(*self.page_coord[0:2]) + xs = slice(*self.page_coord[2:4]) + prediction_regions_org[ys, xs] = prediction_regions_page + confidence_matrix[ys, xs] = confidence_matrix_page - #print("inside 2 ", time.time()-t_in) - if num_col_classifier == 1 or num_col_classifier == 2: - if self.image_org.shape[0]/self.image_org.shape[1] > 2.5: - self.logger.debug("resized to %dx%d for %d cols", - img_resized.shape[1], img_resized.shape[0], num_col_classifier) - prediction_regions_org, confidence_matrix = self.do_prediction_new_concept( - True, img_resized, self.model_zoo.get("region_1_2"), n_batch_inference=1, - thresholding_for_some_classes_in_light_version=True, - threshold_art_class_layout=self.threshold_art_class_layout) else: - prediction_regions_org = np.zeros((self.image_org.shape[0], self.image_org.shape[1], 3)) - confidence_matrix = np.zeros((self.image_org.shape[0], self.image_org.shape[1])) - prediction_regions_page, confidence_matrix_page = self.do_prediction_new_concept( - False, self.image_page_org_size, self.model_zoo.get("region_1_2"), n_batch_inference=1, - thresholding_for_artificial_class_in_light_version=True, - threshold_art_class_layout=self.threshold_art_class_layout) - ys = slice(*self.page_coord[0:2]) - xs = slice(*self.page_coord[2:4]) - prediction_regions_org[ys, xs] = prediction_regions_page - confidence_matrix[ys, xs] = confidence_matrix_page - - else: - new_h = (900+ (num_col_classifier-3)*100) - img_resized = resize_image(img_bin, int(new_h * img_bin.shape[0] /img_bin.shape[1]), new_h) - self.logger.debug("resized to %dx%d (new_h=%d) for %d cols", - img_resized.shape[1], img_resized.shape[0], new_h, num_col_classifier) - prediction_regions_org, confidence_matrix = self.do_prediction_new_concept( - True, img_resized, self.model_zoo.get("region_1_2"), n_batch_inference=2, - thresholding_for_some_classes_in_light_version=True, - threshold_art_class_layout=self.threshold_art_class_layout) - ###prediction_regions_org = self.do_prediction(True, img_bin, self.model_zoo.get_model("region"), - ###n_batch_inference=3, - ###thresholding_for_some_classes_in_light_version=True) - #print("inside 3 ", time.time()-t_in) - #plt.imshow(prediction_regions_org[:,:,0]) - #plt.show() - - prediction_regions_org = resize_image(prediction_regions_org, img_height_h, img_width_h ) - confidence_matrix = resize_image(confidence_matrix, img_height_h, img_width_h ) - img_bin = resize_image(img_bin, img_height_h, img_width_h ) - prediction_regions_org=prediction_regions_org[:,:,0] - - mask_lines_only = (prediction_regions_org[:,:] ==3)*1 - mask_texts_only = (prediction_regions_org[:,:] ==1)*1 - mask_texts_only = mask_texts_only.astype('uint8') - - ##if num_col_classifier == 1 or num_col_classifier == 2: - ###mask_texts_only = cv2.erode(mask_texts_only, KERNEL, iterations=1) - ##mask_texts_only = cv2.dilate(mask_texts_only, KERNEL, iterations=1) - - mask_texts_only = cv2.dilate(mask_texts_only, kernel=np.ones((2,2), np.uint8), iterations=1) - mask_images_only=(prediction_regions_org[:,:] ==2)*1 - - polygons_seplines, hir_seplines = return_contours_of_image(mask_lines_only) - test_khat = np.zeros(prediction_regions_org.shape) - test_khat = cv2.fillPoly(test_khat, pts=polygons_seplines, color=(1,1,1)) - - #plt.imshow(test_khat[:,:]) - #plt.show() - #for jv in range(1): - #print(jv, hir_seplines[0][232][3]) - #test_khat = np.zeros(prediction_regions_org.shape) - #test_khat = cv2.fillPoly(test_khat, pts = [polygons_seplines[232]], color=(1,1,1)) - #plt.imshow(test_khat[:,:]) + new_h = (900+ (num_col_classifier-3)*100) + img_resized = resize_image(img_bin, int(new_h * img_bin.shape[0] /img_bin.shape[1]), new_h) + self.logger.debug("resized to %dx%d (new_h=%d) for %d cols", + img_resized.shape[1], img_resized.shape[0], new_h, num_col_classifier) + prediction_regions_org, confidence_matrix = self.do_prediction_new_concept( + True, img_resized, self.model_region_1_2, n_batch_inference=2, + thresholding_for_some_classes_in_light_version=True) + ###prediction_regions_org = self.do_prediction(True, img_bin, self.model_region, n_batch_inference=3, thresholding_for_some_classes_in_light_version=True) + #print("inside 3 ", time.time()-t_in) + #plt.imshow(prediction_regions_org[:,:,0]) #plt.show() - polygons_seplines = filter_contours_area_of_image( - mask_lines_only, polygons_seplines, hir_seplines, max_area=1, min_area=0.00001, dilate=1) + prediction_regions_org = resize_image(prediction_regions_org, img_height_h, img_width_h ) + confidence_matrix = resize_image(confidence_matrix, img_height_h, img_width_h ) + img_bin = resize_image(img_bin, img_height_h, img_width_h ) + prediction_regions_org=prediction_regions_org[:,:,0] - test_khat = np.zeros(prediction_regions_org.shape) - test_khat = cv2.fillPoly(test_khat, pts = polygons_seplines, color=(1,1,1)) + mask_lines_only = (prediction_regions_org[:,:] ==3)*1 + mask_texts_only = (prediction_regions_org[:,:] ==1)*1 + mask_texts_only = mask_texts_only.astype('uint8') - #plt.imshow(test_khat[:,:]) + ##if num_col_classifier == 1 or num_col_classifier == 2: + ###mask_texts_only = cv2.erode(mask_texts_only, KERNEL, iterations=1) + ##mask_texts_only = cv2.dilate(mask_texts_only, KERNEL, iterations=1) + + mask_texts_only = cv2.dilate(mask_texts_only, kernel=np.ones((2,2), np.uint8), iterations=1) + mask_images_only=(prediction_regions_org[:,:] ==2)*1 + + polygons_lines_xml, hir_lines_xml = return_contours_of_image(mask_lines_only) + test_khat = np.zeros(prediction_regions_org.shape) + test_khat = cv2.fillPoly(test_khat, pts=polygons_lines_xml, color=(1,1,1)) + + #plt.imshow(test_khat[:,:]) + #plt.show() + #for jv in range(1): + #print(jv, hir_lines_xml[0][232][3]) + #test_khat = np.zeros(prediction_regions_org.shape) + #test_khat = cv2.fillPoly(test_khat, pts = [polygons_lines_xml[232]], color=(1,1,1)) + #plt.imshow(test_khat[:,:]) + #plt.show() + + polygons_lines_xml = filter_contours_area_of_image( + mask_lines_only, polygons_lines_xml, hir_lines_xml, max_area=1, min_area=0.00001) + + test_khat = np.zeros(prediction_regions_org.shape) + test_khat = cv2.fillPoly(test_khat, pts = polygons_lines_xml, color=(1,1,1)) + + #plt.imshow(test_khat[:,:]) + #plt.show() + #sys.exit() + + polygons_of_only_texts = return_contours_of_interested_region(mask_texts_only,1,0.00001) + ##polygons_of_only_texts = self.dilate_textregions_contours(polygons_of_only_texts) + polygons_of_only_lines = return_contours_of_interested_region(mask_lines_only,1,0.00001) + + text_regions_p_true = np.zeros(prediction_regions_org.shape) + text_regions_p_true = cv2.fillPoly(text_regions_p_true, pts=polygons_of_only_lines, color=(3,3,3)) + + text_regions_p_true[:,:][mask_images_only[:,:] == 1] = 2 + text_regions_p_true = cv2.fillPoly(text_regions_p_true, pts = polygons_of_only_texts, color=(1,1,1)) + + #plt.imshow(textline_mask_tot_ea) + #plt.show() + + textline_mask_tot_ea[(text_regions_p_true==0) | (text_regions_p_true==4) ] = 0 + + #plt.imshow(textline_mask_tot_ea) + #plt.show() + #print("inside 4 ", time.time()-t_in) + self.logger.debug("exit get_regions_light_v") + return text_regions_p_true, erosion_hurts, polygons_lines_xml, textline_mask_tot_ea, img_bin, confidence_matrix + else: + img_bin = resize_image(img_bin,img_height_h, img_width_h ) + self.logger.debug("exit get_regions_light_v") + return None, erosion_hurts, None, textline_mask_tot_ea, img_bin, None + + def get_regions_from_xy_2models(self,img,is_image_enhanced, num_col_classifier): + self.logger.debug("enter get_regions_from_xy_2models") + erosion_hurts = False + img_org = np.copy(img) + img_height_h = img_org.shape[0] + img_width_h = img_org.shape[1] + + ratio_y=1.3 + ratio_x=1 + + img = resize_image(img_org, int(img_org.shape[0]*ratio_y), int(img_org.shape[1]*ratio_x)) + prediction_regions_org_y = self.do_prediction(True, img, self.model_region) + prediction_regions_org_y = resize_image(prediction_regions_org_y, img_height_h, img_width_h ) + + #plt.imshow(prediction_regions_org_y[:,:,0]) #plt.show() - #sys.exit() + prediction_regions_org_y = prediction_regions_org_y[:,:,0] + mask_zeros_y = (prediction_regions_org_y[:,:]==0)*1 - polygons_of_only_texts = return_contours_of_interested_region(mask_texts_only,1,0.00001) - ##polygons_of_only_texts = dilate_textregion_contours(polygons_of_only_texts) - polygons_of_only_lines = return_contours_of_interested_region(mask_lines_only,1,0.00001) + ##img_only_regions_with_sep = ( (prediction_regions_org_y[:,:] != 3) & (prediction_regions_org_y[:,:] != 0) )*1 + img_only_regions_with_sep = (prediction_regions_org_y == 1).astype(np.uint8) + try: + img_only_regions = cv2.erode(img_only_regions_with_sep[:,:], KERNEL, iterations=20) + _, _ = find_num_col(img_only_regions, num_col_classifier, self.tables, multiplier=6.0) + img = resize_image(img_org, int(img_org.shape[0]), int(img_org.shape[1]*(1.2 if is_image_enhanced else 1))) - text_regions_p_true = np.zeros(prediction_regions_org.shape) - text_regions_p_true = cv2.fillPoly(text_regions_p_true, pts=polygons_of_only_lines, color=(3,3,3)) + prediction_regions_org = self.do_prediction(True, img, self.model_region) + prediction_regions_org = resize_image(prediction_regions_org, img_height_h, img_width_h ) - text_regions_p_true[:,:][mask_images_only[:,:] == 1] = 2 - text_regions_p_true = cv2.fillPoly(text_regions_p_true, pts = polygons_of_only_texts, color=(1,1,1)) + prediction_regions_org=prediction_regions_org[:,:,0] + prediction_regions_org[(prediction_regions_org[:,:]==1) & (mask_zeros_y[:,:]==1)]=0 - textline_mask_tot_ea[(text_regions_p_true==0) | (text_regions_p_true==4) ] = 0 - #plt.imshow(textline_mask_tot_ea) - #plt.show() - #print("inside 4 ", time.time()-t_in) - self.logger.debug("exit get_regions_light_v") - return (text_regions_p_true, - erosion_hurts, - polygons_seplines, - polygons_of_only_texts, - textline_mask_tot_ea, - img_bin, - confidence_matrix) + img = resize_image(img_org, int(img_org.shape[0]), int(img_org.shape[1])) - def do_order_of_regions( + prediction_regions_org2 = self.do_prediction(True, img, self.model_region_p2, marginal_of_patch_percent=0.2) + prediction_regions_org2=resize_image(prediction_regions_org2, img_height_h, img_width_h ) + + mask_zeros2 = (prediction_regions_org2[:,:,0] == 0) + mask_lines2 = (prediction_regions_org2[:,:,0] == 3) + text_sume_early = (prediction_regions_org[:,:] == 1).sum() + prediction_regions_org_copy = np.copy(prediction_regions_org) + prediction_regions_org_copy[(prediction_regions_org_copy[:,:]==1) & (mask_zeros2[:,:]==1)] = 0 + text_sume_second = ((prediction_regions_org_copy[:,:]==1)*1).sum() + rate_two_models = 100. * text_sume_second / text_sume_early + + self.logger.info("ratio_of_two_models: %s", rate_two_models) + if not(is_image_enhanced and rate_two_models < RATIO_OF_TWO_MODEL_THRESHOLD): + prediction_regions_org = np.copy(prediction_regions_org_copy) + + prediction_regions_org[(mask_lines2[:,:]==1) & (prediction_regions_org[:,:]==0)]=3 + mask_lines_only=(prediction_regions_org[:,:]==3)*1 + prediction_regions_org = cv2.erode(prediction_regions_org[:,:], KERNEL, iterations=2) + prediction_regions_org = cv2.dilate(prediction_regions_org[:,:], KERNEL, iterations=2) + + if rate_two_models<=40: + if self.input_binary: + prediction_bin = np.copy(img_org) + else: + prediction_bin = self.do_prediction(True, img_org, self.model_bin, n_batch_inference=5) + prediction_bin = resize_image(prediction_bin, img_height_h, img_width_h ) + prediction_bin = 255 * (prediction_bin[:,:,0]==0) + prediction_bin = np.repeat(prediction_bin[:, :, np.newaxis], 3, axis=2) + + ratio_y=1 + ratio_x=1 + + img = resize_image(prediction_bin, int(img_org.shape[0]*ratio_y), int(img_org.shape[1]*ratio_x)) + + prediction_regions_org = self.do_prediction(True, img, self.model_region) + prediction_regions_org = resize_image(prediction_regions_org, img_height_h, img_width_h ) + prediction_regions_org=prediction_regions_org[:,:,0] + + mask_lines_only=(prediction_regions_org[:,:]==3)*1 + + mask_texts_only=(prediction_regions_org[:,:]==1)*1 + mask_images_only=(prediction_regions_org[:,:]==2)*1 + + polygons_lines_xml, hir_lines_xml = return_contours_of_image(mask_lines_only) + polygons_lines_xml = filter_contours_area_of_image( + mask_lines_only, polygons_lines_xml, hir_lines_xml, max_area=1, min_area=0.00001) + + polygons_of_only_texts = return_contours_of_interested_region(mask_texts_only, 1, 0.00001) + polygons_of_only_lines = return_contours_of_interested_region(mask_lines_only, 1, 0.00001) + + text_regions_p_true = np.zeros(prediction_regions_org.shape) + text_regions_p_true = cv2.fillPoly(text_regions_p_true,pts = polygons_of_only_lines, color=(3, 3, 3)) + text_regions_p_true[:,:][mask_images_only[:,:] == 1] = 2 + + text_regions_p_true=cv2.fillPoly(text_regions_p_true,pts=polygons_of_only_texts, color=(1,1,1)) + + self.logger.debug("exit get_regions_from_xy_2models") + return text_regions_p_true, erosion_hurts, polygons_lines_xml + except: + if self.input_binary: + prediction_bin = np.copy(img_org) + prediction_bin = self.do_prediction(True, img_org, self.model_bin, n_batch_inference=5) + prediction_bin = resize_image(prediction_bin, img_height_h, img_width_h ) + prediction_bin = 255 * (prediction_bin[:,:,0]==0) + prediction_bin = np.repeat(prediction_bin[:, :, np.newaxis], 3, axis=2) + else: + prediction_bin = np.copy(img_org) + ratio_y=1 + ratio_x=1 + + + img = resize_image(prediction_bin, int(img_org.shape[0]*ratio_y), int(img_org.shape[1]*ratio_x)) + prediction_regions_org = self.do_prediction(True, img, self.model_region) + prediction_regions_org = resize_image(prediction_regions_org, img_height_h, img_width_h ) + prediction_regions_org=prediction_regions_org[:,:,0] + + #mask_lines_only=(prediction_regions_org[:,:]==3)*1 + #img = resize_image(img_org, int(img_org.shape[0]*1), int(img_org.shape[1]*1)) + + #prediction_regions_org = self.do_prediction(True, img, self.model_region) + + #prediction_regions_org = resize_image(prediction_regions_org, img_height_h, img_width_h ) + + #prediction_regions_org = prediction_regions_org[:,:,0] + + #prediction_regions_org[(prediction_regions_org[:,:] == 1) & (mask_zeros_y[:,:] == 1)]=0 + + + mask_lines_only = (prediction_regions_org == 3)*1 + mask_texts_only = (prediction_regions_org == 1)*1 + mask_images_only= (prediction_regions_org == 2)*1 + + polygons_lines_xml, hir_lines_xml = return_contours_of_image(mask_lines_only) + polygons_lines_xml = filter_contours_area_of_image( + mask_lines_only, polygons_lines_xml, hir_lines_xml, max_area=1, min_area=0.00001) + + polygons_of_only_texts = return_contours_of_interested_region(mask_texts_only,1,0.00001) + polygons_of_only_lines = return_contours_of_interested_region(mask_lines_only,1,0.00001) + + text_regions_p_true = np.zeros(prediction_regions_org.shape) + text_regions_p_true = cv2.fillPoly(text_regions_p_true, pts = polygons_of_only_lines, color=(3,3,3)) + + text_regions_p_true[:,:][mask_images_only[:,:] == 1] = 2 + text_regions_p_true = cv2.fillPoly(text_regions_p_true, pts = polygons_of_only_texts, color=(1,1,1)) + + erosion_hurts = True + self.logger.debug("exit get_regions_from_xy_2models") + return text_regions_p_true, erosion_hurts, polygons_lines_xml + + def do_order_of_regions_full_layout( self, contours_only_text_parent, contours_only_text_parent_h, boxes, textline_mask_tot): - self.logger.debug("enter do_order_of_regions") - contours_only_text_parent = np.array(contours_only_text_parent) - contours_only_text_parent_h = np.array(contours_only_text_parent_h) + self.logger.debug("enter do_order_of_regions_full_layout") boxes = np.array(boxes, dtype=int) # to be on the safe side - c_boxes = np.stack((0.5 * boxes[:, 2:4].sum(axis=1), - 0.5 * boxes[:, 0:2].sum(axis=1))) - cx_main, cy_main, mx_main, Mx_main, my_main, My_main, mxy_main = find_new_features_of_contours( + cx_text_only, cy_text_only, x_min_text_only, _, _, _, y_cor_x_min_main = find_new_features_of_contours( contours_only_text_parent) - cx_head, cy_head, mx_head, Mx_head, my_head, My_head, mxy_head = find_new_features_of_contours( + cx_text_only_h, cy_text_only_h, x_min_text_only_h, _, _, _, y_cor_x_min_main_h = find_new_features_of_contours( contours_only_text_parent_h) - def match_boxes(only_centers: bool): - arg_text_con_main = np.zeros(len(contours_only_text_parent), dtype=int) - for ii in range(len(contours_only_text_parent)): + try: + arg_text_con = [] + for ii in range(len(cx_text_only)): check_if_textregion_located_in_a_box = False - for jj, box in enumerate(boxes): - if ((cx_main[ii] >= box[0] and - cx_main[ii] < box[1] and - cy_main[ii] >= box[2] and - cy_main[ii] < box[3]) if only_centers else - (mx_main[ii] >= box[0] and - Mx_main[ii] < box[1] and - my_main[ii] >= box[2] and - My_main[ii] < box[3])): - arg_text_con_main[ii] = jj + for jj in range(len(boxes)): + if (x_min_text_only[ii] + 80 >= boxes[jj][0] and + x_min_text_only[ii] + 80 < boxes[jj][1] and + y_cor_x_min_main[ii] >= boxes[jj][2] and + y_cor_x_min_main[ii] < boxes[jj][3]): + arg_text_con.append(jj) check_if_textregion_located_in_a_box = True break if not check_if_textregion_located_in_a_box: - dists_tr_from_box = np.linalg.norm(c_boxes - np.array([[cy_main[ii]], [cx_main[ii]]]), axis=0) - pcontained_in_box = ((boxes[:, 2] <= cy_main[ii]) & (cy_main[ii] < boxes[:, 3]) & - (boxes[:, 0] <= cx_main[ii]) & (cx_main[ii] < boxes[:, 1])) - ind_min = np.argmin(np.ma.masked_array(dists_tr_from_box, ~pcontained_in_box)) - arg_text_con_main[ii] = ind_min - args_contours_main = np.arange(len(contours_only_text_parent)) - order_by_con_main = np.zeros_like(arg_text_con_main) + dists_tr_from_box = [math.sqrt((cx_text_only[ii] - boxes[jj][1]) ** 2 + + (cy_text_only[ii] - boxes[jj][2]) ** 2) + for jj in range(len(boxes))] + ind_min = np.argmin(dists_tr_from_box) + arg_text_con.append(ind_min) + args_contours = np.array(range(len(arg_text_con))) + arg_text_con_h = [] + for ii in range(len(cx_text_only_h)): + check_if_textregion_located_in_a_box = False + for jj in range(len(boxes)): + if (x_min_text_only_h[ii] + 80 >= boxes[jj][0] and + x_min_text_only_h[ii] + 80 < boxes[jj][1] and + y_cor_x_min_main_h[ii] >= boxes[jj][2] and + y_cor_x_min_main_h[ii] < boxes[jj][3]): + arg_text_con_h.append(jj) + check_if_textregion_located_in_a_box = True + break + if not check_if_textregion_located_in_a_box: + dists_tr_from_box = [math.sqrt((cx_text_only_h[ii] - boxes[jj][1]) ** 2 + + (cy_text_only_h[ii] - boxes[jj][2]) ** 2) + for jj in range(len(boxes))] + ind_min = np.argmin(dists_tr_from_box) + arg_text_con_h.append(ind_min) + args_contours_h = np.array(range(len(arg_text_con_h))) - arg_text_con_head = np.zeros(len(contours_only_text_parent_h), dtype=int) - for ii in range(len(contours_only_text_parent_h)): - check_if_textregion_located_in_a_box = False - for jj, box in enumerate(boxes): - if ((cx_head[ii] >= box[0] and - cx_head[ii] < box[1] and - cy_head[ii] >= box[2] and - cy_head[ii] < box[3]) if only_centers else - (mx_head[ii] >= box[0] and - Mx_head[ii] < box[1] and - my_head[ii] >= box[2] and - My_head[ii] < box[3])): - arg_text_con_head[ii] = jj - check_if_textregion_located_in_a_box = True - break - if not check_if_textregion_located_in_a_box: - dists_tr_from_box = np.linalg.norm(c_boxes - np.array([[cy_head[ii]], [cx_head[ii]]]), axis=0) - pcontained_in_box = ((boxes[:, 2] <= cy_head[ii]) & (cy_head[ii] < boxes[:, 3]) & - (boxes[:, 0] <= cx_head[ii]) & (cx_head[ii] < boxes[:, 1])) - ind_min = np.argmin(np.ma.masked_array(dists_tr_from_box, ~pcontained_in_box)) - arg_text_con_head[ii] = ind_min - args_contours_head = np.arange(len(contours_only_text_parent_h)) - order_by_con_head = np.zeros_like(arg_text_con_head) + order_by_con_head = np.zeros(len(arg_text_con_h)) + order_by_con_main = np.zeros(len(arg_text_con)) ref_point = 0 order_of_texts_tot = [] id_of_texts_tot = [] - for iij, box in enumerate(boxes): - ys = slice(*box[2:4]) - xs = slice(*box[0:2]) - args_contours_box_main = args_contours_main[arg_text_con_main == iij] - args_contours_box_head = args_contours_head[arg_text_con_head == iij] - con_inter_box = contours_only_text_parent[args_contours_box_main] - con_inter_box_h = contours_only_text_parent_h[args_contours_box_head] + for iij in range(len(boxes)): + ys = slice(*boxes[iij][2:4]) + xs = slice(*boxes[iij][0:2]) + args_contours_box = args_contours[np.array(arg_text_con) == iij] + args_contours_box_h = args_contours_h[np.array(arg_text_con_h) == iij] + con_inter_box = [] + con_inter_box_h = [] - indexes_sorted, kind_of_texts_sorted, index_by_kind_sorted = order_of_regions( - textline_mask_tot[ys, xs], con_inter_box, con_inter_box_h, box[2]) + for box in args_contours_box: + con_inter_box.append(contours_only_text_parent[box]) + + for box in args_contours_box_h: + con_inter_box_h.append(contours_only_text_parent_h[box]) + + indexes_sorted, matrix_of_orders, kind_of_texts_sorted, index_by_kind_sorted = order_of_regions( + textline_mask_tot[ys, xs], con_inter_box, con_inter_box_h, boxes[iij][2]) order_of_texts, id_of_texts = order_and_id_of_texts( con_inter_box, con_inter_box_h, - indexes_sorted, index_by_kind_sorted, kind_of_texts_sorted, ref_point) + matrix_of_orders, indexes_sorted, index_by_kind_sorted, kind_of_texts_sorted, ref_point) - indexes_sorted_main = indexes_sorted[kind_of_texts_sorted == 1] - indexes_by_type_main = index_by_kind_sorted[kind_of_texts_sorted == 1] - indexes_sorted_head = indexes_sorted[kind_of_texts_sorted == 2] - indexes_by_type_head = index_by_kind_sorted[kind_of_texts_sorted == 2] + indexes_sorted_main = np.array(indexes_sorted)[np.array(kind_of_texts_sorted) == 1] + indexes_by_type_main = np.array(index_by_kind_sorted)[np.array(kind_of_texts_sorted) == 1] + indexes_sorted_head = np.array(indexes_sorted)[np.array(kind_of_texts_sorted) == 2] + indexes_by_type_head = np.array(index_by_kind_sorted)[np.array(kind_of_texts_sorted) == 2] - for zahler, _ in enumerate(args_contours_box_main): + for zahler, _ in enumerate(args_contours_box): arg_order_v = indexes_sorted_main[zahler] - order_by_con_main[args_contours_box_main[indexes_by_type_main[zahler]]] = \ - np.flatnonzero(indexes_sorted == arg_order_v) + ref_point + order_by_con_main[args_contours_box[indexes_by_type_main[zahler]]] = \ + np.where(indexes_sorted == arg_order_v)[0][0] + ref_point - for zahler, _ in enumerate(args_contours_box_head): + for zahler, _ in enumerate(args_contours_box_h): arg_order_v = indexes_sorted_head[zahler] - order_by_con_head[args_contours_box_head[indexes_by_type_head[zahler]]] = \ - np.flatnonzero(indexes_sorted == arg_order_v) + ref_point + order_by_con_head[args_contours_box_h[indexes_by_type_head[zahler]]] = \ + np.where(indexes_sorted == arg_order_v)[0][0] + ref_point for jji in range(len(id_of_texts)): order_of_texts_tot.append(order_of_texts[jji] + ref_point) id_of_texts_tot.append(id_of_texts[jji]) ref_point += len(id_of_texts) - order_of_texts_tot = np.concatenate((order_by_con_main, - order_by_con_head)) - order_text_new = np.argsort(order_of_texts_tot) - return order_text_new, id_of_texts_tot + order_of_texts_tot = [] + for tj1 in range(len(contours_only_text_parent)): + order_of_texts_tot.append(int(order_by_con_main[tj1])) + + for tj1 in range(len(contours_only_text_parent_h)): + order_of_texts_tot.append(int(order_by_con_head[tj1])) + + order_text_new = [] + for iii in range(len(order_of_texts_tot)): + order_text_new.append(np.where(np.array(order_of_texts_tot) == iii)[0][0]) - try: - results = match_boxes(False) except Exception as why: self.logger.error(why) - results = match_boxes(True) + arg_text_con = [] + for ii in range(len(cx_text_only)): + check_if_textregion_located_in_a_box = False + for jj in range(len(boxes)): + if (cx_text_only[ii] >= boxes[jj][0] and + cx_text_only[ii] < boxes[jj][1] and + cy_text_only[ii] >= boxes[jj][2] and + cy_text_only[ii] < boxes[jj][3]): + # this is valid if the center of region identify in which box it is located + arg_text_con.append(jj) + check_if_textregion_located_in_a_box = True + break - self.logger.debug("exit do_order_of_regions") - return results + if not check_if_textregion_located_in_a_box: + dists_tr_from_box = [math.sqrt((cx_text_only[ii] - boxes[jj][1]) ** 2 + + (cy_text_only[ii] - boxes[jj][2]) ** 2) + for jj in range(len(boxes))] + ind_min = np.argmin(dists_tr_from_box) + arg_text_con.append(ind_min) + args_contours = np.array(range(len(arg_text_con))) + order_by_con_main = np.zeros(len(arg_text_con)) + + ############################# head + + arg_text_con_h = [] + for ii in range(len(cx_text_only_h)): + check_if_textregion_located_in_a_box = False + for jj in range(len(boxes)): + if (cx_text_only_h[ii] >= boxes[jj][0] and + cx_text_only_h[ii] < boxes[jj][1] and + cy_text_only_h[ii] >= boxes[jj][2] and + cy_text_only_h[ii] < boxes[jj][3]): + # this is valid if the center of region identify in which box it is located + arg_text_con_h.append(jj) + check_if_textregion_located_in_a_box = True + break + if not check_if_textregion_located_in_a_box: + dists_tr_from_box = [math.sqrt((cx_text_only_h[ii] - boxes[jj][1]) ** 2 + + (cy_text_only_h[ii] - boxes[jj][2]) ** 2) + for jj in range(len(boxes))] + ind_min = np.argmin(dists_tr_from_box) + arg_text_con_h.append(ind_min) + args_contours_h = np.array(range(len(arg_text_con_h))) + order_by_con_head = np.zeros(len(arg_text_con_h)) + + ref_point = 0 + order_of_texts_tot = [] + id_of_texts_tot = [] + for iij, _ in enumerate(boxes): + ys = slice(*boxes[iij][2:4]) + xs = slice(*boxes[iij][0:2]) + args_contours_box = args_contours[np.array(arg_text_con) == iij] + args_contours_box_h = args_contours_h[np.array(arg_text_con_h) == iij] + con_inter_box = [] + con_inter_box_h = [] + + for box in args_contours_box: + con_inter_box.append(contours_only_text_parent[box]) + + for box in args_contours_box_h: + con_inter_box_h.append(contours_only_text_parent_h[box]) + + indexes_sorted, matrix_of_orders, kind_of_texts_sorted, index_by_kind_sorted = order_of_regions( + textline_mask_tot[ys, xs], con_inter_box, con_inter_box_h, boxes[iij][2]) + + order_of_texts, id_of_texts = order_and_id_of_texts( + con_inter_box, con_inter_box_h, + matrix_of_orders, indexes_sorted, index_by_kind_sorted, kind_of_texts_sorted, ref_point) + + indexes_sorted_main = np.array(indexes_sorted)[np.array(kind_of_texts_sorted) == 1] + indexes_by_type_main = np.array(index_by_kind_sorted)[np.array(kind_of_texts_sorted) == 1] + indexes_sorted_head = np.array(indexes_sorted)[np.array(kind_of_texts_sorted) == 2] + indexes_by_type_head = np.array(index_by_kind_sorted)[np.array(kind_of_texts_sorted) == 2] + + for zahler, _ in enumerate(args_contours_box): + arg_order_v = indexes_sorted_main[zahler] + order_by_con_main[args_contours_box[indexes_by_type_main[zahler]]] = \ + np.where(indexes_sorted == arg_order_v)[0][0] + ref_point + + for zahler, _ in enumerate(args_contours_box_h): + arg_order_v = indexes_sorted_head[zahler] + order_by_con_head[args_contours_box_h[indexes_by_type_head[zahler]]] = \ + np.where(indexes_sorted == arg_order_v)[0][0] + ref_point + + for jji, _ in enumerate(id_of_texts): + order_of_texts_tot.append(order_of_texts[jji] + ref_point) + id_of_texts_tot.append(id_of_texts[jji]) + ref_point += len(id_of_texts) + + order_of_texts_tot = [] + for tj1 in range(len(contours_only_text_parent)): + order_of_texts_tot.append(int(order_by_con_main[tj1])) + + for tj1 in range(len(contours_only_text_parent_h)): + order_of_texts_tot.append(int(order_by_con_head[tj1])) + + order_text_new = [] + for iii in range(len(order_of_texts_tot)): + order_text_new.append(np.where(np.array(order_of_texts_tot) == iii)[0][0]) + + self.logger.debug("exit do_order_of_regions_full_layout") + return order_text_new, id_of_texts_tot + + def do_order_of_regions_no_full_layout( + self, contours_only_text_parent, contours_only_text_parent_h, boxes, textline_mask_tot): + + self.logger.debug("enter do_order_of_regions_no_full_layout") + boxes = np.array(boxes, dtype=int) # to be on the safe side + cx_text_only, cy_text_only, x_min_text_only, _, _, _, y_cor_x_min_main = find_new_features_of_contours( + contours_only_text_parent) + + try: + arg_text_con = [] + for ii in range(len(cx_text_only)): + check_if_textregion_located_in_a_box = False + for jj in range(len(boxes)): + if (x_min_text_only[ii] + 80 >= boxes[jj][0] and + x_min_text_only[ii] + 80 < boxes[jj][1] and + y_cor_x_min_main[ii] >= boxes[jj][2] and + y_cor_x_min_main[ii] < boxes[jj][3]): + arg_text_con.append(jj) + check_if_textregion_located_in_a_box = True + break + if not check_if_textregion_located_in_a_box: + dists_tr_from_box = [math.sqrt((cx_text_only[ii] - boxes[jj][1]) ** 2 + + (cy_text_only[ii] - boxes[jj][2]) ** 2) + for jj in range(len(boxes))] + ind_min = np.argmin(dists_tr_from_box) + arg_text_con.append(ind_min) + args_contours = np.array(range(len(arg_text_con))) + order_by_con_main = np.zeros(len(arg_text_con)) + + ref_point = 0 + order_of_texts_tot = [] + id_of_texts_tot = [] + for iij in range(len(boxes)): + ys = slice(*boxes[iij][2:4]) + xs = slice(*boxes[iij][0:2]) + args_contours_box = args_contours[np.array(arg_text_con) == iij] + con_inter_box = [] + con_inter_box_h = [] + for i in range(len(args_contours_box)): + con_inter_box.append(contours_only_text_parent[args_contours_box[i]]) + + indexes_sorted, matrix_of_orders, kind_of_texts_sorted, index_by_kind_sorted = order_of_regions( + textline_mask_tot[ys, xs], con_inter_box, con_inter_box_h, boxes[iij][2]) + + order_of_texts, id_of_texts = order_and_id_of_texts( + con_inter_box, con_inter_box_h, + matrix_of_orders, indexes_sorted, index_by_kind_sorted, kind_of_texts_sorted, ref_point) + + indexes_sorted_main = np.array(indexes_sorted)[np.array(kind_of_texts_sorted) == 1] + indexes_by_type_main = np.array(index_by_kind_sorted)[np.array(kind_of_texts_sorted) == 1] + + for zahler, _ in enumerate(args_contours_box): + arg_order_v = indexes_sorted_main[zahler] + order_by_con_main[args_contours_box[indexes_by_type_main[zahler]]] = \ + np.where(indexes_sorted == arg_order_v)[0][0] + ref_point + + for jji, _ in enumerate(id_of_texts): + order_of_texts_tot.append(order_of_texts[jji] + ref_point) + id_of_texts_tot.append(id_of_texts[jji]) + ref_point += len(id_of_texts) + + order_of_texts_tot = [] + for tj1 in range(len(contours_only_text_parent)): + order_of_texts_tot.append(int(order_by_con_main[tj1])) + + order_text_new = [] + for iii in range(len(order_of_texts_tot)): + order_text_new.append(np.where(np.array(order_of_texts_tot) == iii)[0][0]) + + except Exception as why: + self.logger.error(why) + arg_text_con = [] + for ii in range(len(cx_text_only)): + check_if_textregion_located_in_a_box = False + for jj in range(len(boxes)): + if (cx_text_only[ii] >= boxes[jj][0] and + cx_text_only[ii] < boxes[jj][1] and + cy_text_only[ii] >= boxes[jj][2] and + cy_text_only[ii] < boxes[jj][3]): + # this is valid if the center of region identify in which box it is located + arg_text_con.append(jj) + check_if_textregion_located_in_a_box = True + break + if not check_if_textregion_located_in_a_box: + dists_tr_from_box = [math.sqrt((cx_text_only[ii] - boxes[jj][1]) ** 2 + + (cy_text_only[ii] - boxes[jj][2]) ** 2) + for jj in range(len(boxes))] + ind_min = np.argmin(dists_tr_from_box) + arg_text_con.append(ind_min) + args_contours = np.array(range(len(arg_text_con))) + order_by_con_main = np.zeros(len(arg_text_con)) + + ref_point = 0 + order_of_texts_tot = [] + id_of_texts_tot = [] + for iij in range(len(boxes)): + ys = slice(*boxes[iij][2:4]) + xs = slice(*boxes[iij][0:2]) + args_contours_box = args_contours[np.array(arg_text_con) == iij] + con_inter_box = [] + con_inter_box_h = [] + for i in range(len(args_contours_box)): + con_inter_box.append(contours_only_text_parent[args_contours_box[i]]) + + indexes_sorted, matrix_of_orders, kind_of_texts_sorted, index_by_kind_sorted = order_of_regions( + textline_mask_tot[ys, xs], con_inter_box, con_inter_box_h, boxes[iij][2]) + + order_of_texts, id_of_texts = order_and_id_of_texts( + con_inter_box, con_inter_box_h, + matrix_of_orders, indexes_sorted, index_by_kind_sorted, kind_of_texts_sorted, ref_point) + + indexes_sorted_main = np.array(indexes_sorted)[np.array(kind_of_texts_sorted) == 1] + indexes_by_type_main = np.array(index_by_kind_sorted)[np.array(kind_of_texts_sorted) == 1] + + for zahler, _ in enumerate(args_contours_box): + arg_order_v = indexes_sorted_main[zahler] + order_by_con_main[args_contours_box[indexes_by_type_main[zahler]]] = \ + np.where(indexes_sorted == arg_order_v)[0][0] + ref_point + + for jji, _ in enumerate(id_of_texts): + order_of_texts_tot.append(order_of_texts[jji] + ref_point) + id_of_texts_tot.append(id_of_texts[jji]) + ref_point += len(id_of_texts) + + order_of_texts_tot = [] + + for tj1 in range(len(contours_only_text_parent)): + order_of_texts_tot.append(int(order_by_con_main[tj1])) + + order_text_new = [] + for iii in range(len(order_of_texts_tot)): + order_text_new.append(np.where(np.array(order_of_texts_tot) == iii)[0][0]) + + self.logger.debug("exit do_order_of_regions_no_full_layout") + return order_text_new, id_of_texts_tot def check_iou_of_bounding_box_and_contour_for_tables( self, layout, table_prediction_early, pixel_table, num_col_classifier): layout_org = np.copy(layout) - layout_org[layout_org == pixel_table] = 0 - layout = (layout == pixel_table).astype(np.uint8) * 1 - _, thresh = cv2.threshold(layout, 0, 255, 0) + layout_org[:,:,0][layout_org[:,:,0]==pixel_table] = 0 + layout = (layout[:,:,0]==pixel_table)*1 + + layout =np.repeat(layout[:, :, np.newaxis], 3, axis=2) + layout = layout.astype(np.uint8) + imgray = cv2.cvtColor(layout, cv2.COLOR_BGR2GRAY ) + _, thresh = cv2.threshold(imgray, 0, 255, 0) contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) - cnt_size = np.array([cv2.contourArea(cnt) for cnt in contours]) + cnt_size = np.array([cv2.contourArea(contours[j]) + for j in range(len(contours))]) contours_new = [] - for i, contour in enumerate(contours): - x, y, w, h = cv2.boundingRect(contour) + for i in range(len(contours)): + x, y, w, h = cv2.boundingRect(contours[i]) iou = cnt_size[i] /float(w*h) *100 if iou<80: - layout_contour = np.zeros(layout_org.shape[:2]) - layout_contour = cv2.fillPoly(layout_contour, pts=[contour] ,color=1) + layout_contour = np.zeros((layout_org.shape[0], layout_org.shape[1])) + layout_contour= cv2.fillPoly(layout_contour,pts=[contours[i]] ,color=(1,1,1)) layout_contour_sum = layout_contour.sum(axis=0) layout_contour_sum_diff = np.diff(layout_contour_sum) @@ -1854,42 +2469,44 @@ class Eynollah: layout_contour=cv2.erode(layout_contour[:,:], KERNEL, iterations=5) layout_contour=cv2.dilate(layout_contour[:,:], KERNEL, iterations=5) + layout_contour =np.repeat(layout_contour[:, :, np.newaxis], 3, axis=2) layout_contour = layout_contour.astype(np.uint8) - _, thresh = cv2.threshold(layout_contour, 0, 255, 0) + + imgray = cv2.cvtColor(layout_contour, cv2.COLOR_BGR2GRAY ) + _, thresh = cv2.threshold(imgray, 0, 255, 0) contours_sep, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) for ji in range(len(contours_sep) ): contours_new.append(contours_sep[ji]) if num_col_classifier>=2: - only_recent_contour_image = np.zeros(layout.shape[:2]) - only_recent_contour_image = cv2.fillPoly(only_recent_contour_image, - pts=[contours_sep[ji]], color=1) + only_recent_contour_image = np.zeros((layout.shape[0],layout.shape[1])) + only_recent_contour_image= cv2.fillPoly(only_recent_contour_image, pts=[contours_sep[ji]], color=(1,1,1)) table_pixels_masked_from_early_pre = only_recent_contour_image * table_prediction_early iou_in = 100. * table_pixels_masked_from_early_pre.sum() / only_recent_contour_image.sum() #print(iou_in,'iou_in_in1') if iou_in>30: - layout_org = cv2.fillPoly(layout_org, pts=[contours_sep[ji]], color=pixel_table) + layout_org= cv2.fillPoly(layout_org, pts=[contours_sep[ji]], color=3 * (pixel_table,)) else: pass else: - layout_org= cv2.fillPoly(layout_org, pts=[contours_sep[ji]], color=pixel_table) + layout_org= cv2.fillPoly(layout_org, pts=[contours_sep[ji]], color=3 * (pixel_table,)) else: - contours_new.append(contour) + contours_new.append(contours[i]) if num_col_classifier>=2: - only_recent_contour_image = np.zeros(layout.shape[:2]) - only_recent_contour_image = cv2.fillPoly(only_recent_contour_image, pts=[contour],color=1) + only_recent_contour_image = np.zeros((layout.shape[0],layout.shape[1])) + only_recent_contour_image= cv2.fillPoly(only_recent_contour_image,pts=[contours[i]] ,color=(1,1,1)) table_pixels_masked_from_early_pre = only_recent_contour_image * table_prediction_early iou_in = 100. * table_pixels_masked_from_early_pre.sum() / only_recent_contour_image.sum() #print(iou_in,'iou_in') if iou_in>30: - layout_org = cv2.fillPoly(layout_org, pts=[contour], color=pixel_table) + layout_org= cv2.fillPoly(layout_org, pts=[contours[i]], color=3 * (pixel_table,)) else: pass else: - layout_org = cv2.fillPoly(layout_org, pts=[contour], color=pixel_table) + layout_org= cv2.fillPoly(layout_org, pts=[contours[i]], color=3 * (pixel_table,)) return layout_org, contours_new @@ -1936,52 +2553,58 @@ class Eynollah: pass boxes = np.array(boxes, dtype=int) # to be on the safe side - img_comm = np.zeros(image_revised_1.shape, dtype=np.uint8) + img_comm_e = np.zeros(image_revised_1.shape) + img_comm = np.repeat(img_comm_e[:, :, np.newaxis], 3, axis=2) + for indiv in np.unique(image_revised_1): - image_col = (image_revised_1 == indiv).astype(np.uint8) * 255 - _, thresh = cv2.threshold(image_col, 0, 255, 0) + image_col=(image_revised_1==indiv)*255 + img_comm_in=np.repeat(image_col[:, :, np.newaxis], 3, axis=2) + img_comm_in=img_comm_in.astype(np.uint8) + + imgray = cv2.cvtColor(img_comm_in, cv2.COLOR_BGR2GRAY) + ret, thresh = cv2.threshold(imgray, 0, 255, 0) contours,hirarchy=cv2.findContours(thresh.copy(), cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE) if indiv==pixel_table: - main_contours = filter_contours_area_of_image_tables(thresh, contours, hirarchy, - max_area=1, min_area=0.001) + main_contours = filter_contours_area_of_image_tables(thresh, contours, hirarchy, max_area = 1, min_area = 0.001) else: - main_contours = filter_contours_area_of_image_tables(thresh, contours, hirarchy, - max_area=1, min_area=min_area) + main_contours = filter_contours_area_of_image_tables(thresh, contours, hirarchy, max_area = 1, min_area = min_area) - img_comm = cv2.fillPoly(img_comm, pts=main_contours, color=indiv) + img_comm = cv2.fillPoly(img_comm, pts = main_contours, color = (indiv, indiv, indiv)) + img_comm = img_comm.astype(np.uint8) - if not isNaN(slope_mean_hor): - image_revised_last = np.zeros(image_regions_eraly_p.shape[:2]) + if not self.isNaN(slope_mean_hor): + image_revised_last = np.zeros((image_regions_eraly_p.shape[0], image_regions_eraly_p.shape[1],3)) for i in range(len(boxes)): box_ys = slice(*boxes[i][2:4]) box_xs = slice(*boxes[i][0:2]) image_box = img_comm[box_ys, box_xs] try: - image_box_tabels_1 = (image_box == pixel_table) * 1 + image_box_tabels_1=(image_box[:,:,0]==pixel_table)*1 contours_tab,_=return_contours_of_image(image_box_tabels_1) contours_tab=filter_contours_area_of_image_tables(image_box_tabels_1,contours_tab,_,1,0.003) - image_box_tabels_1 = (image_box == pixel_line).astype(np.uint8) * 1 - image_box_tabels_and_m_text = ( (image_box == pixel_table) | - (image_box == 1) ).astype(np.uint8) * 1 + image_box_tabels_1=(image_box[:,:,0]==pixel_line)*1 - image_box_tabels_1 = cv2.dilate(image_box_tabels_1, KERNEL, iterations=5) + image_box_tabels_and_m_text=( (image_box[:,:,0]==pixel_table) | (image_box[:,:,0]==1) )*1 + image_box_tabels_and_m_text=image_box_tabels_and_m_text.astype(np.uint8) - contours_table_m_text, _ = return_contours_of_image(image_box_tabels_and_m_text) - _, thresh = cv2.threshold(image_box_tabels_1, 0, 255, 0) - contours_line, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) + image_box_tabels_1=image_box_tabels_1.astype(np.uint8) + image_box_tabels_1 = cv2.dilate(image_box_tabels_1,KERNEL,iterations = 5) + + contours_table_m_text,_=return_contours_of_image(image_box_tabels_and_m_text) + image_box_tabels=np.repeat(image_box_tabels_1[:, :, np.newaxis], 3, axis=2) + + image_box_tabels=image_box_tabels.astype(np.uint8) + imgray = cv2.cvtColor(image_box_tabels, cv2.COLOR_BGR2GRAY) + ret, thresh = cv2.threshold(imgray, 0, 255, 0) + + contours_line,hierachy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE) y_min_main_line ,y_max_main_line=find_features_of_contours(contours_line) y_min_main_tab ,y_max_main_tab=find_features_of_contours(contours_tab) - (cx_tab_m_text, cy_tab_m_text, - x_min_tab_m_text, x_max_tab_m_text, - y_min_tab_m_text, y_max_tab_m_text, - _) = find_new_features_of_contours(contours_table_m_text) - (cx_tabl, cy_tabl, - x_min_tabl, x_max_tabl, - y_min_tabl, y_max_tabl, - _) = find_new_features_of_contours(contours_tab) + cx_tab_m_text,cy_tab_m_text ,x_min_tab_m_text , x_max_tab_m_text, y_min_tab_m_text ,y_max_tab_m_text, _= find_new_features_of_contours(contours_table_m_text) + cx_tabl,cy_tabl ,x_min_tabl , x_max_tabl, y_min_tabl ,y_max_tabl,_= find_new_features_of_contours(contours_tab) if len(y_min_main_tab )>0: y_down_tabs=[] @@ -1991,30 +2614,22 @@ class Eynollah: y_down_tab=[] y_up_tab=[] for i_l in range(len(y_min_main_line)): - if (y_min_main_tab[i_t] > y_min_main_line[i_l] and - y_max_main_tab[i_t] > y_min_main_line[i_l] and - y_min_main_tab[i_t] > y_max_main_line[i_l] and - y_max_main_tab[i_t] > y_min_main_line[i_l]): + if y_min_main_tab[i_t]>y_min_main_line[i_l] and y_max_main_tab[i_t]>y_min_main_line[i_l] and y_min_main_tab[i_t]>y_max_main_line[i_l] and y_max_main_tab[i_t]>y_min_main_line[i_l]: pass - elif (y_min_main_tab[i_t] < y_max_main_line[i_l] and - y_max_main_tab[i_t] < y_max_main_line[i_l] and - y_max_main_tab[i_t] < y_min_main_line[i_l] and - y_min_main_tab[i_t] < y_min_main_line[i_l]): + elif y_min_main_tab[i_t]0: for ijv in range(len(y_min_tab_col1)): - image_revised_last[int(y_min_tab_col1[ijv]):int(y_max_tab_col1[ijv])] = pixel_table + image_revised_last[int(y_min_tab_col1[ijv]):int(y_max_tab_col1[ijv]),:,:]=pixel_table return image_revised_last + def do_order_of_regions(self, *args, **kwargs): + if self.full_layout: + return self.do_order_of_regions_full_layout(*args, **kwargs) + return self.do_order_of_regions_no_full_layout(*args, **kwargs) + def get_tables_from_model(self, img, num_col_classifier): img_org = np.copy(img) img_height_h = img_org.shape[0] img_width_h = img_org.shape[1] patches = False - prediction_table, _ = self.do_prediction_new_concept(patches, img, self.model_zoo.get("table")) - prediction_table = prediction_table.astype(np.int16) - return prediction_table[:,:,0] + if self.light_version: + prediction_table, _ = self.do_prediction_new_concept(patches, img, self.model_table) + prediction_table = prediction_table.astype(np.int16) + return prediction_table[:,:,0] + else: + if num_col_classifier < 4 and num_col_classifier > 2: + prediction_table = self.do_prediction(patches, img, self.model_table) + pre_updown = self.do_prediction(patches, cv2.flip(img[:,:,:], -1), self.model_table) + pre_updown = cv2.flip(pre_updown, -1) + + prediction_table[:,:,0][pre_updown[:,:,0]==1]=1 + prediction_table = prediction_table.astype(np.int16) + + elif num_col_classifier ==2: + height_ext = 0 # img.shape[0] // 4 + h_start = height_ext // 2 + width_ext = img.shape[1] // 8 + w_start = width_ext // 2 + + img_new = np.zeros((img.shape[0] + height_ext, + img.shape[1] + width_ext, + img.shape[2])).astype(float) + ys = slice(h_start, h_start + img.shape[0]) + xs = slice(w_start, w_start + img.shape[1]) + img_new[ys, xs] = img + + prediction_ext = self.do_prediction(patches, img_new, self.model_table) + pre_updown = self.do_prediction(patches, cv2.flip(img_new[:,:,:], -1), self.model_table) + pre_updown = cv2.flip(pre_updown, -1) + + prediction_table = prediction_ext[ys, xs] + prediction_table_updown = pre_updown[ys, xs] + + prediction_table[:,:,0][prediction_table_updown[:,:,0]==1]=1 + prediction_table = prediction_table.astype(np.int16) + elif num_col_classifier ==1: + height_ext = 0 # img.shape[0] // 4 + h_start = height_ext // 2 + width_ext = img.shape[1] // 4 + w_start = width_ext // 2 + + img_new =np.zeros((img.shape[0] + height_ext, + img.shape[1] + width_ext, + img.shape[2])).astype(float) + ys = slice(h_start, h_start + img.shape[0]) + xs = slice(w_start, w_start + img.shape[1]) + img_new[ys, xs] = img + + prediction_ext = self.do_prediction(patches, img_new, self.model_table) + pre_updown = self.do_prediction(patches, cv2.flip(img_new[:,:,:], -1), self.model_table) + pre_updown = cv2.flip(pre_updown, -1) + + prediction_table = prediction_ext[ys, xs] + prediction_table_updown = pre_updown[ys, xs] + + prediction_table[:,:,0][prediction_table_updown[:,:,0]==1]=1 + prediction_table = prediction_table.astype(np.int16) + else: + prediction_table = np.zeros(img.shape) + img_w_half = img.shape[1] // 2 + + pre1 = self.do_prediction(patches, img[:,0:img_w_half,:], self.model_table) + pre2 = self.do_prediction(patches, img[:,img_w_half:,:], self.model_table) + pre_full = self.do_prediction(patches, img[:,:,:], self.model_table) + pre_updown = self.do_prediction(patches, cv2.flip(img[:,:,:], -1), self.model_table) + pre_updown = cv2.flip(pre_updown, -1) + + prediction_table_full_erode = cv2.erode(pre_full[:,:,0], KERNEL, iterations=4) + prediction_table_full_erode = cv2.dilate(prediction_table_full_erode, KERNEL, iterations=4) + + prediction_table_full_updown_erode = cv2.erode(pre_updown[:,:,0], KERNEL, iterations=4) + prediction_table_full_updown_erode = cv2.dilate(prediction_table_full_updown_erode, KERNEL, iterations=4) + + prediction_table[:,0:img_w_half,:] = pre1[:,:,:] + prediction_table[:,img_w_half:,:] = pre2[:,:,:] + + prediction_table[:,:,0][prediction_table_full_erode[:,:]==1]=1 + prediction_table[:,:,0][prediction_table_full_updown_erode[:,:]==1]=1 + prediction_table = prediction_table.astype(np.int16) + + #prediction_table_erode = cv2.erode(prediction_table[:,:,0], self.kernel, iterations=6) + #prediction_table_erode = cv2.dilate(prediction_table_erode, self.kernel, iterations=6) + + prediction_table_erode = cv2.erode(prediction_table[:,:,0], KERNEL, iterations=20) + prediction_table_erode = cv2.dilate(prediction_table_erode, KERNEL, iterations=20) + return prediction_table_erode.astype(np.int16) def run_graphics_and_columns_light( self, text_regions_p_1, textline_mask_tot_ea, @@ -2078,21 +2781,10 @@ class Eynollah: if self.plotter: self.plotter.save_page_image(image_page) - - if not self.ignore_page_extraction: - mask_page = np.zeros((text_regions_p_1.shape[0], text_regions_p_1.shape[1])).astype(np.int8) - mask_page = cv2.fillPoly(mask_page, pts=[cont_page[0]], color=(1,)) - - text_regions_p_1[mask_page==0] = 0 - textline_mask_tot_ea[mask_page==0] = 0 - + text_regions_p_1 = text_regions_p_1[page_coord[0] : page_coord[1], page_coord[2] : page_coord[3]] textline_mask_tot_ea = textline_mask_tot_ea[page_coord[0] : page_coord[1], page_coord[2] : page_coord[3]] img_bin_light = img_bin_light[page_coord[0] : page_coord[1], page_coord[2] : page_coord[3]] - - ###text_regions_p_1 = text_regions_p_1[page_coord[0] : page_coord[1], page_coord[2] : page_coord[3]] - ###textline_mask_tot_ea = textline_mask_tot_ea[page_coord[0] : page_coord[1], page_coord[2] : page_coord[3]] - ###img_bin_light = img_bin_light[page_coord[0] : page_coord[1], page_coord[2] : page_coord[3]] mask_images = (text_regions_p_1[:, :] == 2) * 1 mask_images = mask_images.astype(np.uint8) @@ -2117,9 +2809,6 @@ class Eynollah: num_col = num_col + 1 if not num_column_is_classified: num_col_classifier = num_col + 1 - num_col_classifier = min(self.num_col_upper or num_col_classifier, - max(self.num_col_lower or num_col_classifier, - num_col_classifier)) except Exception as why: self.logger.error(why) num_col = None @@ -2147,12 +2836,58 @@ class Eynollah: return page_coord, image_page, textline_mask_tot_ea, img_bin_light, cont_page + def run_graphics_and_columns( + self, text_regions_p_1, + num_col_classifier, num_column_is_classified, erosion_hurts): - def run_enhancement(self): + t_in_gr = time.time() + img_g = self.imread(grayscale=True, uint8=True) + + img_g3 = np.zeros((img_g.shape[0], img_g.shape[1], 3)) + img_g3 = img_g3.astype(np.uint8) + img_g3[:, :, 0] = img_g[:, :] + img_g3[:, :, 1] = img_g[:, :] + img_g3[:, :, 2] = img_g[:, :] + + image_page, page_coord, cont_page = self.extract_page() + + if self.tables: + table_prediction = self.get_tables_from_model(image_page, num_col_classifier) + else: + table_prediction = np.zeros((image_page.shape[0], image_page.shape[1])).astype(np.int16) + + if self.plotter: + self.plotter.save_page_image(image_page) + + text_regions_p_1 = text_regions_p_1[page_coord[0] : page_coord[1], page_coord[2] : page_coord[3]] + mask_images = (text_regions_p_1[:, :] == 2) * 1 + mask_images = mask_images.astype(np.uint8) + mask_images = cv2.erode(mask_images[:, :], KERNEL, iterations=10) + mask_lines = (text_regions_p_1[:, :] == 3) * 1 + mask_lines = mask_lines.astype(np.uint8) + img_only_regions_with_sep = ((text_regions_p_1[:, :] != 3) & (text_regions_p_1[:, :] != 0)) * 1 + img_only_regions_with_sep = img_only_regions_with_sep.astype(np.uint8) + + if erosion_hurts: + img_only_regions = np.copy(img_only_regions_with_sep[:,:]) + else: + img_only_regions = cv2.erode(img_only_regions_with_sep[:,:], KERNEL, iterations=6) + try: + num_col, _ = find_num_col(img_only_regions, num_col_classifier, self.tables, multiplier=6.0) + num_col = num_col + 1 + if not num_column_is_classified: + num_col_classifier = num_col + 1 + except Exception as why: + self.logger.error(why) + num_col = None + return (num_col, num_col_classifier, img_only_regions, page_coord, image_page, mask_images, mask_lines, + text_regions_p_1, cont_page, table_prediction) + + def run_enhancement(self, light_version): t_in = time.time() self.logger.info("Resizing and enhancing image...") is_image_enhanced, img_org, img_res, num_col_classifier, num_column_is_classified, img_bin = \ - self.resize_and_enhance_image_with_column_classifier() + self.resize_and_enhance_image_with_column_classifier(light_version) self.logger.info("Image was %senhanced.", '' if is_image_enhanced else 'not ') scale = 1 if is_image_enhanced: @@ -2164,10 +2899,12 @@ class Eynollah: else: self.get_image_and_scales_after_enhancing(img_org, img_res) else: - self.get_image_and_scales(img_org, img_res, scale) + if self.allow_enhancement: + self.get_image_and_scales(img_org, img_res, scale) + else: + self.get_image_and_scales(img_org, img_res, scale) if self.allow_scaling: - img_org, img_res, is_image_enhanced = \ - self.resize_image_with_column_classifier(is_image_enhanced, img_bin) + img_org, img_res, is_image_enhanced = self.resize_image_with_column_classifier(is_image_enhanced, img_bin) self.get_image_and_scales_after_enhancing(img_org, img_res) #print("enhancement in ", time.time()-t_in) return img_res, is_image_enhanced, num_col_classifier, num_column_is_classified @@ -2176,11 +2913,9 @@ class Eynollah: scaler_h_textline = 1#1.3 # 1.2#1.2 scaler_w_textline = 1#1.3 # 0.9#1 #print(image_page.shape) - textline_mask_tot_ea, _ = self.textline_contours(image_page, True, - scaler_h_textline, - scaler_w_textline, - num_col_classifier) - textline_mask_tot_ea = textline_mask_tot_ea.astype(np.int16) + textline_mask_tot_ea, _ = self.textline_contours(image_page, True, scaler_h_textline, scaler_w_textline, num_col_classifier) + if self.textline_light: + textline_mask_tot_ea = textline_mask_tot_ea.astype(np.int16) if self.plotter: self.plotter.save_plot_of_textlines(textline_mask_tot_ea, image_page) @@ -2190,21 +2925,24 @@ class Eynollah: #print(textline_mask_tot_ea.shape, 'textline_mask_tot_ea deskew') slope_deskew = return_deskew_slop(cv2.erode(textline_mask_tot_ea, KERNEL, iterations=2), 2, 30, True, map=self.executor.map, logger=self.logger, plotter=self.plotter) + slope_first = 0 + if self.plotter: self.plotter.save_deskewed_image(slope_deskew) self.logger.info("slope_deskew: %.2f°", slope_deskew) - return slope_deskew + return slope_deskew, slope_first def run_marginals( - self, textline_mask_tot_ea, mask_images, mask_lines, + self, image_page, textline_mask_tot_ea, mask_images, mask_lines, num_col_classifier, slope_deskew, text_regions_p_1, table_prediction): - textline_mask_tot = textline_mask_tot_ea[:, :] + image_page_rotated, textline_mask_tot = image_page[:, :], textline_mask_tot_ea[:, :] textline_mask_tot[mask_images[:, :] == 1] = 0 text_regions_p_1[mask_lines[:, :] == 1] = 3 text_regions_p = text_regions_p_1[:, :] text_regions_p = np.array(text_regions_p) + if num_col_classifier in (1, 2): try: regions_without_separators = (text_regions_p[:, :] == 1) * 1 @@ -2213,11 +2951,14 @@ class Eynollah: regions_without_separators = regions_without_separators.astype(np.uint8) text_regions_p = get_marginals( rotate_image(regions_without_separators, slope_deskew), text_regions_p, - num_col_classifier, slope_deskew, kernel=KERNEL) + num_col_classifier, slope_deskew, light_version=self.light_version, kernel=KERNEL) except Exception as e: self.logger.error("exception %s", e) - return textline_mask_tot, text_regions_p + if self.plotter: + self.plotter.save_plot_of_layout_main_all(text_regions_p, image_page) + self.plotter.save_plot_of_layout_main(text_regions_p, image_page) + return textline_mask_tot, text_regions_p, image_page_rotated def run_boxes_no_full_layout( self, image_page, textline_mask_tot, text_regions_p, @@ -2234,9 +2975,7 @@ class Eynollah: regions_without_separators_d = (text_regions_p_1_n[:, :] == 1) * 1 if self.tables: regions_without_separators_d[table_prediction_n[:,:] == 1] = 1 - regions_without_separators = (text_regions_p[:, :] == 1) * 1 - # ( (text_regions_p[:,:]==1) | (text_regions_p[:,:]==2) )*1 - #self.return_regions_without_separators_new(text_regions_p[:,:,0],img_only_regions) + regions_without_separators = (text_regions_p[:, :] == 1) * 1 # ( (text_regions_p[:,:]==1) | (text_regions_p[:,:]==2) )*1 #self.return_regions_without_separators_new(text_regions_p[:,:,0],img_only_regions) #print(time.time()-t_0_box,'time box in 1') if self.tables: regions_without_separators[table_prediction ==1 ] = 1 @@ -2247,11 +2986,13 @@ class Eynollah: pixel_lines = 3 if np.abs(slope_deskew) < SLOPE_THRESHOLD: _, _, matrix_of_lines_ch, splitter_y_new, _ = find_number_of_columns_in_document( - text_regions_p, num_col_classifier, self.tables, pixel_lines) + np.repeat(text_regions_p[:, :, np.newaxis], 3, axis=2), + num_col_classifier, self.tables, pixel_lines) if np.abs(slope_deskew) >= SLOPE_THRESHOLD: _, _, matrix_of_lines_ch_d, splitter_y_new_d, _ = find_number_of_columns_in_document( - text_regions_p_1_n, num_col_classifier, self.tables, pixel_lines) + np.repeat(text_regions_p_1_n[:, :, np.newaxis], 3, axis=2), + num_col_classifier, self.tables, pixel_lines) #print(time.time()-t_0_box,'time box in 2') self.logger.info("num_col_classifier: %s", num_col_classifier) @@ -2272,6 +3013,20 @@ class Eynollah: self.logger.debug("len(boxes): %s", len(boxes)) #print(time.time()-t_0_box,'time box in 3.1') + if self.tables: + if self.light_version: + pass + else: + text_regions_p_tables = np.copy(text_regions_p) + text_regions_p_tables[:,:][(table_prediction[:,:] == 1)] = 10 + pixel_line = 3 + img_revised_tab2 = self.add_tables_heuristic_to_layout( + text_regions_p_tables, boxes, 0, splitter_y_new, peaks_neg_tot_tables, text_regions_p_tables, + num_col_classifier , 0.000005, pixel_line) + #print(time.time()-t_0_box,'time box in 3.2') + img_revised_tab2, contoures_tables = self.check_iou_of_bounding_box_and_contour_for_tables( + img_revised_tab2, table_prediction, 10, num_col_classifier) + #print(time.time()-t_0_box,'time box in 3.3') else: boxes_d, peaks_neg_tot_tables_d = return_boxes_of_images_by_order_of_reading_new( splitter_y_new_d, regions_without_separators_d, matrix_of_lines_ch_d, @@ -2279,24 +3034,61 @@ class Eynollah: boxes = None self.logger.debug("len(boxes): %s", len(boxes_d)) + if self.tables: + if self.light_version: + pass + else: + text_regions_p_tables = np.copy(text_regions_p_1_n) + text_regions_p_tables =np.round(text_regions_p_tables) + text_regions_p_tables[:,:][(text_regions_p_tables[:,:] != 3) & (table_prediction_n[:,:] == 1)] = 10 + + pixel_line = 3 + img_revised_tab2 = self.add_tables_heuristic_to_layout( + text_regions_p_tables, boxes_d, 0, splitter_y_new_d, peaks_neg_tot_tables_d, text_regions_p_tables, + num_col_classifier, 0.000005, pixel_line) + img_revised_tab2_d,_ = self.check_iou_of_bounding_box_and_contour_for_tables( + img_revised_tab2, table_prediction_n, 10, num_col_classifier) + + img_revised_tab2_d_rotated = rotate_image(img_revised_tab2_d, -slope_deskew) + img_revised_tab2_d_rotated = np.round(img_revised_tab2_d_rotated) + img_revised_tab2_d_rotated = img_revised_tab2_d_rotated.astype(np.int8) + img_revised_tab2_d_rotated = resize_image(img_revised_tab2_d_rotated, text_regions_p.shape[0], text_regions_p.shape[1]) #print(time.time()-t_0_box,'time box in 4') self.logger.info("detecting boxes took %.1fs", time.time() - t1) if self.tables: - text_regions_p[table_prediction == 1] = 10 - img_revised_tab = text_regions_p[:,:] + if self.light_version: + text_regions_p[:,:][table_prediction[:,:]==1] = 10 + img_revised_tab=text_regions_p[:,:] + else: + if np.abs(slope_deskew) < SLOPE_THRESHOLD: + img_revised_tab = np.copy(img_revised_tab2[:,:,0]) + img_revised_tab[:,:][(text_regions_p[:,:] == 1) & (img_revised_tab[:,:] != 10)] = 1 + else: + img_revised_tab = np.copy(text_regions_p[:,:]) + img_revised_tab[:,:][img_revised_tab[:,:] == 10] = 0 + img_revised_tab[:,:][img_revised_tab2_d_rotated[:,:,0] == 10] = 10 + + text_regions_p[:,:][text_regions_p[:,:]==10] = 0 + text_regions_p[:,:][img_revised_tab[:,:]==10] = 10 else: - img_revised_tab = text_regions_p[:,:] + img_revised_tab=text_regions_p[:,:] #img_revised_tab = text_regions_p[:, :] - polygons_of_images = return_contours_of_interested_region(text_regions_p, 2) + if self.light_version: + polygons_of_images = return_contours_of_interested_region(text_regions_p, 2) + else: + polygons_of_images = return_contours_of_interested_region(img_revised_tab, 2) pixel_img = 4 min_area_mar = 0.00001 - marginal_mask = (text_regions_p[:,:]==pixel_img)*1 - marginal_mask = marginal_mask.astype('uint8') - marginal_mask = cv2.dilate(marginal_mask, KERNEL, iterations=2) + if self.light_version: + marginal_mask = (text_regions_p[:,:]==pixel_img)*1 + marginal_mask = marginal_mask.astype('uint8') + marginal_mask = cv2.dilate(marginal_mask, KERNEL, iterations=2) - polygons_of_marginals = return_contours_of_interested_region(marginal_mask, 1, min_area_mar) + polygons_of_marginals = return_contours_of_interested_region(marginal_mask, 1, min_area_mar) + else: + polygons_of_marginals = return_contours_of_interested_region(text_regions_p, pixel_img, min_area_mar) pixel_img = 10 contours_tables = return_contours_of_interested_region(text_regions_p, pixel_img, min_area_mar) @@ -2314,43 +3106,130 @@ class Eynollah: self.logger.debug('enter run_boxes_full_layout') t_full0 = time.time() if self.tables: - text_regions_p[:,:][table_prediction[:,:]==1] = 10 - img_revised_tab = text_regions_p[:,:] - if np.abs(slope_deskew) >= SLOPE_THRESHOLD: - _, textline_mask_tot_d, text_regions_p_1_n, table_prediction_n = \ - rotation_not_90_func(image_page, textline_mask_tot, text_regions_p, - table_prediction, slope_deskew) + if self.light_version: + text_regions_p[:,:][table_prediction[:,:]==1] = 10 + img_revised_tab = text_regions_p[:,:] + if np.abs(slope_deskew) >= SLOPE_THRESHOLD: + image_page_rotated_n, textline_mask_tot_d, text_regions_p_1_n, table_prediction_n = \ + rotation_not_90_func(image_page, textline_mask_tot, text_regions_p, table_prediction, slope_deskew) - text_regions_p_1_n = resize_image(text_regions_p_1_n, - text_regions_p.shape[0], - text_regions_p.shape[1]) - textline_mask_tot_d = resize_image(textline_mask_tot_d, - text_regions_p.shape[0], - text_regions_p.shape[1]) - table_prediction_n = resize_image(table_prediction_n, - text_regions_p.shape[0], - text_regions_p.shape[1]) + text_regions_p_1_n = resize_image(text_regions_p_1_n,text_regions_p.shape[0],text_regions_p.shape[1]) + textline_mask_tot_d = resize_image(textline_mask_tot_d,text_regions_p.shape[0],text_regions_p.shape[1]) + table_prediction_n = resize_image(table_prediction_n,text_regions_p.shape[0],text_regions_p.shape[1]) + + regions_without_separators_d = (text_regions_p_1_n[:,:] == 1)*1 + regions_without_separators_d[table_prediction_n[:,:] == 1] = 1 + else: + text_regions_p_1_n = None + textline_mask_tot_d = None + regions_without_separators_d = None + # regions_without_separators = ( text_regions_p[:,:]==1 | text_regions_p[:,:]==2 )*1 + #self.return_regions_without_separators_new(text_regions_p[:,:,0],img_only_regions) + regions_without_separators = (text_regions_p[:,:] == 1)*1 + regions_without_separators[table_prediction == 1] = 1 - regions_without_separators_d = (text_regions_p_1_n[:,:] == 1)*1 - regions_without_separators_d[table_prediction_n[:,:] == 1] = 1 else: - text_regions_p_1_n = None - textline_mask_tot_d = None - regions_without_separators_d = None - # regions_without_separators = ( text_regions_p[:,:]==1 | text_regions_p[:,:]==2 )*1 - #self.return_regions_without_separators_new(text_regions_p[:,:,0],img_only_regions) - regions_without_separators = (text_regions_p[:,:] == 1)*1 - regions_without_separators[table_prediction == 1] = 1 + if np.abs(slope_deskew) >= SLOPE_THRESHOLD: + image_page_rotated_n, textline_mask_tot_d, text_regions_p_1_n, table_prediction_n = \ + rotation_not_90_func(image_page, textline_mask_tot, text_regions_p, table_prediction, slope_deskew) + text_regions_p_1_n = resize_image(text_regions_p_1_n,text_regions_p.shape[0],text_regions_p.shape[1]) + textline_mask_tot_d = resize_image(textline_mask_tot_d,text_regions_p.shape[0],text_regions_p.shape[1]) + table_prediction_n = resize_image(table_prediction_n,text_regions_p.shape[0],text_regions_p.shape[1]) + + regions_without_separators_d = (text_regions_p_1_n[:,:] == 1)*1 + regions_without_separators_d[table_prediction_n[:,:] == 1] = 1 + else: + text_regions_p_1_n = None + textline_mask_tot_d = None + regions_without_separators_d = None + + # regions_without_separators = ( text_regions_p[:,:]==1 | text_regions_p[:,:]==2 )*1 + #self.return_regions_without_separators_new(text_regions_p[:,:,0],img_only_regions) + regions_without_separators = (text_regions_p[:,:] == 1)*1 + regions_without_separators[table_prediction == 1] = 1 + + pixel_lines=3 + if np.abs(slope_deskew) < SLOPE_THRESHOLD: + num_col, _, matrix_of_lines_ch, splitter_y_new, _ = find_number_of_columns_in_document( + np.repeat(text_regions_p[:, :, np.newaxis], 3, axis=2), + num_col_classifier, self.tables, pixel_lines) + + if np.abs(slope_deskew) >= SLOPE_THRESHOLD: + num_col_d, _, matrix_of_lines_ch_d, splitter_y_new_d, _ = find_number_of_columns_in_document( + np.repeat(text_regions_p_1_n[:, :, np.newaxis], 3, axis=2), + num_col_classifier, self.tables, pixel_lines) + + if num_col_classifier>=3: + if np.abs(slope_deskew) < SLOPE_THRESHOLD: + regions_without_separators = regions_without_separators.astype(np.uint8) + regions_without_separators = cv2.erode(regions_without_separators[:,:], KERNEL, iterations=6) + + if np.abs(slope_deskew) >= SLOPE_THRESHOLD: + regions_without_separators_d = regions_without_separators_d.astype(np.uint8) + regions_without_separators_d = cv2.erode(regions_without_separators_d[:,:], KERNEL, iterations=6) + else: + pass + + if np.abs(slope_deskew) < SLOPE_THRESHOLD: + boxes, peaks_neg_tot_tables = return_boxes_of_images_by_order_of_reading_new( + splitter_y_new, regions_without_separators, matrix_of_lines_ch, + num_col_classifier, erosion_hurts, self.tables, self.right2left) + text_regions_p_tables = np.copy(text_regions_p) + text_regions_p_tables[:,:][(table_prediction[:,:]==1)] = 10 + pixel_line = 3 + img_revised_tab2 = self.add_tables_heuristic_to_layout( + text_regions_p_tables, boxes, 0, splitter_y_new, peaks_neg_tot_tables, text_regions_p_tables, + num_col_classifier , 0.000005, pixel_line) + + img_revised_tab2,contoures_tables = self.check_iou_of_bounding_box_and_contour_for_tables( + img_revised_tab2, table_prediction, 10, num_col_classifier) + else: + boxes_d, peaks_neg_tot_tables_d = return_boxes_of_images_by_order_of_reading_new( + splitter_y_new_d, regions_without_separators_d, matrix_of_lines_ch_d, + num_col_classifier, erosion_hurts, self.tables, self.right2left) + text_regions_p_tables = np.copy(text_regions_p_1_n) + text_regions_p_tables = np.round(text_regions_p_tables) + text_regions_p_tables[:,:][(text_regions_p_tables[:,:]!=3) & (table_prediction_n[:,:]==1)] = 10 + + pixel_line = 3 + img_revised_tab2 = self.add_tables_heuristic_to_layout( + text_regions_p_tables, boxes_d, 0, splitter_y_new_d, peaks_neg_tot_tables_d, text_regions_p_tables, + num_col_classifier, 0.000005, pixel_line) + + img_revised_tab2_d,_ = self.check_iou_of_bounding_box_and_contour_for_tables( + img_revised_tab2, table_prediction_n, 10, num_col_classifier) + img_revised_tab2_d_rotated = rotate_image(img_revised_tab2_d, -slope_deskew) + + img_revised_tab2_d_rotated = np.round(img_revised_tab2_d_rotated) + img_revised_tab2_d_rotated = img_revised_tab2_d_rotated.astype(np.int8) + + img_revised_tab2_d_rotated = resize_image(img_revised_tab2_d_rotated, text_regions_p.shape[0], text_regions_p.shape[1]) + + if np.abs(slope_deskew) < 0.13: + img_revised_tab = np.copy(img_revised_tab2[:,:,0]) + else: + img_revised_tab = np.copy(text_regions_p[:,:]) + img_revised_tab[:,:][img_revised_tab[:,:] == 10] = 0 + img_revised_tab[:,:][img_revised_tab2_d_rotated[:,:,0] == 10] = 10 + + ##img_revised_tab=img_revised_tab2[:,:,0] + #img_revised_tab=text_regions_p[:,:] + text_regions_p[:,:][text_regions_p[:,:]==10] = 0 + text_regions_p[:,:][img_revised_tab[:,:]==10] = 10 + #img_revised_tab[img_revised_tab2[:,:,0]==10] =10 pixel_img = 4 min_area_mar = 0.00001 - marginal_mask = (text_regions_p[:,:]==pixel_img)*1 - marginal_mask = marginal_mask.astype('uint8') - marginal_mask = cv2.dilate(marginal_mask, KERNEL, iterations=2) + if self.light_version: + marginal_mask = (text_regions_p[:,:]==pixel_img)*1 + marginal_mask = marginal_mask.astype('uint8') + marginal_mask = cv2.dilate(marginal_mask, KERNEL, iterations=2) - polygons_of_marginals = return_contours_of_interested_region(marginal_mask, 1, min_area_mar) + polygons_of_marginals = return_contours_of_interested_region(marginal_mask, 1, min_area_mar) + else: + polygons_of_marginals = return_contours_of_interested_region(text_regions_p, pixel_img, min_area_mar) pixel_img = 10 contours_tables = return_contours_of_interested_region(text_regions_p, pixel_img, min_area_mar) @@ -2363,21 +3242,19 @@ class Eynollah: image_page = image_page.astype(np.uint8) #print("full inside 1", time.time()- t_full0) regions_fully, regions_fully_only_drop = self.extract_text_regions_new( - img_bin_light, + img_bin_light if self.light_version else image_page, False, cols=num_col_classifier) #print("full inside 2", time.time()- t_full0) # 6 is the separators lable in old full layout model # 4 is the drop capital class in old full layout model # in the new full layout drop capital is 3 and separators are 5 - - # the separators in full layout will not be written on layout - if not self.reading_order_machine_based: - text_regions_p[:,:][regions_fully[:,:,0]==5]=6 + + text_regions_p[:,:][regions_fully[:,:,0]==5]=6 ###regions_fully[:, :, 0][regions_fully_only_drop[:, :, 0] == 3] = 4 #text_regions_p[:,:][regions_fully[:,:,0]==6]=6 ##regions_fully_only_drop = put_drop_out_from_only_drop_model(regions_fully_only_drop, text_regions_p) - ##regions_fully[:, :, 0][regions_fully_only_drop[:, :] == 4] = 4 + ##regions_fully[:, :, 0][regions_fully_only_drop[:, :, 0] == 4] = 4 drop_capital_label_in_full_layout_model = 3 drops = (regions_fully[:,:,0]==drop_capital_label_in_full_layout_model)*1 @@ -2396,8 +3273,7 @@ class Eynollah: ##else: ##regions_fully_np = filter_small_drop_capitals_from_no_patch_layout(regions_fully_np, text_regions_p) - ###regions_fully = boosting_headers_by_longshot_region_segmentation(regions_fully, - ### regions_fully_np, img_only_regions) + ###regions_fully = boosting_headers_by_longshot_region_segmentation(regions_fully, regions_fully_np, img_only_regions) # plt.imshow(regions_fully[:,:,0]) # plt.show() text_regions_p[:, :][regions_fully[:, :, 0] == drop_capital_label_in_full_layout_model] = 4 @@ -2429,121 +3305,19 @@ class Eynollah: regions_without_separators_d, regions_fully, regions_without_separators, polygons_of_marginals, contours_tables) + @staticmethod + def our_load_model(model_file): + if model_file.endswith('.h5') and Path(model_file[:-3]).exists(): + # prefer SavedModel over HDF5 format if it exists + model_file = model_file[:-3] + try: + model = load_model(model_file, compile=False) + except: + model = load_model(model_file, compile=False, custom_objects={ + "PatchEncoder": PatchEncoder, "Patches": Patches}) + return model + def do_order_of_regions_with_model(self, contours_only_text_parent, contours_only_text_parent_h, text_regions_p): - - height1 =672#448 - width1 = 448#224 - - height2 =672#448 - width2= 448#224 - - height3 =672#448 - width3 = 448#224 - - inference_bs = 3 - - ver_kernel = np.ones((5, 1), dtype=np.uint8) - hor_kernel = np.ones((1, 5), dtype=np.uint8) - - - min_cont_size_to_be_dilated = 10 - if len(contours_only_text_parent)>min_cont_size_to_be_dilated: - (cx_conts, cy_conts, - x_min_conts, x_max_conts, - y_min_conts, y_max_conts, - _) = find_new_features_of_contours(contours_only_text_parent) - args_cont_located = np.array(range(len(contours_only_text_parent))) - - diff_y_conts = np.abs(y_max_conts[:]-y_min_conts) - diff_x_conts = np.abs(x_max_conts[:]-x_min_conts) - - mean_x = statistics.mean(diff_x_conts) - median_x = statistics.median(diff_x_conts) - - - diff_x_ratio= diff_x_conts/mean_x - - args_cont_located_excluded = args_cont_located[diff_x_ratio>=1.3] - args_cont_located_included = args_cont_located[diff_x_ratio<1.3] - - contours_only_text_parent_excluded = [contours_only_text_parent[ind] - #contours_only_text_parent[diff_x_ratio>=1.3] - for ind in range(len(contours_only_text_parent)) - if diff_x_ratio[ind]>=1.3] - contours_only_text_parent_included = [contours_only_text_parent[ind] - #contours_only_text_parent[diff_x_ratio<1.3] - for ind in range(len(contours_only_text_parent)) - if diff_x_ratio[ind]<1.3] - - cx_conts_excluded = [cx_conts[ind] - #cx_conts[diff_x_ratio>=1.3] - for ind in range(len(cx_conts)) - if diff_x_ratio[ind]>=1.3] - cx_conts_included = [cx_conts[ind] - #cx_conts[diff_x_ratio<1.3] - for ind in range(len(cx_conts)) - if diff_x_ratio[ind]<1.3] - cy_conts_excluded = [cy_conts[ind] - #cy_conts[diff_x_ratio>=1.3] - for ind in range(len(cy_conts)) - if diff_x_ratio[ind]>=1.3] - cy_conts_included = [cy_conts[ind] - #cy_conts[diff_x_ratio<1.3] - for ind in range(len(cy_conts)) - if diff_x_ratio[ind]<1.3] - - #print(diff_x_ratio, 'ratio') - text_regions_p = text_regions_p.astype('uint8') - - if len(contours_only_text_parent_excluded)>0: - textregion_par = np.zeros((text_regions_p.shape[0], text_regions_p.shape[1])).astype('uint8') - textregion_par = cv2.fillPoly(textregion_par, pts=contours_only_text_parent_included, color=(1,1)) - else: - textregion_par = (text_regions_p[:,:]==1)*1 - textregion_par = textregion_par.astype('uint8') - - text_regions_p_textregions_dilated = cv2.erode(textregion_par , hor_kernel, iterations=2) - text_regions_p_textregions_dilated = cv2.dilate(text_regions_p_textregions_dilated , ver_kernel, iterations=4) - text_regions_p_textregions_dilated = cv2.erode(text_regions_p_textregions_dilated , hor_kernel, iterations=1) - text_regions_p_textregions_dilated = cv2.dilate(text_regions_p_textregions_dilated , ver_kernel, iterations=5) - text_regions_p_textregions_dilated[text_regions_p[:,:]>1] = 0 - - - contours_only_dilated, hir_on_text_dilated = return_contours_of_image(text_regions_p_textregions_dilated) - contours_only_dilated = return_parent_contours(contours_only_dilated, hir_on_text_dilated) - - indexes_of_located_cont, center_x_coordinates_of_located, center_y_coordinates_of_located = \ - self.return_indexes_of_contours_located_inside_another_list_of_contours( - contours_only_dilated, contours_only_text_parent_included, - cx_conts_included, cy_conts_included, args_cont_located_included) - - - if len(args_cont_located_excluded)>0: - for ind in args_cont_located_excluded: - indexes_of_located_cont.append(np.array([ind])) - contours_only_dilated.append(contours_only_text_parent[ind]) - center_y_coordinates_of_located.append(0) - - array_list = [np.array([elem]) if isinstance(elem, int) else elem for elem in indexes_of_located_cont] - flattened_array = np.concatenate([arr.ravel() for arr in array_list]) - #print(len( np.unique(flattened_array)), 'indexes_of_located_cont uniques') - - missing_textregions = list( set(range(len(contours_only_text_parent))) - set(flattened_array) ) - #print(missing_textregions, 'missing_textregions') - - for ind in missing_textregions: - indexes_of_located_cont.append(np.array([ind])) - contours_only_dilated.append(contours_only_text_parent[ind]) - center_y_coordinates_of_located.append(0) - - - if contours_only_text_parent_h: - for vi in range(len(contours_only_text_parent_h)): - indexes_of_located_cont.append(int(vi+len(contours_only_text_parent))) - - array_list = [np.array([elem]) if isinstance(elem, int) else elem for elem in indexes_of_located_cont] - flattened_array = np.concatenate([arr.ravel() for arr in array_list]) - y_len = text_regions_p.shape[0] x_len = text_regions_p.shape[1] @@ -2552,46 +3326,41 @@ class Eynollah: img_poly[text_regions_p[:,:]==2] = 2 img_poly[text_regions_p[:,:]==3] = 4 img_poly[text_regions_p[:,:]==6] = 5 - + img_header_and_sep = np.zeros((y_len,x_len), dtype='uint8') if contours_only_text_parent_h: _, cy_main, x_min_main, x_max_main, y_min_main, y_max_main, _ = find_new_features_of_contours( contours_only_text_parent_h) for j in range(len(cy_main)): img_header_and_sep[int(y_max_main[j]):int(y_max_main[j])+12, - int(x_min_main[j]):int(x_max_main[j])] = 1 - co_text_all_org = contours_only_text_parent + contours_only_text_parent_h - if len(contours_only_text_parent)>min_cont_size_to_be_dilated: - co_text_all = contours_only_dilated + contours_only_text_parent_h - else: - co_text_all = contours_only_text_parent + contours_only_text_parent_h + int(x_min_main[j]):int(x_max_main[j])] = 1 + co_text_all = contours_only_text_parent + contours_only_text_parent_h else: - co_text_all_org = contours_only_text_parent - if len(contours_only_text_parent)>min_cont_size_to_be_dilated: - co_text_all = contours_only_dilated - else: - co_text_all = contours_only_text_parent + co_text_all = contours_only_text_parent if not len(co_text_all): return [], [] - labels_con = np.zeros((int(y_len /6.), int(x_len/6.), len(co_text_all)), dtype=bool) - co_text_all = [(i/6).astype(int) for i in co_text_all] + labels_con = np.zeros((y_len, x_len, len(co_text_all)), dtype=bool) for i in range(len(co_text_all)): img = labels_con[:,:,i].astype(np.uint8) - - #img = cv2.resize(img, (int(img.shape[1]/6), int(img.shape[0]/6)), interpolation=cv2.INTER_NEAREST) - cv2.fillPoly(img, pts=[co_text_all[i]], color=(1,)) labels_con[:,:,i] = img + height1 =672#448 + width1 = 448#224 + + height2 =672#448 + width2= 448#224 + + height3 =672#448 + width3 = 448#224 labels_con = resize_image(labels_con.astype(np.uint8), height1, width1).astype(bool) img_header_and_sep = resize_image(img_header_and_sep, height1, width1) img_poly = resize_image(img_poly, height3, width3) - - + inference_bs = 3 input_1 = np.zeros((inference_bs, height1, width1, 3)) ordered = [list(range(len(co_text_all)))] index_update = 0 @@ -2619,7 +3388,7 @@ class Eynollah: tot_counter += 1 batch.append(j) if tot_counter % inference_bs == 0 or tot_counter == len(ij_list): - y_pr = self.model_zoo.get("reading_order").predict(input_1 , verbose=0) + y_pr = self.model_reading_order.predict(input_1 , verbose=0) for jb, j in enumerate(batch): if y_pr[jb][0]>=0.5: post_list.append(j) @@ -2641,61 +3410,595 @@ class Eynollah: break ordered = [i[0] for i in ordered] - - if len(contours_only_text_parent)>min_cont_size_to_be_dilated: - org_contours_indexes = [] - for ind in range(len(ordered)): - region_with_curr_order = ordered[ind] - if region_with_curr_order < len(contours_only_dilated): - if np.isscalar(indexes_of_located_cont[region_with_curr_order]): - org_contours_indexes.extend([indexes_of_located_cont[region_with_curr_order]]) - else: - arg_sort_located_cont = np.argsort(center_y_coordinates_of_located[region_with_curr_order]) - org_contours_indexes.extend( - np.array(indexes_of_located_cont[region_with_curr_order])[arg_sort_located_cont]) - else: - org_contours_indexes.extend([indexes_of_located_cont[region_with_curr_order]]) - - region_ids = ['region_%04d' % i for i in range(len(co_text_all_org))] - return org_contours_indexes, region_ids + region_ids = ['region_%04d' % i for i in range(len(co_text_all))] + return ordered, region_ids + + def return_start_and_end_of_common_text_of_textline_ocr(self, textline_image, ind_tot): + width = np.shape(textline_image)[1] + height = np.shape(textline_image)[0] + common_window = int(0.2*width) + + width1 = int ( width/2. - common_window ) + width2 = int ( width/2. + common_window ) + + img_sum = np.sum(textline_image[:,:,0], axis=0) + sum_smoothed = gaussian_filter1d(img_sum, 3) + + peaks_real, _ = find_peaks(sum_smoothed, height=0) + if len(peaks_real)>70: + print(len(peaks_real), 'len(peaks_real)') + + peaks_real = peaks_real[(peaks_realwidth1)] + + arg_sort = np.argsort(sum_smoothed[peaks_real]) + arg_sort4 =arg_sort[::-1][:4] + peaks_sort_4 = peaks_real[arg_sort][::-1][:4] + argsort_sorted = np.argsort(peaks_sort_4) + + first_4_sorted = peaks_sort_4[argsort_sorted] + y_4_sorted = sum_smoothed[peaks_real][arg_sort4[argsort_sorted]] + #print(first_4_sorted,'first_4_sorted') + + arg_sortnew = np.argsort(y_4_sorted) + peaks_final =np.sort( first_4_sorted[arg_sortnew][2:] ) + + #plt.figure(ind_tot) + #plt.imshow(textline_image) + #plt.plot([peaks_final[0], peaks_final[0]], [0, height-1]) + #plt.plot([peaks_final[1], peaks_final[1]], [0, height-1]) + #plt.savefig('./'+str(ind_tot)+'.png') + + return peaks_final[0], peaks_final[1] else: - region_ids = ['region_%04d' % i for i in range(len(co_text_all_org))] - return ordered, region_ids + pass - def filter_contours_inside_a_bigger_one(self, contours, contours_d_ordered, image, - marginal_cnts=None, type_contour="textregion"): - if type_contour == "textregion": - areas = np.array(list(map(cv2.contourArea, contours))) + def return_start_and_end_of_common_text_of_textline_ocr_without_common_section(self, textline_image, ind_tot): + width = np.shape(textline_image)[1] + height = np.shape(textline_image)[0] + common_window = int(0.06*width) + + width1 = int ( width/2. - common_window ) + width2 = int ( width/2. + common_window ) + + img_sum = np.sum(textline_image[:,:,0], axis=0) + sum_smoothed = gaussian_filter1d(img_sum, 3) + + peaks_real, _ = find_peaks(sum_smoothed, height=0) + if len(peaks_real)>70: + #print(len(peaks_real), 'len(peaks_real)') + + peaks_real = peaks_real[(peaks_realwidth1)] + + arg_max = np.argmax(sum_smoothed[peaks_real]) + peaks_final = peaks_real[arg_max] + + #plt.figure(ind_tot) + #plt.imshow(textline_image) + #plt.plot([peaks_final, peaks_final], [0, height-1]) + ##plt.plot([peaks_final[1], peaks_final[1]], [0, height-1]) + #plt.savefig('./'+str(ind_tot)+'.png') + + return peaks_final + else: + return None + + def return_start_and_end_of_common_text_of_textline_ocr_new_splitted( + self, peaks_real, sum_smoothed, start_split, end_split): + + peaks_real = peaks_real[(peaks_realstart_split)] + + arg_sort = np.argsort(sum_smoothed[peaks_real]) + arg_sort4 =arg_sort[::-1][:4] + peaks_sort_4 = peaks_real[arg_sort][::-1][:4] + argsort_sorted = np.argsort(peaks_sort_4) + + first_4_sorted = peaks_sort_4[argsort_sorted] + y_4_sorted = sum_smoothed[peaks_real][arg_sort4[argsort_sorted]] + #print(first_4_sorted,'first_4_sorted') + + arg_sortnew = np.argsort(y_4_sorted) + peaks_final =np.sort( first_4_sorted[arg_sortnew][3:] ) + return peaks_final[0] + + def return_start_and_end_of_common_text_of_textline_ocr_new(self, textline_image, ind_tot): + width = np.shape(textline_image)[1] + height = np.shape(textline_image)[0] + common_window = int(0.15*width) + + width1 = int ( width/2. - common_window ) + width2 = int ( width/2. + common_window ) + mid = int(width/2.) + + img_sum = np.sum(textline_image[:,:,0], axis=0) + sum_smoothed = gaussian_filter1d(img_sum, 3) + + peaks_real, _ = find_peaks(sum_smoothed, height=0) + if len(peaks_real)>70: + peak_start = self.return_start_and_end_of_common_text_of_textline_ocr_new_splitted( + peaks_real, sum_smoothed, width1, mid+2) + peak_end = self.return_start_and_end_of_common_text_of_textline_ocr_new_splitted( + peaks_real, sum_smoothed, mid-2, width2) + + #plt.figure(ind_tot) + #plt.imshow(textline_image) + #plt.plot([peak_start, peak_start], [0, height-1]) + #plt.plot([peak_end, peak_end], [0, height-1]) + #plt.savefig('./'+str(ind_tot)+'.png') + + return peak_start, peak_end + else: + pass + + def return_ocr_of_textline_without_common_section( + self, textline_image, model_ocr, processor, device, width_textline, h2w_ratio,ind_tot): + + if h2w_ratio > 0.05: + pixel_values = processor(textline_image, return_tensors="pt").pixel_values + generated_ids = model_ocr.generate(pixel_values.to(device)) + generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] + else: + #width = np.shape(textline_image)[1] + #height = np.shape(textline_image)[0] + #common_window = int(0.3*width) + #width1 = int ( width/2. - common_window ) + #width2 = int ( width/2. + common_window ) + + split_point = self.return_start_and_end_of_common_text_of_textline_ocr_without_common_section( + textline_image, ind_tot) + if split_point: + image1 = textline_image[:, :split_point,:]# image.crop((0, 0, width2, height)) + image2 = textline_image[:, split_point:,:]#image.crop((width1, 0, width, height)) + + #pixel_values1 = processor(image1, return_tensors="pt").pixel_values + #pixel_values2 = processor(image2, return_tensors="pt").pixel_values + + pixel_values_merged = processor([image1,image2], return_tensors="pt").pixel_values + generated_ids_merged = model_ocr.generate(pixel_values_merged.to(device)) + generated_text_merged = processor.batch_decode(generated_ids_merged, skip_special_tokens=True) + + #print(generated_text_merged,'generated_text_merged') + + #generated_ids1 = model_ocr.generate(pixel_values1.to(device)) + #generated_ids2 = model_ocr.generate(pixel_values2.to(device)) + + #generated_text1 = processor.batch_decode(generated_ids1, skip_special_tokens=True)[0] + #generated_text2 = processor.batch_decode(generated_ids2, skip_special_tokens=True)[0] + + #generated_text = generated_text1 + ' ' + generated_text2 + generated_text = generated_text_merged[0] + ' ' + generated_text_merged[1] + + #print(generated_text1,'generated_text1') + #print(generated_text2, 'generated_text2') + #print('########################################') + else: + pixel_values = processor(textline_image, return_tensors="pt").pixel_values + generated_ids = model_ocr.generate(pixel_values.to(device)) + generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] + + #print(generated_text,'generated_text') + #print('########################################') + return generated_text + + def return_ocr_of_textline( + self, textline_image, model_ocr, processor, device, width_textline, h2w_ratio,ind_tot): + + if h2w_ratio > 0.05: + pixel_values = processor(textline_image, return_tensors="pt").pixel_values + generated_ids = model_ocr.generate(pixel_values.to(device)) + generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] + else: + #width = np.shape(textline_image)[1] + #height = np.shape(textline_image)[0] + #common_window = int(0.3*width) + #width1 = int ( width/2. - common_window ) + #width2 = int ( width/2. + common_window ) + + try: + width1, width2 = self.return_start_and_end_of_common_text_of_textline_ocr_new(textline_image, ind_tot) + + image1 = textline_image[:, :width2,:]# image.crop((0, 0, width2, height)) + image2 = textline_image[:, width1:,:]#image.crop((width1, 0, width, height)) + + pixel_values1 = processor(image1, return_tensors="pt").pixel_values + pixel_values2 = processor(image2, return_tensors="pt").pixel_values + + generated_ids1 = model_ocr.generate(pixel_values1.to(device)) + generated_ids2 = model_ocr.generate(pixel_values2.to(device)) + + generated_text1 = processor.batch_decode(generated_ids1, skip_special_tokens=True)[0] + generated_text2 = processor.batch_decode(generated_ids2, skip_special_tokens=True)[0] + #print(generated_text1,'generated_text1') + #print(generated_text2, 'generated_text2') + #print('########################################') + + match = sq(None, generated_text1, generated_text2).find_longest_match( + 0, len(generated_text1), 0, len(generated_text2)) + generated_text = generated_text1 + generated_text2[match.b+match.size:] + except: + pixel_values = processor(textline_image, return_tensors="pt").pixel_values + generated_ids = model_ocr.generate(pixel_values.to(device)) + generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] + + return generated_text + + def return_textline_contour_with_added_box_coordinate(self, textline_contour, box_ind): + textline_contour[:,0] = textline_contour[:,0] + box_ind[2] + textline_contour[:,1] = textline_contour[:,1] + box_ind[0] + return textline_contour + + def return_list_of_contours_with_desired_order(self, ls_cons, sorted_indexes): + return [ls_cons[sorted_indexes[index]] for index in range(len(sorted_indexes))] + + def return_it_in_two_groups(self, x_differential): + split = [ind if x_differential[ind]!=x_differential[ind+1] else -1 + for ind in range(len(x_differential)-1)] + split_masked = list( np.array(split[:])[np.array(split[:])!=-1] ) + if 0 not in split_masked: + split_masked.insert(0, -1) + split_masked.append(len(x_differential)-1) + + split_masked = np.array(split_masked) +1 + + sums = [np.sum(x_differential[split_masked[ind]:split_masked[ind+1]]) + for ind in range(len(split_masked)-1)] + + indexes_to_bec_changed = [ind if (np.abs(sums[ind-1]) > np.abs(sums[ind]) and + np.abs(sums[ind+1]) > np.abs(sums[ind])) else -1 + for ind in range(1,len(sums)-1)] + indexes_to_bec_changed_filtered = np.array(indexes_to_bec_changed)[np.array(indexes_to_bec_changed)!=-1] + + x_differential_new = np.copy(x_differential) + for i in indexes_to_bec_changed_filtered: + i_slice = slice(split_masked[i], split_masked[i+1]) + x_differential_new[i_slice] = -1 * np.array(x_differential)[i_slice] + + return x_differential_new + + def dilate_textregions_contours_textline_version(self, all_found_textline_polygons): + #print(all_found_textline_polygons) + for j in range(len(all_found_textline_polygons)): + for ij in range(len(all_found_textline_polygons[j])): + con_ind = all_found_textline_polygons[j][ij] + area = cv2.contourArea(con_ind) + con_ind = con_ind.astype(float) + + x_differential = np.diff( con_ind[:,0,0]) + y_differential = np.diff( con_ind[:,0,1]) + + x_differential = gaussian_filter1d(x_differential, 0.1) + y_differential = gaussian_filter1d(y_differential, 0.1) + + x_min = float(np.min( con_ind[:,0,0] )) + y_min = float(np.min( con_ind[:,0,1] )) + + x_max = float(np.max( con_ind[:,0,0] )) + y_max = float(np.max( con_ind[:,0,1] )) + + x_differential_mask_nonzeros = [ ind/abs(ind) if ind!=0 else ind for ind in x_differential] + y_differential_mask_nonzeros = [ ind/abs(ind) if ind!=0 else ind for ind in y_differential] + + abs_diff=abs(abs(x_differential)- abs(y_differential) ) + + inc_x = np.zeros(len(x_differential)+1) + inc_y = np.zeros(len(x_differential)+1) + + if (y_max-y_min) <= (x_max-x_min): + dilation_m1 = round(area / (x_max-x_min) * 0.12) + else: + dilation_m1 = round(area / (y_max-y_min) * 0.12) + + if dilation_m1>8: + dilation_m1 = 8 + if dilation_m1<6: + dilation_m1 = 6 + #print(dilation_m1, 'dilation_m1') + dilation_m1 = 6 + dilation_m2 = int(dilation_m1/2.) +1 + + for i in range(len(x_differential)): + if abs_diff[i]==0: + inc_x[i+1] = dilation_m2*(-1*y_differential_mask_nonzeros[i]) + inc_y[i+1] = dilation_m2*(x_differential_mask_nonzeros[i]) + elif abs_diff[i]!=0 and x_differential_mask_nonzeros[i]==0 and y_differential_mask_nonzeros[i]!=0: + inc_x[i+1]= dilation_m1*(-1*y_differential_mask_nonzeros[i]) + elif abs_diff[i]!=0 and x_differential_mask_nonzeros[i]!=0 and y_differential_mask_nonzeros[i]==0: + inc_y[i+1] = dilation_m1*(x_differential_mask_nonzeros[i]) + + elif abs_diff[i]!=0 and abs_diff[i]>=3: + if abs(x_differential[i])>abs(y_differential[i]): + inc_y[i+1] = dilation_m1*(x_differential_mask_nonzeros[i]) + else: + inc_x[i+1]= dilation_m1*(-1*y_differential_mask_nonzeros[i]) + else: + inc_x[i+1] = dilation_m2*(-1*y_differential_mask_nonzeros[i]) + inc_y[i+1] = dilation_m2*(x_differential_mask_nonzeros[i]) + + inc_x[0] = inc_x[-1] + inc_y[0] = inc_y[-1] + + con_scaled = con_ind*1 + + con_scaled[:,0, 0] = con_ind[:,0,0] + np.array(inc_x)[:] + con_scaled[:,0, 1] = con_ind[:,0,1] + np.array(inc_y)[:] + + con_scaled[:,0, 1][con_scaled[:,0, 1]<0] = 0 + con_scaled[:,0, 0][con_scaled[:,0, 0]<0] = 0 + + area_scaled = cv2.contourArea(con_scaled.astype(np.int32)) + + con_ind = con_ind.astype(np.int32) + + results = [cv2.pointPolygonTest(con_ind, (con_scaled[ind,0, 0], con_scaled[ind,0, 1]), False) + for ind in range(len(con_scaled[:,0, 1])) ] + results = np.array(results) + #print(results,'results') + results[results==0] = 1 + + diff_result = np.diff(results) + + indices_2 = [ind for ind in range(len(diff_result)) if diff_result[ind]==2] + indices_m2 = [ind for ind in range(len(diff_result)) if diff_result[ind]==-2] + + if results[0]==1: + con_scaled[:indices_m2[0]+1,0, 1] = con_ind[:indices_m2[0]+1,0,1] + con_scaled[:indices_m2[0]+1,0, 0] = con_ind[:indices_m2[0]+1,0,0] + #indices_2 = indices_2[1:] + indices_m2 = indices_m2[1:] + + if len(indices_2)>len(indices_m2): + con_scaled[indices_2[-1]+1:,0, 1] = con_ind[indices_2[-1]+1:,0,1] + con_scaled[indices_2[-1]+1:,0, 0] = con_ind[indices_2[-1]+1:,0,0] + indices_2 = indices_2[:-1] + + for ii in range(len(indices_2)): + con_scaled[indices_2[ii]+1:indices_m2[ii]+1,0, 1] = con_scaled[indices_2[ii],0, 1] + con_scaled[indices_2[ii]+1:indices_m2[ii]+1,0, 0] = con_scaled[indices_2[ii],0, 0] + + all_found_textline_polygons[j][ij][:,0,1] = con_scaled[:,0, 1] + all_found_textline_polygons[j][ij][:,0,0] = con_scaled[:,0, 0] + return all_found_textline_polygons + + def dilate_textregions_contours(self, all_found_textline_polygons): + #print(all_found_textline_polygons) + for j in range(len(all_found_textline_polygons)): + con_ind = all_found_textline_polygons[j] + #print(len(con_ind[:,0,0]),'con_ind[:,0,0]') + area = cv2.contourArea(con_ind) + con_ind = con_ind.astype(float) + + x_differential = np.diff( con_ind[:,0,0]) + y_differential = np.diff( con_ind[:,0,1]) + + x_differential = gaussian_filter1d(x_differential, 0.1) + y_differential = gaussian_filter1d(y_differential, 0.1) + + x_min = float(np.min( con_ind[:,0,0] )) + y_min = float(np.min( con_ind[:,0,1] )) + + x_max = float(np.max( con_ind[:,0,0] )) + y_max = float(np.max( con_ind[:,0,1] )) + + x_differential_mask_nonzeros = [ ind/abs(ind) if ind!=0 else ind for ind in x_differential] + y_differential_mask_nonzeros = [ ind/abs(ind) if ind!=0 else ind for ind in y_differential] + + abs_diff=abs(abs(x_differential)- abs(y_differential) ) + + inc_x = np.zeros(len(x_differential)+1) + inc_y = np.zeros(len(x_differential)+1) + + if (y_max-y_min) <= (x_max-x_min): + dilation_m1 = round(area / (x_max-x_min) * 0.12) + else: + dilation_m1 = round(area / (y_max-y_min) * 0.12) + + if dilation_m1>8: + dilation_m1 = 8 + if dilation_m1<6: + dilation_m1 = 6 + #print(dilation_m1, 'dilation_m1') + dilation_m1 = 6 + dilation_m2 = int(dilation_m1/2.) +1 + + for i in range(len(x_differential)): + if abs_diff[i]==0: + inc_x[i+1] = dilation_m2*(-1*y_differential_mask_nonzeros[i]) + inc_y[i+1] = dilation_m2*(x_differential_mask_nonzeros[i]) + elif abs_diff[i]!=0 and x_differential_mask_nonzeros[i]==0 and y_differential_mask_nonzeros[i]!=0: + inc_x[i+1]= dilation_m1*(-1*y_differential_mask_nonzeros[i]) + elif abs_diff[i]!=0 and x_differential_mask_nonzeros[i]!=0 and y_differential_mask_nonzeros[i]==0: + inc_y[i+1] = dilation_m1*(x_differential_mask_nonzeros[i]) + + elif abs_diff[i]!=0 and abs_diff[i]>=3: + if abs(x_differential[i])>abs(y_differential[i]): + inc_y[i+1] = dilation_m1*(x_differential_mask_nonzeros[i]) + else: + inc_x[i+1]= dilation_m1*(-1*y_differential_mask_nonzeros[i]) + else: + inc_x[i+1] = dilation_m2*(-1*y_differential_mask_nonzeros[i]) + inc_y[i+1] = dilation_m2*(x_differential_mask_nonzeros[i]) + + inc_x[0] = inc_x[-1] + inc_y[0] = inc_y[-1] + + con_scaled = con_ind*1 + + con_scaled[:,0, 0] = con_ind[:,0,0] + np.array(inc_x)[:] + con_scaled[:,0, 1] = con_ind[:,0,1] + np.array(inc_y)[:] + + con_scaled[:,0, 1][con_scaled[:,0, 1]<0] = 0 + con_scaled[:,0, 0][con_scaled[:,0, 0]<0] = 0 + + area_scaled = cv2.contourArea(con_scaled.astype(np.int32)) + + con_ind = con_ind.astype(np.int32) + + results = [cv2.pointPolygonTest(con_ind, (con_scaled[ind,0, 0], con_scaled[ind,0, 1]), False) + for ind in range(len(con_scaled[:,0, 1])) ] + results = np.array(results) + #print(results,'results') + results[results==0] = 1 + + diff_result = np.diff(results) + indices_2 = [ind for ind in range(len(diff_result)) if diff_result[ind]==2] + indices_m2 = [ind for ind in range(len(diff_result)) if diff_result[ind]==-2] + + if results[0]==1: + con_scaled[:indices_m2[0]+1,0, 1] = con_ind[:indices_m2[0]+1,0,1] + con_scaled[:indices_m2[0]+1,0, 0] = con_ind[:indices_m2[0]+1,0,0] + #indices_2 = indices_2[1:] + indices_m2 = indices_m2[1:] + + if len(indices_2)>len(indices_m2): + con_scaled[indices_2[-1]+1:,0, 1] = con_ind[indices_2[-1]+1:,0,1] + con_scaled[indices_2[-1]+1:,0, 0] = con_ind[indices_2[-1]+1:,0,0] + indices_2 = indices_2[:-1] + + for ii in range(len(indices_2)): + con_scaled[indices_2[ii]+1:indices_m2[ii]+1,0, 1] = con_scaled[indices_2[ii],0, 1] + con_scaled[indices_2[ii]+1:indices_m2[ii]+1,0, 0] = con_scaled[indices_2[ii],0, 0] + + all_found_textline_polygons[j][:,0,1] = con_scaled[:,0, 1] + all_found_textline_polygons[j][:,0,0] = con_scaled[:,0, 0] + return all_found_textline_polygons + + def dilate_textline_contours(self, all_found_textline_polygons): + for j in range(len(all_found_textline_polygons)): + for ij in range(len(all_found_textline_polygons[j])): + con_ind = all_found_textline_polygons[j][ij] + area = cv2.contourArea(con_ind) + + con_ind = con_ind.astype(float) + + x_differential = np.diff( con_ind[:,0,0]) + y_differential = np.diff( con_ind[:,0,1]) + + x_differential = gaussian_filter1d(x_differential, 3) + y_differential = gaussian_filter1d(y_differential, 3) + + x_min = float(np.min( con_ind[:,0,0] )) + y_min = float(np.min( con_ind[:,0,1] )) + + x_max = float(np.max( con_ind[:,0,0] )) + y_max = float(np.max( con_ind[:,0,1] )) + + x_differential_mask_nonzeros = [ ind/abs(ind) if ind!=0 else ind for ind in x_differential] + y_differential_mask_nonzeros = [ ind/abs(ind) if ind!=0 else ind for ind in y_differential] + + abs_diff=abs(abs(x_differential)- abs(y_differential) ) + + inc_x = np.zeros(len(x_differential)+1) + inc_y = np.zeros(len(x_differential)+1) + + if (y_max-y_min) <= (x_max-x_min): + dilation_m1 = round(area / (x_max-x_min) * 0.35) + else: + dilation_m1 = round(area / (y_max-y_min) * 0.35) + + if dilation_m1>12: + dilation_m1 = 12 + if dilation_m1<4: + dilation_m1 = 4 + #print(dilation_m1, 'dilation_m1') + dilation_m2 = int(dilation_m1/2.) +1 + + for i in range(len(x_differential)): + if abs_diff[i]==0: + inc_x[i+1] = dilation_m2*(-1*y_differential_mask_nonzeros[i]) + inc_y[i+1] = dilation_m2*(x_differential_mask_nonzeros[i]) + elif abs_diff[i]!=0 and x_differential_mask_nonzeros[i]==0 and y_differential_mask_nonzeros[i]!=0: + inc_x[i+1]= dilation_m1*(-1*y_differential_mask_nonzeros[i]) + elif abs_diff[i]!