mirror of
https://github.com/qurator-spk/eynollah.git
synced 2025-10-06 22:50:14 +02:00
Merge pull request #178 from qurator-spk/prepare-release-v0.5.0
Prepare release v0.5.0
This commit is contained in:
Konstantin Baierer
GitHub
commit
882e242946
26 changed files with 8225 additions and 922 deletions
9
.github/workflows/test-eynollah.yml
vendored
9
.github/workflows/test-eynollah.yml
vendored
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@ -27,7 +27,12 @@ jobs:
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- uses: actions/cache@v4
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id: seg_model_cache
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with:
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path: models_eynollah
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path: models_layout_v0_5_0
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key: ${{ runner.os }}-models
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- uses: actions/cache@v4
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id: ocr_model_cache
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with:
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path: models_ocr_v0_5_0
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key: ${{ runner.os }}-models
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- uses: actions/cache@v4
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id: bin_model_cache
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|
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@ -35,7 +40,7 @@ jobs:
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path: default-2021-03-09
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key: ${{ runner.os }}-modelbin
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- name: Download models
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if: steps.seg_model_cache.outputs.cache-hit != 'true' || steps.bin_model_cache.outputs.cache-hit != 'true'
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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
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run: make models
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- name: Set up Python ${{ matrix.python-version }}
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uses: actions/setup-python@v5
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1
.gitignore
vendored
1
.gitignore
vendored
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@ -5,3 +5,4 @@ models_eynollah*
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output.html
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/build
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/dist
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*.tif
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|
|
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@ -7,7 +7,13 @@ Versioned according to [Semantic Versioning](http://semver.org/).
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Fixed:
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* restoring the contour in the original image caused an error due to an empty tuple
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* restoring the contour in the original image caused an error due to an empty tuple, #154
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Added:
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* `eynollah machine-based-reading-order` CLI to run reading order detection, #175
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* `eynollah enhancement` CLI to run image enhancement, #175
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* Improved models for page extraction and reading order detection, #175
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## [0.4.0] - 2025-04-07
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50
Makefile
50
Makefile
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@ -9,12 +9,15 @@ DOCKER ?= docker
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#SEG_MODEL := https://qurator-data.de/eynollah/2021-04-25/models_eynollah.tar.gz
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#SEG_MODEL := https://qurator-data.de/eynollah/2022-04-05/models_eynollah_renamed.tar.gz
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SEG_MODEL := https://qurator-data.de/eynollah/2022-04-05/models_eynollah.tar.gz
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# SEG_MODEL := https://qurator-data.de/eynollah/2022-04-05/models_eynollah.tar.gz
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#SEG_MODEL := https://github.com/qurator-spk/eynollah/releases/download/v0.3.0/models_eynollah.tar.gz
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#SEG_MODEL := https://github.com/qurator-spk/eynollah/releases/download/v0.3.1/models_eynollah.tar.gz
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SEG_MODEL := https://zenodo.org/records/17194824/files/models_layout_v0_5_0.tar.gz?download=1
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BIN_MODEL := https://github.com/qurator-spk/sbb_binarization/releases/download/v0.0.11/saved_model_2021_03_09.zip
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OCR_MODEL := https://zenodo.org/records/17194824/files/models_ocr_v0_5_0.tar.gz?download=1
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PYTEST_ARGS ?= -vv
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# BEGIN-EVAL makefile-parser --make-help Makefile
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@ -28,7 +31,7 @@ help:
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@echo " install Install package with pip"
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@echo " install-dev Install editable with pip"
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@echo " deps-test Install test dependencies with pip"
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@echo " models Download and extract models to $(CURDIR)/models_eynollah"
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@echo " models Download and extract models to $(CURDIR)/models_layout_v0_5_0"
|
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@echo " smoke-test Run simple CLI check"
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@echo " ocrd-test Run OCR-D CLI check"
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@echo " test Run unit tests"
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@ -44,14 +47,20 @@ help:
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|||
# END-EVAL
|
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||||
|
||||
# Download and extract models to $(PWD)/models_eynollah
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models: models_eynollah default-2021-03-09
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# Download and extract models to $(PWD)/models_layout_v0_5_0
|
||||
models: models_layout_v0_5_0 models_ocr_v0_5_0 default-2021-03-09
|
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|
||||
models_eynollah: models_eynollah.tar.gz
|
||||
tar zxf models_eynollah.tar.gz
|
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models_layout_v0_5_0: models_layout_v0_5_0.tar.gz
|
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tar zxf models_layout_v0_5_0.tar.gz
|
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|
||||
models_eynollah.tar.gz:
|
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wget $(SEG_MODEL)
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models_layout_v0_5_0.tar.gz:
|
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wget -O $@ $(SEG_MODEL)
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|
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models_ocr_v0_5_0: models_ocr_v0_5_0.tar.gz
|
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tar zxf models_ocr_v0_5_0.tar.gz
|
||||
|
||||
models_ocr_v0_5_0.tar.gz:
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wget -O $@ $(OCR_MODEL)
|
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|
||||
default-2021-03-09: $(notdir $(BIN_MODEL))
|
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unzip $(notdir $(BIN_MODEL))
|
||||
|
|
@ -73,20 +82,28 @@ install:
|
|||
install-dev:
|
||||
$(PIP) install -e .$(and $(EXTRAS),[$(EXTRAS)])
|
||||
|
||||
deps-test: models_eynollah
|
||||
deps-test: models_layout_v0_5_0
|
||||
$(PIP) install -r requirements-test.txt
|
||||
|
||||
smoke-test: TMPDIR != mktemp -d
|
||||
smoke-test: tests/resources/kant_aufklaerung_1784_0020.tif
|
||||
# layout analysis:
|
||||
eynollah layout -i $< -o $(TMPDIR) -m $(CURDIR)/models_eynollah
|
||||
eynollah layout -i $< -o $(TMPDIR) -m $(CURDIR)/models_layout_v0_5_0
|
||||
fgrep -q http://schema.primaresearch.org/PAGE/gts/pagecontent/2019-07-15 $(TMPDIR)/$(basename $(<F)).xml
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fgrep -c -e TextRegion -e ImageRegion -e SeparatorRegion $(TMPDIR)/$(basename $(<F)).xml
|
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# directory mode (skip one, add one):
|
||||
eynollah layout -di $(<D) -o $(TMPDIR) -m $(CURDIR)/models_eynollah
|
||||
# layout, directory mode (skip one, add one):
|
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eynollah layout -di $(<D) -o $(TMPDIR) -m $(CURDIR)/models_layout_v0_5_0
|
||||
test -s $(TMPDIR)/euler_rechenkunst01_1738_0025.xml
|
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# mbreorder, directory mode (overwrite):
|
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eynollah machine-based-reading-order -di $(<D) -o $(TMPDIR) -m $(CURDIR)/models_layout_v0_5_0
|
||||
fgrep -q http://schema.primaresearch.org/PAGE/gts/pagecontent/2019-07-15 $(TMPDIR)/$(basename $(<F)).xml
|
||||
fgrep -c -e RegionRefIndexed $(TMPDIR)/$(basename $(<F)).xml
|
||||
# binarize:
|
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eynollah binarization -m $(CURDIR)/default-2021-03-09 $< $(TMPDIR)/$(<F)
|
||||
eynollah binarization -m $(CURDIR)/default-2021-03-09 -i $< -o $(TMPDIR)/$(<F)
|
||||
test -s $(TMPDIR)/$(<F)
|
||||
@set -x; test "$$(identify -format '%w %h' $<)" = "$$(identify -format '%w %h' $(TMPDIR)/$(<F))"
|
||||
# enhance:
|
||||
eynollah enhancement -m $(CURDIR)/models_layout_v0_5_0 -sos -i $< -o $(TMPDIR) -O
|
||||
test -s $(TMPDIR)/$(<F)
|
||||
@set -x; test "$$(identify -format '%w %h' $<)" = "$$(identify -format '%w %h' $(TMPDIR)/$(<F))"
|
||||
$(RM) -r $(TMPDIR)
|
||||
|
|
@ -97,7 +114,7 @@ ocrd-test: tests/resources/kant_aufklaerung_1784_0020.tif
|
|||
cp $< $(TMPDIR)
|
||||
ocrd workspace -d $(TMPDIR) init
|
||||
ocrd workspace -d $(TMPDIR) add -G OCR-D-IMG -g PHYS_0020 -i OCR-D-IMG_0020 $(<F)
|
||||
ocrd-eynollah-segment -w $(TMPDIR) -I OCR-D-IMG -O OCR-D-SEG -P models $(CURDIR)/models_eynollah
|
||||
ocrd-eynollah-segment -w $(TMPDIR) -I OCR-D-IMG -O OCR-D-SEG -P models $(CURDIR)/models_layout_v0_5_0
|
||||
result=$$(ocrd workspace -d $(TMPDIR) find -G OCR-D-SEG); \
|
||||
fgrep -q http://schema.primaresearch.org/PAGE/gts/pagecontent/2019-07-15 $(TMPDIR)/$$result && \
|
||||
fgrep -c -e TextRegion -e ImageRegion -e SeparatorRegion $(TMPDIR)/$$result
|
||||
|
|
@ -106,8 +123,9 @@ ocrd-test: tests/resources/kant_aufklaerung_1784_0020.tif
|
|||
$(RM) -r $(TMPDIR)
|
||||
|
||||
# Run unit tests
|
||||
test: export EYNOLLAH_MODELS=$(CURDIR)/models_eynollah
|
||||
test: export SBBBIN_MODELS=$(CURDIR)/default-2021-03-09
|
||||
test: export MODELS_LAYOUT=$(CURDIR)/models_layout_v0_5_0
|
||||
test: export MODELS_OCR=$(CURDIR)/models_ocr_v0_5_0
|
||||
test: export MODELS_BIN=$(CURDIR)/default-2021-03-09
|
||||
test:
|
||||
$(PYTHON) -m pytest tests --durations=0 --continue-on-collection-errors $(PYTEST_ARGS)
|
||||
|
||||
|
|
|
|||
91
README.md
91
README.md
|
|
@ -1,5 +1,6 @@
|
|||
# Eynollah
|
||||
> Document Layout Analysis with Deep Learning and Heuristics
|
||||
|
||||
> Document Layout Analysis, Binarization and OCR with Deep Learning and Heuristics
|
||||
|
||||
[](https://pypi.org/project/eynollah/)
|
||||
[](https://github.com/qurator-spk/eynollah/actions/workflows/test-eynollah.yml)
|
||||
|
|
@ -19,9 +20,11 @@
|
|||
* Output in [PAGE-XML](https://github.com/PRImA-Research-Lab/PAGE-XML)
|
||||
* [OCR-D](https://github.com/qurator-spk/eynollah#use-as-ocr-d-processor) interface
|
||||
|
||||
:warning: Development is currently focused on achieving the best possible quality of results for a wide variety of historical documents and therefore processing can be very slow. We aim to improve this, but contributions are welcome.
|
||||
:warning: Development is currently focused on achieving the best possible quality of results for a wide variety of
|
||||
historical documents and therefore processing can be very slow. We aim to improve this, but contributions are welcome.
|
||||
|
||||
## Installation
|
||||
|
||||
Python `3.8-3.11` with Tensorflow `<2.13` on Linux are currently supported.
|
||||
|
||||
For (limited) GPU support the CUDA toolkit needs to be installed.
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||||
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|
@ -41,19 +44,40 @@ cd eynollah; pip install -e .
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|||
|
||||
Alternatively, you can run `make install` or `make install-dev` for editable installation.
|
||||
|
||||
To also install the dependencies for the OCR engines:
|
||||
|
||||
```
|
||||
pip install "eynollah[OCR]"
|
||||
# or
|
||||
make install EXTRAS=OCR
|
||||
```
|
||||
|
||||
## Models
|
||||
Pre-trained models can be downloaded from [qurator-data.de](https://qurator-data.de/eynollah/) or [huggingface](https://huggingface.co/SBB?search_models=eynollah).
|
||||
Pretrained models can be downloaded from [zenodo](https://zenodo.org/records/17194824) or [huggingface](https://huggingface.co/SBB?search_models=eynollah).
|
||||
|
||||
For documentation on methods and models, have a look at [`models.md`](https://github.com/qurator-spk/eynollah/tree/main/docs/models.md).
|
||||
|
||||
## Train
|
||||
|
||||
In case you want to train your own model with Eynollah, have a look at [`train.md`](https://github.com/qurator-spk/eynollah/tree/main/docs/train.md).
|
||||
|
||||
## Usage
|
||||
The command-line interface can be called like this:
|
||||
|
||||
Eynollah supports five use cases: layout analysis (segmentation), binarization,
|
||||
image enhancement, text recognition (OCR), and (trainable) reading order detection.
|
||||
|
||||
### Layout Analysis
|
||||
|
||||
The layout analysis module is responsible for detecting layouts, identifying text lines, and determining reading order
|
||||
using both heuristic methods or a machine-based reading order detection model.
|
||||
|
||||
Note that there are currently two supported ways for reading order detection: either as part of layout analysis based
|
||||
on image input, or, currently under development, for given layout analysis results based on PAGE-XML data as input.
