diff --git a/.github/workflows/test-eynollah.yml b/.github/workflows/test-eynollah.yml index 466e690..82de94d 100644 --- a/.github/workflows/test-eynollah.yml +++ b/.github/workflows/test-eynollah.yml @@ -24,61 +24,59 @@ jobs: sudo rm -rf "$AGENT_TOOLSDIRECTORY" df -h - uses: actions/checkout@v4 - - uses: actions/cache/restore@v4 - id: seg_model_cache + + # - name: Lint with ruff + # uses: astral-sh/ruff-action@v3 + # with: + # src: "./src" + + - name: Try to restore models_eynollah + uses: actions/cache/restore@v4 + id: all_model_cache with: - path: models_layout_v0_5_0 - key: seg-models - - uses: actions/cache/restore@v4 - id: ocr_model_cache - with: - path: models_ocr_v0_5_1 - key: ocr-models - - uses: actions/cache/restore@v4 - id: bin_model_cache - with: - path: default-2021-03-09 - key: bin-models + path: models_eynollah + key: models_eynollah-${{ hashFiles('src/eynollah/model_zoo/default_specs.py') }} + - name: Download models - if: steps.seg_model_cache.outputs.cache-hit != 'true' || steps.bin_model_cache.outputs.cache-hit != 'true' || steps.ocr_model_cache.outputs.cache-hit != true - run: make models + if: steps.all_model_cache.outputs.cache-hit != 'true' + run: | + make models + ls -la models_eynollah + - uses: actions/cache/save@v4 - if: steps.seg_model_cache.outputs.cache-hit != 'true' + if: steps.all_model_cache.outputs.cache-hit != 'true' with: - path: models_layout_v0_5_0 - key: seg-models - - uses: actions/cache/save@v4 - if: steps.ocr_model_cache.outputs.cache-hit != 'true' - with: - path: models_ocr_v0_5_1 - key: ocr-models - - uses: actions/cache/save@v4 - if: steps.bin_model_cache.outputs.cache-hit != 'true' - with: - path: default-2021-03-09 - key: bin-models + path: models_eynollah + key: models_eynollah-${{ hashFiles('src/eynollah/model_zoo/default_specs.py') }} + - 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 - ls -l models_* - - name: Lint with ruff - uses: astral-sh/ruff-action@v3 - with: - src: "./src" + - 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: @@ -88,12 +86,15 @@ 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 fd64f0b..49835a7 100644 --- a/.gitignore +++ b/.gitignore @@ -11,3 +11,4 @@ output.html *.tif *.sw? TAGS +uv.lock diff --git a/Makefile b/Makefile index 29dd877..c1458df 100644 --- a/Makefile +++ b/Makefile @@ -6,23 +6,17 @@ EXTRAS ?= DOCKER_BASE_IMAGE ?= docker.io/ocrd/core-cuda-tf2:latest DOCKER_TAG ?= ocrd/eynollah DOCKER ?= docker +WGET = wget -O #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://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 -SEG_MODELFILE = $(notdir $(patsubst %?download=1,%,$(SEG_MODEL))) -SEG_MODELNAME = $(SEG_MODELFILE:%.tar.gz=%) - -BIN_MODEL := https://github.com/qurator-spk/sbb_binarization/releases/download/v0.0.11/saved_model_2021_03_09.zip -BIN_MODELFILE = $(notdir $(BIN_MODEL)) -BIN_MODELNAME := default-2021-03-09 - -OCR_MODEL := https://zenodo.org/records/17236998/files/models_ocr_v0_5_1.tar.gz?download=1 -OCR_MODELFILE = $(notdir $(patsubst %?download=1,%,$(OCR_MODEL))) -OCR_MODELNAME = $(OCR_MODELFILE:%.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 @@ -38,7 +32,7 @@ help: @echo " install-dev Install editable with pip" @echo " deps-test Install test dependencies with pip" @echo " models Download and extract models to $(CURDIR):" - @echo " $(BIN_MODELNAME) $(SEG_MODELNAME) $(OCR_MODELNAME)" + @echo " $(EYNOLLAH_MODELS_DIR)" @echo " smoke-test Run simple CLI check" @echo " ocrd-test Run OCR-D CLI check" @echo " test Run unit tests" @@ -47,34 +41,22 @@ help: @echo " EXTRAS comma-separated list of features (like 'OCR,plotting') for 'install' [$(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 " 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 " OCR_MODEL URL of 'models' archive to download for binarization 'test' [$(OCR_MODEL)]" + @echo " ALL_MODELS URL of archive of all models [$(ALL_MODELS)]" @echo "" # END-EVAL - -# Download and extract models to $(PWD)/models_layout_v0_5_0 -models: $(BIN_MODELNAME) $(SEG_MODELNAME) $(OCR_MODELNAME) +# 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: $(BIN_MODELFILE) $(SEG_MODELFILE) $(OCR_MODELFILE) +.INTERMEDIATE: $(EYNOLLAH_MODELS_ZIP) -$(BIN_MODELFILE): - wget -O $@ $(BIN_MODEL) -$(SEG_MODELFILE): - wget -O $@ $(SEG_MODEL) -$(OCR_MODELFILE): - wget -O $@ $(OCR_MODEL) +$(EYNOLLAH_MODELS_ZIP): + $(WGET) $@ $(EYNOLLAH_MODELS_URL) -$(BIN_MODELNAME): $(BIN_MODELFILE) - mkdir $@ - unzip -d $@ $< -$(SEG_MODELNAME): $(SEG_MODELFILE) - tar zxf $< -$(OCR_MODELNAME): $(OCR_MODELFILE) - tar zxf $< +$(EYNOLLAH_MODELS_DIR): $(EYNOLLAH_MODELS_ZIP) + unzip $< build: $(PIP) install build @@ -88,56 +70,48 @@ install: install-dev: $(PIP) install -e .$(and $(EXTRAS),[$(EXTRAS)]) -ifeq (OCR,$(findstring OCR, $(EXTRAS))) -deps-test: $(OCR_MODELNAME) -endif -deps-test: $(BIN_MODELNAME) $(SEG_MODELNAME) +deps-test: $(PIP) install -r requirements-test.txt -ifeq (OCR,$(findstring OCR, $(EXTRAS))) - ln -rs $(OCR_MODELNAME)/* $(SEG_MODELNAME)/ -endif smoke-test: TMPDIR != mktemp -d -smoke-test: tests/resources/kant_aufklaerung_1784_0020.tif +smoke-test: tests/resources/2files/kant_aufklaerung_1784_0020.tif # layout analysis: - eynollah layout -i $< -o $(TMPDIR) -m $(CURDIR)/$(SEG_MODELNAME) + eynollah -m $(CURDIR) layout -i $< -o $(TMPDIR) fgrep -q http://schema.primaresearch.org/PAGE/gts/pagecontent/2019-07-15 $(TMPDIR)/$(basename $(` | save image regions detected to this directory | | `-sd ` | save deskewed image to this directory | @@ -120,9 +117,6 @@ The following options can be used to further configure the processing: | `-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 | -| `-ocr` | do ocr | -| `-tr` | apply transformer ocr. Default model is a CNN-RNN model | -| `-bs_ocr` | ocr inference batch size. Default bs for trocr and cnn_rnn models are 2 and 8 respectively | | `-ncu` | upper limit of columns in document image | | `-ncl` | lower limit of columns in document image | | `-slro` | skip layout detection and reading order | diff --git a/requirements-ocr.txt b/requirements-ocr.txt index 9f31ebb..8f3b062 100644 --- a/requirements-ocr.txt +++ b/requirements-ocr.txt @@ -1,2 +1,2 @@ -torch <= 2.0.1 +torch transformers <= 4.30.2 diff --git a/requirements.txt b/requirements.txt index db1d7df..bbacd48 100644 --- a/requirements.txt +++ b/requirements.txt @@ -6,3 +6,4 @@ tensorflow < 2.13 numba <= 0.58.1 scikit-image biopython +tabulate diff --git a/src/eynollah/cli.py b/src/eynollah/cli.py deleted file mode 100644 index c9bad52..0000000 --- a/src/eynollah/cli.py +++ /dev/null @@ -1,579 +0,0 @@ -import sys -import click -import logging -from ocrd_utils import initLogging, getLevelName, getLogger -from eynollah.eynollah import Eynollah, Eynollah_ocr -from eynollah.sbb_binarize import SbbBinarizer -from eynollah.image_enhancer import Enhancer -from eynollah.mb_ro_on_layout import machine_based_reading_order_on_layout - -@click.group() -def main(): - pass - -@main.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.option( - "--model", - "-m", - help="directory of models", - type=click.Path(exists=True, file_okay=False), - required=True, -) -@click.option( - "--log_level", - "-l", - type=click.Choice(['OFF', 'DEBUG', 'INFO', 'WARN', 'ERROR']), - help="Override log level globally to this", -) - -def machine_based_reading_order(input, dir_in, out, model, log_level): - 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) - if log_level: - orderer.logger.setLevel(getLevelName(log_level)) - - orderer.run(xml_filename=input, - dir_in=dir_in, - dir_out=out, - ) - - -@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.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.option( - "--log_level", - "-l", - type=click.Choice(['OFF', 'DEBUG', 'INFO', 'WARN', 'ERROR']), - help="Override log level globally to this", -) -def binarization(patches, model_dir, input_image, dir_in, output, log_level): - assert bool(input_image) != bool(dir_in), "Either -i (single input) or -di (directory) must be provided, but not both." - binarizer = SbbBinarizer(model_dir) - if log_level: - binarizer.log.setLevel(getLevelName(log_level)) - binarizer.run(image_path=input_image, use_patches=patches, output=output, dir_in=dir_in) - - -@main.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( - "--model", - "-m", - help="directory of models", - type=click.Path(exists=True, file_okay=False), - required=True, -) - -@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.option( - "--log_level", - "-l", - type=click.Choice(['OFF', 'DEBUG', 'INFO', 'WARN', 'ERROR']), - help="Override log level globally to this", -) - -def enhancement(image, out, overwrite, dir_in, model, num_col_upper, num_col_lower, save_org_scale, log_level): - assert bool(image) != bool(dir_in), "Either -i (single input) or -di (directory) must be provided, but not both." - initLogging() - enhancer = Enhancer( - model, - num_col_upper=num_col_upper, - num_col_lower=num_col_lower, - save_org_scale=save_org_scale, - ) - if log_level: - enhancer.logger.setLevel(getLevelName(log_level)) - enhancer.run(overwrite=overwrite, - dir_in=dir_in, - image_filename=image, - dir_out=out, - ) - -@main.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( - "--model", - "-m", - help="directory of models", - type=click.Path(exists=True, file_okay=False), - required=True, -) -@click.option( - "--model_version", - "-mv", - help="override default versions of model categories", - type=(str, str), - multiple=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( - "--transformer_ocr", - "-tr/-notr", - is_flag=True, - help="if this parameter set to true, this tool will apply transformer ocr", -) -@click.option( - "--batch_size_ocr", - "-bs_ocr", - help="number of inference batch size of ocr model. Default b_s for trocr and cnn_rnn models are 2 and 8 respectively", -) -@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.", -) -# TODO move to top-level CLI context -@click.option( - "--log_level", - "-l", - type=click.Choice(['OFF', 'DEBUG', 'INFO', 'WARN', 'ERROR']), - help="Override 'eynollah' log level globally to this", -) -# -@click.option( - "--setup-logging", - is_flag=True, - help="Setup a basic console logger", -) - -def layout(image, out, overwrite, dir_in, model, model_version, 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, transformer_ocr, batch_size_ocr, num_col_upper, num_col_lower, threshold_art_class_textline, threshold_art_class_layout, skip_layout_and_reading_order, ignore_page_extraction, log_level, setup_logging): - if setup_logging: - console_handler = logging.StreamHandler(sys.stdout) - console_handler.setLevel(logging.INFO) - formatter = logging.Formatter('%(message)s') - console_handler.setFormatter(formatter) - getLogger('eynollah').addHandler(console_handler) - getLogger('eynollah').setLevel(logging.INFO) - else: - initLogging() - 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 bool(image) != bool(dir_in), "Either -i (single input) or -di (directory) must be provided, but not both." - eynollah = Eynollah( - model, - model_versions=model_version, - extract_only_images=extract_only_images, - 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, - transformer_ocr=transformer_ocr, - batch_size_ocr=batch_size_ocr, - 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, - ) - if log_level: - eynollah.logger.setLevel(getLevelName(log_level)) - 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, - ) - -@main.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' suffix).\nPerform 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( - "--model", - "-m", - help="directory of models", - type=click.Path(exists=True, file_okay=False), -) -@click.option( - "--model_name", - help="Specific model file path to use for OCR", - type=click.Path(exists=True, file_okay=False), -) -@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( - "--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( - "--dataset_abbrevation", - "-ds_pref", - 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( - "--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.option( - "--log_level", - "-l", - type=click.Choice(['OFF', 'DEBUG', 'INFO', 'WARN', 'ERROR']), - help="Override log level globally to this", -) - -def ocr(image, dir_in, dir_in_bin, dir_xmls, out, dir_out_image_text, overwrite, model, model_name, tr_ocr, export_textline_images_and_text, do_not_mask_with_textline_contour, batch_size, dataset_abbrevation, min_conf_value_of_textline_text, log_level): - initLogging() - - assert bool(model) != bool(model_name), "Either -m (model directory) or --model_name (specific model name) must be provided." - assert not export_textline_images_and_text or not tr_ocr, "Exporting textline and text -etit can not be set alongside transformer ocr -tr_ocr" - assert not export_textline_images_and_text or not model, "Exporting textline and text -etit can not be set alongside model -m" - assert not export_textline_images_and_text or not batch_size, "Exporting textline and text -etit can not be set alongside batch size -bs" - assert not export_textline_images_and_text or not dir_in_bin, "Exporting textline and text -etit can not be set alongside directory of bin images -dib" - assert not export_textline_images_and_text or not dir_out_image_text, "Exporting textline and text -etit can not be set alongside directory of images with predicted text -doit" - assert bool(image) != bool(dir_in), "Either -i (single image) or -di (directory) must be provided, but not both." - eynollah_ocr = Eynollah_ocr( - dir_models=model, - model_name=model_name, - 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, - batch_size=batch_size, - pref_of_dataset=dataset_abbrevation, - min_conf_value_of_textline_text=min_conf_value_of_textline_text, - ) - if log_level: - eynollah_ocr.logger.setLevel(getLevelName(log_level)) - 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, - ) - -if __name__ == "__main__": - main() diff --git a/src/eynollah/cli/__init__.py b/src/eynollah/cli/__init__.py new file mode 100644 index 0000000..05dafa1 --- /dev/null +++ b/src/eynollah/cli/__init__.py @@ -0,0 +1,22 @@ +# 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 new file mode 100644 index 0000000..b374fa8 --- /dev/null +++ b/src/eynollah/cli/cli.py @@ -0,0 +1,66 @@ +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 new file mode 100644 index 0000000..d4a6e31 --- /dev/null +++ b/src/eynollah/cli/cli_binarize.py @@ -0,0 +1,44 @@ +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 new file mode 100644 index 0000000..fa4158b --- /dev/null +++ b/src/eynollah/cli/cli_enhance.py @@ -0,0 +1,63 @@ +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 new file mode 100644 index 0000000..0add5b5 --- /dev/null +++ b/src/eynollah/cli/cli_extract_images.py @@ -0,0 +1,100 @@ +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 new file mode 100644 index 0000000..df66993 --- /dev/null +++ b/src/eynollah/cli/cli_layout.py @@ -0,0 +1,223 @@ +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 new file mode 100644 index 0000000..f3de596 --- /dev/null +++ b/src/eynollah/cli/cli_models.py @@ -0,0 +1,69 @@ +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 