=0 and x_differential_mask_nonzeros[i]!=0 and y_differential_mask_nonzeros[i]==0: + inc_y[i+1] = dilation_m1*(x_differential_mask_nonzeros[i]) + + elif abs_diff[i]!=0 and abs_diff[i]>=3: + if abs(x_differential[i])>abs(y_differential[i]): + inc_y[i+1] = dilation_m1*(x_differential_mask_nonzeros[i]) + else: + inc_x[i+1]= dilation_m1*(-1*y_differential_mask_nonzeros[i]) + else: + inc_x[i+1] = dilation_m2*(-1*y_differential_mask_nonzeros[i]) + inc_y[i+1] = dilation_m2*(x_differential_mask_nonzeros[i]) + + inc_x[0] = inc_x[-1] + inc_y[0] = inc_y[-1] + + con_scaled = con_ind*1 + + con_scaled[:,0, 0] = con_ind[:,0,0] + np.array(inc_x)[:] + con_scaled[:,0, 1] = con_ind[:,0,1] + np.array(inc_y)[:] + + con_scaled[:,0, 1][con_scaled[:,0, 1]<0] = 0 + con_scaled[:,0, 0][con_scaled[:,0, 0]<0] = 0 + + con_ind = con_ind.astype(np.int32) + + results = [cv2.pointPolygonTest(con_ind, (con_scaled[ind,0, 0], con_scaled[ind,0, 1]), False) + for ind in range(len(con_scaled[:,0, 1])) ] + results = np.array(results) + results[results==0] = 1 + + diff_result = np.diff(results) + + indices_2 = [ind for ind in range(len(diff_result)) if diff_result[ind]==2] + indices_m2 = [ind for ind in range(len(diff_result)) if diff_result[ind]==-2] + + if results[0]==1: + con_scaled[:indices_m2[0]+1,0, 1] = con_ind[:indices_m2[0]+1,0,1] + con_scaled[:indices_m2[0]+1,0, 0] = con_ind[:indices_m2[0]+1,0,0] + indices_m2 = indices_m2[1:] + + if len(indices_2)>len(indices_m2): + con_scaled[indices_2[-1]+1:,0, 1] = con_ind[indices_2[-1]+1:,0,1] + con_scaled[indices_2[-1]+1:,0, 0] = con_ind[indices_2[-1]+1:,0,0] + indices_2 = indices_2[:-1] + + for ii in range(len(indices_2)): + con_scaled[indices_2[ii]+1:indices_m2[ii]+1,0, 1] = con_scaled[indices_2[ii],0, 1] + con_scaled[indices_2[ii]+1:indices_m2[ii]+1,0, 0] = con_scaled[indices_2[ii],0, 0] + + all_found_textline_polygons[j][ij][:,0,1] = con_scaled[:,0, 1] + all_found_textline_polygons[j][ij][:,0,0] = con_scaled[:,0, 0] + return all_found_textline_polygons + + def filter_contours_inside_a_bigger_one(self,contours, contours_d_ordered, image, marginal_cnts=None, type_contour="textregion"): + if type_contour=="textregion": + areas = [cv2.contourArea(contours[j]) for j in range(len(contours))] area_tot = image.shape[0]*image.shape[1] - areas_ratio = areas / area_tot - cx_main, cy_main = find_center_of_contours(contours) - contours_index_small = np.flatnonzero(areas_ratio < 1e-3) - contours_index_large = np.flatnonzero(areas_ratio >= 1e-3) + M_main = [cv2.moments(contours[j]) + for j in range(len(contours))] + cx_main = [(M_main[j]["m10"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))] + cy_main = [(M_main[j]["m01"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))] - #contours_> = [contours[ind] for ind in contours_index_large] + areas_ratio = np.array(areas)/ area_tot + contours_index_small = [ind for ind in range(len(contours)) if areas_ratio[ind] < 1e-3] + contours_index_big = [ind for ind in range(len(contours)) if areas_ratio[ind] >= 1e-3] + + #contours_> = [contours[ind] for ind in contours_index_big] indexes_to_be_removed = [] for ind_small in contours_index_small: - results = [cv2.pointPolygonTest(contours[ind_large], (cx_main[ind_small], - cy_main[ind_small]), - False) - for ind_large in contours_index_large] - results = np.array(results) - if np.any(results==1): - indexes_to_be_removed.append(ind_small) - elif marginal_cnts: - results_marginal = [cv2.pointPolygonTest(marginal_cnt, - (cx_main[ind_small], - cy_main[ind_small]), - False) - for marginal_cnt in marginal_cnts] + results = [cv2.pointPolygonTest(contours[ind], (cx_main[ind_small], cy_main[ind_small]), False) + for ind in contours_index_big] + if marginal_cnts: + results_marginal = [cv2.pointPolygonTest(marginal_cnts[ind], (cx_main[ind_small], cy_main[ind_small]), False) + for ind in range(len(marginal_cnts))] results_marginal = np.array(results_marginal) + if np.any(results_marginal==1): indexes_to_be_removed.append(ind_small) - contours = np.delete(contours, indexes_to_be_removed, axis=0) - if len(contours_d_ordered): - contours_d_ordered = np.delete(contours_d_ordered, indexes_to_be_removed, axis=0) + results = np.array(results) + + if np.any(results==1): + indexes_to_be_removed.append(ind_small) + + if len(indexes_to_be_removed)>0: + indexes_to_be_removed = np.unique(indexes_to_be_removed) + indexes_to_be_removed = np.sort(indexes_to_be_removed)[::-1] + for ind in indexes_to_be_removed: + contours.pop(ind) + if len(contours_d_ordered)>0: + contours_d_ordered.pop(ind) return contours, contours_d_ordered @@ -2703,206 +4006,260 @@ class Eynollah: contours_txtline_of_all_textregions = [] indexes_of_textline_tot = [] index_textline_inside_textregion = [] - for ind_region, textlines in enumerate(contours): - contours_txtline_of_all_textregions.extend(textlines) - index_textline_inside_textregion.extend(list(range(len(textlines)))) - indexes_of_textline_tot.extend([ind_region] * len(textlines)) - areas_tot = np.array(list(map(cv2.contourArea, contours_txtline_of_all_textregions))) + for jj in range(len(contours)): + contours_txtline_of_all_textregions = contours_txtline_of_all_textregions + contours[jj] + + ind_textline_inside_tr = list(range(len(contours[jj]))) + index_textline_inside_textregion = index_textline_inside_textregion + ind_textline_inside_tr + ind_ins = [jj] * len(contours[jj]) + indexes_of_textline_tot = indexes_of_textline_tot + ind_ins + + M_main_tot = [cv2.moments(contours_txtline_of_all_textregions[j]) + for j in range(len(contours_txtline_of_all_textregions))] + cx_main_tot = [(M_main_tot[j]["m10"] / (M_main_tot[j]["m00"] + 1e-32)) for j in range(len(M_main_tot))] + cy_main_tot = [(M_main_tot[j]["m01"] / (M_main_tot[j]["m00"] + 1e-32)) for j in range(len(M_main_tot))] + + areas_tot = [cv2.contourArea(con_ind) for con_ind in contours_txtline_of_all_textregions] area_tot_tot = image.shape[0]*image.shape[1] - cx_main_tot, cy_main_tot = find_center_of_contours(contours_txtline_of_all_textregions) - textline_in_textregion_index_to_del = {} + textregion_index_to_del = [] + textline_in_textregion_index_to_del = [] for ij in range(len(contours_txtline_of_all_textregions)): + args_all = list(np.array(range(len(contours_txtline_of_all_textregions)))) + args_all.pop(ij) + + areas_without = np.array(areas_tot)[args_all] area_of_con_interest = areas_tot[ij] - args_without = np.delete(np.arange(len(contours_txtline_of_all_textregions)), ij) - areas_without = areas_tot[args_without] - args_with_bigger_area = args_without[areas_without > 1.5*area_of_con_interest] + + args_with_bigger_area = np.array(args_all)[areas_without > 1.5*area_of_con_interest] if len(args_with_bigger_area)>0: - results = [cv2.pointPolygonTest(contours_txtline_of_all_textregions[ind], - (cx_main_tot[ij], - cy_main_tot[ij]), - False) + results = [cv2.pointPolygonTest(contours_txtline_of_all_textregions[ind], (cx_main_tot[ij], cy_main_tot[ij]), False) for ind in args_with_bigger_area ] results = np.array(results) if np.any(results==1): #print(indexes_of_textline_tot[ij], index_textline_inside_textregion[ij]) - textline_in_textregion_index_to_del.setdefault( - indexes_of_textline_tot[ij], list()).append( - index_textline_inside_textregion[ij]) - #contours[indexes_of_textline_tot[ij]].pop(index_textline_inside_textregion[ij]) + textregion_index_to_del.append(int(indexes_of_textline_tot[ij])) + textline_in_textregion_index_to_del.append(int(index_textline_inside_textregion[ij])) + #contours[int(indexes_of_textline_tot[ij])].pop(int(index_textline_inside_textregion[ij])) - for textregion_index_to_del in textline_in_textregion_index_to_del: - contours[textregion_index_to_del] = list(np.delete( - contours[textregion_index_to_del], - textline_in_textregion_index_to_del[textregion_index_to_del], - # needed so numpy does not flatten the entire result when 0 left - axis=0)) + textregion_index_to_del = np.array(textregion_index_to_del) + textline_in_textregion_index_to_del = np.array(textline_in_textregion_index_to_del) + for ind_u_a_trs in np.unique(textregion_index_to_del): + textline_in_textregion_index_to_del_ind = textline_in_textregion_index_to_del[textregion_index_to_del==ind_u_a_trs] + textline_in_textregion_index_to_del_ind = np.sort(textline_in_textregion_index_to_del_ind)[::-1] + for ittrd in textline_in_textregion_index_to_del_ind: + contours[ind_u_a_trs].pop(ittrd) return contours - - def return_indexes_of_contours_located_inside_another_list_of_contours( - self, contours, contours_loc, cx_main_loc, cy_main_loc, indexes_loc): - indexes_of_located_cont = [] - center_x_coordinates_of_located = [] - center_y_coordinates_of_located = [] - #M_main_tot = [cv2.moments(contours_loc[j]) - #for j in range(len(contours_loc))] - #cx_main_loc = [(M_main_tot[j]["m10"] / (M_main_tot[j]["m00"] + 1e-32)) for j in range(len(M_main_tot))] - #cy_main_loc = [(M_main_tot[j]["m01"] / (M_main_tot[j]["m00"] + 1e-32)) for j in range(len(M_main_tot))] - - for ij in range(len(contours)): - results = [cv2.pointPolygonTest(contours[ij], (cx_main_loc[ind], cy_main_loc[ind]), False) - for ind in range(len(cy_main_loc)) ] - results = np.array(results) - indexes_in = np.where((results == 0) | (results == 1)) - # [(results == 0) | (results == 1)]#np.where((results == 0) | (results == 1)) - indexes = indexes_loc[indexes_in] - - indexes_of_located_cont.append(indexes) - center_x_coordinates_of_located.append(np.array(cx_main_loc)[indexes_in] ) - center_y_coordinates_of_located.append(np.array(cy_main_loc)[indexes_in] ) - - return indexes_of_located_cont, center_x_coordinates_of_located, center_y_coordinates_of_located - def filter_contours_without_textline_inside( - self, contours_par, contours_textline, - contours_only_text_parent_d_ordered, - conf_contours_textregions): + self, contours,text_con_org, contours_textline, contours_only_text_parent_d_ordered, conf_contours_textregions): + ###contours_txtline_of_all_textregions = [] + ###for jj in range(len(contours_textline)): + ###contours_txtline_of_all_textregions = contours_txtline_of_all_textregions + contours_textline[jj] - assert len(contours_par) == len(contours_textline) - indices = np.arange(len(contours_textline)) - indices = np.delete(indices, np.flatnonzero([len(lines) == 0 for lines in contours_textline])) - def filterfun(lis): - if len(lis) == 0: - return [] - return list(np.array(lis)[indices]) + ###M_main_textline = [cv2.moments(contours_txtline_of_all_textregions[j]) + ### for j in range(len(contours_txtline_of_all_textregions))] + ###cx_main_textline = [(M_main_textline[j]["m10"] / (M_main_textline[j]["m00"] + 1e-32)) + ### for j in range(len(M_main_textline))] + ###cy_main_textline = [(M_main_textline[j]["m01"] / (M_main_textline[j]["m00"] + 1e-32)) + ### for j in range(len(M_main_textline))] - return (filterfun(contours_par), - filterfun(contours_textline), - filterfun(contours_only_text_parent_d_ordered), - filterfun(conf_contours_textregions), - # indices - ) + ###M_main = [cv2.moments(contours[j]) for j in range(len(contours))] + ###cx_main = [(M_main[j]["m10"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))] + ###cy_main = [(M_main[j]["m01"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))] - def separate_marginals_to_left_and_right_and_order_from_top_to_down( - self, polygons_of_marginals, all_found_textline_polygons_marginals, all_box_coord_marginals, - slopes_marginals, mid_point_of_page_width): - cx_marg, cy_marg = find_center_of_contours(polygons_of_marginals) - cx_marg = np.array(cx_marg) - cy_marg = np.array(cy_marg) + ###contours_with_textline = [] + ###for ind_tr, con_tr in enumerate(contours): + ###results = [cv2.pointPolygonTest(con_tr, (cx_main_textline[index_textline_con], cy_main_textline[index_textline_con]), False) + ### for index_textline_con in range(len(contours_txtline_of_all_textregions)) ] + ###results = np.array(results) + ###if np.any(results==1): + ###contours_with_textline.append(con_tr) - def split(lis): - array = np.array(lis) - return (list(array[cx_marg < mid_point_of_page_width]), - list(array[cx_marg >= mid_point_of_page_width])) + textregion_index_to_del = [] + for index_textregion, textlines_textregion in enumerate(contours_textline): + if len(textlines_textregion)==0: + textregion_index_to_del.append(index_textregion) - (poly_marg_left, - poly_marg_right) = \ - split(polygons_of_marginals) + uniqe_args_trs = np.unique(textregion_index_to_del) + uniqe_args_trs_sorted = np.sort(uniqe_args_trs)[::-1] - (all_found_textline_polygons_marginals_left, - all_found_textline_polygons_marginals_right) = \ - split(all_found_textline_polygons_marginals) - - (all_box_coord_marginals_left, - all_box_coord_marginals_right) = \ - split(all_box_coord_marginals) - - (slopes_marg_left, - slopes_marg_right) = \ - split(slopes_marginals) - - (cy_marg_left, - cy_marg_right) = \ - split(cy_marg) + for ind_u_a_trs in uniqe_args_trs_sorted: + conf_contours_textregions.pop(ind_u_a_trs) + contours.pop(ind_u_a_trs) + contours_textline.pop(ind_u_a_trs) + text_con_org.pop(ind_u_a_trs) + if len(contours_only_text_parent_d_ordered) > 0: + contours_only_text_parent_d_ordered.pop(ind_u_a_trs) - order_left = np.argsort(cy_marg_left) - order_right = np.argsort(cy_marg_right) - def sort_left(lis): - return list(np.array(lis)[order_left]) - def sort_right(lis): - return list(np.array(lis)[order_right]) - - ordered_left_marginals = sort_left(poly_marg_left) - ordered_right_marginals = sort_right(poly_marg_right) - - ordered_left_marginals_textline = sort_left(all_found_textline_polygons_marginals_left) - ordered_right_marginals_textline = sort_right(all_found_textline_polygons_marginals_right) - - ordered_left_marginals_bbox = sort_left(all_box_coord_marginals_left) - ordered_right_marginals_bbox = sort_right(all_box_coord_marginals_right) - - ordered_left_slopes_marginals = sort_left(slopes_marg_left) - ordered_right_slopes_marginals = sort_right(slopes_marg_right) - - return (ordered_left_marginals, - ordered_right_marginals, - ordered_left_marginals_textline, - ordered_right_marginals_textline, - ordered_left_marginals_bbox, - ordered_right_marginals_bbox, - ordered_left_slopes_marginals, - ordered_right_slopes_marginals) + return contours, text_con_org, conf_contours_textregions, contours_textline, contours_only_text_parent_d_ordered, np.array(range(len(contours))) - def run(self, - overwrite: bool = False, - image_filename: Optional[str] = None, - dir_in: Optional[str] = None, - dir_out: Optional[str] = None, - dir_of_cropped_images: Optional[str] = None, - dir_of_layout: Optional[str] = None, - dir_of_deskewed: Optional[str] = None, - dir_of_all: Optional[str] = None, - dir_save_page: Optional[str] = None, - ): + def dilate_textlines(self, all_found_textline_polygons): + for j in range(len(all_found_textline_polygons)): + for i in range(len(all_found_textline_polygons[j])): + con_ind = all_found_textline_polygons[j][i] + con_ind = con_ind.astype(float) + + x_differential = np.diff( con_ind[:,0,0]) + y_differential = np.diff( con_ind[:,0,1]) + + x_min = float(np.min( con_ind[:,0,0] )) + y_min = float(np.min( con_ind[:,0,1] )) + + x_max = float(np.max( con_ind[:,0,0] )) + y_max = float(np.max( con_ind[:,0,1] )) + + if (y_max - y_min) > (x_max - x_min) and (x_max - x_min)<70: + x_biger_than_x = np.abs(x_differential) > np.abs(y_differential) + mult = x_biger_than_x*x_differential + + arg_min_mult = np.argmin(mult) + arg_max_mult = np.argmax(mult) + + if y_differential[0]==0: + y_differential[0] = 0.1 + if y_differential[-1]==0: + y_differential[-1]= 0.1 + y_differential = [y_differential[ind] if y_differential[ind] != 0 + else 0.5 * (y_differential[ind-1] + y_differential[ind+1]) + for ind in range(len(y_differential))] + + if y_differential[0]==0.1: + y_differential[0] = y_differential[1] + if y_differential[-1]==0.1: + y_differential[-1] = y_differential[-2] + y_differential.append(y_differential[0]) + + y_differential = [-1 if y_differential[ind] < 0 else 1 + for ind in range(len(y_differential))] + y_differential = self.return_it_in_two_groups(y_differential) + y_differential = np.array(y_differential) + + con_scaled = con_ind*1 + con_scaled[:,0, 0] = con_ind[:,0,0] - 8*y_differential + con_scaled[arg_min_mult,0, 1] = con_ind[arg_min_mult,0,1] + 8 + con_scaled[arg_min_mult+1,0, 1] = con_ind[arg_min_mult+1,0,1] + 8 + + try: + con_scaled[arg_min_mult-1,0, 1] = con_ind[arg_min_mult-1,0,1] + 5 + con_scaled[arg_min_mult+2,0, 1] = con_ind[arg_min_mult+2,0,1] + 5 + except: + pass + + con_scaled[arg_max_mult,0, 1] = con_ind[arg_max_mult,0,1] - 8 + con_scaled[arg_max_mult+1,0, 1] = con_ind[arg_max_mult+1,0,1] - 8 + + try: + con_scaled[arg_max_mult-1,0, 1] = con_ind[arg_max_mult-1,0,1] - 5 + con_scaled[arg_max_mult+2,0, 1] = con_ind[arg_max_mult+2,0,1] - 5 + except: + pass + + else: + y_biger_than_x = np.abs(y_differential) > np.abs(x_differential) + mult = y_biger_than_x*y_differential + + arg_min_mult = np.argmin(mult) + arg_max_mult = np.argmax(mult) + + if x_differential[0]==0: + x_differential[0] = 0.1 + if x_differential[-1]==0: + x_differential[-1]= 0.1 + x_differential = [x_differential[ind] if x_differential[ind] != 0 + else 0.5 * (x_differential[ind-1] + x_differential[ind+1]) + for ind in range(len(x_differential))] + + if x_differential[0]==0.1: + x_differential[0] = x_differential[1] + if x_differential[-1]==0.1: + x_differential[-1] = x_differential[-2] + x_differential.append(x_differential[0]) + + x_differential = [-1 if x_differential[ind] < 0 else 1 + for ind in range(len(x_differential))] + x_differential = self.return_it_in_two_groups(x_differential) + x_differential = np.array(x_differential) + + con_scaled = con_ind*1 + con_scaled[:,0, 1] = con_ind[:,0,1] + 8*x_differential + con_scaled[arg_min_mult,0, 0] = con_ind[arg_min_mult,0,0] + 8 + con_scaled[arg_min_mult+1,0, 0] = con_ind[arg_min_mult+1,0,0] + 8 + + try: + con_scaled[arg_min_mult-1,0, 0] = con_ind[arg_min_mult-1,0,0] + 5 + con_scaled[arg_min_mult+2,0, 0] = con_ind[arg_min_mult+2,0,0] + 5 + except: + pass + + con_scaled[arg_max_mult,0, 0] = con_ind[arg_max_mult,0,0] - 8 + con_scaled[arg_max_mult+1,0, 0] = con_ind[arg_max_mult+1,0,0] - 8 + + try: + con_scaled[arg_max_mult-1,0, 0] = con_ind[arg_max_mult-1,0,0] - 5 + con_scaled[arg_max_mult+2,0, 0] = con_ind[arg_max_mult+2,0,0] - 5 + except: + pass + + con_scaled[:,0, 1][con_scaled[:,0, 1]<0] = 0 + con_scaled[:,0, 0][con_scaled[:,0, 0]<0] = 0 + + all_found_textline_polygons[j][i][:,0,1] = con_scaled[:,0, 1] + all_found_textline_polygons[j][i][:,0,0] = con_scaled[:,0, 0] + + return all_found_textline_polygons + + def delete_regions_without_textlines( + self, slopes, all_found_textline_polygons, boxes_text, txt_con_org, + contours_only_text_parent, index_by_text_par_con): + + slopes_rem = [] + all_found_textline_polygons_rem = [] + boxes_text_rem = [] + txt_con_org_rem = [] + contours_only_text_parent_rem = [] + index_by_text_par_con_rem = [] + + for i, ind_con in enumerate(all_found_textline_polygons): + if len(ind_con): + all_found_textline_polygons_rem.append(ind_con) + slopes_rem.append(slopes[i]) + boxes_text_rem.append(boxes_text[i]) + txt_con_org_rem.append(txt_con_org[i]) + contours_only_text_parent_rem.append(contours_only_text_parent[i]) + index_by_text_par_con_rem.append(index_by_text_par_con[i]) + + index_sort = np.argsort(index_by_text_par_con_rem) + indexes_new = np.array(range(len(index_by_text_par_con_rem))) + + index_by_text_par_con_rem_sort = [indexes_new[index_sort==j][0] + for j in range(len(index_by_text_par_con_rem))] + + return (slopes_rem, all_found_textline_polygons_rem, boxes_text_rem, txt_con_org_rem, + contours_only_text_parent_rem, index_by_text_par_con_rem_sort) + + def run(self, image_filename : Optional[str] = None, dir_in : Optional[str] = None, overwrite : bool = False): """ Get image and scales, then extract the page of scanned image """ self.logger.debug("enter run") t0_tot = time.time() - # Log enabled features directly - enabled_modes = [] - if self.full_layout: - enabled_modes.append("Full layout analysis") - if self.tables: - enabled_modes.append("Table detection") - if enabled_modes: - self.logger.info("Enabled modes: " + ", ".join(enabled_modes)) - if self.enable_plotting: - self.logger.info("Saving debug plots") - if dir_of_cropped_images: - self.logger.info(f"Saving cropped images to: {dir_of_cropped_images}") - if dir_of_layout: - self.logger.info(f"Saving layout plots to: {dir_of_layout}") - if dir_of_deskewed: - self.logger.info(f"Saving deskewed images to: {dir_of_deskewed}") - if dir_in: - ls_imgs = [os.path.join(dir_in, image_filename) - for image_filename in filter(is_image_filename, - os.listdir(dir_in))] + self.ls_imgs = os.listdir(dir_in) elif image_filename: - ls_imgs = [image_filename] + self.ls_imgs = [image_filename] else: raise ValueError("run requires either a single image filename or a directory") - for img_filename in ls_imgs: + for img_filename in self.ls_imgs: self.logger.info(img_filename) t0 = time.time() - self.reset_file_name_dir(img_filename, dir_out) - if self.enable_plotting: - self.plotter = EynollahPlotter(dir_out=dir_out, - dir_of_all=dir_of_all, - dir_save_page=dir_save_page, - dir_of_deskewed=dir_of_deskewed, - dir_of_cropped_images=dir_of_cropped_images, - dir_of_layout=dir_of_layout, - image_filename_stem=Path(image_filename).stem) + self.reset_file_name_dir(os.path.join(dir_in or "", img_filename)) #print("text region early -11 in %.1fs", time.time() - t0) if os.path.exists(self.writer.output_filename): if overwrite: @@ -2913,149 +4270,134 @@ class Eynollah: pcgts = self.run_single() self.logger.info("Job done in %.1fs", time.time() - t0) + #print("Job done in %.1fs" % (time.time() - t0)) self.writer.write_pagexml(pcgts) if dir_in: self.logger.info("All jobs done in %.1fs", time.time() - t0_tot) + print("all Job done in %.1fs", time.time() - t0_tot) def run_single(self): t0 = time.time() - - self.logger.info(f"Processing file: {self.writer.image_filename}") - self.logger.info("Step 1/5: Image Enhancement") - - img_res, is_image_enhanced, num_col_classifier, num_column_is_classified = \ - self.run_enhancement() - - self.logger.info(f"Image: {self.image.shape[1]}x{self.image.shape[0]}, " - f"{self.dpi} DPI, {num_col_classifier} columns") - if is_image_enhanced: - self.logger.info("Enhancement applied") - - self.logger.info(f"Enhancement complete ({time.time() - t0:.1f}s)") - + img_res, is_image_enhanced, num_col_classifier, num_column_is_classified = self.run_enhancement(self.light_version) + self.logger.info("Enhancing took %.1fs ", time.time() - t0) + if self.extract_only_images: + text_regions_p_1, erosion_hurts, polygons_lines_xml, polygons_of_images, image_page, page_coord, cont_page = \ + self.get_regions_light_v_extract_only_images(img_res, is_image_enhanced, num_col_classifier) + ocr_all_textlines = None + pcgts = self.writer.build_pagexml_no_full_layout( + [], page_coord, [], [], [], [], + polygons_of_images, [], [], [], [], [], + cont_page, [], [], ocr_all_textlines, []) + if self.plotter: + self.plotter.write_images_into_directory(polygons_of_images, image_page) + return pcgts - # Basic Processing Mode if self.skip_layout_and_reading_order: - self.logger.info("Step 2/5: Basic Processing Mode") - self.logger.info("Skipping layout analysis and reading order detection") - - _ ,_, _, _, textline_mask_tot_ea, img_bin_light, _ = \ - self.get_regions_light_v(img_res, is_image_enhanced, num_col_classifier,) + _ ,_, _, textline_mask_tot_ea, img_bin_light, _ = \ + self.get_regions_light_v(img_res, is_image_enhanced, num_col_classifier, + skip_layout_and_reading_order=self.skip_layout_and_reading_order) page_coord, image_page, textline_mask_tot_ea, img_bin_light, cont_page = \ self.run_graphics_and_columns_without_layout(textline_mask_tot_ea, img_bin_light) + ##all_found_textline_polygons =self.scale_contours_new(textline_mask_tot_ea) cnt_clean_rot_raw, hir_on_cnt_clean_rot = return_contours_of_image(textline_mask_tot_ea) all_found_textline_polygons = filter_contours_area_of_image( textline_mask_tot_ea, cnt_clean_rot_raw, hir_on_cnt_clean_rot, max_area=1, min_area=0.00001) - - cx_main_tot, cy_main_tot = find_center_of_contours(all_found_textline_polygons) - w_h_textlines = [cv2.boundingRect(polygon)[2:] - for polygon in all_found_textline_polygons] - w_h_textlines = [w / float(h) for w, h in w_h_textlines] - all_found_textline_polygons = self.get_textlines_of_a_textregion_sorted( - #all_found_textline_polygons[::-1] - all_found_textline_polygons, cx_main_tot, cy_main_tot, w_h_textlines) - all_found_textline_polygons = [ all_found_textline_polygons ] - all_found_textline_polygons = dilate_textline_contours(all_found_textline_polygons) + all_found_textline_polygons=[ all_found_textline_polygons ] + + all_found_textline_polygons = self.dilate_textregions_contours_textline_version( + all_found_textline_polygons) all_found_textline_polygons = self.filter_contours_inside_a_bigger_one( all_found_textline_polygons, None, textline_mask_tot_ea, type_contour="textline") - + + order_text_new = [0] slopes =[0] - conf_contours_textregions =[0] - + id_of_texts_tot =['region_0001'] + + polygons_of_images = [] + slopes_marginals = [] + polygons_of_marginals = [] + all_found_textline_polygons_marginals = [] + all_box_coord_marginals = [] + polygons_lines_xml = [] + contours_tables = [] + ocr_all_textlines = None + conf_contours_textregions =None pcgts = self.writer.build_pagexml_no_full_layout( - found_polygons_text_region=cont_page, - page_coord=page_coord, - order_of_texts=order_text_new, - all_found_textline_polygons=all_found_textline_polygons, - all_box_coord=page_coord, - found_polygons_text_region_img=[], - found_polygons_marginals_left=[], - found_polygons_marginals_right=[], - all_found_textline_polygons_marginals_left=[], - all_found_textline_polygons_marginals_right=[], - all_box_coord_marginals_left=[], - all_box_coord_marginals_right=[], - slopes=slopes, - slopes_marginals_left=[], - slopes_marginals_right=[], - cont_page=cont_page, - polygons_seplines=[], - found_polygons_tables=[], - ) - self.logger.info("Basic processing complete") + cont_page, page_coord, order_text_new, id_of_texts_tot, + all_found_textline_polygons, page_coord, polygons_of_images, polygons_of_marginals, + all_found_textline_polygons_marginals, all_box_coord_marginals, slopes, slopes_marginals, + cont_page, polygons_lines_xml, contours_tables, ocr_all_textlines, conf_contours_textregions) return pcgts #print("text region early -1 in %.1fs", time.time() - t0) t1 = time.time() - self.logger.info("Step 2/5: Layout Analysis") - - self.logger.info("Using light version processing") - text_regions_p_1 ,erosion_hurts, polygons_seplines, polygons_text_early, \ - textline_mask_tot_ea, img_bin_light, confidence_matrix = \ - self.get_regions_light_v(img_res, is_image_enhanced, num_col_classifier) - #print("text region early -2 in %.1fs", time.time() - t0) + if self.light_version: + text_regions_p_1 ,erosion_hurts, polygons_lines_xml, textline_mask_tot_ea, img_bin_light, confidence_matrix = \ + self.get_regions_light_v(img_res, is_image_enhanced, num_col_classifier) + #print("text region early -2 in %.1fs", time.time() - t0) - if num_col_classifier == 1 or num_col_classifier ==2: - if num_col_classifier == 1: - img_w_new = 1000 + if num_col_classifier == 1 or num_col_classifier ==2: + if num_col_classifier == 1: + img_w_new = 1000 + else: + img_w_new = 1300 + img_h_new = img_w_new * textline_mask_tot_ea.shape[0] // textline_mask_tot_ea.shape[1] + + textline_mask_tot_ea_deskew = resize_image(textline_mask_tot_ea,img_h_new, img_w_new ) + + slope_deskew, slope_first = self.run_deskew(textline_mask_tot_ea_deskew) else: - img_w_new = 1300 - img_h_new = img_w_new * textline_mask_tot_ea.shape[0] // textline_mask_tot_ea.shape[1] - - textline_mask_tot_ea_deskew = resize_image(textline_mask_tot_ea,img_h_new, img_w_new ) - slope_deskew = self.run_deskew(textline_mask_tot_ea_deskew) + slope_deskew, slope_first = self.run_deskew(textline_mask_tot_ea) + #print("text region early -2,5 in %.1fs", time.time() - t0) + #self.logger.info("Textregion detection took %.1fs ", time.time() - t1t) + num_col, num_col_classifier, img_only_regions, page_coord, image_page, mask_images, mask_lines, \ + text_regions_p_1, cont_page, table_prediction, textline_mask_tot_ea, img_bin_light = \ + self.run_graphics_and_columns_light(text_regions_p_1, textline_mask_tot_ea, + num_col_classifier, num_column_is_classified, erosion_hurts, img_bin_light) + #self.logger.info("run graphics %.1fs ", time.time() - t1t) + #print("text region early -3 in %.1fs", time.time() - t0) + textline_mask_tot_ea_org = np.copy(textline_mask_tot_ea) + #print("text region early -4 in %.1fs", time.time() - t0) else: - slope_deskew = self.run_deskew(textline_mask_tot_ea) - #print("text region early -2,5 in %.1fs", time.time() - t0) - #self.logger.info("Textregion detection took %.1fs ", time.time() - t1t) - num_col, num_col_classifier, img_only_regions, page_coord, image_page, mask_images, mask_lines, \ - text_regions_p_1, cont_page, table_prediction, textline_mask_tot_ea, img_bin_light = \ - self.run_graphics_and_columns_light(text_regions_p_1, textline_mask_tot_ea, - num_col_classifier, num_column_is_classified, - erosion_hurts, img_bin_light) - #self.logger.info("run graphics %.1fs ", time.time() - t1t) - #print("text region early -3 in %.1fs", time.time() - t0) - textline_mask_tot_ea_org = np.copy(textline_mask_tot_ea) + text_regions_p_1 ,erosion_hurts, polygons_lines_xml = \ + self.get_regions_from_xy_2models(img_res, is_image_enhanced, + num_col_classifier) + self.logger.info("Textregion detection took %.1fs ", time.time() - t1) + confidence_matrix = np.zeros((text_regions_p_1.shape[:2])) + t1 = time.time() + num_col, num_col_classifier, img_only_regions, page_coord, image_page, mask_images, mask_lines, \ + text_regions_p_1, cont_page, table_prediction = \ + self.run_graphics_and_columns(text_regions_p_1, num_col_classifier, num_column_is_classified, erosion_hurts) + self.logger.info("Graphics detection took %.1fs ", time.time() - t1) + #self.logger.info('cont_page %s', cont_page) #plt.imshow(table_prediction) #plt.show() - self.logger.info(f"Layout analysis complete ({time.time() - t1:.1f}s)") - if not num_col and len(polygons_text_early) == 0: - self.logger.info("No columns detected - generating empty PAGE-XML") - + if not num_col: + self.logger.info("No columns detected, outputting an empty PAGE-XML") + ocr_all_textlines = None pcgts = self.writer.build_pagexml_no_full_layout( - found_polygons_text_region=[], - page_coord=page_coord, - order_of_texts=[], - all_found_textline_polygons=[], - all_box_coord=[], - found_polygons_text_region_img=[], - found_polygons_marginals_left=[], - found_polygons_marginals_right=[], - all_found_textline_polygons_marginals_left=[], - all_found_textline_polygons_marginals_right=[], - all_box_coord_marginals_left=[], - all_box_coord_marginals_right=[], - slopes=[], - slopes_marginals_left=[], - slopes_marginals_right=[], - cont_page=cont_page, - polygons_seplines=[], - found_polygons_tables=[], - ) + [], page_coord, [], [], [], [], [], [], [], [], [], [], + cont_page, [], [], ocr_all_textlines, []) return pcgts #print("text region early in %.1fs", time.time() - t0) t1 = time.time() - if num_col_classifier in (1,2): + if not self.light_version: + textline_mask_tot_ea = self.run_textline(image_page) + self.logger.info("textline detection took %.1fs", time.time() - t1) + t1 = time.time() + slope_deskew, slope_first = self.run_deskew(textline_mask_tot_ea) + self.logger.info("deskewing took %.1fs", time.time() - t1) + elif num_col_classifier in (1,2): org_h_l_m = textline_mask_tot_ea.shape[0] org_w_l_m = textline_mask_tot_ea.shape[1] if num_col_classifier == 1: @@ -3071,348 +4413,317 @@ class Eynollah: text_regions_p_1 = resize_image(text_regions_p_1,img_h_new, img_w_new ) table_prediction = resize_image(table_prediction,img_h_new, img_w_new ) - textline_mask_tot, text_regions_p = \ - self.run_marginals(textline_mask_tot_ea, mask_images, mask_lines, + textline_mask_tot, text_regions_p, image_page_rotated = \ + self.run_marginals(image_page, textline_mask_tot_ea, mask_images, mask_lines, num_col_classifier, slope_deskew, text_regions_p_1, table_prediction) - if self.plotter: - self.plotter.save_plot_of_layout_main_all(text_regions_p, image_page) - self.plotter.save_plot_of_layout_main(text_regions_p, image_page) - if image_page.size: - # if ratio of text regions to page area is smaller that 30%, - # then deskew angle will not be allowed to exceed 45 - if (abs(slope_deskew) > 45 and - ((text_regions_p == 1).sum() + - (text_regions_p == 4).sum()) / float(image_page.size) <= 0.3): - slope_deskew = 0 - - # if there is no main text, then relabel marginalia as main - if (text_regions_p == 1).sum() == 0: - text_regions_p[text_regions_p == 4] = 1 - - self.logger.info("Step 3/5: Text Line Detection") - - if self.curved_line: - self.logger.info("Mode: Curved line detection") - - if num_col_classifier in (1,2): + if self.light_version and num_col_classifier in (1,2): image_page = resize_image(image_page,org_h_l_m, org_w_l_m ) textline_mask_tot_ea = resize_image(textline_mask_tot_ea,org_h_l_m, org_w_l_m ) text_regions_p = resize_image(text_regions_p,org_h_l_m, org_w_l_m ) textline_mask_tot = resize_image(textline_mask_tot,org_h_l_m, org_w_l_m ) text_regions_p_1 = resize_image(text_regions_p_1,org_h_l_m, org_w_l_m ) table_prediction = resize_image(table_prediction,org_h_l_m, org_w_l_m ) + image_page_rotated = resize_image(image_page_rotated,org_h_l_m, org_w_l_m ) - self.logger.info(f"Detection of marginals took {time.time() - t1:.1f}s") + self.logger.info("detection of marginals took %.1fs", time.time() - t1) + #print("text region early 2 marginal in %.1fs", time.time() - t0) ## birdan sora chock chakir t1 = time.time() if not self.full_layout: - polygons_of_images, img_revised_tab, text_regions_p_1_n, \ - textline_mask_tot_d, regions_without_separators_d, \ + polygons_of_images, img_revised_tab, text_regions_p_1_n, textline_mask_tot_d, regions_without_separators_d, \ boxes, boxes_d, polygons_of_marginals, contours_tables = \ self.run_boxes_no_full_layout(image_page, textline_mask_tot, text_regions_p, slope_deskew, num_col_classifier, table_prediction, erosion_hurts) - ###polygons_of_marginals = dilate_textregion_contours(polygons_of_marginals) + ###polygons_of_marginals = self.dilate_textregions_contours(polygons_of_marginals) else: - polygons_of_images, img_revised_tab, text_regions_p_1_n, \ - textline_mask_tot_d, regions_without_separators_d, \ + polygons_of_images, img_revised_tab, text_regions_p_1_n, textline_mask_tot_d, regions_without_separators_d, \ regions_fully, regions_without_separators, polygons_of_marginals, contours_tables = \ self.run_boxes_full_layout(image_page, textline_mask_tot, text_regions_p, slope_deskew, num_col_classifier, img_only_regions, table_prediction, erosion_hurts, - img_bin_light) - ###polygons_of_marginals = dilate_textregion_contours(polygons_of_marginals) - drop_label_in_full_layout = 4 - textline_mask_tot_ea_org[img_revised_tab==drop_label_in_full_layout] = 0 + img_bin_light if self.light_version else None) + ###polygons_of_marginals = self.dilate_textregions_contours(polygons_of_marginals) + if self.light_version: + drop_label_in_full_layout = 4 + textline_mask_tot_ea_org[img_revised_tab==drop_label_in_full_layout] = 0 - text_only = (img_revised_tab[:, :] == 1) * 1 + text_only = ((img_revised_tab[:, :] == 1)) * 1 if np.abs(slope_deskew) >= SLOPE_THRESHOLD: - text_only_d = (text_regions_p_1_n[:, :] == 1) * 1 + text_only_d = ((text_regions_p_1_n[:, :] == 1)) * 1 #print("text region early 2 in %.1fs", time.time() - t0) ###min_con_area = 0.000005 contours_only_text, hir_on_text = return_contours_of_image(text_only) contours_only_text_parent = return_parent_contours(contours_only_text, hir_on_text) - contours_only_text_parent_d_ordered = [] - contours_only_text_parent_d = [] - if len(contours_only_text_parent) > 0: - areas_tot_text = np.prod(text_only.shape) areas_cnt_text = np.array([cv2.contourArea(c) for c in contours_only_text_parent]) - areas_cnt_text = areas_cnt_text / float(areas_tot_text) + areas_cnt_text = areas_cnt_text / float(text_only.shape[0] * text_only.shape[1]) #self.logger.info('areas_cnt_text %s', areas_cnt_text) - contours_only_text_parent = np.array(contours_only_text_parent)[areas_cnt_text > MIN_AREA_REGION] - areas_cnt_text_parent = areas_cnt_text[areas_cnt_text > MIN_AREA_REGION] - + contours_biggest = contours_only_text_parent[np.argmax(areas_cnt_text)] + contours_only_text_parent = [c for jz, c in enumerate(contours_only_text_parent) + if areas_cnt_text[jz] > MIN_AREA_REGION] + areas_cnt_text_parent = [area for area in areas_cnt_text if area > MIN_AREA_REGION] index_con_parents = np.argsort(areas_cnt_text_parent) - contours_only_text_parent = contours_only_text_parent[index_con_parents] - areas_cnt_text_parent = areas_cnt_text_parent[index_con_parents] - centers = np.stack(find_center_of_contours(contours_only_text_parent)) # [2, N] + contours_only_text_parent = self.return_list_of_contours_with_desired_order( + contours_only_text_parent, index_con_parents) - center0 = centers[:, -1:] # [2, 1] + ##try: + ##contours_only_text_parent = \ + ##list(np.array(contours_only_text_parent,dtype=object)[index_con_parents]) + ##except: + ##contours_only_text_parent = \ + ##list(np.array(contours_only_text_parent,dtype=np.int32)[index_con_parents]) + ##areas_cnt_text_parent = list(np.array(areas_cnt_text_parent)[index_con_parents]) + areas_cnt_text_parent = self.return_list_of_contours_with_desired_order( + areas_cnt_text_parent, index_con_parents) + + cx_bigest_big, cy_biggest_big, _, _, _, _, _ = find_new_features_of_contours([contours_biggest]) + cx_bigest, cy_biggest, _, _, _, _, _ = find_new_features_of_contours(contours_only_text_parent) if np.abs(slope_deskew) >= SLOPE_THRESHOLD: contours_only_text_d, hir_on_text_d = return_contours_of_image(text_only_d) contours_only_text_parent_d = return_parent_contours(contours_only_text_d, hir_on_text_d) - areas_tot_text_d = np.prod(text_only_d.shape) areas_cnt_text_d = np.array([cv2.contourArea(c) for c in contours_only_text_parent_d]) - areas_cnt_text_d = areas_cnt_text_d / float(areas_tot_text_d) + areas_cnt_text_d = areas_cnt_text_d / float(text_only_d.shape[0] * text_only_d.shape[1]) - contours_only_text_parent_d = np.array(contours_only_text_parent_d)[areas_cnt_text_d > MIN_AREA_REGION] - areas_cnt_text_d = areas_cnt_text_d[areas_cnt_text_d > MIN_AREA_REGION] - - if len(contours_only_text_parent_d): + if len(areas_cnt_text_d)>0: + contours_biggest_d = contours_only_text_parent_d[np.argmax(areas_cnt_text_d)] index_con_parents_d = np.argsort(areas_cnt_text_d) - contours_only_text_parent_d = np.array(contours_only_text_parent_d)[index_con_parents_d] - areas_cnt_text_d = areas_cnt_text_d[index_con_parents_d] + contours_only_text_parent_d = self.return_list_of_contours_with_desired_order( + contours_only_text_parent_d, index_con_parents_d) + #try: + #contours_only_text_parent_d = \ + #list(np.array(contours_only_text_parent_d,dtype=object)[index_con_parents_d]) + #except: + #contours_only_text_parent_d = \ + #list(np.array(contours_only_text_parent_d,dtype=np.int32)[index_con_parents_d]) + #areas_cnt_text_d = list(np.array(areas_cnt_text_d)[index_con_parents_d]) + areas_cnt_text_d = self.return_list_of_contours_with_desired_order( + areas_cnt_text_d, index_con_parents_d) - centers_d = np.stack(find_center_of_contours(contours_only_text_parent_d)) # [2, N] + cx_bigest_d_big, cy_biggest_d_big, _, _, _, _, _ = find_new_features_of_contours([contours_biggest_d]) + cx_bigest_d, cy_biggest_d, _, _, _, _, _ = find_new_features_of_contours(contours_only_text_parent_d) + try: + if len(cx_bigest_d) >= 5: + cx_bigest_d_last5 = cx_bigest_d[-5:] + cy_biggest_d_last5 = cy_biggest_d[-5:] + dists_d = [math.sqrt((cx_bigest_big[0] - cx_bigest_d_last5[j]) ** 2 + + (cy_biggest_big[0] - cy_biggest_d_last5[j]) ** 2) + for j in range(len(cy_biggest_d_last5))] + ind_largest = len(cx_bigest_d) -5 + np.argmin(dists_d) + else: + cx_bigest_d_last5 = cx_bigest_d[-len(cx_bigest_d):] + cy_biggest_d_last5 = cy_biggest_d[-len(cx_bigest_d):] + dists_d = [math.sqrt((cx_bigest_big[0]-cx_bigest_d_last5[j])**2 + + (cy_biggest_big[0]-cy_biggest_d_last5[j])**2) + for j in range(len(cy_biggest_d_last5))] + ind_largest = len(cx_bigest_d) - len(cx_bigest_d) + np.argmin(dists_d) - center0_d = centers_d[:, -1:].copy() # [2, 1] + cx_bigest_d_big[0] = cx_bigest_d[ind_largest] + cy_biggest_d_big[0] = cy_biggest_d[ind_largest] + except Exception as why: + self.logger.error(why) - # find the largest among the largest 5 deskewed contours - # that is also closest to the largest original contour - last5_centers_d = centers_d[:, -5:] - dists_d = np.linalg.norm(center0 - last5_centers_d, axis=0) - ind_largest = len(contours_only_text_parent_d) - last5_centers_d.shape[1] + np.argmin(dists_d) - center0_d[:, 0] = centers_d[:, ind_largest] - - # order new contours the same way as the undeskewed contours - # (by calculating the offset of the largest contours, respectively, - # of the new and undeskewed image; then for each contour, - # finding the closest new contour, with proximity calculated - # as distance of their centers modulo offset vector) (h, w) = text_only.shape[:2] center = (w // 2.0, h // 2.0) M = cv2.getRotationMatrix2D(center, slope_deskew, 1.0) M_22 = np.array(M)[:2, :2] - center0 = np.dot(M_22, center0) # [2, 1] - offset = center0 - center0_d # [2, 1] + p_big = np.dot(M_22, [cx_bigest_big, cy_biggest_big]) + x_diff = p_big[0] - cx_bigest_d_big + y_diff = p_big[1] - cy_biggest_d_big - centers = np.dot(M_22, centers) - offset # [2,N] - # add dimension for area (so only contours of similar size will be considered close) - centers = np.append(centers, areas_cnt_text_parent[np.newaxis], axis=0) - centers_d = np.append(centers_d, areas_cnt_text_d[np.newaxis], axis=0) - - dists = np.zeros((len(contours_only_text_parent), len(contours_only_text_parent_d))) + contours_only_text_parent_d_ordered = [] for i in range(len(contours_only_text_parent)): - dists[i] = np.linalg.norm(centers[:, i:i + 1] - centers_d, axis=0) - corresp = np.zeros(dists.shape, dtype=bool) - # keep searching next-closest until at least one correspondence on each side - while not np.all(corresp.sum(axis=1)) and not np.all(corresp.sum(axis=0)): - idx = np.nanargmin(dists) - i, j = np.unravel_index(idx, dists.shape) - dists[i, j] = np.nan - corresp[i, j] = True - #print("original/deskewed adjacency", corresp.nonzero()) - contours_only_text_parent_d_ordered = np.zeros_like(contours_only_text_parent) - contours_only_text_parent_d_ordered = contours_only_text_parent_d[np.argmax(corresp, axis=1)] - # img1 = np.zeros(text_only_d.shape[:2], dtype=np.uint8) - # for i in range(len(contours_only_text_parent)): - # cv2.fillPoly(img1, pts=[contours_only_text_parent_d_ordered[i]], color=i + 1) - # plt.subplot(2, 2, 1, title="direct corresp contours") - # plt.imshow(img1) - # img2 = np.zeros(text_only_d.shape[:2], dtype=np.uint8) - # join deskewed regions mapping to single original ones - for i in range(len(contours_only_text_parent)): - if np.count_nonzero(corresp[i]) > 1: - indices = np.flatnonzero(corresp[i]) - #print("joining", indices) - polygons_d = [contour2polygon(contour) - for contour in contours_only_text_parent_d[indices]] - contour_d = polygon2contour(join_polygons(polygons_d)) - contours_only_text_parent_d_ordered[i] = contour_d - # cv2.fillPoly(img2, pts=[contour_d], color=i + 1) - # plt.subplot(2, 2, 3, title="joined contours") - # plt.imshow(img2) - # img3 = np.zeros(text_only_d.shape[:2], dtype=np.uint8) - # split deskewed regions mapping to multiple original ones - def deskew(polygon): - polygon = shapely.affinity.rotate(polygon, -slope_deskew, origin=center) - polygon = shapely.affinity.translate(polygon, *offset.squeeze()) - return polygon - for j in range(len(contours_only_text_parent_d)): - if np.count_nonzero(corresp[:, j]) > 1: - indices = np.flatnonzero(corresp[:, j]) - #print("splitting along", indices) - polygons = [deskew(contour2polygon(contour)) - for contour in contours_only_text_parent[indices]] - polygon_d = contour2polygon(contours_only_text_parent_d[j]) - polygons_d = [make_intersection(polygon_d, polygon) - for polygon in polygons] - # ignore where there is no actual overlap - indices = indices[np.flatnonzero(polygons_d)] - contours_d = [polygon2contour(polygon_d) - for polygon_d in polygons_d - if polygon_d] - contours_only_text_parent_d_ordered[indices] = contours_d - # cv2.fillPoly(img3, pts=contours_d, color=j + 1) - # plt.subplot(2, 2, 4, title="split contours") - # plt.imshow(img3) - # img4 = np.zeros(text_only_d.shape[:2], dtype=np.uint8) - # for i in range(len(contours_only_text_parent)): - # cv2.fillPoly(img4, pts=[contours_only_text_parent_d_ordered[i]], color=i + 1) - # plt.subplot(2, 2, 2, title="result contours") - # plt.imshow(img4) - # plt.show() + p = np.dot(M_22, [cx_bigest[i], cy_biggest[i]]) + p[0] = p[0] - x_diff[0] + p[1] = p[1] - y_diff[0] + dists = [math.sqrt((p[0] - cx_bigest_d[j]) ** 2 + + (p[1] - cy_biggest_d[j]) ** 2) + for j in range(len(cx_bigest_d))] + contours_only_text_parent_d_ordered.append(contours_only_text_parent_d[np.argmin(dists)]) + # img2=np.zeros((text_only.shape[0],text_only.shape[1],3)) + # img2=cv2.fillPoly(img2,pts=[contours_only_text_parent_d[np.argmin(dists)]] ,color=(1,1,1)) + # plt.imshow(img2[:,:,0]) + # plt.show() + else: + contours_only_text_parent_d_ordered = [] + contours_only_text_parent_d = [] + contours_only_text_parent = [] + else: + contours_only_text_parent_d_ordered = [] + contours_only_text_parent_d = [] + #contours_only_text_parent = [] if not len(contours_only_text_parent): # stop early empty_marginals = [[]] * len(polygons_of_marginals) if self.full_layout: pcgts = self.writer.build_pagexml_full_layout( - found_polygons_text_region=[], - found_polygons_text_region_h=[], - page_coord=page_coord, - order_of_texts=[], - all_found_textline_polygons=[], - all_found_textline_polygons_h=[], - all_box_coord=[], - all_box_coord_h=[], - found_polygons_text_region_img=polygons_of_images, - found_polygons_tables=contours_tables, - found_polygons_drop_capitals=[], - found_polygons_marginals_left=polygons_of_marginals, - found_polygons_marginals_right=polygons_of_marginals, - all_found_textline_polygons_marginals_left=empty_marginals, - all_found_textline_polygons_marginals_right=empty_marginals, - all_box_coord_marginals_left=empty_marginals, - all_box_coord_marginals_right=empty_marginals, - slopes=[], - slopes_h=[], - slopes_marginals_left=[], - slopes_marginals_right=[], - cont_page=cont_page, - polygons_seplines=polygons_seplines - ) + [], [], page_coord, [], [], [], [], [], [], + polygons_of_images, contours_tables, [], + polygons_of_marginals, empty_marginals, empty_marginals, [], [], [], + cont_page, polygons_lines_xml, [], [], []) else: pcgts = self.writer.build_pagexml_no_full_layout( - found_polygons_text_region=[], - page_coord=page_coord, - order_of_texts=[], - all_found_textline_polygons=[], - all_box_coord=[], - found_polygons_text_region_img=polygons_of_images, - found_polygons_marginals_left=polygons_of_marginals, - found_polygons_marginals_right=polygons_of_marginals, - all_found_textline_polygons_marginals_left=empty_marginals, - all_found_textline_polygons_marginals_right=empty_marginals, - all_box_coord_marginals_left=empty_marginals, - all_box_coord_marginals_right=empty_marginals, - slopes=[], - slopes_marginals_left=[], - slopes_marginals_right=[], - cont_page=cont_page, - polygons_seplines=polygons_seplines, - found_polygons_tables=contours_tables - ) + [], page_coord, [], [], [], [], + polygons_of_images, + polygons_of_marginals, empty_marginals, empty_marginals, [], [], + cont_page, polygons_lines_xml, contours_tables, [], []) return pcgts + ## check the ro order + + + + #print("text region early 3 in %.1fs", time.time() - t0) - contours_only_text_parent = dilate_textregion_contours(contours_only_text_parent) - contours_only_text_parent , contours_only_text_parent_d_ordered = self.filter_contours_inside_a_bigger_one( - contours_only_text_parent, contours_only_text_parent_d_ordered, text_only, - marginal_cnts=polygons_of_marginals) - #print("text region early 3.5 in %.1fs", time.time() - t0) - conf_contours_textregions = get_textregion_contours_in_org_image_light( - contours_only_text_parent, self.image, confidence_matrix) - #contours_only_text_parent = dilate_textregion_contours(contours_only_text_parent) + if self.light_version: + contours_only_text_parent = self.dilate_textregions_contours( + contours_only_text_parent) + contours_only_text_parent , contours_only_text_parent_d_ordered = self.filter_contours_inside_a_bigger_one( + contours_only_text_parent, contours_only_text_parent_d_ordered, text_only, marginal_cnts=polygons_of_marginals) + #print("text region early 3.5 in %.1fs", time.time() - t0) + txt_con_org , conf_contours_textregions = get_textregion_contours_in_org_image_light( + contours_only_text_parent, self.image, slope_first, confidence_matrix, map=self.executor.map) + #txt_con_org = self.dilate_textregions_contours(txt_con_org) + #contours_only_text_parent = self.dilate_textregions_contours(contours_only_text_parent) + else: + txt_con_org , conf_contours_textregions = get_textregion_contours_in_org_image_light( + contours_only_text_parent, self.image, slope_first, confidence_matrix, map=self.executor.map) #print("text region early 4 in %.1fs", time.time() - t0) - boxes_text = get_text_region_boxes_by_given_contours(contours_only_text_parent) - boxes_marginals = get_text_region_boxes_by_given_contours(polygons_of_marginals) + boxes_text, _ = get_text_region_boxes_by_given_contours(contours_only_text_parent) + boxes_marginals, _ = get_text_region_boxes_by_given_contours(polygons_of_marginals) #print("text region early 5 in %.1fs", time.time() - t0) ## birdan sora chock chakir if not self.curved_line: - all_found_textline_polygons, \ - all_box_coord, slopes = self.get_slopes_and_deskew_new_light2( - contours_only_text_parent, textline_mask_tot_ea_org, - boxes_text, slope_deskew) - all_found_textline_polygons_marginals, \ - all_box_coord_marginals, slopes_marginals = self.get_slopes_and_deskew_new_light2( - polygons_of_marginals, textline_mask_tot_ea_org, - boxes_marginals, slope_deskew) + if self.light_version: + if self.textline_light: + all_found_textline_polygons, boxes_text, txt_con_org, contours_only_text_parent, \ + all_box_coord, index_by_text_par_con, slopes = self.get_slopes_and_deskew_new_light2( + txt_con_org, contours_only_text_parent, textline_mask_tot_ea_org, + image_page_rotated, boxes_text, slope_deskew) + all_found_textline_polygons_marginals, boxes_marginals, _, polygons_of_marginals, \ + all_box_coord_marginals, _, slopes_marginals = self.get_slopes_and_deskew_new_light2( + polygons_of_marginals, polygons_of_marginals, textline_mask_tot_ea_org, + image_page_rotated, boxes_marginals, slope_deskew) - all_found_textline_polygons = dilate_textline_contours( - all_found_textline_polygons) - all_found_textline_polygons = self.filter_contours_inside_a_bigger_one( - all_found_textline_polygons, None, textline_mask_tot_ea_org, type_contour="textline") - all_found_textline_polygons_marginals = dilate_textline_contours( - all_found_textline_polygons_marginals) - contours_only_text_parent, all_found_textline_polygons, \ - contours_only_text_parent_d_ordered, conf_contours_textregions = \ - self.filter_contours_without_textline_inside( - contours_only_text_parent, all_found_textline_polygons, - contours_only_text_parent_d_ordered, conf_contours_textregions) + #slopes, all_found_textline_polygons, boxes_text, txt_con_org, contours_only_text_parent, index_by_text_par_con = \ + # self.delete_regions_without_textlines(slopes, all_found_textline_polygons, + # boxes_text, txt_con_org, contours_only_text_parent, index_by_text_par_con) + #slopes_marginals, all_found_textline_polygons_marginals, boxes_marginals, polygons_of_marginals, polygons_of_marginals, _ = \ + # self.delete_regions_without_textlines(slopes_marginals, all_found_textline_polygons_marginals, + # boxes_marginals, polygons_of_marginals, polygons_of_marginals, np.array(range(len(polygons_of_marginals)))) + #all_found_textline_polygons = self.dilate_textlines(all_found_textline_polygons) + #####all_found_textline_polygons = self.dilate_textline_contours(all_found_textline_polygons) + all_found_textline_polygons = self.dilate_textregions_contours_textline_version( + all_found_textline_polygons) + all_found_textline_polygons = self.filter_contours_inside_a_bigger_one( + all_found_textline_polygons, None, textline_mask_tot_ea_org, type_contour="textline") + all_found_textline_polygons_marginals = self.dilate_textregions_contours_textline_version( + all_found_textline_polygons_marginals) + contours_only_text_parent, txt_con_org, conf_contours_textregions, all_found_textline_polygons, contours_only_text_parent_d_ordered, \ + index_by_text_par_con = self.filter_contours_without_textline_inside( + contours_only_text_parent, txt_con_org, all_found_textline_polygons, contours_only_text_parent_d_ordered, conf_contours_textregions) + else: + textline_mask_tot_ea = cv2.erode(textline_mask_tot_ea, kernel=KERNEL, iterations=1) + all_found_textline_polygons, boxes_text, txt_con_org, contours_only_text_parent, all_box_coord, \ + index_by_text_par_con, slopes = self.get_slopes_and_deskew_new_light( + txt_con_org, contours_only_text_parent, textline_mask_tot_ea, + image_page_rotated, boxes_text, slope_deskew) + all_found_textline_polygons_marginals, boxes_marginals, _, polygons_of_marginals, \ + all_box_coord_marginals, _, slopes_marginals = self.get_slopes_and_deskew_new_light( + polygons_of_marginals, polygons_of_marginals, textline_mask_tot_ea, + image_page_rotated, boxes_marginals, slope_deskew) + #all_found_textline_polygons = self.filter_contours_inside_a_bigger_one( + # all_found_textline_polygons, textline_mask_tot_ea_org, type_contour="textline") + else: + textline_mask_tot_ea = cv2.erode(textline_mask_tot_ea, kernel=KERNEL, iterations=1) + all_found_textline_polygons, boxes_text, txt_con_org, contours_only_text_parent, \ + all_box_coord, index_by_text_par_con, slopes = self.get_slopes_and_deskew_new( + txt_con_org, contours_only_text_parent, textline_mask_tot_ea, + image_page_rotated, boxes_text, slope_deskew) + all_found_textline_polygons_marginals, boxes_marginals, _, polygons_of_marginals, \ + all_box_coord_marginals, _, slopes_marginals = self.get_slopes_and_deskew_new( + polygons_of_marginals, polygons_of_marginals, textline_mask_tot_ea, + image_page_rotated, boxes_marginals, slope_deskew) else: scale_param = 1 textline_mask_tot_ea_erode = cv2.erode(textline_mask_tot_ea, kernel=KERNEL, iterations=2) - all_found_textline_polygons, \ - all_box_coord, slopes = self.get_slopes_and_deskew_new_curved( - contours_only_text_parent, textline_mask_tot_ea_erode, - boxes_text, text_only, + all_found_textline_polygons, boxes_text, txt_con_org, contours_only_text_parent, \ + all_box_coord, index_by_text_par_con, slopes = self.get_slopes_and_deskew_new_curved( + txt_con_org, contours_only_text_parent, textline_mask_tot_ea_erode, + image_page_rotated, boxes_text, text_only, num_col_classifier, scale_param, slope_deskew) all_found_textline_polygons = small_textlines_to_parent_adherence2( all_found_textline_polygons, textline_mask_tot_ea, num_col_classifier) - all_found_textline_polygons_marginals, \ - all_box_coord_marginals, slopes_marginals = self.get_slopes_and_deskew_new_curved( - polygons_of_marginals, textline_mask_tot_ea_erode, - boxes_marginals, text_only, + all_found_textline_polygons_marginals, boxes_marginals, _, polygons_of_marginals, \ + all_box_coord_marginals, _, slopes_marginals = self.get_slopes_and_deskew_new_curved( + polygons_of_marginals, polygons_of_marginals, textline_mask_tot_ea_erode, + image_page_rotated, boxes_marginals, text_only, num_col_classifier, scale_param, slope_deskew) all_found_textline_polygons_marginals = small_textlines_to_parent_adherence2( all_found_textline_polygons_marginals, textline_mask_tot_ea, num_col_classifier) - - mid_point_of_page_width = text_regions_p.shape[1] / 2. - (polygons_of_marginals_left, polygons_of_marginals_right, - all_found_textline_polygons_marginals_left, all_found_textline_polygons_marginals_right, - all_box_coord_marginals_left, all_box_coord_marginals_right, - slopes_marginals_left, slopes_marginals_right) = \ - self.separate_marginals_to_left_and_right_and_order_from_top_to_down( - polygons_of_marginals, all_found_textline_polygons_marginals, all_box_coord_marginals, - slopes_marginals, mid_point_of_page_width) - - #print(len(polygons_of_marginals), len(ordered_left_marginals), len(ordered_right_marginals), 'marginals ordred') + #print("text region early 6 in %.1fs", time.time() - t0) if self.full_layout: - fun = check_any_text_region_in_model_one_is_main_or_header_light + if np.abs(slope_deskew) >= SLOPE_THRESHOLD: + contours_only_text_parent_d_ordered = self.return_list_of_contours_with_desired_order( + contours_only_text_parent_d_ordered, index_by_text_par_con) + #try: + #contours_only_text_parent_d_ordered = \ + #list(np.array(contours_only_text_parent_d_ordered, dtype=np.int32)[index_by_text_par_con]) + #except: + #contours_only_text_parent_d_ordered = \ + #list(np.array(contours_only_text_parent_d_ordered, dtype=object)[index_by_text_par_con]) + else: + #takes long timee + contours_only_text_parent_d_ordered = None + if self.light_version: + fun = check_any_text_region_in_model_one_is_main_or_header_light + else: + fun = check_any_text_region_in_model_one_is_main_or_header text_regions_p, contours_only_text_parent, contours_only_text_parent_h, all_box_coord, all_box_coord_h, \ all_found_textline_polygons, all_found_textline_polygons_h, slopes, slopes_h, \ contours_only_text_parent_d_ordered, contours_only_text_parent_h_d_ordered, \ conf_contours_textregions, conf_contours_textregions_h = fun( - text_regions_p, regions_fully, contours_only_text_parent, - all_box_coord, all_found_textline_polygons, - slopes, contours_only_text_parent_d_ordered, conf_contours_textregions) + text_regions_p, regions_fully, contours_only_text_parent, + all_box_coord, all_found_textline_polygons, slopes, contours_only_text_parent_d_ordered, conf_contours_textregions) if self.plotter: self.plotter.save_plot_of_layout(text_regions_p, image_page) self.plotter.save_plot_of_layout_all(text_regions_p, image_page) - label_img = 4 - polygons_of_drop_capitals = return_contours_of_interested_region(text_regions_p, label_img, - min_area=0.00003) - ##all_found_textline_polygons = adhere_drop_capital_region_into_corresponding_textline( - ##text_regions_p, polygons_of_drop_capitals, contours_only_text_parent, contours_only_text_parent_h, - ##all_box_coord, all_box_coord_h, all_found_textline_polygons, all_found_textline_polygons_h, - ##kernel=KERNEL, curved_line=self.curved_line) + pixel_img = 4 + polygons_of_drop_capitals = return_contours_of_interested_region_by_min_size(text_regions_p, pixel_img) + all_found_textline_polygons = adhere_drop_capital_region_into_corresponding_textline( + text_regions_p, polygons_of_drop_capitals, contours_only_text_parent, contours_only_text_parent_h, + all_box_coord, all_box_coord_h, all_found_textline_polygons, all_found_textline_polygons_h, + kernel=KERNEL, curved_line=self.curved_line, textline_light=self.textline_light) if not self.reading_order_machine_based: - label_seps = 6 + pixel_seps = 6 if not self.headers_off: if np.abs(slope_deskew) < SLOPE_THRESHOLD: num_col, _, matrix_of_lines_ch, splitter_y_new, _ = find_number_of_columns_in_document( - text_regions_p, num_col_classifier, self.tables, label_seps, contours_only_text_parent_h) + np.repeat(text_regions_p[:, :, np.newaxis], 3, axis=2), + num_col_classifier, self.tables, pixel_seps, contours_only_text_parent_h) else: _, _, matrix_of_lines_ch_d, splitter_y_new_d, _ = find_number_of_columns_in_document( - text_regions_p_1_n, num_col_classifier, self.tables, label_seps, contours_only_text_parent_h_d_ordered) + np.repeat(text_regions_p_1_n[:, :, np.newaxis], 3, axis=2), + num_col_classifier, self.tables, pixel_seps, contours_only_text_parent_h_d_ordered) elif self.headers_off: if np.abs(slope_deskew) < SLOPE_THRESHOLD: num_col, _, matrix_of_lines_ch, splitter_y_new, _ = find_number_of_columns_in_document( - text_regions_p, num_col_classifier, self.tables, label_seps) + np.repeat(text_regions_p[:, :, np.newaxis], 3, axis=2), + num_col_classifier, self.tables, pixel_seps) else: _, _, matrix_of_lines_ch_d, splitter_y_new_d, _ = find_number_of_columns_in_document( - text_regions_p_1_n, num_col_classifier, self.tables, label_seps) + np.repeat(text_regions_p_1_n[:, :, np.newaxis], 3, axis=2), + num_col_classifier, self.tables, pixel_seps) if num_col_classifier >= 3: if np.abs(slope_deskew) < SLOPE_THRESHOLD: @@ -3425,30 +4736,42 @@ class Eynollah: if np.abs(slope_deskew) < SLOPE_THRESHOLD: boxes, peaks_neg_tot_tables = return_boxes_of_images_by_order_of_reading_new( splitter_y_new, regions_without_separators, matrix_of_lines_ch, - num_col_classifier, erosion_hurts, self.tables, self.right2left, - logger=self.logger) + num_col_classifier, erosion_hurts, self.tables, self.right2left) else: boxes_d, peaks_neg_tot_tables_d = return_boxes_of_images_by_order_of_reading_new( splitter_y_new_d, regions_without_separators_d, matrix_of_lines_ch_d, - num_col_classifier, erosion_hurts, self.tables, self.right2left, - logger=self.logger) - else: - contours_only_text_parent_h = [] - contours_only_text_parent_h_d_ordered = [] + num_col_classifier, erosion_hurts, self.tables, self.right2left) if self.plotter: self.plotter.write_images_into_directory(polygons_of_images, image_page) t_order = time.time() - self.logger.info("Step 4/5: Reading Order Detection") + if self.full_layout: + if self.reading_order_machine_based: + order_text_new, id_of_texts_tot = self.do_order_of_regions_with_model( + contours_only_text_parent, contours_only_text_parent_h, text_regions_p) + else: + if np.abs(slope_deskew) < SLOPE_THRESHOLD: + order_text_new, id_of_texts_tot = self.do_order_of_regions( + contours_only_text_parent, contours_only_text_parent_h, boxes, textline_mask_tot) + else: + order_text_new, id_of_texts_tot = self.do_order_of_regions( + contours_only_text_parent_d_ordered, contours_only_text_parent_h_d_ordered, boxes_d, textline_mask_tot_d) + self.logger.info("detection of reading order took %.1fs", time.time() - t_order) - if self.reading_order_machine_based: - self.logger.info("Using machine-based detection") - if self.right2left: - self.logger.info("Right-to-left mode enabled") - if self.headers_off: - self.logger.info("Headers ignored in reading order") + if self.ocr: + ocr_all_textlines = [] + else: + ocr_all_textlines = None + pcgts = self.writer.build_pagexml_full_layout( + contours_only_text_parent, contours_only_text_parent_h, page_coord, order_text_new, id_of_texts_tot, + all_found_textline_polygons, all_found_textline_polygons_h, all_box_coord, all_box_coord_h, + polygons_of_images, contours_tables, polygons_of_drop_capitals, polygons_of_marginals, + all_found_textline_polygons_marginals, all_box_coord_marginals, slopes, slopes_h, slopes_marginals, + cont_page, polygons_lines_xml, ocr_all_textlines, conf_contours_textregions, conf_contours_textregions_h) + return pcgts + contours_only_text_parent_h = None if self.reading_order_machine_based: order_text_new, id_of_texts_tot = self.do_order_of_regions_with_model( contours_only_text_parent, contours_only_text_parent_h, text_regions_p) @@ -3457,61 +4780,654 @@ class Eynollah: order_text_new, id_of_texts_tot = self.do_order_of_regions( contours_only_text_parent, contours_only_text_parent_h, boxes, textline_mask_tot) else: + contours_only_text_parent_d_ordered = self.return_list_of_contours_with_desired_order( + contours_only_text_parent_d_ordered, index_by_text_par_con) + #try: + #contours_only_text_parent_d_ordered = \ + #list(np.array(contours_only_text_parent_d_ordered, dtype=object)[index_by_text_par_con]) + #except: + #contours_only_text_parent_d_ordered = \ + #list(np.array(contours_only_text_parent_d_ordered, dtype=np.int32)[index_by_text_par_con]) order_text_new, id_of_texts_tot = self.do_order_of_regions( - contours_only_text_parent_d_ordered, contours_only_text_parent_h_d_ordered, - boxes_d, textline_mask_tot_d) - self.logger.info(f"Detection of reading order took {time.time() - t_order:.1f}s") + contours_only_text_parent_d_ordered, contours_only_text_parent_h, boxes_d, textline_mask_tot_d) - self.logger.info("Step 5/5: Output Generation") + if self.ocr: + device = cuda.get_current_device() + device.reset() + gc.collect() + model_ocr = VisionEncoderDecoderModel.from_pretrained(self.model_ocr_dir) + device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-printed") + torch.cuda.empty_cache() + model_ocr.to(device) + + ind_tot = 0 + #cv2.imwrite('./img_out.png', image_page) + ocr_all_textlines = [] + for indexing, ind_poly_first in enumerate(all_found_textline_polygons): + ocr_textline_in_textregion = [] + for indexing2, ind_poly in enumerate(ind_poly_first): + if not (self.textline_light or self.curved_line): + ind_poly = copy.deepcopy(ind_poly) + box_ind = all_box_coord[indexing] + #print(ind_poly,np.shape(ind_poly), 'ind_poly') + #print(box_ind) + ind_poly = self.return_textline_contour_with_added_box_coordinate(ind_poly, box_ind) + #print(ind_poly_copy) + ind_poly[ind_poly<0] = 0 + x, y, w, h = cv2.boundingRect(ind_poly) + #print(ind_poly_copy, np.shape(ind_poly_copy)) + #print(x, y, w, h, h/float(w),'ratio') + h2w_ratio = h/float(w) + mask_poly = np.zeros(image_page.shape) + if not self.light_version: + img_poly_on_img = np.copy(image_page) + else: + img_poly_on_img = np.copy(img_bin_light) + mask_poly = cv2.fillPoly(mask_poly, pts=[ind_poly], color=(1, 1, 1)) + + if self.textline_light: + mask_poly = cv2.dilate(mask_poly, KERNEL, iterations=1) + img_poly_on_img[:,:,0][mask_poly[:,:,0] ==0] = 255 + img_poly_on_img[:,:,1][mask_poly[:,:,0] ==0] = 255 + img_poly_on_img[:,:,2][mask_poly[:,:,0] ==0] = 255 + + img_croped = img_poly_on_img[y:y+h, x:x+w, :] + #cv2.imwrite('./extracted_lines/'+str(ind_tot)+'.jpg', img_croped) + text_ocr = self.return_ocr_of_textline_without_common_section(img_croped, model_ocr, processor, device, w, h2w_ratio, ind_tot) + ocr_textline_in_textregion.append(text_ocr) + ind_tot = ind_tot +1 + ocr_all_textlines.append(ocr_textline_in_textregion) - if self.full_layout: - pcgts = self.writer.build_pagexml_full_layout( - found_polygons_text_region=contours_only_text_parent, - found_polygons_text_region_h=contours_only_text_parent_h, - page_coord=page_coord, - order_of_texts=order_text_new, - all_found_textline_polygons=all_found_textline_polygons, - all_found_textline_polygons_h=all_found_textline_polygons_h, - all_box_coord=all_box_coord, - all_box_coord_h=all_box_coord_h, - found_polygons_text_region_img=polygons_of_images, - found_polygons_tables=contours_tables, - found_polygons_drop_capitals=polygons_of_drop_capitals, - found_polygons_marginals_left=polygons_of_marginals_left, - found_polygons_marginals_right=polygons_of_marginals_right, - all_found_textline_polygons_marginals_left=all_found_textline_polygons_marginals_left, - all_found_textline_polygons_marginals_right=all_found_textline_polygons_marginals_right, - all_box_coord_marginals_left=all_box_coord_marginals_left, - all_box_coord_marginals_right=all_box_coord_marginals_right, - slopes=slopes, - slopes_h=slopes_h, - slopes_marginals_left=slopes_marginals_left, - slopes_marginals_right=slopes_marginals_right, - cont_page=cont_page, - polygons_seplines=polygons_seplines, - conf_contours_textregions=conf_contours_textregions, - conf_contours_textregions_h=conf_contours_textregions_h - ) else: - pcgts = self.writer.build_pagexml_no_full_layout( - found_polygons_text_region=contours_only_text_parent, - page_coord=page_coord, - order_of_texts=order_text_new, - all_found_textline_polygons=all_found_textline_polygons, - all_box_coord=all_box_coord, - found_polygons_text_region_img=polygons_of_images, - found_polygons_marginals_left=polygons_of_marginals_left, - found_polygons_marginals_right=polygons_of_marginals_right, - all_found_textline_polygons_marginals_left=all_found_textline_polygons_marginals_left, - all_found_textline_polygons_marginals_right=all_found_textline_polygons_marginals_right, - all_box_coord_marginals_left=all_box_coord_marginals_left, - all_box_coord_marginals_right=all_box_coord_marginals_right, - slopes=slopes, - slopes_marginals_left=slopes_marginals_left, - slopes_marginals_right=slopes_marginals_right, - cont_page=cont_page, - polygons_seplines=polygons_seplines, - found_polygons_tables=contours_tables, - ) - + ocr_all_textlines = None + #print(ocr_all_textlines) + self.logger.info("detection of reading order took %.1fs", time.time() - t_order) + pcgts = self.writer.build_pagexml_no_full_layout( + txt_con_org, page_coord, order_text_new, id_of_texts_tot, + all_found_textline_polygons, all_box_coord, polygons_of_images, polygons_of_marginals, + all_found_textline_polygons_marginals, all_box_coord_marginals, slopes, slopes_marginals, + cont_page, polygons_lines_xml, contours_tables, ocr_all_textlines, conf_contours_textregions) return pcgts + + +class Eynollah_ocr: + def __init__( + self, + dir_models, + dir_xmls=None, + dir_in=None, + dir_in_bin=None, + dir_out=None, + dir_out_image_text=None, + tr_ocr=False, + export_textline_images_and_text=False, + do_not_mask_with_textline_contour=False, + draw_texts_on_image=False, + prediction_with_both_of_rgb_and_bin=False, + logger=None, + ): + self.dir_in = dir_in + self.dir_in_bin = dir_in_bin + self.dir_out = dir_out + self.dir_xmls = dir_xmls + self.dir_models = dir_models + self.tr_ocr = tr_ocr + self.export_textline_images_and_text = export_textline_images_and_text + self.do_not_mask_with_textline_contour = do_not_mask_with_textline_contour + self.draw_texts_on_image = draw_texts_on_image + self.dir_out_image_text = dir_out_image_text + self.prediction_with_both_of_rgb_and_bin = prediction_with_both_of_rgb_and_bin + if tr_ocr: + self.processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-printed") + self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + self.model_ocr_dir = dir_models + "/trocr_model_ens_of_3_checkpoints_201124" + self.model_ocr = VisionEncoderDecoderModel.from_pretrained(self.model_ocr_dir) + self.model_ocr.to(self.device) + + else: + self.model_ocr_dir = dir_models + "/model_step_75000_ocr"#"/model_0_ocr_cnnrnn"#"/model_23_ocr_cnnrnn" + model_ocr = load_model(self.model_ocr_dir , compile=False) + + self.prediction_model = tf.keras.models.Model( + model_ocr.get_layer(name = "image").input, + model_ocr.get_layer(name = "dense2").output) + + + with open(os.path.join(self.model_ocr_dir, "characters_org.txt"),"r") as config_file: + characters = json.load(config_file) + + + AUTOTUNE = tf.data.AUTOTUNE + + # Mapping characters to integers. + char_to_num = StringLookup(vocabulary=list(characters), mask_token=None) + + # Mapping integers back to original characters. + self.num_to_char = StringLookup( + vocabulary=char_to_num.get_vocabulary(), mask_token=None, invert=True + ) + + def decode_batch_predictions(self, pred, max_len = 128): + # input_len is the product of the batch size and the + # number of time steps. + input_len = np.ones(pred.shape[0]) * pred.shape[1] + + # Decode CTC predictions using greedy search. + # decoded is a tuple with 2 elements. + decoded = tf.keras.backend.ctc_decode(pred, + input_length = input_len, + beam_width = 100) + # The outputs are in the first element of the tuple. + # Additionally, the first element is actually a list, + # therefore we take the first element of that list as well. + #print(decoded,'decoded') + decoded = decoded[0][0][:, :max_len] + + #print(decoded, decoded.shape,'decoded') + + output = [] + for d in decoded: + # Convert the predicted indices to the corresponding chars. + d = tf.strings.reduce_join(self.num_to_char(d)) + d = d.numpy().decode("utf-8") + output.append(d) + return output + + + def distortion_free_resize(self, image, img_size): + w, h = img_size + image = tf.image.resize(image, size=(h, w), preserve_aspect_ratio=True) + + # Check tha amount of padding needed to be done. + pad_height = h - tf.shape(image)[0] + pad_width = w - tf.shape(image)[1] + + # Only necessary if you want to do same amount of padding on both sides. + if pad_height % 2 != 0: + height = pad_height // 2 + pad_height_top = height + 1 + pad_height_bottom = height + else: + pad_height_top = pad_height_bottom = pad_height // 2 + + if pad_width % 2 != 0: + width = pad_width // 2 + pad_width_left = width + 1 + pad_width_right = width + else: + pad_width_left = pad_width_right = pad_width // 2 + + image = tf.pad( + image, + paddings=[ + [pad_height_top, pad_height_bottom], + [pad_width_left, pad_width_right], + [0, 0], + ], + ) + + image = tf.transpose(image, (1, 0, 2)) + image = tf.image.flip_left_right(image) + return image + + def return_start_and_end_of_common_text_of_textline_ocr_without_common_section(self, textline_image): + width = np.shape(textline_image)[1] + height = np.shape(textline_image)[0] + common_window = int(0.06*width) + + width1 = int ( width/2. - common_window ) + width2 = int ( width/2. + common_window ) + + img_sum = np.sum(textline_image[:,:,0], axis=0) + sum_smoothed = gaussian_filter1d(img_sum, 3) + + peaks_real, _ = find_peaks(sum_smoothed, height=0) + + if len(peaks_real)>70: + + peaks_real = peaks_real[(peaks_realwidth1)] + + arg_max = np.argmax(sum_smoothed[peaks_real]) + + peaks_final = peaks_real[arg_max] + + return peaks_final + else: + return None + + # Function to fit text inside the given area + def fit_text_single_line(self, draw, text, font_path, max_width, max_height): + initial_font_size = 50 + font_size = initial_font_size + while font_size > 10: # Minimum font size + font = ImageFont.truetype(font_path, font_size) + text_bbox = draw.textbbox((0, 0), text, font=font) # Get text bounding box + text_width = text_bbox[2] - text_bbox[0] + text_height = text_bbox[3] - text_bbox[1] + + if text_width <= max_width and text_height <= max_height: + return font # Return the best-fitting font + + font_size -= 2 # Reduce font size and retry + + return ImageFont.truetype(font_path, 10) # Smallest font fallback + + def return_textlines_split_if_needed(self, textline_image, textline_image_bin): + + split_point = self.return_start_and_end_of_common_text_of_textline_ocr_without_common_section(textline_image) + if split_point: + image1 = textline_image[:, :split_point,:]# image.crop((0, 0, width2, height)) + image2 = textline_image[:, split_point:,:]#image.crop((width1, 0, width, height)) + if self.prediction_with_both_of_rgb_and_bin: + image1_bin = textline_image_bin[:, :split_point,:]# image.crop((0, 0, width2, height)) + image2_bin = textline_image_bin[:, split_point:,:]#image.crop((width1, 0, width, height)) + return [image1, image2], [image1_bin, image2_bin] + else: + return [image1, image2], None + else: + return None, None + def preprocess_and_resize_image_for_ocrcnn_model(self, img, image_height, image_width): + ratio = image_height /float(img.shape[0]) + w_ratio = int(ratio * img.shape[1]) + + if w_ratio <= image_width: + width_new = w_ratio + else: + width_new = image_width + + if width_new == 0: + width_new = img.shape[1] + + img = resize_image(img, image_height, width_new) + img_fin = np.ones((image_height, image_width, 3))*255 + img_fin[:,:+width_new,:] = img[:,:,:] + img_fin = img_fin / 255. + return img_fin + + def run(self): + ls_imgs = os.listdir(self.dir_in) + + if self.tr_ocr: + b_s = 2 + for ind_img in ls_imgs: + t0 = time.time() + file_name = ind_img.split('.')[0] + dir_img = os.path.join(self.dir_in, ind_img) + dir_xml = os.path.join(self.dir_xmls, file_name+'.xml') + out_file_ocr = os.path.join(self.dir_out, file_name+'.xml') + img = cv2.imread(dir_img) + + ##file_name = Path(dir_xmls).stem + tree1 = ET.parse(dir_xml, parser = ET.XMLParser(encoding="utf-8")) + root1=tree1.getroot() + alltags=[elem.tag for elem in root1.iter()] + link=alltags[0].split('}')[0]+'}' + + name_space = alltags[0].split('}')[0] + name_space = name_space.split('{')[1] + + region_tags=np.unique([x for x in alltags if x.endswith('TextRegion')]) + + + + cropped_lines = [] + cropped_lines_region_indexer = [] + cropped_lines_meging_indexing = [] + + indexer_text_region = 0 + for nn in root1.iter(region_tags): + for child_textregion in nn: + if child_textregion.tag.endswith("TextLine"): + + for child_textlines in child_textregion: + if child_textlines.tag.endswith("Coords"): + cropped_lines_region_indexer.append(indexer_text_region) + p_h=child_textlines.attrib['points'].split(' ') + textline_coords = np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) + x,y,w,h = cv2.boundingRect(textline_coords) + + h2w_ratio = h/float(w) + + img_poly_on_img = np.copy(img) + mask_poly = np.zeros(img.shape) + mask_poly = cv2.fillPoly(mask_poly, pts=[textline_coords], color=(1, 1, 1)) + + mask_poly = mask_poly[y:y+h, x:x+w, :] + img_crop = img_poly_on_img[y:y+h, x:x+w, :] + img_crop[mask_poly==0] = 255 + + if h2w_ratio > 0.05: + cropped_lines.append(img_crop) + cropped_lines_meging_indexing.append(0) + else: + splited_images, _ = self.return_textlines_split_if_needed(img_crop, None) + #print(splited_images) + if splited_images: + cropped_lines.append(splited_images[0]) + cropped_lines_meging_indexing.append(1) + cropped_lines.append(splited_images[1]) + cropped_lines_meging_indexing.append(-1) + else: + cropped_lines.append(img_crop) + cropped_lines_meging_indexing.append(0) + indexer_text_region = indexer_text_region +1 + + + extracted_texts = [] + n_iterations = math.ceil(len(cropped_lines) / b_s) + + for i in range(n_iterations): + if i==(n_iterations-1): + n_start = i*b_s + imgs = cropped_lines[n_start:] + else: + n_start = i*b_s + n_end = (i+1)*b_s + imgs = cropped_lines[n_start:n_end] + pixel_values_merged = self.processor(imgs, return_tensors="pt").pixel_values + generated_ids_merged = self.model_ocr.generate(pixel_values_merged.to(self.device)) + generated_text_merged = self.processor.batch_decode(generated_ids_merged, skip_special_tokens=True) + + extracted_texts = extracted_texts + generated_text_merged + + extracted_texts_merged = [extracted_texts[ind] if cropped_lines_meging_indexing[ind]==0 else extracted_texts[ind]+extracted_texts[ind+1] if cropped_lines_meging_indexing[ind]==1 else None for ind in range(len(cropped_lines_meging_indexing))] + + extracted_texts_merged = [ind for ind in extracted_texts_merged if ind is not None] + #print(extracted_texts_merged, len(extracted_texts_merged)) + + unique_cropped_lines_region_indexer = np.unique(cropped_lines_region_indexer) + + #print(len(unique_cropped_lines_region_indexer), 'unique_cropped_lines_region_indexer') + text_by_textregion = [] + for ind in unique_cropped_lines_region_indexer: + extracted_texts_merged_un = np.array(extracted_texts_merged)[np.array(cropped_lines_region_indexer)==ind] + + text_by_textregion.append(" ".join(extracted_texts_merged_un)) + + #print(len(text_by_textregion) , indexer_text_region, "text_by_textregion") + + + #print(time.time() - t0 ,'elapsed time') + + + indexer = 0 + indexer_textregion = 0 + for nn in root1.iter(region_tags): + text_subelement_textregion = ET.SubElement(nn, 'TextEquiv') + unicode_textregion = ET.SubElement(text_subelement_textregion, 'Unicode') + + + has_textline = False + for child_textregion in nn: + if child_textregion.tag.endswith("TextLine"): + text_subelement = ET.SubElement(child_textregion, 'TextEquiv') + unicode_textline = ET.SubElement(text_subelement, 'Unicode') + unicode_textline.text = extracted_texts_merged[indexer] + indexer = indexer + 1 + has_textline = True + if has_textline: + unicode_textregion.text = text_by_textregion[indexer_textregion] + indexer_textregion = indexer_textregion + 1 + + + + ET.register_namespace("",name_space) + tree1.write(out_file_ocr,xml_declaration=True,method='xml',encoding="utf8",default_namespace=None) + #print("Job done in %.1fs", time.time() - t0) + else: + max_len = 512 + padding_token = 299 + image_width = 512#max_len * 4 + image_height = 32 + b_s = 8 + + + img_size=(image_width, image_height) + + for ind_img in ls_imgs: + t0 = time.time() + file_name = ind_img.split('.')[0] + dir_img = os.path.join(self.dir_in, ind_img) + dir_xml = os.path.join(self.dir_xmls, file_name+'.xml') + out_file_ocr = os.path.join(self.dir_out, file_name+'.xml') + img = cv2.imread(dir_img) + if self.prediction_with_both_of_rgb_and_bin: + cropped_lines_bin = [] + dir_img_bin = os.path.join(self.dir_in_bin, file_name+'.png') + img_bin = cv2.imread(dir_img_bin) + + if self.draw_texts_on_image: + out_image_with_text = os.path.join(self.dir_out_image_text, file_name+'.png') + image_text = Image.new("RGB", (img.shape[1], img.shape[0]), "white") + draw = ImageDraw.Draw(image_text) + total_bb_coordinates = [] + + tree1 = ET.parse(dir_xml, parser = ET.XMLParser(encoding="utf-8")) + root1=tree1.getroot() + alltags=[elem.tag for elem in root1.iter()] + link=alltags[0].split('}')[0]+'}' + + name_space = alltags[0].split('}')[0] + name_space = name_space.split('{')[1] + + region_tags=np.unique([x for x in alltags if x.endswith('TextRegion')]) + + cropped_lines = [] + cropped_lines_region_indexer = [] + cropped_lines_meging_indexing = [] + + tinl = time.time() + indexer_text_region = 0 + indexer_textlines = 0 + for nn in root1.iter(region_tags): + for child_textregion in nn: + if child_textregion.tag.endswith("TextLine"): + for child_textlines in child_textregion: + if child_textlines.tag.endswith("Coords"): + cropped_lines_region_indexer.append(indexer_text_region) + p_h=child_textlines.attrib['points'].split(' ') + textline_coords = np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) + + x,y,w,h = cv2.boundingRect(textline_coords) + + if self.draw_texts_on_image: + total_bb_coordinates.append([x,y,w,h]) + + h2w_ratio = h/float(w) + + img_poly_on_img = np.copy(img) + if self.prediction_with_both_of_rgb_and_bin: + img_poly_on_img_bin = np.copy(img_bin) + img_crop_bin = img_poly_on_img_bin[y:y+h, x:x+w, :] + + mask_poly = np.zeros(img.shape) + mask_poly = cv2.fillPoly(mask_poly, pts=[textline_coords], color=(1, 1, 1)) + + mask_poly = mask_poly[y:y+h, x:x+w, :] + img_crop = img_poly_on_img[y:y+h, x:x+w, :] + if not self.do_not_mask_with_textline_contour: + img_crop[mask_poly==0] = 255 + if self.prediction_with_both_of_rgb_and_bin: + img_crop_bin[mask_poly==0] = 255 + + if not self.export_textline_images_and_text: + if h2w_ratio > 0.1: + img_fin = self.preprocess_and_resize_image_for_ocrcnn_model(img_crop, image_height, image_width) + cropped_lines.append(img_fin) + cropped_lines_meging_indexing.append(0) + if self.prediction_with_both_of_rgb_and_bin: + img_fin = self.preprocess_and_resize_image_for_ocrcnn_model(img_crop_bin, image_height, image_width) + cropped_lines_bin.append(img_fin) + else: + if self.prediction_with_both_of_rgb_and_bin: + splited_images, splited_images_bin = self.return_textlines_split_if_needed(img_crop, img_crop_bin) + else: + splited_images, splited_images_bin = self.return_textlines_split_if_needed(img_crop, None) + if splited_images: + img_fin = self.preprocess_and_resize_image_for_ocrcnn_model(splited_images[0], image_height, image_width) + cropped_lines.append(img_fin) + cropped_lines_meging_indexing.append(1) + img_fin = self.preprocess_and_resize_image_for_ocrcnn_model(splited_images[1], image_height, image_width) + + cropped_lines.append(img_fin) + cropped_lines_meging_indexing.append(-1) + + if self.prediction_with_both_of_rgb_and_bin: + img_fin = self.preprocess_and_resize_image_for_ocrcnn_model(splited_images_bin[0], image_height, image_width) + cropped_lines_bin.append(img_fin) + img_fin = self.preprocess_and_resize_image_for_ocrcnn_model(splited_images_bin[1], image_height, image_width) + cropped_lines_bin.append(img_fin) + + else: + img_fin = self.preprocess_and_resize_image_for_ocrcnn_model(img_crop, image_height, image_width) + cropped_lines.append(img_fin) + cropped_lines_meging_indexing.append(0) + + if self.prediction_with_both_of_rgb_and_bin: + img_fin = self.preprocess_and_resize_image_for_ocrcnn_model(img_crop_bin, image_height, image_width) + cropped_lines_bin.append(img_fin) + + if self.export_textline_images_and_text: + if child_textlines.tag.endswith("TextEquiv"): + for cheild_text in child_textlines: + if cheild_text.tag.endswith("Unicode"): + textline_text = cheild_text.text + if textline_text: + with open(os.path.join(self.dir_out, file_name+'_line_'+str(indexer_textlines)+'.txt'), 'w') as text_file: + text_file.write(textline_text) + + cv2.imwrite(os.path.join(self.dir_out, file_name+'_line_'+str(indexer_textlines)+'.png'), img_crop ) + + indexer_textlines+=1 + + if not self.export_textline_images_and_text: + indexer_text_region = indexer_text_region +1 + + if not self.export_textline_images_and_text: + extracted_texts = [] + + n_iterations = math.ceil(len(cropped_lines) / b_s) + + for i in range(n_iterations): + if i==(n_iterations-1): + n_start = i*b_s + imgs = cropped_lines[n_start:] + imgs = np.array(imgs) + imgs = imgs.reshape(imgs.shape[0], image_height, image_width, 3) + if self.prediction_with_both_of_rgb_and_bin: + imgs_bin = cropped_lines_bin[n_start:] + imgs_bin = np.array(imgs_bin) + imgs_bin = imgs_bin.reshape(imgs_bin.shape[0], image_height, image_width, 3) + else: + n_start = i*b_s + n_end = (i+1)*b_s + imgs = cropped_lines[n_start:n_end] + imgs = np.array(imgs).reshape(b_s, image_height, image_width, 3) + + if self.prediction_with_both_of_rgb_and_bin: + imgs_bin = cropped_lines_bin[n_start:n_end] + imgs_bin = np.array(imgs_bin).reshape(b_s, image_height, image_width, 3) + + + preds = self.prediction_model.predict(imgs, verbose=0) + if self.prediction_with_both_of_rgb_and_bin: + preds_bin = self.prediction_model.predict(imgs_bin, verbose=0) + preds = (preds + preds_bin) / 2. + + pred_texts = self.decode_batch_predictions(preds) + + for ib in range(imgs.shape[0]): + pred_texts_ib = pred_texts[ib].strip("[UNK]") + extracted_texts.append(pred_texts_ib) + + extracted_texts_merged = [extracted_texts[ind] if cropped_lines_meging_indexing[ind]==0 else extracted_texts[ind]+" "+extracted_texts[ind+1] if cropped_lines_meging_indexing[ind]==1 else None for ind in range(len(cropped_lines_meging_indexing))] + + extracted_texts_merged = [ind for ind in extracted_texts_merged if ind is not None] + unique_cropped_lines_region_indexer = np.unique(cropped_lines_region_indexer) + + + if self.draw_texts_on_image: + + font_path = "NotoSans-Regular.ttf" # Make sure this file exists! + font = ImageFont.truetype(font_path, 40) + + for indexer_text, bb_ind in enumerate(total_bb_coordinates): + + + x_bb = bb_ind[0] + y_bb = bb_ind[1] + w_bb = bb_ind[2] + h_bb = bb_ind[3] + + font = self.fit_text_single_line(draw, extracted_texts_merged[indexer_text], font_path, w_bb, int(h_bb*0.4) ) + + ##draw.rectangle([x_bb, y_bb, x_bb + w_bb, y_bb + h_bb], outline="red", width=2) + + text_bbox = draw.textbbox((0, 0), extracted_texts_merged[indexer_text], font=font) + text_width = text_bbox[2] - text_bbox[0] + text_height = text_bbox[3] - text_bbox[1] + + text_x = x_bb + (w_bb - text_width) // 2 # Center horizontally + text_y = y_bb + (h_bb - text_height) // 2 # Center vertically + + # Draw the text + draw.text((text_x, text_y), extracted_texts_merged[indexer_text], fill="black", font=font) + image_text.save(out_image_with_text) + + text_by_textregion = [] + for ind in unique_cropped_lines_region_indexer: + extracted_texts_merged_un = np.array(extracted_texts_merged)[np.array(cropped_lines_region_indexer)==ind] + text_by_textregion.append(" ".join(extracted_texts_merged_un)) + + indexer = 0 + indexer_textregion = 0 + for nn in root1.iter(region_tags): + + is_textregion_text = False + for childtest in nn: + if childtest.tag.endswith("TextEquiv"): + is_textregion_text = True + + if not is_textregion_text: + text_subelement_textregion = ET.SubElement(nn, 'TextEquiv') + unicode_textregion = ET.SubElement(text_subelement_textregion, 'Unicode') + + + has_textline = False + for child_textregion in nn: + if child_textregion.tag.endswith("TextLine"): + + is_textline_text = False + for childtest2 in child_textregion: + if childtest2.tag.endswith("TextEquiv"): + is_textline_text = True + + + if not is_textline_text: + text_subelement = ET.SubElement(child_textregion, 'TextEquiv') + unicode_textline = ET.SubElement(text_subelement, 'Unicode') + unicode_textline.text = extracted_texts_merged[indexer] + else: + for childtest3 in child_textregion: + if childtest3.tag.endswith("TextEquiv"): + for child_uc in childtest3: + if child_uc.tag.endswith("Unicode"): + child_uc.text = extracted_texts_merged[indexer] + + indexer = indexer + 1 + has_textline = True + if has_textline: + if is_textregion_text: + for child4 in nn: + if child4.tag.endswith("TextEquiv"): + for childtr_uc in child4: + if childtr_uc.tag.endswith("Unicode"): + childtr_uc.text = text_by_textregion[indexer_textregion] + else: + unicode_textregion.text = text_by_textregion[indexer_textregion] + indexer_textregion = indexer_textregion + 1 + + ET.register_namespace("",name_space) + tree1.write(out_file_ocr,xml_declaration=True,method='xml',encoding="utf8",default_namespace=None) + #print("Job done in %.1fs", time.time() - t0) diff --git a/src/eynollah/eynollah_imports.py b/src/eynollah/eynollah_imports.py deleted file mode 100644 index f04cfdc..0000000 --- a/src/eynollah/eynollah_imports.py +++ /dev/null @@ -1,10 +0,0 @@ -""" -Load libraries with possible race conditions once. This must be imported as the first module of eynollah. -""" -from ocrd_utils import tf_disable_interactive_logs -from torch import * -tf_disable_interactive_logs() -import tensorflow.keras -from shapely import * -imported_libs = True -__all__ = ['imported_libs'] diff --git a/src/eynollah/eynollah_ocr.py b/src/eynollah/eynollah_ocr.py deleted file mode 100644 index 3c918e5..0000000 --- a/src/eynollah/eynollah_ocr.py +++ /dev/null @@ -1,837 +0,0 @@ -# FIXME: fix all of those... -# pyright: reportOptionalSubscript=false - -from logging import Logger, getLogger -from typing import List, Optional -from pathlib import Path -import os -import gc -import math -from dataclasses import dataclass - -import cv2 -from cv2.typing import MatLike -from xml.etree import ElementTree as ET -from PIL import Image, ImageDraw -import numpy as np -from eynollah.model_zoo import EynollahModelZoo -from eynollah.utils.font import get_font -from eynollah.utils.xml import etree_namespace_for_element_tag -try: - import torch -except ImportError: - torch = None - - -from .utils import is_image_filename -from .utils.resize import resize_image -from .utils.utils_ocr import ( - break_curved_line_into_small_pieces_and_then_merge, - decode_batch_predictions, - fit_text_single_line, - get_contours_and_bounding_boxes, - get_orientation_moments, - preprocess_and_resize_image_for_ocrcnn_model, - return_textlines_split_if_needed, - rotate_image_with_padding, -) - -# TODO: refine typing -@dataclass -class EynollahOcrResult: - extracted_texts_merged: List - extracted_conf_value_merged: Optional[List] - cropped_lines_region_indexer: List - total_bb_coordinates:List - -class Eynollah_ocr: - def __init__( - self, - *, - model_zoo: EynollahModelZoo, - tr_ocr=False, - batch_size: Optional[int]=None, - do_not_mask_with_textline_contour: bool=False, - min_conf_value_of_textline_text : Optional[float]=None, - logger: Optional[Logger]=None, - ): - self.tr_ocr = tr_ocr - # masking for OCR and GT generation, relevant for skewed lines and bounding boxes - self.do_not_mask_with_textline_contour = do_not_mask_with_textline_contour - self.logger = logger if logger else getLogger('eynollah.ocr') - self.model_zoo = model_zoo - - self.min_conf_value_of_textline_text = min_conf_value_of_textline_text if min_conf_value_of_textline_text else 0.3 - self.b_s = 2 if batch_size is None and tr_ocr else 8 if batch_size is None else batch_size - - if tr_ocr: - self.model_zoo.load_model('trocr_processor') - self.model_zoo.load_model('ocr', 'tr') - self.model_zoo.get('ocr').to(self.device) - else: - self.model_zoo.load_model('ocr', '') - self.model_zoo.load_model('num_to_char') - self.model_zoo.load_model('characters') - self.end_character = len(self.model_zoo.get('characters', list)) + 2 - - @property - def device(self): - assert torch - if torch.cuda.is_available(): - self.logger.info("Using GPU acceleration") - return torch.device("cuda:0") - else: - self.logger.info("Using CPU processing") - return torch.device("cpu") - - def run_trocr( - self, - *, - img: MatLike, - page_tree: ET.ElementTree, - page_ns, - tr_ocr_input_height_and_width, - ) -> EynollahOcrResult: - - total_bb_coordinates = [] - - - cropped_lines = [] - cropped_lines_region_indexer = [] - cropped_lines_meging_indexing = [] - - extracted_texts = [] - - indexer_text_region = 0 - indexer_b_s = 0 - - for nn in page_tree.getroot().iter(f'{{{page_ns}}}TextRegion'): - for child_textregion in nn: - if child_textregion.tag.endswith("TextLine"): - - for child_textlines in child_textregion: - if child_textlines.tag.endswith("Coords"): - cropped_lines_region_indexer.append(indexer_text_region) - p_h=child_textlines.attrib['points'].split(' ') - textline_coords = np.array( [ [int(x.split(',')[0]), - int(x.split(',')[1]) ] - for x in p_h] ) - x,y,w,h = cv2.boundingRect(textline_coords) - - total_bb_coordinates.append([x,y,w,h]) - - h2w_ratio = h/float(w) - - img_poly_on_img = np.copy(img) - mask_poly = np.zeros(img.shape) - mask_poly = cv2.fillPoly(mask_poly, pts=[textline_coords], color=(1, 1, 1)) - - mask_poly = mask_poly[y:y+h, x:x+w, :] - img_crop = img_poly_on_img[y:y+h, x:x+w, :] - img_crop[mask_poly==0] = 255 - - self.logger.debug("processing %d lines for '%s'", - len(cropped_lines), nn.attrib['id']) - if h2w_ratio > 0.1: - cropped_lines.append(resize_image(img_crop, - tr_ocr_input_height_and_width, - tr_ocr_input_height_and_width) ) - cropped_lines_meging_indexing.append(0) - indexer_b_s+=1 - if indexer_b_s==self.b_s: - imgs = cropped_lines[:] - cropped_lines = [] - indexer_b_s = 0 - - pixel_values_merged = self.model_zoo.get('trocr_processor')(imgs, return_tensors="pt").pixel_values - generated_ids_merged = self.model_zoo.get('ocr').generate( - pixel_values_merged.to(self.device)) - generated_text_merged = self.model_zoo.get('trocr_processor').batch_decode( - generated_ids_merged, skip_special_tokens=True) - - extracted_texts = extracted_texts + generated_text_merged - - else: - splited_images, _ = return_textlines_split_if_needed(img_crop, None) - #print(splited_images) - if splited_images: - cropped_lines.append(resize_image(splited_images[0], - tr_ocr_input_height_and_width, - tr_ocr_input_height_and_width)) - cropped_lines_meging_indexing.append(1) - indexer_b_s+=1 - - if indexer_b_s==self.b_s: - imgs = cropped_lines[:] - cropped_lines = [] - indexer_b_s = 0 - - pixel_values_merged = self.model_zoo.get('trocr_processor')(imgs, return_tensors="pt").pixel_values - generated_ids_merged = self.model_zoo.get('ocr').generate( - pixel_values_merged.to(self.device)) - generated_text_merged = self.model_zoo.get('trocr_processor').batch_decode( - generated_ids_merged, skip_special_tokens=True) - - extracted_texts = extracted_texts + generated_text_merged - - - cropped_lines.append(resize_image(splited_images[1], - tr_ocr_input_height_and_width, - tr_ocr_input_height_and_width)) - cropped_lines_meging_indexing.append(-1) - indexer_b_s+=1 - - if indexer_b_s==self.b_s: - imgs = cropped_lines[:] - cropped_lines = [] - indexer_b_s = 0 - - pixel_values_merged = self.model_zoo.get('trocr_processor')(imgs, return_tensors="pt").pixel_values - generated_ids_merged = self.model_zoo.get('ocr').generate( - pixel_values_merged.to(self.device)) - generated_text_merged = self.model_zoo.get('trocr_processor').batch_decode( - generated_ids_merged, skip_special_tokens=True) - - extracted_texts = extracted_texts + generated_text_merged - - else: - cropped_lines.append(img_crop) - cropped_lines_meging_indexing.append(0) - indexer_b_s+=1 - - if indexer_b_s==self.b_s: - imgs = cropped_lines[:] - cropped_lines = [] - indexer_b_s = 0 - - pixel_values_merged = self.model_zoo.get('trocr_processor')(imgs, return_tensors="pt").pixel_values - generated_ids_merged = self.model_zoo.get('ocr').generate( - pixel_values_merged.to(self.device)) - generated_text_merged = self.model_zoo.get('trocr_processor').batch_decode( - generated_ids_merged, skip_special_tokens=True) - - extracted_texts = extracted_texts + generated_text_merged - - - - indexer_text_region = indexer_text_region +1 - - if indexer_b_s!