|
||||
|
||||
The command-line interface for layout analysis can be called like this:
|
||||
|
||||
```sh
|
||||
eynollah \
|
||||
eynollah layout \
|
||||
-i <single image file> | -di <directory containing image files> \
|
||||
-o <output directory> \
|
||||
-m <directory containing model files> \
|
||||
|
|
@ -66,6 +90,7 @@ The following options can be used to further configure the processing:
|
|||
|-------------------|:-------------------------------------------------------------------------------|
|
||||
| `-fl` | full layout analysis including all steps and segmentation classes |
|
||||
| `-light` | lighter and faster but simpler method for main region detection and deskewing |
|
||||
| `-tll` | this indicates the light textline and should be passed with light version |
|
||||
| `-tab` | apply table detection |
|
||||
| `-ae` | apply enhancement (the resulting image is saved to the output directory) |
|
||||
| `-as` | apply scaling |
|
||||
|
|
@ -80,9 +105,51 @@ The following options can be used to further configure the processing:
|
|||
| `-sp <directory>` | save cropped page image to this directory |
|
||||
| `-sa <directory>` | save all (plot, enhanced/binary image, layout) to this directory |
|
||||
|
||||
If no option is set, the tool performs layout detection of main regions (background, text, images, separators and marginals).
|
||||
If no option is set, the tool performs layout detection of main regions (background, text, images, separators
|
||||
and marginals).
|
||||
The best output quality is produced when RGB images are used as input rather than greyscale or binarized images.
|
||||
|
||||
### Binarization
|
||||
|
||||
The binarization module performs document image binarization using pretrained pixelwise segmentation models.
|
||||
|
||||
The command-line interface for binarization of single image can be called like this:
|
||||
|
||||
```sh
|
||||
eynollah binarization \
|
||||
-i <single image file> | -di <directory containing image files> \
|
||||
-o <output directory> \
|
||||
-m <directory containing model files> \
|
||||
```
|
||||
|
||||
### OCR
|
||||
|
||||
The OCR module performs text recognition from images using two main families of pretrained models: CNN-RNN–based OCR and Transformer-based OCR.
|
||||
|
||||
The command-line interface for ocr can be called like this:
|
||||
|
||||
```sh
|
||||
eynollah ocr \
|
||||
-i <single image file> | -di <directory containing image files> \
|
||||
-dx <directory of xmls> \
|
||||
-o <output directory> \
|
||||
-m <path to directory containing model files> | --model_name <path to specific model> \
|
||||
```
|
||||
|
||||
### Machine-based-reading-order
|
||||
|
||||
The machine-based reading-order module employs a pretrained model to identify the reading order from layouts represented in PAGE-XML files.
|
||||
|
||||
The command-line interface for machine based reading order can be called like this:
|
||||
|
||||
```sh
|
||||
eynollah machine-based-reading-order \
|
||||
-i <single image file> | -di <directory containing image files> \
|
||||
-xml <xml file name> | -dx <directory containing xml files> \
|
||||
-m <path to directory containing model files> \
|
||||
-o <output directory>
|
||||
```
|
||||
|
||||
#### Use as OCR-D processor
|
||||
|
||||
Eynollah ships with a CLI interface to be used as [OCR-D](https://ocr-d.de) [processor](https://ocr-d.de/en/spec/cli),
|
||||
|
|
@ -90,8 +157,7 @@ formally described in [`ocrd-tool.json`](https://github.com/qurator-spk/eynollah
|
|||
|
||||
In this case, the source image file group with (preferably) RGB images should be used as input like this:
|
||||
|
||||
ocrd-eynollah-segment -I OCR-D-IMG -O OCR-D-SEG -P models 2022-04-05
|
||||
|
||||
ocrd-eynollah-segment -I OCR-D-IMG -O OCR-D-SEG -P models eynollah_layout_v0_5_0
|
||||
|
||||
If the input file group is PAGE-XML (from a previous OCR-D workflow step), Eynollah behaves as follows:
|
||||
- existing regions are kept and ignored (i.e. in effect they might overlap segments from Eynollah results)
|
||||
|
|
@ -103,15 +169,20 @@ If the input file group is PAGE-XML (from a previous OCR-D workflow step), Eynol
|
|||
(because some other preprocessing step was in effect like `denoised`), then
|
||||
the output PAGE-XML will be based on that as new top-level (`@imageFilename`)
|
||||
|
||||
|
||||
ocrd-eynollah-segment -I OCR-D-XYZ -O OCR-D-SEG -P models 2022-04-05
|
||||
ocrd-eynollah-segment -I OCR-D-XYZ -O OCR-D-SEG -P models eynollah_layout_v0_5_0
|
||||
|
||||
Still, in general, it makes more sense to add other workflow steps **after** Eynollah.
|
||||
|
||||
There is also an OCR-D processor for the binarization:
|
||||
|
||||
ocrd-sbb-binarize -I OCR-D-IMG -O OCR-D-BIN -P models default-2021-03-09
|
||||
|
||||
#### Additional documentation
|
||||
|
||||
Please check the [wiki](https://github.com/qurator-spk/eynollah/wiki).
|
||||
|
||||
## How to cite
|
||||
|
||||
If you find this tool useful in your work, please consider citing our paper:
|
||||
|
||||
```bibtex
|
||||
|
|
|
|||
|
|
@ -1,5 +1,6 @@
|
|||
# Models documentation
|
||||
This suite of 14 models presents a document layout analysis (DLA) system for historical documents implemented by
|
||||
|
||||
This suite of 15 models presents a document layout analysis (DLA) system for historical documents implemented by
|
||||
pixel-wise segmentation using a combination of a ResNet50 encoder with various U-Net decoders. In addition, heuristic
|
||||
methods are applied to detect marginals and to determine the reading order of text regions.
|
||||
|
||||
|
|
@ -23,6 +24,7 @@ See the flowchart below for the different stages and how they interact:
|
|||
## Models
|
||||
|
||||
### Image enhancement
|
||||
|
||||
Model card: [Image Enhancement](https://huggingface.co/SBB/eynollah-enhancement)
|
||||
|
||||
This model addresses image resolution, specifically targeting documents with suboptimal resolution. In instances where
|
||||
|
|
@ -30,12 +32,14 @@ the detection of document layout exhibits inadequate performance, the proposed e
|
|||
the quality and clarity of the images, thus facilitating enhanced visual interpretation and analysis.
|
||||
|
||||
### Page extraction / border detection
|
||||
|
||||
Model card: [Page Extraction/Border Detection](https://huggingface.co/SBB/eynollah-page-extraction)
|
||||
|
||||
A problem that can negatively affect OCR are black margins around a page caused by document scanning. A deep learning
|
||||
model helps to crop to the page borders by using a pixel-wise segmentation method.
|
||||
|
||||
### Column classification
|
||||
|
||||
Model card: [Column Classification](https://huggingface.co/SBB/eynollah-column-classifier)
|
||||
|
||||
This model is a trained classifier that recognizes the number of columns in a document by use of a training set with
|
||||
|
|
@ -43,6 +47,7 @@ manual classification of all documents into six classes with either one, two, th
|
|||
respectively.
|
||||
|
||||
### Binarization
|
||||
|
||||
Model card: [Binarization](https://huggingface.co/SBB/eynollah-binarization)
|
||||
|
||||
This model is designed to tackle the intricate task of document image binarization, which involves segmentation of the
|
||||
|
|
@ -52,6 +57,7 @@ capability of the model enables improved accuracy and reliability in subsequent
|
|||
enhanced document understanding and interpretation.
|
||||
|
||||
### Main region detection
|
||||
|
||||
Model card: [Main Region Detection](https://huggingface.co/SBB/eynollah-main-regions)
|
||||
|
||||
This model has employed a different set of labels, including an artificial class specifically designed to encompass the
|
||||
|
|
@ -61,6 +67,7 @@ during the inference phase. By incorporating this methodology, improved efficien
|
|||
model's ability to accurately identify and classify text regions within documents.
|
||||
|
||||
### Main region detection (with scaling augmentation)
|
||||
|
||||
Model card: [Main Region Detection (with scaling augmentation)](https://huggingface.co/SBB/eynollah-main-regions-aug-scaling)
|
||||
|
||||
Utilizing scaling augmentation, this model leverages the capability to effectively segment elements of extremely high or
|
||||
|
|
@ -69,12 +76,14 @@ categorizing and isolating such elements, thereby enhancing its overall performa
|
|||
documents with varying scale characteristics.
|
||||
|
||||
### Main region detection (with rotation augmentation)
|
||||
|
||||
Model card: [Main Region Detection (with rotation augmentation)](https://huggingface.co/SBB/eynollah-main-regions-aug-rotation)
|
||||
|
||||
This model takes advantage of rotation augmentation. This helps the tool to segment the vertical text regions in a
|
||||
robust way.
|
||||
|
||||
### Main region detection (ensembled)
|
||||
|
||||
Model card: [Main Region Detection (ensembled)](https://huggingface.co/SBB/eynollah-main-regions-ensembled)
|
||||
|
||||
The robustness of this model is attained through an ensembling technique that combines the weights from various epochs.
|
||||
|
|
@ -82,16 +91,19 @@ By employing this approach, the model achieves a high level of resilience and st
|
|||
strengths of multiple epochs to enhance its overall performance and deliver consistent and reliable results.
|
||||
|
||||
### Full region detection (1,2-column documents)
|
||||
|
||||
Model card: [Full Region Detection (1,2-column documents)](https://huggingface.co/SBB/eynollah-full-regions-1column)
|
||||
|
||||
This model deals with documents comprising of one and two columns.
|
||||
|
||||
### Full region detection (3,n-column documents)
|
||||
|
||||
Model card: [Full Region Detection (3,n-column documents)](https://huggingface.co/SBB/eynollah-full-regions-3pluscolumn)
|
||||
|
||||
This model is responsible for detecting headers and drop capitals in documents with three or more columns.
|
||||
|
||||
### Textline detection
|
||||
|
||||
Model card: [Textline Detection](https://huggingface.co/SBB/eynollah-textline)
|
||||
|
||||
The method for textline detection combines deep learning and heuristics. In the deep learning part, an image-to-image
|
||||
|
|
@ -106,6 +118,7 @@ segmentation is first deskewed and then the textlines are separated with the sam
|
|||
textline bounding boxes. Later, the strap is rotated back into its original orientation.
|
||||
|
||||
### Textline detection (light)
|
||||
|
||||
Model card: [Textline Detection Light (simpler but faster method)](https://huggingface.co/SBB/eynollah-textline_light)
|
||||
|
||||
The method for textline detection combines deep learning and heuristics. In the deep learning part, an image-to-image
|
||||
|
|
@ -119,6 +132,7 @@ enhancing the model's ability to accurately identify and delineate individual te
|
|||
eliminates the need for additional heuristics in extracting textline contours.
|
||||
|
||||
### Table detection
|
||||
|
||||
Model card: [Table Detection](https://huggingface.co/SBB/eynollah-tables)
|
||||
|
||||
The objective of this model is to perform table segmentation in historical document images. Due to the pixel-wise
|
||||
|
|
@ -128,17 +142,21 @@ effectively identify and delineate tables within the historical document images,
|
|||
enabling subsequent analysis and interpretation.
|
||||
|
||||
### Image detection
|
||||
|
||||
Model card: [Image Detection](https://huggingface.co/SBB/eynollah-image-extraction)
|
||||
|
||||
This model is used for the task of illustration detection only.
|
||||
|
||||
### Reading order detection
|
||||
|
||||
Model card: [Reading Order Detection]()
|
||||
|
||||
TODO
|
||||
|
||||
## Heuristic methods
|
||||
|
||||
Additionally, some heuristic methods are employed to further improve the model predictions:
|
||||
|
||||
* After border detection, the largest contour is determined by a bounding box, and the image cropped to these coordinates.
|
||||
* For text region detection, the image is scaled up to make it easier for the model to detect background space between text regions.
|
||||
* A minimum area is defined for text regions in relation to the overall image dimensions, so that very small regions that are noise can be filtered out.
|
||||
|
|
|
|||
|
|
@ -1,4 +1,5 @@
|
|||
# Training documentation
|
||||
|
||||
This aims to assist users in preparing training datasets, training models, and performing inference with trained models.
|
||||
We cover various use cases including pixel-wise segmentation, image classification, image enhancement, and machine-based
|
||||
reading order detection. For each use case, we provide guidance on how to generate the corresponding training dataset.
|
||||
|
|
@ -11,6 +12,7 @@ The following three tasks can all be accomplished using the code in the
|
|||
* inference with the trained model
|
||||
|
||||
## Generate training dataset
|
||||
|
||||
The script `generate_gt_for_training.py` is used for generating training datasets. As the results of the following
|
||||
command demonstrates, the dataset generator provides three different commands:
|
||||
|
||||
|
|
@ -23,14 +25,19 @@ These three commands are:
|
|||
* pagexml2label
|
||||
|
||||
### image-enhancement
|
||||
|
||||
Generating a training dataset for image enhancement is quite straightforward. All that is needed is a set of
|
||||
high-resolution images. The training dataset can then be generated using the following command:
|
||||
|
||||
`python generate_gt_for_training.py image-enhancement -dis "dir of high resolution images" -dois "dir where degraded
|
||||
images will be written" -dols "dir where the corresponding high resolution image will be written as label" -scs
|
||||
"degrading scales json file"`
|
||||
```sh
|
||||
python generate_gt_for_training.py image-enhancement \
|
||||
-dis "dir of high resolution images" \
|
||||
-dois "dir where degraded images will be written" \
|
||||
-dols "dir where the corresponding high resolution image will be written as label" \
|
||||
-scs "degrading scales json file"
|
||||
```
|
||||
|
||||
The scales JSON file is a dictionary with a key named 'scales' and values representing scales smaller than 1. Images are
|
||||
The scales JSON file is a dictionary with a key named `scales` and values representing scales smaller than 1. Images are
|
||||
downscaled based on these scales and then upscaled again to their original size. This process causes the images to lose
|
||||
resolution at different scales. The degraded images are used as input images, and the original high-resolution images
|
||||
serve as labels. The enhancement model can be trained with this generated dataset. The scales JSON file looks like this:
|
||||
|
|
@ -42,6 +49,7 @@ serve as labels. The enhancement model can be trained with this generated datase
|
|||
```
|
||||
|
||||
### machine-based-reading-order
|
||||
|
||||
For machine-based reading order, we aim to determine the reading priority between two sets of text regions. The model's
|
||||
input is a three-channel image: the first and last channels contain information about each of the two text regions,
|
||||
while the middle channel encodes prominent layout elements necessary for reading order, such as separators and headers.