new file mode 100644 index 0000000..406af61 --- /dev/null +++ b/src/eynollah/cli/cli_ocr.py @@ -0,0 +1,103 @@ +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 new file mode 100644 index 0000000..0f44b7f --- /dev/null +++ b/src/eynollah/cli/cli_readingorder.py @@ -0,0 +1,35 @@ +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 new file mode 100644 index 0000000..9942cf8 --- /dev/null +++ b/src/eynollah/extract_images.py @@ -0,0 +1,281 @@ +""" +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 13acba6..9383c5e 100644 --- a/src/eynollah/eynollah.py +++ b/src/eynollah/eynollah.py @@ -1,47 +1,47 @@ +""" +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 -""" -document layout analysis (segmentation) with output in PAGE-XML -""" +# 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 -# cannot use importlib.resources until we move to 3.9+ forimportlib.resources.files +import logging import sys -if sys.version_info < (3, 10): - import importlib_resources -else: - import importlib.resources as importlib_resources from difflib import SequenceMatcher as sq -from PIL import Image, ImageDraw, ImageFont import math import os -import sys import time -from typing import Dict, List, Optional, Tuple -import atexit -import warnings +from typing import Optional from functools import partial from pathlib import Path from multiprocessing import cpu_count import gc -import copy -import json from concurrent.futures 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 numba import cuda from skimage.morphology import skeletonize -from ocrd import OcrdPage -from ocrd_utils import getLogger, tf_disable_interactive_logs +from ocrd_utils import tf_disable_interactive_logs import statistics +tf_disable_interactive_logs() + +import tensorflow as tf try: import torch except ImportError: @@ -50,36 +50,18 @@ 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_contours_mean_y_diff, find_center_of_contours, 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_textline, return_parent_contours, dilate_textregion_contours, dilate_textline_contours, @@ -92,53 +74,29 @@ from .utils.rotate import ( rotate_image, rotation_not_90_func, rotation_not_90_func_full_layout, - rotation_image_new -) -from .utils.utils_ocr import ( - return_start_and_end_of_common_text_of_textline_ocr_without_common_section, - return_textline_contour_with_added_box_coordinate, - preprocess_and_resize_image_for_ocrcnn_model, - return_textlines_split_if_needed, - decode_batch_predictions, - return_rnn_cnn_ocr_of_given_textlines, - fit_text_single_line, - break_curved_line_into_small_pieces_and_then_merge, - get_orientation_moments, - rotate_image_with_padding, - get_contours_and_bounding_boxes ) from .utils.separate_lines import ( - 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, - boosting_headers_by_longshot_region_segmentation, + isNaN, crop_image_inside_box, box2rect, - box2slice, 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 check_dpi, pil2cv +from .utils.pil_cv2 import pil2cv from .utils.xml import order_and_id_of_texts from .plot import EynollahPlotter from .writer import EynollahXmlWriter @@ -155,109 +113,47 @@ 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, - dir_models : str, - model_versions: List[Tuple[str, str]] = [], - extract_only_images : bool =False, + *, + model_zoo: EynollahModelZoo, 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, - transformer_ocr: bool = False, - batch_size_ocr: Optional[int] = None, 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, ): - self.logger = getLogger('eynollah') + 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.reading_order_machine_based = reading_order_machine_based 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 - self.tr = transformer_ocr - if not batch_size_ocr: - self.b_s_ocr = 8 - else: - self.b_s_ocr = int(batch_size_ocr) if num_col_upper: self.num_col_upper = int(num_col_upper) else: @@ -297,93 +193,11 @@ class Eynollah: self.logger.warning("no GPU device available") self.logger.info("Loading models...") - self.setup_models(dir_models, model_versions) + self.setup_models() self.logger.info(f"Model initialization complete ({time.time() - t_start:.1f}s)") - @staticmethod - def our_load_model(model_file, basedir=""): - if basedir: - model_file = os.path.join(basedir, 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 setup_models(self): - def setup_models(self, basedir: Path, model_versions: List[Tuple[str, str]] = []): - self.model_versions = { - "enhancement": "eynollah-enhancement_20210425", - "binarization": "eynollah-binarization_20210425", - "col_classifier": "eynollah-column-classifier_20210425", - "page": "model_eynollah_page_extraction_20250915", - #?: "eynollah-main-regions-aug-scaling_20210425", - "region": ( # early layout - "eynollah-main-regions_20231127_672_org_ens_11_13_16_17_18" if self.extract_only_images else - "eynollah-main-regions_20220314" if self.light_version else - "eynollah-main-regions-ensembled_20210425"), - "region_p2": ( # early layout, non-light, 2nd part - "eynollah-main-regions-aug-rotation_20210425"), - "region_1_2": ( # early layout, light, 1-or-2-column - #"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" - "modelens_e_l_all_sp_0_1_2_3_4_171024"), - "region_fl_np": ( # full layout / no patches - #"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" - "modelens_full_lay_1__4_3_091124"), - "region_fl": ( # full layout / with patches - #"eynollah-full-regions-3+column_20210425" - ##"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" - "modelens_full_lay_1__4_3_091124"), - "reading_order": ( - #"model_mb_ro_aug_ens_11" - #"model_step_3200000_mb_ro" - #"model_ens_reading_order_machine_based" - #"model_mb_ro_aug_ens_8" - #"model_ens_reading_order_machine_based" - "model_eynollah_reading_order_20250824"), - "textline": ( - #"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" - "modelens_textline_0_1__2_4_16092024" if self.textline_light else - #"eynollah-textline_20210425" - "modelens_textline_0_1__2_4_16092024"), - "table": ( - None if not self.tables else - "modelens_table_0t4_201124" if self.light_version else - "eynollah-tables_20210319"), - "ocr": ( - None if not self.ocr else - "model_eynollah_ocr_trocr_20250919" if self.tr else - "model_eynollah_ocr_cnnrnn_20250930") - } - # override defaults from CLI - for key, val in model_versions: - assert key in self.model_versions, "unknown model category '%s'" % key - self.logger.warning("overriding default model %s version %s to %s", key, self.model_versions[key], val) - self.model_versions[key] = val # 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) @@ -393,77 +207,44 @@ class Eynollah: "page", "region" ] - if not self.extract_only_images: - loadable.append("textline") - if self.light_version: - loadable.append("region_1_2") - else: - loadable.append("region_p2") - # if self.allow_enhancement:? - loadable.append("enhancement") - 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") + 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.models = {name: self.our_load_model(self.model_versions[name], basedir) - for name in loadable - } - - if self.ocr: - ocr_model_dir = os.path.join(basedir, self.model_versions["ocr"]) - if self.tr: - self.models["ocr"] = VisionEncoderDecoderModel.from_pretrained(ocr_model_dir) - if torch.cuda.is_available(): - self.logger.info("Using GPU acceleration") - self.device = torch.device("cuda:0") - else: - self.logger.info("Using CPU processing") - self.device = torch.device("cpu") - #self.processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-handwritten") - self.processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-printed") - else: - ocr_model = load_model(ocr_model_dir, compile=False) - self.models["ocr"] = tf.keras.models.Model( - ocr_model.get_layer(name = "image").input, - ocr_model.get_layer(name = "dense2").output) - - with open(os.path.join(ocr_model_dir, "characters_org.txt"), "r") as config_file: - characters = json.load(config_file) - # 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 - ) + 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 - if hasattr(self, 'models') and getattr(self, 'models'): - for model_name in list(self.models): - if self.models[model_name]: - del self.models[model_name] + 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") 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) - if self.light_version: - self.dpi = 100 - else: - self.dpi = check_dpi(image_filename) + self.dpi = 100 else: ret['img'] = pil2cv(image_pil) - if self.light_version: - self.dpi = 100 - else: - self.dpi = check_dpi(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) @@ -477,8 +258,7 @@ class Eynollah: self.writer = EynollahXmlWriter( dir_out=dir_out, image_filename=image_filename, - curved_line=self.curved_line, - textline_light = self.textline_light) + curved_line=self.curved_line) def imread(self, grayscale=False, uint8=True): key = 'img' @@ -488,14 +268,11 @@ 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.models["enhancement"].layers[-1].output_shape[1] - img_width_model = self.models["enhancement"].layers[-1].output_shape[2] + 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] 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: @@ -536,7 +313,7 @@ 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.models["enhancement"].predict(img_patch, verbose=0) + label_p_pred = self.model_zoo.get("enhancement").predict(img_patch, verbose=0) seg = label_p_pred[0, :, :, :] * 255 if i == 0 and j == 0: @@ -660,27 +437,6 @@ 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: @@ -711,7 +467,7 @@ class Eynollah: img_in[0, :, :, 1] = img_1ch[:, :] img_in[0, :, :, 2] = img_1ch[:, :] - label_p_pred = self.models["col_classifier"].predict(img_in, verbose=0) + label_p_pred = self.model_zoo.get("col_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) @@ -723,13 +479,13 @@ class Eynollah: return img, img_new, is_image_enhanced - def resize_and_enhance_image_with_column_classifier(self, light_version): + def resize_and_enhance_image_with_column_classifier(self): 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.models["binarization"], n_batch_inference=5) + 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) @@ -769,7 +525,7 @@ class Eynollah: img_in[0, :, :, 1] = img_1ch[:, :] img_in[0, :, :, 2] = img_1ch[:, :] - label_p_pred = self.models["col_classifier"].predict(img_in, verbose=0) + 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): @@ -790,7 +546,7 @@ class Eynollah: img_in[0, :, :, 1] = img_1ch[:, :] img_in[0, :, :, 2] = img_1ch[:, :] - label_p_pred = self.models["col_classifier"].predict(img_in, verbose=0) + 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: @@ -804,33 +560,25 @@ 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 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) + 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) is_image_enhanced = True else: - 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 + 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 @@ -845,8 +593,8 @@ 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 = self.img_hight_int / float(self.image.shape[0]) - self.scale_x = self.img_width_int / float(self.image.shape[1]) + 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.image = resize_image(self.image, self.img_hight_int, self.img_width_int) @@ -1161,136 +909,6 @@ class Eynollah: gc.collect() return prediction_true - 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): - - 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: - #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, @@ -1642,7 +1260,7 @@ class Eynollah: 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.models["page"]) + 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) @@ -1690,7 +1308,7 @@ class Eynollah: else: img = self.imread() img = cv2.GaussianBlur(img, (5, 5), 0) - img_page_prediction = self.do_prediction(False, img, self.models["page"]) + 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) @@ -1716,11 +1334,10 @@ class Eynollah: self.logger.debug("enter extract_text_regions") img_height_h = img.shape[0] img_width_h = img.shape[1] - model_region = self.models["region_fl"] if patches else self.models["region_fl_np"] + model_region = self.model_zoo.get("region_fl") if patches else self.model_zoo.get("region_fl_np") - if self.light_version: - thresholding_for_fl_light_version = True - elif not patches: + thresholding_for_fl_light_version = True + if not patches: img = otsu_copy_binary(img).astype(np.uint8) prediction_regions = None thresholding_for_fl_light_version = False @@ -1751,54 +1368,12 @@ class Eynollah: self.logger.debug("enter extract_text_regions") img_height_h = img.shape[0] img_width_h = img.shape[1] - model_region = self.models["region_fl"] if patches else self.models["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) + model_region = self.model_zoo.get("region_fl") if patches else self.model_zoo.get("region_fl_np") 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, prediction_regions2 + 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) @@ -1906,40 +1481,6 @@ class Eynollah: all_box_coord, slopes) - def get_slopes_and_deskew_new_light(self, contours, contours_par, textline_mask_tot, boxes, slope_deskew): - if not len(contours): - return [], [], [] - self.logger.debug("enter get_slopes_and_deskew_new_light") - with share_ndarray(textline_mask_tot) as textline_mask_tot_shared: - results = self.executor.map(partial(do_work_of_slopes_new_light, - textline_mask_tot_ea=textline_mask_tot_shared, - slope_deskew=slope_deskew, - textline_light=self.textline_light, - logger=self.logger,), - boxes, contours, contours_par) - results = list(results) # exhaust prior to release - #textline_polygons, box_coord, 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, boxes, slope_deskew): - if not len(contours): - return [], [], [] - self.logger.debug("enter get_slopes_and_deskew_new") - with share_ndarray(textline_mask_tot) as textline_mask_tot_shared: - results = self.executor.map(partial(do_work_of_slopes_new, - textline_mask_tot_ea=textline_mask_tot_shared, - slope_deskew=slope_deskew, - MAX_SLOPE=MAX_SLOPE, - KERNEL=KERNEL, - logger=self.logger, - plotter=self.plotter,), - boxes, contours, contours_par) - results = list(results) # exhaust prior to release - #textline_polygons, box_coord, 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_par, textline_mask_tot, boxes, mask_texts_only, num_col, scale_par, slope_deskew): if not len(contours_par): @@ -1947,6 +1488,7 @@ class Eynollah: 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, @@ -1972,85 +1514,24 @@ 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.models["textline"], + prediction_textline = self.do_prediction(use_patches, img, self.model_zoo.get("textline"), marginal_of_patch_percent=0.15, n_batch_inference=3, - thresholding_for_artificial_class_in_light_version=self.textline_light, threshold_art_class_textline=self.threshold_art_class_textline) - #if not self.textline_light: - #if num_col_classifier==1: - #prediction_textline_nopatch = self.do_prediction(False, img, self.models["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 - """ - else: - textline_mask_tot_ea_art = textline_mask_tot_ea_art.astype('uint8') - hor_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (8, 1)) - - kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3)) - ##cv2.imwrite('textline_mask_tot_ea_art.png', textline_mask_tot_ea_art) - textline_mask_tot_ea_art = cv2.dilate(textline_mask_tot_ea_art, hor_kernel, iterations=1) - - ###cv2.imwrite('dil_textline_mask_tot_ea_art.png', dil_textline_mask_tot_ea_art) - - textline_mask_tot_ea_art = textline_mask_tot_ea_art.astype('uint8') - - #print(np.shape(dil_textline_mask_tot_ea_art), np.unique(dil_textline_mask_tot_ea_art), 'dil_textline_mask_tot_ea_art') - tsk = time.time() - skeleton_art_textline = skeletonize(textline_mask_tot_ea_art[:,:,0]) - - skeleton_art_textline = skeleton_art_textline*1 - - skeleton_art_textline = skeleton_art_textline.astype('uint8') - - skeleton_art_textline = cv2.dilate(skeleton_art_textline, kernel, iterations=1) - - #print(np.unique(skeleton_art_textline), np.shape(skeleton_art_textline)) - - #print(skeleton_art_textline, np.unique(skeleton_art_textline)) - - #cv2.imwrite('skeleton_art_textline.png', skeleton_art_textline) - - - prediction_textline[:,:,0][skeleton_art_textline[:,:]==1]=2 - - #cv2.imwrite('prediction_textline1.png', prediction_textline[:,:,0]) - - ##hor_kernel2 = cv2.getStructuringElement(cv2.MORPH_RECT, (4, 1)) - ##ver_kernel2 = cv2.getStructuringElement(cv2.MORPH_RECT, (1, 3)) - ##textline_mask_tot_ea_main = (prediction_textline[:,:]==1)*1 - ##textline_mask_tot_ea_main = textline_mask_tot_ea_main.astype('uint8') - - ##dil_textline_mask_tot_ea_main = cv2.erode(textline_mask_tot_ea_main, ver_kernel2, iterations=1) - - ##dil_textline_mask_tot_ea_main = cv2.dilate(textline_mask_tot_ea_main, hor_kernel2, iterations=1) - - ##dil_textline_mask_tot_ea_main = cv2.dilate(textline_mask_tot_ea_main, ver_kernel2, iterations=1) - - ##prediction_textline[:,:][dil_textline_mask_tot_ea_main[:,:]==1]=1 - - """ 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 - if not self.textline_light: - prediction_textline[:,:][old_art[:,:]==1]=2 #cv2.imwrite('prediction_textline2.png', prediction_textline[:,:,0]) - prediction_textline_longshot = self.do_prediction(False, img, self.models["textline"]) + prediction_textline_longshot = self.do_prediction(False, img, self.model_zoo.get("textline")) prediction_textline_longshot_true_size = resize_image(prediction_textline_longshot, img_h, img_w) @@ -2061,113 +1542,7 @@ class Eynollah: (prediction_textline_longshot_true_size[:, :, 0]==1).astype(np.uint8)) - 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.models["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_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) - 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 = box2rect(box) - # 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 = box2rect(box) - # 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_seplines, - polygons_of_images_fin, - image_page, - page_coord, - cont_page) + def get_regions_light_v(self,img,is_image_enhanced, num_col_classifier): self.logger.debug("enter get_regions_light_v") @@ -2199,7 +1574,7 @@ 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.models["binarization"], n_batch_inference=5) + ###prediction_bin = self.do_prediction(True, img_resized, self.model_zoo.get_model("binarization"), n_batch_inference=5) ####print("inside bin ", time.time()-t_bin) ###prediction_bin=prediction_bin[:,:,0] @@ -2213,15 +1588,7 @@ class Eynollah: ###img_bin = np.copy(prediction_bin) ###else: ###img_bin = np.copy(img_resized) - if (self.ocr and self.tr) and not self.input_binary: - prediction_bin = self.do_prediction(True, img_resized, self.models["binarization"], 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) + img_bin = np.copy(img_resized) #print("inside 1 ", time.time()-t_in) ###textline_mask_tot_ea = self.run_textline(img_bin) @@ -2246,14 +1613,14 @@ class Eynollah: 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.models["region_1_2"], n_batch_inference=1, + 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.models["region_1_2"], n_batch_inference=1, + 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]) @@ -2267,10 +1634,10 @@ class Eynollah: 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.models["region_1_2"], n_batch_inference=2, + 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.models["region"], + ###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) @@ -2339,145 +1706,6 @@ class Eynollah: img_bin, confidence_matrix) - 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.models["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() - prediction_regions_org_y = prediction_regions_org_y[:,:,0] - mask_zeros_y = (prediction_regions_org_y[:,:]==0)*1 - - ##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))) - - prediction_regions_org = self.do_prediction(True, img, self.models["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 - - img = resize_image(img_org, int(img_org.shape[0]), int(img_org.shape[1])) - - prediction_regions_org2 = self.do_prediction(True, img, self.models["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.models["binarization"], 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.models["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_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)) - - self.logger.debug("exit get_regions_from_xy_2models") - return text_regions_p_true, erosion_hurts, polygons_seplines, polygons_of_only_texts - except: - if self.input_binary: - prediction_bin = np.copy(img_org) - prediction_bin = self.do_prediction(True, img_org, self.models["binarization"], 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.models["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.models["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_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)) - - erosion_hurts = True - self.logger.debug("exit get_regions_from_xy_2models") - return text_regions_p_true, erosion_hurts, polygons_seplines, polygons_of_only_texts - def do_order_of_regions( self, contours_only_text_parent, contours_only_text_parent_h, boxes, textline_mask_tot): @@ -2723,7 +1951,7 @@ class Eynollah: img_comm = cv2.fillPoly(img_comm, pts=main_contours, color=indiv) - if not self.isNaN(slope_mean_hor): + if not isNaN(slope_mean_hor): image_revised_last = np.zeros(image_regions_eraly_p.shape[:2]) for i in range(len(boxes)): box_ys = slice(*boxes[i][2:4]) @@ -2822,92 +2050,9 @@ class Eynollah: img_height_h = img_org.shape[0] img_width_h = img_org.shape[1] patches = False - if self.light_version: - prediction_table, _ = self.do_prediction_new_concept(patches, img, self.models["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.models["table"]) - pre_updown = self.do_prediction(patches, cv2.flip(img[:,:,:], -1), self.models["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.models["table"]) - pre_updown = self.do_prediction(patches, cv2.flip(img_new[:,:,:], -1), self.models["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.models["table"]) - pre_updown = self.do_prediction(patches, cv2.flip(img_new[:,:,:], -1), self.models["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.models["table"]) - pre2 = self.do_prediction(patches, img[:,img_w_half:,:], self.models["table"]) - pre_full = self.do_prediction(patches, img[:,:,:], self.models["table"]) - pre_updown = self.do_prediction(patches, cv2.flip(img[:,:,:], -1), self.models["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) + 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] def run_graphics_and_columns_light( self, text_regions_p_1, textline_mask_tot_ea, @@ -3002,58 +2147,12 @@ 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): - 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): + 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(light_version) + self.resize_and_enhance_image_with_column_classifier() self.logger.info("Image was %senhanced.", '' if is_image_enhanced else 'not ') scale = 1 if is_image_enhanced: @@ -3065,10 +2164,7 @@ class Eynollah: else: self.get_image_and_scales_after_enhancing(img_org, img_res) else: - 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) + 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) @@ -3084,8 +2180,7 @@ class Eynollah: scaler_h_textline, scaler_w_textline, num_col_classifier) - if self.textline_light: - textline_mask_tot_ea = textline_mask_tot_ea.astype(np.int16) + 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) @@ -3118,7 +2213,7 @@ 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, light_version=self.light_version, kernel=KERNEL) + num_col_classifier, slope_deskew, kernel=KERNEL) except Exception as e: self.logger.error("exception %s", e) @@ -3177,20 +2272,6 @@ 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, @@ -3198,63 +2279,24 @@ 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: - 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) - 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 == 10] = 10 - - text_regions_p[text_regions_p == 10] = 0 - text_regions_p[img_revised_tab == 10] = 10 + text_regions_p[table_prediction == 1] = 10 + img_revised_tab = text_regions_p[:,:] else: img_revised_tab = text_regions_p[:,:] #img_revised_tab = text_regions_p[:, :] - 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) + polygons_of_images = return_contours_of_interested_region(text_regions_p, 2) pixel_img = 4 min_area_mar = 0.00001 - 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) + 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) - else: - polygons_of_marginals = return_contours_of_interested_region(text_regions_p, pixel_img, min_area_mar) + polygons_of_marginals = return_contours_of_interested_region(marginal_mask, 1, min_area_mar) pixel_img = 10 contours_tables = return_contours_of_interested_region(text_regions_p, pixel_img, min_area_mar) @@ -3272,144 +2314,43 @@ class Eynollah: self.logger.debug('enter run_boxes_full_layout') t_full0 = time.time() if self.tables: - if self.light_version: - 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) + 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) - 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 + 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: - 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) + 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 - 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( - text_regions_p, 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( - text_regions_p_1_n, 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) - 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 == 10] = 10 - - ##img_revised_tab = img_revised_tab2[:,:] - #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 == 10] = 10 pixel_img = 4 min_area_mar = 0.00001 - 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) + 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) - else: - polygons_of_marginals = return_contours_of_interested_region(text_regions_p, pixel_img, min_area_mar) + polygons_of_marginals = return_contours_of_interested_region(marginal_mask, 1, min_area_mar) pixel_img = 10 contours_tables = return_contours_of_interested_region(text_regions_p, pixel_img, min_area_mar) @@ -3422,7 +2363,7 @@ 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 if self.light_version else image_page, + img_bin_light, False, cols=num_col_classifier) #print("full inside 2", time.time()- t_full0) # 6 is the separators lable in old full layout model @@ -3506,7 +2447,7 @@ class Eynollah: min_cont_size_to_be_dilated = 10 - if len(contours_only_text_parent)>min_cont_size_to_be_dilated and self.light_version: + 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, @@ -3620,13 +2561,13 @@ class Eynollah: 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 and self.light_version: + 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 and self.light_version: + 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 @@ -3678,7 +2619,7 @@ class Eynollah: tot_counter += 1 batch.append(j) if tot_counter % inference_bs == 0 or tot_counter == len(ij_list): - y_pr = self.models["reading_order"].predict(input_1 , verbose=0) + y_pr = self.model_zoo.get("reading_order").predict(input_1 , verbose=0) for jb, j in enumerate(batch): if y_pr[jb][0]>=0.5: post_list.append(j) @@ -3701,7 +2642,7 @@ class Eynollah: ordered = [i[0] for i in ordered] - if len(contours_only_text_parent)>min_cont_size_to_be_dilated and self.light_version: + 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] @@ -3721,211 +2662,6 @@ class Eynollah: region_ids = ['region_%04d' % i for i in range(len(co_text_all_org))] 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: - pass - - 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 = 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)) - - #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_list_of_contours_with_desired_order(self, ls_cons, sorted_indexes): - return list(np.array(ls_cons)[np.array(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 filter_contours_inside_a_bigger_one(self, contours, contours_d_ordered, image, marginal_cnts=None, type_contour="textregion"): if type_contour == "textregion": @@ -4130,14 +2866,8 @@ class Eynollah: # Log enabled features directly enabled_modes = [] - if self.light_version: - enabled_modes.append("Light version") - if self.textline_light: - enabled_modes.append("Light textline detection") if self.full_layout: enabled_modes.append("Full layout analysis") - if self.ocr: - enabled_modes.append("OCR") if self.tables: enabled_modes.append("Table detection") if enabled_modes: @@ -4195,7 +2925,7 @@ class Eynollah: self.logger.info("Step 1/5: Image Enhancement") img_res, is_image_enhanced, num_col_classifier, num_column_is_classified = \ - self.run_enhancement(self.light_version) + 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") @@ -4205,23 +2935,6 @@ class Eynollah: self.logger.info(f"Enhancement complete ({time.time() - t0:.1f}s)") - # Image Extraction Mode - if self.extract_only_images: - self.logger.info("Step 2/5: Image Extraction Mode") - - text_regions_p_1, erosion_hurts, polygons_seplines, 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) - pcgts = self.writer.build_pagexml_no_full_layout( - [], page_coord, [], [], [], [], - polygons_of_images, [], [], [], [], [], [], [], [], [], - cont_page, [], []) - if self.plotter: - self.plotter.write_images_into_directory(polygons_of_images, image_page) - - self.logger.info("Image extraction complete") - return pcgts - # Basic Processing Mode if self.skip_layout_and_reading_order: self.logger.info("Step 2/5: Basic Processing Mode") @@ -4254,26 +2967,28 @@ class Eynollah: order_text_new = [0] slopes =[0] - id_of_texts_tot =['region_0001'] conf_contours_textregions =[0] - if self.ocr and not self.tr: - gc.collect() - ocr_all_textlines = return_rnn_cnn_ocr_of_given_textlines( - image_page, all_found_textline_polygons, np.zeros((len(all_found_textline_polygons), 4)), - self.models["ocr"], self.b_s_ocr, self.num_to_char, textline_light=True) - else: - ocr_all_textlines = None - pcgts = self.writer.build_pagexml_no_full_layout( - cont_page, page_coord, order_text_new, id_of_texts_tot, - all_found_textline_polygons, page_coord, [], - [], [], [], [], [], [], - slopes, [], [], - cont_page, [], [], - ocr_all_textlines=ocr_all_textlines, - conf_contours_textregion=conf_contours_textregions, - skip_layout_reading_order=True) + 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") return pcgts @@ -4281,49 +2996,34 @@ class Eynollah: t1 = time.time() self.logger.info("Step 2/5: Layout Analysis") - if self.light_version: - 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) + 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 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 = self.run_deskew(textline_mask_tot_ea_deskew) + if num_col_classifier == 1 or num_col_classifier ==2: + if num_col_classifier == 1: + img_w_new = 1000 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) + 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) else: - text_regions_p_1, erosion_hurts, polygons_seplines, polygons_text_early = \ - self.get_regions_from_xy_2models(img_res, is_image_enhanced, - num_col_classifier) - self.logger.info(f"Textregion detection took {time.time() - t1:.1f}s") - confidence_matrix = np.zeros((text_regions_p_1.shape[:2])) + 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) - 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(f"Graphics detection took {time.time() - t1:.1f}s") - #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)") @@ -4332,19 +3032,30 @@ class Eynollah: self.logger.info("No columns detected - generating empty PAGE-XML") pcgts = self.writer.build_pagexml_no_full_layout( - [], page_coord, [], [], [], [], [], [], [], [], [], [], [], [], [], [], - cont_page, [], []) + 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=[], + ) return pcgts #print("text region early in %.1fs", time.time() - t0) t1 = time.time() - if not self.light_version: - textline_mask_tot_ea = self.run_textline(image_page) - self.logger.info(f"Textline detection took {time.time() - t1:.1f}s") - t1 = time.time() - slope_deskew = self.run_deskew(textline_mask_tot_ea) - self.logger.info(f"Deskewing took {time.time() - t1:.1f}s") - elif num_col_classifier in (1,2): + if 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: @@ -4383,10 +3094,8 @@ class Eynollah: if self.curved_line: self.logger.info("Mode: Curved line detection") - elif self.textline_light: - self.logger.info("Mode: Light detection") - if self.light_version and num_col_classifier in (1,2): + if 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 ) @@ -4410,11 +3119,10 @@ class Eynollah: 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 if self.light_version else None) + img_bin_light) ###polygons_of_marginals = dilate_textregion_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 + 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 @@ -4555,88 +3263,89 @@ class Eynollah: empty_marginals = [[]] * len(polygons_of_marginals) if self.full_layout: pcgts = self.writer.build_pagexml_full_layout( - [], [], page_coord, [], [], [], [], [], [], - polygons_of_images, contours_tables, [], - polygons_of_marginals, polygons_of_marginals, - empty_marginals, empty_marginals, - empty_marginals, empty_marginals, - [], [], [], [], - cont_page, polygons_seplines) + 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 + ) else: pcgts = self.writer.build_pagexml_no_full_layout( - [], page_coord, [], [], [], [], - polygons_of_images, - polygons_of_marginals, polygons_of_marginals, - empty_marginals, empty_marginals, - empty_marginals, empty_marginals, - [], [], [], - cont_page, polygons_seplines, contours_tables) + 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 + ) return pcgts #print("text region early 3 in %.1fs", time.time() - t0) - if self.light_version: - 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) - else: - 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) + 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) #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) #print("text region early 5 in %.1fs", time.time() - t0) ## birdan sora chock chakir if not self.curved_line: - if self.light_version: - if self.textline_light: - 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) + 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) - 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) - else: - textline_mask_tot_ea = cv2.erode(textline_mask_tot_ea, kernel=KERNEL, iterations=1) - all_found_textline_polygons, \ - all_box_coord, slopes = self.get_slopes_and_deskew_new_light( - contours_only_text_parent, contours_only_text_parent, textline_mask_tot_ea, - boxes_text, slope_deskew) - all_found_textline_polygons_marginals, \ - all_box_coord_marginals, slopes_marginals = self.get_slopes_and_deskew_new_light( - polygons_of_marginals, polygons_of_marginals, textline_mask_tot_ea, - 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, \ - all_box_coord, slopes = self.get_slopes_and_deskew_new( - contours_only_text_parent, contours_only_text_parent, textline_mask_tot_ea, - boxes_text, slope_deskew) - all_found_textline_polygons_marginals, \ - all_box_coord_marginals, slopes_marginals = self.get_slopes_and_deskew_new( - polygons_of_marginals, polygons_of_marginals, textline_mask_tot_ea, - 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) else: scale_param = 1 textline_mask_tot_ea_erode = cv2.erode(textline_mask_tot_ea, kernel=KERNEL, iterations=2) @@ -4667,10 +3376,7 @@ class Eynollah: #print(len(polygons_of_marginals), len(ordered_left_marginals), len(ordered_right_marginals), 'marginals ordred') if self.full_layout: - 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 + fun = check_any_text_region_in_model_one_is_main_or_header_light 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, \ @@ -4689,7 +3395,7 @@ class Eynollah: ##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) + ##kernel=KERNEL, curved_line=self.curved_line) if not self.reading_order_machine_based: label_seps = 6 @@ -4756,1084 +3462,56 @@ class Eynollah: boxes_d, textline_mask_tot_d) self.logger.info(f"Detection of reading order took {time.time() - t_order:.1f}s") - ocr_all_textlines = None - ocr_all_textlines_marginals_left = None - ocr_all_textlines_marginals_right = None - ocr_all_textlines_h = None - ocr_all_textlines_drop = None - if self.ocr: - self.logger.info("Step 4.5/5: OCR Processing") - - if not self.tr: - gc.collect() - - if len(all_found_textline_polygons): - ocr_all_textlines = return_rnn_cnn_ocr_of_given_textlines( - image_page, all_found_textline_polygons, all_box_coord, - self.models["ocr"], self.b_s_ocr, self.num_to_char, self.textline_light, self.curved_line) - - if len(all_found_textline_polygons_marginals_left): - ocr_all_textlines_marginals_left = return_rnn_cnn_ocr_of_given_textlines( - image_page, all_found_textline_polygons_marginals_left, all_box_coord_marginals_left, - self.models["ocr"], self.b_s_ocr, self.num_to_char, self.textline_light, self.curved_line) - - if len(all_found_textline_polygons_marginals_right): - ocr_all_textlines_marginals_right = return_rnn_cnn_ocr_of_given_textlines( - image_page, all_found_textline_polygons_marginals_right, all_box_coord_marginals_right, - self.models["ocr"], self.b_s_ocr, self.num_to_char, self.textline_light, self.curved_line) - - if self.full_layout and len(all_found_textline_polygons): - ocr_all_textlines_h = return_rnn_cnn_ocr_of_given_textlines( - image_page, all_found_textline_polygons_h, all_box_coord_h, - self.models["ocr"], self.b_s_ocr, self.num_to_char, self.textline_light, self.curved_line) - - if self.full_layout and len(polygons_of_drop_capitals): - ocr_all_textlines_drop = return_rnn_cnn_ocr_of_given_textlines( - image_page, polygons_of_drop_capitals, np.zeros((len(polygons_of_drop_capitals), 4)), - self.models["ocr"], self.b_s_ocr, self.num_to_char, self.textline_light, self.curved_line) - - else: - if self.light_version: - self.logger.info("Using light version OCR") - if self.textline_light: - self.logger.info("Using light text line detection for OCR") - self.logger.info("Processing text lines...") - - gc.collect() - - torch.cuda.empty_cache() - self.models["ocr"].to(self.device) - - ind_tot = 0 - #cv2.imwrite('./img_out.png', image_page) - ocr_all_textlines = [] - # FIXME: what about lines in marginals / headings / drop-capitals here? - 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 = 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, self.models["ocr"], self.processor, self.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) - self.logger.info("Step 5/5: Output Generation") if self.full_layout: 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_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, slopes_h, slopes_marginals_left, slopes_marginals_right, - cont_page, polygons_seplines, ocr_all_textlines, ocr_all_textlines_h, - ocr_all_textlines_marginals_left, ocr_all_textlines_marginals_right, - ocr_all_textlines_drop, - conf_contours_textregions, conf_contours_textregions_h) + 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( - contours_only_text_parent, page_coord, order_text_new, id_of_texts_tot, - all_found_textline_polygons, all_box_coord, polygons_of_images, - 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, slopes_marginals_left, slopes_marginals_right, - cont_page, polygons_seplines, contours_tables, - ocr_all_textlines=ocr_all_textlines, - ocr_all_textlines_marginals_left=ocr_all_textlines_marginals_left, - ocr_all_textlines_marginals_right=ocr_all_textlines_marginals_right, - conf_contours_textregions=conf_contours_textregions) + 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, + ) return pcgts - - -class Eynollah_ocr: - def __init__( - self, - dir_models, - model_name=None, - dir_xmls=None, - tr_ocr=False, - batch_size=None, - export_textline_images_and_text=False, - do_not_mask_with_textline_contour=False, - pref_of_dataset=None, - min_conf_value_of_textline_text : Optional[float]=None, - logger=None, - ): - self.model_name = model_name - 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.pref_of_dataset = pref_of_dataset - self.logger = logger if logger else getLogger('eynollah') - - if not export_textline_images_and_text: - if min_conf_value_of_textline_text: - self.min_conf_value_of_textline_text = float(min_conf_value_of_textline_text) - else: - self.min_conf_value_of_textline_text = 0.3 - 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") - if self.model_name: - self.model_ocr_dir = self.model_name - else: - self.model_ocr_dir = dir_models + "/model_eynollah_ocr_trocr_20250919" - self.model_ocr = VisionEncoderDecoderModel.from_pretrained(self.model_ocr_dir) - self.model_ocr.to(self.device) - if not batch_size: - self.b_s = 2 - else: - self.b_s = int(batch_size) - - else: - if self.model_name: - self.model_ocr_dir = self.model_name - else: - self.model_ocr_dir = dir_models + "/model_eynollah_ocr_cnnrnn_20250930" - 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) - if not batch_size: - self.b_s = 8 - else: - self.b_s = int(batch_size) - - 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 - ) - self.end_character = len(characters) + 2 - - def run(self, overwrite: bool = False, - dir_in: Optional[str] = None, - dir_in_bin: Optional[str] = None, - image_filename: Optional[str] = None, - dir_xmls: Optional[str] = None, - dir_out_image_text: Optional[str] = None, - dir_out: Optional[str] = None, - ): - 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: - ls_imgs = [image_filename] - - if self.tr_ocr: - tr_ocr_input_height_and_width = 384 - for dir_img in ls_imgs: - file_name = Path(dir_img).stem - dir_xml = os.path.join(dir_xmls, file_name+'.xml') - out_file_ocr = os.path.join(dir_out, file_name+'.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) - continue - - img = cv2.imread(dir_img) - - if dir_out_image_text: - out_image_with_text = os.path.join(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 = [] - - ##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 = [] - - extracted_texts = [] - - indexer_text_region = 0 - indexer_b_s = 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 dir_out_image_text: - 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.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 - - 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.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 - - - 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.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 - - 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.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 - - - - 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.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 = [] - ####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.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 - - 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)) - - unique_cropped_lines_region_indexer = np.unique(cropped_lines_region_indexer) - - if dir_out_image_text: - - #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: - font = ImageFont.truetype(font=font, size=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 = 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) - - #print(len(unique_cropped_lines_region_indexer), 'unique_cropped_lines_region_indexer') - #######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] - #######text_by_textregion.append(" ".join(extracted_texts_merged_un)) - - 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 root1.iter(region_tags): - #id_textregion = nn.attrib['id'] - #id_textregions.append(id_textregion) - #textregions_by_existing_ids.append(text_by_textregion[indexer_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') - ##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"): - ##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 - - ###sample_order = [(id_to_order[tid], text) - ### for tid, text in zip(id_textregions, textregions_by_existing_ids) - ### if tid in id_to_order] - - ##ordered_texts_sample = [text for _, text in sorted(sample_order)] - ##tot_page_text = ' '.join(ordered_texts_sample) - - ##for page_element in root1.iter(link+'Page'): - ##text_page = ET.SubElement(page_element, 'TextEquiv') - ##unicode_textpage = ET.SubElement(text_page, 'Unicode') - ##unicode_textpage.text = tot_page_text - - ET.register_namespace("",name_space) - tree1.write(out_file_ocr,xml_declaration=True,method='xml',encoding="utf-8",default_namespace=None) - else: - ###max_len = 280#512#280#512 - ###padding_token = 1500#299#1500#299 - image_width = 512#max_len * 4 - image_height = 32 - - - img_size=(image_width, image_height) - - for dir_img in ls_imgs: - file_name = Path(dir_img).stem - dir_xml = os.path.join(dir_xmls, file_name+'.xml') - out_file_ocr = os.path.join(dir_out, file_name+'.