=0: - imgs = cropped_lines[:] - cropped_lines = [] - indexer_b_s = 0 - - pixel_values_merged = self.model_zoo.get('trocr_processor')(imgs, return_tensors="pt").pixel_values - generated_ids_merged = self.model_zoo.get('ocr').generate(pixel_values_merged.to(self.device)) - generated_text_merged = self.model_zoo.get('trocr_processor').batch_decode(generated_ids_merged, skip_special_tokens=True) - - extracted_texts = extracted_texts + generated_text_merged - - ####extracted_texts = [] - ####n_iterations = math.ceil(len(cropped_lines) / self.b_s) - - ####for i in range(n_iterations): - ####if i==(n_iterations-1): - ####n_start = i*self.b_s - ####imgs = cropped_lines[n_start:] - ####else: - ####n_start = i*self.b_s - ####n_end = (i+1)*self.b_s - ####imgs = cropped_lines[n_start:n_end] - ####pixel_values_merged = self.model_zoo.get('trocr_processor')(imgs, return_tensors="pt").pixel_values - ####generated_ids_merged = self.model_ocr.generate( - #### pixel_values_merged.to(self.device)) - ####generated_text_merged = self.model_zoo.get('trocr_processor').batch_decode( - #### generated_ids_merged, skip_special_tokens=True) - - ####extracted_texts = extracted_texts + generated_text_merged - - del cropped_lines - gc.collect() - - extracted_texts_merged = [extracted_texts[ind] - if cropped_lines_meging_indexing[ind]==0 - else extracted_texts[ind]+" "+extracted_texts[ind+1] - if cropped_lines_meging_indexing[ind]==1 - else None - for ind in range(len(cropped_lines_meging_indexing))] - - extracted_texts_merged = [ind for ind in extracted_texts_merged if ind is not None] - #print(extracted_texts_merged, len(extracted_texts_merged)) - - return EynollahOcrResult( - extracted_texts_merged=extracted_texts_merged, - extracted_conf_value_merged=None, - cropped_lines_region_indexer=cropped_lines_region_indexer, - total_bb_coordinates=total_bb_coordinates, - ) - - def run_cnn( - self, - *, - img: MatLike, - img_bin: Optional[MatLike], - page_tree: ET.ElementTree, - page_ns, - image_width, - image_height, - ) -> EynollahOcrResult: - - total_bb_coordinates = [] - - cropped_lines = [] - img_crop_bin = None - imgs_bin = None - imgs_bin_ver_flipped = None - cropped_lines_bin = [] - cropped_lines_ver_index = [] - cropped_lines_region_indexer = [] - cropped_lines_meging_indexing = [] - - indexer_text_region = 0 - for nn in page_tree.getroot().iter(f'{{{page_ns}}}TextRegion'): - try: - type_textregion = nn.attrib['type'] - except: - type_textregion = 'paragraph' - for child_textregion in nn: - if child_textregion.tag.endswith("TextLine"): - for child_textlines in child_textregion: - if child_textlines.tag.endswith("Coords"): - cropped_lines_region_indexer.append(indexer_text_region) - p_h=child_textlines.attrib['points'].split(' ') - textline_coords = np.array( [ [int(x.split(',')[0]), - int(x.split(',')[1]) ] - for x in p_h] ) - - x,y,w,h = cv2.boundingRect(textline_coords) - - angle_radians = math.atan2(h, w) - # Convert to degrees - angle_degrees = math.degrees(angle_radians) - if type_textregion=='drop-capital': - angle_degrees = 0 - - total_bb_coordinates.append([x,y,w,h]) - - w_scaled = w * image_height/float(h) - - img_poly_on_img = np.copy(img) - if img_bin: - img_poly_on_img_bin = np.copy(img_bin) - img_crop_bin = img_poly_on_img_bin[y:y+h, x:x+w, :] - - mask_poly = np.zeros(img.shape) - mask_poly = cv2.fillPoly(mask_poly, pts=[textline_coords], color=(1, 1, 1)) - - - mask_poly = mask_poly[y:y+h, x:x+w, :] - img_crop = img_poly_on_img[y:y+h, x:x+w, :] - - # print(file_name, angle_degrees, w*h, - # mask_poly[:,:,0].sum(), - # mask_poly[:,:,0].sum() /float(w*h) , - # 'didi') - - if angle_degrees > 3: - better_des_slope = get_orientation_moments(textline_coords) - - img_crop = rotate_image_with_padding(img_crop, better_des_slope) - if img_bin: - img_crop_bin = rotate_image_with_padding(img_crop_bin, better_des_slope) - - mask_poly = rotate_image_with_padding(mask_poly, better_des_slope) - mask_poly = mask_poly.astype('uint8') - - #new bounding box - x_n, y_n, w_n, h_n = get_contours_and_bounding_boxes(mask_poly[:,:,0]) - - mask_poly = mask_poly[y_n:y_n+h_n, x_n:x_n+w_n, :] - img_crop = img_crop[y_n:y_n+h_n, x_n:x_n+w_n, :] - - if not self.do_not_mask_with_textline_contour: - img_crop[mask_poly==0] = 255 - if img_bin: - img_crop_bin = img_crop_bin[y_n:y_n+h_n, x_n:x_n+w_n, :] - if not self.do_not_mask_with_textline_contour: - img_crop_bin[mask_poly==0] = 255 - - if mask_poly[:,:,0].sum() /float(w_n*h_n) < 0.50 and w_scaled > 90: - if img_bin: - img_crop, img_crop_bin = \ - break_curved_line_into_small_pieces_and_then_merge( - img_crop, mask_poly, img_crop_bin) - else: - img_crop, _ = \ - break_curved_line_into_small_pieces_and_then_merge( - img_crop, mask_poly) - - else: - better_des_slope = 0 - if not self.do_not_mask_with_textline_contour: - img_crop[mask_poly==0] = 255 - if img_bin: - if not self.do_not_mask_with_textline_contour: - img_crop_bin[mask_poly==0] = 255 - if type_textregion=='drop-capital': - pass - else: - if mask_poly[:,:,0].sum() /float(w*h) < 0.50 and w_scaled > 90: - if img_bin: - img_crop, img_crop_bin = \ - break_curved_line_into_small_pieces_and_then_merge( - img_crop, mask_poly, img_crop_bin) - else: - img_crop, _ = \ - break_curved_line_into_small_pieces_and_then_merge( - img_crop, mask_poly) - - if w_scaled < 750:#1.5*image_width: - img_fin = preprocess_and_resize_image_for_ocrcnn_model( - img_crop, image_height, image_width) - cropped_lines.append(img_fin) - if abs(better_des_slope) > 45: - cropped_lines_ver_index.append(1) - else: - cropped_lines_ver_index.append(0) - - cropped_lines_meging_indexing.append(0) - if img_bin: - img_fin = preprocess_and_resize_image_for_ocrcnn_model( - img_crop_bin, image_height, image_width) - cropped_lines_bin.append(img_fin) - else: - splited_images, splited_images_bin = return_textlines_split_if_needed( - img_crop, img_crop_bin if img_bin else None) - if splited_images: - img_fin = preprocess_and_resize_image_for_ocrcnn_model( - splited_images[0], image_height, image_width) - cropped_lines.append(img_fin) - cropped_lines_meging_indexing.append(1) - - if abs(better_des_slope) > 45: - cropped_lines_ver_index.append(1) - else: - cropped_lines_ver_index.append(0) - - img_fin = preprocess_and_resize_image_for_ocrcnn_model( - splited_images[1], image_height, image_width) - - cropped_lines.append(img_fin) - cropped_lines_meging_indexing.append(-1) - - if abs(better_des_slope) > 45: - cropped_lines_ver_index.append(1) - else: - cropped_lines_ver_index.append(0) - - if img_bin: - img_fin = preprocess_and_resize_image_for_ocrcnn_model( - splited_images_bin[0], image_height, image_width) - cropped_lines_bin.append(img_fin) - img_fin = preprocess_and_resize_image_for_ocrcnn_model( - splited_images_bin[1], image_height, image_width) - cropped_lines_bin.append(img_fin) - - else: - img_fin = preprocess_and_resize_image_for_ocrcnn_model( - img_crop, image_height, image_width) - cropped_lines.append(img_fin) - cropped_lines_meging_indexing.append(0) - - if abs(better_des_slope) > 45: - cropped_lines_ver_index.append(1) - else: - cropped_lines_ver_index.append(0) - - if img_bin: - img_fin = preprocess_and_resize_image_for_ocrcnn_model( - img_crop_bin, image_height, image_width) - cropped_lines_bin.append(img_fin) - - - indexer_text_region = indexer_text_region +1 - - extracted_texts = [] - extracted_conf_value = [] - - n_iterations = math.ceil(len(cropped_lines) / self.b_s) - - # FIXME: copy pasta - for i in range(n_iterations): - if i==(n_iterations-1): - n_start = i*self.b_s - imgs = cropped_lines[n_start:] - imgs = np.array(imgs) - imgs = imgs.reshape(imgs.shape[0], image_height, image_width, 3) - - ver_imgs = np.array( cropped_lines_ver_index[n_start:] ) - indices_ver = np.where(ver_imgs == 1)[0] - - #print(indices_ver, 'indices_ver') - if len(indices_ver)>0: - imgs_ver_flipped = imgs[indices_ver, : ,: ,:] - imgs_ver_flipped = imgs_ver_flipped[:,::-1,::-1,:] - #print(imgs_ver_flipped, 'imgs_ver_flipped') - - else: - imgs_ver_flipped = None - - if img_bin: - imgs_bin = cropped_lines_bin[n_start:] - imgs_bin = np.array(imgs_bin) - imgs_bin = imgs_bin.reshape(imgs_bin.shape[0], image_height, image_width, 3) - - if len(indices_ver)>0: - imgs_bin_ver_flipped = imgs_bin[indices_ver, : ,: ,:] - imgs_bin_ver_flipped = imgs_bin_ver_flipped[:,::-1,::-1,:] - #print(imgs_ver_flipped, 'imgs_ver_flipped') - - else: - imgs_bin_ver_flipped = None - else: - n_start = i*self.b_s - n_end = (i+1)*self.b_s - imgs = cropped_lines[n_start:n_end] - imgs = np.array(imgs).reshape(self.b_s, image_height, image_width, 3) - - ver_imgs = np.array( cropped_lines_ver_index[n_start:n_end] ) - indices_ver = np.where(ver_imgs == 1)[0] - #print(indices_ver, 'indices_ver') - - if len(indices_ver)>0: - imgs_ver_flipped = imgs[indices_ver, : ,: ,:] - imgs_ver_flipped = imgs_ver_flipped[:,::-1,::-1,:] - #print(imgs_ver_flipped, 'imgs_ver_flipped') - else: - imgs_ver_flipped = None - - - if img_bin: - imgs_bin = cropped_lines_bin[n_start:n_end] - imgs_bin = np.array(imgs_bin).reshape(self.b_s, image_height, image_width, 3) - - - if len(indices_ver)>0: - imgs_bin_ver_flipped = imgs_bin[indices_ver, : ,: ,:] - imgs_bin_ver_flipped = imgs_bin_ver_flipped[:,::-1,::-1,:] - #print(imgs_ver_flipped, 'imgs_ver_flipped') - else: - imgs_bin_ver_flipped = None - - - self.logger.debug("processing next %d lines", len(imgs)) - preds = self.model_zoo.get('ocr').predict(imgs, verbose=0) - - if len(indices_ver)>0: - preds_flipped = self.model_zoo.get('ocr').predict(imgs_ver_flipped, verbose=0) - preds_max_fliped = np.max(preds_flipped, axis=2 ) - preds_max_args_flipped = np.argmax(preds_flipped, axis=2 ) - pred_max_not_unk_mask_bool_flipped = preds_max_args_flipped[:,:]!=self.end_character - masked_means_flipped = \ - np.sum(preds_max_fliped * pred_max_not_unk_mask_bool_flipped, axis=1) / \ - np.sum(pred_max_not_unk_mask_bool_flipped, axis=1) - masked_means_flipped[np.isnan(masked_means_flipped)] = 0 - - preds_max = np.max(preds, axis=2 ) - preds_max_args = np.argmax(preds, axis=2 ) - pred_max_not_unk_mask_bool = preds_max_args[:,:]!=self.end_character - - masked_means = \ - np.sum(preds_max * pred_max_not_unk_mask_bool, axis=1) / \ - np.sum(pred_max_not_unk_mask_bool, axis=1) - masked_means[np.isnan(masked_means)] = 0 - - masked_means_ver = masked_means[indices_ver] - #print(masked_means_ver, 'pred_max_not_unk') - - indices_where_flipped_conf_value_is_higher = \ - np.where(masked_means_flipped > masked_means_ver)[0] - - #print(indices_where_flipped_conf_value_is_higher, 'indices_where_flipped_conf_value_is_higher') - if len(indices_where_flipped_conf_value_is_higher)>0: - indices_to_be_replaced = indices_ver[indices_where_flipped_conf_value_is_higher] - preds[indices_to_be_replaced,:,:] = \ - preds_flipped[indices_where_flipped_conf_value_is_higher, :, :] - - if img_bin: - preds_bin = self.model_zoo.get('ocr').predict(imgs_bin, verbose=0) - - if len(indices_ver)>0: - preds_flipped = self.model_zoo.get('ocr').predict(imgs_bin_ver_flipped, verbose=0) - preds_max_fliped = np.max(preds_flipped, axis=2 ) - preds_max_args_flipped = np.argmax(preds_flipped, axis=2 ) - pred_max_not_unk_mask_bool_flipped = preds_max_args_flipped[:,:]!=self.end_character - masked_means_flipped = \ - np.sum(preds_max_fliped * pred_max_not_unk_mask_bool_flipped, axis=1) / \ - np.sum(pred_max_not_unk_mask_bool_flipped, axis=1) - masked_means_flipped[np.isnan(masked_means_flipped)] = 0 - - preds_max = np.max(preds, axis=2 ) - preds_max_args = np.argmax(preds, axis=2 ) - pred_max_not_unk_mask_bool = preds_max_args[:,:]!=self.end_character - - masked_means = \ - np.sum(preds_max * pred_max_not_unk_mask_bool, axis=1) / \ - np.sum(pred_max_not_unk_mask_bool, axis=1) - masked_means[np.isnan(masked_means)] = 0 - - masked_means_ver = masked_means[indices_ver] - #print(masked_means_ver, 'pred_max_not_unk') - - indices_where_flipped_conf_value_is_higher = \ - np.where(masked_means_flipped > masked_means_ver)[0] - - #print(indices_where_flipped_conf_value_is_higher, 'indices_where_flipped_conf_value_is_higher') - if len(indices_where_flipped_conf_value_is_higher)>0: - indices_to_be_replaced = indices_ver[indices_where_flipped_conf_value_is_higher] - preds_bin[indices_to_be_replaced,:,:] = \ - preds_flipped[indices_where_flipped_conf_value_is_higher, :, :] - - preds = (preds + preds_bin) / 2. - - pred_texts = decode_batch_predictions(preds, self.model_zoo.get('num_to_char')) - - preds_max = np.max(preds, axis=2 ) - preds_max_args = np.argmax(preds, axis=2 ) - pred_max_not_unk_mask_bool = preds_max_args[:,:]!=self.end_character - masked_means = \ - np.sum(preds_max * pred_max_not_unk_mask_bool, axis=1) / \ - np.sum(pred_max_not_unk_mask_bool, axis=1) - - for ib in range(imgs.shape[0]): - pred_texts_ib = pred_texts[ib].replace("[UNK]", "") - if masked_means[ib] >= self.min_conf_value_of_textline_text: - extracted_texts.append(pred_texts_ib) - extracted_conf_value.append(masked_means[ib]) - else: - extracted_texts.append("") - extracted_conf_value.append(0) - del cropped_lines - del cropped_lines_bin - gc.collect() - - extracted_texts_merged = [extracted_texts[ind] - if cropped_lines_meging_indexing[ind]==0 - else extracted_texts[ind]+" "+extracted_texts[ind+1] - if cropped_lines_meging_indexing[ind]==1 - else None - for ind in range(len(cropped_lines_meging_indexing))] - - extracted_conf_value_merged = [extracted_conf_value[ind] # type: ignore - if cropped_lines_meging_indexing[ind]==0 - else (extracted_conf_value[ind]+extracted_conf_value[ind+1])/2. - if cropped_lines_meging_indexing[ind]==1 - else None - for ind in range(len(cropped_lines_meging_indexing))] - - extracted_conf_value_merged: List[float] = [extracted_conf_value_merged[ind_cfm] - for ind_cfm in range(len(extracted_texts_merged)) - if extracted_texts_merged[ind_cfm] is not None] - - extracted_texts_merged = [ind for ind in extracted_texts_merged if ind is not None] - - return EynollahOcrResult( - extracted_texts_merged=extracted_texts_merged, - extracted_conf_value_merged=extracted_conf_value_merged, - cropped_lines_region_indexer=cropped_lines_region_indexer, - total_bb_coordinates=total_bb_coordinates, - ) - - def write_ocr( - self, - *, - result: EynollahOcrResult, - page_tree: ET.ElementTree, - out_file_ocr, - page_ns, - img, - out_image_with_text, - ): - cropped_lines_region_indexer = result.cropped_lines_region_indexer - total_bb_coordinates = result.total_bb_coordinates - extracted_texts_merged = result.extracted_texts_merged - extracted_conf_value_merged = result.extracted_conf_value_merged - - unique_cropped_lines_region_indexer = np.unique(cropped_lines_region_indexer) - if out_image_with_text: - image_text = Image.new("RGB", (img.shape[1], img.shape[0]), "white") - draw = ImageDraw.Draw(image_text) - font = get_font() - - for indexer_text, bb_ind in enumerate(total_bb_coordinates): - x_bb = bb_ind[0] - y_bb = bb_ind[1] - w_bb = bb_ind[2] - h_bb = bb_ind[3] - - font = fit_text_single_line(draw, extracted_texts_merged[indexer_text], - font.path, w_bb, int(h_bb*0.4) ) - - ##draw.rectangle([x_bb, y_bb, x_bb + w_bb, y_bb + h_bb], outline="red", width=2) - - text_bbox = draw.textbbox((0, 0), extracted_texts_merged[indexer_text], font=font) - text_width = text_bbox[2] - text_bbox[0] - text_height = text_bbox[3] - text_bbox[1] - - text_x = x_bb + (w_bb - text_width) // 2 # Center horizontally - text_y = y_bb + (h_bb - text_height) // 2 # Center vertically - - # Draw the text - draw.text((text_x, text_y), extracted_texts_merged[indexer_text], fill="black", font=font) - image_text.save(out_image_with_text) - - text_by_textregion = [] - for ind in unique_cropped_lines_region_indexer: - ind = np.array(cropped_lines_region_indexer)==ind - extracted_texts_merged_un = np.array(extracted_texts_merged)[ind] - if len(extracted_texts_merged_un)>1: - text_by_textregion_ind = "" - next_glue = "" - for indt in range(len(extracted_texts_merged_un)): - if (extracted_texts_merged_un[indt].endswith('⸗') or - extracted_texts_merged_un[indt].endswith('-') or - extracted_texts_merged_un[indt].endswith('¬')): - text_by_textregion_ind += next_glue + extracted_texts_merged_un[indt][:-1] - next_glue = "" - else: - text_by_textregion_ind += next_glue + extracted_texts_merged_un[indt] - next_glue = " " - text_by_textregion.append(text_by_textregion_ind) - else: - text_by_textregion.append(" ".join(extracted_texts_merged_un)) - - indexer = 0 - indexer_textregion = 0 - for nn in page_tree.getroot().iter(f'{{{page_ns}}}TextRegion'): - - is_textregion_text = False - for childtest in nn: - if childtest.tag.endswith("TextEquiv"): - is_textregion_text = True - - if not is_textregion_text: - text_subelement_textregion = ET.SubElement(nn, 'TextEquiv') - unicode_textregion = ET.SubElement(text_subelement_textregion, 'Unicode') - - - has_textline = False - for child_textregion in nn: - if child_textregion.tag.endswith("TextLine"): - - is_textline_text = False - for childtest2 in child_textregion: - if childtest2.tag.endswith("TextEquiv"): - is_textline_text = True - - - if not is_textline_text: - text_subelement = ET.SubElement(child_textregion, 'TextEquiv') - if extracted_conf_value_merged: - text_subelement.set('conf', f"{extracted_conf_value_merged[indexer]:.2f}") - unicode_textline = ET.SubElement(text_subelement, 'Unicode') - unicode_textline.text = extracted_texts_merged[indexer] - else: - for childtest3 in child_textregion: - if childtest3.tag.endswith("TextEquiv"): - for child_uc in childtest3: - if child_uc.tag.endswith("Unicode"): - if extracted_conf_value_merged: - childtest3.set('conf', f"{extracted_conf_value_merged[indexer]:.2f}") - child_uc.text = extracted_texts_merged[indexer] - - indexer = indexer + 1 - has_textline = True - if has_textline: - if is_textregion_text: - for child4 in nn: - if child4.tag.endswith("TextEquiv"): - for childtr_uc in child4: - if childtr_uc.tag.endswith("Unicode"): - childtr_uc.text = text_by_textregion[indexer_textregion] - else: - unicode_textregion.text = text_by_textregion[indexer_textregion] - indexer_textregion = indexer_textregion + 1 - - ET.register_namespace("",page_ns) - page_tree.write(out_file_ocr, xml_declaration=True, method='xml', encoding="utf-8", default_namespace=None) - - def run( - self, - *, - overwrite: bool = False, - dir_in: Optional[str] = None, - dir_in_bin: Optional[str] = None, - image_filename: Optional[str] = None, - dir_xmls: str, - dir_out_image_text: Optional[str] = None, - dir_out: str, - ): - """ - Run OCR. - - Args: - - dir_in_bin (str): Prediction with RGB and binarized images for selected pages, should not be the default - """ - if dir_in: - ls_imgs = [os.path.join(dir_in, image_filename) - for image_filename in filter(is_image_filename, - os.listdir(dir_in))] - else: - assert image_filename - ls_imgs = [image_filename] - - for img_filename in ls_imgs: - file_stem = Path(img_filename).stem - page_file_in = os.path.join(dir_xmls, file_stem+'.xml') - out_file_ocr = os.path.join(dir_out, file_stem+'.xml') - - if os.path.exists(out_file_ocr): - if overwrite: - self.logger.warning("will overwrite existing output file '%s'", out_file_ocr) - else: - self.logger.warning("will skip input for existing output file '%s'", out_file_ocr) - return - - img = cv2.imread(img_filename) - - page_tree = ET.parse(page_file_in, parser = ET.XMLParser(encoding="utf-8")) - page_ns = etree_namespace_for_element_tag(page_tree.getroot().tag) - - out_image_with_text = None - if dir_out_image_text: - out_image_with_text = os.path.join(dir_out_image_text, file_stem + '.png') - - img_bin = None - if dir_in_bin: - img_bin = cv2.imread(os.path.join(dir_in_bin, file_stem+'.png')) - - - if self.tr_ocr: - result = self.run_trocr( - img=img, - page_tree=page_tree, - page_ns=page_ns, - - tr_ocr_input_height_and_width = 384 - ) - else: - result = self.run_cnn( - img=img, - page_tree=page_tree, - page_ns=page_ns, - - img_bin=img_bin, - image_width=512, - image_height=32, - ) - - self.write_ocr( - result=result, - img=img, - page_tree=page_tree, - page_ns=page_ns, - out_file_ocr=out_file_ocr, - out_image_with_text=out_image_with_text, - ) diff --git a/src/eynollah/image_enhancer.py b/src/eynollah/image_enhancer.py deleted file mode 100644 index babbd55..0000000 --- a/src/eynollah/image_enhancer.py +++ /dev/null @@ -1,712 +0,0 @@ -""" -Image enhancer. The output can be written as same scale of input or in new predicted scale. -""" - -# FIXME: fix all of those... -# pyright: reportUnboundVariable=false -# pyright: reportCallIssue=false -# pyright: reportArgumentType=false - -import logging -import os -import time -from typing import Optional -from pathlib import Path -import gc - -import cv2 -from keras.models import Model -import numpy as np -import tensorflow as tf # type: ignore -from skimage.morphology import skeletonize - -from .model_zoo import EynollahModelZoo -from .utils.resize import resize_image -from .utils.pil_cv2 import pil2cv -from .utils import ( - is_image_filename, - crop_image_inside_box -) - -DPI_THRESHOLD = 298 -KERNEL = np.ones((5, 5), np.uint8) - - -class Enhancer: - def __init__( - self, - *, - model_zoo: EynollahModelZoo, - num_col_upper : Optional[int] = None, - num_col_lower : Optional[int] = None, - save_org_scale : bool = False, - ): - self.input_binary = False - self.save_org_scale = save_org_scale - if num_col_upper: - self.num_col_upper = int(num_col_upper) - else: - self.num_col_upper = num_col_upper - if num_col_lower: - self.num_col_lower = int(num_col_lower) - else: - self.num_col_lower = num_col_lower - - self.logger = logging.getLogger('eynollah.enhance') - self.model_zoo = model_zoo - for v in ['binarization', 'enhancement', 'col_classifier', 'page']: - self.model_zoo.load_model(v) - - try: - for device in tf.config.list_physical_devices('GPU'): - tf.config.experimental.set_memory_growth(device, True) - except: - self.logger.warning("no GPU device available") - - def cache_images(self, image_filename=None, image_pil=None, dpi=None): - ret = {} - if image_filename: - ret['img'] = cv2.imread(image_filename) - self.dpi = 100 - else: - ret['img'] = pil2cv(image_pil) - self.dpi = 100 - ret['img_grayscale'] = cv2.cvtColor(ret['img'], cv2.COLOR_BGR2GRAY) - for prefix in ('', '_grayscale'): - ret[f'img{prefix}_uint8'] = ret[f'img{prefix}'].astype(np.uint8) - self._imgs = ret - if dpi is not None: - self.dpi = dpi - - def reset_file_name_dir(self, image_filename, dir_out): - self.cache_images(image_filename=image_filename) - self.output_filename = os.path.join(dir_out, Path(image_filename).stem +'.png') - - def imread(self, grayscale=False, uint8=True): - key = 'img' - if grayscale: - key += '_grayscale' - if uint8: - key += '_uint8' - return self._imgs[key].copy() - - def predict_enhancement(self, img): - self.logger.debug("enter predict_enhancement") - - img_height_model = self.model_zoo.get('enhancement', Model).layers[-1].output_shape[1] - img_width_model = self.model_zoo.get('enhancement', Model).layers[-1].output_shape[2] - if img.shape[0] < img_height_model: - img = cv2.resize(img, (img.shape[1], img_width_model), interpolation=cv2.INTER_NEAREST) - if img.shape[1] < img_width_model: - img = cv2.resize(img, (img_height_model, img.shape[0]), interpolation=cv2.INTER_NEAREST) - margin = int(0.1 * img_width_model) - width_mid = img_width_model - 2 * margin - height_mid = img_height_model - 2 * margin - img = img / 255. - img_h = img.shape[0] - img_w = img.shape[1] - - prediction_true = np.zeros((img_h, img_w, 3)) - nxf = img_w / float(width_mid) - nyf = img_h / float(height_mid) - nxf = int(nxf) + 1 if nxf > int(nxf) else int(nxf) - nyf = int(nyf) + 1 if nyf > int(nyf) else int(nyf) - - for i in range(nxf): - for j in range(nyf): - if i == 0: - index_x_d = i * width_mid - index_x_u = index_x_d + img_width_model - else: - index_x_d = i * width_mid - index_x_u = index_x_d + img_width_model - if j == 0: - index_y_d = j * height_mid - index_y_u = index_y_d + img_height_model - else: - index_y_d = j * height_mid - index_y_u = index_y_d + img_height_model - - if index_x_u > img_w: - index_x_u = img_w - index_x_d = img_w - img_width_model - if index_y_u > img_h: - index_y_u = img_h - index_y_d = img_h - img_height_model - - img_patch = img[np.newaxis, index_y_d:index_y_u, index_x_d:index_x_u, :] - label_p_pred = self.model_zoo.get('enhancement', Model).predict(img_patch, verbose='0') - seg = label_p_pred[0, :, :, :] * 255 - - if i == 0 and j == 0: - prediction_true[index_y_d + 0:index_y_u - margin, - index_x_d + 0:index_x_u - margin] = \ - seg[0:-margin or None, - 0:-margin or None] - elif i == nxf - 1 and j == nyf - 1: - prediction_true[index_y_d + margin:index_y_u - 0, - index_x_d + margin:index_x_u - 0] = \ - seg[margin:, - margin:] - elif i == 0 and j == nyf - 1: - prediction_true[index_y_d + margin:index_y_u - 0, - index_x_d + 0:index_x_u - margin] = \ - seg[margin:, - 0:-margin or None] - elif i == nxf - 1 and j == 0: - prediction_true[index_y_d + 0:index_y_u - margin, - index_x_d + margin:index_x_u - 0] = \ - seg[0:-margin or None, - margin:] - elif i == 0 and j != 0 and j != nyf - 1: - prediction_true[index_y_d + margin:index_y_u - margin, - index_x_d + 0:index_x_u - margin] = \ - seg[margin:-margin or None, - 0:-margin or None] - elif i == nxf - 1 and j != 0 and j != nyf - 1: - prediction_true[index_y_d + margin:index_y_u - margin, - index_x_d + margin:index_x_u - 0] = \ - seg[margin:-margin or None, - margin:] - elif i != 0 and i != nxf - 1 and j == 0: - prediction_true[index_y_d + 0:index_y_u - margin, - index_x_d + margin:index_x_u - margin] = \ - seg[0:-margin or None, - margin:-margin or None] - elif i != 0 and i != nxf - 1 and j == nyf - 1: - prediction_true[index_y_d + margin:index_y_u - 0, - index_x_d + margin:index_x_u - margin] = \ - seg[margin:, - margin:-margin or None] - else: - prediction_true[index_y_d + margin:index_y_u - margin, - index_x_d + margin:index_x_u - margin] = \ - seg[margin:-margin or None, - margin:-margin or None] - - prediction_true = prediction_true.astype(int) - return prediction_true - - def calculate_width_height_by_columns(self, img, num_col, width_early, label_p_pred): - self.logger.debug("enter calculate_width_height_by_columns") - if num_col == 1: - img_w_new = 2000 - elif num_col == 2: - img_w_new = 2400 - elif num_col == 3: - img_w_new = 3000 - elif num_col == 4: - img_w_new = 4000 - elif num_col == 5: - img_w_new = 5000 - elif num_col == 6: - img_w_new = 6500 - else: - img_w_new = width_early - img_h_new = img_w_new * img.shape[0] // img.shape[1] - - if img_h_new >= 8000: - img_new = np.copy(img) - num_column_is_classified = False - else: - img_new = resize_image(img, img_h_new, img_w_new) - num_column_is_classified = True - - return img_new, num_column_is_classified - - def early_page_for_num_of_column_classification(self,img_bin): - self.logger.debug("enter early_page_for_num_of_column_classification") - if self.input_binary: - img = np.copy(img_bin).astype(np.uint8) - else: - img = self.imread() - img = cv2.GaussianBlur(img, (5, 5), 0) - img_page_prediction = self.do_prediction(False, img, self.model_zoo.get('page')) - - imgray = cv2.cvtColor(img_page_prediction, cv2.COLOR_BGR2GRAY) - _, thresh = cv2.threshold(imgray, 0, 255, 0) - thresh = cv2.dilate(thresh, KERNEL, iterations=3) - contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) - if len(contours)>0: - cnt_size = np.array([cv2.contourArea(contours[j]) - for j in range(len(contours))]) - cnt = contours[np.argmax(cnt_size)] - box = cv2.boundingRect(cnt) - else: - box = [0, 0, img.shape[1], img.shape[0]] - cropped_page, page_coord = crop_image_inside_box(box, img) - - self.logger.debug("exit early_page_for_num_of_column_classification") - return cropped_page, page_coord - - def calculate_width_height_by_columns_1_2(self, img, num_col, width_early, label_p_pred): - self.logger.debug("enter calculate_width_height_by_columns") - if num_col == 1: - img_w_new = 1000 - else: - img_w_new = 1300 - img_h_new = img_w_new * img.shape[0] // img.shape[1] - - if label_p_pred[0][int(num_col - 1)] < 0.9 and img_w_new < width_early: - img_new = np.copy(img) - num_column_is_classified = False - #elif label_p_pred[0][int(num_col - 1)] < 0.8 and img_h_new >= 8000: - elif img_h_new >= 8000: - img_new = np.copy(img) - num_column_is_classified = False - else: - img_new = resize_image(img, img_h_new, img_w_new) - num_column_is_classified = True - - return img_new, num_column_is_classified - - def resize_and_enhance_image_with_column_classifier(self): - self.logger.debug("enter resize_and_enhance_image_with_column_classifier") - dpi = 0#self.dpi - self.logger.info("Detected %s DPI", dpi) - if self.input_binary: - img = self.imread() - prediction_bin = self.do_prediction(True, img, self.model_zoo.get('binarization'), n_batch_inference=5) - prediction_bin = 255 * (prediction_bin[:,:,0]==0) - prediction_bin = np.repeat(prediction_bin[:, :, np.newaxis], 3, axis=2).astype(np.uint8) - img= np.copy(prediction_bin) - img_bin = prediction_bin - else: - img = self.imread() - self.h_org, self.w_org = img.shape[:2] - img_bin = None - - width_early = img.shape[1] - t1 = time.time() - _, page_coord = self.early_page_for_num_of_column_classification(img_bin) - - self.image_page_org_size = img[page_coord[0] : page_coord[1], page_coord[2] : page_coord[3], :] - self.page_coord = page_coord - - if self.num_col_upper and not self.num_col_lower: - num_col = self.num_col_upper - label_p_pred = [np.ones(6)] - elif self.num_col_lower and not self.num_col_upper: - num_col = self.num_col_lower - label_p_pred = [np.ones(6)] - elif not self.num_col_upper and not self.num_col_lower: - if self.input_binary: - img_in = np.copy(img) - img_in = img_in / 255.0 - img_in = cv2.resize(img_in, (448, 448), interpolation=cv2.INTER_NEAREST) - img_in = img_in.reshape(1, 448, 448, 3) - else: - img_1ch = self.imread(grayscale=True) - width_early = img_1ch.shape[1] - img_1ch = img_1ch[page_coord[0] : page_coord[1], page_coord[2] : page_coord[3]] - - img_1ch = img_1ch / 255.0 - img_1ch = cv2.resize(img_1ch, (448, 448), interpolation=cv2.INTER_NEAREST) - img_in = np.zeros((1, img_1ch.shape[0], img_1ch.shape[1], 3)) - img_in[0, :, :, 0] = img_1ch[:, :] - img_in[0, :, :, 1] = img_1ch[:, :] - img_in[0, :, :, 2] = img_1ch[:, :] - - label_p_pred = self.model_zoo.get('col_classifier').predict(img_in, verbose=0) - num_col = np.argmax(label_p_pred[0]) + 1 - elif (self.num_col_upper and self.num_col_lower) and (self.num_col_upper!=self.num_col_lower): - if self.input_binary: - img_in = np.copy(img) - img_in = img_in / 255.0 - img_in = cv2.resize(img_in, (448, 448), interpolation=cv2.INTER_NEAREST) - img_in = img_in.reshape(1, 448, 448, 3) - else: - img_1ch = self.imread(grayscale=True) - width_early = img_1ch.shape[1] - img_1ch = img_1ch[page_coord[0] : page_coord[1], page_coord[2] : page_coord[3]] - - img_1ch = img_1ch / 255.0 - img_1ch = cv2.resize(img_1ch, (448, 448), interpolation=cv2.INTER_NEAREST) - img_in = np.zeros((1, img_1ch.shape[0], img_1ch.shape[1], 3)) - img_in[0, :, :, 0] = img_1ch[:, :] - img_in[0, :, :, 1] = img_1ch[:, :] - img_in[0, :, :, 2] = img_1ch[:, :] - - label_p_pred = self.model_zoo.get('col_classifier').predict(img_in, verbose=0) - num_col = np.argmax(label_p_pred[0]) + 1 - - if num_col > self.num_col_upper: - num_col = self.num_col_upper - label_p_pred = [np.ones(6)] - if num_col < self.num_col_lower: - num_col = self.num_col_lower - label_p_pred = [np.ones(6)] - else: - num_col = self.num_col_upper - label_p_pred = [np.ones(6)] - - self.logger.info("Found %d columns (%s)", num_col, np.around(label_p_pred, decimals=5)) - - if dpi < DPI_THRESHOLD: - if num_col in (1,2): - img_new, num_column_is_classified = self.calculate_width_height_by_columns_1_2( - img, num_col, width_early, label_p_pred) - else: - img_new, num_column_is_classified = self.calculate_width_height_by_columns( - img, num_col, width_early, label_p_pred) - image_res = np.copy(img_new) - is_image_enhanced = True - - else: - num_column_is_classified = True - image_res = np.copy(img) - is_image_enhanced = False - - self.logger.debug("exit resize_and_enhance_image_with_column_classifier") - return is_image_enhanced, img, image_res, num_col, num_column_is_classified, img_bin - def do_prediction( - self, patches, img, model, - n_batch_inference=1, marginal_of_patch_percent=0.1, - thresholding_for_some_classes_in_light_version=False, - thresholding_for_artificial_class_in_light_version=False, thresholding_for_fl_light_version=False, threshold_art_class_textline=0.1): - - self.logger.debug("enter do_prediction") - img_height_model = model.layers[-1].output_shape[1] - img_width_model = model.layers[-1].output_shape[2] - - if not patches: - img_h_page = img.shape[0] - img_w_page = img.shape[1] - img = img / float(255.0) - img = resize_image(img, img_height_model, img_width_model) - - label_p_pred = model.predict(img[np.newaxis], verbose=0) - seg = np.argmax(label_p_pred, axis=3)[0] - - if thresholding_for_artificial_class_in_light_version: - seg_art = label_p_pred[0,:,:,2] - - seg_art[seg_art0] =1 - - skeleton_art = skeletonize(seg_art) - skeleton_art = skeleton_art*1 - - seg[skeleton_art==1]=2 - - if thresholding_for_fl_light_version: - seg_header = label_p_pred[0,:,:,2] - - seg_header[seg_header<0.2] = 0 - seg_header[seg_header>0] =1 - - seg[seg_header==1]=2 - - seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2) - prediction_true = resize_image(seg_color, img_h_page, img_w_page).astype(np.uint8) - return prediction_true - - if img.shape[0] < img_height_model: - img = resize_image(img, img_height_model, img.shape[1]) - if img.shape[1] < img_width_model: - img = resize_image(img, img.shape[0], img_width_model) - - self.logger.debug("Patch size: %sx%s", img_height_model, img_width_model) - margin = int(marginal_of_patch_percent * img_height_model) - width_mid = img_width_model - 2 * margin - height_mid = img_height_model - 2 * margin - img = img / 255. - #img = img.astype(np.float16) - img_h = img.shape[0] - img_w = img.shape[1] - prediction_true = np.zeros((img_h, img_w, 3)) - mask_true = np.zeros((img_h, img_w)) - nxf = img_w / float(width_mid) - nyf = img_h / float(height_mid) - nxf = int(nxf) + 1 if nxf > int(nxf) else int(nxf) - nyf = int(nyf) + 1 if nyf > int(nyf) else int(nyf) - - list_i_s = [] - list_j_s = [] - list_x_u = [] - list_x_d = [] - list_y_u = [] - list_y_d = [] - - batch_indexer = 0 - img_patch = np.zeros((n_batch_inference, img_height_model, img_width_model, 3)) - for i in range(nxf): - for j in range(nyf): - if i == 0: - index_x_d = i * width_mid - index_x_u = index_x_d + img_width_model - else: - index_x_d = i * width_mid - index_x_u = index_x_d + img_width_model - if j == 0: - index_y_d = j * height_mid - index_y_u = index_y_d + img_height_model - else: - index_y_d = j * height_mid - index_y_u = index_y_d + img_height_model - if index_x_u > img_w: - index_x_u = img_w - index_x_d = img_w - img_width_model - if index_y_u > img_h: - index_y_u = img_h - index_y_d = img_h - img_height_model - - list_i_s.append(i) - list_j_s.append(j) - list_x_u.append(index_x_u) - list_x_d.append(index_x_d) - list_y_d.append(index_y_d) - list_y_u.append(index_y_u) - - img_patch[batch_indexer,:,:,:] = img[index_y_d:index_y_u, index_x_d:index_x_u, :] - batch_indexer += 1 - - if (batch_indexer == n_batch_inference or - # last batch - i == nxf - 1 and j == nyf - 1): - self.logger.debug("predicting patches on %s", str(img_patch.shape)) - label_p_pred = model.predict(img_patch, verbose=0) - seg = np.argmax(label_p_pred, axis=3) - - if thresholding_for_some_classes_in_light_version: - seg_not_base = label_p_pred[:,:,:,4] - seg_not_base[seg_not_base>0.03] =1 - seg_not_base[seg_not_base<1] =0 - - seg_line = label_p_pred[:,:,:,3] - seg_line[seg_line>0.1] =1 - seg_line[seg_line<1] =0 - - seg_background = label_p_pred[:,:,:,0] - seg_background[seg_background>0.25] =1 - seg_background[seg_background<1] =0 - - seg[seg_not_base==1]=4 - seg[seg_background==1]=0 - seg[(seg_line==1) & (seg==0)]=3 - if thresholding_for_artificial_class_in_light_version: - seg_art = label_p_pred[:,:,:,2] - - seg_art[seg_art0] =1 - - ##seg[seg_art==1]=2 - - indexer_inside_batch = 0 - for i_batch, j_batch in zip(list_i_s, list_j_s): - seg_in = seg[indexer_inside_batch] - - if thresholding_for_artificial_class_in_light_version: - seg_in_art = seg_art[indexer_inside_batch] - - index_y_u_in = list_y_u[indexer_inside_batch] - index_y_d_in = list_y_d[indexer_inside_batch] - - index_x_u_in = list_x_u[indexer_inside_batch] - index_x_d_in = list_x_d[indexer_inside_batch] - - if i_batch == 0 and j_batch == 0: - prediction_true[index_y_d_in + 0:index_y_u_in - margin, - index_x_d_in + 0:index_x_u_in - margin] = \ - seg_in[0:-margin or None, - 0:-margin or None, - np.newaxis] - if thresholding_for_artificial_class_in_light_version: - prediction_true[index_y_d_in + 0:index_y_u_in - margin, - index_x_d_in + 0:index_x_u_in - margin, 1] = \ - seg_in_art[0:-margin or None, - 0:-margin or None] - - elif i_batch == nxf - 1 and j_batch == nyf - 1: - prediction_true[index_y_d_in + margin:index_y_u_in - 0, - index_x_d_in + margin:index_x_u_in - 0] = \ - seg_in[margin:, - margin:, - np.newaxis] - if thresholding_for_artificial_class_in_light_version: - prediction_true[index_y_d_in + margin:index_y_u_in - 0, - index_x_d_in + margin:index_x_u_in - 0, 1] = \ - seg_in_art[margin:, - margin:] - - elif i_batch == 0 and j_batch == nyf - 1: - prediction_true[index_y_d_in + margin:index_y_u_in - 0, - index_x_d_in + 0:index_x_u_in - margin] = \ - seg_in[margin:, - 0:-margin or None, - np.newaxis] - if thresholding_for_artificial_class_in_light_version: - prediction_true[index_y_d_in + margin:index_y_u_in - 0, - index_x_d_in + 0:index_x_u_in - margin, 1] = \ - seg_in_art[margin:, - 0:-margin or None] - - elif i_batch == nxf - 1 and j_batch == 0: - prediction_true[index_y_d_in + 0:index_y_u_in - margin, - index_x_d_in + margin:index_x_u_in - 0] = \ - seg_in[0:-margin or None, - margin:, - np.newaxis] - if thresholding_for_artificial_class_in_light_version: - prediction_true[index_y_d_in + 0:index_y_u_in - margin, - index_x_d_in + margin:index_x_u_in - 0, 1] = \ - seg_in_art[0:-margin or None, - margin:] - - elif i_batch == 0 and j_batch != 0 and j_batch != nyf - 1: - prediction_true[index_y_d_in + margin:index_y_u_in - margin, - index_x_d_in + 0:index_x_u_in - margin] = \ - seg_in[margin:-margin or None, - 0:-margin or None, - np.newaxis] - if thresholding_for_artificial_class_in_light_version: - prediction_true[index_y_d_in + margin:index_y_u_in - margin, - index_x_d_in + 0:index_x_u_in - margin, 1] = \ - seg_in_art[margin:-margin or None, - 0:-margin or None] - - elif i_batch == nxf - 1 and j_batch != 0 and j_batch != nyf - 1: - prediction_true[index_y_d_in + margin:index_y_u_in - margin, - index_x_d_in + margin:index_x_u_in - 0] = \ - seg_in[margin:-margin or None, - margin:, - np.newaxis] - if thresholding_for_artificial_class_in_light_version: - prediction_true[index_y_d_in + margin:index_y_u_in - margin, - index_x_d_in + margin:index_x_u_in - 0, 1] = \ - seg_in_art[margin:-margin or None, - margin:] - - elif i_batch != 0 and i_batch != nxf - 1 and j_batch == 0: - prediction_true[index_y_d_in + 0:index_y_u_in - margin, - index_x_d_in + margin:index_x_u_in - margin] = \ - seg_in[0:-margin or None, - margin:-margin or None, - np.newaxis] - if thresholding_for_artificial_class_in_light_version: - prediction_true[index_y_d_in + 0:index_y_u_in - margin, - index_x_d_in + margin:index_x_u_in - margin, 1] = \ - seg_in_art[0:-margin or None, - margin:-margin or None] - - elif i_batch != 0 and i_batch != nxf - 1 and j_batch == nyf - 1: - prediction_true[index_y_d_in + margin:index_y_u_in - 0, - index_x_d_in + margin:index_x_u_in - margin] = \ - seg_in[margin:, - margin:-margin or None, - np.newaxis] - if thresholding_for_artificial_class_in_light_version: - prediction_true[index_y_d_in + margin:index_y_u_in - 0, - index_x_d_in + margin:index_x_u_in - margin, 1] = \ - seg_in_art[margin:, - margin:-margin or None] - - else: - prediction_true[index_y_d_in + margin:index_y_u_in - margin, - index_x_d_in + margin:index_x_u_in - margin] = \ - seg_in[margin:-margin or None, - margin:-margin or None, - np.newaxis] - if thresholding_for_artificial_class_in_light_version: - prediction_true[index_y_d_in + margin:index_y_u_in - margin, - index_x_d_in + margin:index_x_u_in - margin, 1] = \ - seg_in_art[margin:-margin or None, - margin:-margin or None] - indexer_inside_batch += 1 - - - list_i_s = [] - list_j_s = [] - list_x_u = [] - list_x_d = [] - list_y_u = [] - list_y_d = [] - - batch_indexer = 0 - img_patch[:] = 0 - - prediction_true = prediction_true.astype(np.uint8) - - if thresholding_for_artificial_class_in_light_version: - kernel_min = np.ones((3, 3), np.uint8) - prediction_true[:,:,0][prediction_true[:,:,0]==2] = 0 - - skeleton_art = skeletonize(prediction_true[:,:,1]) - skeleton_art = skeleton_art*1 - - skeleton_art = skeleton_art.astype('uint8') - - skeleton_art = cv2.dilate(skeleton_art, kernel_min, iterations=1) - - prediction_true[:,:,0][skeleton_art==1]=2 - #del model - gc.collect() - return prediction_true - - def run_enhancement(self): - t_in = time.time() - self.logger.info("Resizing and enhancing image...") - is_image_enhanced, img_org, img_res, num_col_classifier, num_column_is_classified, img_bin = \ - self.resize_and_enhance_image_with_column_classifier() - - self.logger.info("Image was %senhanced.", '' if is_image_enhanced else 'not ') - return img_res, is_image_enhanced, num_col_classifier, num_column_is_classified - - - def run_single(self): - t0 = time.time() - img_res, is_image_enhanced, num_col_classifier, num_column_is_classified = self.run_enhancement() - - return img_res, is_image_enhanced - - - def run(self, - overwrite: bool = False, - image_filename: Optional[str] = None, - dir_in: Optional[str] = None, - dir_out: Optional[str] = None, - ): - """ - Get image and scales, then extract the page of scanned image - """ - self.logger.debug("enter run") - t0_tot = time.time() - - if dir_in: - ls_imgs = [os.path.join(dir_in, image_filename) - for image_filename in filter(is_image_filename, - os.listdir(dir_in))] - elif image_filename: - ls_imgs = [image_filename] - else: - raise ValueError("run requires either a single image filename or a directory") - - for img_filename in ls_imgs: - self.logger.info(img_filename) - t0 = time.time() - - self.reset_file_name_dir(img_filename, dir_out) - #print("text region early -11 in %.1fs", time.time() - t0) - - if os.path.exists(self.output_filename): - if overwrite: - self.logger.warning("will overwrite existing output file '%s'", self.output_filename) - else: - self.logger.warning("will skip input for existing output file '%s'", self.output_filename) - continue - - did_resize = False - image_enhanced, did_enhance = self.run_single() - if self.save_org_scale: - image_enhanced = resize_image(image_enhanced, self.h_org, self.w_org) - did_resize = True - - self.logger.info( - "Image %s was %senhanced%s.", - img_filename, - '' if did_enhance else 'not ', - 'and resized' if did_resize else '' - ) - - cv2.imwrite(self.output_filename, image_enhanced) - diff --git a/src/eynollah/mb_ro_on_layout.py b/src/eynollah/mb_ro_on_layout.py deleted file mode 100644 index eec544c..0000000 --- a/src/eynollah/mb_ro_on_layout.py +++ /dev/null @@ -1,804 +0,0 @@ -""" -Machine learning based reading order detection -""" - -# pyright: reportCallIssue=false -# pyright: reportUnboundVariable=false -# pyright: reportArgumentType=false - -import logging -import os -import time -from typing import Optional -from pathlib import Path -import xml.etree.ElementTree as ET - -import cv2 -from keras.models import Model -import numpy as np -import statistics -import tensorflow as tf - -from .model_zoo import EynollahModelZoo -from .utils.resize import resize_image -from .utils.contour import ( - find_new_features_of_contours, - return_contours_of_image, - return_parent_contours, -) -from .utils import is_xml_filename - -DPI_THRESHOLD = 298 -KERNEL = np.ones((5, 5), np.uint8) - - -class machine_based_reading_order_on_layout: - def __init__( - self, - *, - model_zoo: EynollahModelZoo, - logger : Optional[logging.Logger] = None, - ): - self.logger = logger or logging.getLogger('eynollah.mbreorder') - self.model_zoo = model_zoo - - try: - for device in tf.config.list_physical_devices('GPU'): - tf.config.experimental.set_memory_growth(device, True) - except: - self.logger.warning("no GPU device available") - - self.model_zoo.load_model('reading_order') - - def read_xml(self, xml_file): - tree1 = ET.parse(xml_file, parser = ET.XMLParser(encoding='utf-8')) - root1=tree1.getroot() - alltags=[elem.tag for elem in root1.iter()] - link=alltags[0].split('}')[0]+'}' - - index_tot_regions = [] - tot_region_ref = [] - - y_len, x_len = 0, 0 - for jj in root1.iter(link+'Page'): - y_len=int(jj.attrib['imageHeight']) - x_len=int(jj.attrib['imageWidth']) - - for jj in root1.iter(link+'RegionRefIndexed'): - index_tot_regions.append(jj.attrib['index']) - tot_region_ref.append(jj.attrib['regionRef']) - - if (link+'PrintSpace' in alltags) or (link+'Border' in alltags): - co_printspace = [] - if link+'PrintSpace' in alltags: - region_tags_printspace = np.unique([x for x in alltags if x.endswith('PrintSpace')]) - else: - region_tags_printspace = np.unique([x for x in alltags if x.endswith('Border')]) - - for tag in region_tags_printspace: - if link+'PrintSpace' in alltags: - tag_endings_printspace = ['}PrintSpace','}printspace'] - else: - tag_endings_printspace = ['}Border','}border'] - - if tag.endswith(tag_endings_printspace[0]) or tag.endswith(tag_endings_printspace[1]): - for nn in root1.iter(tag): - c_t_in = [] - sumi = 0 - for vv in nn.iter(): - # check the format of coords - if vv.tag == link + 'Coords': - coords = bool(vv.attrib) - if coords: - p_h = vv.attrib['points'].split(' ') - c_t_in.append( - np.array([[int(x.split(',')[0]), int(x.split(',')[1])] for x in p_h])) - break - else: - pass - - if vv.tag == link + 'Point': - c_t_in.append([int(float(vv.attrib['x'])), int(float(vv.attrib['y']))]) - sumi += 1 - elif vv.tag != link + 'Point' and sumi >= 1: - break - co_printspace.append(np.array(c_t_in)) - img_printspace = np.zeros( (y_len,x_len,3) ) - img_printspace=cv2.fillPoly(img_printspace, pts =co_printspace, color=(1,1,1)) - img_printspace = img_printspace.astype(np.uint8) - - imgray = cv2.cvtColor(img_printspace, cv2.COLOR_BGR2GRAY) - _, thresh = cv2.threshold(imgray, 0, 255, 0) - contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) - cnt_size = np.array([cv2.contourArea(contours[j]) for j in range(len(contours))]) - cnt = contours[np.argmax(cnt_size)] - x, y, w, h = cv2.boundingRect(cnt) - - bb_coord_printspace = [x, y, w, h] - - else: - bb_coord_printspace = None - - - region_tags=np.unique([x for x in alltags if x.endswith('Region')]) - co_text_paragraph=[] - co_text_drop=[] - co_text_heading=[] - co_text_header=[] - co_text_marginalia=[] - co_text_catch=[] - co_text_page_number=[] - co_text_signature_mark=[] - co_sep=[] - co_img=[] - co_table=[] - co_graphic=[] - co_graphic_text_annotation=[] - co_graphic_decoration=[] - co_noise=[] - - co_text_paragraph_text=[] - co_text_drop_text=[] - co_text_heading_text=[] - co_text_header_text=[] - co_text_marginalia_text=[] - co_text_catch_text=[] - co_text_page_number_text=[] - co_text_signature_mark_text=[] - co_sep_text=[] - co_img_text=[] - co_table_text=[] - co_graphic_text=[] - co_graphic_text_annotation_text=[] - co_graphic_decoration_text=[] - co_noise_text=[] - - id_paragraph = [] - id_header = [] - id_heading = [] - id_marginalia = [] - - for tag in region_tags: - if tag.endswith('}TextRegion') or tag.endswith('}Textregion'): - for nn in root1.iter(tag): - for child2 in nn: - tag2 = child2.tag - if tag2.endswith('}TextEquiv') or tag2.endswith('}TextEquiv'): - for childtext2 in child2: - if childtext2.tag.endswith('}Unicode') or childtext2.tag.endswith('}Unicode'): - if "type" in nn.attrib and nn.attrib['type']=='drop-capital': - co_text_drop_text.append(childtext2.text) - elif "type" in nn.attrib and nn.attrib['type']=='heading': - co_text_heading_text.append(childtext2.text) - elif "type" in nn.attrib and nn.attrib['type']=='signature-mark': - co_text_signature_mark_text.append(childtext2.text) - elif "type" in nn.attrib and nn.attrib['type']=='header': - co_text_header_text.append(childtext2.text) - ###elif "type" in nn.attrib and nn.attrib['type']=='catch-word': - ###co_text_catch_text.append(childtext2.text) - ###elif "type" in nn.attrib and nn.attrib['type']=='page-number': - ###co_text_page_number_text.append(childtext2.text) - elif "type" in nn.attrib and nn.attrib['type']=='marginalia': - co_text_marginalia_text.append(childtext2.text) - else: - co_text_paragraph_text.append(childtext2.text) - c_t_in_drop=[] - c_t_in_paragraph=[] - c_t_in_heading=[] - c_t_in_header=[] - c_t_in_page_number=[] - c_t_in_signature_mark=[] - c_t_in_catch=[] - c_t_in_marginalia=[] - - - sumi=0 - for vv in nn.iter(): - # check the format of coords - if vv.tag==link+'Coords': - - coords=bool(vv.attrib) - if coords: - #print('birda1') - p_h=vv.attrib['points'].split(' ') - - - - if "type" in nn.attrib and nn.attrib['type']=='drop-capital': - - c_t_in_drop.append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) ) - - elif "type" in nn.attrib and nn.attrib['type']=='heading': - ##id_heading.append(nn.attrib['id']) - c_t_in_heading.append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) ) - - - elif "type" in nn.attrib and nn.attrib['type']=='signature-mark': - - c_t_in_signature_mark.append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) ) - #print(c_t_in_paragraph) - elif "type" in nn.attrib and nn.attrib['type']=='header': - #id_header.append(nn.attrib['id']) - c_t_in_header.append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) ) - - - ###elif "type" in nn.attrib and nn.attrib['type']=='catch-word': - ###c_t_in_catch.append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) ) - - - ###elif "type" in nn.attrib and nn.attrib['type']=='page-number': - - ###c_t_in_page_number.append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) ) - - elif "type" in nn.attrib and nn.attrib['type']=='marginalia': - #id_marginalia.append(nn.attrib['id']) - - c_t_in_marginalia.append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) ) - else: - #id_paragraph.append(nn.attrib['id']) - - c_t_in_paragraph.append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) ) - - break - else: - pass - - - if vv.tag==link+'Point': - if "type" in nn.attrib and nn.attrib['type']=='drop-capital': - - c_t_in_drop.append([ int(float(vv.attrib['x'])) , int(float(vv.attrib['y'])) ]) - sumi+=1 - - elif "type" in nn.attrib and nn.attrib['type']=='heading': - #id_heading.append(nn.attrib['id']) - c_t_in_heading.append([ int(float(vv.attrib['x'])) , int(float(vv.attrib['y'])) ]) - sumi+=1 - - - elif "type" in nn.attrib and nn.attrib['type']=='signature-mark': - - c_t_in_signature_mark.append([ int(float(vv.attrib['x'])) , int(float(vv.attrib['y'])) ]) - sumi+=1 - elif "type" in nn.attrib and nn.attrib['type']=='header': - #id_header.append(nn.attrib['id']) - c_t_in_header.append([ int(float(vv.attrib['x'])) , int(float(vv.attrib['y'])) ]) - sumi+=1 - - - ###elif "type" in nn.attrib and nn.attrib['type']=='catch-word': - ###c_t_in_catch.append([ int(float(vv.attrib['x'])) , int(float(vv.attrib['y'])) ]) - ###sumi+=1 - - ###elif "type" in nn.attrib and nn.attrib['type']=='page-number': - - ###c_t_in_page_number.append([ int(float(vv.attrib['x'])) , int(float(vv.attrib['y'])) ]) - ###sumi+=1 - - elif "type" in nn.attrib and nn.attrib['type']=='marginalia': - #id_marginalia.append(nn.attrib['id']) - - c_t_in_marginalia.append([ int(float(vv.attrib['x'])) , int(float(vv.attrib['y'])) ]) - sumi+=1 - - else: - #id_paragraph.append(nn.attrib['id']) - c_t_in_paragraph.append([ int(float(vv.attrib['x'])) , int(float(vv.attrib['y'])) ]) - sumi+=1 - - elif vv.tag!=link+'Point' and sumi>=1: - break - - if len(c_t_in_drop)>0: - co_text_drop.append(np.array(c_t_in_drop)) - if len(c_t_in_paragraph)>0: - co_text_paragraph.append(np.array(c_t_in_paragraph)) - id_paragraph.append(nn.attrib['id']) - if len(c_t_in_heading)>0: - co_text_heading.append(np.array(c_t_in_heading)) - id_heading.append(nn.attrib['id']) - - if len(c_t_in_header)>0: - co_text_header.append(np.array(c_t_in_header)) - id_header.append(nn.attrib['id']) - if len(c_t_in_page_number)>0: - co_text_page_number.append(np.array(c_t_in_page_number)) - if len(c_t_in_catch)>0: - co_text_catch.append(np.array(c_t_in_catch)) - - if len(c_t_in_signature_mark)>0: - co_text_signature_mark.append(np.array(c_t_in_signature_mark)) - - if len(c_t_in_marginalia)>0: - co_text_marginalia.append(np.array(c_t_in_marginalia)) - id_marginalia.append(nn.attrib['id']) - - - elif tag.endswith('}GraphicRegion') or tag.endswith('}graphicregion'): - for nn in root1.iter(tag): - c_t_in=[] - c_t_in_text_annotation=[] - c_t_in_decoration=[] - sumi=0 - for vv in nn.iter(): - # check the format of coords - if vv.tag==link+'Coords': - coords=bool(vv.attrib) - if coords: - p_h=vv.attrib['points'].split(' ') - - if "type" in nn.attrib and nn.attrib['type']=='handwritten-annotation': - c_t_in_text_annotation.append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) ) - - elif "type" in nn.attrib and nn.attrib['type']=='decoration': - c_t_in_decoration.append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) ) - - else: - c_t_in.append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) ) - - - break - else: - pass - - - if vv.tag==link+'Point': - if "type" in nn.attrib and nn.attrib['type']=='handwritten-annotation': - c_t_in_text_annotation.append([ int(float(vv.attrib['x'])) , int(float(vv.attrib['y'])) ]) - sumi+=1 - - elif "type" in nn.attrib and nn.attrib['type']=='decoration': - c_t_in_decoration.append([ int(float(vv.attrib['x'])) , int(float(vv.attrib['y'])) ]) - sumi+=1 - - else: - c_t_in.append([ int(float(vv.attrib['x'])) , int(float(vv.attrib['y'])) ]) - sumi+=1 - - if len(c_t_in_text_annotation)>0: - co_graphic_text_annotation.append(np.array(c_t_in_text_annotation)) - if len(c_t_in_decoration)>0: - co_graphic_decoration.append(np.array(c_t_in_decoration)) - if len(c_t_in)>0: - co_graphic.append(np.array(c_t_in)) - - - - elif tag.endswith('}ImageRegion') or tag.endswith('}imageregion'): - for nn in root1.iter(tag): - c_t_in=[] - sumi=0 - for vv in nn.iter(): - # check the format of coords - if vv.tag==link+'Coords': - coords=bool(vv.attrib) - if coords: - p_h=vv.attrib['points'].split(' ') - c_t_in.append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) ) - break - else: - pass - - - if vv.tag==link+'Point': - c_t_in.append([ int(float(vv.attrib['x'])) , int(float(vv.attrib['y'])) ]) - sumi+=1 - elif vv.tag!=link+'Point' and sumi>=1: - break - co_img.append(np.array(c_t_in)) - co_img_text.append(' ') - - - elif tag.endswith('}SeparatorRegion') or tag.endswith('}separatorregion'): - for nn in root1.iter(tag): - c_t_in=[] - sumi=0 - for vv in nn.iter(): - # check the format of coords - if vv.tag==link+'Coords': - coords=bool(vv.attrib) - if coords: - p_h=vv.attrib['points'].split(' ') - c_t_in.append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) ) - break - else: - pass - - - if vv.tag==link+'Point': - c_t_in.append([ int(float(vv.attrib['x'])) , int(float(vv.attrib['y'])) ]) - sumi+=1 - elif vv.tag!=link+'Point' and sumi>=1: - break - co_sep.append(np.array(c_t_in)) - - - - elif tag.endswith('}TableRegion') or tag.endswith('}tableregion'): - for nn in root1.iter(tag): - c_t_in=[] - sumi=0 - for vv in nn.iter(): - # check the format of coords - if vv.tag==link+'Coords': - coords=bool(vv.attrib) - if coords: - p_h=vv.attrib['points'].split(' ') - c_t_in.append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) ) - break - else: - pass - - - if vv.tag==link+'Point': - c_t_in.append([ int(float(vv.attrib['x'])) , int(float(vv.attrib['y'])) ]) - sumi+=1 - - elif vv.tag!=link+'Point' and sumi>=1: - break - co_table.append(np.array(c_t_in)) - co_table_text.append(' ') - - elif tag.endswith('}NoiseRegion') or tag.endswith('}noiseregion'): - for nn in root1.iter(tag): - c_t_in=[] - sumi=0 - for vv in nn.iter(): - # check the format of coords - if vv.tag==link+'Coords': - coords=bool(vv.attrib) - if coords: - p_h=vv.attrib['points'].split(' ') - c_t_in.append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) ) - break - else: - pass - - - if vv.tag==link+'Point': - c_t_in.append([ int(float(vv.attrib['x'])) , int(float(vv.attrib['y'])) ]) - sumi+=1 - - elif vv.tag!=link+'Point' and sumi>=1: - break - co_noise.append(np.array(c_t_in)) - co_noise_text.append(' ') - - img = np.zeros( (y_len,x_len,3) ) - img_poly=cv2.fillPoly(img, pts =co_text_paragraph, color=(1,1,1)) - - img_poly=cv2.fillPoly(img, pts =co_text_heading, color=(2,2,2)) - img_poly=cv2.fillPoly(img, pts =co_text_header, color=(2,2,2)) - img_poly=cv2.fillPoly(img, pts =co_text_marginalia, color=(3,3,3)) - img_poly=cv2.fillPoly(img, pts =co_img, color=(4,4,4)) - img_poly=cv2.fillPoly(img, pts =co_sep, color=(5,5,5)) - - return tree1, root1, bb_coord_printspace, id_paragraph, id_header+id_heading, co_text_paragraph, co_text_header+co_text_heading,\ - tot_region_ref,x_len, y_len,index_tot_regions, img_poly - - def return_indexes_of_contours_loctaed_inside_another_list_of_contours(self, contours, contours_loc, cx_main_loc, cy_main_loc, indexes_loc): - indexes_of_located_cont = [] - center_x_coordinates_of_located = [] - center_y_coordinates_of_located = [] - #M_main_tot = [cv2.moments(contours_loc[j]) - #for j in range(len(contours_loc))] - #cx_main_loc = [(M_main_tot[j]["m10"] / (M_main_tot[j]["m00"] + 1e-32)) for j in range(len(M_main_tot))] - #cy_main_loc = [(M_main_tot[j]["m01"] / (M_main_tot[j]["m00"] + 1e-32)) for j in range(len(M_main_tot))] - - for ij in range(len(contours)): - results = [cv2.pointPolygonTest(contours[ij], (cx_main_loc[ind], cy_main_loc[ind]), False) - for ind in range(len(cy_main_loc)) ] - results = np.array(results) - indexes_in = np.where((results == 0) | (results == 1)) - indexes = indexes_loc[indexes_in]# [(results == 0) | (results == 1)]#np.where((results == 0) | (results == 1)) - - indexes_of_located_cont.append(indexes) - center_x_coordinates_of_located.append(np.array(cx_main_loc)[indexes_in] ) - center_y_coordinates_of_located.append(np.array(cy_main_loc)[indexes_in] ) - - return indexes_of_located_cont, center_x_coordinates_of_located, center_y_coordinates_of_located - - def do_order_of_regions_with_model(self, contours_only_text_parent, contours_only_text_parent_h, text_regions_p): - height1 =672#448 - width1 = 448#224 - - height2 =672#448 - width2= 448#224 - - height3 =672#448 - width3 = 448#224 - - inference_bs = 3 - - ver_kernel = np.ones((5, 1), dtype=np.uint8) - hor_kernel = np.ones((1, 5), dtype=np.uint8) - - - min_cont_size_to_be_dilated = 10 - if len(contours_only_text_parent)>min_cont_size_to_be_dilated: - cx_conts, cy_conts, x_min_conts, x_max_conts, y_min_conts, y_max_conts, _ = find_new_features_of_contours(contours_only_text_parent) - args_cont_located = np.array(range(len(contours_only_text_parent))) - - diff_y_conts = np.abs(y_max_conts[:]-y_min_conts) - diff_x_conts = np.abs(x_max_conts[:]-x_min_conts) - - mean_x = statistics.mean(diff_x_conts) - median_x = statistics.median(diff_x_conts) - - - diff_x_ratio= diff_x_conts/mean_x - - args_cont_located_excluded = args_cont_located[diff_x_ratio>=1.3] - args_cont_located_included = args_cont_located[diff_x_ratio<1.3] - - contours_only_text_parent_excluded = [contours_only_text_parent[ind] for ind in range(len(contours_only_text_parent)) if diff_x_ratio[ind]>=1.3]#contours_only_text_parent[diff_x_ratio>=1.3] - contours_only_text_parent_included = [contours_only_text_parent[ind] for ind in range(len(contours_only_text_parent)) if diff_x_ratio[ind]<1.3]#contours_only_text_parent[diff_x_ratio<1.3] - - - cx_conts_excluded = [cx_conts[ind] for ind in range(len(cx_conts)) if diff_x_ratio[ind]>=1.3]#cx_conts[diff_x_ratio>=1.3] - cx_conts_included = [cx_conts[ind] for ind in range(len(cx_conts)) if diff_x_ratio[ind]<1.3]#cx_conts[diff_x_ratio<1.3] - - cy_conts_excluded = [cy_conts[ind] for ind in range(len(cy_conts)) if diff_x_ratio[ind]>=1.3]#cy_conts[diff_x_ratio>=1.3] - cy_conts_included = [cy_conts[ind] for ind in range(len(cy_conts)) if diff_x_ratio[ind]<1.3]#cy_conts[diff_x_ratio<1.3] - - #print(diff_x_ratio, 'ratio') - text_regions_p = text_regions_p.astype('uint8') - - if len(contours_only_text_parent_excluded)>0: - textregion_par = np.zeros((text_regions_p.shape[0], text_regions_p.shape[1])).astype('uint8') - textregion_par = cv2.fillPoly(textregion_par, pts=contours_only_text_parent_included, color=(1,1)) - else: - textregion_par = (text_regions_p[:,:]==1)*1 - textregion_par = textregion_par.astype('uint8') - - text_regions_p_textregions_dilated = cv2.erode(textregion_par , hor_kernel, iterations=2) - text_regions_p_textregions_dilated = cv2.dilate(text_regions_p_textregions_dilated , ver_kernel, iterations=4) - text_regions_p_textregions_dilated = cv2.erode(text_regions_p_textregions_dilated , hor_kernel, iterations=1) - text_regions_p_textregions_dilated = cv2.dilate(text_regions_p_textregions_dilated , ver_kernel, iterations=5) - text_regions_p_textregions_dilated[text_regions_p[:,:]>1] = 0 - - - contours_only_dilated, hir_on_text_dilated = return_contours_of_image(text_regions_p_textregions_dilated) - contours_only_dilated = return_parent_contours(contours_only_dilated, hir_on_text_dilated) - - indexes_of_located_cont, center_x_coordinates_of_located, center_y_coordinates_of_located = self.return_indexes_of_contours_loctaed_inside_another_list_of_contours(contours_only_dilated, contours_only_text_parent_included, cx_conts_included, cy_conts_included, args_cont_located_included) - - - if len(args_cont_located_excluded)>0: - for ind in args_cont_located_excluded: - indexes_of_located_cont.append(np.array([ind])) - contours_only_dilated.append(contours_only_text_parent[ind]) - center_y_coordinates_of_located.append(0) - - array_list = [np.array([elem]) if isinstance(elem, int) else elem for elem in indexes_of_located_cont] - flattened_array = np.concatenate([arr.ravel() for arr in array_list]) - #print(len( np.unique(flattened_array)), 'indexes_of_located_cont uniques') - - missing_textregions = list( set(np.array(range(len(contours_only_text_parent))) ) - set(np.unique(flattened_array)) ) - #print(missing_textregions, 'missing_textregions') - - for ind in missing_textregions: - indexes_of_located_cont.append(np.array([ind])) - contours_only_dilated.append(contours_only_text_parent[ind]) - center_y_coordinates_of_located.append(0) - - - if contours_only_text_parent_h: - for vi in range(len(contours_only_text_parent_h)): - indexes_of_located_cont.append(int(vi+len(contours_only_text_parent))) - - array_list = [np.array([elem]) if isinstance(elem, int) else elem for elem in indexes_of_located_cont] - flattened_array = np.concatenate([arr.ravel() for arr in array_list]) - - y_len = text_regions_p.shape[0] - x_len = text_regions_p.shape[1] - - img_poly = np.zeros((y_len,x_len), dtype='uint8') - ###img_poly[text_regions_p[:,:]==1] = 1 - ###img_poly[text_regions_p[:,:]==2] = 2 - ###img_poly[text_regions_p[:,:]==3] = 4 - ###img_poly[text_regions_p[:,:]==6] = 5 - - ##img_poly[text_regions_p[:,:]==1] = 1 - ##img_poly[text_regions_p[:,:]==2] = 2 - ##img_poly[text_regions_p[:,:]==3] = 3 - ##img_poly[text_regions_p[:,:]==4] = 4 - ##img_poly[text_regions_p[:,:]==5] = 5 - - img_poly = np.copy(text_regions_p) - - img_header_and_sep = np.zeros((y_len,x_len), dtype='uint8') - if contours_only_text_parent_h: - _, cy_main, x_min_main, x_max_main, y_min_main, y_max_main, _ = find_new_features_of_contours( - contours_only_text_parent_h) - for j in range(len(cy_main)): - img_header_and_sep[int(y_max_main[j]):int(y_max_main[j])+12, - int(x_min_main[j]):int(x_max_main[j])] = 1 - co_text_all_org = contours_only_text_parent + contours_only_text_parent_h - if len(contours_only_text_parent)>min_cont_size_to_be_dilated: - co_text_all = contours_only_dilated + contours_only_text_parent_h - else: - co_text_all = contours_only_text_parent + contours_only_text_parent_h - else: - co_text_all_org = contours_only_text_parent - if len(contours_only_text_parent)>min_cont_size_to_be_dilated: - co_text_all = contours_only_dilated - else: - co_text_all = contours_only_text_parent - - if not len(co_text_all): - return [], [] - - labels_con = np.zeros((int(y_len /6.), int(x_len/6.), len(co_text_all)), dtype=bool) - - co_text_all = [(i/6).astype(int) for i in co_text_all] - for i in range(len(co_text_all)): - img = labels_con[:,:,i].astype(np.uint8) - - #img = cv2.resize(img, (int(img.shape[1]/6), int(img.shape[0]/6)), interpolation=cv2.INTER_NEAREST) - - cv2.fillPoly(img, pts=[co_text_all[i]], color=(1,)) - labels_con[:,:,i] = img - - - labels_con = resize_image(labels_con.astype(np.uint8), height1, width1).astype(bool) - img_header_and_sep = resize_image(img_header_and_sep, height1, width1) - img_poly = resize_image(img_poly, height3, width3) - - - - input_1 = np.zeros((inference_bs, height1, width1, 3)) - ordered = [list(range(len(co_text_all)))] - index_update = 0 - #print(labels_con.shape[2],"number of regions for reading order") - while index_update>=0: - ij_list = ordered.pop(index_update) - i = ij_list.pop(0) - - ante_list = [] - post_list = [] - tot_counter = 0 - batch = [] - for j in ij_list: - img1 = labels_con[:,:,i].astype(float) - img2 = labels_con[:,:,j].astype(float) - img1[img_poly==5] = 2 - img2[img_poly==5] = 2 - img1[img_header_and_sep==1] = 3 - img2[img_header_and_sep==1] = 3 - - input_1[len(batch), :, :, 0] = img1 / 3. - input_1[len(batch), :, :, 2] = img2 / 3. - input_1[len(batch), :, :, 1] = img_poly / 5. - - tot_counter += 1 - batch.append(j) - if tot_counter % inference_bs == 0 or tot_counter == len(ij_list): - y_pr = self.model_zoo.get('reading_order', Model).predict(input_1 , verbose='0') - for jb, j in enumerate(batch): - if y_pr[jb][0]>=0.5: - post_list.append(j) - else: - ante_list.append(j) - batch = [] - - if len(ante_list): - ordered.insert(index_update, ante_list) - index_update += 1 - ordered.insert(index_update, [i]) - if len(post_list): - ordered.insert(index_update + 1, post_list) - - index_update = -1 - for index_next, ij_list in enumerate(ordered): - if len(ij_list) > 1: - index_update = index_next - break - - ordered = [i[0] for i in ordered] - - ##id_all_text = np.array(id_all_text)[index_sort] - - - if len(contours_only_text_parent)>min_cont_size_to_be_dilated: - org_contours_indexes = [] - for ind in range(len(ordered)): - region_with_curr_order = ordered[ind] - if region_with_curr_order < len(contours_only_dilated): - if np.isscalar(indexes_of_located_cont[region_with_curr_order]): - org_contours_indexes = org_contours_indexes + [indexes_of_located_cont[region_with_curr_order]] - else: - arg_sort_located_cont = np.argsort(center_y_coordinates_of_located[region_with_curr_order]) - org_contours_indexes = org_contours_indexes + list(np.array(indexes_of_located_cont[region_with_curr_order])[arg_sort_located_cont]) ##org_contours_indexes + list ( - else: - org_contours_indexes = org_contours_indexes + [indexes_of_located_cont[region_with_curr_order]] - - region_ids = ['region_%04d' % i for i in range(len(co_text_all_org))] - return org_contours_indexes, region_ids - else: - region_ids = ['region_%04d' % i for i in range(len(co_text_all_org))] - return ordered, region_ids - - - - - def run(self, - overwrite: bool = False, - xml_filename: Optional[str] = None, - dir_in: Optional[str] = None, - dir_out: Optional[str] = None, - ): - """ - Get image and scales, then extract the page of scanned image - """ - self.logger.debug("enter run") - t0_tot = time.time() - - if dir_in: - ls_xmls = [os.path.join(dir_in, xml_filename) - for xml_filename in filter(is_xml_filename, - os.listdir(dir_in))] - elif xml_filename: - ls_xmls = [xml_filename] - else: - raise ValueError("run requires either a single image filename or a directory") - - for xml_filename in ls_xmls: - self.logger.info(xml_filename) - t0 = time.time() - - file_name = Path(xml_filename).stem - (tree_xml, root_xml, bb_coord_printspace, id_paragraph, id_header, - co_text_paragraph, co_text_header, tot_region_ref, - x_len, y_len, index_tot_regions, img_poly) = self.read_xml(xml_filename) - - id_all_text = id_paragraph + id_header - - order_text_new, id_of_texts_tot = self.do_order_of_regions_with_model(co_text_paragraph, co_text_header, img_poly[:,:,0]) - - id_all_text = np.array(id_all_text)[order_text_new] - - alltags=[elem.tag for elem in root_xml.iter()] - - - - link=alltags[0].split('}')[0]+'}' - name_space = alltags[0].split('}')[0] - name_space = name_space.split('{')[1] - - page_element = root_xml.find(link+'Page') - - - old_ro = root_xml.find(".//{*}ReadingOrder") - - if old_ro is not None: - page_element.remove(old_ro) - - #print(old_ro, 'old_ro') - ro_subelement = ET.Element('ReadingOrder') - - ro_subelement2 = ET.SubElement(ro_subelement, 'OrderedGroup') - ro_subelement2.set('id', "ro357564684568544579089") - - for index, id_text in enumerate(id_all_text): - new_element_2 = ET.SubElement(ro_subelement2, 'RegionRefIndexed') - new_element_2.set('regionRef', id_all_text[index]) - new_element_2.set('index', str(index)) - - if (link+'PrintSpace' in alltags) or (link+'Border' in alltags): - page_element.insert(1, ro_subelement) - else: - page_element.insert(0, ro_subelement) - - alltags=[elem.tag for elem in root_xml.iter()] - - ET.register_namespace("",name_space) - assert dir_out - tree_xml.write(os.path.join(dir_out, file_name+'.xml'), - xml_declaration=True, - method='xml', - encoding="utf-8", - default_namespace=None) - - #sys.exit() - diff --git a/src/eynollah/model_zoo/__init__.py b/src/eynollah/model_zoo/__init__.py deleted file mode 100644 index e1dc985..0000000 --- a/src/eynollah/model_zoo/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -__all__ = [ - 'EynollahModelZoo', -] -from .model_zoo import EynollahModelZoo diff --git a/src/eynollah/model_zoo/default_specs.py b/src/eynollah/model_zoo/default_specs.py deleted file mode 100644 index b9a1a2c..0000000 --- a/src/eynollah/model_zoo/default_specs.py +++ /dev/null @@ -1,252 +0,0 @@ -from .specs import EynollahModelSpec, EynollahModelSpecSet - -# NOTE: This needs to change whenever models/versions change -ZENODO = "https://zenodo.org/records/17295988/files" -MODELS_VERSION = "v0_7_0" - -def dist_url(dist_name: str="layout") -> str: - return f'{ZENODO}/models_{dist_name}_{MODELS_VERSION}.zip' - -DEFAULT_MODEL_SPECS = EynollahModelSpecSet([ - - EynollahModelSpec( - category="enhancement", - variant='', - filename="models_eynollah/eynollah-enhancement_20210425", - dist_url=dist_url(), - type='Keras', - ), - - EynollahModelSpec( - category="binarization", - variant='hybrid', - filename="models_eynollah/eynollah-binarization-hybrid_20230504/model_bin_hybrid_trans_cnn_sbb_ens", - dist_url=dist_url(), - type='Keras', - ), - - EynollahModelSpec( - category="binarization", - variant='20210309', - filename="models_eynollah/eynollah-binarization_20210309", - dist_url=dist_url("extra"), - type='Keras', - ), - - EynollahModelSpec( - category="binarization", - variant='', - filename="models_eynollah/eynollah-binarization_20210425", - dist_url=dist_url("extra"), - type='Keras', - ), - - EynollahModelSpec( - category="col_classifier", - variant='', - filename="models_eynollah/eynollah-column-classifier_20210425", - dist_url=dist_url(), - type='Keras', - ), - - EynollahModelSpec( - category="page", - variant='', - filename="models_eynollah/model_eynollah_page_extraction_20250915", - dist_url=dist_url(), - type='Keras', - ), - - EynollahModelSpec( - category="region", - variant='', - filename="models_eynollah/eynollah-main-regions-ensembled_20210425", - dist_url=dist_url(), - type='Keras', - ), - - EynollahModelSpec( - category="extract_images", - variant='', - filename="models_eynollah/eynollah-main-regions_20231127_672_org_ens_11_13_16_17_18", - dist_url=dist_url(), - type='Keras', - ), - - EynollahModelSpec( - category="region", - variant='', - filename="models_eynollah/eynollah-main-regions_20220314", - dist_url=dist_url(), - help="early layout", - type='Keras', - ), - - EynollahModelSpec( - category="region_p2", - variant='non-light', - filename="models_eynollah/eynollah-main-regions-aug-rotation_20210425", - dist_url=dist_url('extra'), - help="early layout, non-light, 2nd part", - type='Keras', - ), - - EynollahModelSpec( - category="region_1_2", - variant='', - #filename="models_eynollah/modelens_12sp_elay_0_3_4__3_6_n", - #filename="models_eynollah/modelens_earlylayout_12spaltige_2_3_5_6_7_8", - #filename="models_eynollah/modelens_early12_sp_2_3_5_6_7_8_9_10_12_14_15_16_18", - #filename="models_eynollah/modelens_1_2_4_5_early_lay_1_2_spaltige", - #filename="models_eynollah/model_3_eraly_layout_no_patches_1_2_spaltige", - filename="models_eynollah/modelens_e_l_all_sp_0_1_2_3_4_171024", - dist_url=dist_url("layout"), - help="early layout, light, 1-or-2-column", - type='Keras', - ), - - EynollahModelSpec( - category="region_fl_np", - variant='', - #'filename="models_eynollah/modelens_full_lay_1_3_031124", - #'filename="models_eynollah/modelens_full_lay_13__3_19_241024", - #'filename="models_eynollah/model_full_lay_13_241024", - #'filename="models_eynollah/modelens_full_lay_13_17_231024", - #'filename="models_eynollah/modelens_full_lay_1_2_221024", - #'filename="models_eynollah/eynollah-full-regions-1column_20210425", - filename="models_eynollah/modelens_full_lay_1__4_3_091124", - dist_url=dist_url(), - help="full layout / no patches", - type='Keras', - ), - - # FIXME: Why is region_fl and region_fl_np the same model? - EynollahModelSpec( - category="region_fl", - variant='', - # filename="models_eynollah/eynollah-full-regions-3+column_20210425", - # filename="models_eynollah/model_2_full_layout_new_trans", - # filename="models_eynollah/modelens_full_lay_1_3_031124", - # filename="models_eynollah/modelens_full_lay_13__3_19_241024", - # filename="models_eynollah/model_full_lay_13_241024", - # filename="models_eynollah/modelens_full_lay_13_17_231024", - # filename="models_eynollah/modelens_full_lay_1_2_221024", - # filename="models_eynollah/modelens_full_layout_24_till_28", - # filename="models_eynollah/model_2_full_layout_new_trans", - filename="models_eynollah/modelens_full_lay_1__4_3_091124", - dist_url=dist_url(), - help="full layout / with patches", - type='Keras', - ), - - EynollahModelSpec( - category="reading_order", - variant='', - #filename="models_eynollah/model_mb_ro_aug_ens_11", - #filename="models_eynollah/model_step_3200000_mb_ro", - #filename="models_eynollah/model_ens_reading_order_machine_based", - #filename="models_eynollah/model_mb_ro_aug_ens_8", - #filename="models_eynollah/model_ens_reading_order_machine_based", - filename="models_eynollah/model_eynollah_reading_order_20250824", - dist_url=dist_url(), - type='Keras', - ), - - EynollahModelSpec( - category="textline", - variant='non-light', - #filename="models_eynollah/modelens_textline_1_4_16092024", - #filename="models_eynollah/model_textline_ens_3_4_5_6_artificial", - #filename="models_eynollah/modelens_textline_1_3_4_20240915", - #filename="models_eynollah/model_textline_ens_3_4_5_6_artificial", - #filename="models_eynollah/modelens_textline_9_12_13_14_15", - #filename="models_eynollah/eynollah-textline_20210425", - filename="models_eynollah/modelens_textline_0_1__2_4_16092024", - dist_url=dist_url('extra'), - type='Keras', - ), - - EynollahModelSpec( - category="textline", - variant='', - #filename="models_eynollah/eynollah-textline_light_20210425", - filename="models_eynollah/modelens_textline_0_1__2_4_16092024", - dist_url=dist_url(), - type='Keras', - ), - - EynollahModelSpec( - category="table", - variant='non-light', - filename="models_eynollah/eynollah-tables_20210319", - dist_url=dist_url('extra'), - type='Keras', - ), - - EynollahModelSpec( - category="table", - variant='', - filename="models_eynollah/modelens_table_0t4_201124", - dist_url=dist_url(), - type='Keras', - ), - - EynollahModelSpec( - category="ocr", - variant='', - filename="models_eynollah/model_eynollah_ocr_cnnrnn_20250930", - dist_url=dist_url("ocr"), - type='Keras', - ), - - EynollahModelSpec( - category="ocr", - variant='degraded', - filename="models_eynollah/model_eynollah_ocr_cnnrnn__degraded_20250805/", - help="slightly better at degraded Fraktur", - dist_url=dist_url("ocr"), - type='Keras', - ), - - EynollahModelSpec( - category="num_to_char", - variant='', - filename="characters_org.txt", - dist_url=dist_url("ocr"), - type='decoder', - ), - - EynollahModelSpec( - category="characters", - variant='', - filename="characters_org.txt", - dist_url=dist_url("ocr"), - type='List[str]', - ), - - EynollahModelSpec( - category="ocr", - variant='tr', - filename="models_eynollah/model_eynollah_ocr_trocr_20250919", - dist_url=dist_url("ocr"), - help='much slower transformer-based', - type='Keras', - ), - - EynollahModelSpec( - category="trocr_processor", - variant='', - filename="models_eynollah/model_eynollah_ocr_trocr_20250919", - dist_url=dist_url("ocr"), - type='TrOCRProcessor', - ), - - EynollahModelSpec( - category="trocr_processor", - variant='htr', - filename="models_eynollah/microsoft/trocr-base-handwritten", - dist_url=dist_url("extra"), - type='TrOCRProcessor', - ), - -]) diff --git a/src/eynollah/model_zoo/model_zoo.py b/src/eynollah/model_zoo/model_zoo.py deleted file mode 100644 index 80d0aa7..0000000 --- a/src/eynollah/model_zoo/model_zoo.py +++ /dev/null @@ -1,203 +0,0 @@ -import json -import logging -from copy import deepcopy -from pathlib import Path -from typing import Dict, List, Optional, Tuple, Type, Union - -from ocrd_utils import tf_disable_interactive_logs -tf_disable_interactive_logs() - -from keras.layers import StringLookup -from keras.models import Model as KerasModel -from keras.models import load_model -from tabulate import tabulate -from ..patch_encoder import PatchEncoder, Patches -from .specs import EynollahModelSpecSet -from .default_specs import DEFAULT_MODEL_SPECS -from .types import AnyModel, T - - -class EynollahModelZoo: - """ - Wrapper class that handles storage and loading of models for all eynollah runners. - """ - - model_basedir: Path - specs: EynollahModelSpecSet - - def __init__( - self, - basedir: str, - model_overrides: Optional[List[Tuple[str, str, str]]] = None, - ) -> None: - self.model_basedir = Path(basedir) - self.logger = logging.getLogger('eynollah.model_zoo') - if not self.model_basedir.exists(): - self.logger.warning(f"Model basedir does not exist: {basedir}. Set eynollah --model-basedir to the correct directory.") - self.specs = deepcopy(DEFAULT_MODEL_SPECS) - self._overrides = [] - if model_overrides: - self.override_models(*model_overrides) - self._loaded: Dict[str, AnyModel] = {} - - @property - def model_overrides(self): - return self._overrides - - def override_models( - self, - *model_overrides: Tuple[str, str, str], - ): - """ - Override the default model versions - """ - for model_category, model_variant, model_filename in model_overrides: - spec = self.specs.get(model_category, model_variant) - self.logger.warning("Overriding filename for model spec %s to %s", spec, model_filename) - self.specs.get(model_category, model_variant).filename = model_filename - self._overrides += model_overrides - - def model_path( - self, - model_category: str, - model_variant: str = '', - absolute: bool = True, - ) -> Path: - """ - Translate model_{type,variant} tuple into an absolute (or relative) Path - """ - spec = self.specs.get(model_category, model_variant) - if spec.category in ('characters', 'num_to_char'): - return self.model_path('ocr') / spec.filename - if not Path(spec.filename).is_absolute() and absolute: - model_path = Path(self.model_basedir).joinpath(spec.filename) - else: - model_path = Path(spec.filename) - return model_path - - def load_models( - self, - *all_load_args: Union[str, Tuple[str], Tuple[str, str], Tuple[str, str, str]], - ) -> Dict: - """ - Load all models by calling load_model and return a dictionary mapping model_category to loaded model - """ - ret = {} - for load_args in all_load_args: - if isinstance(load_args, str): - ret[load_args] = self.load_model(load_args) - else: - ret[load_args[0]] = self.load_model(*load_args) - return ret - - def load_model( - self, - model_category: str, - model_variant: str = '', - model_path_override: Optional[str] = None, - ) -> AnyModel: - """ - Load any model - """ - if model_path_override: - self.override_models((model_category, model_variant, model_path_override)) - model_path = self.model_path(model_category, model_variant) - if model_path.suffix == '.h5' and Path(model_path.stem).exists(): - # prefer SavedModel over HDF5 format if it exists - model_path = Path(model_path.stem) - if model_category == 'ocr': - model = self._load_ocr_model(variant=model_variant) - elif model_category == 'num_to_char': - model = self._load_num_to_char() - elif model_category == 'characters': - model = self._load_characters() - elif model_category == 'trocr_processor': - from transformers import TrOCRProcessor - model = TrOCRProcessor.from_pretrained(model_path) - else: - try: - model = load_model(model_path, compile=False) - except Exception as e: - self.logger.exception(e) - model = load_model( - model_path, compile=False, custom_objects={"PatchEncoder": PatchEncoder, "Patches": Patches} - ) - self._loaded[model_category] = model - return model # type: ignore - - def get(self, model_category: str, model_type: Optional[Type[T]] = None) -> T: - if model_category not in self._loaded: - raise ValueError(f'Model "{model_category} not previously loaded with "load_model(..)"') - ret = self._loaded[model_category] - if model_type: - assert isinstance(ret, model_type) - return ret # type: ignore # FIXME: convince typing that we're returning generic type - - def _load_ocr_model(self, variant: str) -> AnyModel: - """ - Load OCR model - """ - ocr_model_dir = self.model_path('ocr', variant) - if variant == 'tr': - from transformers import VisionEncoderDecoderModel - ret = VisionEncoderDecoderModel.from_pretrained(ocr_model_dir) - assert isinstance(ret, VisionEncoderDecoderModel) - return ret - else: - ocr_model = load_model(ocr_model_dir, compile=False) - assert isinstance(ocr_model, KerasModel) - return KerasModel( - ocr_model.get_layer(name="image").input, # type: ignore - ocr_model.get_layer(name="dense2").output, # type: ignore - ) - - def _load_characters(self) -> List[str]: - """ - Load encoding for OCR - """ - with open(self.model_path('num_to_char'), "r") as config_file: - return json.load(config_file) - - def _load_num_to_char(self) -> StringLookup: - """ - Load decoder for OCR - """ - characters = self._load_characters() - # Mapping characters to integers. - char_to_num = StringLookup(vocabulary=characters, mask_token=None) - # Mapping integers back to original characters. - return StringLookup(vocabulary=char_to_num.get_vocabulary(), mask_token=None, invert=True) - - def __str__(self): - return tabulate( - [ - [ - spec.type, - spec.category, - spec.variant, - spec.help, - f'Yes, at {self.model_path(spec.category, spec.variant)}' - if self.model_path(spec.category, spec.variant).exists() - else f'No, download {spec.dist_url}', - # self.model_path(spec.category, spec.variant), - ] - for spec in sorted(self.specs.specs, key=lambda x: x.dist_url) - ], - headers=[ - 'Type', - 'Category', - 'Variant', - 'Help', - 'Used in', - 'Installed', - ], - tablefmt='github', - ) - - def shutdown(self): - """ - Ensure that a loaded models is not referenced by ``self._loaded`` anymore - """ - if hasattr(self, '_loaded') and getattr(self, '_loaded'): - for needle in list(self._loaded.keys()): - del self._loaded[needle] diff --git a/src/eynollah/model_zoo/specs.py b/src/eynollah/model_zoo/specs.py deleted file mode 100644 index 3c47b7b..0000000 --- a/src/eynollah/model_zoo/specs.py +++ /dev/null @@ -1,52 +0,0 @@ -from dataclasses import dataclass -from typing import Dict, List, Set, Tuple - - -@dataclass -class EynollahModelSpec(): - """ - Describing a single model abstractly. - """ - category: str - # Relative filename to the models_eynollah directory in the dists - filename: str - # URL to the smallest model distribution containing this model (link to Zenodo) - dist_url: str - type: str - variant: str = '' - help: str = '' - -class EynollahModelSpecSet(): - """ - List of all used models for eynollah. - """ - specs: List[EynollahModelSpec] - - def __init__(self, specs: List[EynollahModelSpec]) -> None: - self.specs = sorted(specs, key=lambda x: x.category + '0' + x.variant) - self.categories: Set[str] = set([spec.category for spec in self.specs]) - self.variants: Dict[str, Set[str]] = { - spec.category: set([x.variant for x in self.specs if x.category == spec.category]) - for spec in self.specs - } - self._index_category_variant: Dict[Tuple[str, str], EynollahModelSpec] = { - (spec.category, spec.variant): spec - for spec in self.specs - } - - def asdict(self) -> Dict[str, Dict[str, str]]: - return { - spec.category: { - spec.variant: spec.filename - } - for spec in self.specs - } - - def get(self, category: str, variant: str) -> EynollahModelSpec: - if category not in self.categories: - raise ValueError(f"Unknown category '{category}', must be one of {self.categories}") - if variant not in self.variants[category]: - raise ValueError(f"Unknown variant {variant} for {category}. Known variants: {self.variants[category]}") - return self._index_category_variant[(category, variant)] - - diff --git a/src/eynollah/model_zoo/types.py b/src/eynollah/model_zoo/types.py deleted file mode 100644 index 43f6859..0000000 --- a/src/eynollah/model_zoo/types.py +++ /dev/null @@ -1,7 +0,0 @@ -from typing import TypeVar - -# NOTE: Creating an actual union type requires loading transformers which is expensive and error-prone -# from transformers import TrOCRProcessor, VisionEncoderDecoderModel -# AnyModel = Union[VisionEncoderDecoderModel, TrOCRProcessor, KerasModel, List] -AnyModel = object -T = TypeVar('T') diff --git a/src/eynollah/ocrd-tool.json b/src/eynollah/ocrd-tool.json index 3b500fc..e972ec8 100644 --- a/src/eynollah/ocrd-tool.json +++ b/src/eynollah/ocrd-tool.json @@ -1,5 +1,5 @@ { - "version": "0.7.0", + "version": "0.4.0", "git_url": "https://github.com/qurator-spk/eynollah", "dockerhub": "ocrd/eynollah", "tools": { @@ -29,6 +29,16 @@ "type": "boolean", "default": true, "description": "Try to detect all element subtypes, including drop-caps and headings" + }, + "light_version": { + "type": "boolean", + "default": true, + "description": "Try to detect all element subtypes in light version (faster+simpler method for main region detection and deskewing)" + }, + "textline_light": { + "type": "boolean", + "default": true, + "description": "Light version need textline light" }, "tables": { "type": "boolean", @@ -55,6 +65,11 @@ "default": false, "description": "if this parameter set to true, this tool would check that input image need resizing and enhancement or not." }, + "textline_light": { + "type": "boolean", + "default": false, + "description": "if this parameter set to true, this tool will try to return contoure of textlines instead of rectangle bounding box of textline with a faster method." + }, "right_to_left": { "type": "boolean", "default": false, @@ -64,39 +79,16 @@ "type": "boolean", "default": false, "description": "ignore the special role of headings during reading order detection" - }, - "reading_order_machine_based": { - "type": "boolean", - "default": false, - "description": "use data-driven (rather than rule-based) reading order detection" } }, "resources": [ - { - "url": "https://zenodo.org/records/17580627/files/models_all_v0_7_0.zip?download=1", - "name": "models_layout_v0_7_0", - "type": "archive", - "size": 6119874002, - "description": "Models for layout detection, reading order detection, textline detection, page extraction, column classification, table detection, binarization, image enhancement and OCR", - "version_range": ">= v0.7.0" - }, - { - "url": "https://zenodo.org/records/17295988/files/models_layout_v0_6_0.tar.gz?download=1", - "name": "models_layout_v0_6_0", - "type": "archive", - "path_in_archive": "models_layout_v0_6_0", - "size": 3525684179, - "description": "Models for layout detection, reading order detection, textline detection, page extraction, column classification, table detection, binarization, image enhancement and OCR", - "version_range": ">= v0.5.0" - }, { "description": "models for eynollah (TensorFlow SavedModel format)", "url": "https://github.com/qurator-spk/eynollah/releases/download/v0.3.1/models_eynollah.tar.gz", "name": "default", "size": 1894627041, "type": "archive", - "path_in_archive": "models_eynollah", - "version_range": ">= v0.3.0, < v0.5.0" + "path_in_archive": "models_eynollah" } ] }, diff --git a/src/eynollah/ocrd_cli.py b/src/eynollah/ocrd_cli.py index acd8d4e..8929927 100644 --- a/src/eynollah/ocrd_cli.py +++ b/src/eynollah/ocrd_cli.py @@ -1,6 +1,3 @@ -# NOTE: For predictable order of imports of torch/shapely/tensorflow -# this must be the first import of the CLI! -from .eynollah_imports import imported_libs from .processor import EynollahProcessor from click import command from ocrd.decorators import ocrd_cli_options, ocrd_cli_wrap_processor diff --git a/src/eynollah/ocrd_cli_binarization.py b/src/eynollah/ocrd_cli_binarization.py index f234520..848bbac 100644 --- a/src/eynollah/ocrd_cli_binarization.py +++ b/src/eynollah/ocrd_cli_binarization.py @@ -1,8 +1,6 @@ -from functools import cached_property from typing import Optional from PIL import Image -from frozendict import frozendict import numpy as np import cv2 from click import command @@ -11,8 +9,6 @@ from ocrd import Processor, OcrdPageResult, OcrdPageResultImage from ocrd_models.ocrd_page import OcrdPage, AlternativeImageType from ocrd.decorators import ocrd_cli_options, ocrd_cli_wrap_processor -from eynollah.model_zoo.model_zoo import EynollahModelZoo - from .sbb_binarize import SbbBinarizer @@ -29,7 +25,7 @@ class SbbBinarizeProcessor(Processor): # already employs GPU (without singleton process atm) max_workers = 1 - @cached_property + @property def executable(self): return 'ocrd-sbb-binarize' @@ -38,9 +34,8 @@ class SbbBinarizeProcessor(Processor): Set up the model prior to processing. """ # resolve relative path via OCR-D ResourceManager - assert isinstance(self.parameter, frozendict) - model_zoo = EynollahModelZoo(basedir=self.parameter['model']) - self.binarizer = SbbBinarizer(model_zoo=model_zoo, logger=self.logger) + model_path = self.resolve_resource(self.parameter['model']) + self.binarizer = SbbBinarizer(model_dir=model_path, logger=self.logger) def process_page_pcgts(self, *input_pcgts: Optional[OcrdPage], page_id: Optional[str] = None) -> OcrdPageResult: """ @@ -103,7 +98,7 @@ class SbbBinarizeProcessor(Processor): line_image_bin = cv2pil(self.binarizer.run(image=pil2cv(line_image), use_patches=True)) # update PAGE (reference the image file): line_image_ref = AlternativeImageType(comments=line_xywh['features'] + ',binarized') - line.add_AlternativeImage(line_image_ref) + line.add_AlternativeImage(region_image_ref) result.images.append(OcrdPageResultImage(line_image_bin, line.id + '.IMG-BIN', line_image_ref)) return result diff --git a/src/eynollah/patch_encoder.py b/src/eynollah/patch_encoder.py deleted file mode 100644 index 939ad7b..0000000 --- a/src/eynollah/patch_encoder.py +++ /dev/null @@ -1,52 +0,0 @@ -from keras import layers -import tensorflow as tf - -projection_dim = 64 -patch_size = 1 -num_patches =21*21#14*14#28*28#14*14#28*28 - -class PatchEncoder(layers.Layer): - - def __init__(self): - super().__init__() - self.projection = layers.Dense(units=projection_dim) - self.position_embedding = layers.Embedding(input_dim=num_patches, output_dim=projection_dim) - - def call(self, patch): - positions = tf.range(start=0, limit=num_patches, delta=1) - encoded = self.projection(patch) + self.position_embedding(positions) - return encoded - - def get_config(self): - config = super().get_config().copy() - config.update({ - 'num_patches': num_patches, - 'projection': self.projection, - 'position_embedding': self.position_embedding, - }) - return config - -class Patches(layers.Layer): - def __init__(self, **kwargs): - super(Patches, self).