|
||||
|
|
@ -52,10 +60,18 @@ For output images, it is necessary to specify the width and height. Additionally
|
|||
to filter out regions smaller than this minimum size. This minimum size is defined as the ratio of the text region area
|
||||
to the image area, with a default value of zero. To run the dataset generator, use the following command:
|
||||
|
||||
`python generate_gt_for_training.py machine-based-reading-order -dx "dir of GT xml files" -domi "dir where output images
|
||||
will be written" -docl "dir where the labels will be written" -ih "height" -iw "width" -min "min area ratio"`
|
||||
```shell
|
||||
python generate_gt_for_training.py machine-based-reading-order \
|
||||
-dx "dir of GT xml files" \
|
||||
-domi "dir where output images will be written" \
|
||||
-docl "dir where the labels will be written" \
|
||||
-ih "height" \
|
||||
-iw "width" \
|
||||
-min "min area ratio"
|
||||
```
|
||||
|
||||
### pagexml2label
|
||||
|
||||
pagexml2label is designed to generate labels from GT page XML files for various pixel-wise segmentation use cases,
|
||||
including 'layout,' 'textline,' 'printspace,' 'glyph,' and 'word' segmentation.
|
||||
To train a pixel-wise segmentation model, we require images along with their corresponding labels. Our training script
|
||||
|
|
@ -119,9 +135,13 @@ graphic region, "stamp" has its own class, while all other types are classified
|
|||
region" are also present in the label. However, other regions like "noise region" and "table region" will not be
|
||||
included in the label PNG file, even if they have information in the page XML files, as we chose not to include them.
|
||||
|
||||
`python generate_gt_for_training.py pagexml2label -dx "dir of GT xml files" -do "dir where output label png files will
|
||||
be written" -cfg "custom config json file" -to "output type which has 2d and 3d. 2d is used for training and 3d is just
|
||||
to visualise the labels" "`
|
||||
```sh
|
||||
python generate_gt_for_training.py pagexml2label \
|
||||
-dx "dir of GT xml files" \
|
||||
-do "dir where output label png files will be written" \
|
||||
-cfg "custom config json file" \
|
||||
-to "output type which has 2d and 3d. 2d is used for training and 3d is just to visualise the labels"
|
||||
```
|
||||
|
||||
We have also defined an artificial class that can be added to the boundary of text region types or text lines. This key
|
||||
is called "artificial_class_on_boundary." If users want to apply this to certain text regions in the layout use case,
|
||||
|
|
@ -169,12 +189,19 @@ in this scenario, since cropping will be applied to the label files, the directo
|
|||
provided to ensure that they are cropped in sync with the labels. This ensures that the correct images and labels
|
||||
required for training are obtained. The command should resemble the following:
|
||||
|
||||
`python generate_gt_for_training.py pagexml2label -dx "dir of GT xml files" -do "dir where output label png files will
|
||||
be written" -cfg "custom config json file" -to "output type which has 2d and 3d. 2d is used for training and 3d is just
|
||||
to visualise the labels" -ps -di "dir where the org images are located" -doi "dir where the cropped output images will
|
||||
be written" `
|
||||
```sh
|
||||
python generate_gt_for_training.py pagexml2label \
|
||||
-dx "dir of GT xml files" \
|
||||
-do "dir where output label png files will be written" \
|
||||
-cfg "custom config json file" \
|
||||
-to "output type which has 2d and 3d. 2d is used for training and 3d is just to visualise the labels" \
|
||||
-ps \
|
||||
-di "dir where the org images are located" \
|
||||
-doi "dir where the cropped output images will be written"
|
||||
```
|
||||
|
||||
## Train a model
|
||||
|
||||
### classification
|
||||
|
||||
For the classification use case, we haven't provided a ground truth generator, as it's unnecessary. For classification,
|
||||
|
|
@ -225,7 +252,9 @@ And the "dir_eval" the same structure as train directory:
|
|||
|
||||
The classification model can be trained using the following command line:
|
||||
|
||||
`python train.py with config_classification.json`
|
||||
```sh
|
||||
python train.py with config_classification.json
|
||||
```
|
||||
|
||||
As evident in the example JSON file above, for classification, we utilize a "f1_threshold_classification" parameter.
|
||||
This parameter is employed to gather all models with an evaluation f1 score surpassing this threshold. Subsequently,
|
||||
|
|
@ -276,6 +305,7 @@ The classification model can be trained like the classification case command lin
|
|||
### Segmentation (Textline, Binarization, Page extraction and layout) and enhancement
|
||||
|
||||
#### Parameter configuration for segmentation or enhancement usecases
|
||||
|
||||
The following parameter configuration can be applied to all segmentation use cases and enhancements. The augmentation,
|
||||
its sub-parameters, and continued training are defined only for segmentation use cases and enhancements, not for
|
||||
classification and machine-based reading order, as you can see in their example config files.
|
||||
|
|
@ -355,6 +385,7 @@ command, similar to the process for classification and reading order:
|
|||
`python train.py with config_classification.json`
|
||||
|
||||
#### Binarization
|
||||
|
||||
An example config json file for binarization can be like this:
|
||||
|
||||
```yaml
|
||||
|
|
@ -550,6 +581,7 @@ For page segmentation (or printspace or border segmentation), the model needs to
|
|||
hence the patches parameter should be set to false.
|
||||
|
||||
#### layout segmentation
|
||||
|
||||
An example config json file for layout segmentation with 5 classes (including background) can be like this:
|
||||
|
||||
```yaml
|
||||
|
|
@ -605,26 +637,41 @@ An example config json file for layout segmentation with 5 classes (including ba
|
|||
## Inference with the trained model
|
||||
|
||||
### classification
|
||||
|
||||
For conducting inference with a trained model, you simply need to execute the following command line, specifying the
|
||||
directory of the model and the image on which to perform inference:
|
||||
|
||||
`python inference.py -m "model dir" -i "image" `
|
||||
```sh
|
||||
python inference.py -m "model dir" -i "image"
|
||||
```
|
||||
|
||||
This will straightforwardly return the class of the image.
|
||||
|
||||
### machine based reading order
|
||||
|
||||
To infer the reading order using a reading order model, we need a page XML file containing layout information but
|
||||
without the reading order. We simply need to provide the model directory, the XML file, and the output directory.
|
||||
The new XML file with the added reading order will be written to the output directory with the same name.
|
||||
We need to run:
|
||||
|
||||
`python inference.py -m "model dir" -xml "page xml file" -o "output dir to write new xml with reading order" `
|
||||
```sh
|
||||
python inference.py \
|
||||
-m "model dir" \
|
||||
-xml "page xml file" \
|
||||
-o "output dir to write new xml with reading order"
|
||||
```
|
||||
|
||||
### Segmentation (Textline, Binarization, Page extraction and layout) and enhancement
|
||||
For conducting inference with a trained model for segmentation and enhancement you need to run the following command
|
||||
line:
|
||||
|
||||
`python inference.py -m "model dir" -i "image" -p -s "output image" `
|
||||
```sh
|
||||
python inference.py \
|
||||
-m "model dir" \
|
||||
-i "image" \
|
||||
-p \
|
||||
-s "output image"
|
||||
```
|
||||
|
||||
Note that in the case of page extraction the -p flag is not needed.
|
||||
|
||||
|
|
|
|||
|
|
@ -46,7 +46,7 @@ optional-dependencies.test = {file = ["requirements-test.txt"]}
|
|||
where = ["src"]
|
||||
|
||||
[tool.setuptools.package-data]
|
||||
"*" = ["*.json", '*.yml', '*.xml', '*.xsd']
|
||||
"*" = ["*.json", '*.yml', '*.xml', '*.xsd', '*.ttf']
|
||||
|
||||
[tool.coverage.run]
|
||||
branch = true
|
||||
|
|
|
|||
|
|
@ -4,4 +4,6 @@ numpy <1.24.0
|
|||
scikit-learn >= 0.23.2
|
||||
tensorflow < 2.13
|
||||
numba <= 0.58.1
|
||||
scikit-image
|
||||
loky
|
||||
biopython
|
||||
|
|
|
|||
BIN
src/eynollah/Charis-Regular.ttf
Normal file
BIN
src/eynollah/Charis-Regular.ttf
Normal file
Binary file not shown.
|
|
@ -1,8 +1,11 @@
|
|||
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():
|
||||
|
|
@ -10,79 +13,98 @@ def main():
|
|||
|
||||
@main.command()
|
||||
@click.option(
|
||||
"--dir_xml",
|
||||
"-dx",
|
||||
help="directory of GT page-xml files",
|
||||
"--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(
|
||||
"--dir_out_modal_image",
|
||||
"-domi",
|
||||
help="directory where ground truth images would be written",
|
||||
"--out",
|
||||
"-o",
|
||||
help="directory for output images",
|
||||
type=click.Path(exists=True, file_okay=False),
|
||||
required=True,
|
||||
)
|
||||
@click.option(
|
||||
"--dir_out_classes",
|
||||
"-docl",
|
||||
help="directory where ground truth classes would be written",
|
||||
"--model",
|
||||
"-m",
|
||||
help="directory of models",
|
||||
type=click.Path(exists=True, file_okay=False),
|
||||
required=True,
|
||||
)
|
||||
@click.option(
|
||||
"--input_height",
|
||||
"-ih",
|
||||
help="input height",
|
||||
"--log_level",
|
||||
"-l",
|
||||
type=click.Choice(['OFF', 'DEBUG', 'INFO', 'WARN', 'ERROR']),
|
||||
help="Override log level globally to this",
|
||||
)
|
||||
@click.option(
|
||||
"--input_width",
|
||||
"-iw",
|
||||
help="input width",
|
||||
)
|
||||
@click.option(
|
||||
"--min_area_size",
|
||||
"-min",
|
||||
help="min area size of regions considered for reading order training.",
|
||||
)
|
||||
def machine_based_reading_order(dir_xml, dir_out_modal_image, dir_out_classes, input_height, input_width, min_area_size):
|
||||
xml_files_ind = os.listdir(dir_xml)
|
||||
|
||||
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.argument('input_image', required=False)
|
||||
@click.argument('output_image', required=False)
|
||||
@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",
|
||||
help="directory of input images (instead of --image)",
|
||||
type=click.Path(exists=True, file_okay=False),
|
||||
)
|
||||
@click.option(
|
||||
"--dir_out",
|
||||
"-do",
|
||||
help="directory for output images",
|
||||
type=click.Path(exists=True, file_okay=False),
|
||||
"--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,
|
||||
)
|
||||
def binarization(patches, model_dir, input_image, output_image, dir_in, dir_out):
|
||||
assert (dir_out is None) == (dir_in is None), "Options -di and -do are mutually dependent"
|
||||
assert (input_image is None) == (output_image is None), "INPUT_IMAGE and OUTPUT_IMAGE are mutually dependent"
|
||||
assert (dir_in is None) != (input_image is None), "Specify either -di and -do options, or INPUT_IMAGE and OUTPUT_IMAGE"
|
||||
SbbBinarizer(model_dir).run(image_path=input_image, use_patches=patches, save=output_image, dir_in=dir_in, dir_out=dir_out)
|
||||
|
||||
|
||||
@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="image filename",
|
||||
help="input image filename",
|
||||
type=click.Path(exists=True, dir_okay=False),
|
||||
)
|
||||
|
||||
@click.option(
|
||||
"--out",
|
||||
"-o",
|
||||
help="directory to write output xml data",
|
||||
help="directory for output PAGE-XML files",
|
||||
type=click.Path(exists=True, file_okay=False),
|
||||
required=True,
|
||||
)
|
||||
|
|
@ -95,7 +117,82 @@ def binarization(patches, model_dir, input_image, output_image, dir_in, dir_out)
|
|||
@click.option(
|
||||
"--dir_in",
|
||||
"-di",
|
||||
help="directory of images",
|
||||
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(
|
||||
|
|
@ -225,6 +322,17 @@ def binarization(patches, model_dir, input_image, output_image, dir_in, dir_out)
|
|||
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",
|
||||
|
|
@ -235,23 +343,46 @@ def binarization(patches, model_dir, input_image, output_image, dir_in, dir_out)
|
|||
"-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 log level globally to this",
|
||||
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, save_images, save_layout, save_deskewed, save_all, extract_only_images, save_page, enable_plotting, allow_enhancement, curved_line, textline_light, full_layout, tables, right2left, input_binary, allow_scaling, headers_off, light_version, reading_order_machine_based, do_ocr, num_col_upper, num_col_lower, skip_layout_and_reading_order, ignore_page_extraction, log_level):
|
||||
initLogging()
|
||||
if log_level:
|
||||
getLogger('eynollah').setLevel(getLevelName(log_level))
|
||||
def layout(image, out, overwrite, dir_in, model, save_images, save_layout, save_deskewed, save_all, extract_only_images, save_page, enable_plotting, allow_enhancement, curved_line, textline_light, full_layout, tables, right2left, input_binary, allow_scaling, headers_off, light_version, reading_order_machine_based, do_ocr, 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"
|
||||
|
|
@ -270,17 +401,10 @@ def layout(image, out, overwrite, dir_in, model, save_images, save_layout, save_
|
|||
assert not extract_only_images or not tables, "Image extraction -eoi can not be set alongside tables -tab"
|
||||
assert not extract_only_images or not right2left, "Image extraction -eoi can not be set alongside right2left -r2l"
|
||||
assert not extract_only_images or not headers_off, "Image extraction -eoi can not be set alongside headers_off -ho"
|
||||
assert image or dir_in, "Either a single image -i or a dir_in -di is required"
|
||||
assert bool(image) != bool(dir_in), "Either -i (single input) or -di (directory) must be provided, but not both."