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) - continue - - img = cv2.imread(dir_img) - if dir_in_bin is not None: - cropped_lines_bin = [] - dir_img_bin = os.path.join(dir_in_bin, file_name+'.png') - img_bin = cv2.imread(dir_img_bin) - - if dir_out_image_text: - out_image_with_text = os.path.join(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_ver_index = [] - 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): - 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 - - if dir_out_image_text: - total_bb_coordinates.append([x,y,w,h]) - - w_scaled = w * image_height/float(h) - - img_poly_on_img = np.copy(img) - if dir_in_bin is not None: - 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 self.export_textline_images_and_text: - if not self.do_not_mask_with_textline_contour: - img_crop[mask_poly==0] = 255 - - else: - # 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 dir_in_bin is not None: - 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 dir_in_bin is not None: - 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 dir_in_bin is not None: - 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 dir_in_bin is not None: - 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 dir_in_bin is not None: - 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 not self.export_textline_images_and_text: - 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 dir_in_bin is not None: - 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 dir_in_bin is not None 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 dir_in_bin is not None: - 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 dir_in_bin is not None: - img_fin = 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 img_crop.shape[0]==0 or img_crop.shape[1]==0: - pass - else: - 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 self.pref_of_dataset: - base_name += '_' + self.pref_of_dataset - if not self.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 - - 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 = [] - extracted_conf_value = [] - - 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:] - 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 dir_in_bin is not None: - 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 dir_in_bin is not None: - 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.prediction_model.predict(imgs, verbose=0) - - if len(indices_ver)>0: - preds_flipped = self.prediction_model.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 dir_in_bin is not None: - preds_bin = self.prediction_model.predict(imgs_bin, verbose=0) - - if len(indices_ver)>0: - preds_flipped = self.prediction_model.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.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 - if dir_in_bin is not None: - 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] - 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 = [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] - unique_cropped_lines_region_indexer = np.unique(cropped_lines_region_indexer) - - if dir_out_image_text: - #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: - font = ImageFont.truetype(font=font, size=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 = 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)) - #print(text_by_textregion, 'text_by_textregiontext_by_textregiontext_by_textregiontext_by_textregiontext_by_textregion') - - ###index_tot_regions = [] - ###tot_region_ref = [] - - ###for jj in root1.iter(link+'RegionRefIndexed'): - ###index_tot_regions.append(jj.attrib['index']) - ###tot_region_ref.append(jj.attrib['regionRef']) - - ###id_to_order = {tid: ro for tid, ro in zip(tot_region_ref, index_tot_regions)} - - #id_textregions = [] - #textregions_by_existing_ids = [] - indexer = 0 - indexer_textregion = 0 - for nn in root1.iter(region_tags): - #id_textregion = nn.attrib['id'] - #id_textregions.append(id_textregion) - #textregions_by_existing_ids.append(text_by_textregion[indexer_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') - 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"): - 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 - - ###sample_order = [(id_to_order[tid], text) - ### for tid, text in zip(id_textregions, textregions_by_existing_ids) - ### if tid in id_to_order] - - ##ordered_texts_sample = [text for _, text in sorted(sample_order)] - ##tot_page_text = ' '.join(ordered_texts_sample) - - ##for page_element in root1.iter(link+'Page'): - ##text_page = ET.SubElement(page_element, 'TextEquiv') - ##unicode_textpage = ET.SubElement(text_page, 'Unicode') - ##unicode_textpage.text = tot_page_text - - ET.register_namespace("",name_space) - tree1.write(out_file_ocr,xml_declaration=True,method='xml',encoding="utf-8",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 new file mode 100644 index 0000000..f04cfdc --- /dev/null +++ b/src/eynollah/eynollah_imports.py @@ -0,0 +1,10 @@ +""" +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 new file mode 100644 index 0000000..3c918e5 --- /dev/null +++ b/src/eynollah/eynollah_ocr.py @@ -0,0 +1,837 @@ +# 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 index 9247efe..babbd55 100644 --- a/src/eynollah/image_enhancer.py +++ b/src/eynollah/image_enhancer.py @@ -2,7 +2,12 @@ Image enhancer. The output can be written as same scale of input or in new predicted scale. """ -from logging import Logger +# FIXME: fix all of those... +# pyright: reportUnboundVariable=false +# pyright: reportCallIssue=false +# pyright: reportArgumentType=false + +import logging import os import time from typing import Optional @@ -10,19 +15,18 @@ from pathlib import Path import gc import cv2 +from keras.models import Model import numpy as np -from ocrd_utils import getLogger, tf_disable_interactive_logs -import tensorflow as tf +import tensorflow as tf # type: ignore from skimage.morphology import skeletonize -from tensorflow.keras.models import load_model +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 ) -from .eynollah import PatchEncoder, Patches DPI_THRESHOLD = 298 KERNEL = np.ones((5, 5), np.uint8) @@ -31,14 +35,13 @@ KERNEL = np.ones((5, 5), np.uint8) class Enhancer: def __init__( self, - dir_models : str, + *, + model_zoo: EynollahModelZoo, num_col_upper : Optional[int] = None, num_col_lower : Optional[int] = None, save_org_scale : bool = False, - logger : Optional[Logger] = None, ): self.input_binary = False - self.light_version = False self.save_org_scale = save_org_scale if num_col_upper: self.num_col_upper = int(num_col_upper) @@ -49,12 +52,10 @@ class Enhancer: else: self.num_col_lower = num_col_lower - self.logger = logger if logger else getLogger('enhancement') - self.dir_models = dir_models - self.model_dir_of_binarization = dir_models + "/eynollah-binarization_20210425" - self.model_dir_of_enhancement = dir_models + "/eynollah-enhancement_20210425" - self.model_dir_of_col_classifier = dir_models + "/eynollah-column-classifier_20210425" - self.model_page_dir = dir_models + "/model_eynollah_page_extraction_20250915" + 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'): @@ -62,25 +63,14 @@ class Enhancer: except: self.logger.warning("no GPU device available") - 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_enhancement = self.our_load_model(self.model_dir_of_enhancement) - self.model_bin = self.our_load_model(self.model_dir_of_binarization) - def cache_images(self, image_filename=None, image_pil=None, dpi=None): ret = {} if image_filename: ret['img'] = cv2.imread(image_filename) - if self.light_version: - self.dpi = 100 - else: - self.dpi = 0#check_dpi(image_filename) + self.dpi = 100 else: ret['img'] = pil2cv(image_pil) - if self.light_version: - self.dpi = 100 - else: - self.dpi = 0#check_dpi(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) @@ -100,26 +90,11 @@ class Enhancer: key += '_uint8' return self._imgs[key].copy() - def isNaN(self, num): - return num != num - - @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 predict_enhancement(self, img): self.logger.debug("enter predict_enhancement") - img_height_model = self.model_enhancement.layers[-1].output_shape[1] - img_width_model = self.model_enhancement.layers[-1].output_shape[2] + 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: @@ -160,7 +135,7 @@ class Enhancer: 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_enhancement.predict(img_patch, verbose=0) + 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: @@ -246,7 +221,7 @@ class Enhancer: else: img = self.imread() img = cv2.GaussianBlur(img, (5, 5), 0) - img_page_prediction = self.do_prediction(False, img, self.model_page) + 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) @@ -285,13 +260,13 @@ class Enhancer: return img_new, num_column_is_classified - def resize_and_enhance_image_with_column_classifier(self, light_version): + 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_bin, n_batch_inference=5) + 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) @@ -332,7 +307,7 @@ class Enhancer: img_in[0, :, :, 1] = img_1ch[:, :] img_in[0, :, :, 2] = img_1ch[:, :] - label_p_pred = self.model_classifier.predict(img_in, verbose=0) + 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: @@ -352,7 +327,7 @@ class Enhancer: img_in[0, :, :, 1] = img_1ch[:, :] img_in[0, :, :, 2] = img_1ch[:, :] - label_p_pred = self.model_classifier.predict(img_in, verbose=0) + 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: @@ -368,16 +343,13 @@ class Enhancer: self.logger.info("Found %d columns (%s)", num_col, np.around(label_p_pred, decimals=5)) if dpi < DPI_THRESHOLD: - if light_version and num_col in (1,2): + 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) - if light_version: - image_res = np.copy(img_new) - else: - image_res = self.predict_enhancement(img_new) + image_res = np.copy(img_new) is_image_enhanced = True else: @@ -671,11 +643,11 @@ class Enhancer: gc.collect() return prediction_true - def run_enhancement(self, light_version): + 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(light_version) + 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 @@ -683,9 +655,9 @@ class Enhancer: def run_single(self): t0 = time.time() - img_res, is_image_enhanced, num_col_classifier, num_column_is_classified = self.run_enhancement(light_version=False) + img_res, is_image_enhanced, num_col_classifier, num_column_is_classified = self.run_enhancement() - return img_res + return img_res, is_image_enhanced def run(self, @@ -723,9 +695,18 @@ class Enhancer: self.logger.warning("will skip input for existing output file '%s'", self.output_filename) continue - image_enhanced = self.run_single() + 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 index 1b991ae..eec544c 100644 --- a/src/eynollah/mb_ro_on_layout.py +++ b/src/eynollah/mb_ro_on_layout.py @@ -1,8 +1,12 @@ """ -Image enhancer. The output can be written as same scale of input or in new predicted scale. +Machine learning based reading order detection """ -from logging import Logger +# pyright: reportCallIssue=false +# pyright: reportUnboundVariable=false +# pyright: reportArgumentType=false + +import logging import os import time from typing import Optional @@ -10,12 +14,12 @@ from pathlib import Path import xml.etree.ElementTree as ET import cv2 +from keras.models import Model import numpy as np -from ocrd_utils import getLogger import statistics import tensorflow as tf -from tensorflow.keras.models import load_model +from .model_zoo import EynollahModelZoo from .utils.resize import resize_image from .utils.contour import ( find_new_features_of_contours, @@ -23,7 +27,6 @@ from .utils.contour import ( return_parent_contours, ) from .utils import is_xml_filename -from .eynollah import PatchEncoder, Patches DPI_THRESHOLD = 298 KERNEL = np.ones((5, 5), np.uint8) @@ -32,12 +35,12 @@ KERNEL = np.ones((5, 5), np.uint8) class machine_based_reading_order_on_layout: def __init__( self, - dir_models : str, - logger : Optional[Logger] = None, + *, + model_zoo: EynollahModelZoo, + logger : Optional[logging.Logger] = None, ): - self.logger = logger if logger else getLogger('mbreorder') - self.dir_models = dir_models - self.model_reading_order_dir = dir_models + "/model_eynollah_reading_order_20250824" + self.logger = logger or logging.getLogger('eynollah.mbreorder') + self.model_zoo = model_zoo try: for device in tf.config.list_physical_devices('GPU'): @@ -45,20 +48,7 @@ class machine_based_reading_order_on_layout: except: self.logger.warning("no GPU device available") - self.model_reading_order = self.our_load_model(self.model_reading_order_dir) - self.light_version = True - - @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 + self.model_zoo.load_model('reading_order') def read_xml(self, xml_file): tree1 = ET.parse(xml_file, parser = ET.XMLParser(encoding='utf-8')) @@ -69,6 +59,7 @@ class machine_based_reading_order_on_layout: 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']) @@ -81,13 +72,13 @@ class machine_based_reading_order_on_layout: 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: + 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'] - elif link+'Border' in alltags: + else: tag_endings_printspace = ['}Border','}border'] if tag.endswith(tag_endings_printspace[0]) or tag.endswith(tag_endings_printspace[1]): @@ -524,7 +515,7 @@ class machine_based_reading_order_on_layout: min_cont_size_to_be_dilated = 10 - if len(contours_only_text_parent)>min_cont_size_to_be_dilated and self.light_version: + 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))) @@ -624,13 +615,13 @@ class machine_based_reading_order_on_layout: 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 and self.light_version: + 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 and self.light_version: + 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 @@ -683,7 +674,7 @@ class machine_based_reading_order_on_layout: tot_counter += 1 batch.append(j) if tot_counter % inference_bs == 0 or tot_counter == len(ij_list): - y_pr = self.model_reading_order.predict(input_1 , verbose=0) + 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) @@ -709,7 +700,7 @@ class machine_based_reading_order_on_layout: ##id_all_text = np.array(id_all_text)[index_sort] - if len(contours_only_text_parent)>min_cont_size_to_be_dilated and self.light_version: + 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] @@ -802,6 +793,7 @@ class machine_based_reading_order_on_layout: 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', diff --git a/src/eynollah/model_zoo/__init__.py b/src/eynollah/model_zoo/__init__.py new file mode 100644 index 0000000..e1dc985 --- /dev/null +++ b/src/eynollah/model_zoo/__init__.py @@ -0,0 +1,4 @@ +__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 new file mode 100644 index 0000000..b9a1a2c --- /dev/null +++ b/src/eynollah/model_zoo/default_specs.py @@ -0,0 +1,252 @@ +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 new file mode 100644 index 0000000..80d0aa7 --- /dev/null +++ b/src/eynollah/model_zoo/model_zoo.py @@ -0,0 +1,203 @@ +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 new file mode 100644 index 0000000..3c47b7b --- /dev/null +++ b/src/eynollah/model_zoo/specs.py @@ -0,0 +1,52 @@ +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 new file mode 100644 index 0000000..43f6859 --- /dev/null +++ b/src/eynollah/model_zoo/types.py @@ -0,0 +1,7 @@ +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 dbbdc3b..c694a6f 100644 --- a/src/eynollah/ocrd-tool.json +++ b/src/eynollah/ocrd-tool.json @@ -29,16 +29,6 @@ "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. 