__init__() - self.patch_size = patch_size - - def call(self, images): - batch_size = tf.shape(images)[0] - patches = tf.image.extract_patches( - images=images, - sizes=[1, self.patch_size, self.patch_size, 1], - strides=[1, self.patch_size, self.patch_size, 1], - rates=[1, 1, 1, 1], - padding="VALID", - ) - patch_dims = patches.shape[-1] - patches = tf.reshape(patches, [batch_size, -1, patch_dims]) - return patches - def get_config(self): - - config = super().get_config().copy() - config.update({ - 'patch_size': self.patch_size, - }) - return config diff --git a/src/eynollah/plot.py b/src/eynollah/plot.py index b1b2359..412ae5a 100644 --- a/src/eynollah/plot.py +++ b/src/eynollah/plot.py @@ -12,7 +12,7 @@ from .utils import crop_image_inside_box from .utils.rotate import rotate_image_different from .utils.resize import resize_image -class EynollahPlotter: +class EynollahPlotter(): """ Class collecting all the plotting and image writing methods """ @@ -40,8 +40,8 @@ class EynollahPlotter: self.image_filename_stem = image_filename_stem # XXX TODO hacky these cannot be set at init time self.image_org = image_org - self.scale_x : float = scale_x - self.scale_y : float = scale_y + self.scale_x = scale_x + self.scale_y = scale_y def save_plot_of_layout_main(self, text_regions_p, image_page): if self.dir_of_layout is not None: diff --git a/src/eynollah/processor.py b/src/eynollah/processor.py index 0addaff..8f99489 100644 --- a/src/eynollah/processor.py +++ b/src/eynollah/processor.py @@ -1,9 +1,6 @@ -from functools import cached_property from typing import Optional from ocrd_models import OcrdPage -from ocrd import OcrdPageResultImage, Processor, OcrdPageResult - -from eynollah.model_zoo.model_zoo import EynollahModelZoo +from ocrd import Processor, OcrdPageResult from .eynollah import Eynollah, EynollahXmlWriter @@ -12,25 +9,27 @@ class EynollahProcessor(Processor): # already employs GPU (without singleton process atm) max_workers = 1 - @cached_property - def executable(self) -> str: + @property + def executable(self): return 'ocrd-eynollah-segment' def setup(self) -> None: - assert self.parameter - model_zoo = EynollahModelZoo(basedir=self.parameter['models']) + if self.parameter['textline_light'] and not self.parameter['light_version']: + raise ValueError("Error: You set parameter 'textline_light' to enable light textline detection, " + "but parameter 'light_version' is not enabled") self.eynollah = Eynollah( - model_zoo=model_zoo, + self.resolve_resource(self.parameter['models']), + logger=self.logger, allow_enhancement=self.parameter['allow_enhancement'], curved_line=self.parameter['curved_line'], right2left=self.parameter['right_to_left'], - reading_order_machine_based=self.parameter['reading_order_machine_based'], ignore_page_extraction=self.parameter['ignore_page_extraction'], + light_version=self.parameter['light_version'], + textline_light=self.parameter['textline_light'], full_layout=self.parameter['full_layout'], allow_scaling=self.parameter['allow_scaling'], headers_off=self.parameter['headers_off'], tables=self.parameter['tables'], - logger=self.logger ) self.eynollah.plotter = None @@ -57,8 +56,6 @@ class EynollahProcessor(Processor): - If ``ignore_page_extraction``, then attempt no cropping of the page. - If ``curved_line``, then compute contour polygons for text lines instead of simple bounding boxes. - - If ``reading_order_machine_based``, then detect reading order via - data-driven model instead of geometrical heuristics. Produce a new output file by serialising the resulting hierarchy. """ @@ -88,6 +85,7 @@ class EynollahProcessor(Processor): dir_out=None, image_filename=image_filename, curved_line=self.eynollah.curved_line, + textline_light=self.eynollah.textline_light, pcgts=pcgts) self.eynollah.run_single() return result diff --git a/src/eynollah/sbb_binarize.py b/src/eynollah/sbb_binarize.py index 37ac7c3..f43b6ba 100644 --- a/src/eynollah/sbb_binarize.py +++ b/src/eynollah/sbb_binarize.py @@ -2,57 +2,60 @@ Tool to load model and binarize a given image. """ -# pyright: reportIndexIssue=false -# pyright: reportCallIssue=false -# pyright: reportArgumentType=false -# pyright: reportPossiblyUnboundVariable=false - +import sys +from glob import glob import os import logging -from typing import Optional import numpy as np +from PIL import Image import cv2 from ocrd_utils import tf_disable_interactive_logs - -from eynollah.model_zoo import EynollahModelZoo tf_disable_interactive_logs() import tensorflow as tf +from tensorflow.keras.models import load_model from tensorflow.python.keras import backend as tensorflow_backend -from pathlib import Path -from .utils import is_image_filename + def resize_image(img_in, input_height, input_width): return cv2.resize(img_in, (input_width, input_height), interpolation=cv2.INTER_NEAREST) class SbbBinarizer: - def __init__( - self, - *, - model_zoo: EynollahModelZoo, - logger: Optional[logging.Logger] = None, - ): - self.logger = logger if logger else logging.getLogger('eynollah.binarization') - self.models = (model_zoo.model_path('binarization'), model_zoo.load_model('binarization')) - self.session = self.start_new_session() + def __init__(self, model_dir, logger=None): + self.model_dir = model_dir + self.log = logger if logger else logging.getLogger('SbbBinarizer') + + self.start_new_session() + + self.model_files = glob(self.model_dir+"/*/", recursive = True) + + self.models = [] + for model_file in self.model_files: + self.models.append(self.load_model(model_file)) def start_new_session(self): config = tf.compat.v1.ConfigProto() config.gpu_options.allow_growth = True - session = tf.compat.v1.Session(config=config) # tf.InteractiveSession() - tensorflow_backend.set_session(session) - return session - + self.session = tf.compat.v1.Session(config=config) # tf.InteractiveSession() + tensorflow_backend.set_session(self.session) + def end_session(self): tensorflow_backend.clear_session() self.session.close() del self.session - def predict(self, model, img, use_patches, n_batch_inference=5): + def load_model(self, model_name): + model = load_model(os.path.join(self.model_dir, model_name), compile=False) model_height = model.layers[len(model.layers)-1].output_shape[1] model_width = model.layers[len(model.layers)-1].output_shape[2] + n_classes = model.layers[len(model.layers)-1].output_shape[3] + return model, model_height, model_width, n_classes + + def predict(self, model_in, img, use_patches, n_batch_inference=5): + tensorflow_backend.set_session(self.session) + model, model_height, model_width, n_classes = model_in img_org_h = img.shape[0] img_org_w = img.shape[1] @@ -311,8 +314,8 @@ class SbbBinarizer: prediction_true = prediction_true.astype(np.uint8) return prediction_true[:,:,0] - def run(self, image=None, image_path=None, output=None, use_patches=False, dir_in=None): - # print(dir_in,'dir_in') + def run(self, image=None, image_path=None, save=None, use_patches=False, dir_in=None, dir_out=None): + print(dir_in,'dir_in') if not dir_in: if (image is not None and image_path is not None) or \ (image is None and image_path is None): @@ -320,38 +323,9 @@ class SbbBinarizer: if image_path is not None: image = cv2.imread(image_path) img_last = 0 - model_file, model = self.models - self.logger.info('Predicting %s with model %s', image_path if image_path else '[image]', model_file) - res = self.predict(model, image, use_patches) + for n, (model, model_file) in enumerate(zip(self.models, self.model_files)): + self.log.info('Predicting with model %s [%s/%s]' % (model_file, n + 1, len(self.model_files))) - img_fin = np.zeros((res.shape[0], res.shape[1], 3)) - res[:, :][res[:, :] == 0] = 2 - res = res - 1 - res = res * 255 - img_fin[:, :, 0] = res - img_fin[:, :, 1] = res - img_fin[:, :, 2] = res - - img_fin = img_fin.astype(np.uint8) - img_fin = (res[:, :] == 0) * 255 - img_last = img_last + img_fin - - img_last[:, :][img_last[:, :] > 0] = 255 - img_last = (img_last[:, :] == 0) * 255 - if output: - self.logger.info('Writing binarized image to %s', output) - cv2.imwrite(output, img_last) - return img_last - else: - ls_imgs = list(filter(is_image_filename, os.listdir(dir_in))) - self.logger.info("Found %d image files to binarize in %s", len(ls_imgs), dir_in) - for i, image_path in enumerate(ls_imgs): - self.logger.info('Binarizing [%3d/%d] %s', i + 1, len(ls_imgs), image_path) - image_stem = Path(image_path).stem - image = cv2.imread(os.path.join(dir_in,image_path) ) - img_last = 0 - model_file, model = self.models - self.logger.info('Predicting %s with model %s', image_path if image_path else '[image]', model_file) res = self.predict(model, image, use_patches) img_fin = np.zeros((res.shape[0], res.shape[1], 3)) @@ -366,9 +340,38 @@ class SbbBinarizer: img_fin = (res[:, :] == 0) * 255 img_last = img_last + img_fin + kernel = np.ones((5, 5), np.uint8) + img_last[:, :][img_last[:, :] > 0] = 255 + img_last = (img_last[:, :] == 0) * 255 + if save: + cv2.imwrite(save, img_last) + return img_last + else: + ls_imgs = os.listdir(dir_in) + for image_name in ls_imgs: + image_stem = image_name.split('.')[0] + print(image_name,'image_name') + image = cv2.imread(os.path.join(dir_in,image_name) ) + img_last = 0 + for n, (model, model_file) in enumerate(zip(self.models, self.model_files)): + self.log.info('Predicting with model %s [%s/%s]' % (model_file, n + 1, len(self.model_files))) + + res = self.predict(model, image, use_patches) + + img_fin = np.zeros((res.shape[0], res.shape[1], 3)) + res[:, :][res[:, :] == 0] = 2 + res = res - 1 + res = res * 255 + img_fin[:, :, 0] = res + img_fin[:, :, 1] = res + img_fin[:, :, 2] = res + + img_fin = img_fin.astype(np.uint8) + img_fin = (res[:, :] == 0) * 255 + img_last = img_last + img_fin + + kernel = np.ones((5, 5), np.uint8) img_last[:, :][img_last[:, :] > 0] = 255 img_last = (img_last[:, :] == 0) * 255 - output_filename = os.path.join(output, image_stem + '.png') - self.logger.info('Writing binarized image to %s', output_filename) - cv2.imwrite(output_filename, img_last) + cv2.imwrite(os.path.join(dir_out,image_stem+'.png'), img_last) diff --git a/src/eynollah/training/build_model_load_pretrained_weights_and_save.py b/src/eynollah/training/build_model_load_pretrained_weights_and_save.py deleted file mode 100644 index 40fc1fe..0000000 --- a/src/eynollah/training/build_model_load_pretrained_weights_and_save.py +++ /dev/null @@ -1,24 +0,0 @@ -import click -import tensorflow as tf - -from .models import resnet50_unet - - -def configuration(): - gpu_options = tf.compat.v1.GPUOptions(allow_growth=True) - session = tf.compat.v1.Session(config=tf.compat.v1.ConfigProto(gpu_options=gpu_options)) - -@click.command() -def build_model_load_pretrained_weights_and_save(): - n_classes = 2 - input_height = 224 - input_width = 448 - weight_decay = 1e-6 - pretraining = False - dir_of_weights = 'model_bin_sbb_ens.h5' - - # configuration() - - model = resnet50_unet(n_classes, input_height, input_width, weight_decay, pretraining) - model.load_weights(dir_of_weights) - model.save('./name_in_another_python_version.h5') diff --git a/src/eynollah/training/cli.py b/src/eynollah/training/cli.py deleted file mode 100644 index 3718275..0000000 --- a/src/eynollah/training/cli.py +++ /dev/null @@ -1,30 +0,0 @@ -import os -os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' - -import click -import sys - -from .build_model_load_pretrained_weights_and_save import build_model_load_pretrained_weights_and_save -from .generate_gt_for_training import main as generate_gt_cli -from .inference import main as inference_cli -from .train import ex -from .extract_line_gt import linegt_cli -from .weights_ensembling import main as ensemble_cli - -@click.command(context_settings=dict( - ignore_unknown_options=True, -)) -@click.argument('SACRED_ARGS', nargs=-1, type=click.UNPROCESSED) -def train_cli(sacred_args): - ex.run_commandline([sys.argv[0]] + list(sacred_args)) - -@click.group('training') -def main(): - pass - -main.add_command(build_model_load_pretrained_weights_and_save) -main.add_command(generate_gt_cli, 'generate-gt') -main.add_command(inference_cli, 'inference') -main.add_command(train_cli, 'train') -main.add_command(linegt_cli, 'export_textline_images_and_text') -main.add_command(ensemble_cli, 'ensembling') diff --git a/src/eynollah/training/extract_line_gt.py b/src/eynollah/training/extract_line_gt.py deleted file mode 100644 index 58fc253..0000000 --- a/src/eynollah/training/extract_line_gt.py +++ /dev/null @@ -1,134 +0,0 @@ -from logging import Logger, getLogger -from typing import Optional -from pathlib import Path -import os - -import click -import cv2 -import xml.etree.ElementTree as ET -import numpy as np - -from ..utils import is_image_filename - -@click.command() -@click.option( - "--image", - "-i", - help="input image filename", - type=click.Path(exists=True, dir_okay=False), -) -@click.option( - "--dir_in", - "-di", - help="directory of input images (instead of --image)", - type=click.Path(exists=True, file_okay=False), -) -@click.option( - "--dir_xmls", - "-dx", - help="directory of input PAGE-XML files (in addition to --dir_in; filename stems must match the image files, with '.xml' suffix).", - type=click.Path(exists=True, file_okay=False), - required=True, -) -@click.option( - "--out", - "-o", - 'dir_out', - help="directory for output PAGE-XML files", - type=click.Path(exists=True, file_okay=False), - required=True, -) -@click.option( - "--dataset_abbrevation", - "-ds_pref", - 'pref_of_dataset', - help="in the case of extracting textline and text from a xml GT file user can add an abbrevation of dataset name to generated dataset", -) -@click.option( - "--do_not_mask_with_textline_contour", - "-nmtc/-mtc", - is_flag=True, - help="if this parameter set to true, cropped textline images will not be masked with textline contour.", -) -def linegt_cli( - image, - dir_in, - dir_xmls, - dir_out, - pref_of_dataset, - do_not_mask_with_textline_contour, -): - assert bool(dir_in) ^ bool(image), "Set --dir-in or --image-filename, not both" - if dir_in: - ls_imgs = [ - os.path.join(dir_in, image) for image in filter(is_image_filename, os.listdir(dir_in)) - ] - else: - assert image - ls_imgs = [image] - - for dir_img in ls_imgs: - file_name = Path(dir_img).stem - dir_xml = os.path.join(dir_xmls, file_name + '.xml') - - img = cv2.imread(dir_img) - - total_bb_coordinates = [] - - tree1 = ET.parse(dir_xml, parser=ET.XMLParser(encoding="utf-8")) - root1 = tree1.getroot() - alltags = [elem.tag for elem in root1.iter()] - - name_space = alltags[0].split('}')[0] - name_space = name_space.split('{')[1] - - region_tags = np.unique([x for x in alltags if x.endswith('TextRegion')]) - - cropped_lines_region_indexer = [] - - indexer_text_region = 0 - indexer_textlines = 0 - # FIXME: non recursive, use OCR-D PAGE generateDS API. Or use an existing tool for this purpose altogether - for nn in root1.iter(region_tags): - for child_textregion in nn: - if child_textregion.tag.endswith("TextLine"): - for child_textlines in child_textregion: - if child_textlines.tag.endswith("Coords"): - cropped_lines_region_indexer.append(indexer_text_region) - p_h = child_textlines.attrib['points'].split(' ') - textline_coords = np.array([[int(x.split(',')[0]), int(x.split(',')[1])] for x in p_h]) - - x, y, w, h = cv2.boundingRect(textline_coords) - - total_bb_coordinates.append([x, y, w, h]) - - img_poly_on_img = np.copy(img) - - mask_poly = np.zeros(img.shape) - mask_poly = cv2.fillPoly(mask_poly, pts=[textline_coords], color=(1, 1, 1)) - - mask_poly = mask_poly[y : y + h, x : x + w, :] - img_crop = img_poly_on_img[y : y + h, x : x + w, :] - - if not do_not_mask_with_textline_contour: - img_crop[mask_poly == 0] = 255 - - if img_crop.shape[0] == 0 or img_crop.shape[1] == 0: - continue - if child_textlines.tag.endswith("TextEquiv"): - for cheild_text in child_textlines: - if cheild_text.tag.endswith("Unicode"): - textline_text = cheild_text.text - if textline_text: - base_name = os.path.join( - dir_out, file_name + '_line_' + str(indexer_textlines) - ) - if pref_of_dataset: - base_name += '_' + pref_of_dataset - if not do_not_mask_with_textline_contour: - base_name += '_masked' - - with open(base_name + '.txt', 'w') as text_file: - text_file.write(textline_text) - cv2.imwrite(base_name + '.png', img_crop) - indexer_textlines += 1 diff --git a/src/eynollah/training/generate_gt_for_training.py b/src/eynollah/training/generate_gt_for_training.py deleted file mode 100644 index 30abd04..0000000 --- a/src/eynollah/training/generate_gt_for_training.py +++ /dev/null @@ -1,583 +0,0 @@ -import click -import json -import os -from tqdm import tqdm -from pathlib import Path -from PIL import Image, ImageDraw, ImageFont -import cv2 -import numpy as np - -from eynollah.training.gt_gen_utils import ( - filter_contours_area_of_image, - find_format_of_given_filename_in_dir, - find_new_features_of_contours, - fit_text_single_line, - get_content_of_dir, - get_images_of_ground_truth, - get_layout_contours_for_visualization, - get_textline_contours_and_ocr_text, - get_textline_contours_for_visualization, - overlay_layout_on_image, - read_xml, - resize_image, - visualize_image_from_contours, - visualize_image_from_contours_layout -) - -@click.group() -def main(): - pass - -@main.command() -@click.option( - "--dir_xml", - "-dx", - help="directory of GT page-xml files", - type=click.Path(exists=True, file_okay=False), -) -@click.option( - "--dir_images", - "-di", - help="directory of org images. If print space cropping or scaling is needed for labels it would be great to provide the original images to apply the same function on them. So if -ps is not set true or in config files no columns_width key is given this argumnet can be ignored. File stems in this directory should be the same as those in dir_xml.", - type=click.Path(exists=True, file_okay=False), -) -@click.option( - "--dir_out_images", - "-doi", - help="directory where the output org images after undergoing a process (like print space cropping or scaling) will be written.", - type=click.Path(exists=True, file_okay=False), -) -@click.option( - "--dir_out", - "-do", - help="directory where ground truth label images would be written", - type=click.Path(exists=True, file_okay=False), -) - -@click.option( - "--config", - "-cfg", - help="config file of prefered layout or use case.", - type=click.Path(exists=True, dir_okay=False), -) - -@click.option( - "--type_output", - "-to", - help="this defines how output should be. A 2d image array or a 3d image array encoded with RGB color. Just pass 2d or 3d. The file will be saved one directory up. 2D image array is 3d but only information of one channel would be enough since all channels have the same values.", -) -@click.option( - "--printspace", - "-ps", - is_flag=True, - help="if this parameter set to true, generated labels and in the case of provided org images cropping will be imposed and cropped labels and images will be written in output directories.", -) - -def pagexml2label(dir_xml,dir_out,type_output,config, printspace, dir_images, dir_out_images): - if config: - with open(config) as f: - config_params = json.load(f) - else: - print("passed") - config_params = None - gt_list = get_content_of_dir(dir_xml) - get_images_of_ground_truth(gt_list,dir_xml,dir_out,type_output, config, config_params, printspace, dir_images, dir_out_images) - -@main.command() -@click.option( - "--dir_imgs", - "-dis", - help="directory of images with high resolution.", - type=click.Path(exists=True, file_okay=False), -) -@click.option( - "--dir_out_images", - "-dois", - help="directory where degraded images will be written.", - type=click.Path(exists=True, file_okay=False), -) - -@click.option( - "--dir_out_labels", - "-dols", - help="directory where original images will be written as labels.", - type=click.Path(exists=True, file_okay=False), -) -@click.option( - "--scales", - "-scs", - help="json dictionary where the scales are written.", - type=click.Path(exists=True, dir_okay=False), -) -def image_enhancement(dir_imgs, dir_out_images, dir_out_labels, scales): - ls_imgs = os.listdir(dir_imgs) - with open(scales) as f: - scale_dict = json.load(f) - ls_scales = scale_dict['scales'] - - for img in tqdm(ls_imgs): - img_name = img.split('.')[0] - img_type = img.split('.')[1] - image = cv2.imread(os.path.join(dir_imgs, img)) - for i, scale in enumerate(ls_scales): - height_sc = int(image.shape[0]*scale) - width_sc = int(image.shape[1]*scale) - - image_down_scaled = resize_image(image, height_sc, width_sc) - image_back_to_org_scale = resize_image(image_down_scaled, image.shape[0], image.shape[1]) - - cv2.imwrite(os.path.join(dir_out_images, img_name+'_'+str(i)+'.'+img_type), image_back_to_org_scale) - cv2.imwrite(os.path.join(dir_out_labels, img_name+'_'+str(i)+'.'+img_type), image) - - -@main.command() -@click.option( - "--dir_xml", - "-dx", - help="directory of GT page-xml files", - type=click.Path(exists=True, file_okay=False), -) - -@click.option( - "--dir_out_modal_image", - "-domi", - help="directory where ground truth images would be written", - type=click.Path(exists=True, file_okay=False), -) - -@click.option( - "--dir_out_classes", - "-docl", - help="directory where ground truth classes would be written", - type=click.Path(exists=True, file_okay=False), -) - -@click.option( - "--input_height", - "-ih", - help="input height", -) -@click.option( - "--input_width", - "-iw", - help="input width", -) -@click.option( - "--min_area_size", - "-min", - help="min area size of regions considered for reading order training.", -) - -@click.option( - "--min_area_early", - "-min_early", - help="If you have already generated a training dataset using a specific minimum area value and now wish to create a dataset with a smaller minimum area value, you can avoid regenerating the previous dataset by providing the earlier minimum area value. This will ensure that only the missing data is generated.", -) - -def machine_based_reading_order(dir_xml, dir_out_modal_image, dir_out_classes, input_height, input_width, min_area_size, min_area_early): - xml_files_ind = os.listdir(dir_xml) - xml_files_ind = [ind_xml for ind_xml in xml_files_ind if ind_xml.endswith('.xml')] - input_height = int(input_height) - input_width = int(input_width) - min_area = float(min_area_size) - if min_area_early: - min_area_early = float(min_area_early) - - - indexer_start= 0#55166 - max_area = 1 - #min_area = 0.0001 - - for ind_xml in tqdm(xml_files_ind): - indexer = 0 - #print(ind_xml) - #print('########################') - xml_file = os.path.join(dir_xml,ind_xml ) - f_name = ind_xml.split('.')[0] - _, _, _, file_name, id_paragraph, id_header,co_text_paragraph,co_text_header,tot_region_ref,x_len, y_len,index_tot_regions,img_poly = read_xml(xml_file) - - id_all_text = id_paragraph + id_header - co_text_all = co_text_paragraph + co_text_header - - - _, cy_main, x_min_main, x_max_main, y_min_main, y_max_main, _ = find_new_features_of_contours(co_text_header) - - img_header_and_sep = np.zeros((y_len,x_len), dtype='uint8') - - for j in range(len(cy_main)): - img_header_and_sep[int(y_max_main[j]):int(y_max_main[j])+12,int(x_min_main[j]):int(x_max_main[j]) ] = 1 - - - texts_corr_order_index = [index_tot_regions[tot_region_ref.index(i)] for i in id_all_text ] - texts_corr_order_index_int = [int(x) for x in texts_corr_order_index] - - - co_text_all, texts_corr_order_index_int, regions_ar_less_than_early_min = filter_contours_area_of_image(img_poly, co_text_all, texts_corr_order_index_int, max_area, min_area, min_area_early) - - - arg_array = np.array(range(len(texts_corr_order_index_int))) - - labels_con = np.zeros((y_len,x_len,len(arg_array)),dtype='uint8') - for i in range(len(co_text_all)): - img_label = np.zeros((y_len,x_len,3),dtype='uint8') - img_label=cv2.fillPoly(img_label, pts =[co_text_all[i]], color=(1,1,1)) - - img_label[:,:,0][img_poly[:,:,0]==5] = 2 - img_label[:,:,0][img_header_and_sep[:,:]==1] = 3 - - labels_con[:,:,i] = img_label[:,:,0] - - labels_con = resize_image(labels_con, input_height, input_width) - img_poly = resize_image(img_poly, input_height, input_width) - - - for i in range(len(texts_corr_order_index_int)): - for j in range(len(texts_corr_order_index_int)): - if i!=j: - if regions_ar_less_than_early_min: - if regions_ar_less_than_early_min[i]==1: - input_multi_visual_modal = np.zeros((input_height,input_width,3)).astype(np.int8) - final_f_name = f_name+'_'+str(indexer+indexer_start) - order_class_condition = texts_corr_order_index_int[i]-texts_corr_order_index_int[j] - if order_class_condition<0: - class_type = 1 - else: - class_type = 0 - - input_multi_visual_modal[:,:,0] = labels_con[:,:,i] - input_multi_visual_modal[:,:,1] = img_poly[:,:,0] - input_multi_visual_modal[:,:,2] = labels_con[:,:,j] - - np.save(os.path.join(dir_out_classes,final_f_name+'_missed.npy' ), class_type) - - cv2.imwrite(os.path.join(dir_out_modal_image,final_f_name+'_missed.png' ), input_multi_visual_modal) - indexer = indexer+1 - - else: - input_multi_visual_modal = np.zeros((input_height,input_width,3)).astype(np.int8) - final_f_name = f_name+'_'+str(indexer+indexer_start) - order_class_condition = texts_corr_order_index_int[i]-texts_corr_order_index_int[j] - if order_class_condition<0: - class_type = 1 - else: - class_type = 0 - - input_multi_visual_modal[:,:,0] = labels_con[:,:,i] - input_multi_visual_modal[:,:,1] = img_poly[:,:,0] - input_multi_visual_modal[:,:,2] = labels_con[:,:,j] - - np.save(os.path.join(dir_out_classes,final_f_name+'.npy' ), class_type) - - cv2.imwrite(os.path.join(dir_out_modal_image,final_f_name+'.png' ), input_multi_visual_modal) - indexer = indexer+1 - - -@main.command() -@click.option( - "--xml_file", - "-xml", - help="xml filename", - type=click.Path(exists=True, dir_okay=False), -) -@click.option( - "--dir_xml", - "-dx", - help="directory of GT page-xml files", - type=click.Path(exists=True, file_okay=False), -) - -@click.option( - "--dir_out", - "-o", - help="directory where plots will be written", - type=click.Path(exists=True, file_okay=False), -) - -@click.option( - "--dir_imgs", - "-di", - help="directory where the overlayed plots will be written", ) - -def visualize_reading_order(xml_file, dir_xml, dir_out, dir_imgs): - assert xml_file or dir_xml, "A single xml file -xml or a dir of xml files -dx is required not both of them" - - if dir_xml: - xml_files_ind = os.listdir(dir_xml) - xml_files_ind = [ind_xml for ind_xml in xml_files_ind if ind_xml.endswith('.xml')] - else: - xml_files_ind = [xml_file] - - indexer_start= 0#55166 - #min_area = 0.0001 - - for ind_xml in tqdm(xml_files_ind): - indexer = 0 - #print(ind_xml) - #print('########################') - #xml_file = os.path.join(dir_xml,ind_xml ) - - if dir_xml: - xml_file = os.path.join(dir_xml,ind_xml ) - f_name = Path(ind_xml).stem - else: - xml_file = os.path.join(ind_xml ) - f_name = Path(ind_xml).stem - print(f_name, 'f_name') - - #f_name = ind_xml.split('.')[0] - _, _, _, file_name, id_paragraph, id_header,co_text_paragraph,co_text_header,tot_region_ref,x_len, y_len,index_tot_regions,img_poly = read_xml(xml_file) - - id_all_text = id_paragraph + id_header - co_text_all = co_text_paragraph + co_text_header - - - cx_main, cy_main, x_min_main, x_max_main, y_min_main, y_max_main, _ = find_new_features_of_contours(co_text_all) - - texts_corr_order_index = [int(index_tot_regions[tot_region_ref.index(i)]) for i in id_all_text ] - #texts_corr_order_index_int = [int(x) for x in texts_corr_order_index] - - - #cx_ordered = np.array(cx_main)[np.array(texts_corr_order_index)] - #cx_ordered = cx_ordered.astype(np.int32) - - cx_ordered = [int(val) for (_, val) in sorted(zip(texts_corr_order_index, cx_main), key=lambda x: \ - x[0], reverse=False)] - #cx_ordered = cx_ordered.astype(np.int32) - - cy_ordered = [int(val) for (_, val) in sorted(zip(texts_corr_order_index, cy_main), key=lambda x: \ - x[0], reverse=False)] - #cy_ordered = cy_ordered.astype(np.int32) - - - color = (0, 0, 255) - thickness = 20 - if dir_imgs: - layout = np.zeros( (y_len,x_len,3) ) - layout = cv2.fillPoly(layout, pts =co_text_all, color=(1,1,1)) - - img_file_name_with_format = find_format_of_given_filename_in_dir(dir_imgs, f_name) - img = cv2.imread(os.path.join(dir_imgs, img_file_name_with_format)) - - overlayed = overlay_layout_on_image(layout, img, cx_ordered, cy_ordered, color, thickness) - cv2.imwrite(os.path.join(dir_out, f_name+'.png'), overlayed) - - else: - img = np.zeros( (y_len,x_len,3) ) - img = cv2.fillPoly(img, pts =co_text_all, color=(255,0,0)) - for i in range(len(cx_ordered)-1): - start_point = (int(cx_ordered[i]), int(cy_ordered[i])) - end_point = (int(cx_ordered[i+1]), int(cy_ordered[i+1])) - img = cv2.arrowedLine(img, start_point, end_point, - color, thickness, tipLength = 0.03) - - cv2.imwrite(os.path.join(dir_out, f_name+'.png'), img) - - -@main.command() -@click.option( - "--xml_file", - "-xml", - help="xml filename", - type=click.Path(exists=True, dir_okay=False), -) -@click.option( - "--dir_xml", - "-dx", - help="directory of GT page-xml files", - type=click.Path(exists=True, file_okay=False), -) - -@click.option( - "--dir_out", - "-o", - help="directory where plots will be written", - type=click.Path(exists=True, file_okay=False), -) - -@click.option( - "--dir_imgs", - "-di", - help="directory of images where textline segmentation will be overlayed", ) - -def visualize_textline_segmentation(xml_file, dir_xml, dir_out, dir_imgs): - assert xml_file or dir_xml, "A single xml file -xml or a dir of xml files -dx is required not both of them" - if dir_xml: - xml_files_ind = os.listdir(dir_xml) - xml_files_ind = [ind_xml for ind_xml in xml_files_ind if ind_xml.endswith('.xml')] - else: - xml_files_ind = [xml_file] - - for ind_xml in tqdm(xml_files_ind): - indexer = 0 - #print(ind_xml) - #print('########################') - xml_file = os.path.join(dir_xml,ind_xml ) - f_name = Path(ind_xml).stem - - img_file_name_with_format = find_format_of_given_filename_in_dir(dir_imgs, f_name) - img = cv2.imread(os.path.join(dir_imgs, img_file_name_with_format)) - - co_tetxlines, y_len, x_len = get_textline_contours_for_visualization(xml_file) - - added_image = visualize_image_from_contours(co_tetxlines, img) - - cv2.imwrite(os.path.join(dir_out, f_name+'.png'), added_image) - - - -@main.command() -@click.option( - "--xml_file", - "-xml", - help="xml filename", - type=click.Path(exists=True, dir_okay=False), -) -@click.option( - "--dir_xml", - "-dx", - help="directory of GT page-xml files", - type=click.Path(exists=True, file_okay=False), -) - -@click.option( - "--dir_out", - "-o", - help="directory where plots will be written", - type=click.Path(exists=True, file_okay=False), -) - -@click.option( - "--dir_imgs", - "-di", - help="directory of images where textline segmentation will be overlayed", ) - -def visualize_layout_segmentation(xml_file, dir_xml, dir_out, dir_imgs): - assert xml_file or dir_xml, "A single xml file -xml or a dir of xml files -dx is required not both of them" - if dir_xml: - xml_files_ind = os.listdir(dir_xml) - xml_files_ind = [ind_xml for ind_xml in xml_files_ind if ind_xml.endswith('.xml')] - else: - xml_files_ind = [xml_file] - - for ind_xml in tqdm(xml_files_ind): - indexer = 0 - #print(ind_xml) - #print('########################') - if dir_xml: - xml_file = os.path.join(dir_xml,ind_xml ) - f_name = Path(ind_xml).stem - else: - xml_file = os.path.join(ind_xml ) - f_name = Path(ind_xml).stem - print(f_name, 'f_name') - - img_file_name_with_format = find_format_of_given_filename_in_dir(dir_imgs, f_name) - img = cv2.imread(os.path.join(dir_imgs, img_file_name_with_format)) - - co_text, co_graphic, co_sep, co_img, co_table, co_map, co_noise, y_len, x_len = get_layout_contours_for_visualization(xml_file) - - - added_image = visualize_image_from_contours_layout(co_text['paragraph'], co_text['header']+co_text['heading'], co_text['drop-capital'], co_sep, co_img, co_text['marginalia'], co_table, img) - - cv2.imwrite(os.path.join(dir_out, f_name+'.png'), added_image) - - - - -@main.command() -@click.option( - "--xml_file", - "-xml", - help="xml filename", - type=click.Path(exists=True, dir_okay=False), -) -@click.option( - "--dir_xml", - "-dx", - help="directory of GT page-xml files", - type=click.Path(exists=True, file_okay=False), -) - -@click.option( - "--dir_out", - "-o", - help="directory where plots will be written", - type=click.Path(exists=True, file_okay=False), -) - - -def visualize_ocr_text(xml_file, dir_xml, dir_out): - assert xml_file or dir_xml, "A single xml file -xml or a dir of xml files -dx is required not both of them" - if dir_xml: - xml_files_ind = os.listdir(dir_xml) - xml_files_ind = [ind_xml for ind_xml in xml_files_ind if ind_xml.endswith('.xml')] - else: - xml_files_ind = [xml_file] - - font_path = "Charis-7.000/Charis-Regular.ttf" # Make sure this file exists! - font = ImageFont.truetype(font_path, 40) - - for ind_xml in tqdm(xml_files_ind): - indexer = 0 - #print(ind_xml) - #print('########################') - if dir_xml: - xml_file = os.path.join(dir_xml,ind_xml ) - f_name = Path(ind_xml).stem - else: - xml_file = os.path.join(ind_xml ) - f_name = Path(ind_xml).stem - print(f_name, 'f_name') - - co_tetxlines, y_len, x_len, ocr_texts = get_textline_contours_and_ocr_text(xml_file) - - total_bb_coordinates = [] - - image_text = Image.new("RGB", (x_len, y_len), "white") - draw = ImageDraw.Draw(image_text) - - - - for index, cnt in enumerate(co_tetxlines): - x,y,w,h = cv2.boundingRect(cnt) - #total_bb_coordinates.append([x,y,w,h]) - - #fit_text_single_line - - #x_bb = bb_ind[0] - #y_bb = bb_ind[1] - #w_bb = bb_ind[2] - #h_bb = bb_ind[3] - if ocr_texts[index]: - - - is_vertical = h > 2*w # Check orientation - font = fit_text_single_line(draw, ocr_texts[index], font_path, w, int(h*0.4) ) - - if is_vertical: - - vertical_font = fit_text_single_line(draw, ocr_texts[index], font_path, h, int(w * 0.8)) - - text_img = Image.new("RGBA", (h, w), (255, 255, 255, 0)) # Note: dimensions are swapped - text_draw = ImageDraw.Draw(text_img) - text_draw.text((0, 0), ocr_texts[index], font=vertical_font, fill="black") - - # Rotate text image by 90 degrees - rotated_text = text_img.rotate(90, expand=1) - - # Calculate paste position (centered in bbox) - paste_x = x + (w - rotated_text.width) // 2 - paste_y = y + (h - rotated_text.height) // 2 - - image_text.paste(rotated_text, (paste_x, paste_y), rotated_text) # Use rotated image as mask - else: - text_bbox = draw.textbbox((0, 0), ocr_texts[index], font=font) - text_width = text_bbox[2] - text_bbox[0] - text_height = text_bbox[3] - text_bbox[1] - - text_x = x + (w - text_width) // 2 # Center horizontally - text_y = y + (h - text_height) // 2 # Center vertically - - # Draw the text - draw.text((text_x, text_y), ocr_texts[index], fill="black", font=font) - image_text.save(os.path.join(dir_out, f_name+'.png')) diff --git a/src/eynollah/training/gt_gen_utils.py b/src/eynollah/training/gt_gen_utils.py deleted file mode 100644 index 50bb6fa..0000000 --- a/src/eynollah/training/gt_gen_utils.py +++ /dev/null @@ -1,1908 +0,0 @@ -import os -import numpy as np -import warnings -import xml.etree.ElementTree as ET -from tqdm import tqdm -import cv2 -from shapely import geometry -from pathlib import Path -from PIL import ImageFont - - -KERNEL = np.ones((5, 5), np.uint8) - -with warnings.catch_warnings(): - warnings.simplefilter("ignore") - - -def visualize_image_from_contours_layout(co_par, co_header, co_drop, co_sep, co_image, co_marginal, co_table, co_map, img): - alpha = 0.5 - - blank_image = np.ones( (img.shape[:]), dtype=np.uint8) * 255 - - col_header = (173, 216, 230) - col_drop = (0, 191, 255) - boundary_color = (143, 216, 200)#(0, 0, 255) # Dark gray for the boundary - col_par = (0, 0, 139) # Lighter gray for the filled area - col_image = (0, 100, 0) - col_sep = (255, 0, 0) - col_marginal = (106, 90, 205) - col_table = (0, 90, 205) - col_map = (90, 90, 205) - - if len(co_image)>0: - cv2.drawContours(blank_image, co_image, -1, col_image, thickness=cv2.FILLED) # Fill the contour - - if len(co_sep)>0: - cv2.drawContours(blank_image, co_sep, -1, col_sep, thickness=cv2.FILLED) # Fill the contour - - - if len(co_header)>0: - cv2.drawContours(blank_image, co_header, -1, col_header, thickness=cv2.FILLED) # Fill the contour - - if len(co_par)>0: - cv2.drawContours(blank_image, co_par, -1, col_par, thickness=cv2.FILLED) # Fill the contour - - cv2.drawContours(blank_image, co_par, -1, boundary_color, thickness=1) # Draw the boundary - - if len(co_drop)>0: - cv2.drawContours(blank_image, co_drop, -1, col_drop, thickness=cv2.FILLED) # Fill the contour - - if len(co_marginal)>0: - cv2.drawContours(blank_image, co_marginal, -1, col_marginal, thickness=cv2.FILLED) # Fill the contour - - if len(co_table)>0: - cv2.drawContours(blank_image, co_table, -1, col_table, thickness=cv2.FILLED) # Fill the contour - - if len(co_map)>0: - cv2.drawContours(blank_image, co_map, -1, col_map, thickness=cv2.FILLED) # Fill the contour - - img_final =cv2.cvtColor(blank_image, cv2.COLOR_BGR2RGB) - - added_image = cv2.addWeighted(img,alpha,img_final,1- alpha,0) - return added_image - - -def visualize_image_from_contours(contours, img): - alpha = 0.5 - - blank_image = np.ones( (img.shape[:]), dtype=np.uint8) * 255 - - boundary_color = (0, 0, 255) # Dark gray for the boundary - fill_color = (173, 216, 230) # Lighter gray for the filled area - - cv2.drawContours(blank_image, contours, -1, fill_color, thickness=cv2.FILLED) # Fill the contour - cv2.drawContours(blank_image, contours, -1, boundary_color, thickness=1) # Draw the boundary - - img_final =cv2.cvtColor(blank_image, cv2.COLOR_BGR2RGB) - - added_image = cv2.addWeighted(img,alpha,img_final,1- alpha,0) - return added_image - -def visualize_model_output(prediction, img, task): - if task == "binarization": - prediction = prediction * -1 - prediction = prediction + 1 - added_image = prediction * 255 - layout_only = None - else: - unique_classes = np.unique(prediction[:,:,0]) - rgb_colors = {'0' : [255, 255, 255], - '1' : [255, 0, 0], - '2' : [255, 125, 0], - '3' : [255, 0, 125], - '4' : [125, 125, 125], - '5' : [125, 125, 0], - '6' : [0, 125, 255], - '7' : [0, 125, 0], - '8' : [125, 125, 125], - '9' : [0, 125, 255], - '10' : [125, 0, 125], - '11' : [0, 255, 0], - '12' : [0, 0, 255], - '13' : [0, 255, 255], - '14' : [255, 125, 125], - '15' : [255, 0, 255]} - - layout_only = np.zeros(prediction.shape) - - for unq_class in unique_classes: - rgb_class_unique = rgb_colors[str(int(unq_class))] - layout_only[:,:,0][prediction[:,:,0]==unq_class] = rgb_class_unique[0] - layout_only[:,:,1][prediction[:,:,0]==unq_class] = rgb_class_unique[1] - layout_only[:,:,2][prediction[:,:,0]==unq_class] = rgb_class_unique[2] - - - - img = resize_image(img, layout_only.shape[0], layout_only.shape[1]) - - layout_only = layout_only.astype(np.int32) - img = img.astype(np.int32) - - - - added_image = cv2.addWeighted(img,0.5,layout_only,0.1,0) - - return added_image, layout_only - -def get_content_of_dir(dir_in): - """ - Listing all ground truth page xml files. All files are needed to have xml format. - """ - - gt_all=os.listdir(dir_in) - gt_list = [file for file in gt_all if os.path.splitext(file)[1] == '.xml'] - return gt_list - -def return_parent_contours(contours, hierarchy): - contours_parent = [contours[i] for i in range(len(contours)) if hierarchy[0][i][3] == -1] - return contours_parent -def filter_contours_area_of_image_tables(image, contours, hierarchy, max_area, min_area): - found_polygons_early = list() - - jv = 0 - for c in contours: - if len(np.shape(c)) == 3: - c = c[0] - elif len(np.shape(c)) == 2: - pass - #c = c[0] - if len(c) < 3: # A polygon cannot have less than 3 points - continue - - c_e = [point for point in c] - polygon = geometry.Polygon(c_e) - # area = cv2.contourArea(c) - area = polygon.area - # Check that polygon has area greater than minimal area - if area >= min_area * np.prod(image.shape[:2]) and area <= max_area * np.prod(image.shape[:2]): # and hierarchy[0][jv][3]==-1 : - found_polygons_early.append(np.array([[point] for point in polygon.exterior.coords], dtype=np.int32)) - jv += 1 - return found_polygons_early - -def filter_contours_area_of_image(image, contours, order_index, max_area, min_area, min_early=None): - found_polygons_early = list() - order_index_filtered = list() - regions_ar_less_than_early_min = list() - #jv = 0 - for jv, c in enumerate(contours): - if len(np.shape(c)) == 3: - c = c[0] - elif len(np.shape(c)) == 2: - pass - if len(c) < 3: # A polygon cannot have less than 3 points - continue - c_e = [point for point in c] - polygon = geometry.Polygon(c_e) - area = polygon.area - if area >= min_area * np.prod(image.shape[:2]) and area <= max_area * np.prod(image.shape[:2]): # and hierarchy[0][jv][3]==-1 : - found_polygons_early.append(np.array([[point] for point in polygon.exterior.coords], dtype=np.uint)) - order_index_filtered.append(order_index[jv]) - if min_early: - if area < min_early * np.prod(image.shape[:2]) and area <= max_area * np.prod(image.shape[:2]): # and hierarchy[0][jv][3]==-1 : - regions_ar_less_than_early_min.append(1) - else: - regions_ar_less_than_early_min.append(0) - else: - regions_ar_less_than_early_min = None - - #jv += 1 - return found_polygons_early, order_index_filtered, regions_ar_less_than_early_min - -def return_contours_of_interested_region(region_pre_p, pixel, min_area=0.0002): - - # pixels of images are identified by 5 - if len(region_pre_p.shape) == 3: - cnts_images = (region_pre_p[:, :, 0] == pixel) * 1 - else: - cnts_images = (region_pre_p[:, :] == pixel) * 1 - cnts_images = cnts_images.astype(np.uint8) - cnts_images = np.repeat(cnts_images[:, :, np.newaxis], 3, axis=2) - imgray = cv2.cvtColor(cnts_images, cv2.COLOR_BGR2GRAY) - ret, thresh = cv2.threshold(imgray, 0, 255, 0) - - contours_imgs, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) - - #print(len(contours_imgs), hierarchy) - - contours_imgs = return_parent_contours(contours_imgs, hierarchy) - - #print(len(contours_imgs), "iki") - #contours_imgs = filter_contours_area_of_image_tables(thresh, contours_imgs, hierarchy, max_area=1, min_area=min_area) - - return contours_imgs -def update_region_contours(co_text, img_boundary, erosion_rate, dilation_rate, y_len, x_len, dilation_early=None, erosion_early=None): - co_text_eroded = [] - for con in co_text: - img_boundary_in = np.zeros( (y_len,x_len) ) - img_boundary_in = cv2.fillPoly(img_boundary_in, pts=[con], color=(1, 1, 1)) - - if dilation_early: - img_boundary_in = cv2.dilate(img_boundary_in[:,:], KERNEL, iterations=dilation_early) - - if erosion_early: - img_boundary_in = cv2.erode(img_boundary_in[:,:], KERNEL, iterations=erosion_early) - - #img_boundary_in = cv2.erode(img_boundary_in[:,:], KERNEL, iterations=7)#asiatica - if erosion_rate > 0: - img_boundary_in = cv2.erode(img_boundary_in[:,:], KERNEL, iterations=erosion_rate) - - pixel = 1 - min_size = 0 - - img_boundary_in = img_boundary_in.astype("uint8") - - con_eroded = return_contours_of_interested_region(img_boundary_in,pixel, min_size ) - - try: - if len(con_eroded)>1: - cnt_size = np.array([cv2.contourArea(con_eroded[j]) for j in range(len(con_eroded))]) - cnt = contours[np.argmax(cnt_size)] - co_text_eroded.append(cnt) - else: - co_text_eroded.append(con_eroded[0]) - except: - co_text_eroded.append(con) - - - img_boundary_in_dilated = cv2.dilate(img_boundary_in[:,:], KERNEL, iterations=dilation_rate) - #img_boundary_in_dilated = cv2.dilate(img_boundary_in[:,:], KERNEL, iterations=5) - - boundary = img_boundary_in_dilated[:,:] - img_boundary_in[:,:] - - img_boundary[:,:][boundary[:,:]==1] =1 - return co_text_eroded, img_boundary - -def get_textline_contours_for_visualization(xml_file): - tree1 = ET.parse(xml_file, parser = ET.XMLParser(encoding='utf-8')) - root1=tree1.getroot() - alltags=[elem.tag for elem in root1.iter()] - link=alltags[0].split('}')[0]+'}' - - - - x_len, y_len = 0, 0 - for jj in root1.iter(link+'Page'): - y_len=int(jj.attrib['imageHeight']) - x_len=int(jj.attrib['imageWidth']) - - region_tags = np.unique([x for x in alltags if x.endswith('TextLine')]) - tag_endings = ['}TextLine','}textline'] - co_use_case = [] - - for tag in region_tags: - if tag.endswith(tag_endings[0]) or tag.endswith(tag_endings[1]): - for nn in root1.iter(tag): - c_t_in = [] - sumi = 0 - for vv in nn.iter(): - if vv.tag == link + 'Coords': - coords = bool(vv.attrib) - if coords: - p_h = vv.attrib['points'].split(' ') - c_t_in.append( - np.array([[int(x.split(',')[0]), int(x.split(',')[1])] for x in p_h])) - break - else: - pass - - if vv.tag == link + 'Point': - c_t_in.append([int(float(vv.attrib['x'])), int(float(vv.attrib['y']))]) - sumi += 1 - elif vv.tag != link + 'Point' and sumi >= 1: - break - co_use_case.append(np.array(c_t_in)) - return co_use_case, y_len, x_len - - -def get_textline_contours_and_ocr_text(xml_file): - tree1 = ET.parse(xml_file, parser = ET.XMLParser(encoding='utf-8')) - root1=tree1.getroot() - alltags=[elem.tag for elem in root1.iter()] - link=alltags[0].split('}')[0]+'}' - - - - x_len, y_len = 0, 0 - for jj in root1.iter(link+'Page'): - y_len=int(jj.attrib['imageHeight']) - x_len=int(jj.attrib['imageWidth']) - - region_tags = np.unique([x for x in alltags if x.endswith('TextLine')]) - tag_endings = ['}TextLine','}textline'] - co_use_case = [] - ocr_textlines = [] - - for tag in region_tags: - if tag.endswith(tag_endings[0]) or tag.endswith(tag_endings[1]): - for nn in root1.iter(tag): - c_t_in = [] - ocr_text_in = [''] - sumi = 0 - for vv in nn.iter(): - if vv.tag == link + 'Coords': - for childtest2 in nn: - if childtest2.tag.endswith("TextEquiv"): - for child_uc in childtest2: - if child_uc.tag.endswith("Unicode"): - text = child_uc.text - ocr_text_in[0]= text - - coords = bool(vv.attrib) - if coords: - p_h = vv.attrib['points'].split(' ') - c_t_in.append( - np.array([[int(x.split(',')[0]), int(x.split(',')[1])] for x in p_h])) - break - else: - pass - - - - if vv.tag == link + 'Point': - c_t_in.append([int(float(vv.attrib['x'])), int(float(vv.attrib['y']))]) - sumi += 1 - elif vv.tag != link + 'Point' and sumi >= 1: - break - - - co_use_case.append(np.array(c_t_in)) - ocr_textlines.append(ocr_text_in[0]) - return co_use_case, y_len, x_len, ocr_textlines - -def fit_text_single_line(draw, text, font_path, max_width, max_height): - initial_font_size = 50 - font_size = initial_font_size - while font_size > 10: # Minimum font size - font = ImageFont.truetype(font_path, font_size) - text_bbox = draw.textbbox((0, 0), text, font=font) # Get text bounding box - text_width = text_bbox[2] - text_bbox[0] - text_height = text_bbox[3] - text_bbox[1] - - if text_width <= max_width and text_height <= max_height: - return font # Return the best-fitting font - - font_size -= 2 # Reduce font size and retry - - return ImageFont.truetype(font_path, 10) # Smallest font fallback - -def get_layout_contours_for_visualization(xml_file): - tree1 = ET.parse(xml_file, parser = ET.XMLParser(encoding='utf-8')) - root1=tree1.getroot() - alltags=[elem.tag for elem in root1.iter()] - link=alltags[0].split('}')[0]+'}' - - - x_len, y_len = 0, 0 - for jj in root1.iter(link+'Page'): - y_len=int(jj.attrib['imageHeight']) - x_len=int(jj.attrib['imageWidth']) - - region_tags=np.unique([x for x in alltags if x.endswith('Region')]) - co_text = {'drop-capital':[], "footnote":[], "footnote-continued":[], "heading":[], "signature-mark":[], "header":[], "catch-word":[], "page-number":[], "marginalia":[], "paragraph":[]} - all_defined_textregion_types = list(co_text.keys()) - co_graphic = {"handwritten-annotation":[], "decoration":[], "stamp":[], "signature":[]} - all_defined_graphic_types = list(co_graphic.keys()) - co_sep=[] - co_img=[] - co_table=[] - co_map=[] - co_noise=[] - - types_text = [] - types_graphic = [] - - for tag in region_tags: - if tag.endswith('}TextRegion') or tag.endswith('}Textregion'): - for nn in root1.iter(tag): - c_t_in = {'drop-capital':[], "footnote":[], "footnote-continued":[], "heading":[], "signature-mark":[], "header":[], "catch-word":[], "page-number":[], "marginalia":[], "paragraph":[]} - sumi=0 - for vv in nn.iter(): - # check the format of coords - if vv.tag==link+'Coords': - - coords=bool(vv.attrib) - if coords: - p_h=vv.attrib['points'].split(' ') - - if "rest_as_paragraph" in types_text: - types_text_without_paragraph = [element for element in types_text if element!='rest_as_paragraph' and element!='paragraph'] - if len(types_text_without_paragraph) == 0: - if "type" in nn.attrib: - c_t_in['paragraph'].append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) ) - else: - c_t_in['paragraph'].append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) ) - - elif len(types_text_without_paragraph) >= 1: - if "type" in nn.attrib: - if nn.attrib['type'] in types_text_without_paragraph: - c_t_in[nn.attrib['type']].append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) ) - else: - c_t_in['paragraph'].append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) ) - - else: - c_t_in['paragraph'].append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) ) - - else: - if "type" in nn.attrib: - if nn.attrib['type'] in all_defined_textregion_types: - c_t_in[nn.attrib['type']].append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) ) - else: - c_t_in['paragraph'].append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) ) - - break - else: - pass - - - if vv.tag==link+'Point': - if "rest_as_paragraph" in types_text: - types_text_without_paragraph = [element for element in types_text if element!='rest_as_paragraph' and element!='paragraph'] - if len(types_text_without_paragraph) == 0: - if "type" in nn.attrib: - c_t_in['paragraph'].append( [ int(float(vv.attrib['x'])) , int(float(vv.attrib['y'])) ] ) - sumi+=1 - elif len(types_text_without_paragraph) >= 1: - if "type" in nn.attrib: - if nn.attrib['type'] in types_text_without_paragraph: - c_t_in[nn.attrib['type']].append( [ int(float(vv.attrib['x'])) , int(float(vv.attrib['y'])) ] ) - sumi+=1 - else: - c_t_in['paragraph'].append( [ int(float(vv.attrib['x'])) , int(float(vv.attrib['y'])) ] ) - sumi+=1 - - else: - if "type" in nn.attrib: - if nn.attrib['type'] in all_defined_textregion_types: - c_t_in[nn.attrib['type']].append( [ int(float(vv.attrib['x'])) , int(float(vv.attrib['y'])) ] ) - sumi+=1 - - - elif vv.tag!=link+'Point' and sumi>=1: - break - - for element_text in list(c_t_in.keys()): - if len(c_t_in[element_text])>0: - co_text[element_text].append(np.array(c_t_in[element_text])) - - - if tag.endswith('}GraphicRegion') or tag.endswith('}graphicregion'): - #print('sth') - for nn in root1.iter(tag): - c_t_in_graphic = {"handwritten-annotation":[], "decoration":[], "stamp":[], "signature":[]} - sumi=0 - for vv in nn.iter(): - # check the format of coords - if vv.tag==link+'Coords': - coords=bool(vv.attrib) - if coords: - p_h=vv.attrib['points'].split(' ') - - if "rest_as_decoration" in types_graphic: - types_graphic_without_decoration = [element for element in types_graphic if element!='rest_as_decoration' and element!='decoration'] - if len(types_graphic_without_decoration) == 0: - if "type" in nn.attrib: - c_t_in_graphic['decoration'].append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) ) - elif len(types_graphic_without_decoration) >= 1: - if "type" in nn.attrib: - if nn.attrib['type'] in types_graphic_without_decoration: - c_t_in_graphic[nn.attrib['type']].append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) ) - else: - c_t_in_graphic['decoration'].append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) ) - - else: - if "type" in nn.attrib: - if nn.attrib['type'] in all_defined_graphic_types: - c_t_in_graphic[nn.attrib['type']].append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) ) - - break - else: - pass - - - if vv.tag==link+'Point': - if "rest_as_decoration" in types_graphic: - types_graphic_without_decoration = [element for element in types_graphic if element!='rest_as_decoration' and element!='decoration'] - if len(types_graphic_without_decoration) == 0: - if "type" in nn.attrib: - c_t_in_graphic['decoration'].append( [ int(float(vv.attrib['x'])) , int(float(vv.attrib['y'])) ] ) - sumi+=1 - elif len(types_graphic_without_decoration) >= 1: - if "type" in nn.attrib: - if nn.attrib['type'] in types_graphic_without_decoration: - c_t_in_graphic[nn.attrib['type']].append( [ int(float(vv.attrib['x'])) , int(float(vv.attrib['y'])) ] ) - sumi+=1 - else: - c_t_in_graphic['decoration'].append( [ int(float(vv.attrib['x'])) , int(float(vv.attrib['y'])) ] ) - sumi+=1 - - else: - if "type" in nn.attrib: - if nn.attrib['type'] in all_defined_graphic_types: - c_t_in_graphic[nn.attrib['type']].append( [ int(float(vv.attrib['x'])) , int(float(vv.attrib['y'])) ] ) - sumi+=1 - - elif vv.tag!=link+'Point' and sumi>=1: - break - - for element_graphic in list(c_t_in_graphic.keys()): - if len(c_t_in_graphic[element_graphic])>0: - co_graphic[element_graphic].append(np.array(c_t_in_graphic[element_graphic])) - - - if tag.endswith('}ImageRegion') or tag.endswith('}imageregion'): - for nn in root1.iter(tag): - c_t_in=[] - sumi=0 - for vv in nn.iter(): - if vv.tag==link+'Coords': - coords=bool(vv.attrib) - if coords: - p_h=vv.attrib['points'].split(' ') - c_t_in.append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) ) - break - else: - pass - - - if vv.tag==link+'Point': - c_t_in.append([ int(float(vv.attrib['x'])) , int(float(vv.attrib['y'])) ]) - sumi+=1 - - elif vv.tag!=link+'Point' and sumi>=1: - break - co_img.append(np.array(c_t_in)) - - - if tag.endswith('}SeparatorRegion') or tag.endswith('}separatorregion'): - for nn in root1.iter(tag): - c_t_in=[] - sumi=0 - for vv in nn.iter(): - # check the format of coords - if vv.tag==link+'Coords': - coords=bool(vv.attrib) - if coords: - p_h=vv.attrib['points'].split(' ') - c_t_in.append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) ) - break - else: - pass - - - if vv.tag==link+'Point': - c_t_in.append([ int(float(vv.attrib['x'])) , int(float(vv.attrib['y'])) ]) - sumi+=1 - - elif vv.tag!=link+'Point' and sumi>=1: - break - co_sep.append(np.array(c_t_in)) - - - if tag.endswith('}TableRegion') or tag.endswith('}tableregion'): - #print('sth') - for nn in root1.iter(tag): - c_t_in=[] - sumi=0 - for vv in nn.iter(): - # check the format of coords - if vv.tag==link+'Coords': - coords=bool(vv.attrib) - if coords: - p_h=vv.attrib['points'].split(' ') - c_t_in.append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) ) - break - else: - pass - - - if vv.tag==link+'Point': - c_t_in.append([ int(float(vv.attrib['x'])) , int(float(vv.attrib['y'])) ]) - sumi+=1 - #print(vv.tag,'in') - elif vv.tag!=link+'Point' and sumi>=1: - break - co_table.append(np.array(c_t_in)) - - if tag.endswith('}MapRegion') or tag.endswith('}mapregion'): - #print('sth') - for nn in root1.iter(tag): - c_t_in=[] - sumi=0 - for vv in nn.iter(): - # check the format of coords - if vv.tag==link+'Coords': - coords=bool(vv.attrib) - if coords: - p_h=vv.attrib['points'].split(' ') - c_t_in.append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) ) - break - else: - pass - - - if vv.tag==link+'Point': - c_t_in.append([ int(float(vv.attrib['x'])) , int(float(vv.attrib['y'])) ]) - sumi+=1 - #print(vv.tag,'in') - elif vv.tag!=link+'Point' and sumi>=1: - break - co_map.append(np.array(c_t_in)) - - - if tag.endswith('}NoiseRegion') or tag.endswith('}noiseregion'): - #print('sth') - for nn in root1.iter(tag): - c_t_in=[] - sumi=0 - for vv in nn.iter(): - # check the format of coords - if vv.tag==link+'Coords': - coords=bool(vv.attrib) - if coords: - p_h=vv.attrib['points'].split(' ') - c_t_in.append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) ) - break - else: - pass - - - if vv.tag==link+'Point': - c_t_in.append([ int(float(vv.attrib['x'])) , int(float(vv.attrib['y'])) ]) - sumi+=1 - #print(vv.tag,'in') - elif vv.tag!=link+'Point' and sumi>=1: - break - co_noise.append(np.array(c_t_in)) - return co_text, co_graphic, co_sep, co_img, co_table, co_map, co_noise, y_len, x_len - -def get_images_of_ground_truth(gt_list, dir_in, output_dir, output_type, config_file, config_params, printspace, dir_images, dir_out_images): - """ - Reading the page xml files and write the ground truth images into given output directory. - """ - ## to do: add footnote to text regions - - if dir_images: - ls_org_imgs = os.listdir(dir_images) - ls_org_imgs_stem = [os.path.splitext(item)[0] for item in ls_org_imgs] - for index in tqdm(range(len(gt_list))): - #try: - print(gt_list[index]) - tree1 = ET.parse(dir_in+'/'+gt_list[index], parser = ET.XMLParser(encoding='utf-8')) - root1=tree1.getroot() - alltags=[elem.tag for elem in root1.iter()] - link=alltags[0].split('}')[0]+'}' - - - x_len, y_len = 0, 0 - for jj in root1.iter(link+'Page'): - y_len=int(jj.attrib['imageHeight']) - x_len=int(jj.attrib['imageWidth']) - - if 'columns_width' in list(config_params.keys()): - columns_width_dict = config_params['columns_width'] - metadata_element = root1.find(link+'Metadata') - num_col = None - for child in metadata_element: - tag2 = child.tag - if tag2.endswith('}Comments') or tag2.endswith('}comments'): - text_comments = child.text - num_col = int(text_comments.split('num_col')[1]) - - if num_col: - x_new = columns_width_dict[str(num_col)] - y_new = int ( x_new * (y_len / float(x_len)) ) - - if printspace or "printspace_as_class_in_layout" in list(config_params.keys()): - region_tags = np.unique([x for x in alltags if x.endswith('PrintSpace') or x.endswith('Border')]) - co_use_case = [] - - for tag in region_tags: - tag_endings = ['}PrintSpace','}Border'] - - if tag.endswith(tag_endings[0]) or tag.endswith(tag_endings[1]): - for nn in root1.iter(tag): - c_t_in = [] - sumi = 0 - for vv in nn.iter(): - # check the format of coords - if vv.tag == link + 'Coords': - coords = bool(vv.attrib) - if coords: - p_h = vv.attrib['points'].split(' ') - c_t_in.append( - np.array([[int(x.split(',')[0]), int(x.split(',')[1])] for x in p_h])) - break - else: - pass - - if vv.tag == link + 'Point': - c_t_in.append([int(float(vv.attrib['x'])), int(float(vv.attrib['y']))]) - sumi += 1 - elif vv.tag != link + 'Point' and sumi >= 1: - break - co_use_case.append(np.array(c_t_in)) - - img = np.zeros((y_len, x_len, 3)) - - img_poly = cv2.fillPoly(img, pts=co_use_case, color=(1, 1, 1)) - - img_poly = img_poly.astype(np.uint8) - - imgray = cv2.cvtColor(img_poly, cv2.COLOR_BGR2GRAY) - _, thresh = cv2.threshold(imgray, 0, 255, 0) - - contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) - - cnt_size = np.array([cv2.contourArea(contours[j]) for j in range(len(contours))]) - - try: - cnt = contours[np.argmax(cnt_size)] - x, y, w, h = cv2.boundingRect(cnt) - except: - x, y , w, h = 0, 0, x_len, y_len - - bb_xywh = [x, y, w, h] - - - if config_file and (config_params['use_case']=='textline' or config_params['use_case']=='word' or config_params['use_case']=='glyph' or config_params['use_case']=='printspace'): - keys = list(config_params.keys()) - if "artificial_class_label" in keys: - artificial_class_rgb_color = (255,255,0) - artificial_class_label = config_params['artificial_class_label'] - - textline_rgb_color = (255, 0, 0) - - if config_params['use_case']=='textline': - region_tags = np.unique([x for x in alltags if x.endswith('TextLine')]) - elif config_params['use_case']=='word': - region_tags = np.unique([x for x in alltags if x.endswith('Word')]) - elif config_params['use_case']=='glyph': - region_tags = np.unique([x for x in alltags if x.endswith('Glyph')]) - elif config_params['use_case']=='printspace': - region_tags = np.unique([x for x in alltags if x.endswith('PrintSpace')]) - - co_use_case = [] - - for tag in region_tags: - if config_params['use_case']=='textline': - tag_endings = ['}TextLine','}textline'] - elif config_params['use_case']=='word': - tag_endings = ['}Word','}word'] - elif config_params['use_case']=='glyph': - tag_endings = ['}Glyph','}glyph'] - elif config_params['use_case']=='printspace': - tag_endings = ['}PrintSpace','}printspace'] - - if tag.endswith(tag_endings[0]) or tag.endswith(tag_endings[1]): - for nn in root1.iter(tag): - c_t_in = [] - sumi = 0 - for vv in nn.iter(): - # check the format of coords - if vv.tag == link + 'Coords': - coords = bool(vv.attrib) - if coords: - p_h = vv.attrib['points'].split(' ') - c_t_in.append( - np.array([[int(x.split(',')[0]), int(x.split(',')[1])] for x in p_h])) - break - else: - pass - - if vv.tag == link + 'Point': - c_t_in.append([int(float(vv.attrib['x'])), int(float(vv.attrib['y']))]) - sumi += 1 - elif vv.tag != link + 'Point' and sumi >= 1: - break - co_use_case.append(np.array(c_t_in)) - - - if "artificial_class_label" in keys: - img_boundary = np.zeros((y_len, x_len)) - erosion_rate = 0#1 - dilation_rate = 2 - dilation_early = 0 - erosion_early = 2 - co_use_case, img_boundary = update_region_contours(co_use_case, img_boundary, erosion_rate, dilation_rate, y_len, x_len, dilation_early=dilation_early, erosion_early=erosion_early) - - - img = np.zeros((y_len, x_len, 3)) - if output_type == '2d': - img_poly = cv2.fillPoly(img, pts=co_use_case, color=(1, 1, 1)) - if "artificial_class_label" in keys: - img_mask = np.copy(img_poly) - ##img_poly[:,:][(img_boundary[:,:]==1) & (img_mask[:,:,0]!=1)] = artificial_class_label - img_poly[:,:][img_boundary[:,:]==1] = artificial_class_label - elif output_type == '3d': - img_poly = cv2.fillPoly(img, pts=co_use_case, color=textline_rgb_color) - if "artificial_class_label" in keys: - img_mask = np.copy(img_poly) - img_poly[:,:,0][(img_boundary[:,:]==1) & (img_mask[:,:,0]!=255)] = artificial_class_rgb_color[0] - img_poly[:,:,1][(img_boundary[:,:]==1) & (img_mask[:,:,0]!=255)] = artificial_class_rgb_color[1] - img_poly[:,:,2][(img_boundary[:,:]==1) & (img_mask[:,:,0]!=255)] = artificial_class_rgb_color[2] - - - if printspace and config_params['use_case']!='printspace': - img_poly = img_poly[bb_xywh[1]:bb_xywh[1]+bb_xywh[3], bb_xywh[0]:bb_xywh[0]+bb_xywh[2], :] - - - if 'columns_width' in list(config_params.keys()) and num_col and config_params['use_case']!='printspace': - img_poly = resize_image(img_poly, y_new, x_new) - - try: - xml_file_stem = os.path.splitext(gt_list[index])[0] - cv2.imwrite(os.path.join(output_dir, xml_file_stem + '.png'), img_poly) - except: - xml_file_stem = os.path.splitext(gt_list[index])[0] - cv2.imwrite(os.path.join(output_dir, xml_file_stem + '.png'), img_poly) - - if dir_images: - org_image_name = ls_org_imgs[ls_org_imgs_stem.index(xml_file_stem)] - img_org = cv2.imread(os.path.join(dir_images, org_image_name)) - - if printspace and config_params['use_case']!='printspace': - img_org = img_org[bb_xywh[1]:bb_xywh[1]+bb_xywh[3], bb_xywh[0]:bb_xywh[0]+bb_xywh[2], :] - - if 'columns_width' in list(config_params.keys()) and num_col and config_params['use_case']!='printspace': - img_org = resize_image(img_org, y_new, x_new) - - cv2.imwrite(os.path.join(dir_out_images, org_image_name), img_org) - - - if config_file and config_params['use_case']=='layout': - keys = list(config_params.keys()) - - if "artificial_class_on_boundary" in keys: - elements_with_artificial_class = list(config_params['artificial_class_on_boundary']) - artificial_class_rgb_color = (255,255,0) - artificial_class_label = config_params['artificial_class_label'] - #values = config_params.values() - - if "printspace_as_class_in_layout" in list(config_params.keys()): - printspace_class_rgb_color = (125,125,255) - printspace_class_label = config_params['printspace_as_class_in_layout'] - - if 'textregions' in keys: - types_text_dict = config_params['textregions'] - types_text = list(types_text_dict.keys()) - types_text_label = list(types_text_dict.values()) - if 'graphicregions' in keys: - types_graphic_dict = config_params['graphicregions'] - types_graphic = list(types_graphic_dict.keys()) - types_graphic_label = list(types_graphic_dict.values()) - - - labels_rgb_color = [ (0,0,0), (255,0,0), (255,125,0), (255,0,125), (125,255,125), (125,125,0), (0,125,255), (0,125,0), (125,125,125), (255,0,255), (125,0,125), (0,255,0),(0,0,255), (0,255,255), (255,125,125), (0,125,125), (0,255,125), (255,125,255), (125,255,0), (125,255,255)] - - - region_tags=np.unique([x for x in alltags if x.endswith('Region')]) - co_text = {'drop-capital':[], "footnote":[], "footnote-continued":[], "heading":[], "signature-mark":[], "header":[], "catch-word":[], "page-number":[], "marginalia":[], "paragraph":[]} - all_defined_textregion_types = list(co_text.keys()) - co_graphic = {"handwritten-annotation":[], "decoration":[], "stamp":[], "signature":[]} - all_defined_graphic_types = list(co_graphic.keys()) - co_sep=[] - co_img=[] - co_table=[] - co_map=[] - co_noise=[] - - for tag in region_tags: - if 'textregions' in keys: - if tag.endswith('}TextRegion') or tag.endswith('}Textregion'): - for nn in root1.iter(tag): - c_t_in = {'drop-capital':[], "footnote":[], "footnote-continued":[], "heading":[], "signature-mark":[], "header":[], "catch-word":[], "page-number":[], "marginalia":[], "paragraph":[]} - sumi=0 - for vv in nn.iter(): - # check the format of coords - if vv.tag==link+'Coords': - - coords=bool(vv.attrib) - if coords: - p_h=vv.attrib['points'].split(' ') - - if "rest_as_paragraph" in types_text: - types_text_without_paragraph = [element for element in types_text if element!='rest_as_paragraph' and element!='paragraph'] - if len(types_text_without_paragraph) == 0: - if "type" in nn.attrib: - c_t_in['paragraph'].append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) ) - elif len(types_text_without_paragraph) >= 1: - if "type" in nn.attrib: - if nn.attrib['type'] in types_text_without_paragraph: - c_t_in[nn.attrib['type']].append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) ) - else: - c_t_in['paragraph'].append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) ) - - else: - if "type" in nn.attrib: - if nn.attrib['type'] in all_defined_textregion_types: - c_t_in[nn.attrib['type']].append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) ) - - break - else: - pass - - - if vv.tag==link+'Point': - if "rest_as_paragraph" in types_text: - types_text_without_paragraph = [element for element in types_text if element!='rest_as_paragraph' and element!='paragraph'] - if len(types_text_without_paragraph) == 0: - if "type" in nn.attrib: - c_t_in['paragraph'].append( [ int(float(vv.attrib['x'])) , int(float(vv.attrib['y'])) ] ) - sumi+=1 - elif len(types_text_without_paragraph) >= 1: - if "type" in nn.attrib: - if nn.attrib['type'] in types_text_without_paragraph: - c_t_in[nn.attrib['type']].append( [ int(float(vv.attrib['x'])) , int(float(vv.attrib['y'])) ] ) - sumi+=1 - else: - c_t_in['paragraph'].append( [ int(float(vv.attrib['x'])) , int(float(vv.attrib['y'])) ] ) - sumi+=1 - - else: - if "type" in nn.attrib: - if nn.attrib['type'] in all_defined_textregion_types: - c_t_in[nn.attrib['type']].append( [ int(float(vv.attrib['x'])) , int(float(vv.attrib['y'])) ] ) - sumi+=1 - - - elif vv.tag!=link+'Point' and sumi>=1: - break - - for element_text in list(c_t_in.keys()): - if len(c_t_in[element_text])>0: - co_text[element_text].append(np.array(c_t_in[element_text])) - - if 'graphicregions' in keys: - if tag.endswith('}GraphicRegion') or tag.endswith('}graphicregion'): - #print('sth') - for nn in root1.iter(tag): - c_t_in_graphic = {"handwritten-annotation":[], "decoration":[], "stamp":[], "signature":[]} - sumi=0 - for vv in nn.iter(): - # check the format of coords - if vv.tag==link+'Coords': - coords=bool(vv.attrib) - if coords: - p_h=vv.attrib['points'].split(' ') - - if "rest_as_decoration" in types_graphic: - types_graphic_without_decoration = [element for element in types_graphic if element!='rest_as_decoration' and element!='decoration'] - if len(types_graphic_without_decoration) == 0: - if "type" in nn.attrib: - c_t_in_graphic['decoration'].append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) ) - elif len(types_graphic_without_decoration) >= 1: - if "type" in nn.attrib: - if nn.attrib['type'] in types_graphic_without_decoration: - c_t_in_graphic[nn.attrib['type']].append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) ) - else: - c_t_in_graphic['decoration'].append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) ) - - else: - if "type" in nn.attrib: - if nn.attrib['type'] in all_defined_graphic_types: - c_t_in_graphic[nn.attrib['type']].append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) ) - - break - else: - pass - - - if vv.tag==link+'Point': - if "rest_as_decoration" in types_graphic: - types_graphic_without_decoration = [element for element in types_graphic if element!='rest_as_decoration' and element!='decoration'] - if len(types_graphic_without_decoration) == 0: - if "type" in nn.attrib: - c_t_in_graphic['decoration'].append( [ int(float(vv.attrib['x'])) , int(float(vv.attrib['y'])) ] ) - sumi+=1 - elif len(types_graphic_without_decoration) >= 1: - if "type" in nn.attrib: - if nn.attrib['type'] in types_graphic_without_decoration: - c_t_in_graphic[nn.attrib['type']].append( [ int(float(vv.attrib['x'])) , int(float(vv.attrib['y'])) ] ) - sumi+=1 - else: - c_t_in_graphic['decoration'].append( [ int(float(vv.attrib['x'])) , int(float(vv.attrib['y'])) ] ) - sumi+=1 - - else: - if "type" in nn.attrib: - if nn.attrib['type'] in all_defined_graphic_types: - c_t_in_graphic[nn.attrib['type']].append( [ int(float(vv.attrib['x'])) , int(float(vv.attrib['y'])) ] ) - sumi+=1 - - elif vv.tag!=link+'Point' and sumi>=1: - break - - for element_graphic in list(c_t_in_graphic.keys()): - if len(c_t_in_graphic[element_graphic])>0: - co_graphic[element_graphic].append(np.array(c_t_in_graphic[element_graphic])) - - - if 'imageregion' in keys: - if tag.endswith('}ImageRegion') or tag.endswith('}imageregion'): - for nn in root1.iter(tag): - c_t_in=[] - sumi=0 - for vv in nn.iter(): - if vv.tag==link+'Coords': - coords=bool(vv.attrib) - if coords: - p_h=vv.attrib['points'].split(' ') - c_t_in.append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) ) - break - else: - pass - - - if vv.tag==link+'Point': - c_t_in.append([ int(float(vv.attrib['x'])) , int(float(vv.attrib['y'])) ]) - sumi+=1 - - elif vv.tag!=link+'Point' and sumi>=1: - break - co_img.append(np.array(c_t_in)) - - - if 'separatorregion' in keys: - if tag.endswith('}SeparatorRegion') or tag.endswith('}separatorregion'): - for nn in root1.iter(tag): - c_t_in=[] - sumi=0 - for vv in nn.iter(): - # check the format of coords - if vv.tag==link+'Coords': - coords=bool(vv.attrib) - if coords: - p_h=vv.attrib['points'].split(' ') - c_t_in.append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) ) - break - else: - pass - - - if vv.tag==link+'Point': - c_t_in.append([ int(float(vv.attrib['x'])) , int(float(vv.attrib['y'])) ]) - sumi+=1 - - elif vv.tag!=link+'Point' and sumi>=1: - break - co_sep.append(np.array(c_t_in)) - - - - if 'tableregion' in keys: - if tag.endswith('}TableRegion') or tag.endswith('}tableregion'): - #print('sth') - for nn in root1.iter(tag): - c_t_in=[] - sumi=0 - for vv in nn.iter(): - # check the format of coords - if vv.tag==link+'Coords': - coords=bool(vv.attrib) - if coords: - p_h=vv.attrib['points'].split(' ') - c_t_in.append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) ) - break - else: - pass - - - if vv.tag==link+'Point': - c_t_in.append([ int(float(vv.attrib['x'])) , int(float(vv.attrib['y'])) ]) - sumi+=1 - #print(vv.tag,'in') - elif vv.tag!=link+'Point' and sumi>=1: - break - co_table.append(np.array(c_t_in)) - - if 'mapregion' in keys: - if tag.endswith('}MapRegion') or tag.endswith('}mapregion'): - #print('sth') - for nn in root1.iter(tag): - c_t_in=[] - sumi=0 - for vv in nn.iter(): - # check the format of coords - if vv.tag==link+'Coords': - coords=bool(vv.attrib) - if coords: - p_h=vv.attrib['points'].split(' ') - c_t_in.append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) ) - break - else: - pass - - - if vv.tag==link+'Point': - c_t_in.append([ int(float(vv.attrib['x'])) , int(float(vv.attrib['y'])) ]) - sumi+=1 - #print(vv.tag,'in') - elif vv.tag!=link+'Point' and sumi>=1: - break - co_map.append(np.array(c_t_in)) - - if 'noiseregion' in keys: - if tag.endswith('}NoiseRegion') or tag.endswith('}noiseregion'): - #print('sth') - for nn in root1.iter(tag): - c_t_in=[] - sumi=0 - for vv in nn.iter(): - # check the format of coords - if vv.tag==link+'Coords': - coords=bool(vv.attrib) - if coords: - p_h=vv.attrib['points'].split(' ') - c_t_in.append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) ) - break - else: - pass - - - if vv.tag==link+'Point': - c_t_in.append([ int(float(vv.attrib['x'])) , int(float(vv.attrib['y'])) ]) - sumi+=1 - #print(vv.tag,'in') - elif vv.tag!=link+'Point' and sumi>=1: - break - co_noise.append(np.array(c_t_in)) - - if "artificial_class_on_boundary" in keys: - img_boundary = np.zeros( (y_len,x_len) ) - if "paragraph" in elements_with_artificial_class: - erosion_rate = 2 - dilation_rate = 4 - co_text['paragraph'], img_boundary = update_region_contours(co_text['paragraph'], img_boundary, erosion_rate, dilation_rate, y_len, x_len ) - if "drop-capital" in elements_with_artificial_class: - erosion_rate = 1 - dilation_rate = 3 - co_text["drop-capital"], img_boundary = update_region_contours(co_text["drop-capital"], img_boundary, erosion_rate, dilation_rate, y_len, x_len ) - if "catch-word" in elements_with_artificial_class: - erosion_rate = 0 - dilation_rate = 3#4 - co_text["catch-word"], img_boundary = update_region_contours(co_text["catch-word"], img_boundary, erosion_rate, dilation_rate, y_len, x_len ) - if "page-number" in elements_with_artificial_class: - erosion_rate = 0 - dilation_rate = 3#4 - co_text["page-number"], img_boundary = update_region_contours(co_text["page-number"], img_boundary, erosion_rate, dilation_rate, y_len, x_len ) - if "header" in elements_with_artificial_class: - erosion_rate = 1 - dilation_rate = 4 - co_text["header"], img_boundary = update_region_contours(co_text["header"], img_boundary, erosion_rate, dilation_rate, y_len, x_len ) - if "heading" in elements_with_artificial_class: - erosion_rate = 1 - dilation_rate = 4 - co_text["heading"], img_boundary = update_region_contours(co_text["heading"], img_boundary, erosion_rate, dilation_rate, y_len, x_len ) - if "signature-mark" in elements_with_artificial_class: - erosion_rate = 1 - dilation_rate = 4 - co_text["signature-mark"], img_boundary = update_region_contours(co_text["signature-mark"], img_boundary, erosion_rate, dilation_rate, y_len, x_len ) - if "marginalia" in elements_with_artificial_class: - erosion_rate = 2 - dilation_rate = 4 - co_text["marginalia"], img_boundary = update_region_contours(co_text["marginalia"], img_boundary, erosion_rate, dilation_rate, y_len, x_len ) - if "footnote" in elements_with_artificial_class: - erosion_rate = 0#2 - dilation_rate = 2#4 - co_text["footnote"], img_boundary = update_region_contours(co_text["footnote"], img_boundary, erosion_rate, dilation_rate, y_len, x_len ) - if "footnote-continued" in elements_with_artificial_class: - erosion_rate = 0#2 - dilation_rate = 2#4 - co_text["footnote-continued"], img_boundary = update_region_contours(co_text["footnote-continued"], img_boundary, erosion_rate, dilation_rate, y_len, x_len ) - if "tableregion" in elements_with_artificial_class: - erosion_rate = 0#2 - dilation_rate = 3#4 - co_table, img_boundary = update_region_contours(co_table, img_boundary, erosion_rate, dilation_rate, y_len, x_len ) - if "mapregion" in elements_with_artificial_class: - erosion_rate = 0#2 - dilation_rate = 3#4 - co_map, img_boundary = update_region_contours(co_map, img_boundary, erosion_rate, dilation_rate, y_len, x_len ) - - - - img = np.zeros( (y_len,x_len,3) ) - - if output_type == '3d': - if 'graphicregions' in keys: - if 'rest_as_decoration' in types_graphic: - types_graphic[types_graphic=='rest_as_decoration'] = 'decoration' - for element_graphic in types_graphic: - if element_graphic == 'decoration': - color_label = labels_rgb_color[ config_params['graphicregions']['rest_as_decoration']] - else: - color_label = labels_rgb_color[ config_params['graphicregions'][element_graphic]] - img_poly=cv2.fillPoly(img, pts =co_graphic[element_graphic], color=color_label) - else: - for element_graphic in types_graphic: - color_label = labels_rgb_color[ config_params['graphicregions'][element_graphic]] - img_poly=cv2.fillPoly(img, pts =co_graphic[element_graphic], color=color_label) - - - if 'imageregion' in keys: - img_poly=cv2.fillPoly(img, pts =co_img, color=labels_rgb_color[ config_params['imageregion']]) - if 'tableregion' in keys: - img_poly=cv2.fillPoly(img, pts =co_table, color=labels_rgb_color[ config_params['tableregion']]) - if 'mapregion' in keys: - img_poly=cv2.fillPoly(img, pts =co_map, color=labels_rgb_color[ config_params['mapregion']]) - if 'noiseregion' in keys: - img_poly=cv2.fillPoly(img, pts =co_noise, color=labels_rgb_color[ config_params['noiseregion']]) - - if 'textregions' in keys: - if 'rest_as_paragraph' in types_text: - types_text = ['paragraph'if ttind=='rest_as_paragraph' else ttind for ttind in types_text] - for element_text in types_text: - if element_text == 'paragraph': - color_label = labels_rgb_color[ config_params['textregions']['rest_as_paragraph']] - else: - color_label = labels_rgb_color[ config_params['textregions'][element_text]] - img_poly=cv2.fillPoly(img, pts =co_text[element_text], color=color_label) - else: - for element_text in types_text: - color_label = labels_rgb_color[ config_params['textregions'][element_text]] - img_poly=cv2.fillPoly(img, pts =co_text[element_text], color=color_label) - - - if "artificial_class_on_boundary" in keys: - img_poly[:,:,0][img_boundary[:,:]==1] = artificial_class_rgb_color[0] - img_poly[:,:,1][img_boundary[:,:]==1] = artificial_class_rgb_color[1] - img_poly[:,:,2][img_boundary[:,:]==1] = artificial_class_rgb_color[2] - - if 'separatorregion' in keys: - img_poly=cv2.fillPoly(img, pts =co_sep, color=labels_rgb_color[ config_params['separatorregion']]) - - - if "printspace_as_class_in_layout" in list(config_params.keys()): - printspace_mask = np.zeros((img_poly.shape[0], img_poly.shape[1])) - printspace_mask[bb_xywh[1]:bb_xywh[1]+bb_xywh[3], bb_xywh[0]:bb_xywh[0]+bb_xywh[2]] = 1 - - img_poly[:,:,0][printspace_mask[:,:] == 0] = printspace_class_rgb_color[0] - img_poly[:,:,1][printspace_mask[:,:] == 0] = printspace_class_rgb_color[1] - img_poly[:,:,2][printspace_mask[:,:] == 0] = printspace_class_rgb_color[2] - - - - - elif output_type == '2d': - if 'graphicregions' in keys: - if 'rest_as_decoration' in types_graphic: - types_graphic[types_graphic=='rest_as_decoration'] = 'decoration' - for element_graphic in types_graphic: - if element_graphic == 'decoration': - color_label = config_params['graphicregions']['rest_as_decoration'] - else: - color_label = config_params['graphicregions'][element_graphic] - img_poly=cv2.fillPoly(img, pts =co_graphic[element_graphic], color=color_label) - else: - for element_graphic in types_graphic: - color_label = config_params['graphicregions'][element_graphic] - img_poly=cv2.fillPoly(img, pts =co_graphic[element_graphic], color=color_label) - - - if 'imageregion' in keys: - color_label = config_params['imageregion'] - img_poly=cv2.fillPoly(img, pts =co_img, color=(color_label,color_label,color_label)) - if 'tableregion' in keys: - color_label = config_params['tableregion'] - img_poly=cv2.fillPoly(img, pts =co_table, color=(color_label,color_label,color_label)) - if 'mapregion' in keys: - color_label = config_params['mapregion'] - img_poly=cv2.fillPoly(img, pts =co_map, color=(color_label,color_label,color_label)) - if 'noiseregion' in keys: - color_label = config_params['noiseregion'] - img_poly=cv2.fillPoly(img, pts =co_noise, color=(color_label,color_label,color_label)) - - if 'textregions' in keys: - if 'rest_as_paragraph' in types_text: - types_text = ['paragraph'if ttind=='rest_as_paragraph' else ttind for ttind in types_text] - for element_text in types_text: - if element_text == 'paragraph': - color_label = config_params['textregions']['rest_as_paragraph'] - else: - color_label = config_params['textregions'][element_text] - img_poly=cv2.fillPoly(img, pts =co_text[element_text], color=color_label) - else: - for element_text in types_text: - color_label = config_params['textregions'][element_text] - img_poly=cv2.fillPoly(img, pts =co_text[element_text], color=color_label) - - if "artificial_class_on_boundary" in keys: - img_poly[:,:][img_boundary[:,:]==1] = artificial_class_label - - if 'separatorregion' in keys: - color_label = config_params['separatorregion'] - img_poly=cv2.fillPoly(img, pts =co_sep, color=(color_label,color_label,color_label)) - - if "printspace_as_class_in_layout" in list(config_params.keys()): - printspace_mask = np.zeros((img_poly.shape[0], img_poly.shape[1])) - printspace_mask[bb_xywh[1]:bb_xywh[1]+bb_xywh[3], bb_xywh[0]:bb_xywh[0]+bb_xywh[2]] = 1 - - img_poly[:,:,0][printspace_mask[:,:] == 0] = printspace_class_label - img_poly[:,:,1][printspace_mask[:,:] == 0] = printspace_class_label - img_poly[:,:,2][printspace_mask[:,:] == 0] = printspace_class_label - - - - if printspace: - img_poly = img_poly[bb_xywh[1]:bb_xywh[1]+bb_xywh[3], bb_xywh[0]:bb_xywh[0]+bb_xywh[2], :] - - if 'columns_width' in list(config_params.keys()) and num_col: - img_poly = resize_image(img_poly, y_new, x_new) - - try: - xml_file_stem = os.path.splitext(gt_list[index])[0] - cv2.imwrite(os.path.join(output_dir, xml_file_stem + '.png'), img_poly) - except: - xml_file_stem = os.path.splitext(gt_list[index])[0] - cv2.imwrite(os.path.join(output_dir, xml_file_stem + '.png'), img_poly) - - - if dir_images: - org_image_name = ls_org_imgs[ls_org_imgs_stem.index(xml_file_stem)] - img_org = cv2.imread(os.path.join(dir_images, org_image_name)) - - if printspace: - img_org = img_org[bb_xywh[1]:bb_xywh[1]+bb_xywh[3], bb_xywh[0]:bb_xywh[0]+bb_xywh[2], :] - - if 'columns_width' in list(config_params.keys()) and num_col: - img_org = resize_image(img_org, y_new, x_new) - - cv2.imwrite(os.path.join(dir_out_images, org_image_name), img_org) - - - -def find_new_features_of_contours(contours_main): - - areas_main = np.array([cv2.contourArea(contours_main[j]) for j in range(len(contours_main))]) - M_main = [cv2.moments(contours_main[j]) for j in range(len(contours_main))] - cx_main = [(M_main[j]["m10"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))] - cy_main = [(M_main[j]["m01"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))] - try: - x_min_main = np.array([np.min(contours_main[j][0][:, 0]) for j in range(len(contours_main))]) - - argmin_x_main = np.array([np.argmin(contours_main[j][0][:, 0]) for j in range(len(contours_main))]) - - x_min_from_argmin = np.array([contours_main[j][0][argmin_x_main[j], 0] for j in range(len(contours_main))]) - y_corr_x_min_from_argmin = np.array([contours_main[j][0][argmin_x_main[j], 1] for j in range(len(contours_main))]) - - x_max_main = np.array([np.max(contours_main[j][0][:, 0]) for j in range(len(contours_main))]) - - y_min_main = np.array([np.min(contours_main[j][0][:, 1]) for j in range(len(contours_main))]) - y_max_main = np.array([np.max(contours_main[j][0][:, 1]) for j in range(len(contours_main))]) - except: - x_min_main = np.array([np.min(contours_main[j][:, 0]) for j in range(len(contours_main))]) - - argmin_x_main = np.array([np.argmin(contours_main[j][:, 0]) for j in range(len(contours_main))]) - - x_min_from_argmin = np.array([contours_main[j][argmin_x_main[j], 0] for j in range(len(contours_main))]) - y_corr_x_min_from_argmin = np.array([contours_main[j][argmin_x_main[j], 1] for j in range(len(contours_main))]) - - x_max_main = np.array([np.max(contours_main[j][:, 0]) for j in range(len(contours_main))]) - - y_min_main = np.array([np.min(contours_main[j][:, 1]) for j in range(len(contours_main))]) - y_max_main = np.array([np.max(contours_main[j][:, 1]) for j in range(len(contours_main))]) - - return cx_main, cy_main, x_min_main, x_max_main, y_min_main, y_max_main, y_corr_x_min_from_argmin -def read_xml(xml_file): - file_name = Path(xml_file).stem - tree1 = ET.parse(xml_file, parser = ET.XMLParser(encoding='utf-8')) - root1=tree1.getroot() - alltags=[elem.tag for elem in root1.iter()] - link=alltags[0].split('}')[0]+'}' - - index_tot_regions = [] - tot_region_ref = [] - - for jj in root1.iter(link+'Page'): - y_len=int(jj.attrib['imageHeight']) - x_len=int(jj.attrib['imageWidth']) - - for jj in root1.iter(link+'RegionRefIndexed'): - index_tot_regions.append(jj.attrib['index']) - tot_region_ref.append(jj.attrib['regionRef']) - - if (link+'PrintSpace' in alltags) or (link+'Border' in alltags): - co_printspace = [] - if link+'PrintSpace' in alltags: - region_tags_printspace = np.unique([x for x in alltags if x.endswith('PrintSpace')]) - elif link+'Border' in alltags: - region_tags_printspace = np.unique([x for x in alltags if x.endswith('Border')]) - - for tag in region_tags_printspace: - if link+'PrintSpace' in alltags: - tag_endings_printspace = ['}PrintSpace','}printspace'] - elif link+'Border' in alltags: - tag_endings_printspace = ['}Border','}border'] - - if tag.endswith(tag_endings_printspace[0]) or tag.endswith(tag_endings_printspace[1]): - for nn in root1.iter(tag): - c_t_in = [] - sumi = 0 - for vv in nn.iter(): - # check the format of coords - if vv.tag == link + 'Coords': - coords = bool(vv.attrib) - if coords: - p_h = vv.attrib['points'].split(' ') - c_t_in.append( - np.array([[int(x.split(',')[0]), int(x.split(',')[1])] for x in p_h])) - break - else: - pass - - if vv.tag == link + 'Point': - c_t_in.append([int(float(vv.attrib['x'])), int(float(vv.attrib['y']))]) - sumi += 1 - elif vv.tag != link + 'Point' and sumi >= 1: - break - co_printspace.append(np.array(c_t_in)) - img_printspace = np.zeros( (y_len,x_len,3) ) - img_printspace=cv2.fillPoly(img_printspace, pts =co_printspace, color=(1,1,1)) - img_printspace = img_printspace.astype(np.uint8) - - imgray = cv2.cvtColor(img_printspace, cv2.COLOR_BGR2GRAY) - _, thresh = cv2.threshold(imgray, 0, 255, 0) - contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) - cnt_size = np.array([cv2.contourArea(contours[j]) for j in range(len(contours))]) - cnt = contours[np.argmax(cnt_size)] - x, y, w, h = cv2.boundingRect(cnt) - - bb_coord_printspace = [x, y, w, h] - - else: - bb_coord_printspace = None - - - region_tags=np.unique([x for x in alltags if x.endswith('Region')]) - co_text_paragraph=[] - co_text_drop=[] - co_text_heading=[] - co_text_header=[] - co_text_marginalia=[] - co_text_catch=[] - co_text_page_number=[] - co_text_signature_mark=[] - co_sep=[] - co_img=[] - co_table=[] - co_graphic=[] - co_graphic_text_annotation=[] - co_graphic_decoration=[] - co_noise=[] - - co_text_paragraph_text=[] - co_text_drop_text=[] - co_text_heading_text=[] - co_text_header_text=[] - co_text_marginalia_text=[] - co_text_catch_text=[] - co_text_page_number_text=[] - co_text_signature_mark_text=[] - co_sep_text=[] - co_img_text=[] - co_table_text=[] - co_graphic_text=[] - co_graphic_text_annotation_text=[] - co_graphic_decoration_text=[] - co_noise_text=[] - - id_paragraph = [] - id_header = [] - id_heading = [] - id_marginalia = [] - - for tag in region_tags: - if tag.endswith('}TextRegion') or tag.endswith('}Textregion'): - for nn in root1.iter(tag): - for child2 in nn: - tag2 = child2.tag - if tag2.endswith('}TextEquiv') or tag2.endswith('}TextEquiv'): - for childtext2 in child2: - if childtext2.tag.endswith('}Unicode') or childtext2.tag.endswith('}Unicode'): - if "type" in nn.attrib and nn.attrib['type']=='drop-capital': - co_text_drop_text.append(childtext2.text) - elif "type" in nn.attrib and nn.attrib['type']=='heading': - co_text_heading_text.append(childtext2.text) - elif "type" in nn.attrib and nn.attrib['type']=='signature-mark': - co_text_signature_mark_text.append(childtext2.text) - elif "type" in nn.attrib and nn.attrib['type']=='header': - co_text_header_text.append(childtext2.text) - ###elif "type" in nn.attrib and nn.attrib['type']=='catch-word': - ###co_text_catch_text.append(childtext2.text) - ###elif "type" in nn.attrib and nn.attrib['type']=='page-number': - ###co_text_page_number_text.append(childtext2.text) - elif "type" in nn.attrib and nn.attrib['type']=='marginalia': - co_text_marginalia_text.append(childtext2.text) - else: - co_text_paragraph_text.append(childtext2.text) - c_t_in_drop=[] - c_t_in_paragraph=[] - c_t_in_heading=[] - c_t_in_header=[] - c_t_in_page_number=[] - c_t_in_signature_mark=[] - c_t_in_catch=[] - c_t_in_marginalia=[] - - - sumi=0 - for vv in nn.iter(): - # check the format of coords - if vv.tag==link+'Coords': - - coords=bool(vv.attrib) - if coords: - #print('birda1') - p_h=vv.attrib['points'].split(' ') - - - - if "type" in nn.attrib and nn.attrib['type']=='drop-capital': - - c_t_in_drop.append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) ) - - elif "type" in nn.attrib and nn.attrib['type']=='heading': - ##id_heading.append(nn.attrib['id']) - c_t_in_heading.append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) ) - - - elif "type" in nn.attrib and nn.attrib['type']=='signature-mark': - - c_t_in_signature_mark.append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) ) - #print(c_t_in_paragraph) - elif "type" in nn.attrib and nn.attrib['type']=='header': - #id_header.append(nn.attrib['id']) - c_t_in_header.append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) ) - - - ###elif "type" in nn.attrib and nn.attrib['type']=='catch-word': - ###c_t_in_catch.append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) ) - - - ###elif "type" in nn.attrib and nn.attrib['type']=='page-number': - - ###c_t_in_page_number.append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) ) - - elif "type" in nn.attrib and nn.attrib['type']=='marginalia': - #id_marginalia.append(nn.attrib['id']) - - c_t_in_marginalia.append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) ) - else: - #id_paragraph.append(nn.attrib['id']) - - c_t_in_paragraph.append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) ) - - break - else: - pass - - - if vv.tag==link+'Point': - if "type" in nn.attrib and nn.attrib['type']=='drop-capital': - - c_t_in_drop.append([ int(float(vv.attrib['x'])) , int(float(vv.attrib['y'])) ]) - sumi+=1 - - elif "type" in nn.attrib and nn.attrib['type']=='heading': - #id_heading.append(nn.attrib['id']) - c_t_in_heading.append([ int(float(vv.attrib['x'])) , int(float(vv.attrib['y'])) ]) - sumi+=1 - - - elif "type" in nn.attrib and nn.attrib['type']=='signature-mark': - - c_t_in_signature_mark.append([ int(float(vv.attrib['x'])) , int(float(vv.attrib['y'])) ]) - sumi+=1 - elif "type" in nn.attrib and nn.attrib['type']=='header': - #id_header.append(nn.attrib['id']) - c_t_in_header.append([ int(float(vv.attrib['x'])) , int(float(vv.attrib['y'])) ]) - sumi+=1 - - - ###elif "type" in nn.attrib and nn.attrib['type']=='catch-word': - ###c_t_in_catch.append([ int(float(vv.attrib['x'])) , int(float(vv.attrib['y'])) ]) - ###sumi+=1 - - ###elif "type" in nn.attrib and nn.attrib['type']=='page-number': - - ###c_t_in_page_number.append([ int(float(vv.attrib['x'])) , int(float(vv.attrib['y'])) ]) - ###sumi+=1 - - elif "type" in nn.attrib and nn.attrib['type']=='marginalia': - #id_marginalia.append(nn.attrib['id']) - - c_t_in_marginalia.append([ int(float(vv.attrib['x'])) , int(float(vv.attrib['y'])) ]) - sumi+=1 - - else: - #id_paragraph.append(nn.attrib['id']) - c_t_in_paragraph.append([ int(float(vv.attrib['x'])) , int(float(vv.attrib['y'])) ]) - sumi+=1 - - elif vv.tag!=link+'Point' and sumi>=1: - break - - if len(c_t_in_drop)>0: - co_text_drop.append(np.array(c_t_in_drop)) - if len(c_t_in_paragraph)>0: - co_text_paragraph.append(np.array(c_t_in_paragraph)) - id_paragraph.append(nn.attrib['id']) - if len(c_t_in_heading)>0: - co_text_heading.append(np.array(c_t_in_heading)) - id_heading.append(nn.attrib['id']) - - if len(c_t_in_header)>0: - co_text_header.append(np.array(c_t_in_header)) - id_header.append(nn.attrib['id']) - if len(c_t_in_page_number)>0: - co_text_page_number.append(np.array(c_t_in_page_number)) - if len(c_t_in_catch)>0: - co_text_catch.append(np.array(c_t_in_catch)) - - if len(c_t_in_signature_mark)>0: - co_text_signature_mark.append(np.array(c_t_in_signature_mark)) - - if len(c_t_in_marginalia)>0: - co_text_marginalia.append(np.array(c_t_in_marginalia)) - id_marginalia.append(nn.attrib['id']) - - - elif tag.endswith('}GraphicRegion') or tag.endswith('}graphicregion'): - for nn in root1.iter(tag): - c_t_in=[] - c_t_in_text_annotation=[] - c_t_in_decoration=[] - sumi=0 - for vv in nn.iter(): - # check the format of coords - if vv.tag==link+'Coords': - coords=bool(vv.attrib) - if coords: - p_h=vv.attrib['points'].split(' ') - - if "type" in nn.attrib and nn.attrib['type']=='handwritten-annotation': - c_t_in_text_annotation.append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) ) - - elif "type" in nn.attrib and nn.attrib['type']=='decoration': - c_t_in_decoration.append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) ) - - else: - c_t_in.append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) ) - - - break - else: - pass - - - if vv.tag==link+'Point': - if "type" in nn.attrib and nn.attrib['type']=='handwritten-annotation': - c_t_in_text_annotation.append([ int(float(vv.attrib['x'])) , int(float(vv.attrib['y'])) ]) - sumi+=1 - - elif "type" in nn.attrib and nn.attrib['type']=='decoration': - c_t_in_decoration.append([ int(float(vv.attrib['x'])) , int(float(vv.attrib['y'])) ]) - sumi+=1 - - else: - c_t_in.append([ int(float(vv.attrib['x'])) , int(float(vv.attrib['y'])) ]) - sumi+=1 - - if len(c_t_in_text_annotation)>0: - co_graphic_text_annotation.append(np.array(c_t_in_text_annotation)) - if len(c_t_in_decoration)>0: - co_graphic_decoration.append(np.array(c_t_in_decoration)) - if len(c_t_in)>0: - co_graphic.append(np.array(c_t_in)) - - - - elif tag.endswith('}ImageRegion') or tag.endswith('}imageregion'): - for nn in root1.iter(tag): - c_t_in=[] - sumi=0 - for vv in nn.iter(): - # check the format of coords - if vv.tag==link+'Coords': - coords=bool(vv.attrib) - if coords: - p_h=vv.attrib['points'].split(' ') - c_t_in.append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) ) - break - else: - pass - - - if vv.tag==link+'Point': - c_t_in.append([ int(float(vv.attrib['x'])) , int(float(vv.attrib['y'])) ]) - sumi+=1 - elif vv.tag!=link+'Point' and sumi>=1: - break - co_img.append(np.array(c_t_in)) - co_img_text.append(' ') - - - elif tag.endswith('}SeparatorRegion') or tag.endswith('}separatorregion'): - for nn in root1.iter(tag): - c_t_in=[] - sumi=0 - for vv in nn.iter(): - # check the format of coords - if vv.tag==link+'Coords': - coords=bool(vv.attrib) - if coords: - p_h=vv.attrib['points'].split(' ') - c_t_in.append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) ) - break - else: - pass - - - if vv.tag==link+'Point': - c_t_in.append([ int(float(vv.attrib['x'])) , int(float(vv.attrib['y'])) ]) - sumi+=1 - elif vv.tag!=link+'Point' and sumi>=1: - break - co_sep.append(np.array(c_t_in)) - - - - elif tag.endswith('}TableRegion') or tag.endswith('}tableregion'): - for nn in root1.iter(tag): - c_t_in=[] - sumi=0 - for vv in nn.iter(): - # check the format of coords - if vv.tag==link+'Coords': - coords=bool(vv.attrib) - if coords: - p_h=vv.attrib['points'].split(' ') - c_t_in.append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) ) - break - else: - pass - - - if vv.tag==link+'Point': - c_t_in.append([ int(float(vv.attrib['x'])) , int(float(vv.attrib['y'])) ]) - sumi+=1 - - elif vv.tag!=link+'Point' and sumi>=1: - break - co_table.append(np.array(c_t_in)) - co_table_text.append(' ') - - elif tag.endswith('}NoiseRegion') or tag.endswith('}noiseregion'): - for nn in root1.iter(tag): - c_t_in=[] - sumi=0 - for vv in nn.iter(): - # check the format of coords - if vv.tag==link+'Coords': - coords=bool(vv.attrib) - if coords: - p_h=vv.attrib['points'].split(' ') - c_t_in.append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) ) - break - else: - pass - - - if vv.tag==link+'Point': - c_t_in.append([ int(float(vv.attrib['x'])) , int(float(vv.attrib['y'])) ]) - sumi+=1 - - elif vv.tag!