|
||||
eynollah = Eynollah(
|
||||
model,
|
||||
logger=getLogger('eynollah'),
|
||||
dir_out=out,
|
||||
dir_of_cropped_images=save_images,
|
||||
extract_only_images=extract_only_images,
|
||||
dir_of_layout=save_layout,
|
||||
dir_of_deskewed=save_deskewed,
|
||||
dir_of_all=save_all,
|
||||
dir_save_page=save_page,
|
||||
enable_plotting=enable_plotting,
|
||||
allow_enhancement=allow_enhancement,
|
||||
curved_line=curved_line,
|
||||
|
|
@ -295,54 +419,82 @@ def layout(image, out, overwrite, dir_in, model, save_images, save_layout, save_
|
|||
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,
|
||||
)
|
||||
if dir_in:
|
||||
eynollah.run(dir_in=dir_in, overwrite=overwrite)
|
||||
else:
|
||||
eynollah.run(image_filename=image, overwrite=overwrite)
|
||||
|
||||
|
||||
@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 images",
|
||||
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. This should be given if you want to do prediction based on both rgb and bin images. And all bin images are png files",
|
||||
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(
|
||||
"--out",
|
||||
"-o",
|
||||
help="directory to write output xml data",
|
||||
type=click.Path(exists=True, file_okay=False),
|
||||
required=True,
|
||||
)
|
||||
@click.option(
|
||||
"--dir_xmls",
|
||||
"-dx",
|
||||
help="directory of xmls",
|
||||
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 of images with predicted text",
|
||||
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),
|
||||
required=True,
|
||||
)
|
||||
@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",
|
||||
|
|
@ -363,16 +515,19 @@ def layout(image, out, overwrite, dir_in, model, save_images, save_layout, save_
|
|||
help="if this parameter set to true, cropped textline images will not be masked with textline contour.",
|
||||
)
|
||||
@click.option(
|
||||
"--draw_texts_on_image",
|
||||
"-dtoi/-ndtoi",
|
||||
is_flag=True,
|
||||
help="if this parameter set to true, the predicted texts will be displayed on an image.",
|
||||
"--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(
|
||||
"--prediction_with_both_of_rgb_and_bin",
|
||||
"-brb/-nbrb",
|
||||
is_flag=True,
|
||||
help="If this parameter is set to True, the prediction will be performed using both RGB and binary images. However, this does not necessarily improve results; it may be beneficial for certain document images.",
|
||||
"--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",
|
||||
|
|
@ -381,24 +536,36 @@ def layout(image, out, overwrite, dir_in, model, save_images, save_layout, save_
|
|||
help="Override log level globally to this",
|
||||
)
|
||||
|
||||
def ocr(dir_in, dir_in_bin, out, dir_xmls, dir_out_image_text, model, tr_ocr, export_textline_images_and_text, do_not_mask_with_textline_contour, draw_texts_on_image, prediction_with_both_of_rgb_and_bin, log_level):
|
||||
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()
|
||||
if log_level:
|
||||
getLogger('eynollah').setLevel(getLevelName(log_level))
|
||||
|
||||
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_xmls=dir_xmls,
|
||||
dir_out_image_text=dir_out_image_text,
|
||||
dir_in=dir_in,
|
||||
dir_in_bin=dir_in_bin,
|
||||
dir_out=out,
|
||||
dir_models=model,
|
||||
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,
|
||||
draw_texts_on_image=draw_texts_on_image,
|
||||
prediction_with_both_of_rgb_and_bin=prediction_with_both_of_rgb_and_bin,
|
||||
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,
|
||||
)
|
||||
eynollah_ocr.run()
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
|
|
|||
File diff suppressed because it is too large
Load diff
731
src/eynollah/image_enhancer.py
Normal file
731
src/eynollah/image_enhancer.py
Normal file
|
|
@ -0,0 +1,731 @@
|
|||
"""
|
||||
Image enhancer. The output can be written as same scale of input or in new predicted scale.
|
||||
"""
|
||||
|
||||
from logging import Logger
|
||||
import os
|
||||
import time
|
||||
from typing import Optional
|
||||
import atexit
|
||||
from functools import partial
|
||||
from pathlib import Path
|
||||
from multiprocessing import cpu_count
|
||||
import gc
|
||||
import cv2
|
||||
import numpy as np
|
||||
from ocrd_utils import getLogger, tf_disable_interactive_logs
|
||||
import tensorflow as tf
|
||||
from skimage.morphology import skeletonize
|
||||
from tensorflow.keras.models import load_model
|
||||
from .utils.resize import resize_image
|
||||
from .utils.pil_cv2 import pil2cv
|
||||
from .utils import (
|
||||
is_image_filename,
|
||||
crop_image_inside_box
|
||||
)
|
||||
|
||||
DPI_THRESHOLD = 298
|
||||
KERNEL = np.ones((5, 5), np.uint8)
|
||||
|
||||
|
||||
class Enhancer:
|
||||
def __init__(
|
||||
self,
|
||||
dir_models : str,
|
||||
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)
|
||||
else:
|
||||
self.num_col_upper = num_col_upper
|
||||
if num_col_lower:
|
||||
self.num_col_lower = int(num_col_lower)
|
||||
else:
|
||||
self.num_col_lower = num_col_lower
|
||||
|
||||
self.logger = 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"
|
||||
|
||||
try:
|
||||
for device in tf.config.list_physical_devices('GPU'):
|
||||
tf.config.experimental.set_memory_growth(device, True)
|
||||
except:
|
||||
self.logger.warning("no GPU device available")
|
||||
|
||||
self.model_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)
|
||||
else:
|
||||
ret['img'] = pil2cv(image_pil)
|
||||
if self.light_version:
|
||||
self.dpi = 100
|
||||
else:
|
||||
self.dpi = 0#check_dpi(image_pil)
|
||||
ret['img_grayscale'] = cv2.cvtColor(ret['img'], cv2.COLOR_BGR2GRAY)
|
||||
for prefix in ('', '_grayscale'):
|
||||
ret[f'img{prefix}_uint8'] = ret[f'img{prefix}'].astype(np.uint8)
|
||||
self._imgs = ret
|
||||
if dpi is not None:
|
||||
self.dpi = dpi
|
||||
|
||||
def reset_file_name_dir(self, image_filename, dir_out):
|
||||
self.cache_images(image_filename=image_filename)
|
||||
self.output_filename = os.path.join(dir_out, Path(image_filename).stem +'.png')
|
||||
|
||||
def imread(self, grayscale=False, uint8=True):
|
||||
key = 'img'
|
||||
if grayscale:
|
||||
key += '_grayscale'
|
||||
if uint8:
|
||||
key += '_uint8'
|
||||
return self._imgs[key].copy()
|
||||
|
||||
def 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]
|
||||
if img.shape[0] < img_height_model:
|
||||
img = cv2.resize(img, (img.shape[1], img_width_model), interpolation=cv2.INTER_NEAREST)
|
||||
if img.shape[1] < img_width_model:
|
||||
img = cv2.resize(img, (img_height_model, img.shape[0]), interpolation=cv2.INTER_NEAREST)
|
||||
margin = int(0.1 * img_width_model)
|
||||
width_mid = img_width_model - 2 * margin
|
||||
height_mid = img_height_model - 2 * margin
|
||||
img = img / 255.
|
||||
img_h = img.shape[0]
|
||||
img_w = img.shape[1]
|
||||
|
||||
prediction_true = np.zeros((img_h, img_w, 3))
|
||||
nxf = img_w / float(width_mid)
|
||||
nyf = img_h / float(height_mid)
|
||||
nxf = int(nxf) + 1 if nxf > int(nxf) else int(nxf)
|
||||
nyf = int(nyf) + 1 if nyf > int(nyf) else int(nyf)
|
||||
|
||||
for i in range(nxf):
|
||||
for j in range(nyf):
|
||||
if i == 0:
|
||||
index_x_d = i * width_mid
|
||||
index_x_u = index_x_d + img_width_model
|
||||
else:
|
||||
index_x_d = i * width_mid
|
||||
index_x_u = index_x_d + img_width_model
|
||||
if j == 0:
|
||||
index_y_d = j * height_mid
|
||||
index_y_u = index_y_d + img_height_model
|
||||
else:
|
||||
index_y_d = j * height_mid
|
||||
index_y_u = index_y_d + img_height_model
|
||||
|
||||
if index_x_u > img_w:
|
||||
index_x_u = img_w
|
||||
index_x_d = img_w - img_width_model
|
||||
if index_y_u > img_h:
|
||||
index_y_u = img_h
|
||||
index_y_d = img_h - img_height_model
|
||||
|
||||
img_patch = img[np.newaxis, index_y_d:index_y_u, index_x_d:index_x_u, :]
|
||||
label_p_pred = self.model_enhancement.predict(img_patch, verbose=0)
|
||||
seg = label_p_pred[0, :, :, :] * 255
|
||||
|
||||
if i == 0 and j == 0:
|
||||
prediction_true[index_y_d + 0:index_y_u - margin,
|
||||
index_x_d + 0:index_x_u - margin] = \
|
||||
seg[0:-margin or None,
|
||||
0:-margin or None]
|
||||
elif i == nxf - 1 and j == nyf - 1:
|
||||
prediction_true[index_y_d + margin:index_y_u - 0,
|
||||
index_x_d + margin:index_x_u - 0] = \
|
||||
seg[margin:,
|
||||
margin:]
|
||||
elif i == 0 and j == nyf - 1:
|
||||
prediction_true[index_y_d + margin:index_y_u - 0,
|
||||
index_x_d + 0:index_x_u - margin] = \
|
||||
seg[margin:,
|
||||
0:-margin or None]
|
||||
elif i == nxf - 1 and j == 0:
|
||||
prediction_true[index_y_d + 0:index_y_u - margin,
|
||||
index_x_d + margin:index_x_u - 0] = \
|
||||
seg[0:-margin or None,
|
||||
margin:]
|
||||
elif i == 0 and j != 0 and j != nyf - 1:
|
||||
prediction_true[index_y_d + margin:index_y_u - margin,
|
||||
index_x_d + 0:index_x_u - margin] = \
|
||||
seg[margin:-margin or None,
|
||||
0:-margin or None]
|
||||
elif i == nxf - 1 and j != 0 and j != nyf - 1:
|
||||
prediction_true[index_y_d + margin:index_y_u - margin,
|
||||
index_x_d + margin:index_x_u - 0] = \
|
||||
seg[margin:-margin or None,
|
||||
margin:]
|
||||
elif i != 0 and i != nxf - 1 and j == 0:
|
||||
prediction_true[index_y_d + 0:index_y_u - margin,
|
||||
index_x_d + margin:index_x_u - margin] = \
|
||||
seg[0:-margin or None,
|
||||
margin:-margin or None]
|
||||
elif i != 0 and i != nxf - 1 and j == nyf - 1:
|
||||
prediction_true[index_y_d + margin:index_y_u - 0,
|
||||
index_x_d + margin:index_x_u - margin] = \
|
||||
seg[margin:,
|
||||
margin:-margin or None]
|
||||
else:
|
||||
prediction_true[index_y_d + margin:index_y_u - margin,
|
||||
index_x_d + margin:index_x_u - margin] = \
|
||||
seg[margin:-margin or None,
|
||||
margin:-margin or None]
|
||||
|
||||
prediction_true = prediction_true.astype(int)
|
||||
return prediction_true
|
||||
|
||||
def calculate_width_height_by_columns(self, img, num_col, width_early, label_p_pred):
|
||||
self.logger.debug("enter calculate_width_height_by_columns")
|
||||
if num_col == 1:
|
||||
img_w_new = 2000
|
||||
elif num_col == 2:
|
||||
img_w_new = 2400
|
||||
elif num_col == 3:
|
||||
img_w_new = 3000
|
||||
elif num_col == 4:
|
||||
img_w_new = 4000
|
||||
elif num_col == 5:
|
||||
img_w_new = 5000
|
||||
elif num_col == 6:
|
||||
img_w_new = 6500
|
||||
else:
|
||||
img_w_new = width_early
|
||||
img_h_new = img_w_new * img.shape[0] // img.shape[1]
|
||||
|
||||
if img_h_new >= 8000:
|
||||
img_new = np.copy(img)
|
||||
num_column_is_classified = False
|
||||
else:
|
||||
img_new = resize_image(img, img_h_new, img_w_new)
|
||||
num_column_is_classified = True
|
||||
|
||||
return img_new, num_column_is_classified
|
||||
|
||||
def early_page_for_num_of_column_classification(self,img_bin):
|
||||
self.logger.debug("enter early_page_for_num_of_column_classification")
|
||||
if self.input_binary:
|
||||
img = np.copy(img_bin).astype(np.uint8)
|
||||
else:
|
||||
img = self.imread()
|
||||
img = cv2.GaussianBlur(img, (5, 5), 0)
|
||||
img_page_prediction = self.do_prediction(False, img, self.model_page)
|
||||
|
||||
imgray = cv2.cvtColor(img_page_prediction, cv2.COLOR_BGR2GRAY)
|
||||
_, thresh = cv2.threshold(imgray, 0, 255, 0)
|
||||
thresh = cv2.dilate(thresh, KERNEL, iterations=3)
|
||||
contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
||||
if len(contours)>0:
|
||||
cnt_size = np.array([cv2.contourArea(contours[j])
|
||||
for j in range(len(contours))])
|
||||
cnt = contours[np.argmax(cnt_size)]
|
||||
box = cv2.boundingRect(cnt)
|
||||
else:
|
||||
box = [0, 0, img.shape[1], img.shape[0]]
|
||||
cropped_page, page_coord = crop_image_inside_box(box, img)
|
||||
|
||||
self.logger.debug("exit early_page_for_num_of_column_classification")
|
||||
return cropped_page, page_coord
|
||||
|
||||
def calculate_width_height_by_columns_1_2(self, img, num_col, width_early, label_p_pred):
|
||||
self.logger.debug("enter calculate_width_height_by_columns")
|
||||
if num_col == 1:
|
||||
img_w_new = 1000
|
||||
else:
|
||||
img_w_new = 1300
|
||||
img_h_new = img_w_new * img.shape[0] // img.shape[1]
|
||||
|
||||
if label_p_pred[0][int(num_col - 1)] < 0.9 and img_w_new < width_early:
|
||||
img_new = np.copy(img)
|
||||
num_column_is_classified = False
|
||||
#elif label_p_pred[0][int(num_col - 1)] < 0.8 and img_h_new >= 8000:
|
||||
elif img_h_new >= 8000:
|
||||
img_new = np.copy(img)
|
||||
num_column_is_classified = False
|
||||
else:
|
||||
img_new = resize_image(img, img_h_new, img_w_new)
|
||||
num_column_is_classified = True
|
||||
|
||||
return img_new, num_column_is_classified
|
||||
|
||||
def resize_and_enhance_image_with_column_classifier(self, light_version):
|
||||
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 = 255 * (prediction_bin[:,:,0]==0)
|
||||
prediction_bin = np.repeat(prediction_bin[:, :, np.newaxis], 3, axis=2).astype(np.uint8)
|
||||
img= np.copy(prediction_bin)
|
||||
img_bin = prediction_bin
|
||||
else:
|
||||
img = self.imread()
|
||||
self.h_org, self.w_org = img.shape[:2]
|
||||
img_bin = None
|
||||
|
||||
width_early = img.shape[1]
|
||||
t1 = time.time()
|
||||
_, page_coord = self.early_page_for_num_of_column_classification(img_bin)
|
||||
|
||||
self.image_page_org_size = img[page_coord[0] : page_coord[1], page_coord[2] : page_coord[3], :]
|
||||
self.page_coord = page_coord
|
||||
|
||||
if self.num_col_upper and not self.num_col_lower:
|
||||
num_col = self.num_col_upper
|
||||
label_p_pred = [np.ones(6)]
|
||||
elif self.num_col_lower and not self.num_col_upper:
|
||||
num_col = self.num_col_lower
|
||||
label_p_pred = [np.ones(6)]
|
||||
elif not self.num_col_upper and not self.num_col_lower:
|
||||
if self.input_binary:
|
||||
img_in = np.copy(img)
|
||||
img_in = img_in / 255.0
|
||||
img_in = cv2.resize(img_in, (448, 448), interpolation=cv2.INTER_NEAREST)
|
||||
img_in = img_in.reshape(1, 448, 448, 3)
|
||||
else:
|
||||
img_1ch = self.imread(grayscale=True)
|
||||
width_early = img_1ch.shape[1]
|
||||
img_1ch = img_1ch[page_coord[0] : page_coord[1], page_coord[2] : page_coord[3]]
|
||||
|
||||
img_1ch = img_1ch / 255.0
|
||||
img_1ch = cv2.resize(img_1ch, (448, 448), interpolation=cv2.INTER_NEAREST)
|
||||
img_in = np.zeros((1, img_1ch.shape[0], img_1ch.shape[1], 3))
|
||||
img_in[0, :, :, 0] = img_1ch[:, :]
|
||||
img_in[0, :, :, 1] = img_1ch[:, :]
|
||||
img_in[0, :, :, 2] = img_1ch[:, :]
|
||||
|
||||
label_p_pred = self.model_classifier.predict(img_in, verbose=0)
|
||||
num_col = np.argmax(label_p_pred[0]) + 1
|
||||
elif (self.num_col_upper and self.num_col_lower) and (self.num_col_upper!=self.num_col_lower):
|
||||
if self.input_binary:
|
||||
img_in = np.copy(img)
|
||||
img_in = img_in / 255.0
|
||||
img_in = cv2.resize(img_in, (448, 448), interpolation=cv2.INTER_NEAREST)
|
||||
img_in = img_in.reshape(1, 448, 448, 3)
|
||||
else:
|
||||
img_1ch = self.imread(grayscale=True)
|
||||
width_early = img_1ch.shape[1]
|
||||
img_1ch = img_1ch[page_coord[0] : page_coord[1], page_coord[2] : page_coord[3]]
|
||||
|
||||
img_1ch = img_1ch / 255.0
|
||||
img_1ch = cv2.resize(img_1ch, (448, 448), interpolation=cv2.INTER_NEAREST)
|
||||
img_in = np.zeros((1, img_1ch.shape[0], img_1ch.shape[1], 3))
|
||||
img_in[0, :, :, 0] = img_1ch[:, :]
|
||||
img_in[0, :, :, 1] = img_1ch[:, :]
|
||||
img_in[0, :, :, 2] = img_1ch[:, :]
|
||||
|
||||
label_p_pred = self.model_classifier.predict(img_in, verbose=0)
|
||||
num_col = np.argmax(label_p_pred[0]) + 1
|
||||
|
||||
if num_col > self.num_col_upper:
|
||||
num_col = self.num_col_upper
|
||||
label_p_pred = [np.ones(6)]
|
||||
if num_col < self.num_col_lower:
|
||||
num_col = self.num_col_lower
|
||||
label_p_pred = [np.ones(6)]
|
||||
else:
|
||||
num_col = self.num_col_upper
|
||||
label_p_pred = [np.ones(6)]
|
||||
|
||||
self.logger.info("Found %d columns (%s)", num_col, np.around(label_p_pred, decimals=5))
|
||||
|
||||
if dpi < DPI_THRESHOLD:
|
||||
if light_version and num_col in (1,2):
|
||||
img_new, num_column_is_classified = self.calculate_width_height_by_columns_1_2(
|
||||
img, num_col, width_early, label_p_pred)
|
||||
else:
|
||||
img_new, num_column_is_classified = self.calculate_width_height_by_columns(
|
||||
img, num_col, width_early, label_p_pred)
|
||||
if light_version:
|
||||
image_res = np.copy(img_new)
|
||||
else:
|
||||
image_res = self.predict_enhancement(img_new)
|
||||
is_image_enhanced = True
|
||||
|
||||
else:
|
||||
num_column_is_classified = True
|
||||
image_res = np.copy(img)
|
||||
is_image_enhanced = False
|
||||
|
||||
self.logger.debug("exit resize_and_enhance_image_with_column_classifier")
|
||||
return is_image_enhanced, img, image_res, num_col, num_column_is_classified, img_bin
|
||||
def do_prediction(
|
||||
self, patches, img, model,
|
||||
n_batch_inference=1, marginal_of_patch_percent=0.1,
|
||||
thresholding_for_some_classes_in_light_version=False,
|
||||
thresholding_for_artificial_class_in_light_version=False, thresholding_for_fl_light_version=False, threshold_art_class_textline=0.1):
|
||||
|
||||
self.logger.debug("enter do_prediction")
|
||||
img_height_model = model.layers[-1].output_shape[1]
|
||||
img_width_model = model.layers[-1].output_shape[2]
|
||||
|
||||
if not patches:
|
||||
img_h_page = img.shape[0]
|
||||
img_w_page = img.shape[1]
|
||||
img = img / float(255.0)
|
||||
img = resize_image(img, img_height_model, img_width_model)
|
||||
|
||||
label_p_pred = model.predict(img[np.newaxis], verbose=0)
|
||||
seg = np.argmax(label_p_pred, axis=3)[0]
|
||||
|
||||
if thresholding_for_artificial_class_in_light_version:
|
||||
seg_art = label_p_pred[0,:,:,2]
|
||||
|
||||
seg_art[seg_art<threshold_art_class_textline] = 0
|
||||
seg_art[seg_art>0] =1
|
||||
|
||||
skeleton_art = skeletonize(seg_art)
|
||||
skeleton_art = skeleton_art*1
|
||||
|
||||
seg[skeleton_art==1]=2
|
||||
|
||||
if thresholding_for_fl_light_version:
|
||||
seg_header = label_p_pred[0,:,:,2]
|
||||
|
||||
seg_header[seg_header<0.2] = 0
|
||||
seg_header[seg_header>0] =1
|
||||
|
||||
seg[seg_header==1]=2
|
||||
|
||||
seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2)
|
||||
prediction_true = resize_image(seg_color, img_h_page, img_w_page).astype(np.uint8)
|
||||
return prediction_true
|
||||
|
||||
if img.shape[0] < img_height_model:
|
||||
img = resize_image(img, img_height_model, img.shape[1])
|
||||
if img.shape[1] < img_width_model:
|
||||
img = resize_image(img, img.shape[0], img_width_model)
|
||||
|
||||
self.logger.debug("Patch size: %sx%s", img_height_model, img_width_model)
|
||||
margin = int(marginal_of_patch_percent * img_height_model)
|
||||
width_mid = img_width_model - 2 * margin
|
||||
height_mid = img_height_model - 2 * margin
|
||||
img = img / 255.