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." }, "tables": { "type": "boolean", @@ -83,12 +73,20 @@ }, "resources": [ { - "url": "https://zenodo.org/records/17194824/files/models_layout_v0_5_0.tar.gz?download=1", - "name": "models_layout_v0_5_0", + "url": "https://zenodo.org/records/17580627/files/models_all_v0_7_0.zip?download=1", + "name": "models_layout_v0_7_0", "type": "archive", - "path_in_archive": "models_layout_v0_5_0", + "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", + "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" }, { diff --git a/src/eynollah/ocrd_cli.py b/src/eynollah/ocrd_cli.py index 8929927..acd8d4e 100644 --- a/src/eynollah/ocrd_cli.py +++ b/src/eynollah/ocrd_cli.py @@ -1,3 +1,6 @@ +# 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 848bbac..f234520 100644 --- a/src/eynollah/ocrd_cli_binarization.py +++ b/src/eynollah/ocrd_cli_binarization.py @@ -1,6 +1,8 @@ +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 @@ -9,6 +11,8 @@ 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 @@ -25,7 +29,7 @@ class SbbBinarizeProcessor(Processor): # already employs GPU (without singleton process atm) max_workers = 1 - @property + @cached_property def executable(self): return 'ocrd-sbb-binarize' @@ -34,8 +38,9 @@ class SbbBinarizeProcessor(Processor): Set up the model prior to processing. """ # resolve relative path via OCR-D ResourceManager - model_path = self.resolve_resource(self.parameter['model']) - self.binarizer = SbbBinarizer(model_dir=model_path, logger=self.logger) + assert isinstance(self.parameter, frozendict) + model_zoo = EynollahModelZoo(basedir=self.parameter['model']) + self.binarizer = SbbBinarizer(model_zoo=model_zoo, logger=self.logger) def process_page_pcgts(self, *input_pcgts: Optional[OcrdPage], page_id: Optional[str] = None) -> OcrdPageResult: """ @@ -98,7 +103,7 @@ 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(region_image_ref) + line.add_AlternativeImage(line_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 new file mode 100644 index 0000000..939ad7b --- /dev/null +++ b/src/eynollah/patch_encoder.py @@ -0,0 +1,52 @@ +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 c026e94..b1b2359 100644 --- a/src/eynollah/plot.py +++ b/src/eynollah/plot.py @@ -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 = scale_x - self.scale_y = scale_y + self.scale_x : float = scale_x + self.scale_y : float = 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 12c7356..0addaff 100644 --- a/src/eynollah/processor.py +++ b/src/eynollah/processor.py @@ -3,6 +3,8 @@ from typing import Optional from ocrd_models import OcrdPage from ocrd import OcrdPageResultImage, Processor, OcrdPageResult +from eynollah.model_zoo.model_zoo import EynollahModelZoo + from .eynollah import Eynollah, EynollahXmlWriter class EynollahProcessor(Processor): @@ -16,24 +18,20 @@ class EynollahProcessor(Processor): def setup(self) -> None: assert self.parameter - if self.parameter['textline_light'] != self.parameter['light_version']: - raise ValueError("Error: You must set or unset both parameter 'textline_light' (to enable light textline detection), " - "and parameter 'light_version' (faster+simpler method for main region detection and deskewing)") + model_zoo = EynollahModelZoo(basedir=self.parameter['models']) self.eynollah = Eynollah( - self.resolve_resource(self.parameter['models']), + model_zoo=model_zoo, 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.logger = self.logger self.eynollah.plotter = None def shutdown(self): @@ -90,7 +88,6 @@ 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 3716987..851ac7d 100644 --- a/src/eynollah/sbb_binarize.py +++ b/src/eynollah/sbb_binarize.py @@ -2,18 +2,22 @@ Tool to load model and binarize a given image. """ -import sys -from glob import glob +# pyright: reportIndexIssue=false +# pyright: reportCallIssue=false +# pyright: reportArgumentType=false +# pyright: reportPossiblyUnboundVariable=false + 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 .utils import is_image_filename @@ -23,40 +27,32 @@ def resize_image(img_in, input_height, input_width): class SbbBinarizer: - 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 __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 start_new_session(self): 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) - + session = tf.compat.v1.Session(config=config) # tf.InteractiveSession() + tensorflow_backend.set_session(session) + return session + def end_session(self): tensorflow_backend.clear_session() self.session.close() del self.session - def load_model(self, model_name): - model = load_model(os.path.join(self.model_dir, model_name), compile=False) + def predict(self, model, img, use_patches, n_batch_inference=5): 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] @@ -324,9 +320,38 @@ class SbbBinarizer: if image_path is not None: image = cv2.imread(image_path) 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))) + 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)) + 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 = image_path.split('.')[0] + 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)) @@ -341,38 +366,9 @@ 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 output: - cv2.imwrite(output, img_last) - return img_last - else: - ls_imgs = list(filter(is_image_filename, 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 - cv2.imwrite(os.path.join(output, image_stem + '.png'), img_last) + 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) diff --git a/src/eynollah/training/cli.py b/src/eynollah/training/cli.py index 8ab754d..65a7a8a 100644 --- a/src/eynollah/training/cli.py +++ b/src/eynollah/training/cli.py @@ -8,6 +8,7 @@ from .build_model_load_pretrained_weights_and_save import build_model_load_pretr 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 @click.command(context_settings=dict( ignore_unknown_options=True, @@ -24,3 +25,4 @@ 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') diff --git a/src/eynollah/training/extract_line_gt.py b/src/eynollah/training/extract_line_gt.py new file mode 100644 index 0000000..58fc253 --- /dev/null +++ b/src/eynollah/training/extract_line_gt.py @@ -0,0 +1,134 @@ +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/models.py b/src/eynollah/training/models.py index 7fc34b6..b0494b1 100644 --- a/src/eynollah/training/models.py +++ b/src/eynollah/training/models.py @@ -33,6 +33,25 @@ resnet50_Weights_path = './pretrained_model/resnet50_weights_tf_dim_ordering_tf_ 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) @@ -779,3 +798,85 @@ def machine_based_reading_order_model(n_classes,input_height=224,input_width=224 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 index 97736e0..c15a562 100644 --- a/src/eynollah/training/train.py +++ b/src/eynollah/training/train.py @@ -15,10 +15,13 @@ from eynollah.training.models import ( resnet50_classifier, resnet50_unet, vit_resnet50_unet, - vit_resnet50_unet_transformer_before_cnn + 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, @@ -36,6 +39,7 @@ 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 @@ -62,6 +66,7 @@ class SaveWeightsAfterSteps(Callback): 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 @@ -89,6 +94,7 @@ def config_params(): 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. @@ -101,6 +107,20 @@ def config_params(): 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 @@ -111,14 +131,21 @@ def config_params(): 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. - shuffle_indexes = None + 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. @@ -145,6 +172,7 @@ def config_params(): 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, @@ -155,11 +183,14 @@ def run(_config, n_classes, n_epochs, input_height, 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, scaling_flip, continue_training, transformer_projection_dim, + 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): + 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) @@ -375,6 +406,82 @@ def run(_config, n_classes, n_epochs, input_height, #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) + + if save_interval: + save_weights_callback = SaveWeightsAfterSteps(save_interval, dir_output, _config) + + 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) diff --git a/src/eynollah/training/utils.py b/src/eynollah/training/utils.py index 1278be5..c589957 100644 --- a/src/eynollah/training/utils.py +++ b/src/eynollah/training/utils.py @@ -9,8 +9,238 @@ 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, ImageEnhance +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 + + 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 + +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 + if padding_color == 'black': + img_new = np.zeros((height_new, width_new, img.shape[2])).astype(float) + + img_new[h_start:h_start + img.shape[0], w_start:w_start + img.shape[1], :] = np.copy(img[:, :, :]) + + + return img_new + +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 + +def do_left_in_depth(img): + height, width = img.shape[:2] + + # 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 + ]) + + # 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 do_right_in_depth(img): + height, width = img.shape[:2] + + # 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 + ]) + + # 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 do_up_in_depth(img): + # Get the dimensions of the image + height, width = img.shape[:2] + + # 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 + ]) + + # 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 do_down_in_depth(img): + # Get the dimensions of the image + height, width = img.shape[:2] + + # 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 + ]) + + # 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): @@ -208,7 +438,7 @@ def generate_data_from_folder_evaluation(path_classes, height, width, n_classes, return ret_x/255., ret_y -def generate_data_from_folder_training(path_classes, batchsize, height, width, n_classes, list_classes): +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) @@ -234,8 +464,8 @@ def generate_data_from_folder_training(path_classes, batchsize, height, width, n 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((batchsize, height,width, 3)).astype(np.int16) - ret_y= np.zeros((batchsize, n_classes)).astype(np.int16) + 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)): @@ -259,11 +489,11 @@ def generate_data_from_folder_training(path_classes, batchsize, height, width, n batchcount+=1 - if batchcount>=batchsize: + if batchcount>=n_batch: ret_x = ret_x/255. yield ret_x, ret_y - ret_x= np.zeros((batchsize, height,width, 3)).astype(np.int16) - ret_y= np.zeros((batchsize, n_classes)).astype(np.int16) + 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): @@ -428,10 +658,10 @@ def IoU(Yi, y_predi): #print("Mean IoU: {:4.3f}".format(mIoU)) return mIoU -def generate_arrays_from_folder_reading_order(classes_file_dir, modal_dir, batchsize, height, width, n_classes, thetha, augmentation=False): +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((batchsize, height, width, 3))#.astype(np.int16) - ret_y= np.zeros((batchsize, n_classes)).astype(np.int16) + 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: @@ -446,10 +676,10 @@ def generate_arrays_from_folder_reading_order(classes_file_dir, modal_dir, batch ret_y[batchcount, :] = label_class batchcount+=1 - if batchcount>=batchsize: + if batchcount>=n_batch: yield ret_x, ret_y - ret_x= np.zeros((batchsize, height, width, 3))#.astype(np.int16) - ret_y= np.zeros((batchsize, n_classes)).astype(np.int16) + 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: @@ -464,10 +694,10 @@ def generate_arrays_from_folder_reading_order(classes_file_dir, modal_dir, batch ret_y[batchcount, :] = label_class batchcount+=1 - if batchcount>=batchsize: + if batchcount>=n_batch: yield ret_x, ret_y - ret_x= np.zeros((batchsize, height, width, 3))#.astype(np.int16) - ret_y= np.zeros((batchsize, n_classes)).astype(np.int16) + 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'): @@ -1055,3 +1285,635 @@ def provide_patches(imgs_list_train, segs_list_train, dir_img, dir_seg, dir_flow 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 + while True: + for i in ls_files_images: + f_name = i.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 = 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. + 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 + + if color_padding_rotation: + for index, thetha_ind in enumerate(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 = scale_padd_image_for_ocr(img_out, 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. + 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 + + if rotation_not_90: + for index, thetha_ind in enumerate(thetha): + img_out = rotation_not_90_func_single_image(img, thetha_ind) + img_out = scale_padd_image_for_ocr(img_out, 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. + 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 + + if blur_aug: + for index, blur_type in enumerate(blur_k): + img_out = bluring(img, blur_type) + img_out = scale_padd_image_for_ocr(img_out, 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. + 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 + + if degrading: + for index, deg_scale_ind in enumerate(degrade_scales): + try: + img_out = do_degrading(img, deg_scale_ind) + img_out = scale_padd_image_for_ocr(img_out, input_height, input_width) + except: + img_out = np.copy(img) + img_out = scale_padd_image_for_ocr(img_out, 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. + 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 + + if bin_deg: + for index, deg_scale_ind in enumerate(degrade_scales): + try: + img_out = do_degrading(img_bin_corr, deg_scale_ind) + img_out = scale_padd_image_for_ocr(img_out, input_height, input_width) + except: + img_out = np.copy(img_bin_corr) + img_out = scale_padd_image_for_ocr(img_out, 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. + 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 + + + if brightening: + for index, bright_scale_ind in enumerate(brightness): + try: + img_out = do_brightening(dir_img, bright_scale_ind) + except: + img_out = np.copy(img) + img_out = scale_padd_image_for_ocr(img_out, 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. + 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 + + if padding_white: + for index, padding_size in enumerate(white_padds): + for padd_col in padd_colors: + img_out = do_padding_for_ocr(img, padding_size, padd_col) + img_out = scale_padd_image_for_ocr(img_out, 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. + 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 + + 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_with_overlayed_background = return_binary_image_with_given_rgb_background_and_given_foreground_rgb(img_bin_corr, img_rgb_background_chosen, foreground_rgb_chosen) + + img_out = scale_padd_image_for_ocr(img_with_overlayed_background, 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. + 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 + + + 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_with_overlayed_background = return_binary_image_with_given_rgb_background(img_bin_corr, img_rgb_background_chosen) + + img_out = scale_padd_image_for_ocr(img_with_overlayed_background, 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. + 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 + + if binarization: + img_out = scale_padd_image_for_ocr(img_bin_corr, 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. + 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 + + if image_inversion: + img_out = invert_image(img_bin_corr) + img_out = scale_padd_image_for_ocr(img_out, 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. + 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 + + + if channels_shuffling: + for shuffle_index in shuffle_indexes: + img_out = return_shuffled_channels(img, shuffle_index) + img_out = scale_padd_image_for_ocr(img_out, 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. + 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 + + + if add_red_textlines: + img_red_context = return_image_with_red_elements(img, img_bin_corr) + + img_out = scale_padd_image_for_ocr(img_red_context, 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. + 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 + + if white_noise_strap: + img_out = return_image_with_strapped_white_noises(img) + + img_out = scale_padd_image_for_ocr(img_out, 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. + 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 + + if textline_skewing: + for index, des_scale_ind in enumerate(skewing_amplitudes): + try: + img_out = do_deskewing(img, des_scale_ind) + img_out = scale_padd_image_for_ocr(img_out, input_height, input_width) + except: + img_out = np.copy(img) + img_out = scale_padd_image_for_ocr(img_out, 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. + 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 + + if textline_skewing_bin: + for index, des_scale_ind in enumerate(skewing_amplitudes): + try: + img_out = do_deskewing(img_bin_corr, des_scale_ind) + img_out = scale_padd_image_for_ocr(img_out, input_height, input_width) + except: + img_out = np.copy(img_bin_corr) + img_out = scale_padd_image_for_ocr(img_out, 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. + 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 + + + if textline_left_in_depth: + try: + img_out = do_left_in_depth(img) + img_out = scale_padd_image_for_ocr(img_out, input_height, input_width) + except: + img_out = np.copy(img) + img_out = scale_padd_image_for_ocr(img_out, 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. + 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 + + + if textline_left_in_depth_bin: + try: + img_out = do_left_in_depth(img_bin_corr) + img_out = scale_padd_image_for_ocr(img_out, input_height, input_width) + except: + img_out = np.copy(img_bin_corr) + img_out = scale_padd_image_for_ocr(img_out, 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. + 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 + + + if textline_right_in_depth: + try: + img_out = do_right_in_depth(img) + img_out = scale_padd_image_for_ocr(img_out, input_height, input_width) + except: + img_out = np.copy(img) + img_out = scale_padd_image_for_ocr(img_out, 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. + 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 + + + if textline_right_in_depth_bin: + try: + img_out = do_right_in_depth(img_bin_corr) + img_out = scale_padd_image_for_ocr(img_out, input_height, input_width) + except: + img_out = np.copy(img_bin_corr) + img_out = scale_padd_image_for_ocr(img_out, 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. + 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 + + + if textline_up_in_depth: + try: + img_out = do_up_in_depth(img) + img_out = scale_padd_image_for_ocr(img_out, input_height, input_width) + except: + img_out = np.copy(img) + img_out = scale_padd_image_for_ocr(img_out, 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. + 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 + + + if textline_up_in_depth_bin: + try: + img_out = do_up_in_depth(img_bin_corr) + img_out = scale_padd_image_for_ocr(img_out, input_height, input_width) + except: + img_out = np.copy(img_bin_corr) + img_out = scale_padd_image_for_ocr(img_out, 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. + 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 + + + if textline_down_in_depth: + try: + img_out = do_down_in_depth(img) + img_out = scale_padd_image_for_ocr(img_out, input_height, input_width) + except: + img_out = np.copy(img) + img_out = scale_padd_image_for_ocr(img_out, 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. + 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 + + + if textline_down_in_depth_bin: + try: + img_out = do_down_in_depth(img_bin_corr) + img_out = scale_padd_image_for_ocr(img_out, input_height, input_width) + except: + img_out = np.copy(img_bin_corr) + img_out = scale_padd_image_for_ocr(img_out, 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. + 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 + + if pepper_bin_aug: + for index, pepper_ind in enumerate(pepper_indexes): + img_out = add_salt_and_pepper_noise(img_bin_corr, pepper_ind, pepper_ind) + img_out = scale_padd_image_for_ocr(img_out, 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. + 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 + + + if pepper_aug: + for index, pepper_ind in enumerate(pepper_indexes): + img_out = add_salt_and_pepper_noise(img, pepper_ind, pepper_ind) + img_out = scale_padd_image_for_ocr(img_out, 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. + 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 + + + + else: + + 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. + 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 + + +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 augmentation: + if binarization: + aug_multip = aug_multip + 1 + if image_inversion: + aug_multip = aug_multip + 1 + if add_red_textlines: + aug_multip = aug_multip + 1 + if white_noise_strap: + aug_multip = aug_multip + 1 + if textline_right_in_depth: + aug_multip = aug_multip + 1 + if textline_left_in_depth: + aug_multip = aug_multip + 1 + if textline_up_in_depth: + aug_multip = aug_multip + 1 + if textline_down_in_depth: + aug_multip = aug_multip + 1 + if textline_right_in_depth_bin: + aug_multip = aug_multip + 1 + if textline_left_in_depth_bin: + aug_multip = aug_multip + 1 + if textline_up_in_depth_bin: + aug_multip = aug_multip + 1 + if textline_down_in_depth_bin: + aug_multip = aug_multip + 1 + if adding_rgb_foreground: + aug_multip = aug_multip + number_of_backgrounds_per_image + if adding_rgb_background: + aug_multip = aug_multip + number_of_backgrounds_per_image + if bin_deg: + aug_multip = aug_multip + len(degrade_scales) + if degrading: + aug_multip = aug_multip + len(degrade_scales) + if rotation_not_90: + aug_multip = aug_multip + len(thetha) + if textline_skewing: + aug_multip = aug_multip + len(skewing_amplitudes) + if textline_skewing_bin: + aug_multip = aug_multip + len(skewing_amplitudes) + if color_padding_rotation: + aug_multip = aug_multip + len(thetha_padd)*len(padd_colors) + if channels_shuffling: + aug_multip = aug_multip + len(shuffle_indexes) + if blur_aug: + aug_multip = aug_multip + len(blur_k) + if brightening: + aug_multip = aug_multip + len(brightness) + if padding_white: + aug_multip = aug_multip + len(white_padds)*len(padd_colors) + if pepper_aug: + aug_multip = aug_multip + len(pepper_indexes) + if pepper_bin_aug: + aug_multip = aug_multip + len(pepper_indexes) + + return aug_multip diff --git a/src/eynollah/utils/__init__.py b/src/eynollah/utils/__init__.py index 5ccb2af..29359eb 100644 --- a/src/eynollah/utils/__init__.py +++ b/src/eynollah/utils/__init__.py @@ -19,7 +19,6 @@ from .contour import (contours_in_same_horizon, find_new_features_of_contours, return_contours_of_image, return_parent_contours) - def pairwise(iterable): # pairwise('ABCDEFG') → AB BC CD DE EF FG @@ -393,7 +392,12 @@ 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): +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) diff --git a/src/eynollah/utils/contour.py b/src/eynollah/utils/contour.py index f304db2..6550171 100644 --- a/src/eynollah/utils/contour.py +++ b/src/eynollah/utils/contour.py @@ -357,7 +357,7 @@ def join_polygons(polygons: Sequence[Polygon], scale=20) -> Polygon: 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) + 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) diff --git a/src/eynollah/utils/drop_capitals.py b/src/eynollah/utils/drop_capitals.py index 9f82fac..228a6d9 100644 --- a/src/eynollah/utils/drop_capitals.py +++ b/src/eynollah/utils/drop_capitals.py @@ -19,7 +19,6 @@ 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]) @@ -79,7 +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 or textline_light: + if curved_line: if len(region_with_intersected_drop) > 1: sum_pixels_of_intersection = [] diff --git a/src/eynollah/utils/font.py b/src/eynollah/utils/font.py new file mode 100644 index 0000000..939933e --- /dev/null +++ b/src/eynollah/utils/font.py @@ -0,0 +1,16 @@ + +# 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 eaf0048..9f76fb7 100644 --- a/src/eynollah/utils/marginals.py +++ b/src/eynollah/utils/marginals.py @@ -6,7 +6,7 @@ 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, light_version=False, kernel=None): +def get_marginals(text_with_lines, text_regions, num_col, slope_deskew, kernel=None): mask_marginals=np.zeros((text_with_lines.shape[0],text_with_lines.shape[1])) mask_marginals=mask_marginals.astype(np.uint8) @@ -27,9 +27,8 @@ def get_marginals(text_with_lines, text_regions, num_col, slope_deskew, light_ve text_with_lines=resize_image(text_with_lines,text_with_lines_eroded.shape[0],text_with_lines_eroded.shape[1]) - if light_version: - kernel_hor = np.ones((1, 5), dtype=np.uint8) - text_with_lines = cv2.erode(text_with_lines,kernel_hor,iterations=6) + 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) @@ -43,10 +42,7 @@ def get_marginals(text_with_lines, text_regions, num_col, slope_deskew, light_ve elif thickness_along_y_percent>=30 and thickness_along_y_percent<50: min_textline_thickness=20 else: - if light_version: - min_textline_thickness=45 - else: - min_textline_thickness=40 + min_textline_thickness=45 if thickness_along_y_percent>=14: @@ -128,92 +124,39 @@ def get_marginals(text_with_lines, text_regions, num_col, slope_deskew, light_ve if max_point_of_right_marginal>=text_regions.shape[1]: max_point_of_right_marginal=text_regions.shape[1]-1 - 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] + 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 22ef00d..c220234 100644 --- a/src/eynollah/utils/separate_lines.py +++ b/src/eynollah/utils/separate_lines.py @@ -5,8 +5,6 @@ import numpy as np import cv2 from scipy.signal import find_peaks from scipy.ndimage import gaussian_filter1d -from multiprocessing import Process, Queue, cpu_count -from multiprocessing import Pool from .rotate import rotate_image from .resize import resize_image from .contour import ( @@ -20,9 +18,7 @@ from .contour import ( from .shm import share_ndarray, wrap_ndarray_shared from . import ( find_num_col_deskew, - crop_image_inside_box, box2rect, - box2slice, ) def dedup_separate_lines(img_patch, contour_text_interest, thetha, axis): @@ -1590,65 +1586,6 @@ def get_smallest_skew(img, sigma_des, angles, logger=None, plotter=None, map=map var = 0 return angle, var -@wrap_ndarray_shared(kw='textline_mask_tot_ea') -def do_work_of_slopes_new( - box_text, contour, contour_par, - textline_mask_tot_ea=None, slope_deskew=0.0, - 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 = box2rect(box_text) - 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 not np.prod(img_int_p.shape) or 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)) - - 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] - - 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, crop_coor, slope - @wrap_ndarray_shared(kw='textline_mask_tot_ea') @wrap_ndarray_shared(kw='mask_texts_only') def do_work_of_slopes_new_curved( @@ -1748,7 +1685,7 @@ def do_work_of_slopes_new_curved( @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, textline_light=True, + textline_mask_tot_ea=None, slope_deskew=0, logger=None ): if logger is None: @@ -1765,16 +1702,10 @@ 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)) - 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) + 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) return cnt_clean_rot, crop_coor, slope_deskew diff --git a/src/eynollah/utils/utils_ocr.py b/src/eynollah/utils/utils_ocr.py index 6e71b0f..b738e29 100644 --- a/src/eynollah/utils/utils_ocr.py +++ b/src/eynollah/utils/utils_ocr.py @@ -128,6 +128,7 @@ def return_textlines_split_if_needed(textline_image, textline_image_bin=None): 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)) @@ -379,7 +380,6 @@ def return_rnn_cnn_ocr_of_given_textlines(image, all_box_coord, prediction_model, b_s_ocr, num_to_char, - textline_light=False, curved_line=False): max_len = 512 padding_token = 299 @@ -404,7 +404,7 @@ def return_rnn_cnn_ocr_of_given_textlines(image, else: for indexing2, ind_poly in enumerate(ind_poly_first): cropped_lines_region_indexer.append(indexer_text_region) - if not (textline_light or curved_line): + if not curved_line: ind_poly = copy.deepcopy(ind_poly) box_ind = all_box_coord[indexing] diff --git a/src/eynollah/utils/xml.py b/src/eynollah/utils/xml.py index 88d1df8..ded098e 100644 --- a/src/eynollah/utils/xml.py +++ b/src/eynollah/utils/xml.py @@ -88,3 +88,7 @@ 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 9c3456a..1781230 100644 --- a/src/eynollah/writer.py +++ b/src/eynollah/writer.py @@ -2,15 +2,14 @@ # pylint: disable=import-error from pathlib import Path import os.path -import xml.etree.ElementTree as ET +from typing import Optional +import logging 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, @@ -19,23 +18,21 @@ from ocrd_models.ocrd_page import ( SeparatorRegionType, to_xml ) -import numpy as np class EynollahXmlWriter: - def __init__(self, *, dir_out, image_filename, curved_line,textline_light, pcgts=None): - self.logger = getLogger('eynollah.writer') + def __init__(self, *, dir_out, image_filename, curved_line, pcgts=None): + self.logger = logging.