=link+'Point' and sumi>=1: - break - co_noise.append(np.array(c_t_in)) - co_noise_text.append(' ') - - img = np.zeros( (y_len,x_len,3) ) - img_poly=cv2.fillPoly(img, pts =co_text_paragraph, color=(1,1,1)) - - img_poly=cv2.fillPoly(img, pts =co_text_heading, color=(2,2,2)) - img_poly=cv2.fillPoly(img, pts =co_text_header, color=(2,2,2)) - img_poly=cv2.fillPoly(img, pts =co_text_marginalia, color=(3,3,3)) - img_poly=cv2.fillPoly(img, pts =co_img, color=(4,4,4)) - img_poly=cv2.fillPoly(img, pts =co_sep, color=(5,5,5)) - - return tree1, root1, bb_coord_printspace, file_name, id_paragraph, id_header+id_heading, co_text_paragraph, co_text_header+co_text_heading,\ -tot_region_ref,x_len, y_len,index_tot_regions, img_poly - - - - -# def bounding_box(cnt,color, corr_order_index ): -# x, y, w, h = cv2.boundingRect(cnt) -# x = int(x*scale_w) -# y = int(y*scale_h) -# -# w = int(w*scale_w) -# h = int(h*scale_h) -# -# return [x,y,w,h,int(color), int(corr_order_index)+1] - -def resize_image(seg_in,input_height,input_width): - return cv2.resize(seg_in,(input_width,input_height),interpolation=cv2.INTER_NEAREST) - -def make_image_from_bb(width_l, height_l, bb_all): - bb_all =np.array(bb_all) - img_remade = np.zeros((height_l,width_l )) - - for i in range(bb_all.shape[0]): - img_remade[bb_all[i,1]:bb_all[i,1]+bb_all[i,3],bb_all[i,0]:bb_all[i,0]+bb_all[i,2] ] = 1 - return img_remade - -def update_list_and_return_first_with_length_bigger_than_one(index_element_to_be_updated, innner_index_pr_pos, pr_list, pos_list,list_inp): - list_inp.pop(index_element_to_be_updated) - if len(pr_list)>0: - list_inp.insert(index_element_to_be_updated, pr_list) - else: - index_element_to_be_updated = index_element_to_be_updated -1 - - list_inp.insert(index_element_to_be_updated+1, [innner_index_pr_pos]) - if len(pos_list)>0: - list_inp.insert(index_element_to_be_updated+2, pos_list) - - len_all_elements = [len(i) for i in list_inp] - list_len_bigger_1 = np.where(np.array(len_all_elements)>1) - list_len_bigger_1 = list_len_bigger_1[0] - - if len(list_len_bigger_1)>0: - early_list_bigger_than_one = list_len_bigger_1[0] - else: - early_list_bigger_than_one = -20 - return list_inp, early_list_bigger_than_one - -def overlay_layout_on_image(prediction, img, cx_ordered, cy_ordered, color, thickness): - - unique_classes = np.unique(prediction[:,:,0]) - rgb_colors = {'0' : [255, 255, 255], - '1' : [255, 0, 0], - '2' : [0, 0, 255], - '3' : [255, 0, 125], - '4' : [125, 125, 125], - '5' : [125, 125, 0], - '6' : [0, 125, 255], - '7' : [0, 125, 0], - '8' : [125, 125, 125], - '9' : [0, 125, 255], - '10' : [125, 0, 125], - '11' : [0, 255, 0], - '12' : [255, 125, 0], - '13' : [0, 255, 255], - '14' : [255, 125, 125], - '15' : [255, 0, 255]} - - layout_only = np.zeros(prediction.shape) - - for unq_class in unique_classes: - rgb_class_unique = rgb_colors[str(int(unq_class))] - layout_only[:,:,0][prediction[:,:,0]==unq_class] = rgb_class_unique[0] - layout_only[:,:,1][prediction[:,:,0]==unq_class] = rgb_class_unique[1] - layout_only[:,:,2][prediction[:,:,0]==unq_class] = rgb_class_unique[2] - - - - #img = self.resize_image(img, layout_only.shape[0], layout_only.shape[1]) - - layout_only = layout_only.astype(np.int32) - - for i in range(len(cx_ordered)-1): - start_point = (int(cx_ordered[i]), int(cy_ordered[i])) - end_point = (int(cx_ordered[i+1]), int(cy_ordered[i+1])) - layout_only = cv2.arrowedLine(layout_only, start_point, end_point, - color, thickness, tipLength = 0.03) - - img = img.astype(np.int32) - - - - added_image = cv2.addWeighted(img,0.5,layout_only,0.1,0) - - return added_image - -def find_format_of_given_filename_in_dir(dir_imgs, f_name): - ls_imgs = os.listdir(dir_imgs) - file_interested = [ind for ind in ls_imgs if ind.startswith(f_name+'.')] - return file_interested[0] diff --git a/src/eynollah/training/inference.py b/src/eynollah/training/inference.py deleted file mode 100644 index f74e9e1..0000000 --- a/src/eynollah/training/inference.py +++ /dev/null @@ -1,725 +0,0 @@ -import sys -import os -from typing import Tuple -import warnings -import json - -import numpy as np -import cv2 -from numpy._typing import NDArray -import tensorflow as tf -from keras.models import Model, load_model -from keras import backend as K -import click -from tensorflow.python.keras import backend as tensorflow_backend -import xml.etree.ElementTree as ET - -from .gt_gen_utils import ( - filter_contours_area_of_image, - find_new_features_of_contours, - read_xml, - resize_image, - update_list_and_return_first_with_length_bigger_than_one -) -from .models import ( - PatchEncoder, - Patches -) - -from.utils import (scale_padd_image_for_ocr) -from eynollah.utils.utils_ocr import (decode_batch_predictions) - -with warnings.catch_warnings(): - warnings.simplefilter("ignore") - -__doc__=\ -""" -Tool to load model and predict for given image. -""" - -class sbb_predict: - def __init__(self,image, dir_in, model, task, config_params_model, patches, save, save_layout, ground_truth, xml_file, cpu, out, min_area): - self.image=image - self.dir_in=dir_in - self.patches=patches - self.save=save - self.save_layout=save_layout - self.model_dir=model - self.ground_truth=ground_truth - self.task=task - self.config_params_model=config_params_model - self.xml_file = xml_file - self.out = out - self.cpu = cpu - if min_area: - self.min_area = float(min_area) - else: - self.min_area = 0 - - def resize_image(self,img_in,input_height,input_width): - return cv2.resize( img_in, ( input_width,input_height) ,interpolation=cv2.INTER_NEAREST) - - - def color_images(self,seg): - ann_u=range(self.n_classes) - if len(np.shape(seg))==3: - seg=seg[:,:,0] - - seg_img=np.zeros((np.shape(seg)[0],np.shape(seg)[1],3)).astype(np.uint8) - - for c in ann_u: - c=int(c) - seg_img[:,:,0][seg==c]=c - seg_img[:,:,1][seg==c]=c - seg_img[:,:,2][seg==c]=c - return seg_img - - def otsu_copy_binary(self,img): - img_r=np.zeros((img.shape[0],img.shape[1],3)) - img1=img[:,:,0] - - #print(img.min()) - #print(img[:,:,0].min()) - #blur = cv2.GaussianBlur(img,(5,5)) - #ret3,th3 = cv2.threshold(blur,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU) - _, threshold1 = cv2.threshold(img1, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU) - - - - img_r[:,:,0]=threshold1 - img_r[:,:,1]=threshold1 - img_r[:,:,2]=threshold1 - #img_r=img_r/float(np.max(img_r))*255 - return img_r - - def otsu_copy(self,img): - img_r=np.zeros((img.shape[0],img.shape[1],3)) - #img1=img[:,:,0] - - #print(img.min()) - #print(img[:,:,0].min()) - #blur = cv2.GaussianBlur(img,(5,5)) - #ret3,th3 = cv2.threshold(blur,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU) - _, threshold1 = cv2.threshold(img[:,:,0], 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU) - _, threshold2 = cv2.threshold(img[:,:,1], 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU) - _, threshold3 = cv2.threshold(img[:,:,2], 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU) - - - - img_r[:,:,0]=threshold1 - img_r[:,:,1]=threshold2 - img_r[:,:,2]=threshold3 - ###img_r=img_r/float(np.max(img_r))*255 - return img_r - - def soft_dice_loss(self,y_true, y_pred, epsilon=1e-6): - - axes = tuple(range(1, len(y_pred.shape)-1)) - - numerator = 2. * K.sum(y_pred * y_true, axes) - - denominator = K.sum(K.square(y_pred) + K.square(y_true), axes) - return 1.00 - K.mean(numerator / (denominator + epsilon)) # average over classes and batch - - # def weighted_categorical_crossentropy(self,weights=None): - # - # def loss(y_true, y_pred): - # labels_floats = tf.cast(y_true, tf.float32) - # per_pixel_loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=labels_floats,logits=y_pred) - # - # if weights is not None: - # weight_mask = tf.maximum(tf.reduce_max(tf.constant( - # np.array(weights, dtype=np.float32)[None, None, None]) - # * labels_floats, axis=-1), 1.0) - # per_pixel_loss = per_pixel_loss * weight_mask[:, :, :, None] - # return tf.reduce_mean(per_pixel_loss) - # return self.loss - - - def IoU(self,Yi,y_predi): - ## mean Intersection over Union - ## Mean IoU = TP/(FN + TP + FP) - - IoUs = [] - Nclass = np.unique(Yi) - for c in Nclass: - TP = np.sum( (Yi == c)&(y_predi==c) ) - FP = np.sum( (Yi != c)&(y_predi==c) ) - FN = np.sum( (Yi == c)&(y_predi != c)) - IoU = TP/float(TP + FP + FN) - if self.n_classes>2: - print("class {:02.0f}: #TP={:6.0f}, #FP={:6.0f}, #FN={:5.0f}, IoU={:4.3f}".format(c,TP,FP,FN,IoU)) - IoUs.append(IoU) - if self.n_classes>2: - mIoU = np.mean(IoUs) - print("_________________") - print("Mean IoU: {:4.3f}".format(mIoU)) - return mIoU - elif self.n_classes==2: - mIoU = IoUs[1] - print("_________________") - print("IoU: {:4.3f}".format(mIoU)) - return mIoU - - def start_new_session_and_model(self): - if self.task == "cnn-rnn-ocr": - if self.cpu: - os.environ['CUDA_VISIBLE_DEVICES']='-1' - self.model = load_model(self.model_dir) - self.model = tf.keras.models.Model( - self.model.get_layer(name = "image").input, - self.model.get_layer(name = "dense2").output) - else: - config = tf.compat.v1.ConfigProto() - config.gpu_options.allow_growth = True - - session = tf.compat.v1.Session(config=config) # tf.InteractiveSession() - tensorflow_backend.set_session(session) - - - ##if self.weights_dir!=None: - ##self.model.load_weights(self.weights_dir) - - assert isinstance(self.model, Model) - if self.task != 'classification' and self.task != 'reading_order': - self.img_height=self.model.layers[len(self.model.layers)-1].output_shape[1] - self.img_width=self.model.layers[len(self.model.layers)-1].output_shape[2] - self.n_classes=self.model.layers[len(self.model.layers)-1].output_shape[3] - - def visualize_model_output(self, prediction, img, task) -> Tuple[NDArray, NDArray]: - if task == "binarization": - prediction = prediction * -1 - prediction = prediction + 1 - added_image = prediction * 255 - layout_only = None - else: - unique_classes = np.unique(prediction[:,:,0]) - rgb_colors = {'0' : [255, 255, 255], - '1' : [255, 0, 0], - '2' : [255, 125, 0], - '3' : [255, 0, 125], - '4' : [125, 125, 125], - '5' : [125, 125, 0], - '6' : [0, 125, 255], - '7' : [0, 125, 0], - '8' : [125, 125, 125], - '9' : [0, 125, 255], - '10' : [125, 0, 125], - '11' : [0, 255, 0], - '12' : [0, 0, 255], - '13' : [0, 255, 255], - '14' : [255, 125, 125], - '15' : [255, 0, 255]} - - layout_only = np.zeros(prediction.shape) - - for unq_class in unique_classes: - rgb_class_unique = rgb_colors[str(int(unq_class))] - layout_only[:,:,0][prediction[:,:,0]==unq_class] = rgb_class_unique[0] - layout_only[:,:,1][prediction[:,:,0]==unq_class] = rgb_class_unique[1] - layout_only[:,:,2][prediction[:,:,0]==unq_class] = rgb_class_unique[2] - - - - img = self.resize_image(img, layout_only.shape[0], layout_only.shape[1]) - - layout_only = layout_only.astype(np.int32) - img = img.astype(np.int32) - - - - added_image = cv2.addWeighted(img,0.5,layout_only,0.1,0) - - assert isinstance(added_image, np.ndarray) - assert isinstance(layout_only, np.ndarray) - return added_image, layout_only - - def predict(self, image_dir): - assert isinstance(self.model, Model) - if self.task == 'classification': - classes_names = self.config_params_model['classification_classes_name'] - img_1ch = img=cv2.imread(image_dir, 0) - - img_1ch = img_1ch / 255.0 - img_1ch = cv2.resize(img_1ch, (self.config_params_model['input_height'], self.config_params_model['input_width']), interpolation=cv2.INTER_NEAREST) - img_in = np.zeros((1, img_1ch.shape[0], img_1ch.shape[1], 3)) - img_in[0, :, :, 0] = img_1ch[:, :] - img_in[0, :, :, 1] = img_1ch[:, :] - img_in[0, :, :, 2] = img_1ch[:, :] - - label_p_pred = self.model.predict(img_in, verbose='0') - index_class = np.argmax(label_p_pred[0]) - - print("Predicted Class: {}".format(classes_names[str(int(index_class))])) - elif self.task == "cnn-rnn-ocr": - img=cv2.imread(image_dir) - img = scale_padd_image_for_ocr(img, self.config_params_model['input_height'], self.config_params_model['input_width']) - - img = img / 255. - - with open(os.path.join(self.model_dir, "characters_org.txt"), 'r') as char_txt_f: - characters = json.load(char_txt_f) - - AUTOTUNE = tf.data.AUTOTUNE - - # Mapping characters to integers. - char_to_num = StringLookup(vocabulary=list(characters), mask_token=None) - - # Mapping integers back to original characters. - num_to_char = StringLookup( - vocabulary=char_to_num.get_vocabulary(), mask_token=None, invert=True - ) - preds = self.model.predict(img.reshape(1, img.shape[0], img.shape[1], img.shape[2]), verbose=0) - pred_texts = decode_batch_predictions(preds, num_to_char) - pred_texts = pred_texts[0].replace("[UNK]", "") - return pred_texts - - - elif self.task == 'reading_order': - img_height = self.config_params_model['input_height'] - img_width = self.config_params_model['input_width'] - - tree_xml, root_xml, bb_coord_printspace, file_name, id_paragraph, id_header, co_text_paragraph, co_text_header, tot_region_ref, x_len, y_len, index_tot_regions, img_poly = read_xml(self.xml_file) - _, cy_main, x_min_main, x_max_main, y_min_main, y_max_main, _ = find_new_features_of_contours(co_text_header) - - img_header_and_sep = np.zeros((y_len,x_len), dtype='uint8') - - - for j in range(len(cy_main)): - img_header_and_sep[int(y_max_main[j]):int(y_max_main[j])+12,int(x_min_main[j]):int(x_max_main[j]) ] = 1 - - co_text_all = co_text_paragraph + co_text_header - id_all_text = id_paragraph + id_header - - - ##texts_corr_order_index = [index_tot_regions[tot_region_ref.index(i)] for i in id_all_text ] - ##texts_corr_order_index_int = [int(x) for x in texts_corr_order_index] - texts_corr_order_index_int = list(np.array(range(len(co_text_all)))) - - #print(texts_corr_order_index_int) - - max_area = 1 - #print(np.shape(co_text_all[0]), len( np.shape(co_text_all[0]) ),'co_text_all') - #co_text_all = filter_contours_area_of_image_tables(img_poly, co_text_all, _, max_area, min_area) - #print(co_text_all,'co_text_all') - co_text_all, texts_corr_order_index_int, _ = filter_contours_area_of_image(img_poly, co_text_all, texts_corr_order_index_int, max_area, self.min_area) - - #print(texts_corr_order_index_int) - - #co_text_all = [co_text_all[index] for index in texts_corr_order_index_int] - id_all_text = [id_all_text[index] for index in texts_corr_order_index_int] - - labels_con = np.zeros((y_len,x_len,len(co_text_all)),dtype='uint8') - for i in range(len(co_text_all)): - img_label = np.zeros((y_len,x_len,3),dtype='uint8') - img_label=cv2.fillPoly(img_label, pts =[co_text_all[i]], color=(1,1,1)) - labels_con[:,:,i] = img_label[:,:,0] - - if bb_coord_printspace: - #bb_coord_printspace[x,y,w,h,_,_] - x = bb_coord_printspace[0] - y = bb_coord_printspace[1] - w = bb_coord_printspace[2] - h = bb_coord_printspace[3] - labels_con = labels_con[y:y+h, x:x+w, :] - img_poly = img_poly[y:y+h, x:x+w, :] - img_header_and_sep = img_header_and_sep[y:y+h, x:x+w] - - - - img3= np.copy(img_poly) - labels_con = resize_image(labels_con, img_height, img_width) - - img_header_and_sep = resize_image(img_header_and_sep, img_height, img_width) - - img3= resize_image (img3, img_height, img_width) - img3 = img3.astype(np.uint16) - - inference_bs = 1#4 - - input_1= np.zeros( (inference_bs, img_height, img_width,3)) - - - starting_list_of_regions = [list(range(labels_con.shape[2]))] - - index_update = 0 - index_selected = starting_list_of_regions[0] - - scalibility_num = 0 - while index_update>=0: - ij_list = starting_list_of_regions[index_update] - i = ij_list[0] - ij_list.pop(0) - - - pr_list = [] - post_list = [] - - batch_counter = 0 - tot_counter = 1 - - tot_iteration = len(ij_list) - full_bs_ite= tot_iteration//inference_bs - last_bs = tot_iteration % inference_bs - - jbatch_indexer =[] - for j in ij_list: - img1= np.repeat(labels_con[:,:,i][:, :, np.newaxis], 3, axis=2) - img2 = np.repeat(labels_con[:,:,j][:, :, np.newaxis], 3, axis=2) - - - img2[:,:,0][img3[:,:,0]==5] = 2 - img2[:,:,0][img_header_and_sep[:,:]==1] = 3 - - - - img1[:,:,0][img3[:,:,0]==5] = 2 - img1[:,:,0][img_header_and_sep[:,:]==1] = 3 - - #input_1= np.zeros( (height1, width1,3)) - - - jbatch_indexer.append(j) - - input_1[batch_counter,:,:,0] = img1[:,:,0]/3. - input_1[batch_counter,:,:,2] = img2[:,:,0]/3. - input_1[batch_counter,:,:,1] = img3[:,:,0]/5. - #input_1[batch_counter,:,:,:]= np.zeros( (batch_counter, height1, width1,3)) - batch_counter = batch_counter+1 - - #input_1[:,:,0] = img1[:,:,0]/3. - #input_1[:,:,2] = img2[:,:,0]/3. - #input_1[:,:,1] = img3[:,:,0]/5. - - if batch_counter==inference_bs or ( (tot_counter//inference_bs)==full_bs_ite and tot_counter%inference_bs==last_bs): - y_pr = self.model.predict(input_1 , verbose='0') - scalibility_num = scalibility_num+1 - - if batch_counter==inference_bs: - iteration_batches = inference_bs - else: - iteration_batches = last_bs - for jb in range(iteration_batches): - if y_pr[jb][0]>=0.5: - post_list.append(jbatch_indexer[jb]) - else: - pr_list.append(jbatch_indexer[jb]) - - batch_counter = 0 - jbatch_indexer = [] - - tot_counter = tot_counter+1 - - starting_list_of_regions, index_update = update_list_and_return_first_with_length_bigger_than_one(index_update, i, pr_list, post_list,starting_list_of_regions) - - - index_sort = [i[0] for i in starting_list_of_regions ] - - id_all_text = np.array(id_all_text)[index_sort] - - alltags=[elem.tag for elem in root_xml.iter()] - - - - link=alltags[0].split('}')[0]+'}' - name_space = alltags[0].split('}')[0] - name_space = name_space.split('{')[1] - - page_element = root_xml.find(link+'Page') - assert isinstance(page_element, ET.Element) - - """ - ro_subelement = ET.SubElement(page_element, 'ReadingOrder') - #print(page_element, 'page_element') - - #new_element = ET.Element('ReadingOrder') - - new_element_element = ET.Element('OrderedGroup') - new_element_element.set('id', "ro357564684568544579089") - - for index, id_text in enumerate(id_all_text): - new_element_2 = ET.Element('RegionRefIndexed') - new_element_2.set('regionRef', id_all_text[index]) - new_element_2.set('index', str(index_sort[index])) - - new_element_element.append(new_element_2) - - ro_subelement.append(new_element_element) - """ - ##ro_subelement = ET.SubElement(page_element, 'ReadingOrder') - - ro_subelement = ET.Element('ReadingOrder') - - ro_subelement2 = ET.SubElement(ro_subelement, 'OrderedGroup') - ro_subelement2.set('id', "ro357564684568544579089") - - for index, id_text in enumerate(id_all_text): - new_element_2 = ET.SubElement(ro_subelement2, 'RegionRefIndexed') - new_element_2.set('regionRef', id_all_text[index]) - new_element_2.set('index', str(index)) - - if (link+'PrintSpace' in alltags) or (link+'Border' in alltags): - page_element.insert(1, ro_subelement) - else: - page_element.insert(0, ro_subelement) - - alltags=[elem.tag for elem in root_xml.iter()] - - ET.register_namespace("",name_space) - tree_xml.write(os.path.join(self.out, file_name+'.xml'),xml_declaration=True,method='xml',encoding="utf8",default_namespace=None) - #tree_xml.write('library2.xml') - - else: - if self.patches: - #def textline_contours(img,input_width,input_height,n_classes,model): - - img=cv2.imread(image_dir) - self.img_org = np.copy(img) - - if img.shape[0] < self.img_height: - img = self.resize_image(img, self.img_height, img.shape[1]) - - if img.shape[1] < self.img_width: - img = self.resize_image(img, img.shape[0], self.img_width) - - margin = int(0.1 * self.img_width) - width_mid = self.img_width - 2 * margin - height_mid = self.img_height - 2 * margin - img = img / float(255.0) - - img_h = img.shape[0] - img_w = img.shape[1] - - prediction_true = np.zeros((img_h, img_w, 3)) - nxf = img_w / float(width_mid) - nyf = img_h / float(height_mid) - - nxf = int(nxf) + 1 if nxf > int(nxf) else int(nxf) - nyf = int(nyf) + 1 if nyf > int(nyf) else int(nyf) - - for i in range(nxf): - for j in range(nyf): - if i == 0: - index_x_d = i * width_mid - index_x_u = index_x_d + self.img_width - else: - index_x_d = i * width_mid - index_x_u = index_x_d + self.img_width - if j == 0: - index_y_d = j * height_mid - index_y_u = index_y_d + self.img_height - else: - index_y_d = j * height_mid - index_y_u = index_y_d + self.img_height - - if index_x_u > img_w: - index_x_u = img_w - index_x_d = img_w - self.img_width - if index_y_u > img_h: - index_y_u = img_h - index_y_d = img_h - self.img_height - - img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :] - label_p_pred = self.model.predict(img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2]), - verbose='0') - - if self.task == 'enhancement': - seg = label_p_pred[0, :, :, :] - seg = seg * 255 - elif self.task == 'segmentation' or self.task == 'binarization': - seg = np.argmax(label_p_pred, axis=3)[0] - seg = np.repeat(seg[:, :, np.newaxis], 3, axis=2) - else: - raise ValueError(f"Unhandled task {self.task}") - - - if i == 0 and j == 0: - seg = seg[0 : seg.shape[0] - margin, 0 : seg.shape[1] - margin] - prediction_true[index_y_d + 0 : index_y_u - margin, index_x_d + 0 : index_x_u - margin, :] = seg - elif i == nxf - 1 and j == nyf - 1: - seg = seg[margin : seg.shape[0] - 0, margin : seg.shape[1] - 0] - prediction_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - 0, :] = seg - elif i == 0 and j == nyf - 1: - seg = seg[margin : seg.shape[0] - 0, 0 : seg.shape[1] - margin] - prediction_true[index_y_d + margin : index_y_u - 0, index_x_d + 0 : index_x_u - margin, :] = seg - elif i == nxf - 1 and j == 0: - seg = seg[0 : seg.shape[0] - margin, margin : seg.shape[1] - 0] - prediction_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - 0, :] = seg - elif i == 0 and j != 0 and j != nyf - 1: - seg = seg[margin : seg.shape[0] - margin, 0 : seg.shape[1] - margin] - prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + 0 : index_x_u - margin, :] = seg - elif i == nxf - 1 and j != 0 and j != nyf - 1: - seg = seg[margin : seg.shape[0] - margin, margin : seg.shape[1] - 0] - prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - 0, :] = seg - elif i != 0 and i != nxf - 1 and j == 0: - seg = seg[0 : seg.shape[0] - margin, margin : seg.shape[1] - margin] - prediction_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - margin, :] = seg - elif i != 0 and i != nxf - 1 and j == nyf - 1: - seg = seg[margin : seg.shape[0] - 0, margin : seg.shape[1] - margin] - prediction_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - margin, :] = seg - else: - seg = seg[margin : seg.shape[0] - margin, margin : seg.shape[1] - margin] - prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - margin, :] = seg - prediction_true = prediction_true.astype(int) - prediction_true = cv2.resize(prediction_true, (self.img_org.shape[1], self.img_org.shape[0]), interpolation=cv2.INTER_NEAREST) - return prediction_true - - else: - - img=cv2.imread(image_dir) - self.img_org = np.copy(img) - - width=self.img_width - height=self.img_height - - img=img/255.0 - img=self.resize_image(img,self.img_height,self.img_width) - - - label_p_pred=self.model.predict( - img.reshape(1,img.shape[0],img.shape[1],img.shape[2])) - - if self.task == 'enhancement': - seg = label_p_pred[0, :, :, :] - seg = seg * 255 - elif self.task == 'segmentation' or self.task == 'binarization': - seg = np.argmax(label_p_pred, axis=3)[0] - seg = np.repeat(seg[:, :, np.newaxis], 3, axis=2) - else: - raise ValueError(f"Unhandled task {self.task}") - - prediction_true = seg.astype(int) - - prediction_true = cv2.resize(prediction_true, (self.img_org.shape[1], self.img_org.shape[0]), interpolation=cv2.INTER_NEAREST) - return prediction_true - - - - def run(self): - self.start_new_session_and_model() - if self.image: - res=self.predict(image_dir = self.image) - - if self.task == 'classification' or self.task == 'reading_order': - pass - elif self.task == 'enhancement': - if self.save: - cv2.imwrite(self.save,res) - elif self.task == "cnn-rnn-ocr": - print(f"Detected text: {res}") - else: - img_seg_overlayed, only_layout = self.visualize_model_output(res, self.img_org, self.task) - if self.save: - cv2.imwrite(self.save,img_seg_overlayed) - if self.save_layout: - cv2.imwrite(self.save_layout, only_layout) - - if self.ground_truth: - gt_img=cv2.imread(self.ground_truth) - self.IoU(gt_img[:,:,0],res[:,:,0]) - - else: - ls_images = os.listdir(self.dir_in) - for ind_image in ls_images: - f_name = ind_image.split('.')[0] - image_dir = os.path.join(self.dir_in, ind_image) - res=self.predict(image_dir) - - if self.task == 'classification' or self.task == 'reading_order': - pass - elif self.task == 'enhancement': - self.save = os.path.join(self.out, f_name+'.png') - cv2.imwrite(self.save,res) - elif self.task == "cnn-rnn-ocr": - print(f"Detected text for file name {f_name} is: {res}") - else: - img_seg_overlayed, only_layout = self.visualize_model_output(res, self.img_org, self.task) - self.save = os.path.join(self.out, f_name+'_overlayed.png') - cv2.imwrite(self.save,img_seg_overlayed) - self.save_layout = os.path.join(self.out, f_name+'_layout.png') - cv2.imwrite(self.save_layout, only_layout) - - if self.ground_truth: - gt_img=cv2.imread(self.ground_truth) - self.IoU(gt_img[:,:,0],res[:,:,0]) - - - -@click.command() -@click.option( - "--image", - "-i", - help="image filename", - type=click.Path(exists=True, dir_okay=False), -) -@click.option( - "--dir_in", - "-di", - help="directory of images", - type=click.Path(exists=True, file_okay=False), -) -@click.option( - "--out", - "-o", - help="output directory where xml with detected reading order will be written.", - type=click.Path(exists=True, file_okay=False), -) -@click.option( - "--patches/--no-patches", - "-p/-nop", - is_flag=True, - help="if this parameter set to true, this tool will try to do inference in patches.", -) -@click.option( - "--save", - "-s", - help="save prediction as a png file in current folder.", -) -@click.option( - "--save_layout", - "-sl", - help="save layout prediction only as a png file in current folder.", -) -@click.option( - "--model", - "-m", - help="directory of models", - type=click.Path(exists=True, file_okay=False), - required=True, -) -@click.option( - "--ground_truth", - "-gt", - help="ground truth directory if you want to see the iou of prediction.", -) -@click.option( - "--xml_file", - "-xml", - help="xml file with layout coordinates that reading order detection will be implemented on. The result will be written in the same xml file.", -) -@click.option( - "--cpu", - "-cpu", - help="For OCR, the default device is the GPU. If this parameter is set to true, inference will be performed on the CPU", - is_flag=True, -) -@click.option( - "--min_area", - "-min", - help="min area size of regions considered for reading order detection. The default value is zero and means that all text regions are considered for reading order.", -) -def main(image, dir_in, model, patches, save, save_layout, ground_truth, xml_file, cpu, out, min_area): - assert image or dir_in, "Either a single image -i or a dir_in -di is required" - with open(os.path.join(model,'config.json')) as f: - config_params_model = json.load(f) - task = config_params_model['task'] - if task != 'classification' and task != 'reading_order' and task != "cnn-rnn-ocr": - if image and not save: - print("Error: You used one of segmentation or binarization task with image input but not set -s, you need a filename to save visualized output with -s") - sys.exit(1) - if dir_in and not out: - print("Error: You used one of segmentation or binarization task with dir_in but not set -out") - sys.exit(1) - x=sbb_predict(image, dir_in, model, task, config_params_model, patches, save, save_layout, ground_truth, xml_file, cpu, out, min_area) - x.run() - diff --git a/src/eynollah/training/metrics.py b/src/eynollah/training/metrics.py deleted file mode 100644 index a8f47d7..0000000 --- a/src/eynollah/training/metrics.py +++ /dev/null @@ -1,363 +0,0 @@ -from tensorflow.keras import backend as K -import tensorflow as tf -import numpy as np - - -def focal_loss(gamma=2., alpha=4.): - gamma = float(gamma) - alpha = float(alpha) - - def focal_loss_fixed(y_true, y_pred): - """Focal loss for multi-classification - FL(p_t)=-alpha(1-p_t)^{gamma}ln(p_t) - Notice: y_pred is probability after softmax - gradient is d(Fl)/d(p_t) not d(Fl)/d(x) as described in paper - d(Fl)/d(p_t) * [p_t(1-p_t)] = d(Fl)/d(x) - Focal Loss for Dense Object Detection - https://arxiv.org/abs/1708.02002 - - Arguments: - y_true {tensor} -- ground truth labels, shape of [batch_size, num_cls] - y_pred {tensor} -- model's output, shape of [batch_size, num_cls] - - Keyword Arguments: - gamma {float} -- (default: {2.0}) - alpha {float} -- (default: {4.0}) - - Returns: - [tensor] -- loss. - """ - epsilon = 1.e-9 - y_true = tf.convert_to_tensor(y_true, tf.float32) - y_pred = tf.convert_to_tensor(y_pred, tf.float32) - - model_out = tf.add(y_pred, epsilon) - ce = tf.multiply(y_true, -tf.log(model_out)) - weight = tf.multiply(y_true, tf.pow(tf.subtract(1., model_out), gamma)) - fl = tf.multiply(alpha, tf.multiply(weight, ce)) - reduced_fl = tf.reduce_max(fl, axis=1) - return tf.reduce_mean(reduced_fl) - - return focal_loss_fixed - - -def weighted_categorical_crossentropy(weights=None): - """ weighted_categorical_crossentropy - - Args: - * weights: crossentropy weights - Returns: - * weighted categorical crossentropy function - """ - - def loss(y_true, y_pred): - labels_floats = tf.cast(y_true, tf.float32) - per_pixel_loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=labels_floats, logits=y_pred) - - if weights is not None: - weight_mask = tf.maximum(tf.reduce_max(tf.constant( - np.array(weights, dtype=np.float32)[None, None, None]) - * labels_floats, axis=-1), 1.0) - per_pixel_loss = per_pixel_loss * weight_mask[:, :, :, None] - return tf.reduce_mean(per_pixel_loss) - - return loss - - -def image_categorical_cross_entropy(y_true, y_pred, weights=None): - """ - :param y_true: tensor of shape (batch_size, height, width) representing the ground truth. - :param y_pred: tensor of shape (batch_size, height, width) representing the prediction. - :return: The mean cross-entropy on softmaxed tensors. - """ - - labels_floats = tf.cast(y_true, tf.float32) - per_pixel_loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=labels_floats, logits=y_pred) - - if weights is not None: - weight_mask = tf.maximum( - tf.reduce_max(tf.constant( - np.array(weights, dtype=np.float32)[None, None, None]) - * labels_floats, axis=-1), 1.0) - per_pixel_loss = per_pixel_loss * weight_mask[:, :, :, None] - - return tf.reduce_mean(per_pixel_loss) - - -def class_tversky(y_true, y_pred): - smooth = 1.0 # 1.00 - - y_true = K.permute_dimensions(y_true, (3, 1, 2, 0)) - y_pred = K.permute_dimensions(y_pred, (3, 1, 2, 0)) - - y_true_pos = K.batch_flatten(y_true) - y_pred_pos = K.batch_flatten(y_pred) - true_pos = K.sum(y_true_pos * y_pred_pos, 1) - false_neg = K.sum(y_true_pos * (1 - y_pred_pos), 1) - false_pos = K.sum((1 - y_true_pos) * y_pred_pos, 1) - alpha = 0.2 # 0.5 - beta = 0.8 - return (true_pos + smooth) / (true_pos + alpha * false_neg + beta * false_pos + smooth) - - -def focal_tversky_loss(y_true, y_pred): - pt_1 = class_tversky(y_true, y_pred) - gamma = 1.3 # 4./3.0#1.3#4.0/3.00# 0.75 - return K.sum(K.pow((1 - pt_1), gamma)) - - -def generalized_dice_coeff2(y_true, y_pred): - n_el = 1 - for dim in y_true.shape: - n_el *= int(dim) - n_cl = y_true.shape[-1] - w = K.zeros(shape=(n_cl,)) - w = (K.sum(y_true, axis=(0, 1, 2))) / n_el - w = 1 / (w ** 2 + 0.000001) - numerator = y_true * y_pred - numerator = w * K.sum(numerator, (0, 1, 2)) - numerator = K.sum(numerator) - denominator = y_true + y_pred - denominator = w * K.sum(denominator, (0, 1, 2)) - denominator = K.sum(denominator) - return 2 * numerator / denominator - - -def generalized_dice_coeff(y_true, y_pred): - axes = tuple(range(1, len(y_pred.shape) - 1)) - Ncl = y_pred.shape[-1] - w = K.zeros(shape=(Ncl,)) - w = K.sum(y_true, axis=axes) - w = 1 / (w ** 2 + 0.000001) - # Compute gen dice coef: - numerator = y_true * y_pred - numerator = w * K.sum(numerator, axes) - numerator = K.sum(numerator) - - denominator = y_true + y_pred - denominator = w * K.sum(denominator, axes) - denominator = K.sum(denominator) - - gen_dice_coef = 2 * numerator / denominator - - return gen_dice_coef - - -def generalized_dice_loss(y_true, y_pred): - return 1 - generalized_dice_coeff2(y_true, y_pred) - - -# TODO: document where this is from -def soft_dice_loss(y_true, y_pred, epsilon=1e-6): - """ - Soft dice loss calculation for arbitrary batch size, number of classes, and number of spatial dimensions. - Assumes the `channels_last` format. - - # Arguments - y_true: b x X x Y( x Z...) x c One hot encoding of ground truth - y_pred: b x X x Y( x Z...) x c Network output, must sum to 1 over c channel (such as after softmax) - epsilon: Used for numerical stability to avoid divide by zero errors - - # References - V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation - https://arxiv.org/abs/1606.04797 - More details on Dice loss formulation - https://mediatum.ub.tum.de/doc/1395260/1395260.pdf (page 72) - - Adapted from https://github.com/Lasagne/Recipes/issues/99#issuecomment-347775022 - """ - - # skip the batch and class axis for calculating Dice score - axes = tuple(range(1, len(y_pred.shape) - 1)) - - numerator = 2. * K.sum(y_pred * y_true, axes) - - denominator = K.sum(K.square(y_pred) + K.square(y_true), axes) - return 1.00 - K.mean(numerator / (denominator + epsilon)) # average over classes and batch - - -# TODO: document where this is from -def seg_metrics(y_true, y_pred, metric_name, metric_type='standard', drop_last=True, mean_per_class=False, - verbose=False): - """ - Compute mean metrics of two segmentation masks, via Keras. - - IoU(A,B) = |A & B| / (| A U B|) - Dice(A,B) = 2*|A & B| / (|A| + |B|) - - Args: - y_true: true masks, one-hot encoded. - y_pred: predicted masks, either softmax outputs, or one-hot encoded. - metric_name: metric to be computed, either 'iou' or 'dice'. - metric_type: one of 'standard' (default), 'soft', 'naive'. - In the standard version, y_pred is one-hot encoded and the mean - is taken only over classes that are present (in y_true or y_pred). - The 'soft' version of the metrics are computed without one-hot - encoding y_pred. - The 'naive' version return mean metrics where absent classes contribute - to the class mean as 1.0 (instead of being dropped from the mean). - drop_last = True: boolean flag to drop last class (usually reserved - for background class in semantic segmentation) - mean_per_class = False: return mean along batch axis for each class. - verbose = False: print intermediate results such as intersection, union - (as number of pixels). - Returns: - IoU/Dice of y_true and y_pred, as a float, unless mean_per_class == True - in which case it returns the per-class metric, averaged over the batch. - - Inputs are B*W*H*N tensors, with - B = batch size, - W = width, - H = height, - N = number of classes - """ - - flag_soft = (metric_type == 'soft') - flag_naive_mean = (metric_type == 'naive') - - # always assume one or more classes - num_classes = K.shape(y_true)[-1] - - if not flag_soft: - # get one-hot encoded masks from y_pred (true masks should already be one-hot) - y_pred = K.one_hot(K.argmax(y_pred), num_classes) - y_true = K.one_hot(K.argmax(y_true), num_classes) - - # if already one-hot, could have skipped above command - # keras uses float32 instead of float64, would give error down (but numpy arrays or keras.to_categorical gives float64) - y_true = K.cast(y_true, 'float32') - y_pred = K.cast(y_pred, 'float32') - - # intersection and union shapes are batch_size * n_classes (values = area in pixels) - axes = (1, 2) # W,H axes of each image - intersection = K.sum(K.abs(y_true * y_pred), axis=axes) - mask_sum = K.sum(K.abs(y_true), axis=axes) + K.sum(K.abs(y_pred), axis=axes) - union = mask_sum - intersection # or, np.logical_or(y_pred, y_true) for one-hot - - smooth = .001 - iou = (intersection + smooth) / (union + smooth) - dice = 2 * (intersection + smooth) / (mask_sum + smooth) - - metric = {'iou': iou, 'dice': dice}[metric_name] - - # define mask to be 0 when no pixels are present in either y_true or y_pred, 1 otherwise - mask = K.cast(K.not_equal(union, 0), 'float32') - - if drop_last: - metric = metric[:, :-1] - mask = mask[:, :-1] - - if verbose: - print('intersection, union') - print(K.eval(intersection), K.eval(union)) - print(K.eval(intersection / union)) - - # return mean metrics: remaining axes are (batch, classes) - if flag_naive_mean: - return K.mean(metric) - - # take mean only over non-absent classes - class_count = K.sum(mask, axis=0) - non_zero = tf.greater(class_count, 0) - non_zero_sum = tf.boolean_mask(K.sum(metric * mask, axis=0), non_zero) - non_zero_count = tf.boolean_mask(class_count, non_zero) - - if verbose: - print('Counts of inputs with class present, metrics for non-absent classes') - print(K.eval(class_count), K.eval(non_zero_sum / non_zero_count)) - - return K.mean(non_zero_sum / non_zero_count) - - -# TODO: document where this is from -# TODO: Why a different implementation than IoU from utils? -def mean_iou(y_true, y_pred, **kwargs): - """ - Compute mean Intersection over Union of two segmentation masks, via Keras. - - Calls metrics_k(y_true, y_pred, metric_name='iou'), see there for allowed kwargs. - """ - return seg_metrics(y_true, y_pred, metric_name='iou', **kwargs) - - -def Mean_IOU(y_true, y_pred): - nb_classes = K.int_shape(y_pred)[-1] - iou = [] - true_pixels = K.argmax(y_true, axis=-1) - pred_pixels = K.argmax(y_pred, axis=-1) - void_labels = K.equal(K.sum(y_true, axis=-1), 0) - for i in range(0, nb_classes): # exclude first label (background) and last label (void) - true_labels = K.equal(true_pixels, i) # & ~void_labels - pred_labels = K.equal(pred_pixels, i) # & ~void_labels - inter = tf.to_int32(true_labels & pred_labels) - union = tf.to_int32(true_labels | pred_labels) - legal_batches = K.sum(tf.to_int32(true_labels), axis=1) > 0 - ious = K.sum(inter, axis=1) / K.sum(union, axis=1) - iou.append(K.mean(tf.gather(ious, indices=tf.where(legal_batches)))) # returns average IoU of the same objects - iou = tf.stack(iou) - legal_labels = ~tf.debugging.is_nan(iou) - iou = tf.gather(iou, indices=tf.where(legal_labels)) - return K.mean(iou) - - -def iou_vahid(y_true, y_pred): - nb_classes = tf.shape(y_true)[-1] + tf.to_int32(1) - true_pixels = K.argmax(y_true, axis=-1) - pred_pixels = K.argmax(y_pred, axis=-1) - iou = [] - - for i in tf.range(nb_classes): - tp = K.sum(tf.to_int32(K.equal(true_pixels, i) & K.equal(pred_pixels, i))) - fp = K.sum(tf.to_int32(K.not_equal(true_pixels, i) & K.equal(pred_pixels, i))) - fn = K.sum(tf.to_int32(K.equal(true_pixels, i) & K.not_equal(pred_pixels, i))) - iouh = tp / (tp + fp + fn) - iou.append(iouh) - return K.mean(iou) - - -# TODO: copy from utils? -def IoU_metric(Yi, y_predi): - # mean Intersection over Union - # Mean IoU = TP/(FN + TP + FP) - y_predi = np.argmax(y_predi, axis=3) - y_testi = np.argmax(Yi, axis=3) - IoUs = [] - Nclass = int(np.max(Yi)) + 1 - for c in range(Nclass): - TP = np.sum((Yi == c) & (y_predi == c)) - FP = np.sum((Yi != c) & (y_predi == c)) - FN = np.sum((Yi == c) & (y_predi != c)) - IoU = TP / float(TP + FP + FN) - IoUs.append(IoU) - return K.cast(np.mean(IoUs), dtype='float32') - - -def IoU_metric_keras(y_true, y_pred): - # mean Intersection over Union - # Mean IoU = TP/(FN + TP + FP) - init = tf.global_variables_initializer() - sess = tf.Session() - sess.run(init) - - return IoU_metric(y_true.eval(session=sess), y_pred.eval(session=sess)) - - -# TODO: unused, remove? -def jaccard_distance_loss(y_true, y_pred, smooth=100): - """ - Jaccard = (|X & Y|)/ (|X|+ |Y| - |X & Y|) - = sum(|A*B|)/(sum(|A|)+sum(|B|)-sum(|A*B|)) - - The jaccard distance loss is usefull for unbalanced datasets. This has been - shifted so it converges on 0 and is smoothed to avoid exploding or disapearing - gradient. - - Ref: https://en.wikipedia.org/wiki/Jaccard_index - - @url: https://gist.github.com/wassname/f1452b748efcbeb4cb9b1d059dce6f96 - @author: wassname - """ - intersection = K.sum(K.abs(y_true * y_pred), axis=-1) - sum_ = K.sum(K.abs(y_true) + K.abs(y_pred), axis=-1) - jac = (intersection + smooth) / (sum_ - intersection + smooth) - return (1 - jac) * smooth diff --git a/src/eynollah/training/models.py b/src/eynollah/training/models.py deleted file mode 100644 index a1d52ce..0000000 --- a/src/eynollah/training/models.py +++ /dev/null @@ -1,801 +0,0 @@ -from tensorflow import keras -from keras.layers import ( - Activation, - Add, - AveragePooling2D, - BatchNormalization, - Conv2D, - Dense, - Dropout, - Embedding, - Flatten, - Input, - Lambda, - Layer, - LayerNormalization, - MaxPooling2D, - MultiHeadAttention, - UpSampling2D, - ZeroPadding2D, - add, - concatenate -) -from keras.models import Model -import tensorflow as tf -# from keras import layers, models -from keras.regularizers import l2 - -from eynollah.patch_encoder import Patches, PatchEncoder - -##mlp_head_units = [512, 256]#[2048, 1024] -###projection_dim = 64 -##transformer_layers = 2#8 -##num_heads = 1#4 -resnet50_Weights_path = './pretrained_model/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5' -IMAGE_ORDERING = 'channels_last' -MERGE_AXIS = -1 - - -class CTCLayer(tf.keras.layers.Layer): - def __init__(self, name=None): - super().__init__(name=name) - self.loss_fn = tf.keras.backend.ctc_batch_cost - - def call(self, y_true, y_pred): - batch_len = tf.cast(tf.shape(y_true)[0], dtype="int64") - input_length = tf.cast(tf.shape(y_pred)[1], dtype="int64") - label_length = tf.cast(tf.shape(y_true)[1], dtype="int64") - - input_length = input_length * tf.ones(shape=(batch_len, 1), dtype="int64") - label_length = label_length * tf.ones(shape=(batch_len, 1), dtype="int64") - loss = self.loss_fn(y_true, y_pred, input_length, label_length) - self.add_loss(loss) - - # At test time, just return the computed predictions. - return y_pred - -def mlp(x, hidden_units, dropout_rate): - for units in hidden_units: - x = Dense(units, activation=tf.nn.gelu)(x) - x = Dropout(dropout_rate)(x) - return x - -def one_side_pad(x): - x = ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING)(x) - if IMAGE_ORDERING == 'channels_first': - x = Lambda(lambda x: x[:, :, :-1, :-1])(x) - elif IMAGE_ORDERING == 'channels_last': - x = Lambda(lambda x: x[:, :-1, :-1, :])(x) - return x - - -def identity_block(input_tensor, kernel_size, filters, stage, block): - """The identity block is the block that has no conv layer at shortcut. - # Arguments - input_tensor: input tensor - kernel_size: defualt 3, the kernel size of middle conv layer at main path - filters: list of integers, the filterss of 3 conv layer at main path - stage: integer, current stage label, used for generating layer names - block: 'a','b'..., current block label, used for generating layer names - # Returns - Output tensor for the block. - """ - filters1, filters2, filters3 = filters - - if IMAGE_ORDERING == 'channels_last': - bn_axis = 3 - else: - bn_axis = 1 - - conv_name_base = 'res' + str(stage) + block + '_branch' - bn_name_base = 'bn' + str(stage) + block + '_branch' - - x = Conv2D(filters1, (1, 1), data_format=IMAGE_ORDERING, name=conv_name_base + '2a')(input_tensor) - x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x) - x = Activation('relu')(x) - - x = Conv2D(filters2, kernel_size, data_format=IMAGE_ORDERING, - padding='same', name=conv_name_base + '2b')(x) - x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x) - x = Activation('relu')(x) - - x = Conv2D(filters3, (1, 1), data_format=IMAGE_ORDERING, name=conv_name_base + '2c')(x) - x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x) - - x = add([x, input_tensor]) - x = Activation('relu')(x) - return x - - -def conv_block(input_tensor, kernel_size, filters, stage, block, strides=(2, 2)): - """conv_block is the block that has a conv layer at shortcut - # Arguments - input_tensor: input tensor - kernel_size: defualt 3, the kernel size of middle conv layer at main path - filters: list of integers, the filterss of 3 conv layer at main path - stage: integer, current stage label, used for generating layer names - block: 'a','b'..., current block label, used for generating layer names - # Returns - Output tensor for the block. - Note that from stage 3, the first conv layer at main path is with strides=(2,2) - And the shortcut should have strides=(2,2) as well - """ - filters1, filters2, filters3 = filters - - if IMAGE_ORDERING == 'channels_last': - bn_axis = 3 - else: - bn_axis = 1 - - conv_name_base = 'res' + str(stage) + block + '_branch' - bn_name_base = 'bn' + str(stage) + block + '_branch' - - x = Conv2D(filters1, (1, 1), data_format=IMAGE_ORDERING, strides=strides, - name=conv_name_base + '2a')(input_tensor) - x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x) - x = Activation('relu')(x) - - x = Conv2D(filters2, kernel_size, data_format=IMAGE_ORDERING, padding='same', - name=conv_name_base + '2b')(x) - x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x) - x = Activation('relu')(x) - - x = Conv2D(filters3, (1, 1), data_format=IMAGE_ORDERING, name=conv_name_base + '2c')(x) - x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x) - - shortcut = Conv2D(filters3, (1, 1), data_format=IMAGE_ORDERING, strides=strides, - name=conv_name_base + '1')(input_tensor) - shortcut = BatchNormalization(axis=bn_axis, name=bn_name_base + '1')(shortcut) - - x = add([x, shortcut]) - x = Activation('relu')(x) - return x - - -def resnet50_unet_light(n_classes, input_height=224, input_width=224, task="segmentation", weight_decay=1e-6, pretraining=False): - assert input_height % 32 == 0 - assert input_width % 32 == 0 - - img_input = Input(shape=(input_height, input_width, 3)) - - if IMAGE_ORDERING == 'channels_last': - bn_axis = 3 - else: - bn_axis = 1 - - x = ZeroPadding2D((3, 3), data_format=IMAGE_ORDERING)(img_input) - x = Conv2D(64, (7, 7), data_format=IMAGE_ORDERING, strides=(2, 2), kernel_regularizer=l2(weight_decay), - name='conv1')(x) - f1 = x - - x = BatchNormalization(axis=bn_axis, name='bn_conv1')(x) - x = Activation('relu')(x) - x = MaxPooling2D((3, 3), data_format=IMAGE_ORDERING, strides=(2, 2))(x) - - x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1)) - x = identity_block(x, 3, [64, 64, 256], stage=2, block='b') - x = identity_block(x, 3, [64, 64, 256], stage=2, block='c') - f2 = one_side_pad(x) - - x = conv_block(x, 3, [128, 128, 512], stage=3, block='a') - x = identity_block(x, 3, [128, 128, 512], stage=3, block='b') - x = identity_block(x, 3, [128, 128, 512], stage=3, block='c') - x = identity_block(x, 3, [128, 128, 512], stage=3, block='d') - f3 = x - - x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a') - x = identity_block(x, 3, [256, 256, 1024], stage=4, block='b') - x = identity_block(x, 3, [256, 256, 1024], stage=4, block='c') - x = identity_block(x, 3, [256, 256, 1024], stage=4, block='d') - x = identity_block(x, 3, [256, 256, 1024], stage=4, block='e') - x = identity_block(x, 3, [256, 256, 1024], stage=4, block='f') - f4 = x - - x = conv_block(x, 3, [512, 512, 2048], stage=5, block='a') - x = identity_block(x, 3, [512, 512, 2048], stage=5, block='b') - x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c') - f5 = x - - if pretraining: - model = Model(img_input, x).load_weights(resnet50_Weights_path) - - v512_2048 = Conv2D(512, (1, 1), padding='same', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay))(f5) - v512_2048 = (BatchNormalization(axis=bn_axis))(v512_2048) - v512_2048 = Activation('relu')(v512_2048) - - v512_1024 = Conv2D(512, (1, 1), padding='same', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay))(f4) - v512_1024 = (BatchNormalization(axis=bn_axis))(v512_1024) - v512_1024 = Activation('relu')(v512_1024) - - o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(v512_2048) - o = (concatenate([o, v512_1024], axis=MERGE_AXIS)) - o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o) - o = (Conv2D(512, (3, 3), padding='valid', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay)))(o) - o = (BatchNormalization(axis=bn_axis))(o) - o = Activation('relu')(o) - - o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o) - o = (concatenate([o, f3], axis=MERGE_AXIS)) - o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o) - o = (Conv2D(256, (3, 3), padding='valid', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay)))(o) - o = (BatchNormalization(axis=bn_axis))(o) - o = Activation('relu')(o) - - o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o) - o = (concatenate([o, f2], axis=MERGE_AXIS)) - o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o) - o = (Conv2D(128, (3, 3), padding='valid', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay)))(o) - o = (BatchNormalization(axis=bn_axis))(o) - o = Activation('relu')(o) - - o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o) - o = (concatenate([o, f1], axis=MERGE_AXIS)) - o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o) - o = (Conv2D(64, (3, 3), padding='valid', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay)))(o) - o = (BatchNormalization(axis=bn_axis))(o) - o = Activation('relu')(o) - - o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o) - o = (concatenate([o, img_input], axis=MERGE_AXIS)) - o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o) - o = (Conv2D(32, (3, 3), padding='valid', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay)))(o) - o = (BatchNormalization(axis=bn_axis))(o) - o = Activation('relu')(o) - - o = Conv2D(n_classes, (1, 1), padding='same', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay))(o) - if task == "segmentation": - o = (BatchNormalization(axis=bn_axis))(o) - o = (Activation('softmax'))(o) - else: - o = (Activation('sigmoid'))(o) - - model = Model(img_input, o) - return model - - -def resnet50_unet(n_classes, input_height=224, input_width=224, task="segmentation", weight_decay=1e-6, pretraining=False): - assert input_height % 32 == 0 - assert input_width % 32 == 0 - - img_input = Input(shape=(input_height, input_width, 3)) - - if IMAGE_ORDERING == 'channels_last': - bn_axis = 3 - else: - bn_axis = 1 - - x = ZeroPadding2D((3, 3), data_format=IMAGE_ORDERING)(img_input) - x = Conv2D(64, (7, 7), data_format=IMAGE_ORDERING, strides=(2, 2), kernel_regularizer=l2(weight_decay), - name='conv1')(x) - f1 = x - - x = BatchNormalization(axis=bn_axis, name='bn_conv1')(x) - x = Activation('relu')(x) - x = MaxPooling2D((3, 3), data_format=IMAGE_ORDERING, strides=(2, 2))(x) - - x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1)) - x = identity_block(x, 3, [64, 64, 256], stage=2, block='b') - x = identity_block(x, 3, [64, 64, 256], stage=2, block='c') - f2 = one_side_pad(x) - - x = conv_block(x, 3, [128, 128, 512], stage=3, block='a') - x = identity_block(x, 3, [128, 128, 512], stage=3, block='b') - x = identity_block(x, 3, [128, 128, 512], stage=3, block='c') - x = identity_block(x, 3, [128, 128, 512], stage=3, block='d') - f3 = x - - x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a') - x = identity_block(x, 3, [256, 256, 1024], stage=4, block='b') - x = identity_block(x, 3, [256, 256, 1024], stage=4, block='c') - x = identity_block(x, 3, [256, 256, 1024], stage=4, block='d') - x = identity_block(x, 3, [256, 256, 1024], stage=4, block='e') - x = identity_block(x, 3, [256, 256, 1024], stage=4, block='f') - f4 = x - - x = conv_block(x, 3, [512, 512, 2048], stage=5, block='a') - x = identity_block(x, 3, [512, 512, 2048], stage=5, block='b') - x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c') - f5 = x - - if pretraining: - Model(img_input, x).load_weights(resnet50_Weights_path) - - v1024_2048 = Conv2D(1024, (1, 1), padding='same', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay))( - f5) - v1024_2048 = (BatchNormalization(axis=bn_axis))(v1024_2048) - v1024_2048 = Activation('relu')(v1024_2048) - - o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(v1024_2048) - o = (concatenate([o, f4], axis=MERGE_AXIS)) - o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o) - o = (Conv2D(512, (3, 3), padding='valid', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay)))(o) - o = (BatchNormalization(axis=bn_axis))(o) - o = Activation('relu')(o) - - o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o) - o = (concatenate([o, f3], axis=MERGE_AXIS)) - o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o) - o = (Conv2D(256, (3, 3), padding='valid', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay)))(o) - o = (BatchNormalization(axis=bn_axis))(o) - o = Activation('relu')(o) - - o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o) - o = (concatenate([o, f2], axis=MERGE_AXIS)) - o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o) - o = (Conv2D(128, (3, 3), padding='valid', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay)))(o) - o = (BatchNormalization(axis=bn_axis))(o) - o = Activation('relu')(o) - - o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o) - o = (concatenate([o, f1], axis=MERGE_AXIS)) - o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o) - o = (Conv2D(64, (3, 3), padding='valid', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay)))(o) - o = (BatchNormalization(axis=bn_axis))(o) - o = Activation('relu')(o) - - o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o) - o = (concatenate([o, img_input], axis=MERGE_AXIS)) - o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o) - o = (Conv2D(32, (3, 3), padding='valid', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay)))(o) - o = (BatchNormalization(axis=bn_axis))(o) - o = Activation('relu')(o) - - o = Conv2D(n_classes, (1, 1), padding='same', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay))(o) - if task == "segmentation": - o = (BatchNormalization(axis=bn_axis))(o) - o = (Activation('softmax'))(o) - else: - o = (Activation('sigmoid'))(o) - - model = Model(img_input, o) - - return model - - -def vit_resnet50_unet(n_classes, patch_size_x, patch_size_y, num_patches, mlp_head_units=None, transformer_layers=8, num_heads =4, projection_dim = 64, input_height=224, input_width=224, task="segmentation", weight_decay=1e-6, pretraining=False): - if mlp_head_units is None: - mlp_head_units = [128, 64] - inputs = Input(shape=(input_height, input_width, 3)) - - #transformer_units = [ - #projection_dim * 2, - #projection_dim, - #] # Size of the transformer layers - IMAGE_ORDERING = 'channels_last' - bn_axis=3 - - x = ZeroPadding2D((3, 3), data_format=IMAGE_ORDERING)(inputs) - x = Conv2D(64, (7, 7), data_format=IMAGE_ORDERING, strides=(2, 2),kernel_regularizer=l2(weight_decay), name='conv1')(x) - f1 = x - - x = BatchNormalization(axis=bn_axis, name='bn_conv1')(x) - x = Activation('relu')(x) - x = MaxPooling2D((3, 3), data_format=IMAGE_ORDERING, strides=(2, 2))(x) - - x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1)) - x = identity_block(x, 3, [64, 64, 256], stage=2, block='b') - x = identity_block(x, 3, [64, 64, 256], stage=2, block='c') - f2 = one_side_pad(x) - - x = conv_block(x, 3, [128, 128, 512], stage=3, block='a') - x = identity_block(x, 3, [128, 128, 512], stage=3, block='b') - x = identity_block(x, 3, [128, 128, 512], stage=3, block='c') - x = identity_block(x, 3, [128, 128, 512], stage=3, block='d') - f3 = x - - x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a') - x = identity_block(x, 3, [256, 256, 1024], stage=4, block='b') - x = identity_block(x, 3, [256, 256, 1024], stage=4, block='c') - x = identity_block(x, 3, [256, 256, 1024], stage=4, block='d') - x = identity_block(x, 3, [256, 256, 1024], stage=4, block='e') - x = identity_block(x, 3, [256, 256, 1024], stage=4, block='f') - f4 = x - - x = conv_block(x, 3, [512, 512, 2048], stage=5, block='a') - x = identity_block(x, 3, [512, 512, 2048], stage=5, block='b') - x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c') - f5 = x - - if pretraining: - model = Model(inputs, x).load_weights(resnet50_Weights_path) - - #num_patches = x.shape[1]*x.shape[2] - - #patch_size_y = input_height / x.shape[1] - #patch_size_x = input_width / x.shape[2] - #patch_size = patch_size_x * patch_size_y - patches = Patches(patch_size_x, patch_size_y)(x) - # Encode patches. - encoded_patches = PatchEncoder(num_patches, projection_dim)(patches) - - for _ in range(transformer_layers): - # Layer normalization 1. - x1 = LayerNormalization(epsilon=1e-6)(encoded_patches) - # Create a multi-head attention layer. - attention_output = MultiHeadAttention( - num_heads=num_heads, key_dim=projection_dim, dropout=0.1 - )(x1, x1) - # Skip connection 1. - x2 = Add()([attention_output, encoded_patches]) - # Layer normalization 2. - x3 = LayerNormalization(epsilon=1e-6)(x2) - # MLP. - x3 = mlp(x3, hidden_units=mlp_head_units, dropout_rate=0.1) - # Skip connection 2. - encoded_patches = Add()([x3, x2]) - - assert isinstance(x, Layer) - encoded_patches = tf.reshape(encoded_patches, [-1, x.shape[1], x.shape[2] , int( projection_dim / (patch_size_x * patch_size_y) )]) - - v1024_2048 = Conv2D( 1024 , (1, 1), padding='same', data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay))(encoded_patches) - v1024_2048 = (BatchNormalization(axis=bn_axis))(v1024_2048) - v1024_2048 = Activation('relu')(v1024_2048) - - o = (UpSampling2D( (2, 2), data_format=IMAGE_ORDERING))(v1024_2048) - o = (concatenate([o, f4],axis=MERGE_AXIS)) - o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o) - o = (Conv2D(512, (3, 3), padding='valid', data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay)))(o) - o = (BatchNormalization(axis=bn_axis))(o) - o = Activation('relu')(o) - - o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o) - o = (concatenate([o ,f3], axis=MERGE_AXIS)) - o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o) - o = (Conv2D(256, (3, 3), padding='valid', data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay)))(o) - o = (BatchNormalization(axis=bn_axis))(o) - o = Activation('relu')(o) - - o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o) - o = (concatenate([o, f2], axis=MERGE_AXIS)) - o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o) - o = (Conv2D(128, (3, 3), padding='valid', data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay)))(o) - o = (BatchNormalization(axis=bn_axis))(o) - o = Activation('relu')(o) - - o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o) - o = (concatenate([o, f1], axis=MERGE_AXIS)) - o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o) - o = (Conv2D(64, (3, 3), padding='valid', data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay)))(o) - o = (BatchNormalization(axis=bn_axis))(o) - o = Activation('relu')(o) - - o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o) - o = (concatenate([o, inputs],axis=MERGE_AXIS)) - o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o) - o = (Conv2D(32, (3, 3), padding='valid', data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay)))(o) - o = (BatchNormalization(axis=bn_axis))(o) - o = Activation('relu')(o) - - o = Conv2D(n_classes, (1, 1), padding='same', data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay))(o) - if task == "segmentation": - o = (BatchNormalization(axis=bn_axis))(o) - o = (Activation('softmax'))(o) - else: - o = (Activation('sigmoid'))(o) - - model = Model(inputs=inputs, outputs=o) - - return model - -def vit_resnet50_unet_transformer_before_cnn(n_classes, patch_size_x, patch_size_y, num_patches, mlp_head_units=None, transformer_layers=8, num_heads =4, projection_dim = 64, input_height=224, input_width=224, task="segmentation", weight_decay=1e-6, pretraining=False): - if mlp_head_units is None: - mlp_head_units = [128, 64] - inputs = Input(shape=(input_height, input_width, 3)) - - ##transformer_units = [ - ##projection_dim * 2, - ##projection_dim, - ##] # Size of the transformer layers - IMAGE_ORDERING = 'channels_last' - bn_axis=3 - - patches = Patches(patch_size_x, patch_size_y)(inputs) - # Encode patches. - encoded_patches = PatchEncoder(num_patches, projection_dim)(patches) - - for _ in range(transformer_layers): - # Layer normalization 1. - x1 = LayerNormalization(epsilon=1e-6)(encoded_patches) - # Create a multi-head attention layer. - attention_output = MultiHeadAttention( - num_heads=num_heads, key_dim=projection_dim, dropout=0.1 - )(x1, x1) - # Skip connection 1. - x2 = Add()([attention_output, encoded_patches]) - # Layer normalization 2. - x3 = LayerNormalization(epsilon=1e-6)(x2) - # MLP. - x3 = mlp(x3, hidden_units=mlp_head_units, dropout_rate=0.1) - # Skip connection 2. - encoded_patches = Add()([x3, x2]) - - encoded_patches = tf.reshape(encoded_patches, [-1, input_height, input_width , int( projection_dim / (patch_size_x * patch_size_y) )]) - - encoded_patches = Conv2D(3, (1, 1), padding='same', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay), name='convinput')(encoded_patches) - - x = ZeroPadding2D((3, 3), data_format=IMAGE_ORDERING)(encoded_patches) - x = Conv2D(64, (7, 7), data_format=IMAGE_ORDERING, strides=(2, 2),kernel_regularizer=l2(weight_decay), name='conv1')(x) - f1 = x - - x = BatchNormalization(axis=bn_axis, name='bn_conv1')(x) - x = Activation('relu')(x) - x = MaxPooling2D((3, 3), data_format=IMAGE_ORDERING, strides=(2, 2))(x) - - x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1)) - x = identity_block(x, 3, [64, 64, 256], stage=2, block='b') - x = identity_block(x, 3, [64, 64, 256], stage=2, block='c') - f2 = one_side_pad(x) - - x = conv_block(x, 3, [128, 128, 512], stage=3, block='a') - x = identity_block(x, 3, [128, 128, 512], stage=3, block='b') - x = identity_block(x, 3, [128, 128, 512], stage=3, block='c') - x = identity_block(x, 3, [128, 128, 512], stage=3, block='d') - f3 = x - - x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a') - x = identity_block(x, 3, [256, 256, 1024], stage=4, block='b') - x = identity_block(x, 3, [256, 256, 1024], stage=4, block='c') - x = identity_block(x, 3, [256, 256, 1024], stage=4, block='d') - x = identity_block(x, 3, [256, 256, 1024], stage=4, block='e') - x = identity_block(x, 3, [256, 256, 1024], stage=4, block='f') - f4 = x - - x = conv_block(x, 3, [512, 512, 2048], stage=5, block='a') - x = identity_block(x, 3, [512, 512, 2048], stage=5, block='b') - x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c') - f5 = x - - if pretraining: - model = Model(encoded_patches, x).load_weights(resnet50_Weights_path) - - v1024_2048 = Conv2D( 1024 , (1, 1), padding='same', data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay))(x) - v1024_2048 = (BatchNormalization(axis=bn_axis))(v1024_2048) - v1024_2048 = Activation('relu')(v1024_2048) - - o = (UpSampling2D( (2, 2), data_format=IMAGE_ORDERING))(v1024_2048) - o = (concatenate([o, f4],axis=MERGE_AXIS)) - o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o) - o = (Conv2D(512, (3, 3), padding='valid', data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay)))(o) - o = (BatchNormalization(axis=bn_axis))(o) - o = Activation('relu')(o) - - o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o) - o = (concatenate([o ,f3], axis=MERGE_AXIS)) - o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o) - o = (Conv2D(256, (3, 3), padding='valid', data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay)))(o) - o = (BatchNormalization(axis=bn_axis))(o) - o = Activation('relu')(o) - - o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o) - o = (concatenate([o, f2], axis=MERGE_AXIS)) - o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o) - o = (Conv2D(128, (3, 3), padding='valid', data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay)))(o) - o = (BatchNormalization(axis=bn_axis))(o) - o = Activation('relu')(o) - - o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o) - o = (concatenate([o, f1], axis=MERGE_AXIS)) - o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o) - o = (Conv2D(64, (3, 3), padding='valid', data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay)))(o) - o = (BatchNormalization(axis=bn_axis))(o) - o = Activation('relu')(o) - - o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o) - o = (concatenate([o, inputs],axis=MERGE_AXIS)) - o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o) - o = (Conv2D(32, (3, 3), padding='valid', data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay)))(o) - o = (BatchNormalization(axis=bn_axis))(o) - o = Activation('relu')(o) - - o = Conv2D(n_classes, (1, 1), padding='same', data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay))(o) - if task == "segmentation": - o = (BatchNormalization(axis=bn_axis))(o) - o = (Activation('softmax'))(o) - else: - o = (Activation('sigmoid'))(o) - - model = Model(inputs=inputs, outputs=o) - - return model - -def resnet50_classifier(n_classes,input_height=224,input_width=224,weight_decay=1e-6,pretraining=False): - include_top=True - assert input_height%32 == 0 - assert input_width%32 == 0 - - - img_input = Input(shape=(input_height,input_width , 3 )) - - if IMAGE_ORDERING == 'channels_last': - bn_axis = 3 - else: - bn_axis = 1 - - x = ZeroPadding2D((3, 3), data_format=IMAGE_ORDERING)(img_input) - x = Conv2D(64, (7, 7), data_format=IMAGE_ORDERING, strides=(2, 2),kernel_regularizer=l2(weight_decay), name='conv1')(x) - f1 = x - - x = BatchNormalization(axis=bn_axis, name='bn_conv1')(x) - x = Activation('relu')(x) - x = MaxPooling2D((3, 3) , data_format=IMAGE_ORDERING , strides=(2, 2))(x) - - - x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1)) - x = identity_block(x, 3, [64, 64, 256], stage=2, block='b') - x = identity_block(x, 3, [64, 64, 256], stage=2, block='c') - f2 = one_side_pad(x ) - - - x = conv_block(x, 3, [128, 128, 512], stage=3, block='a') - x = identity_block(x, 3, [128, 128, 512], stage=3, block='b') - x = identity_block(x, 3, [128, 128, 512], stage=3, block='c') - x = identity_block(x, 3, [128, 128, 512], stage=3, block='d') - f3 = x - - x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a') - x = identity_block(x, 3, [256, 256, 1024], stage=4, block='b') - x = identity_block(x, 3, [256, 256, 1024], stage=4, block='c') - x = identity_block(x, 3, [256, 256, 1024], stage=4, block='d') - x = identity_block(x, 3, [256, 256, 1024], stage=4, block='e') - x = identity_block(x, 3, [256, 256, 1024], stage=4, block='f') - f4 = x - - x = conv_block(x, 3, [512, 512, 2048], stage=5, block='a') - x = identity_block(x, 3, [512, 512, 2048], stage=5, block='b') - x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c') - f5 = x - - if pretraining: - Model(img_input, x).load_weights(resnet50_Weights_path) - - x = AveragePooling2D((7, 7), name='avg_pool')(x) - x = Flatten()(x) - - ## - x = Dense(256, activation='relu', name='fc512')(x) - x=Dropout(0.2)(x) - ## - x = Dense(n_classes, activation='softmax', name='fc1000')(x) - model = Model(img_input, x) - - - - - return model - -def machine_based_reading_order_model(n_classes,input_height=224,input_width=224,weight_decay=1e-6,pretraining=False): - assert input_height%32 == 0 - assert input_width%32 == 0 - - img_input = Input(shape=(input_height,input_width , 3 )) - - if IMAGE_ORDERING == 'channels_last': - bn_axis = 3 - else: - bn_axis = 1 - - x1 = ZeroPadding2D((3, 3), data_format=IMAGE_ORDERING)(img_input) - x1 = Conv2D(64, (7, 7), data_format=IMAGE_ORDERING, strides=(2, 2),kernel_regularizer=l2(weight_decay), name='conv1')(x1) - - x1 = BatchNormalization(axis=bn_axis, name='bn_conv1')(x1) - x1 = Activation('relu')(x1) - x1 = MaxPooling2D((3, 3) , data_format=IMAGE_ORDERING , strides=(2, 2))(x1) - - x1 = conv_block(x1, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1)) - x1 = identity_block(x1, 3, [64, 64, 256], stage=2, block='b') - x1 = identity_block(x1, 3, [64, 64, 256], stage=2, block='c') - - x1 = conv_block(x1, 3, [128, 128, 512], stage=3, block='a') - x1 = identity_block(x1, 3, [128, 128, 512], stage=3, block='b') - x1 = identity_block(x1, 3, [128, 128, 512], stage=3, block='c') - x1 = identity_block(x1, 3, [128, 128, 512], stage=3, block='d') - - x1 = conv_block(x1, 3, [256, 256, 1024], stage=4, block='a') - x1 = identity_block(x1, 3, [256, 256, 1024], stage=4, block='b') - x1 = identity_block(x1, 3, [256, 256, 1024], stage=4, block='c') - x1 = identity_block(x1, 3, [256, 256, 1024], stage=4, block='d') - x1 = identity_block(x1, 3, [256, 256, 1024], stage=4, block='e') - x1 = identity_block(x1, 3, [256, 256, 1024], stage=4, block='f') - - x1 = conv_block(x1, 3, [512, 512, 2048], stage=5, block='a') - x1 = identity_block(x1, 3, [512, 512, 2048], stage=5, block='b') - x1 = identity_block(x1, 3, [512, 512, 2048], stage=5, block='c') - - if pretraining: - Model(img_input , x1).load_weights(resnet50_Weights_path) - - x1 = AveragePooling2D((7, 7), name='avg_pool1')(x1) - flattened = Flatten()(x1) - - o = Dense(256, activation='relu', name='fc512')(flattened) - o=Dropout(0.2)(o) - - o = Dense(256, activation='relu', name='fc512a')(o) - o=Dropout(0.2)(o) - - o = Dense(n_classes, activation='sigmoid', name='fc1000')(o) - model = Model(img_input , o) - - return model - -def cnn_rnn_ocr_model(image_height=None, image_width=None, n_classes=None, max_seq=None): - input_img = tf.keras.Input(shape=(image_height, image_width, 3), name="image") - labels = tf.keras.layers.Input(name="label", shape=(None,)) - - x = tf.keras.layers.Conv2D(64,kernel_size=(3,3),padding="same")(input_img) - x = tf.keras.layers.BatchNormalization(name="bn1")(x) - x = tf.keras.layers.Activation("relu", name="relu1")(x) - x = tf.keras.layers.Conv2D(64,kernel_size=(3,3),padding="same")(x) - x = tf.keras.layers.BatchNormalization(name="bn2")(x) - x = tf.keras.layers.Activation("relu", name="relu2")(x) - x = tf.keras.layers.MaxPool2D(pool_size=(1,2),strides=(1,2))(x) - - x = tf.keras.layers.Conv2D(128,kernel_size=(3,3),padding="same")(x) - x = tf.keras.layers.BatchNormalization(name="bn3")(x) - x = tf.keras.layers.Activation("relu", name="relu3")(x) - x = tf.keras.layers.Conv2D(128,kernel_size=(3,3),padding="same")(x) - x = tf.keras.layers.BatchNormalization(name="bn4")(x) - x = tf.keras.layers.Activation("relu", name="relu4")(x) - x = tf.keras.layers.MaxPool2D(pool_size=(1,2),strides=(1,2))(x) - - x = tf.keras.layers.Conv2D(256,kernel_size=(3,3),padding="same")(x) - x = tf.keras.layers.BatchNormalization(name="bn5")(x) - x = tf.keras.layers.Activation("relu", name="relu5")(x) - x = tf.keras.layers.Conv2D(256,kernel_size=(3,3),padding="same")(x) - x = tf.keras.layers.BatchNormalization(name="bn6")(x) - x = tf.keras.layers.Activation("relu", name="relu6")(x) - x = tf.keras.layers.MaxPool2D(pool_size=(2,2),strides=(2,2))(x) - - x = tf.keras.layers.Conv2D(image_width,kernel_size=(3,3),padding="same")(x) - x = tf.keras.layers.BatchNormalization(name="bn7")(x) - x = tf.keras.layers.Activation("relu", name="relu7")(x) - x = tf.keras.layers.Conv2D(image_width,kernel_size=(16,1))(x) - x = tf.keras.layers.BatchNormalization(name="bn8")(x) - x = tf.keras.layers.Activation("relu", name="relu8")(x) - x2d = tf.keras.layers.MaxPool2D(pool_size=(1,2),strides=(1,2))(x) - x4d = tf.keras.layers.MaxPool2D(pool_size=(1,2),strides=(1,2))(x2d) - - - new_shape = (x.shape[1]*x.shape[2], x.shape[3]) - new_shape2 = (x2d.shape[1]*x2d.shape[2], x2d.shape[3]) - new_shape4 = (x4d.shape[1]*x4d.shape[2], x4d.shape[3]) - - x = tf.keras.layers.Reshape(target_shape=new_shape, name="reshape")(x) - x2d = tf.keras.layers.Reshape(target_shape=new_shape2, name="reshape2")(x2d) - x4d = tf.keras.layers.Reshape(target_shape=new_shape4, name="reshape4")(x4d) - - - xrnnorg = tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(image_width, return_sequences=True, dropout=0.25))(x) - xrnn2d = tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(image_width, return_sequences=True, dropout=0.25))(x2d) - xrnn4d = tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(image_width, return_sequences=True, dropout=0.25))(x4d) - - xrnn2d = tf.keras.layers.Reshape(target_shape=(1, xrnn2d.shape[1], xrnn2d.shape[2]), name="reshape6")(xrnn2d) - xrnn4d = tf.keras.layers.Reshape(target_shape=(1, xrnn4d.shape[1], xrnn4d.shape[2]), name="reshape8")(xrnn4d) - - - xrnn2dup = tf.keras.layers.UpSampling2D(size=(1, 2), interpolation="nearest")(xrnn2d) - xrnn4dup = tf.keras.layers.UpSampling2D(size=(1, 4), interpolation="nearest")(xrnn4d) - - xrnn2dup = tf.keras.layers.Reshape(target_shape=(xrnn2dup.shape[2], xrnn2dup.shape[3]), name="reshape10")(xrnn2dup) - xrnn4dup = tf.keras.layers.Reshape(target_shape=(xrnn4dup.shape[2], xrnn4dup.shape[3]), name="reshape12")(xrnn4dup) - - addition = tf.keras.layers.Add()([xrnnorg, xrnn2dup, xrnn4dup]) - - addition_rnn = tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(image_width, return_sequences=True, dropout=0.25))(addition) - - out = tf.keras.layers.Conv1D(max_seq, 1, data_format="channels_first")(addition_rnn) - out = tf.keras.layers.BatchNormalization(name="bn9")(out) - out = tf.keras.layers.Activation("relu", name="relu9")(out) - #out = tf.keras.layers.Conv1D(n_classes, 1, activation='relu', data_format="channels_last")(out) - - out = tf.keras.layers.Dense( - n_classes, activation="softmax", name="dense2" - )(out) - - # Add CTC layer for calculating CTC loss at each step. - output = CTCLayer(name="ctc_loss")(labels, out) - - model = tf.keras.models.Model(inputs=[input_img, labels], outputs=output, name="handwriting_recognizer") - - return model - diff --git a/src/eynollah/training/train.py b/src/eynollah/training/train.py deleted file mode 100644 index 7a0cb3d..0000000 --- a/src/eynollah/training/train.py +++ /dev/null @@ -1,657 +0,0 @@ -import os -import sys -import json - -import click - -from eynollah.training.metrics import ( - soft_dice_loss, - weighted_categorical_crossentropy -) -from eynollah.training.models import ( - PatchEncoder, - Patches, - machine_based_reading_order_model, - resnet50_classifier, - resnet50_unet, - vit_resnet50_unet, - vit_resnet50_unet_transformer_before_cnn, - cnn_rnn_ocr_model -) -from eynollah.training.utils import ( - data_gen, - data_gen_ocr, - return_multiplier_based_on_augmnentations, - generate_arrays_from_folder_reading_order, - generate_data_from_folder_evaluation, - generate_data_from_folder_training, - get_one_hot, - provide_patches, - return_number_of_total_training_data -) - -os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' -import tensorflow as tf -from tensorflow.compat.v1.keras.backend import set_session -from tensorflow.keras.optimizers import SGD, Adam -from sacred import Experiment -from tensorflow.keras.models import load_model -from tqdm import tqdm -from sklearn.metrics import f1_score -from tensorflow.keras.callbacks import Callback -from tensorflow.keras.layers import StringLookup - -import numpy as np -import cv2 - -class SaveWeightsAfterSteps(Callback): - def __init__(self, save_interval, save_path, _config): - super(SaveWeightsAfterSteps, self).__init__() - self.save_interval = save_interval - self.save_path = save_path - self.step_count = 0 - self._config = _config - - def on_train_batch_end(self, batch, logs=None): - self.step_count += 1 - - if self.step_count % self.save_interval ==0: - save_file = f"{self.save_path}/model_step_{self.step_count}" - #os.system('mkdir '+save_file) - - self.model.save(save_file) - - with open(os.path.join(os.path.join(self.save_path, f"model_step_{self.step_count}"),"config.json"), "w") as fp: - json.dump(self._config, fp) # encode dict into JSON - print(f"saved model as steps {self.step_count} to {save_file}") - - - -def configuration(): - config = tf.compat.v1.ConfigProto() - config.gpu_options.allow_growth = True - session = tf.compat.v1.Session(config=config) - set_session(session) - - -def get_dirs_or_files(input_data): - image_input, labels_input = os.path.join(input_data, 'images/'), os.path.join(input_data, 'labels/') - if os.path.isdir(input_data): - # Check if training dir exists - assert os.path.isdir(image_input), "{} is not a directory".format(image_input) - assert os.path.isdir(labels_input), "{} is not a directory".format(labels_input) - return image_input, labels_input - - -ex = Experiment(save_git_info=False) - - -@ex.config -def config_params(): - n_classes = None # Number of classes. In the case of binary classification this should be 2. - n_epochs = 1 # Number of epochs. - input_height = 224 * 1 # Height of model's input in pixels. - input_width = 224 * 1 # Width of model's input in pixels. - weight_decay = 1e-6 # Weight decay of l2 regularization of model layers. - n_batch = 1 # Number of batches at each iteration. - max_len = None # max len for ocr output. - learning_rate = 1e-4 # Set the learning rate. - patches = False # Divides input image into smaller patches (input size of the model) when set to true. For the model to see the full image, like page extraction, set this to false. - augmentation = False # To apply any kind of augmentation, this parameter must be set to true. - flip_aug = False # If true, different types of flipping will be applied to the image. Types of flips are defined with "flip_index" in config_params.json. - blur_aug = False # If true, different types of blurring will be applied to the image. Types of blur are defined with "blur_k" in config_params.json. - padding_white = False # If true, white padding will be applied to the image. - padding_black = False # If true, black padding will be applied to the image. - scaling = False # If true, scaling will be applied to the image. The amount of scaling is defined with "scales" in config_params.json. - shifting = False - degrading = False # If true, degrading will be applied to the image. The amount of degrading is defined with "degrade_scales" in config_params.json. - brightening = False # If true, brightening will be applied to the image. The amount of brightening is defined with "brightness" in config_params.json. - binarization = False # If true, Otsu thresholding will be applied to augment the input with binarized images. - image_inversion = False # If true, and if the binarized images are avilable the image inevrsion will be applied. - white_noise_strap = False # If true, white noise will be applied on some straps on the textline image. - textline_skewing = False # If true, textline images will be skewed for augmentation. - textline_skewing_bin = False # If true, textline image skewing augmentation for binarized images will be applied if already are available. - textline_left_in_depth = False # If true, left side of textline image will be displayed in depth. - textline_left_in_depth_bin = False # If true, left side of textline binarized image (if available) will be displayed in depth. - textline_right_in_depth = False # If true, right side of textline image will be displayed in depth. - textline_right_in_depth_bin = False # If true, right side of textline binarized image (if available) will be displayed in depth. - textline_up_in_depth = False # If true, upper side of textline image will be displayed in depth. - textline_up_in_depth_bin = False # If true, upper side of textline binarized image (if available) will be displayed in depth. - textline_down_in_depth = False # If true, lower side of textline image will be displayed in depth. - textline_down_in_depth_bin = False # If true, lower side of textline binarized image (if available) will be displayed in depth. - pepper_bin_aug = False # If true, pepper noise will be added to textline binarized image (if available). - pepper_aug = False # If true, pepper noise will be added to textline image. - adding_rgb_background = False - adding_rgb_foreground = False - add_red_textlines = False - channels_shuffling = False - dir_train = None # Directory of training dataset with subdirectories having the names "images" and "labels". - dir_eval = None # Directory of validation dataset with subdirectories having the names "images" and "labels". - dir_output = None # Directory where the output model will be saved. - pretraining = False # Set to true to load pretrained weights of ResNet50 encoder. - scaling_bluring = False # If true, a combination of scaling and blurring will be applied to the image. - scaling_binarization = False # If true, a combination of scaling and binarization will be applied to the image. - bin_deg = False # If true, a combination of degrading and binarization will be applied to the image. - rotation = False # If true, a 90 degree rotation will be implemeneted. - color_padding_rotation = False # If true, rotation and padding will be implemeneted. - rotation_not_90 = False # If true rotation based on provided angles with thetha will be implemeneted. - scaling_brightness = False # If true, a combination of scaling and brightening will be applied to the image. - scaling_flip = False # If true, a combination of scaling and flipping will be applied to the image. - thetha = None # Rotate image by these angles for augmentation. - thetha_padd = None # List of angles used for rotation alongside padding - shuffle_indexes = None # List of shuffling indexes like [[0,2,1], [1,2,0], [1,0,2]] - pepper_indexes = None # List of pepper noise indexes like [0.01, 0.005] - white_padds = None # List of padding size in the case of white padding - skewing_amplitudes = None # List of skewing augmentation amplitudes like [5, 8] - blur_k = None # Blur image for augmentation. - scales = None # Scale patches for augmentation. - padd_colors = None # padding colors. A list elements can be only white and black. like ["white", "black"] or only one of them ["white"] - degrade_scales = None # Degrade image for augmentation. - brightness = None # Brighten image for augmentation. - flip_index = None # Flip image for augmentation. - continue_training = False # Set to true if you would like to continue training an already trained a model. - transformer_patchsize_x = None # Patch size of vision transformer patches in x direction. - transformer_patchsize_y = None # Patch size of vision transformer patches in y direction. - transformer_num_patches_xy = None # Number of patches for vision transformer in x and y direction respectively. - transformer_projection_dim = 64 # Transformer projection dimension. Default value is 64. - transformer_mlp_head_units = [128, 64] # Transformer Multilayer Perceptron (MLP) head units. Default value is [128, 64] - transformer_layers = 8 # transformer layers. Default value is 8. - transformer_num_heads = 4 # Transformer number of heads. Default value is 4. - transformer_cnn_first = True # We have two types of vision transformers. In one type, a CNN is applied first, followed by a transformer. In the other type, this order is reversed. If transformer_cnn_first is true, it means the CNN will be applied before the transformer. Default value is true. - index_start = 0 # Index of model to continue training from. E.g. if you trained for 3 epochs and last index is 2, to continue from model_1.h5, set "index_start" to 3 to start naming model with index 3. - dir_of_start_model = '' # Directory containing pretrained encoder to continue training the model. - is_loss_soft_dice = False # Use soft dice as loss function. When set to true, "weighted_loss" must be false. - weighted_loss = False # Use weighted categorical cross entropy as loss fucntion. When set to true, "is_loss_soft_dice" must be false. - data_is_provided = False # Only set this to true when you have already provided the input data and the train and eval data are in "dir_output". - task = "segmentation" # This parameter defines task of model which can be segmentation, enhancement or classification. - f1_threshold_classification = None # This threshold is used to consider models with an evaluation f1 scores bigger than it. The selected model weights undergo a weights ensembling. And avreage ensembled model will be written to output. - classification_classes_name = None # Dictionary of classification classes names. - backbone_type = None # As backbone we have 2 types of backbones. A vision transformer alongside a CNN and we call it "transformer" and only CNN called "nontransformer" - save_interval = None - dir_img_bin = None - number_of_backgrounds_per_image = 1 - dir_rgb_backgrounds = None - dir_rgb_foregrounds = None - characters_txt_file = None # Directory of characters text file needed for cnn_rnn_ocr model training. The file ends with .txt - -@ex.automain -def run( - _config, - n_classes, - n_epochs, - input_height, - input_width, - weight_decay, - weighted_loss, - index_start, - dir_of_start_model, - is_loss_soft_dice, - n_batch, - patches, - augmentation, - flip_aug, - blur_aug, - padding_white, - padding_black, - scaling, - shifting, - degrading, - channels_shuffling, - brightening, - binarization, - adding_rgb_background, - adding_rgb_foreground, - add_red_textlines, - blur_k, - scales, - degrade_scales, - shuffle_indexes, - brightness, - dir_train, - data_is_provided, - scaling_bluring, - scaling_brightness, - scaling_binarization, - rotation, - rotation_not_90, - thetha, - thetha_padd, - scaling_flip, - continue_training, - transformer_projection_dim, - transformer_mlp_head_units, - transformer_layers, - transformer_num_heads, - transformer_cnn_first, - transformer_patchsize_x, - transformer_patchsize_y, - transformer_num_patches_xy, - backbone_type, - save_interval, - flip_index, - dir_eval, - dir_output, - pretraining, - learning_rate, - task, - f1_threshold_classification, - classification_classes_name, - dir_img_bin, - number_of_backgrounds_per_image, - dir_rgb_backgrounds, - dir_rgb_foregrounds, - characters_txt_file, - color_padding_rotation, - bin_deg, - image_inversion, - white_noise_strap, - textline_skewing, - textline_skewing_bin, - textline_left_in_depth, - textline_left_in_depth_bin, - textline_right_in_depth, - textline_right_in_depth_bin, - textline_up_in_depth, - textline_up_in_depth_bin, - textline_down_in_depth, - textline_down_in_depth_bin, - pepper_bin_aug, - pepper_aug, - padd_colors, - pepper_indexes, - white_padds, - skewing_amplitudes, - max_len, -): - - if dir_rgb_backgrounds: - list_all_possible_background_images = os.listdir(dir_rgb_backgrounds) - else: - list_all_possible_background_images = None - - if dir_rgb_foregrounds: - list_all_possible_foreground_rgbs = os.listdir(dir_rgb_foregrounds) - else: - list_all_possible_foreground_rgbs = None - - dir_seg = None - weights = None - model = None - - if task == "segmentation" or task == "enhancement" or task == "binarization": - if data_is_provided: - dir_train_flowing = os.path.join(dir_output, 'train') - dir_eval_flowing = os.path.join(dir_output, 'eval') - - - dir_flow_train_imgs = os.path.join(dir_train_flowing, 'images') - dir_flow_train_labels = os.path.join(dir_train_flowing, 'labels') - - dir_flow_eval_imgs = os.path.join(dir_eval_flowing, 'images') - dir_flow_eval_labels = os.path.join(dir_eval_flowing, 'labels') - - configuration() - - else: - dir_img, dir_seg = get_dirs_or_files(dir_train) - dir_img_val, dir_seg_val = get_dirs_or_files(dir_eval) - - # make first a directory in output for both training and evaluations in order to flow data from these directories. - dir_train_flowing = os.path.join(dir_output, 'train') - dir_eval_flowing = os.path.join(dir_output, 'eval') - - dir_flow_train_imgs = os.path.join(dir_train_flowing, 'images/') - dir_flow_train_labels = os.path.join(dir_train_flowing, 'labels/') - - dir_flow_eval_imgs = os.path.join(dir_eval_flowing, 'images/') - dir_flow_eval_labels = os.path.join(dir_eval_flowing, 'labels/') - - if os.path.isdir(dir_train_flowing): - os.system('rm -rf ' + dir_train_flowing) - os.makedirs(dir_train_flowing) - else: - os.makedirs(dir_train_flowing) - - if os.path.isdir(dir_eval_flowing): - os.system('rm -rf ' + dir_eval_flowing) - os.makedirs(dir_eval_flowing) - else: - os.makedirs(dir_eval_flowing) - - os.mkdir(dir_flow_train_imgs) - os.mkdir(dir_flow_train_labels) - - os.mkdir(dir_flow_eval_imgs) - os.mkdir(dir_flow_eval_labels) - - # set the gpu configuration - configuration() - - imgs_list=np.array(os.listdir(dir_img)) - segs_list=np.array(os.listdir(dir_seg)) - - imgs_list_test=np.array(os.listdir(dir_img_val)) - segs_list_test=np.array(os.listdir(dir_seg_val)) - - # writing patches into a sub-folder in order to be flowed from directory. - provide_patches(imgs_list, segs_list, dir_img, dir_seg, dir_flow_train_imgs, - dir_flow_train_labels, input_height, input_width, blur_k, - blur_aug, padding_white, padding_black, flip_aug, binarization, adding_rgb_background,adding_rgb_foreground, add_red_textlines, channels_shuffling, - scaling, shifting, degrading, brightening, scales, degrade_scales, brightness, - flip_index,shuffle_indexes, scaling_bluring, scaling_brightness, scaling_binarization, - rotation, rotation_not_90, thetha, scaling_flip, task, augmentation=augmentation, - patches=patches, dir_img_bin=dir_img_bin,number_of_backgrounds_per_image=number_of_backgrounds_per_image,list_all_possible_background_images=list_all_possible_background_images, dir_rgb_backgrounds=dir_rgb_backgrounds, dir_rgb_foregrounds=dir_rgb_foregrounds,list_all_possible_foreground_rgbs=list_all_possible_foreground_rgbs) - - provide_patches(imgs_list_test, segs_list_test, dir_img_val, dir_seg_val, - dir_flow_eval_imgs, dir_flow_eval_labels, input_height, input_width, - blur_k, blur_aug, padding_white, padding_black, flip_aug, binarization, adding_rgb_background, adding_rgb_foreground, add_red_textlines, channels_shuffling, - scaling, shifting, degrading, brightening, scales, degrade_scales, brightness, - flip_index, shuffle_indexes, scaling_bluring, scaling_brightness, scaling_binarization, - rotation, rotation_not_90, thetha, scaling_flip, task, augmentation=False, patches=patches,dir_img_bin=dir_img_bin,number_of_backgrounds_per_image=number_of_backgrounds_per_image,list_all_possible_background_images=list_all_possible_background_images, dir_rgb_backgrounds=dir_rgb_backgrounds,dir_rgb_foregrounds=dir_rgb_foregrounds,list_all_possible_foreground_rgbs=list_all_possible_foreground_rgbs ) - - if weighted_loss: - weights = np.zeros(n_classes) - if data_is_provided: - for obj in os.listdir(dir_flow_train_labels): - try: - label_obj = cv2.imread(dir_flow_train_labels + '/' + obj) - label_obj_one_hot = get_one_hot(label_obj, label_obj.shape[0], label_obj.shape[1], n_classes) - weights += (label_obj_one_hot.sum(axis=0)).sum(axis=0) - except: - pass - else: - - assert dir_seg is not None - for obj in os.listdir(dir_seg): - try: - label_obj = cv2.imread(dir_seg + '/' + obj) - label_obj_one_hot = get_one_hot(label_obj, label_obj.shape[0], label_obj.shape[1], n_classes) - weights += (label_obj_one_hot.sum(axis=0)).sum(axis=0) - except: - pass - - weights = 1.00 / weights - - weights = weights / float(np.sum(weights)) - weights = weights / float(np.min(weights)) - weights = weights / float(np.sum(weights)) - - if continue_training: - if backbone_type=='nontransformer': - if is_loss_soft_dice and (task == "segmentation" or task == "binarization"): - model = load_model(dir_of_start_model, compile=True, custom_objects={'soft_dice_loss': soft_dice_loss}) - if weighted_loss and (task == "segmentation" or task == "binarization"): - model = load_model(dir_of_start_model, compile=True, custom_objects={'loss': weighted_categorical_crossentropy(weights)}) - if not is_loss_soft_dice and not weighted_loss: - model = load_model(dir_of_start_model , compile=True) - elif backbone_type=='transformer': - if is_loss_soft_dice and (task == "segmentation" or task == "binarization"): - model = load_model(dir_of_start_model, compile=True, custom_objects={"PatchEncoder": PatchEncoder, "Patches": Patches,'soft_dice_loss': soft_dice_loss}) - if weighted_loss and (task == "segmentation" or task == "binarization"): - model = load_model(dir_of_start_model, compile=True, custom_objects={'loss': weighted_categorical_crossentropy(weights)}) - if not is_loss_soft_dice and not weighted_loss: - model = load_model(dir_of_start_model , compile=True,custom_objects = {"PatchEncoder": PatchEncoder, "Patches": Patches}) - else: - raise ValueError("backbone_type must be 'nontransformer' or 'transformer'") - else: - index_start = 0 - if backbone_type=='nontransformer': - model = resnet50_unet(n_classes, input_height, input_width, task, weight_decay, pretraining) - elif backbone_type=='transformer': - num_patches_x = transformer_num_patches_xy[0] - num_patches_y = transformer_num_patches_xy[1] - num_patches = num_patches_x * num_patches_y - - if transformer_cnn_first: - if input_height != (num_patches_y * transformer_patchsize_y * 32): - print("Error: transformer_patchsize_y or transformer_num_patches_xy height value error . input_height should be equal to ( transformer_num_patches_xy height value * transformer_patchsize_y * 32)") - sys.exit(1) - if input_width != (num_patches_x * transformer_patchsize_x * 32): - print("Error: transformer_patchsize_x or transformer_num_patches_xy width value error . input_width should be equal to ( transformer_num_patches_xy width value * transformer_patchsize_x * 32)") - sys.exit(1) - if (transformer_projection_dim % (transformer_patchsize_y * transformer_patchsize_x)) != 0: - print("Error: transformer_projection_dim error. The remainder when parameter transformer_projection_dim is divided by (transformer_patchsize_y*transformer_patchsize_x) should be zero") - sys.exit(1) - - - model = vit_resnet50_unet(n_classes, transformer_patchsize_x, transformer_patchsize_y, num_patches, transformer_mlp_head_units, transformer_layers, transformer_num_heads, transformer_projection_dim, input_height, input_width, task, weight_decay, pretraining) - else: - if input_height != (num_patches_y * transformer_patchsize_y): - print("Error: transformer_patchsize_y or transformer_num_patches_xy height value error . input_height should be equal to ( transformer_num_patches_xy height value * transformer_patchsize_y)") - sys.exit(1) - if input_width != (num_patches_x * transformer_patchsize_x): - print("Error: transformer_patchsize_x or transformer_num_patches_xy width value error . input_width should be equal to ( transformer_num_patches_xy width value * transformer_patchsize_x)") - sys.exit(1) - if (transformer_projection_dim % (transformer_patchsize_y * transformer_patchsize_x)) != 0: - print("Error: transformer_projection_dim error. The remainder when parameter transformer_projection_dim is divided by (transformer_patchsize_y*transformer_patchsize_x) should be zero") - sys.exit(1) - model = vit_resnet50_unet_transformer_before_cnn(n_classes, transformer_patchsize_x, transformer_patchsize_y, num_patches, transformer_mlp_head_units, transformer_layers, transformer_num_heads, transformer_projection_dim, input_height, input_width, task, weight_decay, pretraining) - - assert model is not None - #if you want to see the model structure just uncomment model summary. - model.summary() - - - if task == "segmentation" or task == "binarization": - if not is_loss_soft_dice and not weighted_loss: - model.compile(loss='categorical_crossentropy', - optimizer=Adam(learning_rate=learning_rate), metrics=['accuracy']) - if is_loss_soft_dice: - model.compile(loss=soft_dice_loss, - optimizer=Adam(learning_rate=learning_rate), metrics=['accuracy']) - if weighted_loss: - model.compile(loss=weighted_categorical_crossentropy(weights), - optimizer=Adam(learning_rate=learning_rate), metrics=['accuracy']) - elif task == "enhancement": - model.compile(loss='mean_squared_error', - optimizer=Adam(learning_rate=learning_rate), metrics=['accuracy']) - - - # generating train and evaluation data - train_gen = data_gen(dir_flow_train_imgs, dir_flow_train_labels, batch_size=n_batch, - input_height=input_height, input_width=input_width, n_classes=n_classes, task=task) - val_gen = data_gen(dir_flow_eval_imgs, dir_flow_eval_labels, batch_size=n_batch, - input_height=input_height, input_width=input_width, n_classes=n_classes, task=task) - - ##img_validation_patches = os.listdir(dir_flow_eval_imgs) - ##score_best=[] - ##score_best.append(0) - - save_weights_callback = SaveWeightsAfterSteps(save_interval, dir_output, _config) if save_interval else None - - for i in tqdm(range(index_start, n_epochs + index_start)): - if save_interval: - model.fit( - train_gen, - steps_per_epoch=int(len(os.listdir(dir_flow_train_imgs)) / n_batch) - 1, - validation_data=val_gen, - validation_steps=1, - epochs=1, callbacks=[save_weights_callback]) - else: - model.fit( - train_gen, - steps_per_epoch=int(len(os.listdir(dir_flow_train_imgs)) / n_batch) - 1, - validation_data=val_gen, - validation_steps=1, - epochs=1) - - model.save(os.path.join(dir_output,'model_'+str(i))) - - with open(os.path.join(os.path.join(dir_output,'model_'+str(i)),"config.json"), "w") as fp: - json.dump(_config, fp) # encode dict into JSON - - #os.system('rm -rf '+dir_train_flowing) - #os.system('rm -rf '+dir_eval_flowing) - - #model.save(dir_output+'/'+'model'+'.h5') - - elif task=="cnn-rnn-ocr": - dir_img, dir_lab = get_dirs_or_files(dir_train) - - with open(characters_txt_file, 'r') as char_txt_f: - characters = json.load(char_txt_f) - - AUTOTUNE = tf.data.AUTOTUNE - - # Mapping characters to integers. - char_to_num = StringLookup(vocabulary=list(characters), mask_token=None) - - # Mapping integers back to original characters. - ##num_to_char = StringLookup( - ##vocabulary=char_to_num.get_vocabulary(), mask_token=None, invert=True - ##) - - padding_token = len(characters) + 5 - ls_files_images = os.listdir(dir_img) - - n_classes = len(char_to_num.get_vocabulary()) + 2 - - if continue_training: - model = load_model(dir_of_start_model) - else: - index_start = 0 - model = cnn_rnn_ocr_model(image_height=input_height, image_width=input_width, n_classes=n_classes, max_seq=max_len) - - print(model.summary()) - - aug_multip = return_multiplier_based_on_augmnentations(augmentation, color_padding_rotation, rotation_not_90, blur_aug, degrading, bin_deg, - brightening, padding_white, adding_rgb_foreground, adding_rgb_background, binarization, - image_inversion, channels_shuffling, add_red_textlines, white_noise_strap, textline_skewing, textline_skewing_bin, textline_left_in_depth, textline_left_in_depth_bin, textline_right_in_depth, textline_right_in_depth_bin, textline_up_in_depth, textline_up_in_depth_bin, textline_down_in_depth, textline_down_in_depth_bin, pepper_bin_aug, pepper_aug, degrade_scales, number_of_backgrounds_per_image, thetha, thetha_padd, brightness, padd_colors, shuffle_indexes, pepper_indexes, skewing_amplitudes, blur_k, white_padds) - - len_dataset = aug_multip*len(ls_files_images) - - train_ds = data_gen_ocr(padding_token, n_batch, input_height, input_width, max_len, dir_train, ls_files_images, - augmentation, color_padding_rotation, rotation_not_90, blur_aug, degrading, bin_deg, brightening, padding_white, - adding_rgb_foreground, adding_rgb_background, binarization, image_inversion, channels_shuffling, add_red_textlines, white_noise_strap, - textline_skewing, textline_skewing_bin, textline_left_in_depth, textline_left_in_depth_bin, textline_right_in_depth, - textline_right_in_depth_bin, textline_up_in_depth, textline_up_in_depth_bin, textline_down_in_depth, textline_down_in_depth_bin, - pepper_bin_aug, pepper_aug, degrade_scales, number_of_backgrounds_per_image, thetha, thetha_padd, brightness, padd_colors, - shuffle_indexes, pepper_indexes, skewing_amplitudes, blur_k, char_to_num, list_all_possible_background_images, list_all_possible_foreground_rgbs, - dir_rgb_backgrounds, dir_rgb_foregrounds, white_padds, dir_img_bin) - - initial_learning_rate = 1e-4 - decay_steps = int (n_epochs * ( len_dataset / n_batch )) - alpha = 0.01 - lr_schedule = 1e-4#tf.keras.optimizers.schedules.CosineDecay(initial_learning_rate, decay_steps, alpha) - - opt = tf.keras.optimizers.Adam(learning_rate=learning_rate)#1e-4)#(lr_schedule) - model.compile(optimizer=opt) - - save_weights_callback = SaveWeightsAfterSteps(save_interval, dir_output, _config) if save_interval else None - - for i in tqdm(range(index_start, n_epochs + index_start)): - if save_interval: - model.fit( - train_ds, - steps_per_epoch=len_dataset / n_batch, - epochs=1, - callbacks=[save_weights_callback] - ) - else: - model.fit( - train_ds, - steps_per_epoch=len_dataset / n_batch, - epochs=1 - ) - - if i >=0: - model.save( os.path.join(dir_output,'model_'+str(i) )) - with open(os.path.join(os.path.join(dir_output,'model_'+str(i)),"config.json"), "w") as fp: - json.dump(_config, fp) # encode dict into JSON - - elif task=='classification': - configuration() - model = resnet50_classifier(n_classes, input_height, input_width, weight_decay, pretraining) - - opt_adam = Adam(learning_rate=0.001) - model.compile(loss='categorical_crossentropy', - optimizer = opt_adam,metrics=['accuracy']) - - - list_classes = list(classification_classes_name.values()) - testX, testY = generate_data_from_folder_evaluation(dir_eval, input_height, input_width, n_classes, list_classes) - - y_tot=np.zeros((testX.shape[0],n_classes)) - - score_best= [0] - - num_rows = return_number_of_total_training_data(dir_train) - weights=[] - - for i in range(n_epochs): - history = model.fit( generate_data_from_folder_training(dir_train, n_batch , input_height, input_width, n_classes, list_classes), steps_per_epoch=num_rows / n_batch, verbose=1)#,class_weight=weights) - - y_pr_class = [] - for jj in range(testY.shape[0]): - y_pr=model.predict(testX[jj,:,:,:].reshape(1,input_height,input_width,3), verbose=0) - y_pr_ind= np.argmax(y_pr,axis=1) - y_pr_class.append(y_pr_ind) - - y_pr_class = np.array(y_pr_class) - f1score=f1_score(np.argmax(testY,axis=1), y_pr_class, average='macro') - print(i,f1score) - - if f1score>score_best[0]: - score_best[0]=f1score - model.save(os.path.join(dir_output,'model_best')) - - if f1score > f1_threshold_classification: - weights.append(model.get_weights() ) - - - if len(weights) >= 1: - new_weights=list() - for weights_list_tuple in zip(*weights): - new_weights.append( [np.array(weights_).mean(axis=0) for weights_ in zip(*weights_list_tuple)] ) - - new_weights = [np.array(x) for x in new_weights] - model_weight_averaged=tf.keras.models.clone_model(model) - model_weight_averaged.set_weights(new_weights) - - model_weight_averaged.save(os.path.join(dir_output,'model_ens_avg')) - with open(os.path.join( os.path.join(dir_output,'model_ens_avg'), "config.json"), "w") as fp: - json.dump(_config, fp) # encode dict into JSON - - with open(os.path.join( os.path.join(dir_output,'model_best'), "config.json"), "w") as fp: - json.dump(_config, fp) # encode dict into JSON - - elif task=='reading_order': - configuration() - model = machine_based_reading_order_model(n_classes,input_height,input_width,weight_decay,pretraining) - - dir_flow_train_imgs = os.path.join(dir_train, 'images') - dir_flow_train_labels = os.path.join(dir_train, 'labels') - - classes = os.listdir(dir_flow_train_labels) - if augmentation: - num_rows = len(classes)*(len(thetha) + 1) - else: - num_rows = len(classes) - #ls_test = os.listdir(dir_flow_train_labels) - - #f1score_tot = [0] - indexer_start = 0 - # opt = SGD(learning_rate=0.01, momentum=0.9) - opt_adam = tf.keras.optimizers.Adam(learning_rate=0.0001) - model.compile(loss="binary_crossentropy", - optimizer = opt_adam,metrics=['accuracy']) - - save_weights_callback = SaveWeightsAfterSteps(save_interval, dir_output, _config) if save_interval else None - - for i in range(n_epochs): - if save_interval: - history = model.fit(generate_arrays_from_folder_reading_order(dir_flow_train_labels, dir_flow_train_imgs, n_batch, input_height, input_width, n_classes, thetha, augmentation), steps_per_epoch=num_rows / n_batch, verbose=1, callbacks=[save_weights_callback]) - else: - history = model.fit(generate_arrays_from_folder_reading_order(dir_flow_train_labels, dir_flow_train_imgs, n_batch, input_height, input_width, n_classes, thetha, augmentation), steps_per_epoch=num_rows / n_batch, verbose=1) - model.save( os.path.join(dir_output,'model_'+str(i+indexer_start) )) - - with open(os.path.join(os.path.join(dir_output,'model_'+str(i)),"config.json"), "w") as fp: - json.dump(_config, fp) # encode dict into JSON - ''' - if f1score>f1score_tot[0]: - f1score_tot[0] = f1score - model_dir = os.path.join(dir_out,'model_best') - model.save(model_dir) - ''' - - diff --git a/src/eynollah/training/utils.py b/src/eynollah/training/utils.py deleted file mode 100644 index 005810f..0000000 --- a/src/eynollah/training/utils.py +++ /dev/null @@ -1,1670 +0,0 @@ -import os -import math -import random -from pathlib import Path -import cv2 -import numpy as np -import seaborn as sns -from scipy.ndimage.interpolation import map_coordinates -from scipy.ndimage.filters import gaussian_filter -from tqdm import tqdm -import imutils -import tensorflow as tf -from tensorflow.keras.utils import to_categorical -from PIL import Image, ImageFile, ImageEnhance - -ImageFile.LOAD_TRUNCATED_IMAGES = True - -def vectorize_label(label, char_to_num, padding_token, max_len): - label = char_to_num(tf.strings.unicode_split(label, input_encoding="UTF-8")) - length = tf.shape(label)[0] - pad_amount = max_len - length - label = tf.pad(label, paddings=[[0, pad_amount]], constant_values=padding_token) - return label - -def scale_padd_image_for_ocr(img, height, width): - ratio = height /float(img.shape[0]) - - w_ratio = int(ratio * img.shape[1]) - - if w_ratio<=width: - width_new = w_ratio - else: - width_new = width - - if width_new <= 0: - width_new = width - - img_res= resize_image (img, height, width_new) - img_fin = np.ones((height, width, 3))*255 - - img_fin[:,:width_new,:] = img_res[:,:,:] - return img_fin - -# TODO: document where this is from -def add_salt_and_pepper_noise(img, salt_prob, pepper_prob): - """ - Add salt-and-pepper noise to an image. - - Parameters: - image: ndarray - Input image. - salt_prob: float - Probability of salt noise. - pepper_prob: float - Probability of pepper noise. - - Returns: - noisy_image: ndarray - Image with salt-and-pepper noise. - """ - # Make a copy of the image - noisy_image = np.copy(img) - - # Generate random noise - total_pixels = img.size - num_salt = int(salt_prob * total_pixels) - num_pepper = int(pepper_prob * total_pixels) - - # Add salt noise - coords = [np.random.randint(0, i - 1, num_salt) for i in img.shape[:2]] - noisy_image[coords[0], coords[1]] = 255 # white pixels - - # Add pepper noise - coords = [np.random.randint(0, i - 1, num_pepper) for i in img.shape[:2]] - noisy_image[coords[0], coords[1]] = 0 # black pixels - - return noisy_image - -def invert_image(img): - img_inv = 255 - img - return img_inv - -def return_image_with_strapped_white_noises(img): - img_w_noised = np.copy(img) - img_h, img_width = img.shape[0], img.shape[1] - n = 9 - p = 0.3 - num_windows = np.random.binomial(n, p, 1)[0] - - if num_windows<1: - num_windows = 1 - - loc_of_windows = np.random.uniform(0,img_width,num_windows).astype(np.int64) - width_windows = np.random.uniform(10,50,num_windows).astype(np.int64) - - for i, loc in enumerate(loc_of_windows): - noise = np.random.normal(0, 50, (img_h, width_windows[i], 3)) - - try: - img_w_noised[:, loc:loc+width_windows[i], : ] = noise[:,:,:] - except: - pass - return img_w_noised - -def do_padding_for_ocr(img, percent_height, padding_color): - padding_size = int( img.shape[0]*percent_height/2. ) - height_new = img.shape[0] + 2*padding_size - width_new = img.shape[1] + 2*padding_size - - h_start = padding_size - w_start = padding_size - - if padding_color == 'white': - img_new = np.ones((height_new, width_new, img.shape[2])).astype(float) * 255 - elif padding_color == 'black': - img_new = np.zeros((height_new, width_new, img.shape[2])).astype(float) - else: - raise ValueError("padding_color must be 'white' or 'black'") - - img_new[h_start:h_start + img.shape[0], w_start:w_start + img.shape[1], :] = np.copy(img[:, :, :]) - - - return img_new - -# TODO: document where this is from -def do_deskewing(img, amplitude): - height, width = img.shape[:2] - - # Generate sinusoidal wave distortion with reduced amplitude - #amplitude = 8 # 5 # Reduce the amplitude for less curvature - frequency = 300 # Increase frequency to stretch the curve - x_indices = np.tile(np.arange(width), (height, 1)) - y_indices = np.arange(height).reshape(-1, 1) + amplitude * np.sin(2 * np.pi * x_indices / frequency) - - # Convert indices to float32 for remapping - map_x = x_indices.astype(np.float32) - map_y = y_indices.astype(np.float32) - - # Apply the remap to create the curve - curved_image = cv2.remap(img, map_x, map_y, interpolation=cv2.INTER_LINEAR, borderMode=cv2.BORDER_CONSTANT) - return curved_image - -# TODO: document where this is from -def do_direction_in_depth(img, direction: str): - height, width = img.shape[:2] - - if direction == 'left': - # Define the original corner points of the image - src_points = np.float32([ - [0, 0], # Top-left corner - [width, 0], # Top-right corner - [0, height], # Bottom-left corner - [width, height] # Bottom-right corner - ]) - - # Define the new corner points for a subtle right-to-left tilt - dst_points = np.float32([ - [2, 13], # Slight inward shift for top-left - [width, 0], # Slight downward shift for top-right - [2, height-13], # Slight inward shift for bottom-left - [width, height] # Slight upward shift for bottom-right - ]) - elif direction == 'right': - # Define the original corner points of the image - src_points = np.float32([ - [0, 0], # Top-left corner - [width, 0], # Top-right corner - [0, height], # Bottom-left corner - [width, height] # Bottom-right corner - ]) - - # Define the new corner points for a subtle right-to-left tilt - dst_points = np.float32([ - [0, 0], # Slight inward shift for top-left - [width, 13], # Slight downward shift for top-right - [0, height], # Slight inward shift for bottom-left - [width, height - 13] # Slight upward shift for bottom-right - ]) - - elif direction == 'up': - # Define the original corner points of the image - src_points = np.float32([ - [0, 0], # Top-left corner - [width, 0], # Top-right corner - [0, height], # Bottom-left corner - [width, height] # Bottom-right corner - ]) - - # Define the new corner points to simulate a tilted perspective - # Make the top part appear closer and the bottom part farther - dst_points = np.float32([ - [50, 0], # Top-left moved inward - [width - 50, 0], # Top-right moved inward - [0, height], # Bottom-left remains the same - [width, height] # Bottom-right remains the same - ]) - elif direction == 'down': - # Define the original corner points of the image - src_points = np.float32([ - [0, 0], # Top-left corner - [width, 0], # Top-right corner - [0, height], # Bottom-left corner - [width, height] # Bottom-right corner - ]) - - # Define the new corner points to simulate a tilted perspective - # Make the top part appear closer and the bottom part farther - dst_points = np.float32([ - [0, 0], # Top-left moved inward - [width, 0], # Top-right moved inward - [50, height], # Bottom-left remains the same - [width - 50, height] # Bottom-right remains the same - ]) - else: - raise ValueError("direction must be 'left', 'right', 'up' or 'down'") - - # Compute the perspective transformation matrix - matrix = cv2.getPerspectiveTransform(src_points, dst_points) - - # Apply the perspective warp - warped_image = cv2.warpPerspective(img, matrix, (width, height)) - return warped_image - - -def return_shuffled_channels(img, channels_order): - """ - channels order in ordinary case is like this [0, 1, 2]. In the case of shuffling the order should be provided. - """ - img_sh = np.copy(img) - - img_sh[:,:,0]= img[:,:,channels_order[0]] - img_sh[:,:,1]= img[:,:,channels_order[1]] - img_sh[:,:,2]= img[:,:,channels_order[2]] - return img_sh - -# TODO: Refactor into one {{{ -def return_binary_image_with_red_textlines(img_bin): - img_red = np.copy(img_bin) - - img_red[:,:,0][img_bin[:,:,0] == 0] = 255 - return img_red - -def return_binary_image_with_given_rgb_background(img_bin, img_rgb_background): - img_rgb_background = resize_image(img_rgb_background ,img_bin.shape[0], img_bin.shape[1]) - - img_final = np.copy(img_bin) - - img_final[:,:,0][img_bin[:,:,0] != 0] = img_rgb_background[:,:,0][img_bin[:,:,0] != 0] - img_final[:,:,1][img_bin[:,:,1] != 0] = img_rgb_background[:,:,1][img_bin[:,:,1] != 0] - img_final[:,:,2][img_bin[:,:,2] != 0] = img_rgb_background[:,:,2][img_bin[:,:,2] != 0] - - return img_final - -def return_binary_image_with_given_rgb_background_and_given_foreground_rgb(img_bin, img_rgb_background, rgb_foreground): - img_rgb_background = resize_image(img_rgb_background ,img_bin.shape[0], img_bin.shape[1]) - - img_final = np.copy(img_bin) - img_foreground = np.zeros(img_bin.shape) - - - img_foreground[:,:,0][img_bin[:,:,0] == 0] = rgb_foreground[0] - img_foreground[:,:,1][img_bin[:,:,0] == 0] = rgb_foreground[1] - img_foreground[:,:,2][img_bin[:,:,0] == 0] = rgb_foreground[2] - - - img_final[:,:,0][img_bin[:,:,0] != 0] = img_rgb_background[:,:,0][img_bin[:,:,0] != 0] - img_final[:,:,1][img_bin[:,:,1] != 0] = img_rgb_background[:,:,1][img_bin[:,:,1] != 0] - img_final[:,:,2][img_bin[:,:,2] != 0] = img_rgb_background[:,:,2][img_bin[:,:,2] != 0] - - img_final = img_final + img_foreground - return img_final - -def return_binary_image_with_given_rgb_background_red_textlines(img_bin, img_rgb_background, img_color): - img_rgb_background = resize_image(img_rgb_background ,img_bin.shape[0], img_bin.shape[1]) - - img_final = np.copy(img_color) - - img_final[:,:,0][img_bin[:,:,0] != 0] = img_rgb_background[:,:,0][img_bin[:,:,0] != 0] - img_final[:,:,1][img_bin[:,:,1] != 0] = img_rgb_background[:,:,1][img_bin[:,:,1] != 0] - img_final[:,:,2][img_bin[:,:,2] != 0] = img_rgb_background[:,:,2][img_bin[:,:,2] != 0] - - return img_final - -def return_image_with_red_elements(img, img_bin): - img_final = np.copy(img) - - img_final[:,:,0][img_bin[:,:,0]==0] = 0 - img_final[:,:,1][img_bin[:,:,0]==0] = 0 - img_final[:,:,2][img_bin[:,:,0]==0] = 255 - return img_final - -# }}} - -def shift_image_and_label(img, label, type_shift): - h_n = int(img.shape[0]*1.06) - w_n = int(img.shape[1]*1.06) - - channel0_avg = int( np.mean(img[:,:,0]) ) - channel1_avg = int( np.mean(img[:,:,1]) ) - channel2_avg = int( np.mean(img[:,:,2]) ) - - h_diff = abs( img.shape[0] - h_n ) - w_diff = abs( img.shape[1] - w_n ) - - h_start = int(h_diff / 2.) - w_start = int(w_diff / 2.) - - img_scaled_padded = np.zeros((h_n, w_n, 3)) - label_scaled_padded = np.zeros((h_n, w_n, 3)) - - img_scaled_padded[:,:,0] = channel0_avg - img_scaled_padded[:,:,1] = channel1_avg - img_scaled_padded[:,:,2] = channel2_avg - - img_scaled_padded[h_start:h_start+img.shape[0], w_start:w_start+img.shape[1],:] = img[:,:,:] - label_scaled_padded[h_start:h_start+img.shape[0], w_start:w_start+img.shape[1],:] = label[:,:,:] - - - if type_shift=="xpos": - img_dis = img_scaled_padded[h_start:h_start+img.shape[0],2*w_start:2*w_start+img.shape[1],:] - label_dis = label_scaled_padded[h_start:h_start+img.shape[0],2*w_start:2*w_start+img.shape[1],:] - elif type_shift=="xmin": - img_dis = img_scaled_padded[h_start:h_start+img.shape[0],:img.shape[1],:] - label_dis = label_scaled_padded[h_start:h_start+img.shape[0],:img.shape[1],:] - elif type_shift=="ypos": - img_dis = img_scaled_padded[2*h_start:2*h_start+img.shape[0],w_start:w_start+img.shape[1],:] - label_dis = label_scaled_padded[2*h_start:2*h_start+img.shape[0],w_start:w_start+img.shape[1],:] - elif type_shift=="ymin": - img_dis = img_scaled_padded[:img.shape[0],w_start:w_start+img.shape[1],:] - label_dis = label_scaled_padded[:img.shape[0],w_start:w_start+img.shape[1],:] - elif type_shift=="xypos": - img_dis = img_scaled_padded[2*h_start:2*h_start+img.shape[0],2*w_start:2*w_start+img.shape[1],:] - label_dis = label_scaled_padded[2*h_start:2*h_start+img.shape[0],2*w_start:2*w_start+img.shape[1],:] - elif type_shift=="xymin": - img_dis = img_scaled_padded[:img.shape[0],:img.shape[1],:] - label_dis = label_scaled_padded[:img.shape[0],:img.shape[1],:] - return img_dis, label_dis - -def scale_image_for_no_patch(img, label, scale): - h_n = int(img.shape[0]*scale) - w_n = int(img.shape[1]*scale) - - channel0_avg = int( np.mean(img[:,:,0]) ) - channel1_avg = int( np.mean(img[:,:,1]) ) - channel2_avg = int( np.mean(img[:,:,2]) ) - - h_diff = img.shape[0] - h_n - w_diff = img.shape[1] - w_n - - h_start = int(h_diff / 2.) - w_start = int(w_diff / 2.) - - img_res = resize_image(img, h_n, w_n) - label_res = resize_image(label, h_n, w_n) - - img_scaled_padded = np.copy(img) - - label_scaled_padded = np.zeros(label.shape) - - img_scaled_padded[:,:,0] = channel0_avg - img_scaled_padded[:,:,1] = channel1_avg - img_scaled_padded[:,:,2] = channel2_avg - - img_scaled_padded[h_start:h_start+h_n, w_start:w_start+w_n,:] = img_res[:,:,:] - label_scaled_padded[h_start:h_start+h_n, w_start:w_start+w_n,:] = label_res[:,:,:] - - return img_scaled_padded, label_scaled_padded - - -def return_number_of_total_training_data(path_classes): - sub_classes = os.listdir(path_classes) - n_tot = 0 - for sub_c in sub_classes: - sub_files = os.listdir(os.path.join(path_classes,sub_c)) - n_tot = n_tot + len(sub_files) - return n_tot - - - -def generate_data_from_folder_evaluation(path_classes, height, width, n_classes, list_classes): - #sub_classes = os.listdir(path_classes) - #n_classes = len(sub_classes) - all_imgs = [] - labels = [] - #dicts =dict() - #indexer= 0 - for indexer, sub_c in enumerate(list_classes): - sub_files = os.listdir(os.path.join(path_classes,sub_c )) - sub_files = [os.path.join(path_classes,sub_c )+'/' + x for x in sub_files] - #print( os.listdir(os.path.join(path_classes,sub_c )) ) - all_imgs = all_imgs + sub_files - sub_labels = list( np.zeros( len(sub_files) ) +indexer ) - - #print( len(sub_labels) ) - labels = labels + sub_labels - #dicts[sub_c] = indexer - #indexer +=1 - - - categories = to_categorical(range(n_classes)).astype(np.int16)#[ [1 , 0, 0 , 0 , 0 , 0] , [0 , 1, 0 , 0 , 0 , 0] , [0 , 0, 1 , 0 , 0 , 0] , [0 , 0, 0 , 1 , 0 , 0] , [0 , 0, 0 , 0 , 1 , 0] , [0 , 0, 0 , 0 , 0 , 1] ] - ret_x= np.zeros((len(labels), height,width, 3)).astype(np.int16) - ret_y= np.zeros((len(labels), n_classes)).astype(np.int16) - - #print(all_imgs) - for i in range(len(all_imgs)): - row = all_imgs[i] - #####img = cv2.imread(row, 0) - #####img= resize_image (img, height, width) - #####img = img.astype(np.uint16) - #####ret_x[i, :,:,0] = img[:,:] - #####ret_x[i, :,:,1] = img[:,:] - #####ret_x[i, :,:,2] = img[:,:] - - img = cv2.imread(row) - img= resize_image (img, height, width) - img = img.astype(np.uint16) - ret_x[i, :,:] = img[:,:,:] - - ret_y[i, :] = categories[ int( labels[i] ) ][:] - - return ret_x/255., ret_y - -def generate_data_from_folder_training(path_classes, n_batch, height, width, n_classes, list_classes): - #sub_classes = os.listdir(path_classes) - #n_classes = len(sub_classes) - - all_imgs = [] - labels = [] - #dicts =dict() - #indexer= 0 - for indexer, sub_c in enumerate(list_classes): - sub_files = os.listdir(os.path.join(path_classes,sub_c )) - sub_files = [os.path.join(path_classes,sub_c )+'/' + x for x in sub_files] - #print( os.listdir(os.path.join(path_classes,sub_c )) ) - all_imgs = all_imgs + sub_files - sub_labels = list( np.zeros( len(sub_files) ) +indexer ) - - #print( len(sub_labels) ) - labels = labels + sub_labels - #dicts[sub_c] = indexer - #indexer +=1 - - ids = np.array(range(len(labels))) - random.shuffle(ids) - - shuffled_labels = np.array(labels)[ids] - shuffled_files = np.array(all_imgs)[ids] - categories = to_categorical(range(n_classes)).astype(np.int16)#[ [1 , 0, 0 , 0 , 0 , 0] , [0 , 1, 0 , 0 , 0 , 0] , [0 , 0, 1 , 0 , 0 , 0] , [0 , 0, 0 , 1 , 0 , 0] , [0 , 0, 0 , 0 , 1 , 0] , [0 , 0, 0 , 0 , 0 , 1] ] - ret_x= np.zeros((n_batch, height,width, 3)).astype(np.int16) - ret_y= np.zeros((n_batch, n_classes)).astype(np.int16) - batchcount = 0 - while True: - for i in range(len(shuffled_files)): - row = shuffled_files[i] - #print(row) - ###img = cv2.imread(row, 0) - ###img= resize_image (img, height, width) - ###img = img.astype(np.uint16) - ###ret_x[batchcount, :,:,0] = img[:,:] - ###ret_x[batchcount, :,:,1] = img[:,:] - ###ret_x[batchcount, :,:,2] = img[:,:] - - img = cv2.imread(row) - img= resize_image (img, height, width) - img = img.astype(np.uint16) - ret_x[batchcount, :,:,:] = img[:,:,:] - - #print(int(shuffled_labels[i]) ) - #print( categories[int(shuffled_labels[i])] ) - ret_y[batchcount, :] = categories[ int( shuffled_labels[i] ) ][:] - - batchcount+=1 - - if batchcount>=n_batch: - ret_x = ret_x/255. - yield ret_x, ret_y - ret_x= np.zeros((n_batch, height,width, 3)).astype(np.int16) - ret_y= np.zeros((n_batch, n_classes)).astype(np.int16) - batchcount = 0 - -def do_brightening(img_in_dir, factor): - im = Image.open(img_in_dir) - enhancer = ImageEnhance.Brightness(im) - out_img = enhancer.enhance(factor) - out_img = out_img.convert('RGB') - opencv_img = np.array(out_img) - opencv_img = opencv_img[:,:,::-1].copy() - return opencv_img - - -def bluring(img_in, kind): - if kind == 'gauss': - img_blur = cv2.GaussianBlur(img_in, (5, 5), 0) - elif kind == "median": - img_blur = cv2.medianBlur(img_in, 5) - elif kind == 'blur': - img_blur = cv2.blur(img_in, (5, 5)) - else: - raise ValueError("kind must be 'gauss', 'median' or 'blur'") - return img_blur - - -# TODO: document where this is from -def elastic_transform(image, alpha, sigma, seedj, random_state=None): - """Elastic deformation of images as described in [Simard2003]_. - .. [Simard2003] Simard, Steinkraus and Platt, "Best Practices for - Convolutional Neural Networks applied to Visual Document Analysis", in - Proc. of the International Conference on Document Analysis and - Recognition, 2003. - """ - if random_state is None: - random_state = np.random.RandomState(seedj) - - shape = image.shape - dx = gaussian_filter((random_state.rand(*shape) * 2 - 1), sigma, mode="constant", cval=0) * alpha - dy = gaussian_filter((random_state.rand(*shape) * 2 - 1), sigma, mode="constant", cval=0) * alpha - dz = np.zeros_like(dx) - - x, y, z = np.meshgrid(np.arange(shape[1]), np.arange(shape[0]), np.arange(shape[2])) - indices = np.reshape(y + dy, (-1, 1)), np.reshape(x + dx, (-1, 1)), np.reshape(z, (-1, 1)) - - distored_image = map_coordinates(image, indices, order=1, mode='reflect') - return distored_image.reshape(image.shape) - - -# TODO: Use one of the utils/rotate.py functions for this -def rotation_90(img): - img_rot = np.zeros((img.shape[1], img.shape[0], img.shape[2])) - img_rot[:, :, 0] = img[:, :, 0].T - img_rot[:, :, 1] = img[:, :, 1].T - img_rot[:, :, 2] = img[:, :, 2].T - return img_rot - - -# TODO: document where this is from -# TODO: Use one of the utils/rotate.py functions for this -def rotatedRectWithMaxArea(w, h, angle): - """ - Given a rectangle of size wxh that has been rotated by 'angle' (in - radians), computes the width and height of the largest possible - axis-aligned rectangle (maximal area) within the rotated rectangle. - """ - if w <= 0 or h <= 0: - return 0, 0 - - width_is_longer = w >= h - side_long, side_short = (w, h) if width_is_longer else (h, w) - - # since the solutions for angle, -angle and 180-angle are all the same, - # if suffices to look at the first quadrant and the absolute values of sin,cos: - sin_a, cos_a = abs(math.sin(angle)), abs(math.cos(angle)) - if side_short <= 2. * sin_a * cos_a * side_long or abs(sin_a - cos_a) < 1e-10: - # half constrained case: two crop corners touch the longer side, - # the other two corners are on the mid-line parallel to the longer line - x = 0.5 * side_short - wr, hr = (x / sin_a, x / cos_a) if width_is_longer else (x / cos_a, x / sin_a) - else: - # fully constrained case: crop touches all 4 sides - cos_2a = cos_a * cos_a - sin_a * sin_a - wr, hr = (w * cos_a - h * sin_a) / cos_2a, (h * cos_a - w * sin_a) / cos_2a - - return wr, hr - - -# TODO: Use one of the utils/rotate.py functions for this -def rotate_max_area(image, rotated, rotated_label, angle): - """ image: cv2 image matrix object - angle: in degree - """ - wr, hr = rotatedRectWithMaxArea(image.shape[1], image.shape[0], - math.radians(angle)) - h, w, _ = rotated.shape - y1 = h // 2 - int(hr / 2) - y2 = y1 + int(hr) - x1 = w // 2 - int(wr / 2) - x2 = x1 + int(wr) - return rotated[y1:y2, x1:x2], rotated_label[y1:y2, x1:x2] - -# TODO: Use one of the utils/rotate.py functions for this -def rotate_max_area_single_image(image, rotated, angle): - """ image: cv2 image matrix object - angle: in degree - """ - wr, hr = rotatedRectWithMaxArea(image.shape[1], image.shape[0], - math.radians(angle)) - h, w, _ = rotated.shape - y1 = h // 2 - int(hr / 2) - y2 = y1 + int(hr) - x1 = w // 2 - int(wr / 2) - x2 = x1 + int(wr) - return rotated[y1:y2, x1:x2] - -# TODO: Use one of the utils/rotate.py functions for this -def rotation_not_90_func(img, label, thetha): - rotated = imutils.rotate(img, thetha) - rotated_label = imutils.rotate(label, thetha) - return rotate_max_area(img, rotated, rotated_label, thetha) - - -# TODO: Use one of the utils/rotate.py functions for this -def rotation_not_90_func_single_image(img, thetha): - rotated = imutils.rotate(img, thetha) - return rotate_max_area_single_image(img, rotated, thetha) - - -def color_images(seg, n_classes): - ann_u = range(n_classes) - if len(np.shape(seg)) == 3: - seg = seg[:, :, 0] - - seg_img = np.zeros((np.shape(seg)[0], np.shape(seg)[1], 3)).astype(float) - colors = sns.color_palette("hls", n_classes) - - for c in ann_u: - c = int(c) - segl = (seg == c) - seg_img[:, :, 0] += segl * (colors[c][0]) - seg_img[:, :, 1] += segl * (colors[c][1]) - seg_img[:, :, 2] += segl * (colors[c][2]) - return seg_img - - -# TODO: use resize_image from utils -def resize_image(seg_in, input_height, input_width): - return cv2.resize(seg_in, (input_width, input_height), interpolation=cv2.INTER_NEAREST) - - -def get_one_hot(seg, input_height, input_width, n_classes): - seg = seg[:, :, 0] - seg_f = np.zeros((input_height, input_width, n_classes)) - for j in range(n_classes): - seg_f[:, :, j] = (seg == j).astype(int) - return seg_f - - -# TODO: document where this is from -def IoU(Yi, y_predi): - ## mean Intersection over Union - ## Mean IoU = TP/(FN + TP + FP) - - IoUs = [] - classes_true = np.unique(Yi) - for c in classes_true: - TP = np.sum((Yi == c) & (y_predi == c)) - FP = np.sum((Yi != c) & (y_predi == c)) - FN = np.sum((Yi == c) & (y_predi != c)) - IoU = TP / float(TP + FP + FN) - #print("class {:02.0f}: #TP={:6.0f}, #FP={:6.0f}, #FN={:5.0f}, IoU={:4.3f}".format(c, TP, FP, FN, IoU)) - IoUs.append(IoU) - mIoU = np.mean(IoUs) - #print("_________________") - #print("Mean IoU: {:4.3f}".format(mIoU)) - return mIoU - -def generate_arrays_from_folder_reading_order(classes_file_dir, modal_dir, n_batch, height, width, n_classes, thetha, augmentation=False): - all_labels_files = os.listdir(classes_file_dir) - ret_x= np.zeros((n_batch, height, width, 3))#.astype(np.int16) - ret_y= np.zeros((n_batch, n_classes)).astype(np.int16) - batchcount = 0 - while True: - for i in all_labels_files: - file_name = os.path.splitext(i)[0] - img = cv2.imread(os.path.join(modal_dir,file_name+'.png')) - - label_class = int( np.load(os.path.join(classes_file_dir,i)) ) - - ret_x[batchcount, :,:,0] = img[:,:,0]/3.0 - ret_x[batchcount, :,:,2] = img[:,:,2]/3.0 - ret_x[batchcount, :,:,1] = img[:,:,1]/5.0 - - ret_y[batchcount, :] = label_class - batchcount+=1 - if batchcount>=n_batch: - yield ret_x, ret_y - ret_x= np.zeros((n_batch, height, width, 3))#.astype(np.int16) - ret_y= np.zeros((n_batch, n_classes)).astype(np.int16) - batchcount = 0 - - if augmentation: - for thetha_i in thetha: - img_rot = rotation_not_90_func_single_image(img, thetha_i) - - img_rot = resize_image(img_rot, height, width) - - ret_x[batchcount, :,:,0] = img_rot[:,:,0]/3.0 - ret_x[batchcount, :,:,2] = img_rot[:,:,2]/3.0 - ret_x[batchcount, :,:,1] = img_rot[:,:,1]/5.0 - - ret_y[batchcount, :] = label_class - batchcount+=1 - if batchcount>=n_batch: - yield ret_x, ret_y - ret_x= np.zeros((n_batch, height, width, 3))#.astype(np.int16) - ret_y= np.zeros((n_batch, n_classes)).astype(np.int16) - batchcount = 0 - -def data_gen(img_folder, mask_folder, batch_size, input_height, input_width, n_classes, task='segmentation'): - c = 0 - n = [f for f in os.listdir(img_folder) if not f.startswith('.')] # os.listdir(img_folder) #List of training images - random.shuffle(n) - while True: - img = np.zeros((batch_size, input_height, input_width, 3)).astype('float') - mask = np.zeros((batch_size, input_height, input_width, n_classes)).astype('float') - - for i in range(c, c + batch_size): # initially from 0 to 16, c = 0. - try: - filename = os.path.splitext(n[i])[0] - - train_img = cv2.imread(img_folder + '/' + n[i]) / 255. - train_img = cv2.resize(train_img, (input_width, input_height), - interpolation=cv2.INTER_NEAREST) # Read an image from folder and resize - - img[i - c] = train_img # add to array - img[0], img[1], and so on. - if task == "segmentation" or task=="binarization": - train_mask = cv2.imread(mask_folder + '/' + filename + '.png') - train_mask = get_one_hot(resize_image(train_mask, input_height, input_width), input_height, input_width, - n_classes) - elif task == "enhancement": - train_mask = cv2.imread(mask_folder + '/' + filename + '.png')/255. - train_mask = resize_image(train_mask, input_height, input_width) - - # train_mask = train_mask.reshape(224, 224, 1) # Add extra dimension for parity with train_img size [512 * 512 * 3] - - mask[i - c] = train_mask - except: - img[i - c] = np.ones((input_height, input_width, 3)).astype('float') - mask[i - c] = np.zeros((input_height, input_width, n_classes)).astype('float') - - c += batch_size - if c + batch_size >= len(os.listdir(img_folder)): - c = 0 - random.shuffle(n) - yield img, mask - - -# TODO: Use otsu_copy from utils -def otsu_copy(img): - img_r = np.zeros(img.shape) - img1 = img[:, :, 0] - img2 = img[:, :, 1] - img3 = img[:, :, 2] - _, threshold1 = cv2.threshold(img1, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU) - _, threshold2 = cv2.threshold(img2, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU) - _, threshold3 = cv2.threshold(img3, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU) - img_r[:, :, 0] = threshold1 - img_r[:, :, 1] = threshold1 - img_r[:, :, 2] = threshold1 - return img_r - - -def get_patches(dir_img_f, dir_seg_f, img, label, height, width, indexer): - if img.shape[0] < height or img.shape[1] < width: - img, label = do_padding(img, label, height, width) - - img_h = img.shape[0] - img_w = img.shape[1] - - nxf = img_w / float(width) - nyf = img_h / float(height) - - if nxf > int(nxf): - nxf = int(nxf) + 1 - if nyf > int(nyf): - nyf = int(nyf) + 1 - - nxf = int(nxf) - nyf = int(nyf) - - for i in range(nxf): - for j in range(nyf): - index_x_d = i * width - index_x_u = (i + 1) * width - - index_y_d = j * height - index_y_u = (j + 1) * height - - if index_x_u > img_w: - index_x_u = img_w - index_x_d = img_w - width - if index_y_u > img_h: - index_y_u = img_h - index_y_d = img_h - height - - img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :] - label_patch = label[index_y_d:index_y_u, index_x_d:index_x_u, :] - - cv2.imwrite(dir_img_f + '/img_' + str(indexer) + '.png', img_patch) - cv2.imwrite(dir_seg_f + '/img_' + str(indexer) + '.png', label_patch) - indexer += 1 - - return indexer - - -def do_padding_with_color(img, padding_color='black'): - index_start_h = 4 - index_start_w = 4 - - img_padded = np.zeros((img.shape[0] + 2*index_start_h, img.shape[1]+ 2*index_start_w, img.shape[2])) - if padding_color == 'white': - img_padded += 255 - img_padded[index_start_h: index_start_h + img.shape[0], index_start_w: index_start_w + img.shape[1], :] = img[:, :, :] - - return img_padded.astype(float) - - -def do_degrading(img, scale): - img_org_h = img.shape[0] - img_org_w = img.shape[1] - - img_res = resize_image(img, int(img_org_h * scale), int(img_org_w * scale)) - - return resize_image(img_res, img_org_h, img_org_w) - -# TODO: How is this different from do_padding_black? -def do_padding_label(img): - img_org_h = img.shape[0] - img_org_w = img.shape[1] - - index_start_h = 4 - index_start_w = 4 - - img_padded = np.zeros((img.shape[0] + 2*index_start_h, img.shape[1] + 2*index_start_w, img.shape[2])) - img_padded[index_start_h: index_start_h + img.shape[0], index_start_w: index_start_w + img.shape[1], :] = img[:, :, :] - - return img_padded.astype(np.int16) - -def do_padding(img, label, height, width): - height_new=img.shape[0] - width_new=img.shape[1] - - h_start = 0 - w_start = 0 - - if img.shape[0] < height: - h_start = int(abs(height - img.shape[0]) / 2.) - height_new = height - - if img.shape[1] < width: - w_start = int(abs(width - img.shape[1]) / 2.) - width_new = width - - img_new = np.ones((height_new, width_new, img.shape[2])).astype(float) * 255 - label_new = np.zeros((height_new, width_new, label.shape[2])).astype(float) - - img_new[h_start:h_start + img.shape[0], w_start:w_start + img.shape[1], :] = np.copy(img[:, :, :]) - label_new[h_start:h_start + label.shape[0], w_start:w_start + label.shape[1], :] = np.copy(label[:, :, :]) - - return img_new,label_new - - -def get_patches_num_scale_new(dir_img_f, dir_seg_f, img, label, height, width, indexer, scaler): - img = resize_image(img, int(img.shape[0] * scaler), int(img.shape[1] * scaler)) - label = resize_image(label, int(label.shape[0] * scaler), int(label.shape[1] * scaler)) - - if img.shape[0] < height or img.shape[1] < width: - img, label = do_padding(img, label, height, width) - - img_h = img.shape[0] - img_w = img.shape[1] - - height_scale = int(height * 1) - width_scale = int(width * 1) - - nxf = img_w / float(width_scale) - nyf = img_h / float(height_scale) - - if nxf > int(nxf): - nxf = int(nxf) + 1 - if nyf > int(nyf): - nyf = int(nyf) + 1 - - nxf = int(nxf) - nyf = int(nyf) - - for i in range(nxf): - for j in range(nyf): - index_x_d = i * width_scale - index_x_u = (i + 1) * width_scale - - index_y_d = j * height_scale - index_y_u = (j + 1) * height_scale - - if index_x_u > img_w: - index_x_u = img_w - index_x_d = img_w - width_scale - if index_y_u > img_h: - index_y_u = img_h - index_y_d = img_h - height_scale - - img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :] - label_patch = label[index_y_d:index_y_u, index_x_d:index_x_u, :] - - cv2.imwrite(dir_img_f + '/img_' + str(indexer) + '.png', img_patch) - cv2.imwrite(dir_seg_f + '/img_' + str(indexer) + '.png', label_patch) - indexer += 1 - - return indexer - - -# TODO: (far) too many args -# TODO: refactor to combine with data_gen_ocr -def provide_patches( - imgs_list_train, - segs_list_train, - dir_img, - dir_seg, - dir_flow_train_imgs, - dir_flow_train_labels, - input_height, - input_width, - blur_k, - blur_aug, - padding_white, - padding_black, - flip_aug, - binarization, - adding_rgb_background, - adding_rgb_foreground, - add_red_textlines, - channels_shuffling, - scaling, - shifting, - degrading, - brightening, - scales, - degrade_scales, - brightness, - flip_index, - shuffle_indexes, - scaling_bluring, - scaling_brightness, - scaling_binarization, - rotation, - rotation_not_90, - thetha, - scaling_flip, - task, - augmentation=False, - patches=False, - dir_img_bin=None, - number_of_backgrounds_per_image=None, - list_all_possible_background_images=None, - dir_rgb_backgrounds=None, - dir_rgb_foregrounds=None, - list_all_possible_foreground_rgbs=None, -): - - # TODO: why sepoarate var if you have seg_i? - indexer = 0 - for im, seg_i in tqdm(zip(imgs_list_train, segs_list_train)): - img_name = os.path.splitext(im)[0] - if task == "segmentation" or task == "binarization": - dir_of_label_file = os.path.join(dir_seg, img_name + '.png') - elif task=="enhancement": - dir_of_label_file = os.path.join(dir_seg, im) - - if not patches: - cv2.imwrite(dir_flow_train_imgs + '/img_' + str(indexer) + '.png', resize_image(cv2.imread(dir_img + '/' + im), input_height, input_width)) - cv2.imwrite(dir_flow_train_labels + '/img_' + str(indexer) + '.png', resize_image(cv2.imread(dir_of_label_file), input_height, input_width)) - indexer += 1 - - if augmentation: - if flip_aug: - for f_i in flip_index: - cv2.imwrite(dir_flow_train_imgs + '/img_' + str(indexer) + '.png', - resize_image(cv2.flip(cv2.imread(dir_img+'/'+im),f_i),input_height,input_width) ) - - cv2.imwrite(dir_flow_train_labels + '/img_' + str(indexer) + '.png', - resize_image(cv2.flip(cv2.imread(dir_of_label_file), f_i), input_height, input_width)) - indexer += 1 - - if blur_aug: - for blur_i in blur_k: - cv2.imwrite(dir_flow_train_imgs + '/img_' + str(indexer) + '.png', - (resize_image(bluring(cv2.imread(dir_img + '/' + im), blur_i), input_height, input_width))) - - cv2.imwrite(dir_flow_train_labels + '/img_' + str(indexer) + '.png', - resize_image(cv2.imread(dir_of_label_file), input_height, input_width)) - indexer += 1 - if brightening: - for factor in brightness: - try: - cv2.imwrite(dir_flow_train_imgs + '/img_' + str(indexer) + '.png', - (resize_image(do_brightening(dir_img + '/' +im, factor), input_height, input_width))) - - cv2.imwrite(dir_flow_train_labels + '/img_' + str(indexer) + '.png', - resize_image(cv2.imread(dir_of_label_file), input_height, input_width)) - indexer += 1 - except: - pass - - if binarization: - - if dir_img_bin: - img_bin_corr = cv2.imread(dir_img_bin + '/' + img_name+'.png') - - cv2.imwrite(dir_flow_train_imgs + '/img_' + str(indexer) + '.png', - resize_image(img_bin_corr, input_height, input_width)) - else: - cv2.imwrite(dir_flow_train_imgs + '/img_' + str(indexer) + '.png', - resize_image(otsu_copy(cv2.imread(dir_img + '/' + im)), input_height, input_width)) - - cv2.imwrite(dir_flow_train_labels + '/img_' + str(indexer) + '.png', - resize_image(cv2.imread(dir_of_label_file), input_height, input_width)) - indexer += 1 - - if degrading: - for degrade_scale_ind in degrade_scales: - cv2.imwrite(dir_flow_train_imgs + '/img_' + str(indexer) + '.png', - (resize_image(do_degrading(cv2.imread(dir_img + '/' + im), degrade_scale_ind), input_height, input_width))) - - cv2.imwrite(dir_flow_train_labels + '/img_' + str(indexer) + '.png', - resize_image(cv2.imread(dir_of_label_file), input_height, input_width)) - indexer += 1 - - if rotation_not_90: - for thetha_i in thetha: - img_max_rotated, label_max_rotated = rotation_not_90_func(cv2.imread(dir_img + '/'+im), - cv2.imread(dir_of_label_file), thetha_i) - - cv2.imwrite(dir_flow_train_imgs + '/img_' + str(indexer) + '.png', resize_image(img_max_rotated, input_height, input_width)) - - cv2.imwrite(dir_flow_train_labels + '/img_' + str(indexer) + '.png', resize_image(label_max_rotated, input_height, input_width)) - indexer += 1 - - if channels_shuffling: - for shuffle_index in shuffle_indexes: - cv2.imwrite(dir_flow_train_imgs + '/img_' + str(indexer) + '.png', - (resize_image(return_shuffled_channels(cv2.imread(dir_img + '/' + im), shuffle_index), input_height, input_width))) - - cv2.imwrite(dir_flow_train_labels + '/img_' + str(indexer) + '.png', - resize_image(cv2.imread(dir_of_label_file), input_height, input_width)) - indexer += 1 - - if scaling: - for sc_ind in scales: - img_scaled, label_scaled = scale_image_for_no_patch(cv2.imread(dir_img + '/'+im), - cv2.imread(dir_of_label_file), sc_ind) - - cv2.imwrite(dir_flow_train_imgs + '/img_' + str(indexer) + '.png', resize_image(img_scaled, input_height, input_width)) - cv2.imwrite(dir_flow_train_labels + '/img_' + str(indexer) + '.png', resize_image(label_scaled, input_height, input_width)) - indexer += 1 - if shifting: - shift_types = ['xpos', 'xmin', 'ypos', 'ymin', 'xypos', 'xymin'] - for st_ind in shift_types: - img_shifted, label_shifted = shift_image_and_label(cv2.imread(dir_img + '/'+im), - cv2.imread(dir_of_label_file), st_ind) - - cv2.imwrite(dir_flow_train_imgs + '/img_' + str(indexer) + '.png', resize_image(img_shifted, input_height, input_width)) - cv2.imwrite(dir_flow_train_labels + '/img_' + str(indexer) + '.png', resize_image(label_shifted, input_height, input_width)) - indexer += 1 - - - if adding_rgb_background: - img_bin_corr = cv2.imread(dir_img_bin + '/' + img_name+'.png') - for i_n in range(number_of_backgrounds_per_image): - background_image_chosen_name = random.choice(list_all_possible_background_images) - img_rgb_background_chosen = cv2.imread(dir_rgb_backgrounds + '/' + background_image_chosen_name) - img_with_overlayed_background = return_binary_image_with_given_rgb_background(img_bin_corr, img_rgb_background_chosen) - - cv2.imwrite(dir_flow_train_imgs + '/img_' + str(indexer) + '.png', resize_image(img_with_overlayed_background, input_height, input_width)) - cv2.imwrite(dir_flow_train_labels + '/img_' + str(indexer) + '.png', - resize_image(cv2.imread(dir_of_label_file), input_height, input_width)) - - indexer += 1 - - if adding_rgb_foreground: - img_bin_corr = cv2.imread(dir_img_bin + '/' + img_name+'.png') - for i_n in range(number_of_backgrounds_per_image): - background_image_chosen_name = random.choice(list_all_possible_background_images) - foreground_rgb_chosen_name = random.choice(list_all_possible_foreground_rgbs) - - img_rgb_background_chosen = cv2.imread(dir_rgb_backgrounds + '/' + background_image_chosen_name) - foreground_rgb_chosen = np.load(dir_rgb_foregrounds + '/' + foreground_rgb_chosen_name) - - img_with_overlayed_background = return_binary_image_with_given_rgb_background_and_given_foreground_rgb(img_bin_corr, img_rgb_background_chosen, foreground_rgb_chosen) - - cv2.imwrite(dir_flow_train_imgs + '/img_' + str(indexer) + '.png', resize_image(img_with_overlayed_background, input_height, input_width)) - cv2.imwrite(dir_flow_train_labels + '/img_' + str(indexer) + '.png', - resize_image(cv2.imread(dir_of_label_file), input_height, input_width)) - - indexer += 1 - - if add_red_textlines: - img_bin_corr = cv2.imread(dir_img_bin + '/' + img_name+'.png') - img_red_context = return_image_with_red_elements(cv2.imread(dir_img + '/'+im), img_bin_corr) - - cv2.imwrite(dir_flow_train_imgs + '/img_' + str(indexer) + '.png', resize_image(img_red_context, input_height, input_width)) - cv2.imwrite(dir_flow_train_labels + '/img_' + str(indexer) + '.png', - resize_image(cv2.imread(dir_of_label_file), input_height, input_width)) - - indexer += 1 - - - - - if patches: - indexer = get_patches(dir_flow_train_imgs, dir_flow_train_labels, - cv2.imread(dir_img + '/' + im), cv2.imread(dir_of_label_file), - input_height, input_width, indexer=indexer) - - if augmentation: - if rotation: - indexer = get_patches(dir_flow_train_imgs, dir_flow_train_labels, - rotation_90(cv2.imread(dir_img + '/' + im)), - rotation_90(cv2.imread(dir_of_label_file)), - input_height, input_width, indexer=indexer) - - if rotation_not_90: - for thetha_i in thetha: - img_max_rotated, label_max_rotated = rotation_not_90_func(cv2.imread(dir_img + '/'+im), - cv2.imread(dir_of_label_file), thetha_i) - indexer = get_patches(dir_flow_train_imgs, dir_flow_train_labels, - img_max_rotated, - label_max_rotated, - input_height, input_width, indexer=indexer) - - if channels_shuffling: - for shuffle_index in shuffle_indexes: - indexer = get_patches(dir_flow_train_imgs, dir_flow_train_labels, - return_shuffled_channels(cv2.imread(dir_img + '/' + im), shuffle_index), - cv2.imread(dir_of_label_file), - input_height, input_width, indexer=indexer) - - if adding_rgb_background: - img_bin_corr = cv2.imread(dir_img_bin + '/' + img_name+'.png') - for i_n in range(number_of_backgrounds_per_image): - background_image_chosen_name = random.choice(list_all_possible_background_images) - img_rgb_background_chosen = cv2.imread(dir_rgb_backgrounds + '/' + background_image_chosen_name) - img_with_overlayed_background = return_binary_image_with_given_rgb_background(img_bin_corr, img_rgb_background_chosen) - - indexer = get_patches(dir_flow_train_imgs, dir_flow_train_labels, - img_with_overlayed_background, - cv2.imread(dir_of_label_file), - input_height, input_width, indexer=indexer) - - - if adding_rgb_foreground: - img_bin_corr = cv2.imread(dir_img_bin + '/' + img_name+'.png') - for i_n in range(number_of_backgrounds_per_image): - background_image_chosen_name = random.choice(list_all_possible_background_images) - foreground_rgb_chosen_name = random.choice(list_all_possible_foreground_rgbs) - - img_rgb_background_chosen = cv2.imread(dir_rgb_backgrounds + '/' + background_image_chosen_name) - foreground_rgb_chosen = np.load(dir_rgb_foregrounds + '/' + foreground_rgb_chosen_name) - - img_with_overlayed_background = return_binary_image_with_given_rgb_background_and_given_foreground_rgb(img_bin_corr, img_rgb_background_chosen, foreground_rgb_chosen) - - indexer = get_patches(dir_flow_train_imgs, dir_flow_train_labels, - img_with_overlayed_background, - cv2.imread(dir_of_label_file), - input_height, input_width, indexer=indexer) - - - if add_red_textlines: - img_bin_corr = cv2.imread(dir_img_bin + '/' + img_name+'.png') - img_red_context = return_image_with_red_elements(cv2.imread(dir_img + '/'+im), img_bin_corr) - - indexer = get_patches(dir_flow_train_imgs, dir_flow_train_labels, - img_red_context, - cv2.imread(dir_of_label_file), - input_height, input_width, indexer=indexer) - - if flip_aug: - for f_i in flip_index: - indexer = get_patches(dir_flow_train_imgs, dir_flow_train_labels, - cv2.flip(cv2.imread(dir_img + '/' + im), f_i), - cv2.flip(cv2.imread(dir_of_label_file), f_i), - input_height, input_width, indexer=indexer) - if blur_aug: - for blur_i in blur_k: - indexer = get_patches(dir_flow_train_imgs, dir_flow_train_labels, - bluring(cv2.imread(dir_img + '/' + im), blur_i), - cv2.imread(dir_of_label_file), - input_height, input_width, indexer=indexer) - if padding_black: - indexer = get_patches(dir_flow_train_imgs, dir_flow_train_labels, - do_padding_black(cv2.imread(dir_img + '/' + im)), - do_padding_label(cv2.imread(dir_of_label_file)), - input_height, input_width, indexer=indexer) - - if padding_white: - indexer = get_patches(dir_flow_train_imgs, dir_flow_train_labels, - do_padding_white(cv2.imread(dir_img + '/'+im)), - do_padding_label(cv2.imread(dir_of_label_file)), - input_height, input_width, indexer=indexer) - - if brightening: - for factor in brightness: - try: - indexer = get_patches(dir_flow_train_imgs, dir_flow_train_labels, - do_brightening(dir_img + '/' +im, factor), - cv2.imread(dir_of_label_file), - input_height, input_width, indexer=indexer) - except: - pass - if scaling: - for sc_ind in scales: - indexer = get_patches_num_scale_new(dir_flow_train_imgs, dir_flow_train_labels, - cv2.imread(dir_img + '/' + im) , - cv2.imread(dir_of_label_file), - input_height, input_width, indexer=indexer, scaler=sc_ind) - - if degrading: - for degrade_scale_ind in degrade_scales: - indexer = get_patches(dir_flow_train_imgs, dir_flow_train_labels, - do_degrading(cv2.imread(dir_img + '/' + im), degrade_scale_ind), - cv2.imread(dir_of_label_file), - input_height, input_width, indexer=indexer) - - if binarization: - if dir_img_bin: - img_bin_corr = cv2.imread(dir_img_bin + '/' + img_name+'.png') - - indexer = get_patches(dir_flow_train_imgs, dir_flow_train_labels, - img_bin_corr, - cv2.imread(dir_of_label_file), - input_height, input_width, indexer=indexer) - - else: - indexer = get_patches(dir_flow_train_imgs, dir_flow_train_labels, - otsu_copy(cv2.imread(dir_img + '/' + im)), - cv2.imread(dir_of_label_file), - input_height, input_width, indexer=indexer) - - if scaling_brightness: - for sc_ind in scales: - for factor in brightness: - try: - indexer = get_patches_num_scale_new(dir_flow_train_imgs, - dir_flow_train_labels, - do_brightening(dir_img + '/' + im, factor) - ,cv2.imread(dir_of_label_file) - ,input_height, input_width, indexer=indexer, scaler=sc_ind) - except: - pass - - if scaling_bluring: - for sc_ind in scales: - for blur_i in blur_k: - indexer = get_patches_num_scale_new(dir_flow_train_imgs, dir_flow_train_labels, - bluring(cv2.imread(dir_img + '/' + im), blur_i), - cv2.imread(dir_of_label_file), - input_height, input_width, indexer=indexer, scaler=sc_ind) - - if scaling_binarization: - for sc_ind in scales: - indexer = get_patches_num_scale_new(dir_flow_train_imgs, dir_flow_train_labels, - otsu_copy(cv2.imread(dir_img + '/' + im)), - cv2.imread(dir_of_label_file), - input_height, input_width, indexer=indexer, scaler=sc_ind) - - if scaling_flip: - for sc_ind in scales: - for f_i in flip_index: - indexer = get_patches_num_scale_new(dir_flow_train_imgs, dir_flow_train_labels, - - cv2.flip( cv2.imread(dir_img + '/' + im), f_i), - cv2.flip(cv2.imread(dir_of_label_file), f_i), - input_height, input_width, indexer=indexer, scaler=sc_ind) - - - -def data_gen_ocr( - padding_token, - n_batch, - input_height, - input_width, - max_len, - dir_train, - ls_files_images, - augmentation, - color_padding_rotation, - rotation_not_90, - blur_aug, - degrading, - bin_deg, - brightening, - padding_white, - adding_rgb_foreground, - adding_rgb_background, - binarization, - image_inversion, - channels_shuffling, - add_red_textlines, - white_noise_strap, - textline_skewing, - textline_skewing_bin, - textline_left_in_depth, - textline_left_in_depth_bin, - textline_right_in_depth, - textline_right_in_depth_bin, - textline_up_in_depth, - textline_up_in_depth_bin, - textline_down_in_depth, - textline_down_in_depth_bin, - pepper_bin_aug, - pepper_aug, - degrade_scales, - number_of_backgrounds_per_image, - thetha, - thetha_padd, - brightness, - padd_colors, - shuffle_indexes, - pepper_indexes, - skewing_amplitudes, - blur_k, - char_to_num, - list_all_possible_background_images, - list_all_possible_foreground_rgbs, - dir_rgb_backgrounds, - dir_rgb_foregrounds, - white_padds, - dir_img_bin=None, -): - - random.shuffle(ls_files_images) - - ret_x= np.zeros((n_batch, input_height, input_width, 3)).astype(np.float32) - ret_y= np.zeros((n_batch, max_len)).astype(np.int16)+padding_token - batchcount = 0 - - def increment_batchcount(img_out, batchcount, ret_x, ret_y): - to_yield = None - img_out = scale_padd_image_for_ocr(img, input_height, input_width) - ret_x[batchcount, :,:,:] = img_out[:,:,:] - ret_y[batchcount, :] = vectorize_label(txt_inp, char_to_num, padding_token, max_len) - batchcount += 1 - if batchcount>=n_batch: - ret_x = ret_x/255. - to_yield = {"image": ret_x, "label": ret_y} - ret_x= np.zeros((n_batch, input_height, input_width, 3)).astype(np.float32) - ret_y= np.zeros((n_batch, max_len)).astype(np.int16)+padding_token - batchcount = 0 - return img_out, batchcount, ret_x, ret_y, to_yield - - # TODO: Why while True + yield, why not return a list? - while True: - for i in ls_files_images: - print(i, 'i') - f_name = Path(i).stem#.split('.')[0] - - txt_inp = open(os.path.join(dir_train, "labels/"+f_name+'.txt'),'r').read().split('\n')[0] - - img = cv2.imread(os.path.join(dir_train, "images/"+i) ) - if dir_img_bin: - img_bin_corr = cv2.imread(os.path.join(dir_img_bin, f_name+'.png') ) - else: - img_bin_corr = None - - - if augmentation: - img_out, batchcount, ret_x, ret_y, to_yield = increment_batchcount(img, batchcount, ret_x, ret_y) - if to_yield: yield to_yield - - if color_padding_rotation: - for thetha_ind in thetha_padd: - for padd_col in padd_colors: - img_out = rotation_not_90_func_single_image(do_padding_for_ocr(img, 1.2, padd_col), thetha_ind) - img_out, batchcount, ret_x, ret_y, to_yield = increment_batchcount(img_out, batchcount, ret_x, ret_y) - if to_yield: yield to_yield - - if rotation_not_90: - for thetha_ind in thetha: - img_out = rotation_not_90_func_single_image(img, thetha_ind) - img_out, batchcount, ret_x, ret_y, to_yield = increment_batchcount(img_out, batchcount, ret_x, ret_y) - if to_yield: yield to_yield - - if blur_aug: - for blur_type in blur_k: - img_out = bluring(img, blur_type) - img_out, batchcount, ret_x, ret_y, to_yield = increment_batchcount(img_out, batchcount, ret_x, ret_y) - if to_yield: yield to_yield - - if degrading: - for deg_scale_ind in degrade_scales: - try: - img_out = do_degrading(img, deg_scale_ind) - # TODO: qualify except - except: - img_out = np.copy(img) - img_out, batchcount, ret_x, ret_y, to_yield = increment_batchcount(img_out, batchcount, ret_x, ret_y) - if to_yield: yield to_yield - - if bin_deg: - for deg_scale_ind in degrade_scales: - try: - img_out = do_degrading(img_bin_corr, deg_scale_ind) - # TODO: qualify except - except: - img_out = np.copy(img_bin_corr) - img_out, batchcount, ret_x, ret_y, to_yield = increment_batchcount(img_out, batchcount, ret_x, ret_y) - if to_yield: yield to_yield - - if brightening: - for bright_scale_ind in brightness: - try: - # FIXME: dir_img is not defined in this scope, will always fail - img_out = do_brightening(dir_img, bright_scale_ind) - # TODO: qualify except - except: - img_out = np.copy(img) - img_out, batchcount, ret_x, ret_y, to_yield = increment_batchcount(img_out, batchcount, ret_x, ret_y) - if to_yield: yield to_yield - - if padding_white: - for padding_size in white_padds: - for padd_col in padd_colors: - img_out = do_padding_for_ocr(img, padding_size, padd_col) - img_out, batchcount, ret_x, ret_y, to_yield = increment_batchcount(img_out, batchcount, ret_x, ret_y) - if to_yield: yield to_yield - - if adding_rgb_foreground: - for i_n in range(number_of_backgrounds_per_image): - background_image_chosen_name = random.choice(list_all_possible_background_images) - foreground_rgb_chosen_name = random.choice(list_all_possible_foreground_rgbs) - - img_rgb_background_chosen = cv2.imread(dir_rgb_backgrounds + '/' + background_image_chosen_name) - foreground_rgb_chosen = np.load(dir_rgb_foregrounds + '/' + foreground_rgb_chosen_name) - - img_out = return_binary_image_with_given_rgb_background_and_given_foreground_rgb(img_bin_corr, img_rgb_background_chosen, foreground_rgb_chosen) - - img_out, batchcount, ret_x, ret_y, to_yield = increment_batchcount(img_out, batchcount, ret_x, ret_y) - if to_yield: yield to_yield - - - if adding_rgb_background: - for i_n in range(number_of_backgrounds_per_image): - background_image_chosen_name = random.choice(list_all_possible_background_images) - img_rgb_background_chosen = cv2.imread(dir_rgb_backgrounds + '/' + background_image_chosen_name) - img_out = return_binary_image_with_given_rgb_background(img_bin_corr, img_rgb_background_chosen) - img_out, batchcount, ret_x, ret_y, to_yield = increment_batchcount(img_out, batchcount, ret_x, ret_y) - if to_yield: yield to_yield - - if binarization: - img_out = scale_padd_image_for_ocr(img_bin_corr, input_height, input_width) - img_out, batchcount, ret_x, ret_y, to_yield = increment_batchcount(img_out, batchcount, ret_x, ret_y) - if to_yield: yield to_yield - - if image_inversion: - img_out = invert_image(img_bin_corr) - img_out, batchcount, ret_x, ret_y, to_yield = increment_batchcount(img_out, batchcount, ret_x, ret_y) - if to_yield: yield to_yield - - if channels_shuffling: - for shuffle_index in shuffle_indexes: - img_out = return_shuffled_channels(img, shuffle_index) - img_out, batchcount, ret_x, ret_y, to_yield = increment_batchcount(img_out, batchcount, ret_x, ret_y) - if to_yield: yield to_yield - - if add_red_textlines: - img_out = return_image_with_red_elements(img, img_bin_corr) - img_out, batchcount, ret_x, ret_y, to_yield = increment_batchcount(img_out, batchcount, ret_x, ret_y) - if to_yield: yield to_yield - - if white_noise_strap: - img_out = return_image_with_strapped_white_noises(img) - img_out, batchcount, ret_x, ret_y, to_yield = increment_batchcount(img_out, batchcount, ret_x, ret_y) - if to_yield: yield to_yield - - if textline_skewing: - for des_scale_ind in skewing_amplitudes: - try: - img_out = do_deskewing(img, des_scale_ind) - # TODO: qualify except - except: - img_out = np.copy(img) - img_out, batchcount, ret_x, ret_y, to_yield = increment_batchcount(img_out, batchcount, ret_x, ret_y) - if to_yield: yield to_yield - - if textline_skewing_bin: - for des_scale_ind in skewing_amplitudes: - try: - img_out = do_deskewing(img_bin_corr, des_scale_ind) - # TODO: qualify except - except: - img_out = np.copy(img_bin_corr) - img_out, batchcount, ret_x, ret_y, to_yield = increment_batchcount(img_out, batchcount, ret_x, ret_y) - if to_yield: yield to_yield - - if textline_left_in_depth: - try: - img_out = do_direction_in_depth(img, 'left') - # TODO: qualify except - except: - img_out = np.copy(img) - img_out, batchcount, ret_x, ret_y, to_yield = increment_batchcount(img_out, batchcount, ret_x, ret_y) - if to_yield: yield to_yield - - if textline_left_in_depth_bin: - try: - img_out = do_direction_in_depth(img_bin_corr, 'left') - # TODO: qualify except - except: - img_out = np.copy(img_bin_corr) - img_out, batchcount, ret_x, ret_y, to_yield = increment_batchcount(img_out, batchcount, ret_x, ret_y) - if to_yield: yield to_yield - - if textline_right_in_depth: - try: - img_out = do_direction_in_depth(img_bin_corr, 'right') - # TODO: qualify except - except: - img_out = np.copy(img) - img_out, batchcount, ret_x, ret_y, to_yield = increment_batchcount(img_out, batchcount, ret_x, ret_y) - if to_yield: yield to_yield - - - if textline_right_in_depth_bin: - try: - img_out = do_direction_in_depth(img_bin_corr, 'right') - # TODO: qualify except - except: - img_out = np.copy(img_bin_corr) - img_out, batchcount, ret_x, ret_y, to_yield = increment_batchcount(img_out, batchcount, ret_x, ret_y) - if to_yield: yield to_yield - - if textline_up_in_depth: - try: - img_out = do_direction_in_depth(img, 'up') - # TODO: qualify except - except: - img_out = np.copy(img) - img_out, batchcount, ret_x, ret_y, to_yield = increment_batchcount(img_out, batchcount, ret_x, ret_y) - if to_yield: yield to_yield - - if textline_up_in_depth_bin: - try: - img_out = do_direction_in_depth(img_bin_corr, 'up') - # TODO: qualify except - except: - img_out = np.copy(img_bin_corr) - img_out, batchcount, ret_x, ret_y, to_yield = increment_batchcount(img_out, batchcount, ret_x, ret_y) - if to_yield: yield to_yield - - if textline_down_in_depth: - try: - img_out = do_direction_in_depth(img, 'down') - # TODO: qualify except - except: - img_out = np.copy(img) - img_out, batchcount, ret_x, ret_y, to_yield = increment_batchcount(img_out, batchcount, ret_x, ret_y) - if to_yield: yield to_yield - - if textline_down_in_depth_bin: - try: - img_out = do_direction_in_depth(img_bin_corr, 'down') - # TODO: qualify except - except: - img_out = np.copy(img_bin_corr) - img_out, batchcount, ret_x, ret_y, to_yield = increment_batchcount(img_out, batchcount, ret_x, ret_y) - if to_yield: yield to_yield - - if pepper_bin_aug: - for pepper_ind in pepper_indexes: - img_out = add_salt_and_pepper_noise(img_bin_corr, pepper_ind, pepper_ind) - img_out, batchcount, ret_x, ret_y, to_yield = increment_batchcount(img_out, batchcount, ret_x, ret_y) - if to_yield: yield to_yield - - if pepper_aug: - for pepper_ind in pepper_indexes: - img_out = add_salt_and_pepper_noise(img, pepper_ind, pepper_ind) - img_out, batchcount, ret_x, ret_y, to_yield = increment_batchcount(img_out, batchcount, ret_x, ret_y) - if to_yield: yield to_yield - - else: - img_out, batchcount, ret_x, ret_y, to_yield = increment_batchcount(img_out, batchcount, ret_x, ret_y) - if to_yield: yield to_yield - - -# TODO: what is aug_multip and why calculate it in this way -def return_multiplier_based_on_augmnentations( - augmentation, - color_padding_rotation, - rotation_not_90, - blur_aug, - degrading, - bin_deg, - brightening, - padding_white, - adding_rgb_foreground, - adding_rgb_background, - binarization, - image_inversion, - channels_shuffling, - add_red_textlines, - white_noise_strap, - textline_skewing, - textline_skewing_bin, - textline_left_in_depth, - textline_left_in_depth_bin, - textline_right_in_depth, - textline_right_in_depth_bin, - textline_up_in_depth, - textline_up_in_depth_bin, - textline_down_in_depth, - textline_down_in_depth_bin, - pepper_bin_aug, - pepper_aug, - degrade_scales, - number_of_backgrounds_per_image, - thetha, - thetha_padd, - brightness, - padd_colors, - shuffle_indexes, - pepper_indexes, - skewing_amplitudes, - blur_k, - white_padds, -): - aug_multip = 1 - if not augmentation: - return 1 - - if binarization: - aug_multip += 1 - if image_inversion: - aug_multip += 1 - if add_red_textlines: - aug_multip += 1 - if white_noise_strap: - aug_multip += 1 - if textline_right_in_depth: - aug_multip += 1 - if textline_left_in_depth: - aug_multip += 1 - if textline_up_in_depth: - aug_multip += 1 - if textline_down_in_depth: - aug_multip += 1 - if textline_right_in_depth_bin: - aug_multip += 1 - if textline_left_in_depth_bin: - aug_multip += 1 - if textline_up_in_depth_bin: - aug_multip += 1 - if textline_down_in_depth_bin: - aug_multip += 1 - if adding_rgb_foreground: - aug_multip += number_of_backgrounds_per_image - if adding_rgb_background: - aug_multip += number_of_backgrounds_per_image - if bin_deg: - aug_multip += len(degrade_scales) - if degrading: - aug_multip += len(degrade_scales) - if rotation_not_90: - aug_multip += len(thetha) - if textline_skewing: - aug_multip += len(skewing_amplitudes) - if textline_skewing_bin: - aug_multip += len(skewing_amplitudes) - if color_padding_rotation: - aug_multip += len(thetha_padd)*len(padd_colors) - if channels_shuffling: - aug_multip += len(shuffle_indexes) - if blur_aug: - aug_multip += len(blur_k) - if brightening: - aug_multip += len(brightness) - if padding_white: - aug_multip += len(white_padds)*len(padd_colors) - if pepper_aug: - aug_multip += len(pepper_indexes) - if pepper_bin_aug: - aug_multip += len(pepper_indexes) - - return aug_multip diff --git a/src/eynollah/training/weights_ensembling.py b/src/eynollah/training/weights_ensembling.py deleted file mode 100644 index 6dce7fd..0000000 --- a/src/eynollah/training/weights_ensembling.py +++ /dev/null @@ -1,136 +0,0 @@ -import sys -from glob import glob -from os import environ, devnull -from os.path import join -from warnings import catch_warnings, simplefilter -import os - -import numpy as np -from PIL import Image -import cv2 -environ['TF_CPP_MIN_LOG_LEVEL'] = '3' -stderr = sys.stderr -sys.stderr = open(devnull, 'w') -import tensorflow as tf -from tensorflow.keras.models import load_model -from tensorflow.python.keras import backend as tensorflow_backend -sys.stderr = stderr -from tensorflow.keras import layers -import tensorflow.keras.losses -from tensorflow.keras.layers import * -import click -import logging - - -class Patches(layers.Layer): - def __init__(self, patch_size_x, patch_size_y): - super(Patches, self).__init__() - self.patch_size_x = patch_size_x - self.patch_size_y = patch_size_y - - def call(self, images): - #print(tf.shape(images)[1],'images') - #print(self.patch_size,'self.patch_size') - batch_size = tf.shape(images)[0] - patches = tf.image.extract_patches( - images=images, - sizes=[1, self.patch_size_y, self.patch_size_x, 1], - strides=[1, self.patch_size_y, self.patch_size_x, 1], - rates=[1, 1, 1, 1], - padding="VALID", - ) - #patch_dims = patches.shape[-1] - patch_dims = tf.shape(patches)[-1] - patches = tf.reshape(patches, [batch_size, -1, patch_dims]) - return patches - def get_config(self): - - config = super().get_config().copy() - config.update({ - 'patch_size_x': self.patch_size_x, - 'patch_size_y': self.patch_size_y, - }) - return config - - - -class PatchEncoder(layers.Layer): - def __init__(self, **kwargs): - super(PatchEncoder, self).__init__() - self.num_patches = num_patches - self.projection = layers.Dense(units=projection_dim) - self.position_embedding = layers.Embedding( - input_dim=num_patches, output_dim=projection_dim - ) - - def call(self, patch): - positions = tf.range(start=0, limit=self.num_patches, delta=1) - encoded = self.projection(patch) + self.position_embedding(positions) - return encoded - def get_config(self): - - config = super().get_config().copy() - config.update({ - 'num_patches': self.num_patches, - 'projection': self.projection, - 'position_embedding': self.position_embedding, - }) - return config - - -def start_new_session(): - ###config = tf.compat.v1.ConfigProto() - ###config.gpu_options.allow_growth = True - - ###self.session = tf.compat.v1.Session(config=config) # tf.InteractiveSession() - ###tensorflow_backend.set_session(self.session) - - config = tf.compat.v1.ConfigProto() - config.gpu_options.allow_growth = True - - session = tf.compat.v1.Session(config=config) # tf.InteractiveSession() - tensorflow_backend.set_session(session) - return session - -def run_ensembling(dir_models, out): - ls_models = os.listdir(dir_models) - - - weights=[] - - for model_name in ls_models: - model = load_model(os.path.join(dir_models,model_name) , compile=False, custom_objects={'PatchEncoder':PatchEncoder, 'Patches': Patches}) - weights.append(model.get_weights()) - - new_weights = list() - - for weights_list_tuple in zip(*weights): - new_weights.append( - [np.array(weights_).mean(axis=0)\ - for weights_ in zip(*weights_list_tuple)]) - - - - new_weights = [np.array(x) for x in new_weights] - - model.set_weights(new_weights) - model.save(out) - os.system('cp '+os.path.join(os.path.join(dir_models,model_name) , "config.json ")+out) - -@click.command() -@click.option( - "--dir_models", - "-dm", - help="directory of models", - type=click.Path(exists=True, file_okay=False), -) -@click.option( - "--out", - "-o", - help="output directory where ensembled model will be written.", - type=click.Path(exists=False, file_okay=False), -) - -def main(dir_models, out): - run_ensembling(dir_models, out) - diff --git a/src/eynollah/utils/__init__.py b/src/eynollah/utils/__init__.py index 29359eb..c5962f8 100644 --- a/src/eynollah/utils/__init__.py +++ b/src/eynollah/utils/__init__.py @@ -1,5 +1,3 @@ -from typing import Tuple -from logging import getLogger import time import math @@ -15,19 +13,9 @@ from scipy.ndimage import gaussian_filter1d from .is_nan import isNaN from .contour import (contours_in_same_horizon, - find_center_of_contours, find_new_features_of_contours, return_contours_of_image, return_parent_contours) -def pairwise(iterable): - # pairwise('ABCDEFG') → AB BC CD DE EF FG - - iterator = iter(iterable) - a = next(iterator, None) - - for b in iterator: - yield a, b - a = b def return_x_start_end_mothers_childs_and_type_of_reading_order( x_min_hor_some, x_max_hor_some, cy_hor_some, peak_points, cy_hor_diff): @@ -310,17 +298,9 @@ def return_x_start_end_mothers_childs_and_type_of_reading_order( x_end_with_child_without_mother, new_main_sep_y) -def box2rect(box: Tuple[int, int, int, int]) -> Tuple[int, int, int, int]: - return (box[1], box[1] + box[3], - box[0], box[0] + box[2]) - -def box2slice(box: Tuple[int, int, int, int]) -> Tuple[slice, slice]: - return (slice(box[1], box[1] + box[3]), - slice(box[0], box[0] + box[2])) - def crop_image_inside_box(box, img_org_copy): - image_box = img_org_copy[box2slice(box)] - return image_box, box2rect(box) + image_box = img_org_copy[box[1] : box[1] + box[3], box[0] : box[0] + box[2]] + return image_box, [box[1], box[1] + box[3], box[0], box[0] + box[2]] def otsu_copy_binary(img): img_r = np.zeros((img.shape[0], img.shape[1], 3)) @@ -392,16 +372,7 @@ def find_num_col_deskew(regions_without_separators, sigma_, multiplier=3.8): z = gaussian_filter1d(regions_without_separators_0, sigma_) return np.std(z) -def find_num_col( - regions_without_separators, - num_col_classifier, - tables, - multiplier=3.8, -): - if not regions_without_separators.any(): - return 0, [] - #plt.imshow(regions_without_separators) - #plt.show() +def find_num_col(regions_without_separators, num_col_classifier, tables, multiplier=3.8): regions_without_separators_0 = regions_without_separators.sum(axis=0) ##plt.plot(regions_without_separators_0) ##plt.show() @@ -421,9 +392,6 @@ def find_num_col( zneg = gaussian_filter1d(zneg, sigma_) peaks_neg, _ = find_peaks(zneg, height=0) - #plt.plot(zneg) - #plt.plot(peaks_neg, zneg[peaks_neg], 'rx') - #plt.show() peaks, _ = find_peaks(z, height=0) peaks_neg = peaks_neg - 10 - 10 @@ -438,13 +406,9 @@ def find_num_col( (peaks_neg < (regions_without_separators.shape[1] - 370))] interest_pos = z[peaks] interest_pos = interest_pos[interest_pos > 10] - if not interest_pos.any(): - return 0, [] # plt.plot(z) # plt.show() interest_neg = z[peaks_neg] - if not interest_neg.any(): - return 0, [] min_peaks_pos = np.min(interest_pos) max_peaks_pos = np.max(interest_pos) @@ -800,7 +764,7 @@ def find_num_col_only_image(regions_without_separators, multiplier=3.8): return len(peaks_fin_true), peaks_fin_true def find_num_col_by_vertical_lines(regions_without_separators, multiplier=3.8): - regions_without_separators_0 = regions_without_separators.sum(axis=0) + regions_without_separators_0 = regions_without_separators[:, :, 0].sum(axis=0) ##plt.plot(regions_without_separators_0) ##plt.show() @@ -827,10 +791,7 @@ def return_regions_without_separators(regions_pre): return regions_without_separators def put_drop_out_from_only_drop_model(layout_no_patch, layout1): - if layout_no_patch.ndim == 3: - layout_no_patch = layout_no_patch[:, :, 0] - - drop_only = (layout_no_patch[:, :] == 4) * 1 + drop_only = (layout_no_patch[:, :, 0] == 4) * 1 contours_drop, hir_on_drop = return_contours_of_image(drop_only) contours_drop_parent = return_parent_contours(contours_drop, hir_on_drop) @@ -856,8 +817,9 @@ def put_drop_out_from_only_drop_model(layout_no_patch, layout1): (map_of_drop_contour_bb == 5).sum()) >= 15: contours_drop_parent_final.append(contours_drop_parent[jj]) - layout_no_patch[:, :][layout_no_patch[:, :] == 4] = 0 - layout_no_patch = cv2.fillPoly(layout_no_patch, pts=contours_drop_parent_final, color=4) + layout_no_patch[:, :, 0][layout_no_patch[:, :, 0] == 4] = 0 + + layout_no_patch = cv2.fillPoly(layout_no_patch, pts=contours_drop_parent_final, color=(4, 4, 4)) return layout_no_patch @@ -931,28 +893,29 @@ def check_any_text_region_in_model_one_is_main_or_header( contours_only_text_parent_main_d=[] contours_only_text_parent_head_d=[] - for ii, con in enumerate(contours_only_text_parent): - img = np.zeros(regions_model_1.shape[:2]) - img = cv2.fillPoly(img, pts=[con], color=255) + for ii in range(len(contours_only_text_parent)): + con=contours_only_text_parent[ii] + img=np.zeros((regions_model_1.shape[0],regions_model_1.shape[1],3)) + img = cv2.fillPoly(img, pts=[con], color=(255, 255, 255)) - all_pixels=((img == 255)*1).sum() - pixels_header=( ( (img == 255) & (regions_model_full[:,:,0]==2) )*1 ).sum() + all_pixels=((img[:,:,0]==255)*1).sum() + pixels_header=( ( (img[:,:,0]==255) & (regions_model_full[:,:,0]==2) )*1 ).sum() pixels_main=all_pixels-pixels_header if (pixels_header>=pixels_main) and ( (length_con[ii]/float(height_con[ii]) )>=1.3 ): - regions_model_1[:,:][(regions_model_1[:,:]==1) & (img == 255) ]=2 + regions_model_1[:,:][(regions_model_1[:,:]==1) & (img[:,:,0]==255) ]=2 contours_only_text_parent_head.append(con) - if len(contours_only_text_parent_d_ordered): + if contours_only_text_parent_d_ordered is not None: contours_only_text_parent_head_d.append(contours_only_text_parent_d_ordered[ii]) all_box_coord_head.append(all_box_coord[ii]) slopes_head.append(slopes[ii]) all_found_textline_polygons_head.append(all_found_textline_polygons[ii]) conf_contours_head.append(None) else: - regions_model_1[:,:][(regions_model_1[:,:]==1) & (img == 255) ]=1 + regions_model_1[:,:][(regions_model_1[:,:]==1) & (img[:,:,0]==255) ]=1 contours_only_text_parent_main.append(con) conf_contours_main.append(conf_contours[ii]) - if len(contours_only_text_parent_d_ordered): + if contours_only_text_parent_d_ordered is not None: contours_only_text_parent_main_d.append(contours_only_text_parent_d_ordered[ii]) all_box_coord_main.append(all_box_coord[ii]) slopes_main.append(slopes[ii]) @@ -992,11 +955,11 @@ def check_any_text_region_in_model_one_is_main_or_header_light( regions_model_full = cv2.resize(regions_model_full, (regions_model_full.shape[1] // zoom, regions_model_full.shape[0] // zoom), interpolation=cv2.INTER_NEAREST) - contours_only_text_parent_z = [(cnt / zoom).astype(int) for cnt in contours_only_text_parent] + contours_only_text_parent = [(i / zoom).astype(int) for i in contours_only_text_parent] ### cx_main, cy_main, x_min_main, x_max_main, y_min_main, y_max_main, y_corr_x_min_from_argmin = \ - find_new_features_of_contours(contours_only_text_parent_z) + find_new_features_of_contours(contours_only_text_parent) length_con=x_max_main-x_min_main height_con=y_max_main-y_min_main @@ -1019,39 +982,35 @@ def check_any_text_region_in_model_one_is_main_or_header_light( contours_only_text_parent_main_d=[] contours_only_text_parent_head_d=[] - for ii, con in enumerate(contours_only_text_parent_z): - img = np.zeros(regions_model_1.shape[:2]) - img = cv2.fillPoly(img, pts=[con], color=255) + for ii in range(len(contours_only_text_parent)): + con=contours_only_text_parent[ii] + img=np.zeros((regions_model_1.shape[0], regions_model_1.shape[1], 3)) + img = cv2.fillPoly(img, pts=[con], color=(255, 255, 255)) - all_pixels = (img == 255).sum() - pixels_header=((img == 255) & + all_pixels = (img[:,:,0]==255).sum() + pixels_header=((img[:,:,0]==255) & (regions_model_full[:,:,0]==2)).sum() pixels_main = all_pixels - pixels_header - if (( pixels_header / float(pixels_main) >= 0.6 and - length_con[ii] / float(height_con[ii]) >= 1.3 and - length_con[ii] / float(height_con[ii]) <= 3 ) or - ( pixels_header / float(pixels_main) >= 0.3 and - length_con[ii] / float(height_con[ii]) >=3 )): - - regions_model_1[:,:][(regions_model_1[:,:]==1) & (img == 255) ] = 2 - contours_only_text_parent_head.append(contours_only_text_parent[ii]) - conf_contours_head.append(None) # why not conf_contours[ii], too? - if len(contours_only_text_parent_d_ordered): + if (pixels_header/float(pixels_main)>=0.3) and ( (length_con[ii]/float(height_con[ii]) )>=1.3 ): + regions_model_1[:,:][(regions_model_1[:,:]==1) & (img[:,:,0]==255) ]=2 + contours_only_text_parent_head.append(con) + if contours_only_text_parent_d_ordered is not None: contours_only_text_parent_head_d.append(contours_only_text_parent_d_ordered[ii]) all_box_coord_head.append(all_box_coord[ii]) slopes_head.append(slopes[ii]) all_found_textline_polygons_head.append(all_found_textline_polygons[ii]) - + conf_contours_head.append(None) else: - regions_model_1[:,:][(regions_model_1[:,:]==1) & (img == 255) ] = 1 - contours_only_text_parent_main.append(contours_only_text_parent[ii]) + regions_model_1[:,:][(regions_model_1[:,:]==1) & (img[:,:,0]==255) ]=1 + contours_only_text_parent_main.append(con) conf_contours_main.append(conf_contours[ii]) - if len(contours_only_text_parent_d_ordered): + if contours_only_text_parent_d_ordered is not None: contours_only_text_parent_main_d.append(contours_only_text_parent_d_ordered[ii]) all_box_coord_main.append(all_box_coord[ii]) slopes_main.append(slopes[ii]) all_found_textline_polygons_main.append(all_found_textline_polygons[ii]) + #print(all_pixels,pixels_main,pixels_header) ### to make it faster @@ -1059,6 +1018,8 @@ def check_any_text_region_in_model_one_is_main_or_header_light( # regions_model_full = cv2.resize(img, (regions_model_full.shape[1] // zoom, # regions_model_full.shape[0] // zoom), # interpolation=cv2.INTER_NEAREST) + contours_only_text_parent_head = [(i * zoom).astype(int) for i in contours_only_text_parent_head] + contours_only_text_parent_main = [(i * zoom).astype(int) for i in contours_only_text_parent_main] ### return (regions_model_1, @@ -1124,11 +1085,11 @@ def small_textlines_to_parent_adherence2(textlines_con, textline_iamge, num_col) textlines_big.append(textlines_tot[i]) textlines_big_org_form.append(textlines_tot_org_form[i]) - img_textline_s = np.zeros(textline_iamge.shape[:2]) - img_textline_s = cv2.fillPoly(img_textline_s, pts=textlines_small, color=1) + img_textline_s = np.zeros((textline_iamge.shape[0], textline_iamge.shape[1])) + img_textline_s = cv2.fillPoly(img_textline_s, pts=textlines_small, color=(1, 1, 1)) - img_textline_b = np.zeros(textline_iamge.shape[:2]) - img_textline_b = cv2.fillPoly(img_textline_b, pts=textlines_big, color=1) + img_textline_b = np.zeros((textline_iamge.shape[0], textline_iamge.shape[1])) + img_textline_b = cv2.fillPoly(img_textline_b, pts=textlines_big, color=(1, 1, 1)) sum_small_big_all = img_textline_s + img_textline_b sum_small_big_all2 = (sum_small_big_all[:, :] == 2) * 1 @@ -1140,11 +1101,11 @@ def small_textlines_to_parent_adherence2(textlines_con, textline_iamge, num_col) # print(len(textlines_small),'small') intersections = [] for z2 in range(len(textlines_big)): - img_text = np.zeros(textline_iamge.shape[:2]) - img_text = cv2.fillPoly(img_text, pts=[textlines_small[z1]], color=1) + img_text = np.zeros((textline_iamge.shape[0], textline_iamge.shape[1])) + img_text = cv2.fillPoly(img_text, pts=[textlines_small[z1]], color=(1, 1, 1)) - img_text2 = np.zeros(textline_iamge.shape[:2]) - img_text2 = cv2.fillPoly(img_text2, pts=[textlines_big[z2]], color=1) + img_text2 = np.zeros((textline_iamge.shape[0], textline_iamge.shape[1])) + img_text2 = cv2.fillPoly(img_text2, pts=[textlines_big[z2]], color=(1, 1, 1)) sum_small_big = img_text2 + img_text sum_small_big_2 = (sum_small_big[:, :] == 2) * 1 @@ -1170,17 +1131,19 @@ def small_textlines_to_parent_adherence2(textlines_con, textline_iamge, num_col) index_small_textlines = list(np.where(np.array(dis_small_from_bigs_tot) == z)[0]) # print(z,index_small_textlines) - img_text2 = np.zeros(textline_iamge.shape[:2], dtype=np.uint8) - img_text2 = cv2.fillPoly(img_text2, pts=[textlines_big[z]], color=255) + img_text2 = np.zeros((textline_iamge.shape[0], textline_iamge.shape[1], 3)) + img_text2 = cv2.fillPoly(img_text2, pts=[textlines_big[z]], color=(255, 255, 255)) textlines_big_with_change.append(z) for k in index_small_textlines: - img_text2 = cv2.fillPoly(img_text2, pts=[textlines_small[k]], color=255) + img_text2 = cv2.fillPoly(img_text2, pts=[textlines_small[k]], color=(255, 255, 255)) textlines_small_with_change.append(k) - _, thresh = cv2.threshold(img_text2, 0, 255, 0) - cont, _ = cv2.findContours(thresh.astype(np.uint8), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) + img_text2 = img_text2.astype(np.uint8) + imgray = cv2.cvtColor(img_text2, cv2.COLOR_BGR2GRAY) + ret, thresh = cv2.threshold(imgray, 0, 255, 0) + cont, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) # print(cont[0],type(cont)) textlines_big_with_change_con.append(cont) @@ -1192,51 +1155,111 @@ def small_textlines_to_parent_adherence2(textlines_con, textline_iamge, num_col) # print(textlines_big_with_change,'textlines_big_with_change') # print(textlines_small_with_change,'textlines_small_with_change') # print(textlines_big) - - textlines_con_changed.append(textlines_big_org_form) + textlines_con_changed.append(textlines_big_org_form) + else: + textlines_con_changed.append(textlines_big_org_form) return textlines_con_changed -def order_of_regions(textline_mask, contours_main, contours_head, y_ref): +def order_of_regions(textline_mask, contours_main, contours_header, y_ref): ##plt.imshow(textline_mask) ##plt.show() - y = textline_mask.sum(axis=1) # horizontal projection profile + """ + print(len(contours_main),'contours_main') + mada_n=textline_mask.sum(axis=1) + y=mada_n[:] + + y_help=np.zeros(len(y)+40) + y_help[20:len(y)+20]=y + x=np.arange(len(y)) + + peaks_real, _ = find_peaks(gaussian_filter1d(y, 3), height=0) + ##plt.imshow(textline_mask[:,:]) + ##plt.show() + + sigma_gaus=8 + z= gaussian_filter1d(y_help, sigma_gaus) + zneg_rev=-y_help+np.max(y_help) + zneg=np.zeros(len(zneg_rev)+40) + zneg[20:len(zneg_rev)+20]=zneg_rev + zneg= gaussian_filter1d(zneg, sigma_gaus) + + peaks, _ = find_peaks(z, height=0) + peaks_neg, _ = find_peaks(zneg, height=0) + peaks_neg=peaks_neg-20-20 + peaks=peaks-20 + """ + textline_sum_along_width = textline_mask.sum(axis=1) + + y = textline_sum_along_width[:] y_padded = np.zeros(len(y) + 40) y_padded[20 : len(y) + 20] = y + x = np.arange(len(y)) + + peaks_real, _ = find_peaks(gaussian_filter1d(y, 3), height=0) sigma_gaus = 8 - #z = gaussian_filter1d(y_padded, sigma_gaus) - #peaks, _ = find_peaks(z, height=0) - #peaks = peaks - 20 - zneg_rev = np.max(y_padded) - y_padded + z = gaussian_filter1d(y_padded, sigma_gaus) + zneg_rev = -y_padded + np.max(y_padded) zneg = np.zeros(len(zneg_rev) + 40) zneg[20 : len(zneg_rev) + 20] = zneg_rev zneg = gaussian_filter1d(zneg, sigma_gaus) + peaks, _ = find_peaks(z, height=0) peaks_neg, _ = find_peaks(zneg, height=0) peaks_neg = peaks_neg - 20 - 20 + peaks = peaks - 20 ##plt.plot(z) ##plt.show() - cx_main, cy_main = find_center_of_contours(contours_main) - cx_head, cy_head = find_center_of_contours(contours_head) + if contours_main != None: + areas_main = np.array([cv2.contourArea(contours_main[j]) for j in range(len(contours_main))]) + M_main = [cv2.moments(contours_main[j]) for j in range(len(contours_main))] + cx_main = [(M_main[j]["m10"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))] + cy_main = [(M_main[j]["m01"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))] + x_min_main = np.array([np.min(contours_main[j][:, 0, 0]) for j in range(len(contours_main))]) + x_max_main = np.array([np.max(contours_main[j][:, 0, 0]) for j in range(len(contours_main))]) - peaks_neg_new = np.append(np.insert(peaks_neg, 0, 0), textline_mask.shape[0]) - # offset from bbox of mask - peaks_neg_new += y_ref + y_min_main = np.array([np.min(contours_main[j][:, 0, 1]) for j in range(len(contours_main))]) + y_max_main = np.array([np.max(contours_main[j][:, 0, 1]) for j in range(len(contours_main))]) - # assert not len(cy_main) or np.min(peaks_neg_new) <= np.min(cy_main) and np.max(cy_main) <= np.max(peaks_neg_new) - # assert not len(cy_head) or np.min(peaks_neg_new) <= np.min(cy_head) and np.max(cy_head) <= np.max(peaks_neg_new) + if len(contours_header) != None: + areas_header = np.array([cv2.contourArea(contours_header[j]) for j in range(len(contours_header))]) + M_header = [cv2.moments(contours_header[j]) for j in range(len(contours_header))] + cx_header = [(M_header[j]["m10"] / (M_header[j]["m00"] + 1e-32)) for j in range(len(M_header))] + cy_header = [(M_header[j]["m01"] / (M_header[j]["m00"] + 1e-32)) for j in range(len(M_header))] - matrix_of_orders = np.zeros((len(contours_main) + len(contours_head), 5), dtype=int) - matrix_of_orders[:, 0] = np.arange(len(contours_main) + len(contours_head)) + x_min_header = np.array([np.min(contours_header[j][:, 0, 0]) for j in range(len(contours_header))]) + x_max_header = np.array([np.max(contours_header[j][:, 0, 0]) for j in range(len(contours_header))]) + + y_min_header = np.array([np.min(contours_header[j][:, 0, 1]) for j in range(len(contours_header))]) + y_max_header = np.array([np.max(contours_header[j][:, 0, 1]) for j in range(len(contours_header))]) + # print(cy_main,'mainy') + + peaks_neg_new = [] + peaks_neg_new.append(0 + y_ref) + for iii in range(len(peaks_neg)): + peaks_neg_new.append(peaks_neg[iii] + y_ref) + peaks_neg_new.append(textline_mask.shape[0] + y_ref) + + if len(cy_main) > 0 and np.max(cy_main) > np.max(peaks_neg_new): + cy_main = np.array(cy_main) * (np.max(peaks_neg_new) / np.max(cy_main)) - 10 + if contours_main != None: + indexer_main = np.arange(len(contours_main)) + if contours_main != None: + len_main = len(contours_main) + else: + len_main = 0 + + matrix_of_orders = np.zeros((len(contours_main) + len(contours_header), 5)) + matrix_of_orders[:, 0] = np.arange(len(contours_main) + len(contours_header)) matrix_of_orders[: len(contours_main), 1] = 1 matrix_of_orders[len(contours_main) :, 1] = 2 matrix_of_orders[: len(contours_main), 2] = cx_main - matrix_of_orders[len(contours_main) :, 2] = cx_head + matrix_of_orders[len(contours_main) :, 2] = cx_header matrix_of_orders[: len(contours_main), 3] = cy_main - matrix_of_orders[len(contours_main) :, 3] = cy_head + matrix_of_orders[len(contours_main) :, 3] = cy_header matrix_of_orders[: len(contours_main), 4] = np.arange(len(contours_main)) - matrix_of_orders[len(contours_main) :, 4] = np.arange(len(contours_head)) + matrix_of_orders[len(contours_main) :, 4] = np.arange(len(contours_header)) # print(peaks_neg_new,'peaks_neg_new') # print(matrix_of_orders,'matrix_of_orders') @@ -1244,42 +1267,70 @@ def order_of_regions(textline_mask, contours_main, contours_head, y_ref): final_indexers_sorted = [] final_types = [] final_index_type = [] - for top, bot in pairwise(peaks_neg_new): - indexes_in, types_in, cxs_in, cys_in, typed_indexes_in = \ - matrix_of_orders[(matrix_of_orders[:, 3] >= top) & - (matrix_of_orders[:, 3] < bot)].T + for i in range(len(peaks_neg_new) - 1): + top = peaks_neg_new[i] + down = peaks_neg_new[i + 1] + indexes_in = matrix_of_orders[:, 0][(matrix_of_orders[:, 3] >= top) & + ((matrix_of_orders[:, 3] < down))] + cxs_in = matrix_of_orders[:, 2][(matrix_of_orders[:, 3] >= top) & + ((matrix_of_orders[:, 3] < down))] + cys_in = matrix_of_orders[:, 3][(matrix_of_orders[:, 3] >= top) & + ((matrix_of_orders[:, 3] < down))] + types_of_text = matrix_of_orders[:, 1][(matrix_of_orders[:, 3] >= top) & + (matrix_of_orders[:, 3] < down)] + index_types_of_text = matrix_of_orders[:, 4][(matrix_of_orders[:, 3] >= top) & + (matrix_of_orders[:, 3] < down)] sorted_inside = np.argsort(cxs_in) - final_indexers_sorted.extend(indexes_in[sorted_inside]) - final_types.extend(types_in[sorted_inside]) - final_index_type.extend(typed_indexes_in[sorted_inside]) + ind_in_int = indexes_in[sorted_inside] + ind_in_type = types_of_text[sorted_inside] + ind_ind_type = index_types_of_text[sorted_inside] + for j in range(len(ind_in_int)): + final_indexers_sorted.append(int(ind_in_int[j])) + final_types.append(int(ind_in_type[j])) + final_index_type.append(int(ind_ind_type[j])) ##matrix_of_orders[:len_main,4]=final_indexers_sorted[:] - # assert len(final_indexers_sorted) == len(contours_main) + len(contours_head) - # assert not len(final_indexers_sorted) or max(final_index_type) == max(len(contours_main) + # This fix is applied if the sum of the lengths of contours and contours_h + # does not match final_indexers_sorted. However, this is not the optimal solution.. + if len(cy_main) + len(cy_header) == len(final_index_type): + pass + else: + indexes_missed = set(np.arange(len(cy_main) + len(cy_header))) - set(final_indexers_sorted) + for ind_missed in indexes_missed: + final_indexers_sorted.append(ind_missed) + final_types.append(1) + final_index_type.append(ind_missed) - return np.array(final_indexers_sorted), np.array(final_types), np.array(final_index_type) + return final_indexers_sorted, matrix_of_orders, final_types, final_index_type def combine_hor_lines_and_delete_cross_points_and_get_lines_features_back_new( img_p_in_ver, img_in_hor,num_col_classifier): #img_p_in_ver = cv2.erode(img_p_in_ver, self.kernel, iterations=2) - _, thresh = cv2.threshold(img_p_in_ver, 0, 255, 0) - contours_lines_ver, _ = cv2.findContours(thresh.astype(np.uint8), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) + img_p_in_ver=img_p_in_ver.astype(np.uint8) + img_p_in_ver=np.repeat(img_p_in_ver[:, :, np.newaxis], 3, axis=2) + imgray = cv2.cvtColor(img_p_in_ver, cv2.COLOR_BGR2GRAY) + ret, thresh = cv2.threshold(imgray, 0, 255, 0) + + contours_lines_ver,hierarchy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE) slope_lines_ver, _, x_min_main_ver, _, _, _, y_min_main_ver, y_max_main_ver, cx_main_ver = \ find_features_of_lines(contours_lines_ver) for i in range(len(x_min_main_ver)): img_p_in_ver[int(y_min_main_ver[i]): int(y_min_main_ver[i])+30, int(cx_main_ver[i])-25: - int(cx_main_ver[i])+25] = 0 + int(cx_main_ver[i])+25, 0] = 0 img_p_in_ver[int(y_max_main_ver[i])-30: int(y_max_main_ver[i]), int(cx_main_ver[i])-25: - int(cx_main_ver[i])+25] = 0 + int(cx_main_ver[i])+25, 0] = 0 - _, thresh = cv2.threshold(img_in_hor, 0, 255, 0) - contours_lines_hor, _ = cv2.findContours(thresh.astype(np.uint8), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) + img_in_hor=img_in_hor.astype(np.uint8) + img_in_hor=np.repeat(img_in_hor[:, :, np.newaxis], 3, axis=2) + imgray = cv2.cvtColor(img_in_hor, cv2.COLOR_BGR2GRAY) + ret, thresh = cv2.threshold(imgray, 0, 255, 0) + contours_lines_hor,hierarchy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE) slope_lines_hor, dist_x_hor, x_min_main_hor, x_max_main_hor, cy_main_hor, _, _, _, _ = \ find_features_of_lines(contours_lines_hor) @@ -1335,19 +1386,22 @@ def combine_hor_lines_and_delete_cross_points_and_get_lines_features_back_new( img_p_in=img_in_hor special_separators=[] - img_p_in_ver[img_p_in_ver == 255] = 1 - sep_ver_hor = img_p_in + img_p_in_ver - sep_ver_hor_cross = (sep_ver_hor == 2) * 1 - _, thresh = cv2.threshold(sep_ver_hor_cross.astype(np.uint8), 0, 255, 0) - contours_cross, _ = cv2.findContours(thresh.astype(np.uint8), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) - center_cross = np.array(find_center_of_contours(contours_cross), dtype=int) - for cx, cy in center_cross.T: - img_p_in[cy - 30: cy + 30, cx + 5: cx + 40] = 0 - img_p_in[cy - 30: cy + 30, cx - 40: cx - 4] = 0 + img_p_in_ver[:,:,0][img_p_in_ver[:,:,0]==255]=1 + sep_ver_hor=img_p_in+img_p_in_ver + sep_ver_hor_cross=(sep_ver_hor[:,:,0]==2)*1 + sep_ver_hor_cross=np.repeat(sep_ver_hor_cross[:, :, np.newaxis], 3, axis=2) + sep_ver_hor_cross=sep_ver_hor_cross.astype(np.uint8) + imgray = cv2.cvtColor(sep_ver_hor_cross, cv2.COLOR_BGR2GRAY) + ret, thresh = cv2.threshold(imgray, 0, 255, 0) + contours_cross,_=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE) + cx_cross,cy_cross ,_ , _, _ ,_,_=find_new_features_of_contours(contours_cross) + for ii in range(len(cx_cross)): + img_p_in[int(cy_cross[ii])-30:int(cy_cross[ii])+30,int(cx_cross[ii])+5:int(cx_cross[ii])+40,0]=0 + img_p_in[int(cy_cross[ii])-30:int(cy_cross[ii])+30,int(cx_cross[ii])-40:int(cx_cross[ii])-4,0]=0 else: img_p_in=np.copy(img_in_hor) special_separators=[] - return img_p_in, special_separators + return img_p_in[:,:,0], special_separators def return_points_with_boundies(peaks_neg_fin, first_point, last_point): peaks_neg_tot = [] @@ -1357,11 +1411,11 @@ def return_points_with_boundies(peaks_neg_fin, first_point, last_point): peaks_neg_tot.append(last_point) return peaks_neg_tot -def find_number_of_columns_in_document(region_pre_p, num_col_classifier, tables, label_lines, contours_h=None): +def find_number_of_columns_in_document(region_pre_p, num_col_classifier, tables, pixel_lines, contours_h=None): t_ins_c0 = time.time() - separators_closeup=( (region_pre_p[:,:]==label_lines))*1 - separators_closeup[0:110,:]=0 - separators_closeup[separators_closeup.shape[0]-150:,:]=0 + separators_closeup=( (region_pre_p[:,:,:]==pixel_lines))*1 + separators_closeup[0:110,:,:]=0 + separators_closeup[separators_closeup.shape[0]-150:,:,:]=0 kernel = np.ones((5,5),np.uint8) separators_closeup=separators_closeup.astype(np.uint8) @@ -1373,11 +1427,15 @@ def find_number_of_columns_in_document(region_pre_p, num_col_classifier, tables, separators_closeup_n=separators_closeup_n.astype(np.uint8) separators_closeup_n_binary=np.zeros(( separators_closeup_n.shape[0],separators_closeup_n.shape[1]) ) - separators_closeup_n_binary[:,:]=separators_closeup_n[:,:] + separators_closeup_n_binary[:,:]=separators_closeup_n[:,:,0] separators_closeup_n_binary[:,:][separators_closeup_n_binary[:,:]!=0]=1 - _, thresh_e = cv2.threshold(separators_closeup_n_binary, 0, 255, 0) - contours_line_e, _ = cv2.findContours(thresh_e.astype(np.uint8), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) + gray_early=np.repeat(separators_closeup_n_binary[:, :, np.newaxis], 3, axis=2) + gray_early=gray_early.astype(np.uint8) + imgray_e = cv2.cvtColor(gray_early, cv2.COLOR_BGR2GRAY) + ret_e, thresh_e = cv2.threshold(imgray_e, 0, 255, 0) + + contours_line_e,hierarchy_e=cv2.findContours(thresh_e,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE) _, dist_xe, _, _, _, _, y_min_main, y_max_main, _ = \ find_features_of_lines(contours_line_e) dist_ye = y_max_main - y_min_main @@ -1387,8 +1445,10 @@ def find_number_of_columns_in_document(region_pre_p, num_col_classifier, tables, cnts_hor_e=[] for ce in args_hor_e: cnts_hor_e.append(contours_line_e[ce]) + figs_e=np.zeros(thresh_e.shape) + figs_e=cv2.fillPoly(figs_e,pts=cnts_hor_e,color=(1,1,1)) - separators_closeup_n_binary=cv2.fillPoly(separators_closeup_n_binary, pts=cnts_hor_e, color=0) + separators_closeup_n_binary=cv2.fillPoly(separators_closeup_n_binary, pts=cnts_hor_e, color=(0,0,0)) gray = cv2.bitwise_not(separators_closeup_n_binary) gray=gray.astype(np.uint8) @@ -1408,7 +1468,7 @@ def find_number_of_columns_in_document(region_pre_p, num_col_classifier, tables, kernel = np.ones((5,5),np.uint8) horizontal = cv2.dilate(horizontal,kernel,iterations = 2) horizontal = cv2.erode(horizontal,kernel,iterations = 2) - horizontal = cv2.fillPoly(horizontal, pts=cnts_hor_e, color=255) + horizontal = cv2.fillPoly(horizontal, pts=cnts_hor_e, color=(255,255,255)) rows = vertical.shape[0] verticalsize = rows // 30 @@ -1426,8 +1486,13 @@ def find_number_of_columns_in_document(region_pre_p, num_col_classifier, tables, separators_closeup_new[:,:][vertical[:,:]!=0]=1 separators_closeup_new[:,:][horizontal[:,:]!