|
||||
#img = img.astype(np.float16)
|
||||
img_h = img.shape[0]
|
||||
img_w = img.shape[1]
|
||||
prediction_true = np.zeros((img_h, img_w, 3))
|
||||
mask_true = np.zeros((img_h, img_w))
|
||||
nxf = img_w / float(width_mid)
|
||||
nyf = img_h / float(height_mid)
|
||||
nxf = int(nxf) + 1 if nxf > int(nxf) else int(nxf)
|
||||
nyf = int(nyf) + 1 if nyf > int(nyf) else int(nyf)
|
||||
|
||||
list_i_s = []
|
||||
list_j_s = []
|
||||
list_x_u = []
|
||||
list_x_d = []
|
||||
list_y_u = []
|
||||
list_y_d = []
|
||||
|
||||
batch_indexer = 0
|
||||
img_patch = np.zeros((n_batch_inference, img_height_model, img_width_model, 3))
|
||||
for i in range(nxf):
|
||||
for j in range(nyf):
|
||||
if i == 0:
|
||||
index_x_d = i * width_mid
|
||||
index_x_u = index_x_d + img_width_model
|
||||
else:
|
||||
index_x_d = i * width_mid
|
||||
index_x_u = index_x_d + img_width_model
|
||||
if j == 0:
|
||||
index_y_d = j * height_mid
|
||||
index_y_u = index_y_d + img_height_model
|
||||
else:
|
||||
index_y_d = j * height_mid
|
||||
index_y_u = index_y_d + img_height_model
|
||||
if index_x_u > img_w:
|
||||
index_x_u = img_w
|
||||
index_x_d = img_w - img_width_model
|
||||
if index_y_u > img_h:
|
||||
index_y_u = img_h
|
||||
index_y_d = img_h - img_height_model
|
||||
|
||||
list_i_s.append(i)
|
||||
list_j_s.append(j)
|
||||
list_x_u.append(index_x_u)
|
||||
list_x_d.append(index_x_d)
|
||||
list_y_d.append(index_y_d)
|
||||
list_y_u.append(index_y_u)
|
||||
|
||||
img_patch[batch_indexer,:,:,:] = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
|
||||
batch_indexer += 1
|
||||
|
||||
if (batch_indexer == n_batch_inference or
|
||||
# last batch
|
||||
i == nxf - 1 and j == nyf - 1):
|
||||
self.logger.debug("predicting patches on %s", str(img_patch.shape))
|
||||
label_p_pred = model.predict(img_patch, verbose=0)
|
||||
seg = np.argmax(label_p_pred, axis=3)
|
||||
|
||||
if thresholding_for_some_classes_in_light_version:
|
||||
seg_not_base = label_p_pred[:,:,:,4]
|
||||
seg_not_base[seg_not_base>0.03] =1
|
||||
seg_not_base[seg_not_base<1] =0
|
||||
|
||||
seg_line = label_p_pred[:,:,:,3]
|
||||
seg_line[seg_line>0.1] =1
|
||||
seg_line[seg_line<1] =0
|
||||
|
||||
seg_background = label_p_pred[:,:,:,0]
|
||||
seg_background[seg_background>0.25] =1
|
||||
seg_background[seg_background<1] =0
|
||||
|
||||
seg[seg_not_base==1]=4
|
||||
seg[seg_background==1]=0
|
||||
seg[(seg_line==1) & (seg==0)]=3
|
||||
if thresholding_for_artificial_class_in_light_version:
|
||||
seg_art = label_p_pred[:,:,:,2]
|
||||
|
||||
seg_art[seg_art<threshold_art_class_textline] = 0
|
||||
seg_art[seg_art>0] =1
|
||||
|
||||
##seg[seg_art==1]=2
|
||||
|
||||
indexer_inside_batch = 0
|
||||
for i_batch, j_batch in zip(list_i_s, list_j_s):
|
||||
seg_in = seg[indexer_inside_batch]
|
||||
|
||||
if thresholding_for_artificial_class_in_light_version:
|
||||
seg_in_art = seg_art[indexer_inside_batch]
|
||||
|
||||
index_y_u_in = list_y_u[indexer_inside_batch]
|
||||
index_y_d_in = list_y_d[indexer_inside_batch]
|
||||
|
||||
index_x_u_in = list_x_u[indexer_inside_batch]
|
||||
index_x_d_in = list_x_d[indexer_inside_batch]
|
||||
|
||||
if i_batch == 0 and j_batch == 0:
|
||||
prediction_true[index_y_d_in + 0:index_y_u_in - margin,
|
||||
index_x_d_in + 0:index_x_u_in - margin] = \
|
||||
seg_in[0:-margin or None,
|
||||
0:-margin or None,
|
||||
np.newaxis]
|
||||
if thresholding_for_artificial_class_in_light_version:
|
||||
prediction_true[index_y_d_in + 0:index_y_u_in - margin,
|
||||
index_x_d_in + 0:index_x_u_in - margin, 1] = \
|
||||
seg_in_art[0:-margin or None,
|
||||
0:-margin or None]
|
||||
|
||||
elif i_batch == nxf - 1 and j_batch == nyf - 1:
|
||||
prediction_true[index_y_d_in + margin:index_y_u_in - 0,
|
||||
index_x_d_in + margin:index_x_u_in - 0] = \
|
||||
seg_in[margin:,
|
||||
margin:,
|
||||
np.newaxis]
|
||||
if thresholding_for_artificial_class_in_light_version:
|
||||
prediction_true[index_y_d_in + margin:index_y_u_in - 0,
|
||||
index_x_d_in + margin:index_x_u_in - 0, 1] = \
|
||||
seg_in_art[margin:,
|
||||
margin:]
|
||||
|
||||
elif i_batch == 0 and j_batch == nyf - 1:
|
||||
prediction_true[index_y_d_in + margin:index_y_u_in - 0,
|
||||
index_x_d_in + 0:index_x_u_in - margin] = \
|
||||
seg_in[margin:,
|
||||
0:-margin or None,
|
||||
np.newaxis]
|
||||
if thresholding_for_artificial_class_in_light_version:
|
||||
prediction_true[index_y_d_in + margin:index_y_u_in - 0,
|
||||
index_x_d_in + 0:index_x_u_in - margin, 1] = \
|
||||
seg_in_art[margin:,
|
||||
0:-margin or None]
|
||||
|
||||
elif i_batch == nxf - 1 and j_batch == 0:
|
||||
prediction_true[index_y_d_in + 0:index_y_u_in - margin,
|
||||
index_x_d_in + margin:index_x_u_in - 0] = \
|
||||
seg_in[0:-margin or None,
|
||||
margin:,
|
||||
np.newaxis]
|
||||
if thresholding_for_artificial_class_in_light_version:
|
||||
prediction_true[index_y_d_in + 0:index_y_u_in - margin,
|
||||
index_x_d_in + margin:index_x_u_in - 0, 1] = \
|
||||
seg_in_art[0:-margin or None,
|
||||
margin:]
|
||||
|
||||
elif i_batch == 0 and j_batch != 0 and j_batch != nyf - 1:
|
||||
prediction_true[index_y_d_in + margin:index_y_u_in - margin,
|
||||
index_x_d_in + 0:index_x_u_in - margin] = \
|
||||
seg_in[margin:-margin or None,
|
||||
0:-margin or None,
|
||||
np.newaxis]
|
||||
if thresholding_for_artificial_class_in_light_version:
|
||||
prediction_true[index_y_d_in + margin:index_y_u_in - margin,
|
||||
index_x_d_in + 0:index_x_u_in - margin, 1] = \
|
||||
seg_in_art[margin:-margin or None,
|
||||
0:-margin or None]
|
||||
|
||||
elif i_batch == nxf - 1 and j_batch != 0 and j_batch != nyf - 1:
|
||||
prediction_true[index_y_d_in + margin:index_y_u_in - margin,
|
||||
index_x_d_in + margin:index_x_u_in - 0] = \
|
||||
seg_in[margin:-margin or None,
|
||||
margin:,
|
||||
np.newaxis]
|
||||
if thresholding_for_artificial_class_in_light_version:
|
||||
prediction_true[index_y_d_in + margin:index_y_u_in - margin,
|
||||
index_x_d_in + margin:index_x_u_in - 0, 1] = \
|
||||
seg_in_art[margin:-margin or None,
|
||||
margin:]
|
||||
|
||||
elif i_batch != 0 and i_batch != nxf - 1 and j_batch == 0:
|
||||
prediction_true[index_y_d_in + 0:index_y_u_in - margin,
|
||||
index_x_d_in + margin:index_x_u_in - margin] = \
|
||||
seg_in[0:-margin or None,
|
||||
margin:-margin or None,
|
||||
np.newaxis]
|
||||
if thresholding_for_artificial_class_in_light_version:
|
||||
prediction_true[index_y_d_in + 0:index_y_u_in - margin,
|
||||
index_x_d_in + margin:index_x_u_in - margin, 1] = \
|
||||
seg_in_art[0:-margin or None,
|
||||
margin:-margin or None]
|
||||
|
||||
elif i_batch != 0 and i_batch != nxf - 1 and j_batch == nyf - 1:
|
||||
prediction_true[index_y_d_in + margin:index_y_u_in - 0,
|
||||
index_x_d_in + margin:index_x_u_in - margin] = \
|
||||
seg_in[margin:,
|
||||
margin:-margin or None,
|
||||
np.newaxis]
|
||||
if thresholding_for_artificial_class_in_light_version:
|
||||
prediction_true[index_y_d_in + margin:index_y_u_in - 0,
|
||||
index_x_d_in + margin:index_x_u_in - margin, 1] = \
|
||||
seg_in_art[margin:,
|
||||
margin:-margin or None]
|
||||
|
||||
else:
|
||||
prediction_true[index_y_d_in + margin:index_y_u_in - margin,
|
||||
index_x_d_in + margin:index_x_u_in - margin] = \
|
||||
seg_in[margin:-margin or None,
|
||||
margin:-margin or None,
|
||||
np.newaxis]
|
||||
if thresholding_for_artificial_class_in_light_version:
|
||||
prediction_true[index_y_d_in + margin:index_y_u_in - margin,
|
||||
index_x_d_in + margin:index_x_u_in - margin, 1] = \
|
||||
seg_in_art[margin:-margin or None,
|
||||
margin:-margin or None]
|
||||
indexer_inside_batch += 1
|
||||
|
||||
|
||||
list_i_s = []
|
||||
list_j_s = []
|
||||
list_x_u = []
|
||||
list_x_d = []
|
||||
list_y_u = []
|
||||
list_y_d = []
|
||||
|
||||
batch_indexer = 0
|
||||
img_patch[:] = 0
|
||||
|
||||
prediction_true = prediction_true.astype(np.uint8)
|
||||
|
||||
if thresholding_for_artificial_class_in_light_version:
|
||||
kernel_min = np.ones((3, 3), np.uint8)
|
||||
prediction_true[:,:,0][prediction_true[:,:,0]==2] = 0
|
||||
|
||||
skeleton_art = skeletonize(prediction_true[:,:,1])
|
||||
skeleton_art = skeleton_art*1
|
||||
|
||||
skeleton_art = skeleton_art.astype('uint8')
|
||||
|
||||
skeleton_art = cv2.dilate(skeleton_art, kernel_min, iterations=1)
|
||||
|
||||
prediction_true[:,:,0][skeleton_art==1]=2
|
||||
#del model
|
||||
gc.collect()
|
||||
return prediction_true
|
||||
|
||||
def run_enhancement(self, light_version):
|
||||
t_in = time.time()
|
||||
self.logger.info("Resizing and enhancing image...")
|
||||
is_image_enhanced, img_org, img_res, num_col_classifier, num_column_is_classified, img_bin = \
|
||||
self.resize_and_enhance_image_with_column_classifier(light_version)
|
||||
|
||||
self.logger.info("Image was %senhanced.", '' if is_image_enhanced else 'not ')
|
||||
return img_res, is_image_enhanced, num_col_classifier, num_column_is_classified
|
||||
|
||||
|
||||
def run_single(self):
|
||||
t0 = time.time()
|
||||
img_res, is_image_enhanced, num_col_classifier, num_column_is_classified = self.run_enhancement(light_version=False)
|
||||
|
||||
return img_res
|
||||
|
||||
|
||||
def run(self,
|
||||
overwrite: bool = False,
|
||||
image_filename: Optional[str] = None,
|
||||
dir_in: Optional[str] = None,
|
||||
dir_out: Optional[str] = None,
|
||||
):
|
||||
"""
|
||||
Get image and scales, then extract the page of scanned image
|
||||
"""
|
||||
self.logger.debug("enter run")
|
||||
t0_tot = time.time()
|
||||
|
||||
if dir_in:
|
||||
ls_imgs = [os.path.join(dir_in, image_filename)
|
||||
for image_filename in filter(is_image_filename,
|
||||
os.listdir(dir_in))]
|
||||
elif image_filename:
|
||||
ls_imgs = [image_filename]
|
||||
else:
|
||||
raise ValueError("run requires either a single image filename or a directory")
|
||||
|
||||
for img_filename in ls_imgs:
|
||||
self.logger.info(img_filename)
|
||||
t0 = time.time()
|
||||
|
||||
self.reset_file_name_dir(img_filename, dir_out)
|
||||
#print("text region early -11 in %.1fs", time.time() - t0)
|
||||
|
||||
if os.path.exists(self.output_filename):
|
||||
if overwrite:
|
||||
self.logger.warning("will overwrite existing output file '%s'", self.output_filename)
|
||||
else:
|
||||
self.logger.warning("will skip input for existing output file '%s'", self.output_filename)
|
||||
continue
|
||||
|
||||
image_enhanced = self.run_single()
|
||||
if self.save_org_scale:
|
||||
image_enhanced = resize_image(image_enhanced, self.h_org, self.w_org)
|
||||
|
||||
cv2.imwrite(self.output_filename, image_enhanced)
|
||||
|
||||
813
src/eynollah/mb_ro_on_layout.py
Normal file
813
src/eynollah/mb_ro_on_layout.py
Normal file
|
|
@ -0,0 +1,813 @@
|
|||
"""
|
||||
Image enhancer. The output can be written as same scale of input or in new predicted scale.