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 = 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__ + 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__ @property def image_filename_stem(self): @@ -73,13 +70,9 @@ class EynollahXmlWriter: point = point[0] point_x = point[0] + page_coord[2] point_y = point[1] + page_coord[0] - # FIXME: or actually... not self.textline_light and not self.curved_line or np.abs(slopes[region_idx]) > 45? - if not self.textline_light and not (self.curved_line and np.abs(slopes[region_idx]) <= 45): - point_x += region_bboxes[2] - point_y += region_bboxes[0] point_x = max(0, int(point_x / self.scale_x)) point_y = max(0, int(point_y / self.scale_y)) - points_co += str(point_x) + ',' + str(point_y) + ' ' + points_co += f'{point_x},{point_y} ' coords.set_points(points_co[:-1]) def write_pagexml(self, pcgts): @@ -88,53 +81,94 @@ class EynollahXmlWriter: f.write(to_xml(pcgts)) 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_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, - **kwargs): + 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, [], - page_coord, order_of_texts, id_of_texts, - all_found_textline_polygons, [], - all_box_coord, [], - found_polygons_text_region_img, found_polygons_tables, [], - 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, - **kwargs) + 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, 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_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, + *, + 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') # 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() @@ -152,6 +186,7 @@ class EynollahXmlWriter: 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]) page.add_TextRegion(textregion) @@ -168,6 +203,7 @@ class EynollahXmlWriter: 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) diff --git a/tests/__init__.py b/tests/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/tests/cli_tests/conftest.py b/tests/cli_tests/conftest.py new file mode 100644 index 0000000..601d76b --- /dev/null +++ b/tests/cli_tests/conftest.py @@ -0,0 +1,47 @@ +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 new file mode 100644 index 0000000..aa52957 --- /dev/null +++ b/tests/cli_tests/test_binarization.py @@ -0,0 +1,53 @@ +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 new file mode 100644 index 0000000..b994c5d --- /dev/null +++ b/tests/cli_tests/test_enhance.py @@ -0,0 +1,52 @@ +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 new file mode 100644 index 0000000..7cbe013 --- /dev/null +++ b/tests/cli_tests/test_layout.py @@ -0,0 +1,119 @@ +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 new file mode 100644 index 0000000..e429e98 --- /dev/null +++ b/tests/cli_tests/test_mbreorder.py @@ -0,0 +1,47 @@ +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 new file mode 100644 index 0000000..6bf3080 --- /dev/null +++ b/tests/cli_tests/test_ocr.py @@ -0,0 +1,64 @@ +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 new file mode 100644 index 0000000..69f3d28 --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,37 @@ +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/euler_rechenkunst01_1738_0025.tif b/tests/resources/2files/euler_rechenkunst01_1738_0025.tif similarity index 100% rename from tests/resources/euler_rechenkunst01_1738_0025.tif rename to tests/resources/2files/euler_rechenkunst01_1738_0025.tif diff --git a/tests/resources/euler_rechenkunst01_1738_0025.xml b/tests/resources/2files/euler_rechenkunst01_1738_0025.xml similarity index 100% rename from tests/resources/euler_rechenkunst01_1738_0025.xml rename to tests/resources/2files/euler_rechenkunst01_1738_0025.xml diff --git a/tests/resources/kant_aufklaerung_1784_0020.tif b/tests/resources/2files/kant_aufklaerung_1784_0020.tif similarity index 100% rename from tests/resources/kant_aufklaerung_1784_0020.tif rename to tests/resources/2files/kant_aufklaerung_1784_0020.tif diff --git a/tests/resources/kant_aufklaerung_1784_0020.xml b/tests/resources/2files/kant_aufklaerung_1784_0020.xml similarity index 100% rename from tests/resources/kant_aufklaerung_1784_0020.xml rename to tests/resources/2files/kant_aufklaerung_1784_0020.xml diff --git a/tests/resources/marginalia/estor_rechtsgelehrsamkeit02_1758_0880_800px.jpg b/tests/resources/marginalia/estor_rechtsgelehrsamkeit02_1758_0880_800px.jpg new file mode 100644 index 0000000..9270508 Binary files /dev/null and b/tests/resources/marginalia/estor_rechtsgelehrsamkeit02_1758_0880_800px.jpg differ diff --git a/tests/resources/marginalia/estor_rechtsgelehrsamkeit02_1758_0880_800px.xml b/tests/resources/marginalia/estor_rechtsgelehrsamkeit02_1758_0880_800px.xml new file mode 100644 index 0000000..45240c4 --- /dev/null +++ b/tests/resources/marginalia/estor_rechtsgelehrsamkeit02_1758_0880_800px.xml @@ -0,0 +1,235 @@ + + + + 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 new file mode 100644 index 0000000..2042b28 --- /dev/null +++ b/tests/test_model_zoo.py @@ -0,0 +1,16 @@ +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 deleted file mode 100644 index 79c64c2..0000000 --- a/tests/test_run.py +++ /dev/null @@ -1,351 +0,0 @@ -from os import environ -from pathlib import Path -import pytest -import logging -from PIL import Image -from eynollah.cli import ( - layout as layout_cli, - binarization as binarization_cli, - enhancement as enhancement_cli, - machine_based_reading_order as mbreorder_cli, - ocr as ocr_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() - -MODELS_LAYOUT = environ.get('MODELS_LAYOUT', str(testdir.joinpath('..', 'models_layout_v0_5_0').resolve())) -MODELS_OCR = environ.get('MODELS_OCR', str(testdir.joinpath('..', 'models_ocr_v0_5_1').resolve())) -MODELS_BIN = environ.get('MODELS_BIN', str(testdir.joinpath('..', 'default-2021-03-09').resolve())) - -@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"], - ["--allow_scaling", "--curved-line", "--full-layout", "--reading_order_machine_based", - "--textline_light", "--light_version"], - # -ep ... - # -eoi ... - # FIXME: find out whether OCR extra was installed, otherwise skip these - ["--do_ocr"], - ["--do_ocr", "--light_version", "--textline_light"], - ["--do_ocr", "--transformer_ocr"], - #["--do_ocr", "--transformer_ocr", "--light_version", "--textline_light"], - ["--do_ocr", "--transformer_ocr", "--light_version", "--textline_light", "--full-layout"], - # --skip_layout_and_reading_order - ], ids=str) -def test_run_eynollah_layout_filename(tmp_path, pytestconfig, caplog, options): - infile = testdir.joinpath('resources/kant_aufklaerung_1784_0020.tif') - outfile = tmp_path / 'kant_aufklaerung_1784_0020.xml' - args = [ - '-m', MODELS_LAYOUT, - '-i', str(infile), - '-o', str(outfile.parent), - ] - 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 + options, catch_exceptions=False) - assert result.exit_code == 0, result.stdout - 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 - -@pytest.mark.parametrize( - "options", - [ - ["--tables"], - ["--tables", "--full-layout"], - ["--tables", "--full-layout", "--textline_light", "--light_version"], - ], ids=str) -def test_run_eynollah_layout_filename2(tmp_path, pytestconfig, caplog, options): - infile = testdir.joinpath('resources/euler_rechenkunst01_1738_0025.tif') - outfile = tmp_path / 'euler_rechenkunst01_1738_0025.xml' - args = [ - '-m', MODELS_LAYOUT, - '-i', str(infile), - '-o', str(outfile.parent), - ] - 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 + options, catch_exceptions=False) - assert result.exit_code == 0, result.stdout - 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: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, pytestconfig, caplog): - indir = testdir.joinpath('resources') - outdir = tmp_path - args = [ - '-m', MODELS_LAYOUT, - '-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, catch_exceptions=False) - assert result.exit_code == 0, result.stdout - 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 - -@pytest.mark.parametrize( - "options", - [ - [], # defaults - ["--no-patches"], - ], ids=str) -def test_run_eynollah_binarization_filename(tmp_path, pytestconfig, caplog, options): - infile = testdir.joinpath('resources/kant_aufklaerung_1784_0020.tif') - outfile = tmp_path.joinpath('kant_aufklaerung_1784_0020.png') - args = [ - '-m', MODELS_BIN, - '-i', str(infile), - '-o', str(outfile), - ] - if pytestconfig.getoption('verbose') > 0: - args.extend(['-l', 'DEBUG']) - 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 + options, catch_exceptions=False) - assert result.exit_code == 0, result.stdout - 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, pytestconfig, caplog): - indir = testdir.joinpath('resources') - outdir = tmp_path - args = [ - '-m', MODELS_BIN, - '-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 == 'SbbBinarizer' - runner = CliRunner() - with caplog.filtering(only_eynollah): - result = runner.invoke(binarization_cli, args, catch_exceptions=False) - assert result.exit_code == 0, result.stdout - 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 - -@pytest.mark.parametrize( - "options", - [ - [], # defaults - ["-sos"], - ], ids=str) -def test_run_eynollah_enhancement_filename(tmp_path, pytestconfig, caplog, options): - infile = testdir.joinpath('resources/kant_aufklaerung_1784_0020.tif') - outfile = tmp_path.joinpath('kant_aufklaerung_1784_0020.png') - args = [ - '-m', MODELS_LAYOUT, - '-i', str(infile), - '-o', str(outfile.parent), - ] - if pytestconfig.getoption('verbose') > 0: - args.extend(['-l', 'DEBUG']) - caplog.set_level(logging.INFO) - def only_eynollah(logrec): - return logrec.name == 'enhancement' - runner = CliRunner() - with caplog.filtering(only_eynollah): - result = runner.invoke(enhancement_cli, args + options, catch_exceptions=False) - assert result.exit_code == 0, result.stdout - logmsgs = [logrec.message for logrec in caplog.records] - assert any(True for logmsg in logmsgs if logmsg.startswith('Image was enhanced')), logmsgs - assert outfile.exists() - 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, pytestconfig, caplog): - indir = testdir.joinpath('resources') - outdir = tmp_path - args = [ - '-m', MODELS_LAYOUT, - '-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 == 'enhancement' - runner = CliRunner() - with caplog.filtering(only_eynollah): - result = runner.invoke(enhancement_cli, args, catch_exceptions=False) - assert result.exit_code == 0, result.stdout - logmsgs = [logrec.message for logrec in caplog.records] - assert len([logmsg for logmsg in logmsgs if logmsg.startswith('Image was enhanced')]) == 2 - assert len(list(outdir.iterdir())) == 2 - -def test_run_eynollah_mbreorder_filename(tmp_path, pytestconfig, caplog): - infile = testdir.joinpath('resources/kant_aufklaerung_1784_0020.xml') - outfile = tmp_path.joinpath('kant_aufklaerung_1784_0020.xml') - args = [ - '-m', MODELS_LAYOUT, - '-i', str(infile), - '-o', str(outfile.parent), - ] - if pytestconfig.getoption('verbose') > 0: - args.extend(['-l', 'DEBUG']) - caplog.set_level(logging.INFO) - def only_eynollah(logrec): - return logrec.name == 'mbreorder' - runner = CliRunner() - with caplog.filtering(only_eynollah): - result = runner.invoke(mbreorder_cli, args, catch_exceptions=False) - assert result.exit_code == 0, result.stdout - logmsgs = [logrec.message for logrec in caplog.records] - # FIXME: mbreorder has no logging! - #assert any(True for logmsg in logmsgs if logmsg.startswith('???')), logmsgs - 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, pytestconfig, caplog): - indir = testdir.joinpath('resources') - outdir = tmp_path - args = [ - '-m', MODELS_LAYOUT, - '-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 == 'mbreorder' - runner = CliRunner() - with caplog.filtering(only_eynollah): - result = runner.invoke(mbreorder_cli, args, catch_exceptions=False) - assert result.exit_code == 0, result.stdout - logmsgs = [logrec.message for logrec in caplog.records] - # FIXME: mbreorder has no logging! - #assert len([logmsg for logmsg in logmsgs if logmsg.startswith('???')]) == 2 - assert len(list(outdir.iterdir())) == 2 - -@pytest.mark.parametrize( - "options", - [ - [], # defaults - ["-doit", #str(outrenderfile.parent)], - ], - ["-trocr"], - ], ids=str) -def test_run_eynollah_ocr_filename(tmp_path, pytestconfig, caplog, options): - infile = testdir.joinpath('resources/kant_aufklaerung_1784_0020.tif') - outfile = tmp_path.joinpath('kant_aufklaerung_1784_0020.xml') - outrenderfile = tmp_path.joinpath('render').joinpath('kant_aufklaerung_1784_0020.png') - outrenderfile.parent.mkdir() - args = [ - '-m', MODELS_OCR, - '-i', str(infile), - '-dx', str(infile.parent), - '-o', str(outfile.parent), - ] - if pytestconfig.getoption('verbose') > 0: - args.extend(['-l', 'DEBUG']) - caplog.set_level(logging.DEBUG) - def only_eynollah(logrec): - return logrec.name == 'eynollah' - runner = CliRunner() - if "-doit" in options: - options.insert(options.index("-doit") + 1, str(outrenderfile.parent)) - with caplog.filtering(only_eynollah): - result = runner.invoke(ocr_cli, args + options, catch_exceptions=False) - assert result.exit_code == 0, result.stdout - logmsgs = [logrec.message for logrec in caplog.records] - # FIXME: ocr has no logging! - #assert any(True for logmsg in logmsgs if logmsg.startswith('???')), logmsgs - 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, pytestconfig, caplog): - indir = testdir.joinpath('resources') - outdir = tmp_path - args = [ - '-m', MODELS_OCR, - '-di', str(indir), - '-dx', 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(ocr_cli, args, catch_exceptions=False) - assert result.exit_code == 0, result.stdout - logmsgs = [logrec.message for logrec in caplog.records] - # FIXME: ocr has no logging! - #assert any(True for logmsg in logmsgs if logmsg.startswith('???')), logmsgs - assert len(list(outdir.iterdir())) == 2 diff --git a/train/config_params.json b/train/config_params.json index 1db8026..b01ac08 100644 --- a/train/config_params.json +++ b/train/config_params.json @@ -1,31 +1,50 @@ { "backbone_type" : "transformer", - "task": "segmentation", + "task": "cnn-rnn-ocr", "n_classes" : 2, - "n_epochs" : 0, - "input_height" : 448, - "input_width" : 448, + "max_len": 280, + "n_epochs" : 3, + "input_height" : 32, + "input_width" : 512, "weight_decay" : 1e-6, - "n_batch" : 1, - "learning_rate": 1e-4, + "n_batch" : 4, + "learning_rate": 1e-5, + "save_interval": 1500, "patches" : false, "pretraining" : true, "augmentation" : true, "flip_aug" : false, - "blur_aug" : false, + "blur_aug" : true, "scaling" : false, "adding_rgb_background": true, "adding_rgb_foreground": true, - "add_red_textlines": false, - "channels_shuffling": false, - "degrading": false, - "brightening": false, + "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, - "rotation_not_90": 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, @@ -34,13 +53,18 @@ "transformer_layers": 1, "transformer_num_heads": 1, "transformer_cnn_first": false, - "blur_k" : ["blur","guass","median"], + "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" : [5, -5], + "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, @@ -48,11 +72,12 @@ "weighted_loss": false, "is_loss_soft_dice": false, "data_is_provided": false, - "dir_train": "/home/vahid/Documents/test/sbb_pixelwise_segmentation/test_label/pageextractor_test/train_new", + "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/Documents/test/sbb_pixelwise_segmentation/test_label/pageextractor_test/output_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/Documents/test/sbb_pixelwise_segmentation/test_label/pageextractor_test/train_new/images_bin" + "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" }