=0]=1 - _, thresh = cv2.threshold(vertical, 0, 255, 0) - contours_line_vers, _ = cv2.findContours(thresh.astype(np.uint8), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) + vertical=np.repeat(vertical[:, :, np.newaxis], 3, axis=2) + vertical=vertical.astype(np.uint8) + + imgray = cv2.cvtColor(vertical, cv2.COLOR_BGR2GRAY) + ret, thresh = cv2.threshold(imgray, 0, 255, 0) + + contours_line_vers,hierarchy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE) slope_lines, dist_x, x_min_main, x_max_main, cy_main, slope_lines_org, y_min_main, y_max_main, cx_main = \ find_features_of_lines(contours_line_vers) @@ -1442,8 +1507,11 @@ def find_number_of_columns_in_document(region_pre_p, num_col_classifier, tables, dist_y_ver=y_max_main_ver-y_min_main_ver len_y=separators_closeup.shape[0]/3.0 - _, thresh = cv2.threshold(horizontal, 0, 255, 0) - contours_line_hors, _ = cv2.findContours(thresh.astype(np.uint8), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) + horizontal=np.repeat(horizontal[:, :, np.newaxis], 3, axis=2) + horizontal=horizontal.astype(np.uint8) + imgray = cv2.cvtColor(horizontal, cv2.COLOR_BGR2GRAY) + ret, thresh = cv2.threshold(imgray, 0, 255, 0) + contours_line_hors,hierarchy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE) slope_lines, dist_x, x_min_main, x_max_main, cy_main, slope_lines_org, y_min_main, y_max_main, cx_main = \ find_features_of_lines(contours_line_hors) @@ -1536,7 +1604,7 @@ def find_number_of_columns_in_document(region_pre_p, num_col_classifier, tables, peaks_neg_fin_fin=[] for itiles in args_big_parts: regions_without_separators_tile=regions_without_separators[int(splitter_y_new[itiles]): - int(splitter_y_new[itiles+1]),:] + int(splitter_y_new[itiles+1]),:,0] try: num_col, peaks_neg_fin = find_num_col(regions_without_separators_tile, num_col_classifier, tables, multiplier=7.0) @@ -1558,19 +1626,12 @@ def find_number_of_columns_in_document(region_pre_p, num_col_classifier, tables, def return_boxes_of_images_by_order_of_reading_new( splitter_y_new, regions_without_separators, matrix_of_lines_ch, - num_col_classifier, erosion_hurts, tables, - right2left_readingorder, - logger=None): + num_col_classifier, erosion_hurts, tables, right2left_readingorder): if right2left_readingorder: regions_without_separators = cv2.flip(regions_without_separators,1) - if logger is None: - logger = getLogger(__package__) - logger.debug('enter return_boxes_of_images_by_order_of_reading_new') - boxes=[] peaks_neg_tot_tables = [] - splitter_y_new = np.array(splitter_y_new, dtype=int) for i in range(len(splitter_y_new)-1): #print(splitter_y_new[i],splitter_y_new[i+1]) matrix_new = matrix_of_lines_ch[:,:][(matrix_of_lines_ch[:,6]> splitter_y_new[i] ) & @@ -1583,19 +1644,24 @@ def return_boxes_of_images_by_order_of_reading_new( # 0.1 * (np.abs(splitter_y_new[i+1]-splitter_y_new[i]))): if True: try: - num_col, peaks_neg_fin = find_num_col( - regions_without_separators[splitter_y_new[i]:splitter_y_new[i+1], :], - num_col_classifier, tables, multiplier=6. if erosion_hurts else 7.) + if erosion_hurts: + num_col, peaks_neg_fin = find_num_col( + regions_without_separators[int(splitter_y_new[i]):int(splitter_y_new[i+1]),:], + num_col_classifier, tables, multiplier=6.) + else: + num_col, peaks_neg_fin = find_num_col( + regions_without_separators[int(splitter_y_new[i]):int(splitter_y_new[i+1]),:], + num_col_classifier, tables, multiplier=7.) except: peaks_neg_fin=[] num_col = 0 try: + peaks_neg_fin_org=np.copy(peaks_neg_fin) if (len(peaks_neg_fin)+1)=2 or there_is_sep_with_child==1))): try: - y_grenze = splitter_y_new[i] + 300 + y_grenze=int(splitter_y_new[i])+300 #check if there is a big separator in this y_mains_sep_ohne_grenzen args_early_ys=np.arange(len(y_type_2)) #print(args_early_ys,'args_early_ys') - #print(splitter_y_new[i], splitter_y_new[i+1]) + #print(int(splitter_y_new[i]),int(splitter_y_new[i+1])) - x_starting_up = x_starting[(y_type_2 > splitter_y_new[i]) & + x_starting_up = x_starting[(y_type_2 > int(splitter_y_new[i])) & (y_type_2 <= y_grenze)] - x_ending_up = x_ending[(y_type_2 > splitter_y_new[i]) & + x_ending_up = x_ending[(y_type_2 > int(splitter_y_new[i])) & (y_type_2 <= y_grenze)] - y_type_2_up = y_type_2[(y_type_2 > splitter_y_new[i]) & + y_type_2_up = y_type_2[(y_type_2 > int(splitter_y_new[i])) & (y_type_2 <= y_grenze)] - y_diff_type_2_up = y_diff_type_2[(y_type_2 > splitter_y_new[i]) & + y_diff_type_2_up = y_diff_type_2[(y_type_2 > int(splitter_y_new[i])) & (y_type_2 <= y_grenze)] - args_up = args_early_ys[(y_type_2 > splitter_y_new[i]) & + args_up = args_early_ys[(y_type_2 > int(splitter_y_new[i])) & (y_type_2 <= y_grenze)] if len(y_type_2_up) > 0: y_main_separator_up = y_type_2_up [(x_starting_up==0) & @@ -1707,8 +1776,8 @@ def return_boxes_of_images_by_order_of_reading_new( args_to_be_kept = np.array(list( set(args_early_ys) - set(args_main_to_deleted) )) #print(args_to_be_kept,'args_to_be_kept') boxes.append([0, peaks_neg_tot[len(peaks_neg_tot)-1], - splitter_y_new[i], y_diff_main_separator_up.max()]) - splitter_y_new[i] = y_diff_main_separator_up.max() + int(splitter_y_new[i]), int( np.max(y_diff_main_separator_up))]) + splitter_y_new[i]=[ np.max(y_diff_main_separator_up) ][0] #print(splitter_y_new[i],'splitter_y_new[i]') y_type_2 = y_type_2[args_to_be_kept] @@ -1717,28 +1786,29 @@ def return_boxes_of_images_by_order_of_reading_new( y_diff_type_2 = y_diff_type_2[args_to_be_kept] #print('galdiha') - y_grenze = splitter_y_new[i] + 200 + y_grenze=int(splitter_y_new[i])+200 args_early_ys2=np.arange(len(y_type_2)) - y_type_2_up=y_type_2[(y_type_2 > splitter_y_new[i]) & + y_type_2_up=y_type_2[(y_type_2 > int(splitter_y_new[i])) & (y_type_2 <= y_grenze)] - x_starting_up=x_starting[(y_type_2 > splitter_y_new[i]) & + x_starting_up=x_starting[(y_type_2 > int(splitter_y_new[i])) & (y_type_2 <= y_grenze)] - x_ending_up=x_ending[(y_type_2 > splitter_y_new[i]) & + x_ending_up=x_ending[(y_type_2 > int(splitter_y_new[i])) & (y_type_2 <= y_grenze)] - y_diff_type_2_up=y_diff_type_2[(y_type_2 > splitter_y_new[i]) & + y_diff_type_2_up=y_diff_type_2[(y_type_2 > int(splitter_y_new[i])) & (y_type_2 <= y_grenze)] - args_up2=args_early_ys2[(y_type_2 > splitter_y_new[i]) & + args_up2=args_early_ys2[(y_type_2 > int(splitter_y_new[i])) & (y_type_2 <= y_grenze)] #print(y_type_2_up,x_starting_up,x_ending_up,'didid') - nodes_in = set() + nodes_in = [] for ij in range(len(x_starting_up)): - nodes_in.update(range(x_starting_up[ij], - x_ending_up[ij])) + nodes_in = nodes_in + list(range(x_starting_up[ij], + x_ending_up[ij])) + nodes_in = np.unique(nodes_in) #print(nodes_in,'nodes_in') - if nodes_in == set(range(len(peaks_neg_tot)-1)): + if set(nodes_in)==set(range(len(peaks_neg_tot)-1)): pass - elif nodes_in == set(range(1, len(peaks_neg_tot)-1)): + elif set(nodes_in)==set(range(1, len(peaks_neg_tot)-1)): pass else: #print('burdaydikh') @@ -1753,16 +1823,17 @@ def return_boxes_of_images_by_order_of_reading_new( pass #print('burdaydikh2') elif len(y_diff_main_separator_up)==0: - nodes_in = set() + nodes_in = [] for ij in range(len(x_starting_up)): - nodes_in.update(range(x_starting_up[ij], - x_ending_up[ij])) + nodes_in = nodes_in + list(range(x_starting_up[ij], + x_ending_up[ij])) + nodes_in = np.unique(nodes_in) #print(nodes_in,'nodes_in2') #print(np.array(range(len(peaks_neg_tot)-1)),'np.array(range(len(peaks_neg_tot)-1))') - if nodes_in == set(range(len(peaks_neg_tot)-1)): + if set(nodes_in)==set(range(len(peaks_neg_tot)-1)): pass - elif nodes_in == set(range(1,len(peaks_neg_tot)-1)): + elif set(nodes_in)==set(range(1,len(peaks_neg_tot)-1)): pass else: #print('burdaydikh') @@ -1787,25 +1858,26 @@ def return_boxes_of_images_by_order_of_reading_new( x_end_by_order=[] if (len(x_end_with_child_without_mother)==0 and reading_order_type==0) or reading_order_type==1: if reading_order_type==1: - y_lines_by_order.append(splitter_y_new[i]) + y_lines_by_order.append(int(splitter_y_new[i])) x_start_by_order.append(0) x_end_by_order.append(len(peaks_neg_tot)-2) else: #print(x_start_without_mother,x_end_without_mother,peaks_neg_tot,'dodo') - columns_covered_by_mothers = set() + columns_covered_by_mothers = [] for dj in range(len(x_start_without_mother)): - columns_covered_by_mothers.update( - range(x_start_without_mother[dj], - x_end_without_mother[dj])) - columns_not_covered = list(all_columns - columns_covered_by_mothers) - y_type_2 = np.append(y_type_2, np.ones(len(columns_not_covered) + - len(x_start_without_mother), - dtype=int) * splitter_y_new[i]) - ##y_lines_by_order = np.append(y_lines_by_order, [splitter_y_new[i]] * len(columns_not_covered)) + columns_covered_by_mothers = columns_covered_by_mothers + \ + list(range(x_start_without_mother[dj], + x_end_without_mother[dj])) + columns_covered_by_mothers = list(set(columns_covered_by_mothers)) + + all_columns=np.arange(len(peaks_neg_tot)-1) + columns_not_covered=list(set(all_columns) - set(columns_covered_by_mothers)) + y_type_2 = np.append(y_type_2, [int(splitter_y_new[i])] * (len(columns_not_covered) + len(x_start_without_mother))) + ##y_lines_by_order = np.append(y_lines_by_order, [int(splitter_y_new[i])] * len(columns_not_covered)) ##x_start_by_order = np.append(x_start_by_order, [0] * len(columns_not_covered)) - x_starting = np.append(x_starting, np.array(columns_not_covered, int)) + x_starting = np.append(x_starting, columns_not_covered) x_starting = np.append(x_starting, x_start_without_mother) - x_ending = np.append(x_ending, np.array(columns_not_covered, int) + 1) + x_ending = np.append(x_ending, np.array(columns_not_covered) + 1) x_ending = np.append(x_ending, x_end_without_mother) ind_args=np.arange(len(y_type_2)) @@ -1834,39 +1906,42 @@ def return_boxes_of_images_by_order_of_reading_new( x_end_by_order.append(x_end_column_sort[ii]-1) else: #print(x_start_without_mother,x_end_without_mother,peaks_neg_tot,'dodo') - columns_covered_by_mothers = set() + columns_covered_by_mothers = [] for dj in range(len(x_start_without_mother)): - columns_covered_by_mothers.update( - range(x_start_without_mother[dj], - x_end_without_mother[dj])) - columns_not_covered = list(all_columns - columns_covered_by_mothers) - y_type_2 = np.append(y_type_2, np.ones(len(columns_not_covered) + len(x_start_without_mother), - dtype=int) * splitter_y_new[i]) - ##y_lines_by_order = np.append(y_lines_by_order, [splitter_y_new[i]] * len(columns_not_covered)) + columns_covered_by_mothers = columns_covered_by_mothers + \ + list(range(x_start_without_mother[dj], + x_end_without_mother[dj])) + columns_covered_by_mothers = list(set(columns_covered_by_mothers)) + + all_columns=np.arange(len(peaks_neg_tot)-1) + columns_not_covered=list(set(all_columns) - set(columns_covered_by_mothers)) + y_type_2 = np.append(y_type_2, [int(splitter_y_new[i])] * (len(columns_not_covered) + len(x_start_without_mother))) + ##y_lines_by_order = np.append(y_lines_by_order, [int(splitter_y_new[i])] * len(columns_not_covered)) ##x_start_by_order = np.append(x_start_by_order, [0] * len(columns_not_covered)) - x_starting = np.append(x_starting, np.array(columns_not_covered, int)) + x_starting = np.append(x_starting, columns_not_covered) x_starting = np.append(x_starting, x_start_without_mother) - x_ending = np.append(x_ending, np.array(columns_not_covered, int) + 1) + x_ending = np.append(x_ending, np.array(columns_not_covered) + 1) x_ending = np.append(x_ending, x_end_without_mother) - columns_covered_by_with_child_no_mothers = set() + columns_covered_by_with_child_no_mothers = [] for dj in range(len(x_end_with_child_without_mother)): - columns_covered_by_with_child_no_mothers.update( - range(x_start_with_child_without_mother[dj], - x_end_with_child_without_mother[dj])) - columns_not_covered_child_no_mother = list( - all_columns - columns_covered_by_with_child_no_mothers) + columns_covered_by_with_child_no_mothers = columns_covered_by_with_child_no_mothers + \ + list(range(x_start_with_child_without_mother[dj], + x_end_with_child_without_mother[dj])) + columns_covered_by_with_child_no_mothers = list(set(columns_covered_by_with_child_no_mothers)) + + all_columns = np.arange(len(peaks_neg_tot)-1) + columns_not_covered_child_no_mother = list(set(all_columns) - set(columns_covered_by_with_child_no_mothers)) #indexes_to_be_spanned=[] for i_s in range(len(x_end_with_child_without_mother)): columns_not_covered_child_no_mother.append(x_start_with_child_without_mother[i_s]) columns_not_covered_child_no_mother = np.sort(columns_not_covered_child_no_mother) ind_args = np.arange(len(y_type_2)) - x_end_with_child_without_mother = np.array(x_end_with_child_without_mother, int) - x_start_with_child_without_mother = np.array(x_start_with_child_without_mother, int) + x_end_with_child_without_mother = np.array(x_end_with_child_without_mother) + x_start_with_child_without_mother = np.array(x_start_with_child_without_mother) for i_s_nc in columns_not_covered_child_no_mother: if i_s_nc in x_start_with_child_without_mother: - x_end_biggest_column = \ - x_end_with_child_without_mother[x_start_with_child_without_mother==i_s_nc][0] + x_end_biggest_column = x_end_with_child_without_mother[x_start_with_child_without_mother==i_s_nc][0] args_all_biggest_lines = ind_args[(x_starting==i_s_nc) & (x_ending==x_end_biggest_column)] y_column_nc = y_type_2[args_all_biggest_lines] @@ -1876,7 +1951,7 @@ def return_boxes_of_images_by_order_of_reading_new( for i_c in range(len(y_column_nc)): if i_c==(len(y_column_nc)-1): ind_all_lines_between_nm_wc=ind_args[(y_type_2>y_column_nc[i_c]) & - (y_type_2=i_s_nc) & (x_ending<=x_end_biggest_column)] else: @@ -1892,19 +1967,21 @@ def return_boxes_of_images_by_order_of_reading_new( if len(x_diff_all_between_nm_wc)>0: biggest=np.argmax(x_diff_all_between_nm_wc) - columns_covered_by_mothers = set() + columns_covered_by_mothers = [] for dj in range(len(x_starting_all_between_nm_wc)): - columns_covered_by_mothers.update( - range(x_starting_all_between_nm_wc[dj], - x_ending_all_between_nm_wc[dj])) - child_columns = set(range(i_s_nc, x_end_biggest_column)) - columns_not_covered = list(child_columns - columns_covered_by_mothers) + columns_covered_by_mothers = columns_covered_by_mothers + \ + list(range(x_starting_all_between_nm_wc[dj], + x_ending_all_between_nm_wc[dj])) + columns_covered_by_mothers = list(set(columns_covered_by_mothers)) + + all_columns=np.arange(i_s_nc, x_end_biggest_column) + columns_not_covered = list(set(all_columns) - set(columns_covered_by_mothers)) should_longest_line_be_extended=0 if (len(x_diff_all_between_nm_wc) > 0 and set(list(range(x_starting_all_between_nm_wc[biggest], x_ending_all_between_nm_wc[biggest])) + - list(columns_not_covered)) != child_columns): + list(columns_not_covered)) != set(all_columns)): should_longest_line_be_extended=1 index_lines_so_close_to_top_separator = \ np.arange(len(y_all_between_nm_wc))[(y_all_between_nm_wc>y_column_nc[i_c]) & @@ -1914,12 +1991,9 @@ def return_boxes_of_images_by_order_of_reading_new( np.array(list(set(list(range(len(y_all_between_nm_wc)))) - set(list(index_lines_so_close_to_top_separator)))) if len(indexes_remained_after_deleting_closed_lines) > 0: - y_all_between_nm_wc = \ - y_all_between_nm_wc[indexes_remained_after_deleting_closed_lines] - x_starting_all_between_nm_wc = \ - x_starting_all_between_nm_wc[indexes_remained_after_deleting_closed_lines] - x_ending_all_between_nm_wc = \ - x_ending_all_between_nm_wc[indexes_remained_after_deleting_closed_lines] + y_all_between_nm_wc = y_all_between_nm_wc[indexes_remained_after_deleting_closed_lines] + x_starting_all_between_nm_wc = x_starting_all_between_nm_wc[indexes_remained_after_deleting_closed_lines] + x_ending_all_between_nm_wc = x_ending_all_between_nm_wc[indexes_remained_after_deleting_closed_lines] y_all_between_nm_wc = np.append(y_all_between_nm_wc, y_column_nc[i_c]) x_starting_all_between_nm_wc = np.append(x_starting_all_between_nm_wc, i_s_nc) @@ -1931,14 +2005,14 @@ def return_boxes_of_images_by_order_of_reading_new( x_starting_all_between_nm_wc = np.append(x_starting_all_between_nm_wc, x_starting_all_between_nm_wc[biggest]) x_ending_all_between_nm_wc = np.append(x_ending_all_between_nm_wc, x_ending_all_between_nm_wc[biggest]) except: - logger.exception("cannot append") + pass y_all_between_nm_wc = np.append(y_all_between_nm_wc, [y_column_nc[i_c]] * len(columns_not_covered)) - x_starting_all_between_nm_wc = np.append(x_starting_all_between_nm_wc, np.array(columns_not_covered, int)) - x_ending_all_between_nm_wc = np.append(x_ending_all_between_nm_wc, np.array(columns_not_covered, int) + 1) + x_starting_all_between_nm_wc = np.append(x_starting_all_between_nm_wc, columns_not_covered) + x_ending_all_between_nm_wc = np.append(x_ending_all_between_nm_wc, np.array(columns_not_covered) + 1) ind_args_between=np.arange(len(x_ending_all_between_nm_wc)) - for column in range(int(i_s_nc), int(x_end_biggest_column)): + for column in range(i_s_nc, x_end_biggest_column): ind_args_in_col=ind_args_between[x_starting_all_between_nm_wc==column] #print('babali2') #print(ind_args_in_col,'ind_args_in_col') @@ -1990,7 +2064,7 @@ def return_boxes_of_images_by_order_of_reading_new( x_end_itself=x_end_copy.pop(il) #print(y_copy,'y_copy2') - for column in range(int(x_start_itself), int(x_end_itself)+1): + for column in range(x_start_itself, x_end_itself+1): #print(column,'cols') y_in_cols=[] for yic in range(len(y_copy)): @@ -2004,51 +2078,53 @@ def return_boxes_of_images_by_order_of_reading_new( if len(y_in_cols)>0: y_down=np.min(y_in_cols) else: - y_down=splitter_y_new[i+1] + y_down=[int(splitter_y_new[i+1])][0] #print(y_itself,'y_itself') boxes.append([peaks_neg_tot[column], peaks_neg_tot[column+1], y_itself, y_down]) except: - logger.exception("cannot assign boxes") boxes.append([0, peaks_neg_tot[len(peaks_neg_tot)-1], - splitter_y_new[i], splitter_y_new[i+1]]) + int(splitter_y_new[i]), int(splitter_y_new[i+1])]) else: y_lines_by_order=[] x_start_by_order=[] x_end_by_order=[] if len(x_starting)>0: - columns_covered_by_lines_covered_more_than_2col = set() + all_columns = np.arange(len(peaks_neg_tot)-1) + columns_covered_by_lines_covered_more_than_2col = [] for dj in range(len(x_starting)): - if set(range(x_starting[dj], x_ending[dj])) != all_columns: - columns_covered_by_lines_covered_more_than_2col.update( - range(x_starting[dj], x_ending[dj])) - columns_not_covered = list(all_columns - columns_covered_by_lines_covered_more_than_2col) + if set(list(range(x_starting[dj],x_ending[dj]))) == set(all_columns): + pass + else: + columns_covered_by_lines_covered_more_than_2col = columns_covered_by_lines_covered_more_than_2col + \ + list(range(x_starting[dj],x_ending[dj])) + columns_covered_by_lines_covered_more_than_2col = list(set(columns_covered_by_lines_covered_more_than_2col)) + columns_not_covered = list(set(all_columns) - set(columns_covered_by_lines_covered_more_than_2col)) - y_type_2 = np.append(y_type_2, np.ones(len(columns_not_covered) + 1, - dtype=int) * splitter_y_new[i]) - ##y_lines_by_order = np.append(y_lines_by_order, [splitter_y_new[i]] * len(columns_not_covered)) + y_type_2 = np.append(y_type_2, [int(splitter_y_new[i])] * (len(columns_not_covered) + 1)) + ##y_lines_by_order = np.append(y_lines_by_order, [int(splitter_y_new[i])] * len(columns_not_covered)) ##x_start_by_order = np.append(x_start_by_order, [0] * len(columns_not_covered)) - x_starting = np.append(x_starting, np.array(columns_not_covered, x_starting.dtype)) - x_ending = np.append(x_ending, np.array(columns_not_covered, x_ending.dtype) + 1) + x_starting = np.append(x_starting, columns_not_covered) + x_ending = np.append(x_ending, np.array(columns_not_covered) + 1) if len(new_main_sep_y) > 0: x_starting = np.append(x_starting, 0) - x_ending = np.append(x_ending, len(peaks_neg_tot) - 1) + x_ending = np.append(x_ending, len(peaks_neg_tot)-1) else: x_starting = np.append(x_starting, x_starting[0]) x_ending = np.append(x_ending, x_ending[0]) else: - columns_not_covered = list(all_columns) - y_type_2 = np.append(y_type_2, np.ones(len(columns_not_covered), - dtype=int) * splitter_y_new[i]) - ##y_lines_by_order = np.append(y_lines_by_order, [splitter_y_new[i]] * len(columns_not_covered)) + all_columns = np.arange(len(peaks_neg_tot)-1) + columns_not_covered = list(set(all_columns)) + y_type_2 = np.append(y_type_2, [int(splitter_y_new[i])] * len(columns_not_covered)) + ##y_lines_by_order = np.append(y_lines_by_order, [int(splitter_y_new[i])] * len(columns_not_covered)) ##x_start_by_order = np.append(x_start_by_order, [0] * len(columns_not_covered)) - x_starting = np.append(x_starting, np.array(columns_not_covered, x_starting.dtype)) - x_ending = np.append(x_ending, np.array(columns_not_covered, x_ending.dtype) + 1) + x_starting = np.append(x_starting, columns_not_covered) + x_ending = np.append(x_ending, np.array(columns_not_covered) + 1) - ind_args = np.arange(len(y_type_2)) - + ind_args=np.array(range(len(y_type_2))) + #ind_args=np.array(ind_args) for column in range(len(peaks_neg_tot)-1): #print(column,'column') ind_args_in_col=ind_args[x_starting==column] @@ -2079,6 +2155,7 @@ def return_boxes_of_images_by_order_of_reading_new( x_start_itself=x_start_copy.pop(il) x_end_itself=x_end_copy.pop(il) + #print(y_copy,'y_copy2') for column in range(x_start_itself, x_end_itself+1): #print(column,'cols') y_in_cols=[] @@ -2093,7 +2170,7 @@ def return_boxes_of_images_by_order_of_reading_new( if len(y_in_cols)>0: y_down=np.min(y_in_cols) else: - y_down=splitter_y_new[i+1] + y_down=[int(splitter_y_new[i+1])][0] #print(y_itself,'y_itself') boxes.append([peaks_neg_tot[column], peaks_neg_tot[column+1], @@ -2115,18 +2192,6 @@ def return_boxes_of_images_by_order_of_reading_new( x_end_new = regions_without_separators.shape[1] - boxes[i][0] boxes[i][0] = x_start_new boxes[i][1] = x_end_new - peaks_neg_tot_tables = peaks_neg_tot_tables_new - - logger.debug('exit return_boxes_of_images_by_order_of_reading_new') - return boxes, peaks_neg_tot_tables - -def is_image_filename(fname: str) -> bool: - return fname.lower().endswith(('.jpg', - '.jpeg', - '.png', - '.tif', - '.tiff', - )) - -def is_xml_filename(fname: str) -> bool: - return fname.lower().endswith('.xml') + return boxes, peaks_neg_tot_tables_new + else: + return boxes, peaks_neg_tot_tables diff --git a/src/eynollah/utils/contour.py b/src/eynollah/utils/contour.py index 6550171..a81ccb4 100644 --- a/src/eynollah/utils/contour.py +++ b/src/eynollah/utils/contour.py @@ -1,15 +1,7 @@ -from typing import Sequence, Union -from numbers import Number from functools import partial -import itertools - import cv2 import numpy as np -from scipy.sparse.csgraph import minimum_spanning_tree -from shapely.geometry import Polygon, LineString -from shapely.geometry.polygon import orient -from shapely import set_precision -from shapely.ops import unary_union, nearest_points +from shapely import geometry from .rotate import rotate_image, rotation_image_new @@ -36,31 +28,38 @@ def find_contours_mean_y_diff(contours_main): return np.mean(np.diff(np.sort(np.array(cy_main)))) def get_text_region_boxes_by_given_contours(contours): - return [cv2.boundingRect(contour) - for contour in contours] + boxes = [] + contours_new = [] + for jj in range(len(contours)): + box = cv2.boundingRect(contours[jj]) + boxes.append(box) + contours_new.append(contours[jj]) -def filter_contours_area_of_image(image, contours, hierarchy, max_area=1.0, min_area=0.0, dilate=0): + return boxes, contours_new + +def filter_contours_area_of_image(image, contours, hierarchy, max_area, min_area): found_polygons_early = [] - for jv, contour in enumerate(contours): - if len(contour) < 3: # A polygon cannot have less than 3 points + for jv,c in enumerate(contours): + if len(c) < 3: # A polygon cannot have less than 3 points continue - polygon = contour2polygon(contour, dilate=dilate) + polygon = geometry.Polygon([point[0] for point in c]) area = polygon.area if (area >= min_area * np.prod(image.shape[:2]) and area <= max_area * np.prod(image.shape[:2]) and hierarchy[0][jv][3] == -1): - found_polygons_early.append(polygon2contour(polygon)) + found_polygons_early.append(np.array([[point] + for point in polygon.exterior.coords], dtype=np.uint)) return found_polygons_early -def filter_contours_area_of_image_tables(image, contours, hierarchy, max_area=1.0, min_area=0.0, dilate=0): +def filter_contours_area_of_image_tables(image, contours, hierarchy, max_area, min_area): found_polygons_early = [] - for jv, contour in enumerate(contours): - if len(contour) < 3: # A polygon cannot have less than 3 points + for jv,c in enumerate(contours): + if len(c) < 3: # A polygon cannot have less than 3 points continue - polygon = contour2polygon(contour, dilate=dilate) - # area = cv2.contourArea(contour) + polygon = geometry.Polygon([point[0] for point in c]) + # area = cv2.contourArea(c) area = polygon.area ##print(np.prod(thresh.shape[:2])) # Check that polygon has area greater than minimal area @@ -69,41 +68,66 @@ def filter_contours_area_of_image_tables(image, contours, hierarchy, max_area=1. area <= max_area * np.prod(image.shape[:2]) and # hierarchy[0][jv][3]==-1 True): - # print(contour[0][0][1]) - found_polygons_early.append(polygon2contour(polygon)) + # print(c[0][0][1]) + found_polygons_early.append(np.array([[point] + for point in polygon.exterior.coords], dtype=np.int32)) return found_polygons_early -def find_center_of_contours(contours): - moments = [cv2.moments(contour) for contour in contours] - cx = [feat["m10"] / (feat["m00"] + 1e-32) - for feat in moments] - cy = [feat["m01"] / (feat["m00"] + 1e-32) - for feat in moments] - return cx, cy +def find_new_features_of_contours(contours_main): + areas_main = np.array([cv2.contourArea(contours_main[j]) + for j in range(len(contours_main))]) + M_main = [cv2.moments(contours_main[j]) + for j in range(len(contours_main))] + cx_main = [(M_main[j]["m10"] / (M_main[j]["m00"] + 1e-32)) + for j in range(len(M_main))] + cy_main = [(M_main[j]["m01"] / (M_main[j]["m00"] + 1e-32)) + for j in range(len(M_main))] + try: + x_min_main = np.array([np.min(contours_main[j][:, 0, 0]) + for j in range(len(contours_main))]) + argmin_x_main = np.array([np.argmin(contours_main[j][:, 0, 0]) + for j in range(len(contours_main))]) + x_min_from_argmin = np.array([contours_main[j][argmin_x_main[j], 0, 0] + for j in range(len(contours_main))]) + y_corr_x_min_from_argmin = np.array([contours_main[j][argmin_x_main[j], 0, 1] + for j in range(len(contours_main))]) + x_max_main = np.array([np.max(contours_main[j][:, 0, 0]) + for j in range(len(contours_main))]) + y_min_main = np.array([np.min(contours_main[j][:, 0, 1]) + for j in range(len(contours_main))]) + y_max_main = np.array([np.max(contours_main[j][:, 0, 1]) + for j in range(len(contours_main))]) + except: + x_min_main = np.array([np.min(contours_main[j][:, 0]) + for j in range(len(contours_main))]) + argmin_x_main = np.array([np.argmin(contours_main[j][:, 0]) + for j in range(len(contours_main))]) + x_min_from_argmin = np.array([contours_main[j][argmin_x_main[j], 0] + for j in range(len(contours_main))]) + y_corr_x_min_from_argmin = np.array([contours_main[j][argmin_x_main[j], 1] + for j in range(len(contours_main))]) + x_max_main = np.array([np.max(contours_main[j][:, 0]) + for j in range(len(contours_main))]) + y_min_main = np.array([np.min(contours_main[j][:, 1]) + for j in range(len(contours_main))]) + y_max_main = np.array([np.max(contours_main[j][:, 1]) + for j in range(len(contours_main))]) + # dis_x=np.abs(x_max_main-x_min_main) -def find_new_features_of_contours(contours): - # areas = np.array([cv2.contourArea(contour) for contour in contours]) - cx, cy = find_center_of_contours(contours) - slice_x = np.index_exp[:, 0, 0] - slice_y = np.index_exp[:, 0, 1] - if any(contour.ndim < 3 for contour in contours): - slice_x = np.index_exp[:, 0] - slice_y = np.index_exp[:, 1] - x_min = np.array([np.min(contour[slice_x]) for contour in contours]) - x_max = np.array([np.max(contour[slice_x]) for contour in contours]) - y_min = np.array([np.min(contour[slice_y]) for contour in contours]) - y_max = np.array([np.max(contour[slice_y]) for contour in contours]) - # dis_x=np.abs(x_max-x_min) - y_corr_x_min = np.array([contour[np.argmin(contour[slice_x])][slice_y[1:]] - for contour in contours]) + return cx_main, cy_main, x_min_main, x_max_main, y_min_main, y_max_main, y_corr_x_min_from_argmin - return cx, cy, x_min, x_max, y_min, y_max, y_corr_x_min +def find_features_of_contours(contours_main): + areas_main=np.array([cv2.contourArea(contours_main[j]) for j in range(len(contours_main))]) + M_main=[cv2.moments(contours_main[j]) for j in range(len(contours_main))] + cx_main=[(M_main[j]['m10']/(M_main[j]['m00']+1e-32)) for j in range(len(M_main))] + cy_main=[(M_main[j]['m01']/(M_main[j]['m00']+1e-32)) for j in range(len(M_main))] + x_min_main=np.array([np.min(contours_main[j][:,0,0]) for j in range(len(contours_main))]) + x_max_main=np.array([np.max(contours_main[j][:,0,0]) for j in range(len(contours_main))]) -def find_features_of_contours(contours): - y_min = np.array([np.min(contour[:,0,1]) for contour in contours]) - y_max = np.array([np.max(contour[:,0,1]) for contour in contours]) + y_min_main=np.array([np.min(contours_main[j][:,0,1]) for j in range(len(contours_main))]) + y_max_main=np.array([np.max(contours_main[j][:,0,1]) for j in range(len(contours_main))]) - return y_min, y_max + return y_min_main, y_max_main def return_parent_contours(contours, hierarchy): contours_parent = [contours[i] @@ -111,13 +135,16 @@ def return_parent_contours(contours, hierarchy): if hierarchy[0][i][3] == -1] return contours_parent -def return_contours_of_interested_region(region_pre_p, label, min_area=0.0002): +def return_contours_of_interested_region(region_pre_p, pixel, min_area=0.0002): # pixels of images are identified by 5 - if region_pre_p.ndim == 3: - cnts_images = (region_pre_p[:, :, 0] == label) * 1 + if len(region_pre_p.shape) == 3: + cnts_images = (region_pre_p[:, :, 0] == pixel) * 1 else: - cnts_images = (region_pre_p[:, :] == label) * 1 - _, thresh = cv2.threshold(cnts_images.astype(np.uint8), 0, 255, 0) + cnts_images = (region_pre_p[:, :] == pixel) * 1 + cnts_images = cnts_images.astype(np.uint8) + cnts_images = np.repeat(cnts_images[:, :, np.newaxis], 3, axis=2) + imgray = cv2.cvtColor(cnts_images, cv2.COLOR_BGR2GRAY) + ret, thresh = cv2.threshold(imgray, 0, 255, 0) contours_imgs, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) contours_imgs = return_parent_contours(contours_imgs, hierarchy) @@ -126,11 +153,13 @@ def return_contours_of_interested_region(region_pre_p, label, min_area=0.0002): return contours_imgs def do_work_of_contours_in_image(contour, index_r_con, img, slope_first): - img_copy = np.zeros(img.shape[:2], dtype=np.uint8) - img_copy = cv2.fillPoly(img_copy, pts=[contour], color=1) + img_copy = np.zeros(img.shape) + img_copy = cv2.fillPoly(img_copy, pts=[contour], color=(1, 1, 1)) img_copy = rotation_image_new(img_copy, -slope_first) - _, thresh = cv2.threshold(img_copy, 0, 255, 0) + img_copy = img_copy.astype(np.uint8) + imgray = cv2.cvtColor(img_copy, cv2.COLOR_BGR2GRAY) + ret, thresh = cv2.threshold(imgray, 0, 255, 0) cont_int, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) @@ -153,8 +182,8 @@ def get_textregion_contours_in_org_image(cnts, img, slope_first): cnts_org = [] # print(cnts,'cnts') for i in range(len(cnts)): - img_copy = np.zeros(img.shape[:2], dtype=np.uint8) - img_copy = cv2.fillPoly(img_copy, pts=[cnts[i]], color=1) + img_copy = np.zeros(img.shape) + img_copy = cv2.fillPoly(img_copy, pts=[cnts[i]], color=(1, 1, 1)) # plt.imshow(img_copy) # plt.show() @@ -165,7 +194,9 @@ def get_textregion_contours_in_org_image(cnts, img, slope_first): # plt.imshow(img_copy) # plt.show() - _, thresh = cv2.threshold(img_copy, 0, 255, 0) + img_copy = img_copy.astype(np.uint8) + imgray = cv2.cvtColor(img_copy, cv2.COLOR_BGR2GRAY) + ret, thresh = cv2.threshold(imgray, 0, 255, 0) cont_int, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) cont_int[0][:, 0, 0] = cont_int[0][:, 0, 0] + np.abs(img_copy.shape[1] - img.shape[1]) @@ -182,11 +213,12 @@ def get_textregion_contours_in_org_image_light_old(cnts, img, slope_first): interpolation=cv2.INTER_NEAREST) cnts_org = [] for cnt in cnts: - img_copy = np.zeros(img.shape[:2], dtype=np.uint8) - img_copy = cv2.fillPoly(img_copy, pts=[cnt // zoom], color=1) + img_copy = np.zeros(img.shape) + img_copy = cv2.fillPoly(img_copy, pts=[(cnt / zoom).astype(int)], color=(1, 1, 1)) img_copy = rotation_image_new(img_copy, -slope_first).astype(np.uint8) - _, thresh = cv2.threshold(img_copy, 0, 255, 0) + imgray = cv2.cvtColor(img_copy, cv2.COLOR_BGR2GRAY) + ret, thresh = cv2.threshold(imgray, 0, 255, 0) cont_int, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) cont_int[0][:, 0, 0] = cont_int[0][:, 0, 0] + np.abs(img_copy.shape[1] - img.shape[1]) @@ -196,44 +228,50 @@ def get_textregion_contours_in_org_image_light_old(cnts, img, slope_first): return cnts_org def do_back_rotation_and_get_cnt_back(contour_par, index_r_con, img, slope_first, confidence_matrix): - img_copy = np.zeros(img.shape[:2], dtype=np.uint8) - img_copy = cv2.fillPoly(img_copy, pts=[contour_par], color=1) - confidence_matrix_mapped_with_contour = confidence_matrix * img_copy - confidence_contour = np.sum(confidence_matrix_mapped_with_contour) / float(np.sum(img_copy)) + img_copy = np.zeros(img.shape) + img_copy = cv2.fillPoly(img_copy, pts=[contour_par], color=(1, 1, 1)) + + confidence_matrix_mapped_with_contour = confidence_matrix * img_copy[:,:,0] + confidence_contour = np.sum(confidence_matrix_mapped_with_contour) / float(np.sum(img_copy[:,:,0])) img_copy = rotation_image_new(img_copy, -slope_first).astype(np.uint8) - _, thresh = cv2.threshold(img_copy, 0, 255, 0) + imgray = cv2.cvtColor(img_copy, cv2.COLOR_BGR2GRAY) + ret, thresh = cv2.threshold(imgray, 0, 255, 0) cont_int, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) - if len(cont_int)==0: - cont_int = [contour_par] - confidence_contour = 0 - else: - cont_int[0][:, 0, 0] = cont_int[0][:, 0, 0] + np.abs(img_copy.shape[1] - img.shape[1]) - cont_int[0][:, 0, 1] = cont_int[0][:, 0, 1] + np.abs(img_copy.shape[0] - img.shape[0]) + cont_int[0][:, 0, 0] = cont_int[0][:, 0, 0] + np.abs(img_copy.shape[1] - img.shape[1]) + cont_int[0][:, 0, 1] = cont_int[0][:, 0, 1] + np.abs(img_copy.shape[0] - img.shape[0]) + # print(np.shape(cont_int[0])) return cont_int[0], index_r_con, confidence_contour -def get_textregion_contours_in_org_image_light(cnts, img, confidence_matrix): +def get_textregion_contours_in_org_image_light(cnts, img, slope_first, confidence_matrix, map=map): if not len(cnts): - return [] + return [], [] + + confidence_matrix = cv2.resize(confidence_matrix, (int(img.shape[1]/6), int(img.shape[0]/6)), interpolation=cv2.INTER_NEAREST) + img = cv2.resize(img, (int(img.shape[1]/6), int(img.shape[0]/6)), interpolation=cv2.INTER_NEAREST) + ##cnts = list( (np.array(cnts)/2).astype(np.int16) ) + #cnts = cnts/2 + cnts = [(i/6).astype(int) for i in cnts] + results = map(partial(do_back_rotation_and_get_cnt_back, + img=img, + slope_first=slope_first, + confidence_matrix=confidence_matrix, + ), + cnts, range(len(cnts))) + contours, indexes, conf_contours = tuple(zip(*results)) + return [i*6 for i in contours], list(conf_contours) - confidence_matrix = cv2.resize(confidence_matrix, - (img.shape[1] // 6, img.shape[0] // 6), - interpolation=cv2.INTER_NEAREST) - confs = [] - for cnt in cnts: - cnt_mask = np.zeros(confidence_matrix.shape) - cnt_mask = cv2.fillPoly(cnt_mask, pts=[cnt // 6], color=1.0) - confs.append(np.sum(confidence_matrix * cnt_mask) / np.sum(cnt_mask)) - return confs - -def return_contours_of_interested_textline(region_pre_p, label): +def return_contours_of_interested_textline(region_pre_p, pixel): # pixels of images are identified by 5 - if region_pre_p.ndim == 3: - cnts_images = (region_pre_p[:, :, 0] == label) * 1 + if len(region_pre_p.shape) == 3: + cnts_images = (region_pre_p[:, :, 0] == pixel) * 1 else: - cnts_images = (region_pre_p[:, :] == label) * 1 - _, thresh = cv2.threshold(cnts_images.astype(np.uint8), 0, 255, 0) + cnts_images = (region_pre_p[:, :] == pixel) * 1 + cnts_images = cnts_images.astype(np.uint8) + cnts_images = np.repeat(cnts_images[:, :, np.newaxis], 3, axis=2) + imgray = cv2.cvtColor(cnts_images, cv2.COLOR_BGR2GRAY) + ret, thresh = cv2.threshold(imgray, 0, 255, 0) contours_imgs, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) contours_imgs = return_parent_contours(contours_imgs, hierarchy) @@ -243,123 +281,51 @@ def return_contours_of_interested_textline(region_pre_p, label): def return_contours_of_image(image): if len(image.shape) == 2: + image = np.repeat(image[:, :, np.newaxis], 3, axis=2) image = image.astype(np.uint8) - imgray = image else: image = image.astype(np.uint8) - imgray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) - _, thresh = cv2.threshold(imgray, 0, 255, 0) + imgray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) + ret, thresh = cv2.threshold(imgray, 0, 255, 0) contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) return contours, hierarchy -def dilate_textline_contours(all_found_textline_polygons): - return [[polygon2contour(contour2polygon(contour, dilate=6)) - for contour in region] - for region in all_found_textline_polygons] +def return_contours_of_interested_region_by_min_size(region_pre_p, pixel, min_size=0.00003): + # pixels of images are identified by 5 + if len(region_pre_p.shape) == 3: + cnts_images = (region_pre_p[:, :, 0] == pixel) * 1 + else: + cnts_images = (region_pre_p[:, :] == pixel) * 1 + cnts_images = cnts_images.astype(np.uint8) + cnts_images = np.repeat(cnts_images[:, :, np.newaxis], 3, axis=2) + imgray = cv2.cvtColor(cnts_images, cv2.COLOR_BGR2GRAY) + ret, thresh = cv2.threshold(imgray, 0, 255, 0) -def dilate_textregion_contours(all_found_textline_polygons): - return [polygon2contour(contour2polygon(contour, dilate=6)) - for contour in all_found_textline_polygons] + contours_imgs, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) + contours_imgs = return_parent_contours(contours_imgs, hierarchy) + contours_imgs = filter_contours_area_of_image_tables( + thresh, contours_imgs, hierarchy, max_area=1, min_area=min_size) -def contour2polygon(contour: Union[np.ndarray, Sequence[Sequence[Sequence[Number]]]], dilate=0): - polygon = Polygon([point[0] for point in contour]) - if dilate: - polygon = polygon.buffer(dilate) - if polygon.geom_type == 'GeometryCollection': - # heterogeneous result: filter zero-area shapes (LineString, Point) - polygon = unary_union([geom for geom in polygon.geoms if geom.area > 0]) - if polygon.geom_type == 'MultiPolygon': - # homogeneous result: construct convex hull to connect - polygon = join_polygons(polygon.geoms) - return make_valid(polygon) + return contours_imgs -def polygon2contour(polygon: Polygon) -> np.ndarray: - polygon = np.array(polygon.exterior.coords[:-1], dtype=int) - return np.maximum(0, polygon).astype(int)[:, np.newaxis] +def return_contours_of_interested_region_by_size(region_pre_p, pixel, min_area, max_area): + # pixels of images are identified by 5 + if len(region_pre_p.shape) == 3: + cnts_images = (region_pre_p[:, :, 0] == pixel) * 1 + else: + cnts_images = (region_pre_p[:, :] == pixel) * 1 + cnts_images = cnts_images.astype(np.uint8) + cnts_images = np.repeat(cnts_images[:, :, np.newaxis], 3, axis=2) + imgray = cv2.cvtColor(cnts_images, cv2.COLOR_BGR2GRAY) + ret, thresh = cv2.threshold(imgray, 0, 255, 0) + contours_imgs, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) -def make_intersection(poly1, poly2): - interp = poly1.intersection(poly2) - # post-process - if interp.is_empty or interp.area == 0.0: - return None - if interp.geom_type == 'GeometryCollection': - # heterogeneous result: filter zero-area shapes (LineString, Point) - interp = unary_union([geom for geom in interp.geoms if geom.area > 0]) - if interp.geom_type == 'MultiPolygon': - # homogeneous result: construct convex hull to connect - interp = join_polygons(interp.geoms) - assert interp.geom_type == 'Polygon', interp.wkt - interp = make_valid(interp) - return interp + contours_imgs = return_parent_contours(contours_imgs, hierarchy) + contours_imgs = filter_contours_area_of_image_tables( + thresh, contours_imgs, hierarchy, max_area=max_area, min_area=min_area) -def make_valid(polygon: Polygon) -> Polygon: - """Ensures shapely.geometry.Polygon object is valid by repeated rearrangement/simplification/enlargement.""" - def isint(x): - return isinstance(x, int) or int(x) == x - # make sure rounding does not invalidate - if not all(map(isint, np.array(polygon.exterior.coords).flat)) and polygon.minimum_clearance < 1.0: - polygon = Polygon(np.round(polygon.exterior.coords)) - points = list(polygon.exterior.coords[:-1]) - # try by re-arranging points - for split in range(1, len(points)): - if polygon.is_valid or polygon.simplify(polygon.area).is_valid: - break - # simplification may not be possible (at all) due to ordering - # in that case, try another starting point - polygon = Polygon(points[-split:]+points[:-split]) - # try by simplification - for tolerance in range(int(polygon.area + 1.5)): - if polygon.is_valid: - break - # simplification may require a larger tolerance - polygon = polygon.simplify(tolerance + 1) - # try by enlarging - for tolerance in range(1, int(polygon.area + 2.5)): - if polygon.is_valid: - break - # enlargement may require a larger tolerance - polygon = polygon.buffer(tolerance) - assert polygon.is_valid, polygon.wkt - return polygon + img_ret = np.zeros((region_pre_p.shape[0], region_pre_p.shape[1], 3)) + img_ret = cv2.fillPoly(img_ret, pts=contours_imgs, color=(1, 1, 1)) + + return img_ret[:, :, 0] -def join_polygons(polygons: Sequence[Polygon], scale=20) -> Polygon: - """construct concave hull (alpha shape) from input polygons by connecting their pairwise nearest points""" - # ensure input polygons are simply typed and all oriented equally - polygons = [orient(poly) - for poly in itertools.chain.from_iterable( - [poly.geoms - if poly.geom_type in ['MultiPolygon', 'GeometryCollection'] - else [poly] - for poly in polygons])] - npoly = len(polygons) - if npoly == 1: - return polygons[0] - # find min-dist path through all polygons (travelling salesman) - pairs = itertools.combinations(range(npoly), 2) - dists = np.zeros((npoly, npoly), dtype=float) - for i, j in pairs: - dist = polygons[i].distance(polygons[j]) - if dist < 1e-5: - dist = 1e-5 # if pair merely touches, we still need to get an edge - dists[i, j] = dist - dists[j, i] = dist - dists = minimum_spanning_tree(dists, overwrite=True) - # add bridge polygons (where necessary) - for prevp, nextp in zip(*dists.nonzero()): - prevp = polygons[prevp] - nextp = polygons[nextp] - nearest = nearest_points(prevp, nextp) - bridgep = orient(LineString(nearest).buffer(max(1, scale/5), resolution=1), -1) - polygons.append(bridgep) - jointp = unary_union(polygons) - if jointp.geom_type == 'MultiPolygon': - jointp = unary_union(jointp.geoms) - assert jointp.geom_type == 'Polygon', jointp.wkt - # follow-up calculations will necessarily be integer; - # so anticipate rounding here and then ensure validity - jointp2 = set_precision(jointp, 1.0, mode="keep_collapsed") - if jointp2.geom_type != 'Polygon' or not jointp2.is_valid: - jointp2 = Polygon(np.round(jointp.exterior.coords)) - jointp2 = make_valid(jointp2) - assert jointp2.geom_type == 'Polygon', jointp2.wkt - return jointp2 diff --git a/src/eynollah/utils/counter.py b/src/eynollah/utils/counter.py index e6205c8..9a3ed70 100644 --- a/src/eynollah/utils/counter.py +++ b/src/eynollah/utils/counter.py @@ -3,7 +3,7 @@ from collections import Counter REGION_ID_TEMPLATE = 'region_%04d' LINE_ID_TEMPLATE = 'region_%04d_line_%04d' -class EynollahIdCounter: +class EynollahIdCounter(): def __init__(self, region_idx=0, line_idx=0): self._counter = Counter() diff --git a/src/eynollah/utils/drop_capitals.py b/src/eynollah/utils/drop_capitals.py index 228a6d9..67547d3 100644 --- a/src/eynollah/utils/drop_capitals.py +++ b/src/eynollah/utils/drop_capitals.py @@ -1,7 +1,6 @@ import numpy as np import cv2 from .contour import ( - find_center_of_contours, find_new_features_of_contours, return_contours_of_image, return_parent_contours, @@ -19,11 +18,12 @@ def adhere_drop_capital_region_into_corresponding_textline( all_found_textline_polygons_h, kernel=None, curved_line=False, + textline_light=False, ): # print(np.shape(all_found_textline_polygons),np.shape(all_found_textline_polygons[3]),'all_found_textline_polygonsshape') # print(all_found_textline_polygons[3]) - cx_m, cy_m = find_center_of_contours(contours_only_text_parent) - cx_h, cy_h = find_center_of_contours(contours_only_text_parent_h) + cx_m, cy_m, _, _, _, _, _ = find_new_features_of_contours(contours_only_text_parent) + cx_h, cy_h, _, _, _, _, _ = find_new_features_of_contours(contours_only_text_parent_h) cx_d, cy_d, _, _, y_min_d, y_max_d, _ = find_new_features_of_contours(polygons_of_drop_capitals) img_con_all = np.zeros((text_regions_p.shape[0], text_regions_p.shape[1], 3)) @@ -78,7 +78,7 @@ def adhere_drop_capital_region_into_corresponding_textline( # region_with_intersected_drop=region_with_intersected_drop/3 region_with_intersected_drop = region_with_intersected_drop.astype(np.uint8) # print(np.unique(img_con_all_copy[:,:,0])) - if curved_line: + if curved_line or textline_light: if len(region_with_intersected_drop) > 1: sum_pixels_of_intersection = [] @@ -89,9 +89,9 @@ def adhere_drop_capital_region_into_corresponding_textline( region_final = region_with_intersected_drop[np.argmax(sum_pixels_of_intersection)] - 1 # print(region_final,'region_final') - # cx_t, cy_t = find_center_of_contours(all_found_textline_polygons[int(region_final)]) + # cx_t,cy_t ,_, _, _ ,_,_= find_new_features_of_contours(all_found_textline_polygons[int(region_final)]) try: - cx_t, cy_t = find_center_of_contours(all_found_textline_polygons[int(region_final)]) + cx_t, cy_t, _, _, _, _, _ = find_new_features_of_contours(all_found_textline_polygons[int(region_final)]) # print(all_box_coord[j_cont]) # print(cx_t) # print(cy_t) @@ -153,9 +153,9 @@ def adhere_drop_capital_region_into_corresponding_textline( # areas_main=np.array([cv2.contourArea(all_found_textline_polygons[int(region_final)][0][j] ) for j in range(len(all_found_textline_polygons[int(region_final)]))]) - # cx_t, cy_t = find_center_of_contours(all_found_textline_polygons[int(region_final)]) + # cx_t,cy_t ,_, _, _ ,_,_= find_new_features_of_contours(all_found_textline_polygons[int(region_final)]) try: - cx_t, cy_t = find_center_of_contours(all_found_textline_polygons[int(region_final)]) + cx_t, cy_t, _, _, _, _, _ = find_new_features_of_contours(all_found_textline_polygons[int(region_final)]) # print(all_box_coord[j_cont]) # print(cx_t) # print(cy_t) @@ -208,7 +208,7 @@ def adhere_drop_capital_region_into_corresponding_textline( try: # print(all_found_textline_polygons[j_cont][0]) - cx_t, cy_t = find_center_of_contours(all_found_textline_polygons[int(region_final)]) + cx_t, cy_t, _, _, _, _, _ = find_new_features_of_contours(all_found_textline_polygons[int(region_final)]) # print(all_box_coord[j_cont]) # print(cx_t) # print(cy_t) @@ -261,7 +261,7 @@ def adhere_drop_capital_region_into_corresponding_textline( else: pass - ##cx_t, cy_t = find_center_of_contours(all_found_textline_polygons[int(region_final)]) + ##cx_t,cy_t ,_, _, _ ,_,_= find_new_features_of_contours(all_found_textline_polygons[int(region_final)]) ###print(all_box_coord[j_cont]) ###print(cx_t) ###print(cy_t) @@ -315,9 +315,9 @@ def adhere_drop_capital_region_into_corresponding_textline( region_final = region_with_intersected_drop[np.argmax(sum_pixels_of_intersection)] - 1 # print(region_final,'region_final') - # cx_t, cy_t = find_center_of_contours(all_found_textline_polygons[int(region_final)]) + # cx_t,cy_t ,_, _, _ ,_,_= find_new_features_of_contours(all_found_textline_polygons[int(region_final)]) try: - cx_t, cy_t = find_center_of_contours(all_found_textline_polygons[int(region_final)]) + cx_t, cy_t, _, _, _, _, _ = find_new_features_of_contours(all_found_textline_polygons[int(region_final)]) # print(all_box_coord[j_cont]) # print(cx_t) # print(cy_t) @@ -375,12 +375,12 @@ def adhere_drop_capital_region_into_corresponding_textline( # areas_main=np.array([cv2.contourArea(all_found_textline_polygons[int(region_final)][0][j] ) for j in range(len(all_found_textline_polygons[int(region_final)]))]) - # cx_t, cy_t = find_center_of_contours(all_found_textline_polygons[int(region_final)]) + # cx_t,cy_t ,_, _, _ ,_,_= find_new_features_of_contours(all_found_textline_polygons[int(region_final)]) # print(cx_t,'print') try: # print(all_found_textline_polygons[j_cont][0]) - cx_t, cy_t = find_center_of_contours(all_found_textline_polygons[int(region_final)]) + cx_t, cy_t, _, _, _, _, _ = find_new_features_of_contours(all_found_textline_polygons[int(region_final)]) # print(all_box_coord[j_cont]) # print(cx_t) # print(cy_t) @@ -453,7 +453,7 @@ def adhere_drop_capital_region_into_corresponding_textline( #####try: #####if len(contours_new_parent)==1: ######print(all_found_textline_polygons[j_cont][0]) - #####cx_t, cy_t = find_center_of_contours(all_found_textline_polygons[j_cont]) + #####cx_t,cy_t ,_, _, _ ,_,_= find_new_features_of_contours(all_found_textline_polygons[j_cont]) ######print(all_box_coord[j_cont]) ######print(cx_t) ######print(cy_t) diff --git a/src/eynollah/utils/font.py b/src/eynollah/utils/font.py deleted file mode 100644 index 939933e..0000000 --- a/src/eynollah/utils/font.py +++ /dev/null @@ -1,16 +0,0 @@ - -# cannot use importlib.resources until we move to 3.9+ forimportlib.resources.files -import sys -from PIL import ImageFont - -if sys.version_info < (3, 10): - import importlib_resources -else: - import importlib.resources as importlib_resources - - -def get_font(): - #font_path = "Charis-7.000/Charis-Regular.ttf" # Make sure this file exists! - font = importlib_resources.files(__package__) / "../Charis-Regular.ttf" - with importlib_resources.as_file(font) as font: - return ImageFont.truetype(font=font, size=40) diff --git a/src/eynollah/utils/marginals.py b/src/eynollah/utils/marginals.py index 9f76fb7..a29e50d 100644 --- a/src/eynollah/utils/marginals.py +++ b/src/eynollah/utils/marginals.py @@ -6,10 +6,11 @@ from .contour import find_new_features_of_contours, return_contours_of_intereste from .resize import resize_image from .rotate import rotate_image -def get_marginals(text_with_lines, text_regions, num_col, slope_deskew, kernel=None): +def get_marginals(text_with_lines, text_regions, num_col, slope_deskew, light_version=False, kernel=None): mask_marginals=np.zeros((text_with_lines.shape[0],text_with_lines.shape[1])) mask_marginals=mask_marginals.astype(np.uint8) + text_with_lines=text_with_lines.astype(np.uint8) ##text_with_lines=cv2.erode(text_with_lines,self.kernel,iterations=3) @@ -25,11 +26,8 @@ def get_marginals(text_with_lines, text_regions, num_col, slope_deskew, kernel=N text_with_lines=resize_image(text_with_lines,int(text_with_lines.shape[0]*1.8),text_with_lines.shape[1]) text_with_lines=cv2.erode(text_with_lines,kernel,iterations=7) text_with_lines=resize_image(text_with_lines,text_with_lines_eroded.shape[0],text_with_lines_eroded.shape[1]) - - - kernel_hor = np.ones((1, 5), dtype=np.uint8) - text_with_lines = cv2.erode(text_with_lines,kernel_hor,iterations=6) - + + text_with_lines_y=text_with_lines.sum(axis=0) text_with_lines_y_eroded=text_with_lines_eroded.sum(axis=0) @@ -42,7 +40,8 @@ def get_marginals(text_with_lines, text_regions, num_col, slope_deskew, kernel=N elif thickness_along_y_percent>=30 and thickness_along_y_percent<50: min_textline_thickness=20 else: - min_textline_thickness=45 + min_textline_thickness=40 + if thickness_along_y_percent>=14: @@ -72,7 +71,7 @@ def get_marginals(text_with_lines, text_regions, num_col, slope_deskew, kernel=N peaks, _ = find_peaks(text_with_lines_y_rev, height=0) peaks=np.array(peaks) - peaks=peaks[(peaks>first_nonzero) & (peaks < last_nonzero)] + peaks=peaks[(peaks>first_nonzero) & ((peaks=mask_marginals.shape[1]: @@ -124,39 +121,92 @@ def get_marginals(text_with_lines, text_regions, num_col, slope_deskew, kernel=N if max_point_of_right_marginal>=text_regions.shape[1]: max_point_of_right_marginal=text_regions.shape[1]-1 - text_regions_org = np.copy(text_regions) - text_regions[text_regions[:,:]==1]=4 - - pixel_img=4 - min_area_text=0.00001 - - polygon_mask_marginals_rotated = return_contours_of_interested_region(mask_marginals,1,min_area_text) - - polygon_mask_marginals_rotated = polygon_mask_marginals_rotated[0] + if light_version: + text_regions_org = np.copy(text_regions) + text_regions[text_regions[:,:]==1]=4 + + pixel_img=4 + min_area_text=0.00001 + + polygon_mask_marginals_rotated = return_contours_of_interested_region(mask_marginals,1,min_area_text) + + polygon_mask_marginals_rotated = polygon_mask_marginals_rotated[0] - polygons_of_marginals=return_contours_of_interested_region(text_regions,pixel_img,min_area_text) + polygons_of_marginals=return_contours_of_interested_region(text_regions,pixel_img,min_area_text) - cx_text_only,cy_text_only ,x_min_text_only,x_max_text_only, y_min_text_only ,y_max_text_only,y_cor_x_min_main=find_new_features_of_contours(polygons_of_marginals) + cx_text_only,cy_text_only ,x_min_text_only,x_max_text_only, y_min_text_only ,y_max_text_only,y_cor_x_min_main=find_new_features_of_contours(polygons_of_marginals) - text_regions[(text_regions[:,:]==4)]=1 + text_regions[(text_regions[:,:]==4)]=1 - marginlas_should_be_main_text=[] + marginlas_should_be_main_text=[] - x_min_marginals_left=[] - x_min_marginals_right=[] + x_min_marginals_left=[] + x_min_marginals_right=[] - for i in range(len(cx_text_only)): - results = cv2.pointPolygonTest(polygon_mask_marginals_rotated, (cx_text_only[i], cy_text_only[i]), False) + for i in range(len(cx_text_only)): + results = cv2.pointPolygonTest(polygon_mask_marginals_rotated, (cx_text_only[i], cy_text_only[i]), False) - if results == -1: - marginlas_should_be_main_text.append(polygons_of_marginals[i]) + if results == -1: + marginlas_should_be_main_text.append(polygons_of_marginals[i]) - text_regions_org=cv2.fillPoly(text_regions_org, pts =marginlas_should_be_main_text, color=(4,4)) - text_regions = np.copy(text_regions_org) + text_regions_org=cv2.fillPoly(text_regions_org, pts =marginlas_should_be_main_text, color=(4,4)) + text_regions = np.copy(text_regions_org) + else: + + text_regions[(mask_marginals_rotated[:,:]!=1) & (text_regions[:,:]==1)]=4 + + pixel_img=4 + min_area_text=0.00001 + + polygons_of_marginals=return_contours_of_interested_region(text_regions,pixel_img,min_area_text) + + cx_text_only,cy_text_only ,x_min_text_only,x_max_text_only, y_min_text_only ,y_max_text_only,y_cor_x_min_main=find_new_features_of_contours(polygons_of_marginals) + + text_regions[(text_regions[:,:]==4)]=1 + + marginlas_should_be_main_text=[] + + x_min_marginals_left=[] + x_min_marginals_right=[] + + for i in range(len(cx_text_only)): + x_width_mar=abs(x_min_text_only[i]-x_max_text_only[i]) + y_height_mar=abs(y_min_text_only[i]-y_max_text_only[i]) + + if x_width_mar>16 and y_height_mar/x_width_mar<18: + marginlas_should_be_main_text.append(polygons_of_marginals[i]) + if x_min_text_only[i]<(mid_point-one_third_left): + x_min_marginals_left_new=x_min_text_only[i] + if len(x_min_marginals_left)==0: + x_min_marginals_left.append(x_min_marginals_left_new) + else: + x_min_marginals_left[0]=min(x_min_marginals_left[0],x_min_marginals_left_new) + else: + x_min_marginals_right_new=x_min_text_only[i] + if len(x_min_marginals_right)==0: + x_min_marginals_right.append(x_min_marginals_right_new) + else: + x_min_marginals_right[0]=min(x_min_marginals_right[0],x_min_marginals_right_new) + + if len(x_min_marginals_left)==0: + x_min_marginals_left=[0] + if len(x_min_marginals_right)==0: + x_min_marginals_right=[text_regions.shape[1]-1] + + + text_regions=cv2.fillPoly(text_regions, pts =marginlas_should_be_main_text, color=(4,4)) + + + #text_regions[:,:int(x_min_marginals_left[0])][text_regions[:,:int(x_min_marginals_left[0])]==1]=0 + #text_regions[:,int(x_min_marginals_right[0]):][text_regions[:,int(x_min_marginals_right[0]):]==1]=0 + + + text_regions[:,:int(min_point_of_left_marginal)][text_regions[:,:int(min_point_of_left_marginal)]==1]=0 + text_regions[:,int(max_point_of_right_marginal):][text_regions[:,int(max_point_of_right_marginal):]==1]=0 ###text_regions[:,0:point_left][text_regions[:,0:point_left]==1]=4 diff --git a/src/eynollah/utils/separate_lines.py b/src/eynollah/utils/separate_lines.py index c220234..3499c29 100644 --- a/src/eynollah/utils/separate_lines.py +++ b/src/eynollah/utils/separate_lines.py @@ -15,10 +15,9 @@ from .contour import ( return_contours_of_interested_textline, find_contours_mean_y_diff, ) -from .shm import share_ndarray, wrap_ndarray_shared from . import ( find_num_col_deskew, - box2rect, + crop_image_inside_box, ) def dedup_separate_lines(img_patch, contour_text_interest, thetha, axis): @@ -63,8 +62,7 @@ def dedup_separate_lines(img_patch, contour_text_interest, thetha, axis): peaks_neg_e, _ = find_peaks(y_padded_up_to_down_padded_e, height=0) neg_peaks_max = np.max(y_padded_up_to_down_padded_e[peaks_neg_e]) - arg_neg_must_be_deleted = np.arange(len(peaks_neg_e))[ - y_padded_up_to_down_padded_e[peaks_neg_e] / float(neg_peaks_max) < 0.3] + arg_neg_must_be_deleted = np.arange(len(peaks_neg_e))[y_padded_up_to_down_padded_e[peaks_neg_e] / float(neg_peaks_max) < 0.3] diff_arg_neg_must_be_deleted = np.diff(arg_neg_must_be_deleted) arg_diff = np.array(range(len(diff_arg_neg_must_be_deleted))) @@ -75,14 +73,11 @@ def dedup_separate_lines(img_patch, contour_text_interest, thetha, axis): clusters_to_be_deleted = [] if len(arg_diff_cluster) > 0: - clusters_to_be_deleted.append( - arg_neg_must_be_deleted[0 : arg_diff_cluster[0] + 1]) + clusters_to_be_deleted.append(arg_neg_must_be_deleted[0 : arg_diff_cluster[0] + 1]) for i in range(len(arg_diff_cluster) - 1): - clusters_to_be_deleted.append( - arg_neg_must_be_deleted[arg_diff_cluster[i] + 1 : - arg_diff_cluster[i + 1] + 1]) - clusters_to_be_deleted.append( - arg_neg_must_be_deleted[arg_diff_cluster[len(arg_diff_cluster) - 1] + 1 :]) + clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[i] + 1 : + arg_diff_cluster[i + 1] + 1]) + clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[len(arg_diff_cluster) - 1] + 1 :]) if len(clusters_to_be_deleted) > 0: peaks_new_extra = [] for m in range(len(clusters_to_be_deleted)): @@ -107,15 +102,14 @@ def dedup_separate_lines(img_patch, contour_text_interest, thetha, axis): textline_con_fil = filter_contours_area_of_image(img_patch, textline_con, hierarchy, max_area=1, min_area=0.0008) - if len(np.diff(peaks_new_tot))>1: - y_diff_mean = np.mean(np.diff(peaks_new_tot)) # self.find_contours_mean_y_diff(textline_con_fil) - sigma_gaus = int(y_diff_mean * (7.0 / 40.0)) - else: - sigma_gaus = 12 + y_diff_mean = np.mean(np.diff(peaks_new_tot)) # self.find_contours_mean_y_diff(textline_con_fil) + sigma_gaus = int(y_diff_mean * (7.0 / 40.0)) + # print(sigma_gaus,'sigma_gaus') except: sigma_gaus = 12 if sigma_gaus < 3: sigma_gaus = 3 + # print(sigma_gaus,'sigma') y_padded_smoothed = gaussian_filter1d(y_padded, sigma_gaus) y_padded_up_to_down = -y_padded + np.max(y_padded) @@ -138,12 +132,14 @@ def dedup_separate_lines(img_patch, contour_text_interest, thetha, axis): rotation_matrix) def separate_lines(img_patch, contour_text_interest, thetha, x_help, y_help): - h, w = img_patch.shape[:2] + (h, w) = img_patch.shape[:2] center = (w // 2, h // 2) M = cv2.getRotationMatrix2D(center, -thetha, 1.0) x_d = M[0, 2] y_d = M[1, 2] - rotation_matrix = M[:2, :2] + + thetha = thetha / 180. * np.pi + rotation_matrix = np.array([[np.cos(thetha), -np.sin(thetha)], [np.sin(thetha), np.cos(thetha)]]) contour_text_interest_copy = contour_text_interest.copy() x_cont = contour_text_interest[:, 0, 0] @@ -166,86 +162,83 @@ def separate_lines(img_patch, contour_text_interest, thetha, x_help, y_help): x = np.array(range(len(y))) peaks_real, _ = find_peaks(gaussian_filter1d(y, 3), height=0) - - try: - y_padded_smoothed_e= gaussian_filter1d(y_padded, 2) - y_padded_up_to_down_e=-y_padded+np.max(y_padded) - y_padded_up_to_down_padded_e=np.zeros(len(y_padded_up_to_down_e)+40) - y_padded_up_to_down_padded_e[20:len(y_padded_up_to_down_e)+20]=y_padded_up_to_down_e - y_padded_up_to_down_padded_e= gaussian_filter1d(y_padded_up_to_down_padded_e, 2) - - peaks_e, _ = find_peaks(y_padded_smoothed_e, height=0) - peaks_neg_e, _ = find_peaks(y_padded_up_to_down_padded_e, height=0) - neg_peaks_max=np.max(y_padded_up_to_down_padded_e[peaks_neg_e]) + if 1>0: + try: + y_padded_smoothed_e= gaussian_filter1d(y_padded, 2) + y_padded_up_to_down_e=-y_padded+np.max(y_padded) + y_padded_up_to_down_padded_e=np.zeros(len(y_padded_up_to_down_e)+40) + y_padded_up_to_down_padded_e[20:len(y_padded_up_to_down_e)+20]=y_padded_up_to_down_e + y_padded_up_to_down_padded_e= gaussian_filter1d(y_padded_up_to_down_padded_e, 2) + - arg_neg_must_be_deleted = np.arange(len(peaks_neg_e))[ - y_padded_up_to_down_padded_e[peaks_neg_e]/float(neg_peaks_max)<0.3] - diff_arg_neg_must_be_deleted=np.diff(arg_neg_must_be_deleted) - - arg_diff=np.array(range(len(diff_arg_neg_must_be_deleted))) - arg_diff_cluster=arg_diff[diff_arg_neg_must_be_deleted>1] - peaks_new=peaks_e[:] - peaks_neg_new=peaks_neg_e[:] + peaks_e, _ = find_peaks(y_padded_smoothed_e, height=0) + peaks_neg_e, _ = find_peaks(y_padded_up_to_down_padded_e, height=0) + neg_peaks_max=np.max(y_padded_up_to_down_padded_e[peaks_neg_e]) - clusters_to_be_deleted=[] - if len(arg_diff_cluster)>0: - clusters_to_be_deleted.append(arg_neg_must_be_deleted[0:arg_diff_cluster[0]+1]) - for i in range(len(arg_diff_cluster)-1): - clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[i]+1: - arg_diff_cluster[i+1]+1]) - clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[len(arg_diff_cluster)-1]+1:]) - if len(clusters_to_be_deleted)>0: - peaks_new_extra=[] - for m in range(len(clusters_to_be_deleted)): - min_cluster=np.min(peaks_e[clusters_to_be_deleted[m]]) - max_cluster=np.max(peaks_e[clusters_to_be_deleted[m]]) - peaks_new_extra.append( int( (min_cluster+max_cluster)/2.0) ) - for m1 in range(len(clusters_to_be_deleted[m])): - peaks_new=peaks_new[peaks_new!=peaks_e[clusters_to_be_deleted[m][m1]-1]] - peaks_new=peaks_new[peaks_new!=peaks_e[clusters_to_be_deleted[m][m1]]] - peaks_neg_new=peaks_neg_new[peaks_neg_new!=peaks_neg_e[clusters_to_be_deleted[m][m1]]] - peaks_new_tot=[] - for i1 in peaks_new: - peaks_new_tot.append(i1) - for i1 in peaks_new_extra: - peaks_new_tot.append(i1) - peaks_new_tot=np.sort(peaks_new_tot) - else: - peaks_new_tot=peaks_e[:] + arg_neg_must_be_deleted= np.arange(len(peaks_neg_e))[y_padded_up_to_down_padded_e[peaks_neg_e]/float(neg_peaks_max)<0.3] + diff_arg_neg_must_be_deleted=np.diff(arg_neg_must_be_deleted) + + arg_diff=np.array(range(len(diff_arg_neg_must_be_deleted))) + arg_diff_cluster=arg_diff[diff_arg_neg_must_be_deleted>1] - textline_con,hierarchy=return_contours_of_image(img_patch) - textline_con_fil=filter_contours_area_of_image(img_patch, - textline_con, hierarchy, - max_area=1, min_area=0.0008) + peaks_new=peaks_e[:] + peaks_neg_new=peaks_neg_e[:] - if len(np.diff(peaks_new_tot))>0: + clusters_to_be_deleted=[] + if len(arg_diff_cluster)>0: + clusters_to_be_deleted.append(arg_neg_must_be_deleted[0:arg_diff_cluster[0]+1]) + for i in range(len(arg_diff_cluster)-1): + clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[i]+1: + arg_diff_cluster[i+1]+1]) + clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[len(arg_diff_cluster)-1]+1:]) + if len(clusters_to_be_deleted)>0: + peaks_new_extra=[] + for m in range(len(clusters_to_be_deleted)): + min_cluster=np.min(peaks_e[clusters_to_be_deleted[m]]) + max_cluster=np.max(peaks_e[clusters_to_be_deleted[m]]) + peaks_new_extra.append( int( (min_cluster+max_cluster)/2.0) ) + for m1 in range(len(clusters_to_be_deleted[m])): + peaks_new=peaks_new[peaks_new!=peaks_e[clusters_to_be_deleted[m][m1]-1]] + peaks_new=peaks_new[peaks_new!=peaks_e[clusters_to_be_deleted[m][m1]]] + peaks_neg_new=peaks_neg_new[peaks_neg_new!=peaks_neg_e[clusters_to_be_deleted[m][m1]]] + peaks_new_tot=[] + for i1 in peaks_new: + peaks_new_tot.append(i1) + for i1 in peaks_new_extra: + peaks_new_tot.append(i1) + peaks_new_tot=np.sort(peaks_new_tot) + else: + peaks_new_tot=peaks_e[:] + + textline_con,hierarchy=return_contours_of_image(img_patch) + textline_con_fil=filter_contours_area_of_image(img_patch, + textline_con, hierarchy, + max_area=1, min_area=0.0008) y_diff_mean=np.mean(np.diff(peaks_new_tot))#self.find_contours_mean_y_diff(textline_con_fil) sigma_gaus=int( y_diff_mean * (7./40.0) ) - else: + #print(sigma_gaus,'sigma_gaus') + except: sigma_gaus=12 - - except: - sigma_gaus=12 - if sigma_gaus<3: - sigma_gaus=3 + if sigma_gaus<3: + sigma_gaus=3 + #print(sigma_gaus,'sigma') y_padded_smoothed= gaussian_filter1d(y_padded, sigma_gaus) y_padded_up_to_down=-y_padded+np.max(y_padded) y_padded_up_to_down_padded=np.zeros(len(y_padded_up_to_down)+40) y_padded_up_to_down_padded[20:len(y_padded_up_to_down)+20]=y_padded_up_to_down y_padded_up_to_down_padded= gaussian_filter1d(y_padded_up_to_down_padded, sigma_gaus) + peaks, _ = find_peaks(y_padded_smoothed, height=0) peaks_neg, _ = find_peaks(y_padded_up_to_down_padded, height=0) try: neg_peaks_max=np.max(y_padded_smoothed[peaks]) - arg_neg_must_be_deleted = np.arange(len(peaks_neg))[ - y_padded_up_to_down_padded[peaks_neg]/float(neg_peaks_max)<0.42] + arg_neg_must_be_deleted= np.arange(len(peaks_neg))[y_padded_up_to_down_padded[peaks_neg]/float(neg_peaks_max)<0.42] diff_arg_neg_must_be_deleted=np.diff(arg_neg_must_be_deleted) arg_diff=np.array(range(len(diff_arg_neg_must_be_deleted))) arg_diff_cluster=arg_diff[diff_arg_neg_must_be_deleted>1] - except: arg_neg_must_be_deleted=[] arg_diff_cluster=[] @@ -253,6 +246,7 @@ def separate_lines(img_patch, contour_text_interest, thetha, x_help, y_help): peaks_new=peaks[:] peaks_neg_new=peaks_neg[:] clusters_to_be_deleted=[] + if len(arg_diff_cluster)>=2 and len(arg_diff_cluster)>0: clusters_to_be_deleted.append(arg_neg_must_be_deleted[0:arg_diff_cluster[0]+1]) for i in range(len(arg_diff_cluster)-1): @@ -281,6 +275,21 @@ def separate_lines(img_patch, contour_text_interest, thetha, x_help, y_help): peaks_new_tot.append(i1) peaks_new_tot=np.sort(peaks_new_tot) + ##plt.plot(y_padded_up_to_down_padded) + ##plt.plot(peaks_neg,y_padded_up_to_down_padded[peaks_neg],'*') + ##plt.show() + + ##plt.plot(y_padded_up_to_down_padded) + ##plt.plot(peaks_neg_new,y_padded_up_to_down_padded[peaks_neg_new],'*') + ##plt.show() + + ##plt.plot(y_padded_smoothed) + ##plt.plot(peaks,y_padded_smoothed[peaks],'*') + ##plt.show() + + ##plt.plot(y_padded_smoothed) + ##plt.plot(peaks_new_tot,y_padded_smoothed[peaks_new_tot],'*') + ##plt.show() peaks=peaks_new_tot[:] peaks_neg=peaks_neg_new[:] else: @@ -289,13 +298,11 @@ def separate_lines(img_patch, contour_text_interest, thetha, x_help, y_help): peaks_neg=peaks_neg_new[:] except: pass - if len(y_padded_smoothed[peaks]) > 1: - mean_value_of_peaks=np.mean(y_padded_smoothed[peaks]) - std_value_of_peaks=np.std(y_padded_smoothed[peaks]) - else: - mean_value_of_peaks = np.nan - std_value_of_peaks = np.nan + + mean_value_of_peaks=np.mean(y_padded_smoothed[peaks]) + std_value_of_peaks=np.std(y_padded_smoothed[peaks]) peaks_values=y_padded_smoothed[peaks] + peaks_neg = peaks_neg - 20 - 20 peaks = peaks - 20 for jj in range(len(peaks_neg)): @@ -317,47 +324,33 @@ def separate_lines(img_patch, contour_text_interest, thetha, x_help, y_help): if peaks_values[jj]>mean_value_of_peaks-std_value_of_peaks/2.: point_up = peaks[jj] + first_nonzero - int(1.3 * dis_to_next_up) ##+int(dis_to_next_up*1./4.0) - point_down =y_max_cont-1 - ##peaks[jj] + first_nonzero + int(1.3 * dis_to_next_down) - #point_up - # np.max(y_cont)#peaks[jj] + first_nonzero + int(1.4 * dis_to_next_down) - ###-int(dis_to_next_down*1./4.0) + point_down =y_max_cont-1##peaks[jj] + first_nonzero + int(1.3 * dis_to_next_down) #point_up# np.max(y_cont)#peaks[jj] + first_nonzero + int(1.4 * dis_to_next_down) ###-int(dis_to_next_down*1./4.0) else: point_up = peaks[jj] + first_nonzero - int(1.4 * dis_to_next_up) ##+int(dis_to_next_up*1./4.0) - point_down =y_max_cont-1 - ##peaks[jj] + first_nonzero + int(1.6 * dis_to_next_down) - #point_up - # np.max(y_cont)#peaks[jj] + first_nonzero + int(1.4 * dis_to_next_down) - ###-int(dis_to_next_down*1./4.0) + point_down =y_max_cont-1##peaks[jj] + first_nonzero + int(1.6 * dis_to_next_down) #point_up# np.max(y_cont)#peaks[jj] + first_nonzero + int(1.4 * dis_to_next_down) ###-int(dis_to_next_down*1./4.0) point_down_narrow = peaks[jj] + first_nonzero + int( - 1.4 * dis_to_next_down) - ###-int(dis_to_next_down*1./2) + 1.4 * dis_to_next_down) ###-int(dis_to_next_down*1./2) else: dis_to_next_up = abs(peaks[jj] - peaks_neg[jj]) dis_to_next_down = abs(peaks[jj] - peaks_neg[jj + 1]) if peaks_values[jj]>mean_value_of_peaks-std_value_of_peaks/2.: - point_up = peaks[jj] + first_nonzero - int(1.1 * dis_to_next_up) - ##+int(dis_to_next_up*1./4.0) - point_down = peaks[jj] + first_nonzero + int(1.1 * dis_to_next_down) - ###-int(dis_to_next_down*1./4.0) + point_up = peaks[jj] + first_nonzero - int(1.1 * dis_to_next_up) ##+int(dis_to_next_up*1./4.0) + point_down = peaks[jj] + first_nonzero + int(1.1 * dis_to_next_down) ###-int(dis_to_next_down*1./4.0) else: - point_up = peaks[jj] + first_nonzero - int(1.23 * dis_to_next_up) - ##+int(dis_to_next_up*1./4.0) - point_down = peaks[jj] + first_nonzero + int(1.33 * dis_to_next_down) - ###-int(dis_to_next_down*1./4.0) + point_up = peaks[jj] + first_nonzero - int(1.23 * dis_to_next_up) ##+int(dis_to_next_up*1./4.0) + point_down = peaks[jj] + first_nonzero + int(1.33 * dis_to_next_down) ###-int(dis_to_next_down*1./4.0) point_down_narrow = peaks[jj] + first_nonzero + int( 1.1 * dis_to_next_down) ###-int(dis_to_next_down*1./2) + if point_down_narrow >= img_patch.shape[0]: point_down_narrow = img_patch.shape[0] - 2 - distances = [cv2.pointPolygonTest(contour_text_interest_copy, - tuple(int(x) for x in np.array([xv[mj], peaks[jj] + first_nonzero])), - True) + distances = [cv2.pointPolygonTest(contour_text_interest_copy, tuple(int(x) for x in np.array([xv[mj], peaks[jj] + first_nonzero])), True) for mj in range(len(xv))] distances = np.array(distances) @@ -484,8 +477,7 @@ def separate_lines(img_patch, contour_text_interest, thetha, x_help, y_help): point_up =peaks[jj] + first_nonzero - int(1. / 1.8 * dis_to_next) distances = [cv2.pointPolygonTest(contour_text_interest_copy, - tuple(int(x) for x in np.array([xv[mj], peaks[jj] + first_nonzero])), - True) + tuple(int(x) for x in np.array([xv[mj], peaks[jj] + first_nonzero])), True) for mj in range(len(xv))] distances = np.array(distances) @@ -560,8 +552,7 @@ def separate_lines(img_patch, contour_text_interest, thetha, x_help, y_help): point_down = peaks[jj] + first_nonzero + int(1. / 1.9 * dis_to_next_down) distances = [cv2.pointPolygonTest(contour_text_interest_copy, - tuple(int(x) for x in np.array([xv[mj], peaks[jj] + first_nonzero])), - True) + tuple(int(x) for x in np.array([xv[mj], peaks[jj] + first_nonzero])), True) for mj in range(len(xv))] distances = np.array(distances) @@ -610,6 +601,7 @@ def separate_lines(img_patch, contour_text_interest, thetha, x_help, y_help): [int(x_max), int(point_up)], [int(x_max), int(point_down)], [int(x_min), int(point_down)]])) + return peaks, textline_boxes_rot def separate_lines_vertical(img_patch, contour_text_interest, thetha): @@ -631,8 +623,7 @@ def separate_lines_vertical(img_patch, contour_text_interest, thetha): neg_peaks_max = np.max(y_padded_up_to_down_padded[peaks_neg]) - arg_neg_must_be_deleted = np.arange(len(peaks_neg))[ - y_padded_up_to_down_padded[peaks_neg] / float(neg_peaks_max) < 0.42] + arg_neg_must_be_deleted = np.arange(len(peaks_neg))[y_padded_up_to_down_padded[peaks_neg] / float(neg_peaks_max) < 0.42] diff_arg_neg_must_be_deleted = np.diff(arg_neg_must_be_deleted) arg_diff = np.array(range(len(diff_arg_neg_must_be_deleted))) @@ -642,7 +633,7 @@ def separate_lines_vertical(img_patch, contour_text_interest, thetha): peaks_neg_new = peaks_neg[:] clusters_to_be_deleted = [] - if len(arg_neg_must_be_deleted) >= 2 and len(arg_diff_cluster) >= 2: + if len(arg_diff_cluster) >= 2 and len(arg_diff_cluster) > 0: clusters_to_be_deleted.append(arg_neg_must_be_deleted[0 : arg_diff_cluster[0] + 1]) for i in range(len(arg_diff_cluster) - 1): clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[i] + 1 : @@ -650,7 +641,7 @@ def separate_lines_vertical(img_patch, contour_text_interest, thetha): clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[len(arg_diff_cluster) - 1] + 1 :]) elif len(arg_neg_must_be_deleted) >= 2 and len(arg_diff_cluster) == 0: clusters_to_be_deleted.append(arg_neg_must_be_deleted[:]) - else: + if len(arg_neg_must_be_deleted) == 1: clusters_to_be_deleted.append(arg_neg_must_be_deleted) if len(clusters_to_be_deleted) > 0: peaks_new_extra = [] @@ -676,14 +667,9 @@ def separate_lines_vertical(img_patch, contour_text_interest, thetha): peaks_new_tot = peaks[:] peaks = peaks_new_tot[:] peaks_neg = peaks_neg_new[:] - - if len(y_padded_smoothed[peaks])>1: - mean_value_of_peaks = np.mean(y_padded_smoothed[peaks]) - std_value_of_peaks = np.std(y_padded_smoothed[peaks]) - else: - mean_value_of_peaks = np.nan - std_value_of_peaks = np.nan - + + mean_value_of_peaks = np.mean(y_padded_smoothed[peaks]) + std_value_of_peaks = np.std(y_padded_smoothed[peaks]) peaks_values = y_padded_smoothed[peaks] peaks_neg = peaks_neg - 20 - 20 @@ -701,6 +687,7 @@ def separate_lines_vertical(img_patch, contour_text_interest, thetha): textline_boxes_rot = [] if len(peaks_neg) == len(peaks) + 1 and len(peaks) >= 3: + # print('11') for jj in range(len(peaks)): if jj == (len(peaks) - 1): @@ -708,50 +695,30 @@ def separate_lines_vertical(img_patch, contour_text_interest, thetha): dis_to_next_down = abs(peaks[jj] - peaks_neg[jj + 1]) if peaks_values[jj] > mean_value_of_peaks - std_value_of_peaks / 2.0: - point_up = peaks[jj] + first_nonzero - int(1.3 * dis_to_next_up) - ##+int(dis_to_next_up*1./4.0) - point_down = x_max_cont - 1 - ##peaks[jj] + first_nonzero + int(1.3 * dis_to_next_down) - #point_up - # np.max(y_cont)#peaks[jj] + first_nonzero + int(1.4 * dis_to_next_down) - ###-int(dis_to_next_down*1./4.0) + point_up = peaks[jj] + first_nonzero - int(1.3 * dis_to_next_up) ##+int(dis_to_next_up*1./4.0) + point_down = x_max_cont - 1 ##peaks[jj] + first_nonzero + int(1.3 * dis_to_next_down) #point_up# np.max(y_cont)#peaks[jj] + first_nonzero + int(1.4 * dis_to_next_down) ###-int(dis_to_next_down*1./4.0) else: - point_up = peaks[jj] + first_nonzero - int(1.4 * dis_to_next_up) - ##+int(dis_to_next_up*1./4.0) - point_down = x_max_cont - 1 - ##peaks[jj] + first_nonzero + int(1.6 * dis_to_next_down) - #point_up - # np.max(y_cont) - #peaks[jj] + first_nonzero + int(1.4 * dis_to_next_down) - ###-int(dis_to_next_down*1./4.0) + point_up = peaks[jj] + first_nonzero - int(1.4 * dis_to_next_up) ##+int(dis_to_next_up*1./4.0) + point_down = x_max_cont - 1 ##peaks[jj] + first_nonzero + int(1.6 * dis_to_next_down) #point_up# np.max(y_cont)#peaks[jj] + first_nonzero + int(1.4 * dis_to_next_down) ###-int(dis_to_next_down*1./4.0) - point_down_narrow = peaks[jj] + first_nonzero + int(1.4 * dis_to_next_down) - ###-int(dis_to_next_down*1./2) + point_down_narrow = peaks[jj] + first_nonzero + int(1.4 * dis_to_next_down) ###-int(dis_to_next_down*1./2) else: dis_to_next_up = abs(peaks[jj] - peaks_neg[jj]) dis_to_next_down = abs(peaks[jj] - peaks_neg[jj + 1]) if peaks_values[jj] > mean_value_of_peaks - std_value_of_peaks / 2.0: - point_up = peaks[jj] + first_nonzero - int(1.1 * dis_to_next_up) - ##+int(dis_to_next_up*1./4.0) - point_down = peaks[jj] + first_nonzero + int(1.1 * dis_to_next_down) - ###-int(dis_to_next_down*1./4.0) + point_up = peaks[jj] + first_nonzero - int(1.1 * dis_to_next_up) ##+int(dis_to_next_up*1./4.0) + point_down = peaks[jj] + first_nonzero + int(1.1 * dis_to_next_down) ###-int(dis_to_next_down*1./4.0) else: - point_up = peaks[jj] + first_nonzero - int(1.23 * dis_to_next_up) - ##+int(dis_to_next_up*1./4.0) - point_down = peaks[jj] + first_nonzero + int(1.33 * dis_to_next_down) - ###-int(dis_to_next_down*1./4.0) + point_up = peaks[jj] + first_nonzero - int(1.23 * dis_to_next_up) ##+int(dis_to_next_up*1./4.0) + point_down = peaks[jj] + first_nonzero + int(1.33 * dis_to_next_down) ###-int(dis_to_next_down*1./4.0) - point_down_narrow = peaks[jj] + first_nonzero + int(1.1 * dis_to_next_down) - ###-int(dis_to_next_down*1./2) + point_down_narrow = peaks[jj] + first_nonzero + int(1.1 * dis_to_next_down) ###-int(dis_to_next_down*1./2) if point_down_narrow >= img_patch.shape[0]: point_down_narrow = img_patch.shape[0] - 2 - distances = [cv2.pointPolygonTest(contour_text_interest_copy, - tuple(int(x) for x in np.array([xv[mj], peaks[jj] + first_nonzero])), - True) - for mj in range(len(xv))] + distances = [cv2.pointPolygonTest(contour_text_interest_copy, tuple(int(x) for x in np.array([xv[mj], peaks[jj] + first_nonzero])), True) for mj in range(len(xv))] distances = np.array(distances) xvinside = xv[distances >= 0] @@ -840,8 +807,7 @@ def separate_lines_vertical(img_patch, contour_text_interest, thetha): point_up = peaks[jj] + first_nonzero - int(1.0 / 1.8 * dis_to_next) distances = [cv2.pointPolygonTest(contour_text_interest_copy, - tuple(int(x) for x in np.array([xv[mj], peaks[jj] + first_nonzero])), - True) + tuple(int(x) for x in np.array([xv[mj], peaks[jj] + first_nonzero])), True) for mj in range(len(xv))] distances = np.array(distances) @@ -906,8 +872,7 @@ def separate_lines_vertical(img_patch, contour_text_interest, thetha): point_down = peaks[jj] + first_nonzero + int(1.0 / 1.9 * dis_to_next_down) distances = [cv2.pointPolygonTest(contour_text_interest_copy, - tuple(int(x) for x in np.array([xv[mj], peaks[jj] + first_nonzero])), - True) + tuple(int(x) for x in np.array([xv[mj], peaks[jj] + first_nonzero])), True) for mj in range(len(xv))] distances = np.array(distances) @@ -991,8 +956,7 @@ def separate_lines_new_inside_tiles2(img_patch, thetha): peaks_neg_e, _ = find_peaks(y_padded_up_to_down_padded_e, height=0) neg_peaks_max = np.max(y_padded_up_to_down_padded_e[peaks_neg_e]) - arg_neg_must_be_deleted = np.arange(len(peaks_neg_e))[ - y_padded_up_to_down_padded_e[peaks_neg_e] / float(neg_peaks_max) < 0.3] + arg_neg_must_be_deleted = np.arange(len(peaks_neg_e))[y_padded_up_to_down_padded_e[peaks_neg_e] / float(neg_peaks_max) < 0.3] diff_arg_neg_must_be_deleted = np.diff(arg_neg_must_be_deleted) arg_diff = np.array(range(len(diff_arg_neg_must_be_deleted))) @@ -1005,11 +969,8 @@ def separate_lines_new_inside_tiles2(img_patch, thetha): if len(arg_diff_cluster) > 0: clusters_to_be_deleted.append(arg_neg_must_be_deleted[0 : arg_diff_cluster[0] + 1]) for i in range(len(arg_diff_cluster) - 1): - clusters_to_be_deleted.append( - arg_neg_must_be_deleted[arg_diff_cluster[i] + 1: - arg_diff_cluster[i + 1] + 1]) - clusters_to_be_deleted.append( - arg_neg_must_be_deleted[arg_diff_cluster[len(arg_diff_cluster) - 1] + 1 :]) + clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[i] + 1 : arg_diff_cluster[i + 1] + 1]) + clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[len(arg_diff_cluster) - 1] + 1 :]) if len(clusters_to_be_deleted) > 0: peaks_new_extra = [] for m in range(len(clusters_to_be_deleted)): @@ -1033,16 +994,15 @@ def separate_lines_new_inside_tiles2(img_patch, thetha): textline_con_fil = filter_contours_area_of_image(img_patch, textline_con, hierarchy, max_area=1, min_area=0.0008) - if len(np.diff(peaks_new_tot)): - y_diff_mean = np.mean(np.diff(peaks_new_tot)) # self.find_contours_mean_y_diff(textline_con_fil) - sigma_gaus = int(y_diff_mean * (7.0 / 40.0)) - else: - sigma_gaus = 12 + y_diff_mean = np.mean(np.diff(peaks_new_tot)) # self.find_contours_mean_y_diff(textline_con_fil) + sigma_gaus = int(y_diff_mean * (7.0 / 40.0)) + # print(sigma_gaus,'sigma_gaus') except: sigma_gaus = 12 if sigma_gaus < 3: sigma_gaus = 3 + # print(sigma_gaus,'sigma') y_padded_smoothed = gaussian_filter1d(y_padded, sigma_gaus) y_padded_up_to_down = -y_padded + np.max(y_padded) @@ -1059,15 +1019,14 @@ def separate_lines_new_inside_tiles2(img_patch, thetha): try: neg_peaks_max = np.max(y_padded_smoothed[peaks]) - arg_neg_must_be_deleted = np.arange(len(peaks_neg))[ - y_padded_up_to_down_padded[peaks_neg] / float(neg_peaks_max) < 0.24] + arg_neg_must_be_deleted = np.arange(len(peaks_neg))[y_padded_up_to_down_padded[peaks_neg] / float(neg_peaks_max) < 0.24] diff_arg_neg_must_be_deleted = np.diff(arg_neg_must_be_deleted) arg_diff = np.array(range(len(diff_arg_neg_must_be_deleted))) arg_diff_cluster = arg_diff[diff_arg_neg_must_be_deleted > 1] clusters_to_be_deleted = [] - if len(arg_neg_must_be_deleted) >= 2 and len(arg_diff_cluster) >= 2: + if len(arg_diff_cluster) >= 2 and len(arg_diff_cluster) > 0: clusters_to_be_deleted.append(arg_neg_must_be_deleted[0 : arg_diff_cluster[0] + 1]) for i in range(len(arg_diff_cluster) - 1): clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[i] + 1 : @@ -1075,7 +1034,7 @@ def separate_lines_new_inside_tiles2(img_patch, thetha): clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[len(arg_diff_cluster) - 1] + 1 :]) elif len(arg_neg_must_be_deleted) >= 2 and len(arg_diff_cluster) == 0: clusters_to_be_deleted.append(arg_neg_must_be_deleted[:]) - else: + if len(arg_neg_must_be_deleted) == 1: clusters_to_be_deleted.append(arg_neg_must_be_deleted) if len(clusters_to_be_deleted) > 0: peaks_new_extra = [] @@ -1118,14 +1077,9 @@ def separate_lines_new_inside_tiles2(img_patch, thetha): peaks_new_tot = peaks[:] peaks = peaks_new_tot[:] peaks_neg = peaks_neg_new[:] - - if len(y_padded_smoothed[peaks]) > 1: - mean_value_of_peaks = np.mean(y_padded_smoothed[peaks]) - std_value_of_peaks = np.std(y_padded_smoothed[peaks]) - else: - mean_value_of_peaks = np.nan - std_value_of_peaks = np.nan - + + mean_value_of_peaks = np.mean(y_padded_smoothed[peaks]) + std_value_of_peaks = np.std(y_padded_smoothed[peaks]) peaks_values = y_padded_smoothed[peaks] ###peaks_neg = peaks_neg - 20 - 20 @@ -1135,8 +1089,10 @@ def separate_lines_new_inside_tiles2(img_patch, thetha): if len(peaks_neg_true) > 0: peaks_neg_true = np.array(peaks_neg_true) + peaks_neg_true = peaks_neg_true - 20 - 20 + # print(peaks_neg_true) for i in range(len(peaks_neg_true)): img_patch[peaks_neg_true[i] - 6 : peaks_neg_true[i] + 6, :] = 0 else: @@ -1221,11 +1177,13 @@ def separate_lines_new_inside_tiles(img_path, thetha): if diff_peaks[i] <= cut_off: forest.append(peaks_neg[i + 1]) if diff_peaks[i] > cut_off: + # print(forest[np.argmin(z[forest]) ] ) if not np.isnan(forest[np.argmin(z[forest])]): peaks_neg_true.append(forest[np.argmin(z[forest])]) forest = [] forest.append(peaks_neg[i + 1]) if i == (len(peaks_neg) - 1): + # print(print(forest[np.argmin(z[forest]) ] )) if not np.isnan(forest[np.argmin(z[forest])]): peaks_neg_true.append(forest[np.argmin(z[forest])]) @@ -1242,14 +1200,17 @@ def separate_lines_new_inside_tiles(img_path, thetha): if diff_peaks_pos[i] <= cut_off: forest.append(peaks[i + 1]) if diff_peaks_pos[i] > cut_off: + # print(forest[np.argmin(z[forest]) ] ) if not np.isnan(forest[np.argmax(z[forest])]): peaks_pos_true.append(forest[np.argmax(z[forest])]) forest = [] forest.append(peaks[i + 1]) if i == (len(peaks) - 1): + # print(print(forest[np.argmin(z[forest]) ] )) if not np.isnan(forest[np.argmax(z[forest])]): peaks_pos_true.append(forest[np.argmax(z[forest])]) + # print(len(peaks_neg_true) ,len(peaks_pos_true) ,'lensss') if len(peaks_neg_true) > 0: peaks_neg_true = np.array(peaks_neg_true) @@ -1275,6 +1236,7 @@ def separate_lines_new_inside_tiles(img_path, thetha): """ peaks_neg_true = peaks_neg_true - 20 - 20 + # print(peaks_neg_true) for i in range(len(peaks_neg_true)): img_path[peaks_neg_true[i] - 6 : peaks_neg_true[i] + 6, :] = 0 @@ -1297,123 +1259,166 @@ def separate_lines_new_inside_tiles(img_path, thetha): def separate_lines_vertical_cont(img_patch, contour_text_interest, thetha, box_ind, add_boxes_coor_into_textlines): kernel = np.ones((5, 5), np.uint8) - label = 255 + pixel = 255 min_area = 0 max_area = 1 - if img_patch.ndim == 3: - cnts_images = (img_patch[:, :, 0] == label) * 1 + if len(img_patch.shape) == 3: + cnts_images = (img_patch[:, :, 0] == pixel) * 1 else: - cnts_images = (img_patch[:, :] == label) * 1 - _, thresh = cv2.threshold(cnts_images.astype(np.uint8), 0, 255, 0) - contours_imgs, hierarchy = cv2.findContours(thresh.astype(np.uint8), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) + cnts_images = (img_patch[:, :] == pixel) * 1 + cnts_images = cnts_images.astype(np.uint8) + cnts_images = np.repeat(cnts_images[:, :, np.newaxis], 3, axis=2) + imgray = cv2.cvtColor(cnts_images, cv2.COLOR_BGR2GRAY) + ret, thresh = cv2.threshold(imgray, 0, 255, 0) + contours_imgs, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) contours_imgs = return_parent_contours(contours_imgs, hierarchy) contours_imgs = filter_contours_area_of_image_tables(thresh, contours_imgs, hierarchy, max_area=max_area, min_area=min_area) cont_final = [] + ###print(add_boxes_coor_into_textlines,'ikki') for i in range(len(contours_imgs)): - img_contour = np.zeros(cnts_images.shape[:2], dtype=np.uint8) - img_contour = cv2.fillPoly(img_contour, pts=[contours_imgs[i]], color=255) + img_contour = np.zeros((cnts_images.shape[0], cnts_images.shape[1], 3)) + img_contour = cv2.fillPoly(img_contour, pts=[contours_imgs[i]], color=(255, 255, 255)) + img_contour = img_contour.astype(np.uint8) img_contour = cv2.dilate(img_contour, kernel, iterations=4) - _, threshrot = cv2.threshold(img_contour, 0, 255, 0) - contours_text_rot, _ = cv2.findContours(threshrot.astype(np.uint8), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) + imgrayrot = cv2.cvtColor(img_contour, cv2.COLOR_BGR2GRAY) + _, threshrot = cv2.threshold(imgrayrot, 0, 255, 0) + contours_text_rot, _ = cv2.findContours(threshrot.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) ##contour_text_copy[:, 0, 0] = contour_text_copy[:, 0, 0] - box_ind[ ##0] ##contour_text_copy[:, 0, 1] = contour_text_copy[:, 0, 1] - box_ind[1] ##if add_boxes_coor_into_textlines: + ##print(np.shape(contours_text_rot[0]),'sjppo') ##contours_text_rot[0][:, 0, 0]=contours_text_rot[0][:, 0, 0] + box_ind[0] ##contours_text_rot[0][:, 0, 1]=contours_text_rot[0][:, 0, 1] + box_ind[1] cont_final.append(contours_text_rot[0]) + ##print(cont_final,'nadizzzz') return None, cont_final -def textline_contours_postprocessing(textline_mask, slope, - contour_text_interest, box_ind, - add_boxes_coor_into_textlines=False): - textline_mask = textline_mask * 255 +def textline_contours_postprocessing(textline_mask, slope, contour_text_interest, box_ind, add_boxes_coor_into_textlines=False): + textline_mask = np.repeat(textline_mask[:, :, np.newaxis], 3, axis=2) * 255 + textline_mask = textline_mask.astype(np.uint8) kernel = np.ones((5, 5), np.uint8) textline_mask = cv2.morphologyEx(textline_mask, cv2.MORPH_OPEN, kernel) textline_mask = cv2.morphologyEx(textline_mask, cv2.MORPH_CLOSE, kernel) textline_mask = cv2.erode(textline_mask, kernel, iterations=2) # textline_mask = cv2.erode(textline_mask, kernel, iterations=1) - x_help = 30 - y_help = 2 + # print(textline_mask.shape[0]/float(textline_mask.shape[1]),'miz') + try: + # if np.abs(slope)>.5 and textline_mask.shape[0]/float(textline_mask.shape[1])>3: + # plt.imshow(textline_mask) + # plt.show() - textline_mask_help = np.zeros((textline_mask.shape[0] + int(2 * y_help), - textline_mask.shape[1] + int(2 * x_help))) - textline_mask_help[y_help : y_help + textline_mask.shape[0], - x_help : x_help + textline_mask.shape[1]] = np.copy(textline_mask[:, :]) + # if abs(slope)>1: + # x_help=30 + # y_help=2 + # else: + # x_help=2 + # y_help=2 - dst = rotate_image(textline_mask_help, slope) - dst[dst != 0] = 1 + x_help = 30 + y_help = 2 - # if np.abs(slope)>.5 and textline_mask.shape[0]/float(textline_mask.shape[1])>3: - # plt.imshow(dst) - # plt.show() + textline_mask_help = np.zeros((textline_mask.shape[0] + int(2 * y_help), + textline_mask.shape[1] + int(2 * x_help), 3)) + textline_mask_help[y_help : y_help + textline_mask.shape[0], + x_help : x_help + textline_mask.shape[1], :] = np.copy(textline_mask[:, :, :]) - contour_text_copy = contour_text_interest.copy() - contour_text_copy[:, 0, 0] = contour_text_copy[:, 0, 0] - box_ind[0] - contour_text_copy[:, 0, 1] = contour_text_copy[:, 0, 1] - box_ind[1] + dst = rotate_image(textline_mask_help, slope) + dst = dst[:, :, 0] + dst[dst != 0] = 1 - img_contour = np.zeros((box_ind[3], box_ind[2])) - img_contour = cv2.fillPoly(img_contour, pts=[contour_text_copy], color=255) + # if np.abs(slope)>.5 and textline_mask.shape[0]/float(textline_mask.shape[1])>3: + # plt.imshow(dst) + # plt.show() - img_contour_help = np.zeros((img_contour.shape[0] + int(2 * y_help), - img_contour.shape[1] + int(2 * x_help))) - img_contour_help[y_help : y_help + img_contour.shape[0], - x_help : x_help + img_contour.shape[1]] = np.copy(img_contour[:, :]) + contour_text_copy = contour_text_interest.copy() + contour_text_copy[:, 0, 0] = contour_text_copy[:, 0, 0] - box_ind[0] + contour_text_copy[:, 0, 1] = contour_text_copy[:, 0, 1] - box_ind[1] - img_contour_rot = rotate_image(img_contour_help, slope) + img_contour = np.zeros((box_ind[3], box_ind[2], 3)) + img_contour = cv2.fillPoly(img_contour, pts=[contour_text_copy], color=(255, 255, 255)) - _, threshrot = cv2.threshold(img_contour_rot, 0, 255, 0) - contours_text_rot, _ = cv2.findContours(threshrot.astype(np.uint8), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) + # if np.abs(slope)>.5 and textline_mask.shape[0]/float(textline_mask.shape[1])>3: + # plt.imshow(img_contour) + # plt.show() - len_con_text_rot = [len(contours_text_rot[ib]) for ib in range(len(contours_text_rot))] - ind_big_con = np.argmax(len_con_text_rot) + img_contour_help = np.zeros((img_contour.shape[0] + int(2 * y_help), + img_contour.shape[1] + int(2 * x_help), 3)) + img_contour_help[y_help : y_help + img_contour.shape[0], + x_help : x_help + img_contour.shape[1], :] = np.copy(img_contour[:, :, :]) - if abs(slope) > 45: - _, contours_rotated_clean = separate_lines_vertical_cont( - textline_mask, contours_text_rot[ind_big_con], box_ind, slope, - add_boxes_coor_into_textlines=add_boxes_coor_into_textlines) - else: - _, contours_rotated_clean = separate_lines( - dst, contours_text_rot[ind_big_con], slope, x_help, y_help) + img_contour_rot = rotate_image(img_contour_help, slope) + # plt.imshow(img_contour_rot_help) + # plt.show() + + # plt.imshow(dst_help) + # plt.show() + + # if np.abs(slope)>.5 and textline_mask.shape[0]/float(textline_mask.shape[1])>3: + # plt.imshow(img_contour_rot_help) + # plt.show() + + # plt.imshow(dst_help) + # plt.show() + + img_contour_rot = img_contour_rot.astype(np.uint8) + # dst_help = dst_help.astype(np.uint8) + imgrayrot = cv2.cvtColor(img_contour_rot, cv2.COLOR_BGR2GRAY) + _, threshrot = cv2.threshold(imgrayrot, 0, 255, 0) + contours_text_rot, _ = cv2.findContours(threshrot.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) + + len_con_text_rot = [len(contours_text_rot[ib]) for ib in range(len(contours_text_rot))] + ind_big_con = np.argmax(len_con_text_rot) + + # print('juzaa') + if abs(slope) > 45: + # print(add_boxes_coor_into_textlines,'avval') + _, contours_rotated_clean = separate_lines_vertical_cont( + textline_mask, contours_text_rot[ind_big_con], box_ind, slope, + add_boxes_coor_into_textlines=add_boxes_coor_into_textlines) + else: + _, contours_rotated_clean = separate_lines( + dst, contours_text_rot[ind_big_con], slope, x_help, y_help) + except: + contours_rotated_clean = [] return contours_rotated_clean -def separate_lines_new2(img_crop, thetha, num_col, slope_region, logger=None, plotter=None): +def separate_lines_new2(img_path, thetha, num_col, slope_region, logger=None, plotter=None): if logger is None: logger = getLogger(__package__) - if not np.prod(img_crop.shape): - return img_crop if num_col == 1: - num_patches = int(img_crop.shape[1] / 200.0) + num_patches = int(img_path.shape[1] / 200.0) else: - num_patches = int(img_crop.shape[1] / 140.0) - # num_patches=int(img_crop.shape[1]/200.) + num_patches = int(img_path.shape[1] / 140.0) + # num_patches=int(img_path.shape[1]/200.) if num_patches == 0: num_patches = 1 - img_patch_interest = img_crop[:, :] # [peaks_neg_true[14]-dis_up:peaks_neg_true[15]+dis_down ,:] + img_patch_ineterst = img_path[:, :] # [peaks_neg_true[14]-dis_up:peaks_neg_true[15]+dis_down ,:] - # plt.imshow(img_patch_interest) + # plt.imshow(img_patch_ineterst) # plt.show() - length_x = int(img_crop.shape[1] / float(num_patches)) + length_x = int(img_path.shape[1] / float(num_patches)) # margin = int(0.04 * length_x) just recently this was changed because it break lines into 2 margin = int(0.04 * length_x) + # print(margin,'margin') # if margin<=4: # margin = int(0.08 * length_x) # margin=0 width_mid = length_x - 2 * margin - nxf = img_crop.shape[1] / float(width_mid) + nxf = img_path.shape[1] / float(width_mid) if nxf > int(nxf): nxf = int(nxf) + 1 @@ -1429,12 +1434,12 @@ def separate_lines_new2(img_crop, thetha, num_col, slope_region, logger=None, pl index_x_d = i * width_mid index_x_u = index_x_d + length_x - if index_x_u > img_crop.shape[1]: - index_x_u = img_crop.shape[1] - index_x_d = img_crop.shape[1] - length_x + if index_x_u > img_path.shape[1]: + index_x_u = img_path.shape[1] + index_x_d = img_path.shape[1] - length_x # img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :] - img_xline = img_patch_interest[:, index_x_d:index_x_u] + img_xline = img_patch_ineterst[:, index_x_d:index_x_u] try: assert img_xline.any() @@ -1447,12 +1452,14 @@ def separate_lines_new2(img_crop, thetha, num_col, slope_region, logger=None, pl # if abs(slope_region)>70 and abs(slope_xline)<25: # slope_xline=[slope_region][0] slopes_tile_wise.append(slope_xline) + # print(slope_xline,'xlineeee') img_line_rotated = rotate_image(img_xline, slope_xline) img_line_rotated[:, :][img_line_rotated[:, :] != 0] = 1 - - img_patch_interest = img_crop[:, :] # [peaks_neg_true[14]-dis_up:peaks_neg_true[14]+dis_down ,:] - img_patch_interest_revised = np.zeros(img_patch_interest.shape) + # print(slopes_tile_wise,'slopes_tile_wise') + img_patch_ineterst = img_path[:, :] # [peaks_neg_true[14]-dis_up:peaks_neg_true[14]+dis_down ,:] + + img_patch_ineterst_revised = np.zeros(img_patch_ineterst.shape) for i in range(nxf): if i == 0: @@ -1462,11 +1469,11 @@ def separate_lines_new2(img_crop, thetha, num_col, slope_region, logger=None, pl index_x_d = i * width_mid index_x_u = index_x_d + length_x - if index_x_u > img_crop.shape[1]: - index_x_u = img_crop.shape[1] - index_x_d = img_crop.shape[1] - length_x + if index_x_u > img_path.shape[1]: + index_x_u = img_path.shape[1] + index_x_d = img_path.shape[1] - length_x - img_xline = img_patch_interest[:, index_x_d:index_x_u] + img_xline = img_patch_ineterst[:, index_x_d:index_x_u] img_int = np.zeros((img_xline.shape[0], img_xline.shape[1])) img_int[:, :] = img_xline[:, :] # img_patch_org[:,:,0] @@ -1489,12 +1496,13 @@ def separate_lines_new2(img_crop, thetha, num_col, slope_region, logger=None, pl int(img_int.shape[1] * (1.0)) : int(img_int.shape[1] * (1.0)) + img_int.shape[1]] img_patch_separated_returned_true_size = img_patch_separated_returned_true_size[:, margin : length_x - margin] - img_patch_interest_revised[:, index_x_d + margin : index_x_u - margin] = img_patch_separated_returned_true_size + img_patch_ineterst_revised[:, index_x_d + margin : index_x_u - margin] = img_patch_separated_returned_true_size - return img_patch_interest_revised + # plt.imshow(img_patch_ineterst_revised) + # plt.show() + return img_patch_ineterst_revised -@wrap_ndarray_shared(kw='img') -def do_image_rotation(angle, img=None, sigma_des=1.0, logger=None): +def do_image_rotation(angle, img, sigma_des, logger=None): if logger is None: logger = getLogger(__package__) img_rot = rotate_image(img, angle) @@ -1507,10 +1515,10 @@ def do_image_rotation(angle, img=None, sigma_des=1.0, logger=None): return var def return_deskew_slop(img_patch_org, sigma_des,n_tot_angles=100, - main_page=False, logger=None, plotter=None, map=None): + main_page=False, logger=None, plotter=None, map=map): if main_page and plotter: plotter.save_plot_of_textline_density(img_patch_org) - + img_int=np.zeros((img_patch_org.shape[0],img_patch_org.shape[1])) img_int[:,:]=img_patch_org[:,:]#img_patch_org[:,:,0] @@ -1521,77 +1529,133 @@ def return_deskew_slop(img_patch_org, sigma_des,n_tot_angles=100, onset_y=int((img_resized.shape[0]-img_int.shape[0])/2.) #img_resized=np.zeros((int( img_int.shape[0]*(1.8) ) , int( img_int.shape[1]*(2.6) ) )) - #img_resized[ int( img_int.shape[0]*(.4)):int( img_int.shape[0]*(.4))+img_int.shape[0], - # int( img_int.shape[1]*(.8)):int( img_int.shape[1]*(.8))+img_int.shape[1] ]=img_int[:,:] + #img_resized[ int( img_int.shape[0]*(.4)):int( img_int.shape[0]*(.4))+img_int.shape[0] , int( img_int.shape[1]*(.8)):int( img_int.shape[1]*(.8))+img_int.shape[1] ]=img_int[:,:] img_resized[ onset_y:onset_y+img_int.shape[0] , onset_x:onset_x+img_int.shape[1] ]=img_int[:,:] + #print(img_resized.shape,'img_resizedshape') + #plt.imshow(img_resized) + #plt.show() if main_page and img_patch_org.shape[1] > img_patch_org.shape[0]: + #plt.imshow(img_resized) + #plt.show() angles = np.array([-45, 0, 45, 90,]) - angle, _ = get_smallest_skew(img_resized, sigma_des, angles, map=map, logger=logger, plotter=plotter) + angle = get_smallest_skew(img_resized, sigma_des, angles, map=map, logger=logger, plotter=plotter) angles = np.linspace(angle - 22.5, angle + 22.5, n_tot_angles) - angle, _ = get_smallest_skew(img_resized, sigma_des, angles, map=map, logger=logger, plotter=plotter) + angle = get_smallest_skew(img_resized, sigma_des, angles, map=map, logger=logger, plotter=plotter) elif main_page: - #angles = np.linspace(-12, 12, n_tot_angles)#np.array([0 , 45 , 90 , -45]) - angles = np.concatenate((np.linspace(-12, -7, n_tot_angles // 4), - np.linspace(-6, 6, n_tot_angles // 2), - np.linspace(7, 12, n_tot_angles // 4))) - angle, var = get_smallest_skew(img_resized, sigma_des, angles, map=map, logger=logger, plotter=plotter) + #plt.imshow(img_resized) + #plt.show() + angles = np.linspace(-12, 12, n_tot_angles)#np.array([0 , 45 , 90 , -45]) + angle = get_smallest_skew(img_resized, sigma_des, angles, map=map, logger=logger, plotter=plotter) early_slope_edge=11 if abs(angle) > early_slope_edge: if angle < 0: - angles2 = np.linspace(-90, -12, n_tot_angles) + angles = np.linspace(-90, -12, n_tot_angles) else: - angles2 = np.linspace(90, 12, n_tot_angles) - angle2, var2 = get_smallest_skew(img_resized, sigma_des, angles2, map=map, logger=logger, plotter=plotter) - if var2 > var: - angle = angle2 + angles = np.linspace(90, 12, n_tot_angles) + angle = get_smallest_skew(img_resized, sigma_des, angles, map=map, logger=logger, plotter=plotter) else: angles = np.linspace(-25, 25, int(0.5 * n_tot_angles) + 10) - angle, var = get_smallest_skew(img_resized, sigma_des, angles, map=map, logger=logger, plotter=plotter) + angle = get_smallest_skew(img_resized, sigma_des, angles, map=map, logger=logger, plotter=plotter) early_slope_edge=22 if abs(angle) > early_slope_edge: if angle < 0: - angles2 = np.linspace(-90, -25, int(0.5 * n_tot_angles) + 10) + angles = np.linspace(-90, -25, int(0.5 * n_tot_angles) + 10) else: - angles2 = np.linspace(90, 25, int(0.5 * n_tot_angles) + 10) - angle2, var2 = get_smallest_skew(img_resized, sigma_des, angles2, map=map, logger=logger, plotter=plotter) - if var2 > var: - angle = angle2 + angles = np.linspace(90, 25, int(0.5 * n_tot_angles) + 10) + angle = get_smallest_skew(img_resized, sigma_des, angles, map=map, logger=logger, plotter=plotter) + return angle def get_smallest_skew(img, sigma_des, angles, logger=None, plotter=None, map=map): if logger is None: logger = getLogger(__package__) - if map is None: - results = [do_image_rotation.