|
||||
"""
|
||||
|
||||
from logging import Logger
|
||||
import os
|
||||
import time
|
||||
from typing import Optional
|
||||
import atexit
|
||||
from functools import partial
|
||||
from pathlib import Path
|
||||
from multiprocessing import cpu_count
|
||||
import xml.etree.ElementTree as ET
|
||||
import cv2
|
||||
import numpy as np
|
||||
from ocrd_utils import getLogger
|
||||
import statistics
|
||||
import tensorflow as tf
|
||||
from tensorflow.keras.models import load_model
|
||||
from .utils.resize import resize_image
|
||||
|
||||
from .utils.contour import (
|
||||
find_new_features_of_contours,
|
||||
return_contours_of_image,
|
||||
return_parent_contours,
|
||||
)
|
||||
from .utils import is_xml_filename
|
||||
|
||||
DPI_THRESHOLD = 298
|
||||
KERNEL = np.ones((5, 5), np.uint8)
|
||||
|
||||
|
||||
class machine_based_reading_order_on_layout:
|
||||
def __init__(
|
||||
self,
|
||||
dir_models : str,
|
||||
logger : Optional[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"
|
||||
|
||||
try:
|
||||
for device in tf.config.list_physical_devices('GPU'):
|
||||
tf.config.experimental.set_memory_growth(device, True)
|
||||
except:
|
||||
self.logger.warning("no GPU device available")
|
||||
|
||||
self.model_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
|
||||
|
||||
def read_xml(self, xml_file):
|
||||
tree1 = ET.parse(xml_file, parser = ET.XMLParser(encoding='utf-8'))
|
||||
root1=tree1.getroot()
|
||||
alltags=[elem.tag for elem in root1.iter()]
|
||||
link=alltags[0].split('}')[0]+'}'
|
||||
|
||||
index_tot_regions = []
|
||||
tot_region_ref = []
|
||||
|
||||
for jj in root1.iter(link+'Page'):
|
||||
y_len=int(jj.attrib['imageHeight'])
|
||||
x_len=int(jj.attrib['imageWidth'])
|
||||
|
||||
for jj in root1.iter(link+'RegionRefIndexed'):
|
||||
index_tot_regions.append(jj.attrib['index'])
|
||||
tot_region_ref.append(jj.attrib['regionRef'])
|
||||
|
||||
if (link+'PrintSpace' in alltags) or (link+'Border' in alltags):
|
||||
co_printspace = []
|
||||
if link+'PrintSpace' in alltags:
|
||||
region_tags_printspace = np.unique([x for x in alltags if x.endswith('PrintSpace')])
|
||||
elif link+'Border' in alltags:
|
||||
region_tags_printspace = np.unique([x for x in alltags if x.endswith('Border')])
|
||||
|
||||
for tag in region_tags_printspace:
|
||||
if link+'PrintSpace' in alltags:
|
||||
tag_endings_printspace = ['}PrintSpace','}printspace']
|
||||
elif link+'Border' in alltags:
|
||||
tag_endings_printspace = ['}Border','}border']
|
||||
|
||||
if tag.endswith(tag_endings_printspace[0]) or tag.endswith(tag_endings_printspace[1]):
|
||||
for nn in root1.iter(tag):
|
||||
c_t_in = []
|
||||
sumi = 0
|
||||
for vv in nn.iter():
|
||||
# check the format of coords
|
||||
if vv.tag == link + 'Coords':
|
||||
coords = bool(vv.attrib)
|
||||
if coords:
|
||||
p_h = vv.attrib['points'].split(' ')
|
||||
c_t_in.append(
|
||||
np.array([[int(x.split(',')[0]), int(x.split(',')[1])] for x in p_h]))
|
||||
break
|
||||
else:
|
||||
pass
|
||||
|
||||
if vv.tag == link + 'Point':
|
||||
c_t_in.append([int(float(vv.attrib['x'])), int(float(vv.attrib['y']))])
|
||||
sumi += 1
|
||||
elif vv.tag != link + 'Point' and sumi >= 1:
|
||||
break
|
||||
co_printspace.append(np.array(c_t_in))
|
||||
img_printspace = np.zeros( (y_len,x_len,3) )
|
||||
img_printspace=cv2.fillPoly(img_printspace, pts =co_printspace, color=(1,1,1))
|
||||
img_printspace = img_printspace.astype(np.uint8)
|
||||
|
||||
imgray = cv2.cvtColor(img_printspace, cv2.COLOR_BGR2GRAY)
|
||||
_, thresh = cv2.threshold(imgray, 0, 255, 0)
|
||||
contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
||||
cnt_size = np.array([cv2.contourArea(contours[j]) for j in range(len(contours))])
|
||||
cnt = contours[np.argmax(cnt_size)]
|
||||
x, y, w, h = cv2.boundingRect(cnt)
|
||||
|
||||
bb_coord_printspace = [x, y, w, h]
|
||||
|
||||
else:
|
||||
bb_coord_printspace = None
|
||||
|
||||
|
||||
region_tags=np.unique([x for x in alltags if x.endswith('Region')])
|
||||
co_text_paragraph=[]
|
||||
co_text_drop=[]
|
||||
co_text_heading=[]
|
||||
co_text_header=[]
|
||||
co_text_marginalia=[]
|
||||
co_text_catch=[]
|
||||
co_text_page_number=[]
|
||||
co_text_signature_mark=[]
|
||||
co_sep=[]
|
||||
co_img=[]
|
||||
co_table=[]
|
||||
co_graphic=[]
|
||||
co_graphic_text_annotation=[]
|
||||
co_graphic_decoration=[]
|
||||
co_noise=[]
|
||||
|
||||
co_text_paragraph_text=[]
|
||||
co_text_drop_text=[]
|
||||
co_text_heading_text=[]
|
||||
co_text_header_text=[]
|
||||
co_text_marginalia_text=[]
|
||||
co_text_catch_text=[]
|
||||
co_text_page_number_text=[]
|
||||
co_text_signature_mark_text=[]
|
||||
co_sep_text=[]
|
||||
co_img_text=[]
|
||||
co_table_text=[]
|
||||
co_graphic_text=[]
|
||||
co_graphic_text_annotation_text=[]
|
||||
co_graphic_decoration_text=[]
|
||||
co_noise_text=[]
|
||||
|
||||
id_paragraph = []
|
||||
id_header = []
|
||||
id_heading = []
|
||||
id_marginalia = []
|
||||
|
||||
for tag in region_tags:
|
||||
if tag.endswith('}TextRegion') or tag.endswith('}Textregion'):
|
||||
for nn in root1.iter(tag):
|
||||
for child2 in nn:
|
||||
tag2 = child2.tag
|
||||
if tag2.endswith('}TextEquiv') or tag2.endswith('}TextEquiv'):
|
||||
for childtext2 in child2:
|
||||
if childtext2.tag.endswith('}Unicode') or childtext2.tag.endswith('}Unicode'):
|
||||
if "type" in nn.attrib and nn.attrib['type']=='drop-capital':
|
||||
co_text_drop_text.append(childtext2.text)
|
||||
elif "type" in nn.attrib and nn.attrib['type']=='heading':
|
||||
co_text_heading_text.append(childtext2.text)
|
||||
elif "type" in nn.attrib and nn.attrib['type']=='signature-mark':
|
||||
co_text_signature_mark_text.append(childtext2.text)
|
||||
elif "type" in nn.attrib and nn.attrib['type']=='header':
|
||||
co_text_header_text.append(childtext2.text)
|
||||
###elif "type" in nn.attrib and nn.attrib['type']=='catch-word':
|
||||
###co_text_catch_text.append(childtext2.text)
|
||||
###elif "type" in nn.attrib and nn.attrib['type']=='page-number':
|
||||
###co_text_page_number_text.append(childtext2.text)
|
||||
elif "type" in nn.attrib and nn.attrib['type']=='marginalia':
|
||||
co_text_marginalia_text.append(childtext2.text)
|
||||
else:
|
||||
co_text_paragraph_text.append(childtext2.text)
|
||||
c_t_in_drop=[]
|
||||
c_t_in_paragraph=[]
|
||||
c_t_in_heading=[]
|
||||
c_t_in_header=[]
|
||||
c_t_in_page_number=[]
|
||||
c_t_in_signature_mark=[]
|
||||
c_t_in_catch=[]
|
||||
c_t_in_marginalia=[]
|
||||
|
||||
|
||||
sumi=0
|
||||
for vv in nn.iter():
|
||||
# check the format of coords
|
||||
if vv.tag==link+'Coords':
|
||||
|
||||
coords=bool(vv.attrib)
|
||||
if coords:
|
||||
#print('birda1')
|
||||
p_h=vv.attrib['points'].split(' ')
|
||||
|
||||
|
||||
|
||||
if "type" in nn.attrib and nn.attrib['type']=='drop-capital':
|
||||
|
||||
c_t_in_drop.append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) )
|
||||
|
||||
elif "type" in nn.attrib and nn.attrib['type']=='heading':
|
||||
##id_heading.append(nn.attrib['id'])
|
||||
c_t_in_heading.append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) )
|
||||
|
||||
|
||||
elif "type" in nn.attrib and nn.attrib['type']=='signature-mark':
|
||||
|
||||
c_t_in_signature_mark.append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) )
|
||||
#print(c_t_in_paragraph)
|
||||
elif "type" in nn.attrib and nn.attrib['type']=='header':
|
||||
#id_header.append(nn.attrib['id'])
|
||||
c_t_in_header.append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) )
|
||||
|
||||
|
||||
###elif "type" in nn.attrib and nn.attrib['type']=='catch-word':
|
||||
###c_t_in_catch.append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) )
|
||||
|
||||
|
||||
###elif "type" in nn.attrib and nn.attrib['type']=='page-number':
|
||||
|
||||
###c_t_in_page_number.append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) )
|
||||
|
||||
elif "type" in nn.attrib and nn.attrib['type']=='marginalia':
|
||||
#id_marginalia.append(nn.attrib['id'])
|
||||
|
||||
c_t_in_marginalia.append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) )
|
||||
else:
|
||||
#id_paragraph.append(nn.attrib['id'])
|
||||
|
||||
c_t_in_paragraph.append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) )
|
||||
|
||||
break
|
||||
else:
|
||||
pass
|
||||
|
||||
|
||||
if vv.tag==link+'Point':
|
||||
if "type" in nn.attrib and nn.attrib['type']=='drop-capital':
|
||||
|
||||
c_t_in_drop.append([ int(float(vv.attrib['x'])) , int(float(vv.attrib['y'])) ])
|
||||
sumi+=1
|
||||
|
||||
elif "type" in nn.attrib and nn.attrib['type']=='heading':
|
||||
#id_heading.append(nn.attrib['id'])
|
||||
c_t_in_heading.append([ int(float(vv.attrib['x'])) , int(float(vv.attrib['y'])) ])
|
||||
sumi+=1
|
||||
|
||||
|
||||
elif "type" in nn.attrib and nn.attrib['type']=='signature-mark':
|
||||
|
||||
c_t_in_signature_mark.append([ int(float(vv.attrib['x'])) , int(float(vv.attrib['y'])) ])
|
||||
sumi+=1
|
||||
elif "type" in nn.attrib and nn.attrib['type']=='header':
|
||||
#id_header.append(nn.attrib['id'])
|
||||
c_t_in_header.append([ int(float(vv.attrib['x'])) , int(float(vv.attrib['y'])) ])
|
||||
sumi+=1
|
||||
|
||||
|
||||
###elif "type" in nn.attrib and nn.attrib['type']=='catch-word':
|
||||
###c_t_in_catch.append([ int(float(vv.attrib['x'])) , int(float(vv.attrib['y'])) ])
|
||||
###sumi+=1
|
||||
|
||||
###elif "type" in nn.attrib and nn.attrib['type']=='page-number':
|
||||
|
||||
###c_t_in_page_number.append([ int(float(vv.attrib['x'])) , int(float(vv.attrib['y'])) ])
|
||||
###sumi+=1
|
||||
|
||||
elif "type" in nn.attrib and nn.attrib['type']=='marginalia':
|
||||
#id_marginalia.append(nn.attrib['id'])
|
||||
|
||||
c_t_in_marginalia.append([ int(float(vv.attrib['x'])) , int(float(vv.attrib['y'])) ])
|
||||
sumi+=1
|
||||
|
||||
else:
|
||||
#id_paragraph.append(nn.attrib['id'])
|
||||
c_t_in_paragraph.append([ int(float(vv.attrib['x'])) , int(float(vv.attrib['y'])) ])
|
||||
sumi+=1
|
||||
|
||||
elif vv.tag!=link+'Point' and sumi>=1:
|
||||
break
|
||||
|
||||
if len(c_t_in_drop)>0:
|
||||
co_text_drop.append(np.array(c_t_in_drop))
|
||||
if len(c_t_in_paragraph)>0:
|
||||
co_text_paragraph.append(np.array(c_t_in_paragraph))
|
||||
id_paragraph.append(nn.attrib['id'])
|
||||
if len(c_t_in_heading)>0:
|
||||
co_text_heading.append(np.array(c_t_in_heading))
|
||||
id_heading.append(nn.attrib['id'])
|
||||
|
||||
if len(c_t_in_header)>0:
|
||||
co_text_header.append(np.array(c_t_in_header))
|
||||
id_header.append(nn.attrib['id'])
|
||||
if len(c_t_in_page_number)>0:
|
||||
co_text_page_number.append(np.array(c_t_in_page_number))
|
||||
if len(c_t_in_catch)>0:
|
||||
co_text_catch.append(np.array(c_t_in_catch))
|
||||
|
||||
if len(c_t_in_signature_mark)>0:
|
||||
co_text_signature_mark.append(np.array(c_t_in_signature_mark))
|
||||
|
||||
if len(c_t_in_marginalia)>0:
|
||||
co_text_marginalia.append(np.array(c_t_in_marginalia))
|
||||
id_marginalia.append(nn.attrib['id'])
|
||||
|
||||
|
||||
elif tag.endswith('}GraphicRegion') or tag.endswith('}graphicregion'):
|
||||
for nn in root1.iter(tag):
|
||||
c_t_in=[]
|
||||
c_t_in_text_annotation=[]
|
||||
c_t_in_decoration=[]
|
||||
sumi=0
|
||||
for vv in nn.iter():
|
||||
# check the format of coords
|
||||
if vv.tag==link+'Coords':
|
||||
coords=bool(vv.attrib)
|
||||
if coords:
|
||||
p_h=vv.attrib['points'].split(' ')
|
||||
|
||||
if "type" in nn.attrib and nn.attrib['type']=='handwritten-annotation':
|
||||
c_t_in_text_annotation.append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) )
|
||||
|
||||
elif "type" in nn.attrib and nn.attrib['type']=='decoration':
|
||||
c_t_in_decoration.append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) )
|
||||
|
||||
else:
|
||||
c_t_in.append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) )
|
||||
|
||||
|
||||
break
|
||||
else:
|
||||
pass
|
||||
|
||||
|
||||
if vv.tag==link+'Point':
|
||||
if "type" in nn.attrib and nn.attrib['type']=='handwritten-annotation':
|
||||