__wrapped__(angle, img=img, sigma_des=sigma_des, logger=logger) - for angle in angles] - else: - with share_ndarray(img) as img_shared: - results = list(map(partial(do_image_rotation, img=img_shared, sigma_des=sigma_des, logger=None), - angles)) + results = list(map(partial(do_image_rotation, img=img, sigma_des=sigma_des, logger=logger), angles)) if plotter: plotter.save_plot_of_rotation_angle(angles, results) try: var_res = np.array(results) assert var_res.any() - idx = np.argmax(var_res) - angle = angles[idx] - var = var_res[idx] + angle = angles[np.argmax(var_res)] except: logger.exception("cannot determine best angle among %s", str(angles)) angle = 0 - var = 0 - return angle, var + return angle + +def do_work_of_slopes_new( + box_text, contour, contour_par, index_r_con, + textline_mask_tot_ea, image_page_rotated, slope_deskew, + logger=None, MAX_SLOPE=999, KERNEL=None, plotter=None +): + if KERNEL is None: + KERNEL = np.ones((5, 5), np.uint8) + if logger is None: + logger = getLogger(__package__) + logger.debug('enter do_work_of_slopes_new') + + x, y, w, h = box_text + _, crop_coor = crop_image_inside_box(box_text, image_page_rotated) + mask_textline = np.zeros(textline_mask_tot_ea.shape) + mask_textline = cv2.fillPoly(mask_textline, pts=[contour], color=(1,1,1)) + all_text_region_raw = textline_mask_tot_ea * mask_textline + all_text_region_raw = all_text_region_raw[y: y + h, x: x + w].astype(np.uint8) + img_int_p = all_text_region_raw[:,:] + img_int_p = cv2.erode(img_int_p, KERNEL, iterations=2) + + if img_int_p.shape[0] /img_int_p.shape[1] < 0.1: + slope = 0 + slope_for_all = slope_deskew + all_text_region_raw = textline_mask_tot_ea[y: y + h, x: x + w] + cnt_clean_rot = textline_contours_postprocessing(all_text_region_raw, slope_for_all, contour_par, box_text, 0) + else: + try: + textline_con, hierarchy = return_contours_of_image(img_int_p) + textline_con_fil = filter_contours_area_of_image(img_int_p, textline_con, + hierarchy, + max_area=1, min_area=0.00008) + y_diff_mean = find_contours_mean_y_diff(textline_con_fil) if len(textline_con_fil) > 1 else np.NaN + if np.isnan(y_diff_mean): + slope_for_all = MAX_SLOPE + else: + sigma_des = max(1, int(y_diff_mean * (4.0 / 40.0))) + img_int_p[img_int_p > 0] = 1 + slope_for_all = return_deskew_slop(img_int_p, sigma_des, logger=logger, plotter=plotter) + if abs(slope_for_all) <= 0.5: + slope_for_all = slope_deskew + except: + logger.exception("cannot determine angle of contours") + slope_for_all = MAX_SLOPE + + if slope_for_all == MAX_SLOPE: + slope_for_all = slope_deskew + slope = slope_for_all + + mask_only_con_region = np.zeros(textline_mask_tot_ea.shape) + mask_only_con_region = cv2.fillPoly(mask_only_con_region, pts=[contour_par], color=(1, 1, 1)) + + # plt.imshow(mask_only_con_region) + # plt.show() + all_text_region_raw = textline_mask_tot_ea[y: y + h, x: x + w].copy() + mask_only_con_region = mask_only_con_region[y: y + h, x: x + w] + + ##plt.imshow(textline_mask_tot_ea) + ##plt.show() + ##plt.imshow(all_text_region_raw) + ##plt.show() + ##plt.imshow(mask_only_con_region) + ##plt.show() + + all_text_region_raw[mask_only_con_region == 0] = 0 + cnt_clean_rot = textline_contours_postprocessing(all_text_region_raw, slope_for_all, contour_par, box_text) + + return cnt_clean_rot, box_text, contour, contour_par, crop_coor, index_r_con, slope -@wrap_ndarray_shared(kw='textline_mask_tot_ea') -@wrap_ndarray_shared(kw='mask_texts_only') def do_work_of_slopes_new_curved( - box_text, contour_par, - textline_mask_tot_ea=None, mask_texts_only=None, - num_col=1, scale_par=1.0, slope_deskew=0.0, + box_text, contour, contour_par, index_r_con, + textline_mask_tot_ea, image_page_rotated, mask_texts_only, num_col, scale_par, slope_deskew, logger=None, MAX_SLOPE=999, KERNEL=None, plotter=None ): if KERNEL is None: @@ -1608,7 +1672,7 @@ def do_work_of_slopes_new_curved( # plt.imshow(img_int_p) # plt.show() - if not np.prod(img_int_p.shape) or img_int_p.shape[0] / img_int_p.shape[1] < 0.1: + if img_int_p.shape[0] / img_int_p.shape[1] < 0.1: slope = 0 slope_for_all = slope_deskew else: @@ -1634,7 +1698,7 @@ def do_work_of_slopes_new_curved( slope_for_all = slope_deskew slope = slope_for_all - crop_coor = box2rect(box_text) + _, crop_coor = crop_image_inside_box(box_text, image_page_rotated) if abs(slope_for_all) < 45: textline_region_in_image = np.zeros(textline_mask_tot_ea.shape) @@ -1644,15 +1708,20 @@ def do_work_of_slopes_new_curved( mask_region_in_patch_region = mask_biggest[y : y + h, x : x + w] textline_biggest_region = mask_biggest * textline_mask_tot_ea + # print(slope_for_all,'slope_for_all') textline_rotated_separated = separate_lines_new2(textline_biggest_region[y: y+h, x: x+w], 0, num_col, slope_for_all, logger=logger, plotter=plotter) - + # new line added + ##print(np.shape(textline_rotated_separated),np.shape(mask_biggest)) textline_rotated_separated[mask_region_in_patch_region[:, :] != 1] = 0 + # till here textline_region_in_image[y : y + h, x : x + w] = textline_rotated_separated + # plt.imshow(textline_region_in_image) + # plt.show() pixel_img = 1 cnt_textlines_in_image = return_contours_of_interested_textline(textline_region_in_image, pixel_img) @@ -1667,25 +1736,21 @@ def do_work_of_slopes_new_curved( mask_biggest2 = cv2.dilate(mask_biggest2, KERNEL, iterations=4) pixel_img = 1 - mask_biggest2 = resize_image(mask_biggest2, - int(mask_biggest2.shape[0] * scale_par), - int(mask_biggest2.shape[1] * scale_par)) + mask_biggest2 = resize_image(mask_biggest2, int(mask_biggest2.shape[0] * scale_par), int(mask_biggest2.shape[1] * scale_par)) cnt_textlines_in_image_ind = return_contours_of_interested_textline(mask_biggest2, pixel_img) try: textlines_cnt_per_region.append(cnt_textlines_in_image_ind[0]) except Exception as why: logger.error(why) else: - textlines_cnt_per_region = textline_contours_postprocessing(all_text_region_raw, - slope_for_all, contour_par, - box_text, True) + textlines_cnt_per_region = textline_contours_postprocessing(all_text_region_raw, slope_for_all, contour_par, box_text, True) + # print(np.shape(textlines_cnt_per_region),'textlines_cnt_per_region') - return textlines_cnt_per_region[::-1], crop_coor, slope + return textlines_cnt_per_region[::-1], box_text, contour, contour_par, crop_coor, index_r_con, slope -@wrap_ndarray_shared(kw='textline_mask_tot_ea') def do_work_of_slopes_new_light( - box_text, contour, contour_par, - textline_mask_tot_ea=None, slope_deskew=0, + box_text, contour, contour_par, index_r_con, + textline_mask_tot_ea, image_page_rotated, slope_deskew, textline_light, logger=None ): if logger is None: @@ -1693,7 +1758,7 @@ def do_work_of_slopes_new_light( logger.debug('enter do_work_of_slopes_new_light') x, y, w, h = box_text - crop_coor = box2rect(box_text) + _, crop_coor = crop_image_inside_box(box_text, image_page_rotated) mask_textline = np.zeros(textline_mask_tot_ea.shape) mask_textline = cv2.fillPoly(mask_textline, pts=[contour], color=(1,1,1)) all_text_region_raw = textline_mask_tot_ea * mask_textline @@ -1702,10 +1767,16 @@ def do_work_of_slopes_new_light( mask_only_con_region = np.zeros(textline_mask_tot_ea.shape) mask_only_con_region = cv2.fillPoly(mask_only_con_region, pts=[contour_par], color=(1, 1, 1)) - all_text_region_raw = np.copy(textline_mask_tot_ea) - all_text_region_raw[mask_only_con_region == 0] = 0 - cnt_clean_rot_raw, hir_on_cnt_clean_rot = return_contours_of_image(all_text_region_raw) - cnt_clean_rot = filter_contours_area_of_image(all_text_region_raw, cnt_clean_rot_raw, hir_on_cnt_clean_rot, - max_area=1, min_area=0.00001) + if textline_light: + all_text_region_raw = np.copy(textline_mask_tot_ea) + all_text_region_raw[mask_only_con_region == 0] = 0 + cnt_clean_rot_raw, hir_on_cnt_clean_rot = return_contours_of_image(all_text_region_raw) + cnt_clean_rot = filter_contours_area_of_image(all_text_region_raw, cnt_clean_rot_raw, hir_on_cnt_clean_rot, + max_area=1, min_area=0.00001) + else: + all_text_region_raw = np.copy(textline_mask_tot_ea[y: y + h, x: x + w]) + mask_only_con_region = mask_only_con_region[y: y + h, x: x + w] + all_text_region_raw[mask_only_con_region == 0] = 0 + cnt_clean_rot = textline_contours_postprocessing(all_text_region_raw, slope_deskew, contour_par, box_text) - return cnt_clean_rot, crop_coor, slope_deskew + return cnt_clean_rot, box_text, contour, contour_par, crop_coor, index_r_con, slope_deskew diff --git a/src/eynollah/utils/shm.py b/src/eynollah/utils/shm.py deleted file mode 100644 index 4b51053..0000000 --- a/src/eynollah/utils/shm.py +++ /dev/null @@ -1,45 +0,0 @@ -from multiprocessing import shared_memory -from contextlib import contextmanager -from functools import wraps -import numpy as np - -@contextmanager -def share_ndarray(array: np.ndarray): - size = np.dtype(array.dtype).itemsize * np.prod(array.shape) - shm = shared_memory.SharedMemory(create=True, size=size) - try: - shared_array = np.ndarray(array.shape, dtype=array.dtype, buffer=shm.buf) - shared_array[:] = array[:] - shared_array.flags["WRITEABLE"] = False - yield dict(shape=array.shape, dtype=array.dtype, name=shm.name) - finally: - shm.close() - shm.unlink() - -@contextmanager -def ndarray_shared(array: dict): - shm = shared_memory.SharedMemory(name=array['name']) - try: - array = np.ndarray(array['shape'], dtype=array['dtype'], buffer=shm.buf) - yield array - finally: - shm.close() - -def wrap_ndarray_shared(kw=None): - def wrapper(f): - if kw is None: - @wraps(f) - def shared_func(array, *args, **kwargs): - with ndarray_shared(array) as ndarray: - return f(ndarray, *args, **kwargs) - return shared_func - else: - @wraps(f) - def shared_func(*args, **kwargs): - array = kwargs.pop(kw) - with ndarray_shared(array) as ndarray: - kwargs[kw] = ndarray - return f(*args, **kwargs) - return shared_func - return wrapper - diff --git a/src/eynollah/utils/utils_ocr.py b/src/eynollah/utils/utils_ocr.py deleted file mode 100644 index b738e29..0000000 --- a/src/eynollah/utils/utils_ocr.py +++ /dev/null @@ -1,510 +0,0 @@ -import math -import copy - -import numpy as np -import cv2 -import tensorflow as tf -from scipy.signal import find_peaks -from scipy.ndimage import gaussian_filter1d -from PIL import Image, ImageDraw, ImageFont -from Bio import pairwise2 - -from .resize import resize_image - - -def decode_batch_predictions(pred, num_to_char, max_len = 128): - # input_len is the product of the batch size and the - # number of time steps. - input_len = np.ones(pred.shape[0]) * pred.shape[1] - - # Decode CTC predictions using greedy search. - # decoded is a tuple with 2 elements. - decoded = tf.keras.backend.ctc_decode(pred, - input_length = input_len, - beam_width = 100) - # The outputs are in the first element of the tuple. - # Additionally, the first element is actually a list, - # therefore we take the first element of that list as well. - #print(decoded,'decoded') - decoded = decoded[0][0][:, :max_len] - - #print(decoded, decoded.shape,'decoded') - - output = [] - for d in decoded: - # Convert the predicted indices to the corresponding chars. - d = tf.strings.reduce_join(num_to_char(d)) - d = d.numpy().decode("utf-8") - output.append(d) - return output - - -def distortion_free_resize(image, img_size): - w, h = img_size - image = tf.image.resize(image, size=(h, w), preserve_aspect_ratio=True) - - # Check tha amount of padding needed to be done. - pad_height = h - tf.shape(image)[0] - pad_width = w - tf.shape(image)[1] - - # Only necessary if you want to do same amount of padding on both sides. - if pad_height % 2 != 0: - height = pad_height // 2 - pad_height_top = height + 1 - pad_height_bottom = height - else: - pad_height_top = pad_height_bottom = pad_height // 2 - - if pad_width % 2 != 0: - width = pad_width // 2 - pad_width_left = width + 1 - pad_width_right = width - else: - pad_width_left = pad_width_right = pad_width // 2 - - image = tf.pad( - image, - paddings=[ - [pad_height_top, pad_height_bottom], - [pad_width_left, pad_width_right], - [0, 0], - ], - ) - - image = tf.transpose(image, (1, 0, 2)) - image = tf.image.flip_left_right(image) - return image - -def return_start_and_end_of_common_text_of_textline_ocr_without_common_section(textline_image): - width = np.shape(textline_image)[1] - height = np.shape(textline_image)[0] - common_window = int(0.06*width) - - width1 = int ( width/2. - common_window ) - width2 = int ( width/2. + common_window ) - - img_sum = np.sum(textline_image[:,:,0], axis=0) - sum_smoothed = gaussian_filter1d(img_sum, 3) - - peaks_real, _ = find_peaks(sum_smoothed, height=0) - if len(peaks_real)>70: - - peaks_real = peaks_real[(peaks_realwidth1)] - - arg_max = np.argmax(sum_smoothed[peaks_real]) - peaks_final = peaks_real[arg_max] - return peaks_final - else: - return None - -# Function to fit text inside the given area -def fit_text_single_line(draw, text, font_path, max_width, max_height): - initial_font_size = 50 - font_size = initial_font_size - while font_size > 10: # Minimum font size - font = ImageFont.truetype(font_path, font_size) - text_bbox = draw.textbbox((0, 0), text, font=font) # Get text bounding box - text_width = text_bbox[2] - text_bbox[0] - text_height = text_bbox[3] - text_bbox[1] - - if text_width <= max_width and text_height <= max_height: - return font # Return the best-fitting font - - font_size -= 2 # Reduce font size and retry - - return ImageFont.truetype(font_path, 10) # Smallest font fallback - -def return_textlines_split_if_needed(textline_image, textline_image_bin=None): - - split_point = return_start_and_end_of_common_text_of_textline_ocr_without_common_section(textline_image) - if split_point: - image1 = textline_image[:, :split_point,:]# image.crop((0, 0, width2, height)) - image2 = textline_image[:, split_point:,:]#image.crop((width1, 0, width, height)) - if textline_image_bin is not None: - image1_bin = textline_image_bin[:, :split_point,:]# image.crop((0, 0, width2, height)) - image2_bin = textline_image_bin[:, split_point:,:]#image.crop((width1, 0, width, height)) - return [image1, image2], [image1_bin, image2_bin] - else: - return [image1, image2], None - else: - return None, None - -def preprocess_and_resize_image_for_ocrcnn_model(img, image_height, image_width): - if img.shape[0]==0 or img.shape[1]==0: - img_fin = np.ones((image_height, image_width, 3)) - else: - ratio = image_height /float(img.shape[0]) - w_ratio = int(ratio * img.shape[1]) - - if w_ratio <= image_width: - width_new = w_ratio - else: - width_new = image_width - - if width_new == 0: - width_new = img.shape[1] - - - img = resize_image(img, image_height, width_new) - img_fin = np.ones((image_height, image_width, 3))*255 - - img_fin[:,:width_new,:] = img[:,:,:] - img_fin = img_fin / 255. - return img_fin - -def get_deskewed_contour_and_bb_and_image(contour, image, deskew_angle): - (h_in, w_in) = image.shape[:2] - center = (w_in // 2, h_in // 2) - - rotation_matrix = cv2.getRotationMatrix2D(center, deskew_angle, 1.0) - - cos_angle = abs(rotation_matrix[0, 0]) - sin_angle = abs(rotation_matrix[0, 1]) - new_w = int((h_in * sin_angle) + (w_in * cos_angle)) - new_h = int((h_in * cos_angle) + (w_in * sin_angle)) - - rotation_matrix[0, 2] += (new_w / 2) - center[0] - rotation_matrix[1, 2] += (new_h / 2) - center[1] - - deskewed_image = cv2.warpAffine(image, rotation_matrix, (new_w, new_h)) - - contour_points = np.array(contour, dtype=np.float32) - transformed_points = cv2.transform(np.array([contour_points]), rotation_matrix)[0] - - x, y, w, h = cv2.boundingRect(np.array(transformed_points, dtype=np.int32)) - cropped_textline = deskewed_image[y:y+h, x:x+w] - - return cropped_textline - -def rotate_image_with_padding(image, angle, border_value=(0,0,0)): - # Get image dimensions - (h, w) = image.shape[:2] - - # Calculate the center of the image - center = (w // 2, h // 2) - - # Get the rotation matrix - rotation_matrix = cv2.getRotationMatrix2D(center, angle, 1.0) - - # Compute the new bounding dimensions - cos = abs(rotation_matrix[0, 0]) - sin = abs(rotation_matrix[0, 1]) - new_w = int((h * sin) + (w * cos)) - new_h = int((h * cos) + (w * sin)) - - # Adjust the rotation matrix to account for translation - rotation_matrix[0, 2] += (new_w / 2) - center[0] - rotation_matrix[1, 2] += (new_h / 2) - center[1] - - # Perform the rotation - try: - rotated_image = cv2.warpAffine(image, rotation_matrix, (new_w, new_h), borderValue=border_value) - except: - rotated_image = np.copy(image) - - return rotated_image - -def get_orientation_moments(contour): - moments = cv2.moments(contour) - if moments["mu20"] - moments["mu02"] == 0: # Avoid division by zero - return 90 if moments["mu11"] > 0 else -90 - else: - angle = 0.5 * np.arctan2(2 * moments["mu11"], moments["mu20"] - moments["mu02"]) - return np.degrees(angle) # Convert radians to degrees - - -def get_orientation_moments_of_mask(mask): - mask=mask.astype('uint8') - contours, _ = cv2.findContours(mask[:,:,0], cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) - - largest_contour = max(contours, key=cv2.contourArea) if contours else None - - moments = cv2.moments(largest_contour) - if moments["mu20"] - moments["mu02"] == 0: # Avoid division by zero - return 90 if moments["mu11"] > 0 else -90 - else: - angle = 0.5 * np.arctan2(2 * moments["mu11"], moments["mu20"] - moments["mu02"]) - return np.degrees(angle) # Convert radians to degrees - -def get_contours_and_bounding_boxes(mask): - # Find contours in the binary mask - contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) - - largest_contour = max(contours, key=cv2.contourArea) if contours else None - - # Get the bounding rectangle for the contour - x, y, w, h = cv2.boundingRect(largest_contour) - #bounding_boxes.append((x, y, w, h)) - - return x, y, w, h - -def return_splitting_point_of_image(image_to_spliited): - width = np.shape(image_to_spliited)[1] - height = np.shape(image_to_spliited)[0] - common_window = int(0.03*width) - - width1 = int ( common_window) - width2 = int ( width - common_window ) - - img_sum = np.sum(image_to_spliited[:,:,0], axis=0) - sum_smoothed = gaussian_filter1d(img_sum, 1) - - peaks_real, _ = find_peaks(sum_smoothed, height=0) - peaks_real = peaks_real[(peaks_realwidth1)] - - arg_sort = np.argsort(sum_smoothed[peaks_real]) - peaks_sort_4 = peaks_real[arg_sort][::-1][:3] - - return np.sort(peaks_sort_4) - -def break_curved_line_into_small_pieces_and_then_merge(img_curved, mask_curved, img_bin_curved=None): - peaks_4 = return_splitting_point_of_image(img_curved) - if len(peaks_4)>0: - imgs_tot = [] - - for ind in range(len(peaks_4)+1): - if ind==0: - img = img_curved[:, :peaks_4[ind], :] - if img_bin_curved is not None: - img_bin = img_bin_curved[:, :peaks_4[ind], :] - mask = mask_curved[:, :peaks_4[ind], :] - elif ind==len(peaks_4): - img = img_curved[:, peaks_4[ind-1]:, :] - if img_bin_curved is not None: - img_bin = img_bin_curved[:, peaks_4[ind-1]:, :] - mask = mask_curved[:, peaks_4[ind-1]:, :] - else: - img = img_curved[:, peaks_4[ind-1]:peaks_4[ind], :] - if img_bin_curved is not None: - img_bin = img_bin_curved[:, peaks_4[ind-1]:peaks_4[ind], :] - mask = mask_curved[:, peaks_4[ind-1]:peaks_4[ind], :] - - or_ma = get_orientation_moments_of_mask(mask) - - if img_bin_curved is not None: - imgs_tot.append([img, mask, or_ma, img_bin] ) - else: - imgs_tot.append([img, mask, or_ma] ) - - - w_tot_des_list = [] - w_tot_des = 0 - imgs_deskewed_list = [] - imgs_bin_deskewed_list = [] - - for ind in range(len(imgs_tot)): - img_in = imgs_tot[ind][0] - mask_in = imgs_tot[ind][1] - ori_in = imgs_tot[ind][2] - if img_bin_curved is not None: - img_bin_in = imgs_tot[ind][3] - - if abs(ori_in)<45: - img_in_des = rotate_image_with_padding(img_in, ori_in, border_value=(255,255,255) ) - if img_bin_curved is not None: - img_bin_in_des = rotate_image_with_padding(img_bin_in, ori_in, border_value=(255,255,255) ) - mask_in_des = rotate_image_with_padding(mask_in, ori_in) - mask_in_des = mask_in_des.astype('uint8') - - #new bounding box - x_n, y_n, w_n, h_n = get_contours_and_bounding_boxes(mask_in_des[:,:,0]) - - if w_n==0 or h_n==0: - img_in_des = np.copy(img_in) - if img_bin_curved is not None: - img_bin_in_des = np.copy(img_bin_in) - w_relative = int(32 * img_in_des.shape[1]/float(img_in_des.shape[0]) ) - if w_relative==0: - w_relative = img_in_des.shape[1] - img_in_des = resize_image(img_in_des, 32, w_relative) - if img_bin_curved is not None: - img_bin_in_des = resize_image(img_bin_in_des, 32, w_relative) - else: - mask_in_des = mask_in_des[y_n:y_n+h_n, x_n:x_n+w_n, :] - img_in_des = img_in_des[y_n:y_n+h_n, x_n:x_n+w_n, :] - if img_bin_curved is not None: - img_bin_in_des = img_bin_in_des[y_n:y_n+h_n, x_n:x_n+w_n, :] - - w_relative = int(32 * img_in_des.shape[1]/float(img_in_des.shape[0]) ) - if w_relative==0: - w_relative = img_in_des.shape[1] - img_in_des = resize_image(img_in_des, 32, w_relative) - if img_bin_curved is not None: - img_bin_in_des = resize_image(img_bin_in_des, 32, w_relative) - - - else: - img_in_des = np.copy(img_in) - if img_bin_curved is not None: - img_bin_in_des = np.copy(img_bin_in) - w_relative = int(32 * img_in_des.shape[1]/float(img_in_des.shape[0]) ) - if w_relative==0: - w_relative = img_in_des.shape[1] - img_in_des = resize_image(img_in_des, 32, w_relative) - if img_bin_curved is not None: - img_bin_in_des = resize_image(img_bin_in_des, 32, w_relative) - - w_tot_des+=img_in_des.shape[1] - w_tot_des_list.append(img_in_des.shape[1]) - imgs_deskewed_list.append(img_in_des) - if img_bin_curved is not None: - imgs_bin_deskewed_list.append(img_bin_in_des) - - - - - img_final_deskewed = np.zeros((32, w_tot_des, 3))+255 - if img_bin_curved is not None: - img_bin_final_deskewed = np.zeros((32, w_tot_des, 3))+255 - else: - img_bin_final_deskewed = None - - w_indexer = 0 - for ind in range(len(w_tot_des_list)): - img_final_deskewed[:,w_indexer:w_indexer+w_tot_des_list[ind],:] = imgs_deskewed_list[ind][:,:,:] - if img_bin_curved is not None: - img_bin_final_deskewed[:,w_indexer:w_indexer+w_tot_des_list[ind],:] = imgs_bin_deskewed_list[ind][:,:,:] - w_indexer = w_indexer+w_tot_des_list[ind] - return img_final_deskewed, img_bin_final_deskewed - else: - return img_curved, img_bin_curved - -def return_textline_contour_with_added_box_coordinate(textline_contour, box_ind): - textline_contour[:,0] = textline_contour[:,0] + box_ind[2] - textline_contour[:,1] = textline_contour[:,1] + box_ind[0] - return textline_contour - - -def return_rnn_cnn_ocr_of_given_textlines(image, - all_found_textline_polygons, - all_box_coord, - prediction_model, - b_s_ocr, num_to_char, - curved_line=False): - max_len = 512 - padding_token = 299 - image_width = 512#max_len * 4 - image_height = 32 - ind_tot = 0 - #cv2.imwrite('./img_out.png', image_page) - ocr_all_textlines = [] - cropped_lines_region_indexer = [] - cropped_lines_meging_indexing = [] - cropped_lines = [] - indexer_text_region = 0 - - for indexing, ind_poly_first in enumerate(all_found_textline_polygons): - #ocr_textline_in_textregion = [] - if len(ind_poly_first)==0: - cropped_lines_region_indexer.append(indexer_text_region) - cropped_lines_meging_indexing.append(0) - img_fin = np.ones((image_height, image_width, 3))*1 - cropped_lines.append(img_fin) - - else: - for indexing2, ind_poly in enumerate(ind_poly_first): - cropped_lines_region_indexer.append(indexer_text_region) - if not curved_line: - ind_poly = copy.deepcopy(ind_poly) - box_ind = all_box_coord[indexing] - - ind_poly = return_textline_contour_with_added_box_coordinate(ind_poly, box_ind) - #print(ind_poly_copy) - ind_poly[ind_poly<0] = 0 - x, y, w, h = cv2.boundingRect(ind_poly) - - w_scaled = w * image_height/float(h) - - mask_poly = np.zeros(image.shape) - - img_poly_on_img = np.copy(image) - - mask_poly = cv2.fillPoly(mask_poly, pts=[ind_poly], color=(1, 1, 1)) - - - - mask_poly = mask_poly[y:y+h, x:x+w, :] - img_crop = img_poly_on_img[y:y+h, x:x+w, :] - - img_crop[mask_poly==0] = 255 - - if w_scaled < 640:#1.5*image_width: - img_fin = preprocess_and_resize_image_for_ocrcnn_model(img_crop, image_height, image_width) - cropped_lines.append(img_fin) - cropped_lines_meging_indexing.append(0) - else: - splited_images, splited_images_bin = return_textlines_split_if_needed(img_crop, None) - - if splited_images: - img_fin = preprocess_and_resize_image_for_ocrcnn_model(splited_images[0], - image_height, - image_width) - cropped_lines.append(img_fin) - cropped_lines_meging_indexing.append(1) - - img_fin = preprocess_and_resize_image_for_ocrcnn_model(splited_images[1], - image_height, - image_width) - - cropped_lines.append(img_fin) - cropped_lines_meging_indexing.append(-1) - - else: - img_fin = preprocess_and_resize_image_for_ocrcnn_model(img_crop, - image_height, - image_width) - cropped_lines.append(img_fin) - cropped_lines_meging_indexing.append(0) - - indexer_text_region+=1 - - extracted_texts = [] - - n_iterations = math.ceil(len(cropped_lines) / b_s_ocr) - - for i in range(n_iterations): - if i==(n_iterations-1): - n_start = i*b_s_ocr - imgs = cropped_lines[n_start:] - imgs = np.array(imgs) - imgs = imgs.reshape(imgs.shape[0], image_height, image_width, 3) - - - else: - n_start = i*b_s_ocr - n_end = (i+1)*b_s_ocr - imgs = cropped_lines[n_start:n_end] - imgs = np.array(imgs).reshape(b_s_ocr, image_height, image_width, 3) - - - preds = prediction_model.predict(imgs, verbose=0) - - pred_texts = decode_batch_predictions(preds, num_to_char) - - for ib in range(imgs.shape[0]): - pred_texts_ib = pred_texts[ib].replace("[UNK]", "") - extracted_texts.append(pred_texts_ib) - - extracted_texts_merged = [extracted_texts[ind] - if cropped_lines_meging_indexing[ind]==0 - else extracted_texts[ind]+" "+extracted_texts[ind+1] - if cropped_lines_meging_indexing[ind]==1 - else None - for ind in range(len(cropped_lines_meging_indexing))] - - extracted_texts_merged = [ind for ind in extracted_texts_merged if ind is not None] - unique_cropped_lines_region_indexer = np.unique(cropped_lines_region_indexer) - - ocr_all_textlines = [] - for ind in unique_cropped_lines_region_indexer: - ocr_textline_in_textregion = [] - extracted_texts_merged_un = np.array(extracted_texts_merged)[np.array(cropped_lines_region_indexer)==ind] - for it_ind, text_textline in enumerate(extracted_texts_merged_un): - ocr_textline_in_textregion.append(text_textline) - ocr_all_textlines.append(ocr_textline_in_textregion) - return ocr_all_textlines - -def biopython_align(str1, str2): - alignments = pairwise2.align.globalms(str1, str2, 2, -1, -2, -2) - best_alignment = alignments[0] # Get the best alignment - return best_alignment.seqA, best_alignment.seqB diff --git a/src/eynollah/utils/xml.py b/src/eynollah/utils/xml.py index ded098e..bd95702 100644 --- a/src/eynollah/utils/xml.py +++ b/src/eynollah/utils/xml.py @@ -46,26 +46,24 @@ def create_page_xml(imageFilename, height, width): )) return pcgts -def xml_reading_order(page, order_of_texts, id_of_marginalia_left, id_of_marginalia_right): +def xml_reading_order(page, order_of_texts, id_of_marginalia): region_order = ReadingOrderType() og = OrderedGroupType(id="ro357564684568544579089") page.set_ReadingOrder(region_order) region_order.set_OrderedGroup(og) region_counter = EynollahIdCounter() - - for id_marginal in id_of_marginalia_left: - og.add_RegionRefIndexed(RegionRefIndexedType(index=str(region_counter.get('region')), regionRef=id_marginal)) + for idx_textregion, _ in enumerate(order_of_texts): + og.add_RegionRefIndexed(RegionRefIndexedType(index=str(region_counter.get('region')), regionRef=region_counter.region_id(order_of_texts[idx_textregion] + 1))) region_counter.inc('region') - - for idx_textregion in order_of_texts: - og.add_RegionRefIndexed(RegionRefIndexedType(index=str(region_counter.get('region')), regionRef=region_counter.region_id(idx_textregion + 1))) - region_counter.inc('region') - - for id_marginal in id_of_marginalia_right: + for id_marginal in id_of_marginalia: og.add_RegionRefIndexed(RegionRefIndexedType(index=str(region_counter.get('region')), regionRef=id_marginal)) region_counter.inc('region') -def order_and_id_of_texts(found_polygons_text_region, found_polygons_text_region_h, indexes_sorted, index_of_types, kind_of_texts, ref_point): +def order_and_id_of_texts(found_polygons_text_region, found_polygons_text_region_h, matrix_of_orders, indexes_sorted, index_of_types, kind_of_texts, ref_point): + indexes_sorted = np.array(indexes_sorted) + index_of_types = np.array(index_of_types) + kind_of_texts = np.array(kind_of_texts) + id_of_texts = [] order_of_texts = [] @@ -88,7 +86,3 @@ def order_and_id_of_texts(found_polygons_text_region, found_polygons_text_region order_of_texts.append(interest) return order_of_texts, id_of_texts - -def etree_namespace_for_element_tag(tag: str): - right = tag.find('}') - return tag[1:right] diff --git a/src/eynollah/writer.py b/src/eynollah/writer.py index 1781230..92e353f 100644 --- a/src/eynollah/writer.py +++ b/src/eynollah/writer.py @@ -2,14 +2,15 @@ # pylint: disable=import-error from pathlib import Path import os.path -from typing import Optional -import logging +import xml.etree.ElementTree as ET from .utils.xml import create_page_xml, xml_reading_order from .utils.counter import EynollahIdCounter +from ocrd_utils import getLogger from ocrd_models.ocrd_page import ( BorderType, CoordsType, + PcGtsType, TextLineType, TextEquivType, TextRegionType, @@ -18,21 +19,23 @@ from ocrd_models.ocrd_page import ( SeparatorRegionType, to_xml ) +import numpy as np -class EynollahXmlWriter: +class EynollahXmlWriter(): - def __init__(self, *, dir_out, image_filename, curved_line, pcgts=None): - self.logger = logging.getLogger('eynollah.writer') + def __init__(self, *, dir_out, image_filename, curved_line,textline_light, pcgts=None): + self.logger = getLogger('eynollah.writer') self.counter = EynollahIdCounter() self.dir_out = dir_out self.image_filename = image_filename self.output_filename = os.path.join(self.dir_out or "", self.image_filename_stem) + ".xml" self.curved_line = curved_line + self.textline_light = textline_light self.pcgts = pcgts - self.scale_x: Optional[float] = None # XXX set outside __init__ - self.scale_y: Optional[float] = None # XXX set outside __init__ - self.height_org: Optional[int] = None # XXX set outside __init__ - self.width_org: Optional[int] = None # XXX set outside __init__ + self.scale_x = None # XXX set outside __init__ + self.scale_y = None # XXX set outside __init__ + self.height_org = None # XXX set outside __init__ + self.width_org = None # XXX set outside __init__ @property def image_filename_stem(self): @@ -53,26 +56,111 @@ class EynollahXmlWriter: points_page_print = points_page_print + ' ' return points_page_print[:-1] + def serialize_lines_in_marginal(self, marginal_region, all_found_textline_polygons_marginals, marginal_idx, page_coord, all_box_coord_marginals, slopes_marginals, counter): + for j in range(len(all_found_textline_polygons_marginals[marginal_idx])): + coords = CoordsType() + textline = TextLineType(id=counter.next_line_id, Coords=coords) + marginal_region.add_TextLine(textline) + marginal_region.set_orientation(-slopes_marginals[marginal_idx]) + points_co = '' + for l in range(len(all_found_textline_polygons_marginals[marginal_idx][j])): + if not (self.curved_line or self.textline_light): + if len(all_found_textline_polygons_marginals[marginal_idx][j][l]) == 2: + textline_x_coord = max(0, int((all_found_textline_polygons_marginals[marginal_idx][j][l][0] + all_box_coord_marginals[marginal_idx][2] + page_coord[2]) / self.scale_x) ) + textline_y_coord = max(0, int((all_found_textline_polygons_marginals[marginal_idx][j][l][1] + all_box_coord_marginals[marginal_idx][0] + page_coord[0]) / self.scale_y) ) + else: + textline_x_coord = max(0, int((all_found_textline_polygons_marginals[marginal_idx][j][l][0][0] + all_box_coord_marginals[marginal_idx][2] + page_coord[2]) / self.scale_x) ) + textline_y_coord = max(0, int((all_found_textline_polygons_marginals[marginal_idx][j][l][0][1] + all_box_coord_marginals[marginal_idx][0] + page_coord[0]) / self.scale_y) ) + points_co += str(textline_x_coord) + points_co += ',' + points_co += str(textline_y_coord) + if (self.curved_line or self.textline_light) and np.abs(slopes_marginals[marginal_idx]) <= 45: + if len(all_found_textline_polygons_marginals[marginal_idx][j][l]) == 2: + points_co += str(int((all_found_textline_polygons_marginals[marginal_idx][j][l][0] + page_coord[2]) / self.scale_x)) + points_co += ',' + points_co += str(int((all_found_textline_polygons_marginals[marginal_idx][j][l][1] + page_coord[0]) / self.scale_y)) + else: + points_co += str(int((all_found_textline_polygons_marginals[marginal_idx][j][l][0][0] + page_coord[2]) / self.scale_x)) + points_co += ',' + points_co += str(int((all_found_textline_polygons_marginals[marginal_idx][j][l][0][1] + page_coord[0]) / self.scale_y)) + + elif (self.curved_line or self.textline_light) and np.abs(slopes_marginals[marginal_idx]) > 45: + if len(all_found_textline_polygons_marginals[marginal_idx][j][l]) == 2: + points_co += str(int((all_found_textline_polygons_marginals[marginal_idx][j][l][0] + all_box_coord_marginals[marginal_idx][2] + page_coord[2]) / self.scale_x)) + points_co += ',' + points_co += str(int((all_found_textline_polygons_marginals[marginal_idx][j][l][1] + all_box_coord_marginals[marginal_idx][0] + page_coord[0]) / self.scale_y)) + else: + points_co += str(int((all_found_textline_polygons_marginals[marginal_idx][j][l][0][0] + all_box_coord_marginals[marginal_idx][2] + page_coord[2]) / self.scale_x)) + points_co += ',' + points_co += str(int((all_found_textline_polygons_marginals[marginal_idx][j][l][0][1] + all_box_coord_marginals[marginal_idx][0] + page_coord[0]) / self.scale_y)) + points_co += ' ' + coords.set_points(points_co[:-1]) + def serialize_lines_in_region(self, text_region, all_found_textline_polygons, region_idx, page_coord, all_box_coord, slopes, counter, ocr_all_textlines_textregion): self.logger.debug('enter serialize_lines_in_region') - for j, polygon_textline in enumerate(all_found_textline_polygons[region_idx]): + for j in range(len(all_found_textline_polygons[region_idx])): coords = CoordsType() textline = TextLineType(id=counter.next_line_id, Coords=coords) if ocr_all_textlines_textregion: - # FIXME: add OCR confidence - textline.set_TextEquiv([TextEquivType(Unicode=ocr_all_textlines_textregion[j])]) + textline.set_TextEquiv( [ TextEquivType(Unicode=ocr_all_textlines_textregion[j]) ] ) text_region.add_TextLine(textline) text_region.set_orientation(-slopes[region_idx]) region_bboxes = all_box_coord[region_idx] points_co = '' - for point in polygon_textline: - if len(point) != 2: - point = point[0] - point_x = point[0] + page_coord[2] - point_y = point[1] + page_coord[0] - point_x = max(0, int(point_x / self.scale_x)) - point_y = max(0, int(point_y / self.scale_y)) - points_co += f'{point_x},{point_y} ' + for idx_contour_textline, contour_textline in enumerate(all_found_textline_polygons[region_idx][j]): + if not (self.curved_line or self.textline_light): + if len(contour_textline) == 2: + textline_x_coord = max(0, int((contour_textline[0] + region_bboxes[2] + page_coord[2]) / self.scale_x)) + textline_y_coord = max(0, int((contour_textline[1] + region_bboxes[0] + page_coord[0]) / self.scale_y)) + else: + textline_x_coord = max(0, int((contour_textline[0][0] + region_bboxes[2] + page_coord[2]) / self.scale_x)) + textline_y_coord = max(0, int((contour_textline[0][1] + region_bboxes[0] + page_coord[0]) / self.scale_y)) + points_co += str(textline_x_coord) + points_co += ',' + points_co += str(textline_y_coord) + + if (self.curved_line or self.textline_light) and np.abs(slopes[region_idx]) <= 45: + if len(contour_textline) == 2: + points_co += str(int((contour_textline[0] + page_coord[2]) / self.scale_x)) + points_co += ',' + points_co += str(int((contour_textline[1] + page_coord[0]) / self.scale_y)) + else: + points_co += str(int((contour_textline[0][0] + page_coord[2]) / self.scale_x)) + points_co += ',' + points_co += str(int((contour_textline[0][1] + page_coord[0])/self.scale_y)) + elif (self.curved_line or self.textline_light) and np.abs(slopes[region_idx]) > 45: + if len(contour_textline)==2: + points_co += str(int((contour_textline[0] + region_bboxes[2] + page_coord[2])/self.scale_x)) + points_co += ',' + points_co += str(int((contour_textline[1] + region_bboxes[0] + page_coord[0])/self.scale_y)) + else: + points_co += str(int((contour_textline[0][0] + region_bboxes[2]+page_coord[2])/self.scale_x)) + points_co += ',' + points_co += str(int((contour_textline[0][1] + region_bboxes[0]+page_coord[0])/self.scale_y)) + points_co += ' ' + coords.set_points(points_co[:-1]) + + def serialize_lines_in_dropcapital(self, text_region, all_found_textline_polygons, region_idx, page_coord, all_box_coord, slopes, counter, ocr_all_textlines_textregion): + self.logger.debug('enter serialize_lines_in_region') + for j in range(1): + coords = CoordsType() + textline = TextLineType(id=counter.next_line_id, Coords=coords) + if ocr_all_textlines_textregion: + textline.set_TextEquiv( [ TextEquivType(Unicode=ocr_all_textlines_textregion[j]) ] ) + text_region.add_TextLine(textline) + #region_bboxes = all_box_coord[region_idx] + points_co = '' + for idx_contour_textline, contour_textline in enumerate(all_found_textline_polygons[j]): + if len(contour_textline) == 2: + points_co += str(int((contour_textline[0] + page_coord[2]) / self.scale_x)) + points_co += ',' + points_co += str(int((contour_textline[1] + page_coord[0]) / self.scale_y)) + else: + points_co += str(int((contour_textline[0][0] + page_coord[2]) / self.scale_x)) + points_co += ',' + points_co += str(int((contour_textline[0][1] + page_coord[0])/self.scale_y)) + + points_co += ' ' coords.set_points(points_co[:-1]) def write_pagexml(self, pcgts): @@ -80,210 +168,153 @@ class EynollahXmlWriter: with open(self.output_filename, 'w') as f: f.write(to_xml(pcgts)) - def build_pagexml_no_full_layout( - self, - *, - found_polygons_text_region, - page_coord, - order_of_texts, - all_found_textline_polygons, - all_box_coord, - found_polygons_text_region_img, - found_polygons_marginals_left, - found_polygons_marginals_right, - all_found_textline_polygons_marginals_left, - all_found_textline_polygons_marginals_right, - all_box_coord_marginals_left, - all_box_coord_marginals_right, - slopes, - slopes_marginals_left, - slopes_marginals_right, - cont_page, - polygons_seplines, - found_polygons_tables, - ): - return self.build_pagexml_full_layout( - found_polygons_text_region=found_polygons_text_region, - found_polygons_text_region_h=[], - page_coord=page_coord, - order_of_texts=order_of_texts, - all_found_textline_polygons=all_found_textline_polygons, - all_found_textline_polygons_h=[], - all_box_coord=all_box_coord, - all_box_coord_h=[], - found_polygons_text_region_img=found_polygons_text_region_img, - found_polygons_tables=found_polygons_tables, - found_polygons_drop_capitals=[], - found_polygons_marginals_left=found_polygons_marginals_left, - found_polygons_marginals_right=found_polygons_marginals_right, - all_found_textline_polygons_marginals_left=all_found_textline_polygons_marginals_left, - all_found_textline_polygons_marginals_right=all_found_textline_polygons_marginals_right, - all_box_coord_marginals_left=all_box_coord_marginals_left, - all_box_coord_marginals_right=all_box_coord_marginals_right, - slopes=slopes, - slopes_h=[], - slopes_marginals_left=slopes_marginals_left, - slopes_marginals_right=slopes_marginals_right, - cont_page=cont_page, - polygons_seplines=polygons_seplines, - ) - - def build_pagexml_full_layout( - self, - *, - found_polygons_text_region, - found_polygons_text_region_h, - page_coord, - order_of_texts, - all_found_textline_polygons, - all_found_textline_polygons_h, - all_box_coord, - all_box_coord_h, - found_polygons_text_region_img, - found_polygons_tables, - found_polygons_drop_capitals, - found_polygons_marginals_left, - found_polygons_marginals_right, - all_found_textline_polygons_marginals_left, - all_found_textline_polygons_marginals_right, - all_box_coord_marginals_left, - all_box_coord_marginals_right, - slopes, - slopes_h, - slopes_marginals_left, - slopes_marginals_right, - cont_page, - polygons_seplines, - ocr_all_textlines=None, - ocr_all_textlines_h=None, - ocr_all_textlines_marginals_left=None, - ocr_all_textlines_marginals_right=None, - ocr_all_textlines_drop=None, - conf_contours_textregions=None, - conf_contours_textregions_h=None, - skip_layout_reading_order=False, - ): - self.logger.debug('enter build_pagexml') + def build_pagexml_no_full_layout(self, found_polygons_text_region, page_coord, order_of_texts, id_of_texts, all_found_textline_polygons, all_box_coord, found_polygons_text_region_img, found_polygons_marginals, all_found_textline_polygons_marginals, all_box_coord_marginals, slopes, slopes_marginals, cont_page, polygons_lines_to_be_written_in_xml, found_polygons_tables, ocr_all_textlines, conf_contours_textregion): + self.logger.debug('enter build_pagexml_no_full_layout') # create the file structure pcgts = self.pcgts if self.pcgts else create_page_xml(self.image_filename, self.height_org, self.width_org) page = pcgts.get_Page() - assert page page.set_Border(BorderType(Coords=CoordsType(points=self.calculate_page_coords(cont_page)))) counter = EynollahIdCounter() - if len(order_of_texts): + if len(found_polygons_text_region) > 0: _counter_marginals = EynollahIdCounter(region_idx=len(order_of_texts)) - id_of_marginalia_left = [_counter_marginals.next_region_id - for _ in found_polygons_marginals_left] - id_of_marginalia_right = [_counter_marginals.next_region_id - for _ in found_polygons_marginals_right] - xml_reading_order(page, order_of_texts, id_of_marginalia_left, id_of_marginalia_right) + id_of_marginalia = [_counter_marginals.next_region_id for _ in found_polygons_marginals] + xml_reading_order(page, order_of_texts, id_of_marginalia) - for mm, region_contour in enumerate(found_polygons_text_region): - textregion = TextRegionType( - id=counter.next_region_id, type_='paragraph', - Coords=CoordsType(points=self.calculate_polygon_coords(region_contour, page_coord, - skip_layout_reading_order)) - ) - assert textregion.Coords - if conf_contours_textregions: - textregion.Coords.set_conf(conf_contours_textregions[mm]) + for mm in range(len(found_polygons_text_region)): + textregion = TextRegionType(id=counter.next_region_id, type_='paragraph', + Coords=CoordsType(points=self.calculate_polygon_coords(found_polygons_text_region[mm], page_coord), conf=conf_contours_textregion[mm]), + ) + #textregion.set_conf(conf_contours_textregion[mm]) page.add_TextRegion(textregion) if ocr_all_textlines: ocr_textlines = ocr_all_textlines[mm] else: ocr_textlines = None - self.serialize_lines_in_region(textregion, all_found_textline_polygons, mm, page_coord, - all_box_coord, slopes, counter, ocr_textlines) + self.serialize_lines_in_region(textregion, all_found_textline_polygons, mm, page_coord, all_box_coord, slopes, counter, ocr_textlines) - self.logger.debug('len(found_polygons_text_region_h) %s', len(found_polygons_text_region_h)) - for mm, region_contour in enumerate(found_polygons_text_region_h): - textregion = TextRegionType( - id=counter.next_region_id, type_='heading', - Coords=CoordsType(points=self.calculate_polygon_coords(region_contour, page_coord)) - ) - assert textregion.Coords - if conf_contours_textregions_h: - textregion.Coords.set_conf(conf_contours_textregions_h[mm]) - page.add_TextRegion(textregion) - if ocr_all_textlines_h: - ocr_textlines = ocr_all_textlines_h[mm] - else: - ocr_textlines = None - self.serialize_lines_in_region(textregion, all_found_textline_polygons_h, mm, page_coord, - all_box_coord_h, slopes_h, counter, ocr_textlines) - - for mm, region_contour in enumerate(found_polygons_marginals_left): - marginal = TextRegionType( - id=counter.next_region_id, type_='marginalia', - Coords=CoordsType(points=self.calculate_polygon_coords(region_contour, page_coord)) - ) + for mm in range(len(found_polygons_marginals)): + marginal = TextRegionType(id=counter.next_region_id, type_='marginalia', + Coords=CoordsType(points=self.calculate_polygon_coords(found_polygons_marginals[mm], page_coord))) page.add_TextRegion(marginal) - if ocr_all_textlines_marginals_left: - ocr_textlines = ocr_all_textlines_marginals_left[mm] - else: - ocr_textlines = None - self.serialize_lines_in_region(marginal, all_found_textline_polygons_marginals_left, mm, page_coord, all_box_coord_marginals_left, slopes_marginals_left, counter, ocr_textlines) + self.serialize_lines_in_marginal(marginal, all_found_textline_polygons_marginals, mm, page_coord, all_box_coord_marginals, slopes_marginals, counter) - for mm, region_contour in enumerate(found_polygons_marginals_right): - marginal = TextRegionType( - id=counter.next_region_id, type_='marginalia', - Coords=CoordsType(points=self.calculate_polygon_coords(region_contour, page_coord)) - ) - page.add_TextRegion(marginal) - if ocr_all_textlines_marginals_right: - ocr_textlines = ocr_all_textlines_marginals_right[mm] - else: - ocr_textlines = None - self.serialize_lines_in_region(marginal, all_found_textline_polygons_marginals_right, mm, page_coord, - all_box_coord_marginals_right, slopes_marginals_right, counter, ocr_textlines) + for mm in range(len(found_polygons_text_region_img)): + img_region = ImageRegionType(id=counter.next_region_id, Coords=CoordsType()) + page.add_ImageRegion(img_region) + points_co = '' + for lmm in range(len(found_polygons_text_region_img[mm])): + try: + points_co += str(int((found_polygons_text_region_img[mm][lmm,0,0] + page_coord[2]) / self.scale_x)) + points_co += ',' + points_co += str(int((found_polygons_text_region_img[mm][lmm,0,1] + page_coord[0]) / self.scale_y)) + points_co += ' ' + except: - for mm, region_contour in enumerate(found_polygons_drop_capitals): - dropcapital = TextRegionType( - id=counter.next_region_id, type_='drop-capital', - Coords=CoordsType(points=self.calculate_polygon_coords(region_contour, page_coord)) - ) - page.add_TextRegion(dropcapital) - all_box_coord_drop = [[0, 0, 0, 0]] - slopes_drop = [0] - if ocr_all_textlines_drop: - ocr_textlines = ocr_all_textlines_drop[mm] - else: - ocr_textlines = None - self.serialize_lines_in_region(dropcapital, [[found_polygons_drop_capitals[mm]]], 0, page_coord, - all_box_coord_drop, slopes_drop, counter, ocr_textlines) + points_co += str(int((found_polygons_text_region_img[mm][lmm][0] + page_coord[2])/ self.scale_x )) + points_co += ',' + points_co += str(int((found_polygons_text_region_img[mm][lmm][1] + page_coord[0])/ self.scale_y )) + points_co += ' ' - for region_contour in found_polygons_text_region_img: - page.add_ImageRegion( - ImageRegionType(id=counter.next_region_id, - Coords=CoordsType(points=self.calculate_polygon_coords(region_contour, page_coord)))) + img_region.get_Coords().set_points(points_co[:-1]) - for region_contour in polygons_seplines: - page.add_SeparatorRegion( - SeparatorRegionType(id=counter.next_region_id, - Coords=CoordsType(points=self.calculate_polygon_coords(region_contour, [0, 0, 0, 0])))) - - for region_contour in found_polygons_tables: - page.add_TableRegion( - TableRegionType(id=counter.next_region_id, - Coords=CoordsType(points=self.calculate_polygon_coords(region_contour, page_coord)))) + for mm in range(len(polygons_lines_to_be_written_in_xml)): + sep_hor = SeparatorRegionType(id=counter.next_region_id, Coords=CoordsType()) + page.add_SeparatorRegion(sep_hor) + points_co = '' + for lmm in range(len(polygons_lines_to_be_written_in_xml[mm])): + points_co += str(int((polygons_lines_to_be_written_in_xml[mm][lmm,0,0] ) / self.scale_x)) + points_co += ',' + points_co += str(int((polygons_lines_to_be_written_in_xml[mm][lmm,0,1] ) / self.scale_y)) + points_co += ' ' + sep_hor.get_Coords().set_points(points_co[:-1]) + for mm in range(len(found_polygons_tables)): + tab_region = TableRegionType(id=counter.next_region_id, Coords=CoordsType()) + page.add_TableRegion(tab_region) + points_co = '' + for lmm in range(len(found_polygons_tables[mm])): + points_co += str(int((found_polygons_tables[mm][lmm,0,0] + page_coord[2]) / self.scale_x)) + points_co += ',' + points_co += str(int((found_polygons_tables[mm][lmm,0,1] + page_coord[0]) / self.scale_y)) + points_co += ' ' + tab_region.get_Coords().set_points(points_co[:-1]) return pcgts - def calculate_polygon_coords(self, contour, page_coord, skip_layout_reading_order=False): + def build_pagexml_full_layout(self, found_polygons_text_region, found_polygons_text_region_h, page_coord, order_of_texts, id_of_texts, all_found_textline_polygons, all_found_textline_polygons_h, all_box_coord, all_box_coord_h, found_polygons_text_region_img, found_polygons_tables, found_polygons_drop_capitals, found_polygons_marginals, all_found_textline_polygons_marginals, all_box_coord_marginals, slopes, slopes_h, slopes_marginals, cont_page, polygons_lines_to_be_written_in_xml, ocr_all_textlines, conf_contours_textregion, conf_contours_textregion_h): + self.logger.debug('enter build_pagexml_full_layout') + + # create the file structure + pcgts = self.pcgts if self.pcgts else create_page_xml(self.image_filename, self.height_org, self.width_org) + page = pcgts.get_Page() + page.set_Border(BorderType(Coords=CoordsType(points=self.calculate_page_coords(cont_page)))) + + counter = EynollahIdCounter() + _counter_marginals = EynollahIdCounter(region_idx=len(order_of_texts)) + id_of_marginalia = [_counter_marginals.next_region_id for _ in found_polygons_marginals] + xml_reading_order(page, order_of_texts, id_of_marginalia) + + for mm in range(len(found_polygons_text_region)): + textregion = TextRegionType(id=counter.next_region_id, type_='paragraph', + Coords=CoordsType(points=self.calculate_polygon_coords(found_polygons_text_region[mm], page_coord), conf=conf_contours_textregion[mm])) + page.add_TextRegion(textregion) + + if ocr_all_textlines: + ocr_textlines = ocr_all_textlines[mm] + else: + ocr_textlines = None + self.serialize_lines_in_region(textregion, all_found_textline_polygons, mm, page_coord, all_box_coord, slopes, counter, ocr_textlines) + + self.logger.debug('len(found_polygons_text_region_h) %s', len(found_polygons_text_region_h)) + for mm in range(len(found_polygons_text_region_h)): + textregion = TextRegionType(id=counter.next_region_id, type_='header', + Coords=CoordsType(points=self.calculate_polygon_coords(found_polygons_text_region_h[mm], page_coord))) + page.add_TextRegion(textregion) + + if ocr_all_textlines: + ocr_textlines = ocr_all_textlines[mm] + else: + ocr_textlines = None + self.serialize_lines_in_region(textregion, all_found_textline_polygons_h, mm, page_coord, all_box_coord_h, slopes_h, counter, ocr_textlines) + + for mm in range(len(found_polygons_marginals)): + marginal = TextRegionType(id=counter.next_region_id, type_='marginalia', + Coords=CoordsType(points=self.calculate_polygon_coords(found_polygons_marginals[mm], page_coord))) + page.add_TextRegion(marginal) + self.serialize_lines_in_marginal(marginal, all_found_textline_polygons_marginals, mm, page_coord, all_box_coord_marginals, slopes_marginals, counter) + + for mm in range(len(found_polygons_drop_capitals)): + dropcapital = TextRegionType(id=counter.next_region_id, type_='drop-capital', + Coords=CoordsType(points=self.calculate_polygon_coords(found_polygons_drop_capitals[mm], page_coord))) + page.add_TextRegion(dropcapital) + ###all_box_coord_drop = None + ###slopes_drop = None + ###self.serialize_lines_in_dropcapital(dropcapital, [found_polygons_drop_capitals[mm]], mm, page_coord, all_box_coord_drop, slopes_drop, counter, ocr_all_textlines_textregion=None) + + for mm in range(len(found_polygons_text_region_img)): + page.add_ImageRegion(ImageRegionType(id=counter.next_region_id, Coords=CoordsType(points=self.calculate_polygon_coords(found_polygons_text_region_img[mm], page_coord)))) + + for mm in range(len(polygons_lines_to_be_written_in_xml)): + page.add_SeparatorRegion(ImageRegionType(id=counter.next_region_id, Coords=CoordsType(points=self.calculate_polygon_coords(polygons_lines_to_be_written_in_xml[mm], [0 , 0, 0, 0])))) + + for mm in range(len(found_polygons_tables)): + page.add_TableRegion(TableRegionType(id=counter.next_region_id, Coords=CoordsType(points=self.calculate_polygon_coords(found_polygons_tables[mm], page_coord)))) + + return pcgts + + def calculate_polygon_coords(self, contour, page_coord): self.logger.debug('enter calculate_polygon_coords') coords = '' - for point in contour: - if len(point) != 2: - point = point[0] - point_x = point[0] - point_y = point[1] - if not skip_layout_reading_order: - point_x += page_coord[2] - point_y += page_coord[0] - point_x = int(point_x / self.scale_x) - point_y = int(point_y / self.scale_y) - coords += str(point_x) + ',' + str(point_y) + ' ' + for value_bbox in contour: + if len(value_bbox) == 2: + coords += str(int((value_bbox[0] + page_coord[2]) / self.scale_x)) + coords += ',' + coords += str(int((value_bbox[1] + page_coord[0]) / self.scale_y)) + else: + coords += str(int((value_bbox[0][0] + page_coord[2]) / self.scale_x)) + coords += ',' + coords += str(int((value_bbox[0][1] + page_coord[0]) / self.scale_y)) + coords=coords + ' ' return coords[:-1] diff --git a/src/eynollah/training/__init__.py b/tests/__init__.py similarity index 100% rename from src/eynollah/training/__init__.py rename to tests/__init__.py diff --git a/tests/cli_tests/conftest.py b/tests/cli_tests/conftest.py deleted file mode 100644 index 601d76b..0000000 --- a/tests/cli_tests/conftest.py +++ /dev/null @@ -1,47 +0,0 @@ -from typing import List -import pytest -import logging - -from click.testing import CliRunner, Result -from eynollah.cli import main as eynollah_cli - - -@pytest.fixture -def run_eynollah_ok_and_check_logs( - pytestconfig, - caplog, - model_dir, - eynollah_subcommands, - eynollah_log_filter, -): - """ - Generates a Click Runner for `cli`, injects model_path and logging level - to `args`, runs the command and checks whether the logs generated contain - every fragment in `expected_logs` - """ - - def _run_click_ok_logs( - subcommand: 'str', - args: List[str], - expected_logs: List[str], - ) -> Result: - assert subcommand in eynollah_subcommands, f'subcommand {subcommand} must be one of {eynollah_subcommands}' - args = [ - '-m', model_dir, - subcommand, - *args - ] - if pytestconfig.getoption('verbose') > 0: - args = ['-l', 'DEBUG'] + args - caplog.set_level(logging.INFO) - runner = CliRunner() - with caplog.filtering(eynollah_log_filter): - result = runner.invoke(eynollah_cli, args, catch_exceptions=False) - assert result.exit_code == 0, result.stdout - if expected_logs: - logmsgs = [logrec.message for logrec in caplog.records] - assert any(logmsg.startswith(needle) for needle in expected_logs for logmsg in logmsgs), f'{expected_logs} not in {logmsgs}' - return result - - return _run_click_ok_logs - diff --git a/tests/cli_tests/test_binarization.py b/tests/cli_tests/test_binarization.py deleted file mode 100644 index aa52957..0000000 --- a/tests/cli_tests/test_binarization.py +++ /dev/null @@ -1,53 +0,0 @@ -import pytest -from PIL import Image - -@pytest.mark.parametrize( - "options", - [ - [], # defaults - ["--no-patches"], - ], ids=str) -def test_run_eynollah_binarization_filename( - tmp_path, - run_eynollah_ok_and_check_logs, - resources_dir, - options, -): - infile = resources_dir / '2files/kant_aufklaerung_1784_0020.tif' - outfile = tmp_path / 'kant_aufklaerung_1784_0020.png' - run_eynollah_ok_and_check_logs( - 'binarization', - [ - '-i', str(infile), - '-o', str(outfile), - ] + options, - [ - 'Predicting' - ] - ) - assert outfile.exists() - with Image.open(infile) as original_img: - original_size = original_img.size - with Image.open(outfile) as binarized_img: - binarized_size = binarized_img.size - assert original_size == binarized_size - -def test_run_eynollah_binarization_directory( - tmp_path, - run_eynollah_ok_and_check_logs, - resources_dir, - image_resources, -): - outdir = tmp_path - run_eynollah_ok_and_check_logs( - 'binarization', - [ - '-di', str(resources_dir / '2files'), - '-o', str(outdir), - ], - [ - f'Predicting {image_resources[0].name}', - f'Predicting {image_resources[1].name}', - ] - ) - assert len(list(outdir.iterdir())) == 2 diff --git a/tests/cli_tests/test_enhance.py b/tests/cli_tests/test_enhance.py deleted file mode 100644 index b994c5d..0000000 --- a/tests/cli_tests/test_enhance.py +++ /dev/null @@ -1,52 +0,0 @@ -import pytest -from PIL import Image - -@pytest.mark.parametrize( - "options", - [ - [], # defaults - ["-sos"], - ], ids=str) -def test_run_eynollah_enhancement_filename( - tmp_path, - resources_dir, - run_eynollah_ok_and_check_logs, - options, -): - infile = resources_dir / '2files/kant_aufklaerung_1784_0020.tif' - outfile = tmp_path / 'kant_aufklaerung_1784_0020.png' - run_eynollah_ok_and_check_logs( - 'enhancement', - [ - '-i', str(infile), - '-o', str(outfile.parent), - ] + options, - [ - 'Image was enhanced', - ] - ) - with Image.open(infile) as original_img: - original_size = original_img.size - with Image.open(outfile) as enhanced_img: - enhanced_size = enhanced_img.size - assert (original_size == enhanced_size) == ("-sos" in options) - -def test_run_eynollah_enhancement_directory( - tmp_path, - resources_dir, - image_resources, - run_eynollah_ok_and_check_logs, -): - outdir = tmp_path - run_eynollah_ok_and_check_logs( - 'enhancement', - [ - '-di', str(resources_dir/ '2files'), - '-o', str(outdir), - ], - [ - f'Image {image_resources[0]} was enhanced', - f'Image {image_resources[1]} was enhanced', - ] - ) - assert len(list(outdir.iterdir())) == 2 diff --git a/tests/cli_tests/test_layout.py b/tests/cli_tests/test_layout.py deleted file mode 100644 index 7cbe013..0000000 --- a/tests/cli_tests/test_layout.py +++ /dev/null @@ -1,119 +0,0 @@ -import pytest -from ocrd_modelfactory import page_from_file -from ocrd_models.constants import NAMESPACES as NS - -@pytest.mark.parametrize( - "options", - [ - [], # defaults - #["--allow_scaling", "--curved-line"], - ["--allow_scaling", "--curved-line", "--full-layout"], - ["--allow_scaling", "--curved-line", "--full-layout", "--reading_order_machine_based"], - # -ep ... - # -eoi ... - # --skip_layout_and_reading_order - ], ids=str) -def test_run_eynollah_layout_filename( - tmp_path, - run_eynollah_ok_and_check_logs, - resources_dir, - options, -): - infile = resources_dir / '2files/kant_aufklaerung_1784_0020.tif' - outfile = tmp_path / 'kant_aufklaerung_1784_0020.xml' - run_eynollah_ok_and_check_logs( - 'layout', - [ - '-i', str(infile), - '-o', str(outfile.parent), - ] + options, - [ - str(infile) - ] - ) - assert outfile.exists() - tree = page_from_file(str(outfile)).etree - regions = tree.xpath("//page:TextRegion", namespaces=NS) - assert len(regions) >= 2, "result is inaccurate" - regions = tree.xpath("//page:SeparatorRegion", namespaces=NS) - assert len(regions) >= 2, "result is inaccurate" - lines = tree.xpath("//page:TextLine", namespaces=NS) - assert len(lines) == 31, "result is inaccurate" # 29 paragraph lines, 1 page and 1 catch-word line - -@pytest.mark.parametrize( - "options", - [ - ["--tables"], - ["--tables", "--full-layout"], - ], ids=str) -def test_run_eynollah_layout_filename2( - tmp_path, - resources_dir, - run_eynollah_ok_and_check_logs, - options, -): - infile = resources_dir / '2files/euler_rechenkunst01_1738_0025.tif' - outfile = tmp_path / 'euler_rechenkunst01_1738_0025.xml' - run_eynollah_ok_and_check_logs( - 'layout', - [ - '-i', str(infile), - '-o', str(outfile.parent), - ] + options, - [ - str(infile) - ] - ) - assert outfile.exists() - tree = page_from_file(str(outfile)).etree - regions = tree.xpath("//page:TextRegion", namespaces=NS) - assert len(regions) >= 2, "result is inaccurate" - regions = tree.xpath("//page:TableRegion", namespaces=NS) - # model/decoding is not very precise, so (depending on mode) we can get fractures/splits/FP - assert len(regions) >= 1, "result is inaccurate" - regions = tree.xpath("//page:SeparatorRegion", namespaces=NS) - assert len(regions) >= 2, "result is inaccurate" - lines = tree.xpath("//page:TextLine", namespaces=NS) - assert len(lines) >= 2, "result is inaccurate" # mostly table (if detected correctly), but 1 page and 1 catch-word line - -def test_run_eynollah_layout_directory( - tmp_path, - resources_dir, - run_eynollah_ok_and_check_logs, -): - outdir = tmp_path - run_eynollah_ok_and_check_logs( - 'layout', - [ - '-di', str(resources_dir / '2files'), - '-o', str(outdir), - ], - [ - 'Job done in', - 'All jobs done in', - ] - ) - assert len(list(outdir.iterdir())) == 2 - -# def test_run_eynollah_layout_marginalia( -# tmp_path, -# resources_dir, -# run_eynollah_ok_and_check_logs, -# ): -# outdir = tmp_path -# outfile = outdir / 'estor_rechtsgelehrsamkeit02_1758_0880_800px.xml' -# run_eynollah_ok_and_check_logs( -# 'layout', -# [ -# '-i', str(resources_dir / 'estor_rechtsgelehrsamkeit02_1758_0880_800px.jpg'), -# '-o', str(outdir), -# ], -# [ -# 'Job done in', -# 'All jobs done in', -# ] -# ) -# assert outfile.exists() -# tree = page_from_file(str(outfile)).etree -# regions = tree.xpath('//page:TextRegion[type="marginalia"]', namespaces=NS) -# assert len(regions) == 5, "expected 5 marginalia regions" diff --git a/tests/cli_tests/test_mbreorder.py b/tests/cli_tests/test_mbreorder.py deleted file mode 100644 index e429e98..0000000 --- a/tests/cli_tests/test_mbreorder.py +++ /dev/null @@ -1,47 +0,0 @@ -from ocrd_modelfactory import page_from_file -from ocrd_models.constants import NAMESPACES as NS - -def test_run_eynollah_mbreorder_filename( - tmp_path, - resources_dir, - run_eynollah_ok_and_check_logs, -): - infile = resources_dir / '2files/kant_aufklaerung_1784_0020.xml' - outfile = tmp_path /'kant_aufklaerung_1784_0020.xml' - run_eynollah_ok_and_check_logs( - 'machine-based-reading-order', - [ - '-i', str(infile), - '-o', str(outfile.parent), - ], - [ - # FIXME: mbreorder has no logging! - ] - ) - assert outfile.exists() - #in_tree = page_from_file(str(infile)).etree - #in_order = in_tree.xpath("//page:OrderedGroup//@regionRef", namespaces=NS) - out_tree = page_from_file(str(outfile)).etree - out_order = out_tree.xpath("//page:OrderedGroup//@regionRef", namespaces=NS) - #assert len(out_order) >= 2, "result is inaccurate" - #assert in_order != out_order - assert out_order == ['r_1_1', 'r_2_1', 'r_2_2', 'r_2_3'] - -def test_run_eynollah_mbreorder_directory( - tmp_path, - resources_dir, - run_eynollah_ok_and_check_logs, -): - outdir = tmp_path - run_eynollah_ok_and_check_logs( - 'machine-based-reading-order', - [ - '-di', str(resources_dir / '2files'), - '-o', str(outdir), - ], - [ - # FIXME: mbreorder has no logging! - ] - ) - assert len(list(outdir.iterdir())) == 2 - diff --git a/tests/cli_tests/test_ocr.py b/tests/cli_tests/test_ocr.py deleted file mode 100644 index 6bf3080..0000000 --- a/tests/cli_tests/test_ocr.py +++ /dev/null @@ -1,64 +0,0 @@ -import pytest -from ocrd_modelfactory import page_from_file -from ocrd_models.constants import NAMESPACES as NS - -@pytest.mark.parametrize( - "options", - [ - ["-trocr"], - [], # defaults - ["-doit", #str(outrenderfile.parent)], - ], - ], ids=str) -def test_run_eynollah_ocr_filename( - tmp_path, - run_eynollah_ok_and_check_logs, - resources_dir, - options, -): - infile = resources_dir / '2files/kant_aufklaerung_1784_0020.tif' - outfile = tmp_path / 'kant_aufklaerung_1784_0020.xml' - outrenderfile = tmp_path / 'render' / 'kant_aufklaerung_1784_0020.png' - outrenderfile.parent.mkdir() - if "-doit" in options: - options.insert(options.index("-doit") + 1, str(outrenderfile.parent)) - run_eynollah_ok_and_check_logs( - 'ocr', - [ - '-i', str(infile), - '-dx', str(infile.parent), - '-o', str(outfile.parent), - ] + options, - [ - # FIXME: ocr has no logging! - ] - ) - assert outfile.exists() - if "-doit" in options: - assert outrenderfile.exists() - #in_tree = page_from_file(str(infile)).etree - #in_order = in_tree.xpath("//page:OrderedGroup//@regionRef", namespaces=NS) - out_tree = page_from_file(str(outfile)).etree - out_texts = out_tree.xpath("//page:TextLine/page:TextEquiv[last()]/page:Unicode/text()", namespaces=NS) - assert len(out_texts) >= 2, ("result is inaccurate", out_texts) - assert sum(map(len, out_texts)) > 100, ("result is inaccurate", out_texts) - -def test_run_eynollah_ocr_directory( - tmp_path, - run_eynollah_ok_and_check_logs, - resources_dir, -): - outdir = tmp_path - run_eynollah_ok_and_check_logs( - 'ocr', - [ - '-di', str(resources_dir / '2files'), - '-dx', str(resources_dir / '2files'), - '-o', str(outdir), - ], - [ - # FIXME: ocr has no logging! - ] - ) - assert len(list(outdir.iterdir())) == 2 - diff --git a/tests/conftest.py b/tests/conftest.py deleted file mode 100644 index 69f3d28..0000000 --- a/tests/conftest.py +++ /dev/null @@ -1,37 +0,0 @@ -from glob import glob -import os -import pytest -from pathlib import Path - - -@pytest.fixture() -def tests_dir(): - return Path(__file__).parent.resolve() - -@pytest.fixture() -def model_dir(tests_dir): - return os.environ.get('EYNOLLAH_MODELS_DIR', str(tests_dir.joinpath('..').resolve())) - -@pytest.fixture() -def resources_dir(tests_dir): - return tests_dir / 'resources' - -@pytest.fixture() -def image_resources(resources_dir): - return [Path(x) for x in glob(str(resources_dir / '2files/*.tif'))] - -@pytest.fixture() -def eynollah_log_filter(): - return lambda logrec: logrec.name.startswith('eynollah') - -@pytest.fixture -def eynollah_subcommands(): - return [ - 'binarization', - 'layout', - 'ocr', - 'enhancement', - 'machine-based-reading-order', - 'models', - ] - diff --git a/tests/resources/2files/euler_rechenkunst01_1738_0025.xml b/tests/resources/2files/euler_rechenkunst01_1738_0025.xml deleted file mode 100644 index 1a92f73..0000000 --- a/tests/resources/2files/euler_rechenkunst01_1738_0025.xml +++ /dev/null @@ -1,1626 +0,0 @@ - - - OCR-D - 2016-09-29T14:32:09 - 2018-04-25T08:56:33 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 9 - - - 9 - - - 9 - - - - - - - - - der - - - - - rechten - - - - - gegen - - - - - der - - - - - lincken - - - - - Hand - - - - - bedeutet - - - der rechten gegen der lincken Hand bedeutet - - - - - - - - wie - - - - - folget: - - - wie folget: - - - der rechten gegen der lincken Hand bedeutet -wie folget: - - - - - - - - - I. - - - I. - - - I. - - - - - - - - - 0 - - - - - - - - - - - nichts - - - 0 - nichts - - - - - - - - 1 - - - - - - - - - - - eins - - - 1 - eins - - - - - - - - 2 - - - - - - - - - - - zwey - - - 2 - zwey - - - - - - - - 3 - - - - - - - - - - - drey - - - 3 - drey - - - - - - - - 4 - - - - - - - - - - - vier - - - 4 - vier - - - 0 - nichts -1 - eins -2 - zwey -3 - drey -4 - vier - - - - - - - - - 5 - - - - - - - - - - - fuͤnf - - - 5 - fuͤnf - - - - - - - - 6 - - - - - - - - - - - ſechs - - - 6 - ſechs - - - - - - - 7 - - - - - - - - - - - ſieben - - - 7 - ſieben - - - - - - - - 8 - - - - - - - - - - - acht - - - 8 - acht - - - - - - - - 9 - - - - - - - - - - - neun - - - 9 - neun - - - 5 - fuͤnf -6 - ſechs -7 - ſieben -8 - acht -9 - neun - - - - - - - - - Auf - - - - - der - - - - - zweyten - - - - - Stelle - - - - - aber - - - - - bedeutet. - - - Auf der zweyten Stelle aber bedeutet. - - - Auf der zweyten Stelle aber bedeutet. - - - - - - - - - II. - - - II. - - - II. - - - - - - - - - 0 - - - - - - - - - - - nichts - - - 0 - nichts - - - - - - - - 1 - - - - - - - - - - - zehen - - - 1 - zehen - - - - - - - - 2 - - - - - - - - - - - zwanzig - - - 2 - zwanzig - - - - - - - 3 - - - - - - - - - - - dreyßig - - - 3 - dreyßig - - - - - - - 4 - - - - - - - - - - - vierzig - - - 4 - vierzig - - 0 - nichts -1 - zehen -2 - zwanzig -3 - dreyßig -4 - vierzig - - - - - - - - - 5 - - - - - - - - - - - fuͤnfzig - - - 5 - fuͤnfzig - - - - - - - - 6 - - - - - - - - - - - ſechzig - - - 6 - ſechzig - - - - - - - 7 - - - - - - - - - - - ſiebenzig - - - 7 - ſiebenzig - - - - - - - 8 - - - - - - - - - - - achtzig - - - 8 - achtzig - - - - - - - 9 - - - - - - - - - - - neunzig - - - 9 - neunzig - - 5 - fuͤnfzig -6 - ſechzig -7 - ſiebenzig -8 - achtzig -9 - neunzig - - - - - - - - - Auf - - - - - der - - - - - dritten - - - - - Stelle - - - - - bedeutet. - - - Auf der dritten Stelle bedeutet. - - - Auf der dritten Stelle bedeutet. - - - - - - - - - III. - - - III. - - - III. - - - - - - - - - 0 - - - - - - - - - - - nichts - - - 0 - nichts - - - - - - - - 1 - - - - - - - - - - - hundert - - - 1 - hundert - - - - - - - - 2 - - - - - - - - - - - zwey - - - - - hundert - - - 2 - zwey hundert - - - - - - - - 3 - - - - - - - - - - - drey - - - - - hundert - - - 3 - drey hundert - - - - - - - - 4 - - - - - - - - - - - vier - - - - - hundert - - - 4 - vier hundert - - - 0 - nichts -1 - hundert -2 - zwey hundert -3 - drey hundert -4 - vier hundert - - - - - - - - - 5 - - - - - - - - - - - fuͤnf - - - - - hundert - - - 5 - fuͤnf hundert - - - - - - - - 6 - - - - - - - - - - - ſechs - - - - - hundert - - - 6 - ſechs hundert - - - - - - - 7 - - - - - - - - - - - ſieben - - - - - hundert - - - 7 - ſieben hundert - - - - - - - - 8 - - - - - - - - - - - acht - - - - - hundert - - - 8 - acht hundert - - - - - - - 9 - - - - - - - - - - - neun - - - - - hundert - - - 9 - neun hundert - - - 5 - fuͤnf hundert -6 - ſechs hundert -7 - ſieben hundert -8 - acht hundert -9 - neun hundert - - - - - - - - - Auf - - - - - der - - - - - vierten - - - - - Stelle - - - - - bedeutet. - - - Auf der vierten Stelle bedeutet. - - - Auf der vierten Stelle bedeutet. - - - - - - - - - IV. - - - IV. - - - IV. - - - - - - - - - 0 - - - - - - - - - - - nichts - - - 0 - nichts - - - - - - - - 1 - - - - - - - - - - - tauſend - - - 1 - tauſend - - - - - - - - 2 - - - - - - - - - - - zwey - - - - - tauſend - - - 2 - zwey tauſend - - - - - - - - 3 - - - - - - - - - - - drey - - - - - tauſend - - - 3 - drey tauſend - - - - - - - - 4 - - - - - - - - - - - vier - - - - - tauſend - - - 4 - vier tauſend - - - 0 - nichts -1 - tauſend -2 - zwey tauſend -3 - drey tauſend -4 - vier tauſend - - - - - - - - - 5 - - - - - - - - - - - fuͤnf - - - - - tauſend - - - 5 - fuͤnf tauſend - - - - - - - - 6 - - - - - - - - - - - ſechs - - - - - tauſend - - - 6 - ſechs tauſend - - - - - - - 7 - - - - - - - - - - - ſieben - - - - - tauſend - - - 7 - ſieben tauſend - - - - - - - - 8 - - - - - - - - - - - acht - - - - - tauſend - - - 8 - acht tauſend - - - - - - - 9 - - - - - - - - - - - neun - - - - - tauſend - - - 9 - neun tauſend - - 5 - fuͤnf tauſend -6 - ſechs tauſend -7 - ſieben tauſend -8 - acht tauſend -9 - neun tauſend - - - - - - - - - Auf - - - - - der - - - - - fuͤnften - - - - - Stelle - - - - - bedeutet. - - - Auf der fuͤnften Stelle bedeutet. - - - Auf der fuͤnften Stelle bedeutet. - - - - - - - - - V. - - - V. - - - V. - - - - - - - - - 0 - - - - - - - - - - - nichts - - - 0 - nichts - - - - - - - - 1 - - - - - - - - - - - zehen - - - - - tauſend - - - 1 - zehen tauſend - - - - - - - - 2 - - - - - - - - - - - zwanzig - - - - - tauſend - - - 2 - zwanzig tauſend - - - - - - - - 3 - - - - - - - - - - - dreyßig - - - - - tauſend - - - 3 - dreyßig tauſend - - - - - - - - 4 - - - - - - - - - - - vierzig - - - - - tauſend - - - 4 - vierzig tauſend - - - 0 - nichts -1 - zehen tauſend -2 - zwanzig tauſend -3 - dreyßig tauſend -4 - vierzig tauſend - - - - - - - - - 5 - - - - - - - - - - - fuͤnfzig - - - - - tauſend - - - 5 - fuͤnfzig tauſend - - - - - - - - 6 - - - - - - - - - - - ſechzig - - - - - tauſend - - - 6 - ſechzig tauſend - - - - - - - 7 - - - - - - - - - - - ſiebenzig - - - - - tauſend - - - 7 - ſiebenzig tauſend - - - - - - - - 8 - - - - - - - - - - - achtzig - - - - - tauſend - - - 8 - achtzig tauſend - - - - - - - 9 - - - - - - - - - - - neunzig - - - - - tauſend - - - 9 - neunzig tauſend - - - 5 - fuͤnfzig tauſend -6 - ſechzig tauſend -7 - ſiebenzig tauſend -8 - achtzig tauſend -9 - neunzig tauſend - - - - - - - - A - - - - - 5 - - - A 5 - - A 5 - - - - - - - - - Anf - - - Anf - - Anf - - - - - - - - - - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/tests/resources/2files/kant_aufklaerung_1784_0020.xml b/tests/resources/2files/kant_aufklaerung_1784_0020.xml deleted file mode 100644 index 47484cd..0000000 --- a/tests/resources/2files/kant_aufklaerung_1784_0020.xml +++ /dev/null @@ -1,2129 +0,0 @@ - - - OCR-D - 2016-09-20T11:09:27.431+02:00 - 2018-04-24T17:44:49.605+01:00 - - - - - - - - - - - - - - - - - - - - - - - ( - - - - - - - 484 - - - - - - - ) - - - - - ( 484 ) - - - - ( 484 ) - - - - - - - - - - - gewiegelt - - - - - - - worden - - - - - - - ; - - - - - - - ſo - - - - - - - ſchaͤdlich - - - - - - - iſt - - - - - - - es - - - - - - - Vorurtheile - - - - - - - zu - - - - - gewiegelt worden; ſo ſchaͤdlich iſt es Vorurtheile zu - - - - - - - - - - pflanzen - - - - - - - , - - - - - - - weil - - - - - - - ſie - - - - - - - ſich - - - - - - - zuletzt - - - - - - - an - - - - - - - denen - - - - - - - ſelbſt - - - - - - - raͤchen - - - - - - - , - - - - - pflanzen, weil ſie ſich zuletzt an denen ſelbſt raͤchen, - - - - - - - - - - die - - - - - - - , - - - - - - - oder - - - - - - - deren - - - - - - - Vorgaͤnger - - - - - - - , - - - - - - - ihre - - - - - - - Urheber - - - - - - - geweſen - - - - - die, oder deren Vorgaͤnger, ihre Urheber geweſen - - - - - - - - - - ſind - - - - - - - . - - - - - - - Daher - - - - - - - kann - - - - - - - ein - - - - - - - Publikum - - - - - - - nur - - - - - - - langſam - - - - - - - zur - - - - - ſind. Daher kann ein Publikum nur langſam zur - - - - - - - - - - Aufklaͤrung - - - - - - - gelangen - - - - - - - . - - - - - - - Durch - - - - - - - eine - - - - - - - Revolution - - - - - - - wird - - - - - Aufklaͤrung gelangen. Durch eine Revolution wird - - - - - - - - - - vielleicht - - - - - - - wohl - - - - - - - ein - - - - - - - Abfall - - - - - - - von - - - - - - - perſoͤnlichem - - - - - - - Despo- - - - - - vielleicht wohl ein Abfall von perſoͤnlichem Despo- - - - - - - - - - - tism - - - - - - - und - - - - - - - gewinnſuͤchtiger - - - - - - - oder - - - - - - - herrſchſüchtiger - - - - - - - Be - - - - - - - - - - - - - tism und gewinnſuͤchtiger oder herrſchſüchtiger Be- - - - - - - - - - - druͤkkung - - - - - - - , - - - - - - - aber - - - - - - - niemals - - - - - - - wahre - - - - - - - Reform - - - - - - - der - - - - - - - Den - - - - - - - - - - - - - druͤkkung, aber niemals wahre Reform der Den- - - - - - - - - - - kungsart - - - - - - - zu - - - - - - - Stande - - - - - - - kommen - - - - - - - ; - - - - - - - ſondern - - - - - - - neue - - - - - - - Vor - - - - - - - - - - - - - kungsart zu Stande kommen; ſondern neue Vor- - - - - - - - - - - urtheile - - - - - - - werden - - - - - - - , - - - - - - - eben - - - - - - - ſowohl - - - - - - - als - - - - - - - die - - - - - - - alten - - - - - - - , - - - - - - - zum - - - - - urtheile werden, eben ſowohl als die alten, zum - - - - - - - - - - Leitbande - - - - - - - des - - - - - - - gedankenloſen - - - - - - - großen - - - - - - - Haufens - - - - - Leitbande des gedankenloſen großen Haufens - - - - - - - - - - dienen - - - - - - - . - - - - - dienen. - - - - gewiegelt worden; ſo ſchaͤdlich iſt es Vorurtheile zu -pflanzen, weil ſie ſich zuletzt an denen ſelbſt raͤchen, -die, oder deren Vorgaͤnger, ihre Urheber geweſen -ſind. Daher kann ein Publikum nur langſam zur -Aufklaͤrung gelangen. Durch eine Revolution wird -vielleicht wohl ein Abfall von perſoͤnlichem Despo- -tism und gewinnſuͤchtiger oder herrſchſüchtiger Be- -druͤkkung, aber niemals wahre Reform der Den- -kungsart zu Stande kommen; ſondern neue Vor- -urtheile werden, eben ſowohl als die alten, zum -Leitbande des gedankenloſen großen Haufens -dienen. - - - - - - - - - - - Zu - - - - - - - dieſer - - - - - - - Aufklaͤrung - - - - - - - aber - - - - - - - wird - - - - - - - nichts - - - - - - - erfordert - - - - - Zu dieſer Aufklaͤrung aber wird nichts erfordert - - - - - - - - - - als - - - - - - - Freiheit - - - - - - - ; - - - - - - - und - - - - - - - zwar - - - - - - - die - - - - - - - unſchaͤdlichſte - - - - - - - unter - - - - - als Freiheit; und zwar die unſchaͤdlichſte unter - - - - - - - - - allem - - - - - - - , - - - - - - - was - - - - - - - nur - - - - - - - Freiheit - - - - - - - heißen - - - - - - - mag - - - - - - - , - - - - - - - naͤmlich - - - - - - - die - - - - - - - : - - - - - allem, was nur Freiheit heißen mag, naͤmlich die: - - - - - - - - - - von - - - - - - - ſeiner - - - - - - - Vernunft - - - - - - - in - - - - - - - allen - - - - - - - Stuͤkken - - - - - - - oͤffentlichen - - - - - von ſeiner Vernunft in allen Stuͤkken oͤffentlichen - - - - - - - - - - Gebrauch - - - - - - - zu - - - - - - - machen - - - - - - - . - - - - - - - Nun - - - - - - - hoͤre - - - - - - - ich - - - - - - - aber - - - - - - - von - - - - - - - al - - - - - - - - - - - - - Gebrauch zu machen. Nun hoͤre ich aber von al- - - - - - - - - - - len - - - - - - - Seiten - - - - - - - rufen - - - - - - - : - - - - - - - raͤſonnirt - - - - - - - nicht - - - - - - - ! - - - - - - - Der - - - - - - - Offi - - - - - - - - - - - - - len Seiten rufen: raͤſonnirt nicht! Der Offi- - - - - - - - - - - zier - - - - - - - ſagt - - - - - - - : - - - - - - - raͤſonnirt - - - - - - - nicht - - - - - - - , - - - - - - - ſondern - - - - - - - exercirt - - - - - - - ! - - - - - - - Der - - - - - zier ſagt: raͤſonnirt nicht, ſondern exercirt! Der - - - - - - - - - - Finanzrath - - - - - - - : - - - - - - - raͤſonnirt - - - - - - - nicht - - - - - - - , - - - - - - - ſondern - - - - - - - bezahlt - - - - - - - ! - - - - - - - Der - - - - - Finanzrath: raͤſonnirt nicht, ſondern bezahlt! Der - - - - - - - - - - Geiſtliche - - - - - - - : - - - - - - - raͤſonnirt - - - - - - - nicht - - - - - - - , - - - - - - - ſondern - - - - - - - glaubt - - - - - - - ! - - - - - - - ( - - - - - - - Nur - - - - - Geiſtliche: raͤſonnirt nicht, ſondern glaubt! (Nur - - - - - - - - - - ein - - - - - - - einziger - - - - - - - Herr - - - - - - - in - - - - - - - der - - - - - - - Welt - - - - - - - ſagt - - - - - - - : - - - - - - - raͤſonnirt - - - - - - - , - - - - - - - ſo - - - - - ein einziger Herr in der Welt ſagt: raͤſonnirt, ſo - - - - - - - - - - viel - - - - - - - ihr - - - - - - - wollt - - - - - - - , - - - - - - - und - - - - - - - woruͤber - - - - - - - ihr - - - - - - - wollt - - - - - - - ; - - - - - - - aber - - - - - - - ge - - - - - - - - - - - - - viel ihr wollt, und woruͤber ihr wollt; aber ge- - - - - - - - - - - horcht - - - - - - - ! - - - - - - - ) - - - - - - - Hier - - - - - - - iſt - - - - - - - uͤberall - - - - - - - Einſchraͤnkung - - - - - - - der - - - - - - - Frei - - - - - - - - - - - - - horcht!) Hier iſt uͤberall Einſchraͤnkung der Frei- - - - - - - - - - - heit - - - - - - - . - - - - - - - Welche - - - - - - - Einſchraͤnkung - - - - - - - aber - - - - - - - iſt - - - - - - - der - - - - - - - Aufklaͤ - - - - - - - - - - - - - heit. Welche Einſchraͤnkung aber iſt der Aufklaͤ- - - - - - - - - - - rung - - - - - - - hinderlich - - - - - - - ? - - - - - - - welche - - - - - - - nicht - - - - - - - , - - - - - - - ſondern - - - - - - - ihr - - - - - - - wohl - - - - - - - gar - - - - - rung hinderlich? welche nicht, ſondern ihr wohl gar - - - - - - - - - - befoͤrderlich - - - - - - - ? - - - - - - - — - - - - - - - Ich - - - - - - - antworte - - - - - - - : - - - - - - - der - - - - - - - oͤffentliche - - - - - befoͤrderlich? — Ich antworte: der oͤffentliche - - - - - - - - - - Gebrauch - - - - - - - ſeiner - - - - - - - Vernunft - - - - - - - muß - - - - - - - jederzeit - - - - - - - frei - - - - - - - ſein - - - - - - - , - - - - - Gebrauch ſeiner Vernunft muß jederzeit frei ſein, - - - - - - - - - - und - - - - - - - der - - - - - - - allein - - - - - - - kann - - - - - - - Aufklaͤrung - - - - - - - unter - - - - - - - Menſchen - - - - - - - zu - - - - - und der allein kann Aufklaͤrung unter Menſchen zu - - - - - Zu dieſer Aufklaͤrung aber wird nichts erfordert -als Freiheit; und zwar die unſchaͤdlichſte unter -allem, was nur Freiheit heißen mag, naͤmlich die: -von ſeiner Vernunft in allen Stuͤkken oͤffentlichen -Gebrauch zu machen. Nun hoͤre ich aber von al- -len Seiten rufen: raͤſonnirt nicht! Der Offi- -zier ſagt: raͤſonnirt nicht, ſondern exercirt! Der -Finanzrath: raͤſonnirt nicht, ſondern bezahlt! Der -Geiſtliche: raͤſonnirt nicht, ſondern glaubt! (Nur -ein einziger Herr in der Welt ſagt: raͤſonnirt, ſo -viel ihr wollt, und woruͤber ihr wollt; aber ge- -horcht!) Hier iſt uͤberall Einſchraͤnkung der Frei- -heit. Welche Einſchraͤnkung aber iſt der Aufklaͤ- -rung hinderlich? welche nicht, ſondern ihr wohl gar -befoͤrderlich? — Ich antworte: der oͤffentliche -Gebrauch ſeiner Vernunft muß jederzeit frei ſein, -und der allein kann Aufklaͤrung unter Menſchen zu - - - - - - - - - - - Stan - - - - - - - - - - - - - Stan- - - - - - Stan- - - - - - - - - - - \ No newline at end of file diff --git a/tests/resources/2files/euler_rechenkunst01_1738_0025.tif b/tests/resources/euler_rechenkunst01_1738_0025.tif similarity index 100% rename from tests/resources/2files/euler_rechenkunst01_1738_0025.tif rename to tests/resources/euler_rechenkunst01_1738_0025.tif diff --git a/tests/resources/2files/kant_aufklaerung_1784_0020.tif b/tests/resources/kant_aufklaerung_1784_0020.tif similarity index 100% rename from tests/resources/2files/kant_aufklaerung_1784_0020.tif rename to tests/resources/kant_aufklaerung_1784_0020.tif diff --git a/tests/resources/marginalia/estor_rechtsgelehrsamkeit02_1758_0880_800px.jpg b/tests/resources/marginalia/estor_rechtsgelehrsamkeit02_1758_0880_800px.jpg deleted file mode 100644 index 9270508..0000000 Binary files a/tests/resources/marginalia/estor_rechtsgelehrsamkeit02_1758_0880_800px.jpg and /dev/null differ diff --git a/tests/resources/marginalia/estor_rechtsgelehrsamkeit02_1758_0880_800px.xml b/tests/resources/marginalia/estor_rechtsgelehrsamkeit02_1758_0880_800px.xml deleted file mode 100644 index 45240c4..0000000 --- a/tests/resources/marginalia/estor_rechtsgelehrsamkeit02_1758_0880_800px.xml +++ /dev/null @@ -1,235 +0,0 @@ - - - - SBB_QURATOR - 2025-10-30T16:38:21.180191 - 2025-10-30T16:38:21.180191 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/tests/test_model_zoo.py b/tests/test_model_zoo.py deleted file mode 100644 index 2042b28..0000000 --- a/tests/test_model_zoo.py +++ /dev/null @@ -1,16 +0,0 @@ -from eynollah.model_zoo import EynollahModelZoo - -def test_trocr1( - model_dir, -): - model_zoo = EynollahModelZoo(model_dir) - try: - from transformers import TrOCRProcessor, VisionEncoderDecoderModel - model_zoo.load_model('trocr_processor') - proc = model_zoo.get('trocr_processor', TrOCRProcessor) - assert isinstance(proc, TrOCRProcessor) - model_zoo.load_model('ocr', 'tr') - model = model_zoo.get('ocr', VisionEncoderDecoderModel) - assert isinstance(model, VisionEncoderDecoderModel) - except ImportError: - pass diff --git a/tests/test_run.py b/tests/test_run.py new file mode 100644 index 0000000..607140e --- /dev/null +++ b/tests/test_run.py @@ -0,0 +1,132 @@ +from os import environ +from pathlib import Path +import logging +from PIL import Image +from eynollah.cli import layout as layout_cli, binarization as binarization_cli +from click.testing import CliRunner +from ocrd_modelfactory import page_from_file +from ocrd_models.constants import NAMESPACES as NS + +testdir = Path(__file__).parent.resolve() + +EYNOLLAH_MODELS = environ.get('EYNOLLAH_MODELS', str(testdir.joinpath('..', 'models_eynollah').resolve())) +SBBBIN_MODELS = environ.get('SBBBIN_MODELS', str(testdir.joinpath('..', 'default-2021-03-09').resolve())) + +def test_run_eynollah_layout_filename(tmp_path, subtests, pytestconfig, caplog): + infile = testdir.joinpath('resources/kant_aufklaerung_1784_0020.tif') + outfile = tmp_path / 'kant_aufklaerung_1784_0020.xml' + args = [ + '-m', EYNOLLAH_MODELS, + '-i', str(infile), + '-o', str(outfile.parent), + # subtests write to same location + '--overwrite', + ] + if pytestconfig.getoption('verbose') > 0: + args.extend(['-l', 'DEBUG']) + caplog.set_level(logging.INFO) + def only_eynollah(logrec): + return logrec.name == 'eynollah' + runner = CliRunner() + for options in [ + [], # defaults + ["--allow_scaling", "--curved-line"], + ["--allow_scaling", "--curved-line", "--full-layout"], + ["--allow_scaling", "--curved-line", "--full-layout", "--reading_order_machine_based"], + ["--allow_scaling", "--curved-line", "--full-layout", "--reading_order_machine_based", + "--textline_light", "--light_version"], + # -ep ... + # -eoi ... + # --do_ocr + # --skip_layout_and_reading_order + ]: + with subtests.test(#msg="test CLI", + options=options): + with caplog.filtering(only_eynollah): + result = runner.invoke(layout_cli, args + options, catch_exceptions=False) + print(result) + assert result.exit_code == 0 + logmsgs = [logrec.message for logrec in caplog.records] + assert str(infile) in logmsgs + assert outfile.exists() + tree = page_from_file(str(outfile)).etree + regions = tree.xpath("//page:TextRegion", namespaces=NS) + assert len(regions) >= 2, "result is inaccurate" + regions = tree.xpath("//page:SeparatorRegion", namespaces=NS) + assert len(regions) >= 2, "result is inaccurate" + lines = tree.xpath("//page:TextLine", namespaces=NS) + assert len(lines) == 31, "result is inaccurate" # 29 paragraph lines, 1 page and 1 catch-word line + +def test_run_eynollah_layout_directory(tmp_path, pytestconfig, caplog): + indir = testdir.joinpath('resources') + outdir = tmp_path + args = [ + '-m', EYNOLLAH_MODELS, + '-di', str(indir), + '-o', str(outdir), + ] + if pytestconfig.getoption('verbose') > 0: + args.extend(['-l', 'DEBUG']) + caplog.set_level(logging.INFO) + def only_eynollah(logrec): + return logrec.name == 'eynollah' + runner = CliRunner() + with caplog.filtering(only_eynollah): + result = runner.invoke(layout_cli, args) + print(result) + assert result.exit_code == 0 + logmsgs = [logrec.message for logrec in caplog.records] + assert len([logmsg for logmsg in logmsgs if logmsg.startswith('Job done in')]) == 2 + assert any(logmsg for logmsg in logmsgs if logmsg.startswith('All jobs done in')) + assert len(list(outdir.iterdir())) == 2 + +def test_run_eynollah_binarization_filename(tmp_path, subtests, pytestconfig, caplog): + infile = testdir.joinpath('resources/kant_aufklaerung_1784_0020.tif') + outfile = tmp_path.joinpath('kant_aufklaerung_1784_0020.png') + args = [ + '-m', SBBBIN_MODELS, + str(infile), + str(outfile), + ] + caplog.set_level(logging.INFO) + def only_eynollah(logrec): + return logrec.name == 'SbbBinarizer' + runner = CliRunner() + for options in [ + [], # defaults + ["--no-patches"], + ]: + with subtests.test(#msg="test CLI", + options=options): + with caplog.filtering(only_eynollah): + result = runner.invoke(binarization_cli, args + options) + print(result) + assert result.exit_code == 0 + logmsgs = [logrec.message for logrec in caplog.records] + assert any(True for logmsg in logmsgs if logmsg.startswith('Predicting')) + assert outfile.exists() + with Image.open(infile) as original_img: + original_size = original_img.size + with Image.open(outfile) as binarized_img: + binarized_size = binarized_img.size + assert original_size == binarized_size + +def test_run_eynollah_binarization_directory(tmp_path, subtests, pytestconfig, caplog): + indir = testdir.joinpath('resources') + outdir = tmp_path + args = [ + '-m', SBBBIN_MODELS, + '-di', str(indir), + '-do', str(outdir), + ] + caplog.set_level(logging.INFO) + def only_eynollah(logrec): + return logrec.name == 'SbbBinarizer' + runner = CliRunner() + with caplog.filtering(only_eynollah): + result = runner.invoke(binarization_cli, args) + print(result) + assert result.exit_code == 0 + logmsgs = [logrec.message for logrec in caplog.records] + assert len([logmsg for logmsg in logmsgs if logmsg.startswith('Predicting')]) == 2 + assert len(list(outdir.iterdir())) == 2 diff --git a/tests/test_smoke.py b/tests/test_smoke.py index e2b323a..252213f 100644 --- a/tests/test_smoke.py +++ b/tests/test_smoke.py @@ -2,5 +2,6 @@ def test_utils_import(): import eynollah.utils import eynollah.utils.contour import eynollah.utils.drop_capitals + import eynollah.utils.drop_capitals import eynollah.utils.is_nan import eynollah.utils.rotate diff --git a/train/.gitkeep b/train/.gitkeep deleted file mode 100644 index e69de29..0000000 diff --git a/train/Dockerfile b/train/Dockerfile deleted file mode 100644 index 2456ea4..0000000 --- a/train/Dockerfile +++ /dev/null @@ -1,29 +0,0 @@ -# Use NVIDIA base image -FROM nvidia/cuda:11.8.0-cudnn8-devel-ubuntu20.04 - -# Set the working directory -WORKDIR /app - - -# Set environment variable for GitPython -ENV GIT_PYTHON_REFRESH=quiet - -# Install Python and pip -RUN apt-get update && apt-get install -y --fix-broken && \ - apt-get install -y \ - python3 \ - python3-pip \ - python3-distutils \ - python3-setuptools \ - python3-wheel && \ - rm -rf /var/lib/apt/lists/* - -# Copy and install Python dependencies -COPY requirements.txt . -RUN pip install --no-cache-dir -r requirements.txt - -# Copy the rest of the application -COPY . . - -# Specify the entry point -CMD ["python3", "train.py", "with", "config_params_docker.json"] diff --git a/train/config_params.json b/train/config_params.json deleted file mode 100644 index b01ac08..0000000 --- a/train/config_params.json +++ /dev/null @@ -1,83 +0,0 @@ -{ - "backbone_type" : "transformer", - "task": "cnn-rnn-ocr", - "n_classes" : 2, - "max_len": 280, - "n_epochs" : 3, - "input_height" : 32, - "input_width" : 512, - "weight_decay" : 1e-6, - "n_batch" : 4, - "learning_rate": 1e-5, - "save_interval": 1500, - "patches" : false, - "pretraining" : true, - "augmentation" : true, - "flip_aug" : false, - "blur_aug" : true, - "scaling" : false, - "adding_rgb_background": true, - "adding_rgb_foreground": true, - "add_red_textlines": true, - "white_noise_strap": true, - "textline_right_in_depth": true, - "textline_left_in_depth": true, - "textline_up_in_depth": true, - "textline_down_in_depth": true, - "textline_right_in_depth_bin": true, - "textline_left_in_depth_bin": true, - "textline_up_in_depth_bin": true, - "textline_down_in_depth_bin": true, - "bin_deg": true, - "textline_skewing": true, - "textline_skewing_bin": true, - "channels_shuffling": true, - "degrading": true, - "brightening": true, - "binarization" : true, - "pepper_aug": true, - "pepper_bin_aug": true, - "image_inversion": true, - "scaling_bluring" : false, - "scaling_binarization" : false, - "scaling_flip" : false, - "rotation": false, - "color_padding_rotation": true, - "padding_white": true, - "rotation_not_90": true, - "transformer_num_patches_xy": [56, 56], - "transformer_patchsize_x": 4, - "transformer_patchsize_y": 4, - "transformer_projection_dim": 64, - "transformer_mlp_head_units": [128, 64], - "transformer_layers": 1, - "transformer_num_heads": 1, - "transformer_cnn_first": false, - "blur_k" : ["blur","gauss","median"], - "padd_colors" : ["white", "black"], - "scales" : [0.6, 0.7, 0.8, 0.9], - "brightness" : [1.3, 1.5, 1.7, 2], - "degrade_scales" : [0.2, 0.4], - "pepper_indexes": [0.01, 0.005], - "skewing_amplitudes" : [5, 8], - "flip_index" : [0, 1, -1], - "shuffle_indexes" : [ [0,2,1], [1,2,0], [1,0,2] , [2,1,0]], - "thetha" : [0.1, 0.2, -0.1, -0.2], - "thetha_padd": [-0.6, -1, -1.4, -1.8, 0.6, 1, 1.4, 1.8], - "white_padds" : [0.1, 0.3, 0.5, 0.7, 0.9], - "number_of_backgrounds_per_image": 2, - "continue_training": false, - "index_start" : 0, - "dir_of_start_model" : " ", - "weighted_loss": false, - "is_loss_soft_dice": false, - "data_is_provided": false, - "dir_train": "/home/vahid/extracted_lines/1919_bin/train", - "dir_eval": "/home/vahid/Documents/test/sbb_pixelwise_segmentation/test_label/pageextractor_test/eval_new", - "dir_output": "/home/vahid/extracted_lines/1919_bin/output", - "dir_rgb_backgrounds": "/home/vahid/Documents/1_2_test_eynollah/set_rgb_background", - "dir_rgb_foregrounds": "/home/vahid/Documents/1_2_test_eynollah/out_set_rgb_foreground", - "dir_img_bin": "/home/vahid/extracted_lines/1919_bin/images_bin", - "characters_txt_file":"/home/vahid/Downloads/models_eynollah/model_eynollah_ocr_cnnrnn_20250930/characters_org.txt" - -} diff --git a/train/config_params_docker.json b/train/config_params_docker.json deleted file mode 100644 index 45f87d3..0000000 --- a/train/config_params_docker.json +++ /dev/null @@ -1,54 +0,0 @@ -{ - "backbone_type" : "nontransformer", - "task": "segmentation", - "n_classes" : 3, - "n_epochs" : 1, - "input_height" : 672, - "input_width" : 448, - "weight_decay" : 1e-6, - "n_batch" : 4, - "learning_rate": 1e-4, - "patches" : false, - "pretraining" : true, - "augmentation" : false, - "flip_aug" : false, - "blur_aug" : true, - "scaling" : true, - "adding_rgb_background": false, - "adding_rgb_foreground": false, - "add_red_textlines": false, - "channels_shuffling": true, - "degrading": true, - "brightening": true, - "binarization" : false, - "scaling_bluring" : false, - "scaling_binarization" : false, - "scaling_flip" : false, - "rotation": false, - "rotation_not_90": true, - "transformer_num_patches_xy": [14, 21], - "transformer_patchsize_x": 1, - "transformer_patchsize_y": 1, - "transformer_projection_dim": 64, - "transformer_mlp_head_units": [128, 64], - "transformer_layers": 1, - "transformer_num_heads": 1, - "transformer_cnn_first": true, - "blur_k" : ["blur","gauss","median"], - "scales" : [0.6, 0.7, 0.8, 0.9], - "brightness" : [1.3, 1.5, 1.7, 2], - "degrade_scales" : [0.2, 0.4], - "flip_index" : [0, 1, -1], - "shuffle_indexes" : [ [0,2,1], [1,2,0], [1,0,2] , [2,1,0]], - "thetha" : [5, -5], - "number_of_backgrounds_per_image": 2, - "continue_training": false, - "index_start" : 0, - "dir_of_start_model" : " ", - "weighted_loss": false, - "is_loss_soft_dice": true, - "data_is_provided": false, - "dir_train": "/entry_point_dir/train", - "dir_eval": "/entry_point_dir/eval", - "dir_output": "/entry_point_dir/output" -} diff --git a/train/custom_config_page2label.json b/train/custom_config_page2label.json deleted file mode 100644 index 9116ce3..0000000 --- a/train/custom_config_page2label.json +++ /dev/null @@ -1,8 +0,0 @@ -{ -"use_case": "textline", -"textregions":{ "rest_as_paragraph": 1, "header":2 , "heading":2 , "marginalia":3 }, -"imageregion":4, -"separatorregion":5, -"graphicregions" :{"rest_as_decoration":6}, -"columns_width":{"1":1000, "2":1300, "3":1600, "4":2000, "5":2300, "6":2500} -} diff --git a/train/requirements.txt b/train/requirements.txt deleted file mode 100644 index 63f3813..0000000 --- a/train/requirements.txt +++ /dev/null @@ -1,6 +0,0 @@ -sacred -seaborn -numpy <1.24.0 -tqdm -imutils -scipy diff --git a/train/scales_enhancement.json b/train/scales_enhancement.json deleted file mode 100644 index 58034f0..0000000 --- a/train/scales_enhancement.json +++ /dev/null @@ -1,3 +0,0 @@ -{ - "scales" : [ 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9] -}