c_t_in_text_annotation.append([ int(float(vv.attrib['x'])) , int(float(vv.attrib['y'])) ])
|
||||
sumi+=1
|
||||
|
||||
elif "type" in nn.attrib and nn.attrib['type']=='decoration':
|
||||
c_t_in_decoration.append([ int(float(vv.attrib['x'])) , int(float(vv.attrib['y'])) ])
|
||||
sumi+=1
|
||||
|
||||
else:
|
||||
c_t_in.append([ int(float(vv.attrib['x'])) , int(float(vv.attrib['y'])) ])
|
||||
sumi+=1
|
||||
|
||||
if len(c_t_in_text_annotation)>0:
|
||||
co_graphic_text_annotation.append(np.array(c_t_in_text_annotation))
|
||||
if len(c_t_in_decoration)>0:
|
||||
co_graphic_decoration.append(np.array(c_t_in_decoration))
|
||||
if len(c_t_in)>0:
|
||||
co_graphic.append(np.array(c_t_in))
|
||||
|
||||
|
||||
|
||||
elif tag.endswith('}ImageRegion') or tag.endswith('}imageregion'):
|
||||
for nn in root1.iter(tag):
|
||||
c_t_in=[]
|
||||
sumi=0
|
||||
for vv in nn.iter():
|
||||
# check the format of coords
|
||||
if vv.tag==link+'Coords':
|
||||
coords=bool(vv.attrib)
|
||||
if coords:
|
||||
p_h=vv.attrib['points'].split(' ')
|
||||
c_t_in.append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) )
|
||||
break
|
||||
else:
|
||||
pass
|
||||
|
||||
|
||||
if vv.tag==link+'Point':
|
||||
c_t_in.append([ int(float(vv.attrib['x'])) , int(float(vv.attrib['y'])) ])
|
||||
sumi+=1
|
||||
elif vv.tag!=link+'Point' and sumi>=1:
|
||||
break
|
||||
co_img.append(np.array(c_t_in))
|
||||
co_img_text.append(' ')
|
||||
|
||||
|
||||
elif tag.endswith('}SeparatorRegion') or tag.endswith('}separatorregion'):
|
||||
for nn in root1.iter(tag):
|
||||
c_t_in=[]
|
||||
sumi=0
|
||||
for vv in nn.iter():
|
||||
# check the format of coords
|
||||
if vv.tag==link+'Coords':
|
||||
coords=bool(vv.attrib)
|
||||
if coords:
|
||||
p_h=vv.attrib['points'].split(' ')
|
||||
c_t_in.append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) )
|
||||
break
|
||||
else:
|
||||
pass
|
||||
|
||||
|
||||
if vv.tag==link+'Point':
|
||||
c_t_in.append([ int(float(vv.attrib['x'])) , int(float(vv.attrib['y'])) ])
|
||||
sumi+=1
|
||||
elif vv.tag!=link+'Point' and sumi>=1:
|
||||
break
|
||||
co_sep.append(np.array(c_t_in))
|
||||
|
||||
|
||||
|
||||
elif tag.endswith('}TableRegion') or tag.endswith('}tableregion'):
|
||||
for nn in root1.iter(tag):
|
||||
c_t_in=[]
|
||||
sumi=0
|
||||
for vv in nn.iter():
|
||||
# check the format of coords
|
||||
if vv.tag==link+'Coords':
|
||||
coords=bool(vv.attrib)
|
||||
if coords:
|
||||
p_h=vv.attrib['points'].split(' ')
|
||||
c_t_in.append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) )
|
||||
break
|
||||
else:
|
||||
pass
|
||||
|
||||
|
||||
if vv.tag==link+'Point':
|
||||
c_t_in.append([ int(float(vv.attrib['x'])) , int(float(vv.attrib['y'])) ])
|
||||
sumi+=1
|
||||
|
||||
elif vv.tag!=link+'Point' and sumi>=1:
|
||||
break
|
||||
co_table.append(np.array(c_t_in))
|
||||
co_table_text.append(' ')
|
||||
|
||||
elif tag.endswith('}NoiseRegion') or tag.endswith('}noiseregion'):
|
||||
for nn in root1.iter(tag):
|
||||
c_t_in=[]
|
||||
sumi=0
|
||||
for vv in nn.iter():
|
||||
# check the format of coords
|
||||
if vv.tag==link+'Coords':
|
||||
coords=bool(vv.attrib)
|
||||
if coords:
|
||||
p_h=vv.attrib['points'].split(' ')
|
||||
c_t_in.append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) )
|
||||
break
|
||||
else:
|
||||
pass
|
||||
|
||||
|
||||
if vv.tag==link+'Point':
|
||||
c_t_in.append([ int(float(vv.attrib['x'])) , int(float(vv.attrib['y'])) ])
|
||||
sumi+=1
|
||||
|
||||
elif vv.tag!=link+'Point' and sumi>=1:
|
||||
break
|
||||
co_noise.append(np.array(c_t_in))
|
||||
co_noise_text.append(' ')
|
||||
|
||||
img = np.zeros( (y_len,x_len,3) )
|
||||
img_poly=cv2.fillPoly(img, pts =co_text_paragraph, color=(1,1,1))
|
||||
|
||||
img_poly=cv2.fillPoly(img, pts =co_text_heading, color=(2,2,2))
|
||||
img_poly=cv2.fillPoly(img, pts =co_text_header, color=(2,2,2))
|
||||
img_poly=cv2.fillPoly(img, pts =co_text_marginalia, color=(3,3,3))
|
||||
img_poly=cv2.fillPoly(img, pts =co_img, color=(4,4,4))
|
||||
img_poly=cv2.fillPoly(img, pts =co_sep, color=(5,5,5))
|
||||
|
||||
return tree1, root1, bb_coord_printspace, id_paragraph, id_header+id_heading, co_text_paragraph, co_text_header+co_text_heading,\
|
||||
tot_region_ref,x_len, y_len,index_tot_regions, img_poly
|
||||
|
||||
def return_indexes_of_contours_loctaed_inside_another_list_of_contours(self, contours, contours_loc, cx_main_loc, cy_main_loc, indexes_loc):
|
||||
indexes_of_located_cont = []
|
||||
center_x_coordinates_of_located = []
|
||||
center_y_coordinates_of_located = []
|
||||
#M_main_tot = [cv2.moments(contours_loc[j])
|
||||
#for j in range(len(contours_loc))]
|
||||
#cx_main_loc = [(M_main_tot[j]["m10"] / (M_main_tot[j]["m00"] + 1e-32)) for j in range(len(M_main_tot))]
|
||||
#cy_main_loc = [(M_main_tot[j]["m01"] / (M_main_tot[j]["m00"] + 1e-32)) for j in range(len(M_main_tot))]
|
||||
|
||||
for ij in range(len(contours)):
|
||||
results = [cv2.pointPolygonTest(contours[ij], (cx_main_loc[ind], cy_main_loc[ind]), False)
|
||||
for ind in range(len(cy_main_loc)) ]
|
||||
results = np.array(results)
|
||||
indexes_in = np.where((results == 0) | (results == 1))
|
||||
indexes = indexes_loc[indexes_in]# [(results == 0) | (results == 1)]#np.where((results == 0) | (results == 1))
|
||||
|
||||
indexes_of_located_cont.append(indexes)
|
||||
center_x_coordinates_of_located.append(np.array(cx_main_loc)[indexes_in] )
|
||||
center_y_coordinates_of_located.append(np.array(cy_main_loc)[indexes_in] )
|
||||
|
||||
return indexes_of_located_cont, center_x_coordinates_of_located, center_y_coordinates_of_located
|
||||
|
||||
def do_order_of_regions_with_model(self, contours_only_text_parent, contours_only_text_parent_h, text_regions_p):
|
||||
height1 =672#448
|
||||
width1 = 448#224
|
||||
|
||||
height2 =672#448
|
||||
width2= 448#224
|
||||
|
||||
height3 =672#448
|
||||
width3 = 448#224
|
||||
|
||||
inference_bs = 3
|
||||
|
||||
ver_kernel = np.ones((5, 1), dtype=np.uint8)
|
||||
hor_kernel = np.ones((1, 5), dtype=np.uint8)
|
||||
|
||||
|
||||
min_cont_size_to_be_dilated = 10
|
||||
if len(contours_only_text_parent)>min_cont_size_to_be_dilated and self.light_version:
|
||||
cx_conts, cy_conts, x_min_conts, x_max_conts, y_min_conts, y_max_conts, _ = find_new_features_of_contours(contours_only_text_parent)
|
||||
args_cont_located = np.array(range(len(contours_only_text_parent)))
|
||||
|
||||
diff_y_conts = np.abs(y_max_conts[:]-y_min_conts)
|
||||
diff_x_conts = np.abs(x_max_conts[:]-x_min_conts)
|
||||
|
||||
mean_x = statistics.mean(diff_x_conts)
|
||||
median_x = statistics.median(diff_x_conts)
|
||||
|
||||
|
||||
diff_x_ratio= diff_x_conts/mean_x
|
||||
|
||||
args_cont_located_excluded = args_cont_located[diff_x_ratio>=1.3]
|
||||
args_cont_located_included = args_cont_located[diff_x_ratio<1.3]
|
||||
|
||||
contours_only_text_parent_excluded = [contours_only_text_parent[ind] for ind in range(len(contours_only_text_parent)) if diff_x_ratio[ind]>=1.3]#contours_only_text_parent[diff_x_ratio>=1.3]
|
||||
contours_only_text_parent_included = [contours_only_text_parent[ind] for ind in range(len(contours_only_text_parent)) if diff_x_ratio[ind]<1.3]#contours_only_text_parent[diff_x_ratio<1.3]
|
||||
|
||||
|
||||
cx_conts_excluded = [cx_conts[ind] for ind in range(len(cx_conts)) if diff_x_ratio[ind]>=1.3]#cx_conts[diff_x_ratio>=1.3]
|
||||
cx_conts_included = [cx_conts[ind] for ind in range(len(cx_conts)) if diff_x_ratio[ind]<1.3]#cx_conts[diff_x_ratio<1.3]
|
||||
|
||||
cy_conts_excluded = [cy_conts[ind] for ind in range(len(cy_conts)) if diff_x_ratio[ind]>=1.3]#cy_conts[diff_x_ratio>=1.3]
|
||||
cy_conts_included = [cy_conts[ind] for ind in range(len(cy_conts)) if diff_x_ratio[ind]<1.3]#cy_conts[diff_x_ratio<1.3]
|
||||
|
||||
#print(diff_x_ratio, 'ratio')
|
||||
text_regions_p = text_regions_p.astype('uint8')
|
||||
|
||||
if len(contours_only_text_parent_excluded)>0:
|
||||
textregion_par = np.zeros((text_regions_p.shape[0], text_regions_p.shape[1])).astype('uint8')
|
||||
textregion_par = cv2.fillPoly(textregion_par, pts=contours_only_text_parent_included, color=(1,1))
|
||||
else:
|
||||
textregion_par = (text_regions_p[:,:]==1)*1
|
||||
textregion_par = textregion_par.astype('uint8')
|
||||
|
||||
text_regions_p_textregions_dilated = cv2.erode(textregion_par , hor_kernel, iterations=2)
|
||||
text_regions_p_textregions_dilated = cv2.dilate(text_regions_p_textregions_dilated , ver_kernel, iterations=4)
|
||||
text_regions_p_textregions_dilated = cv2.erode(text_regions_p_textregions_dilated , hor_kernel, iterations=1)
|
||||
text_regions_p_textregions_dilated = cv2.dilate(text_regions_p_textregions_dilated , ver_kernel, iterations=5)
|
||||
text_regions_p_textregions_dilated[text_regions_p[:,:]>1] = 0
|
||||
|
||||
|
||||
contours_only_dilated, hir_on_text_dilated = return_contours_of_image(text_regions_p_textregions_dilated)
|
||||
contours_only_dilated = return_parent_contours(contours_only_dilated, hir_on_text_dilated)
|
||||
|
||||
indexes_of_located_cont, center_x_coordinates_of_located, center_y_coordinates_of_located = self.return_indexes_of_contours_loctaed_inside_another_list_of_contours(contours_only_dilated, contours_only_text_parent_included, cx_conts_included, cy_conts_included, args_cont_located_included)
|
||||
|
||||
|
||||
if len(args_cont_located_excluded)>0:
|
||||
for ind in args_cont_located_excluded:
|
||||
indexes_of_located_cont.append(np.array([ind]))
|
||||
contours_only_dilated.append(contours_only_text_parent[ind])
|
||||
center_y_coordinates_of_located.append(0)
|
||||
|
||||
array_list = [np.array([elem]) if isinstance(elem, int) else elem for elem in indexes_of_located_cont]
|
||||
flattened_array = np.concatenate([arr.ravel() for arr in array_list])
|
||||
#print(len( np.unique(flattened_array)), 'indexes_of_located_cont uniques')
|
||||
|
||||
missing_textregions = list( set(np.array(range(len(contours_only_text_parent))) ) - set(np.unique(flattened_array)) )
|
||||
#print(missing_textregions, 'missing_textregions')
|
||||
|
||||
for ind in missing_textregions:
|
||||
indexes_of_located_cont.append(np.array([ind]))
|
||||
contours_only_dilated.append(contours_only_text_parent[ind])
|
||||
center_y_coordinates_of_located.append(0)
|
||||
|
||||
|
||||
if contours_only_text_parent_h:
|
||||
for vi in range(len(contours_only_text_parent_h)):
|
||||
indexes_of_located_cont.append(int(vi+len(contours_only_text_parent)))
|
||||
|
||||
array_list = [np.array([elem]) if isinstance(elem, int) else elem for elem in indexes_of_located_cont]
|
||||
flattened_array = np.concatenate([arr.ravel() for arr in array_list])
|
||||
|
||||
y_len = text_regions_p.shape[0]
|
||||
x_len = text_regions_p.shape[1]
|
||||
|
||||
img_poly = np.zeros((y_len,x_len), dtype='uint8')
|
||||
###img_poly[text_regions_p[:,:]==1] = 1
|
||||
###img_poly[text_regions_p[:,:]==2] = 2
|
||||
###img_poly[text_regions_p[:,:]==3] = 4
|
||||
###img_poly[text_regions_p[:,:]==6] = 5
|
||||
|
||||
##img_poly[text_regions_p[:,:]==1] = 1
|
||||
##img_poly[text_regions_p[:,:]==2] = 2
|
||||
##img_poly[text_regions_p[:,:]==3] = 3
|
||||
##img_poly[text_regions_p[:,:]==4] = 4
|
||||
##img_poly[text_regions_p[:,:]==5] = 5
|
||||
|
||||
img_poly = np.copy(text_regions_p)
|
||||
|
||||
img_header_and_sep = np.zeros((y_len,x_len), dtype='uint8')
|
||||
if contours_only_text_parent_h:
|
||||
_, cy_main, x_min_main, x_max_main, y_min_main, y_max_main, _ = find_new_features_of_contours(
|
||||
contours_only_text_parent_h)
|
||||
for j in range(len(cy_main)):
|
||||
img_header_and_sep[int(y_max_main[j]):int(y_max_main[j])+12,
|
||||
int(x_min_main[j]):int(x_max_main[j])] = 1
|
||||
co_text_all_org = contours_only_text_parent + contours_only_text_parent_h
|
||||
if len(contours_only_text_parent)>min_cont_size_to_be_dilated and self.light_version:
|
||||
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:
|
||||
co_text_all = contours_only_dilated
|
||||
else:
|
||||
co_text_all = contours_only_text_parent
|
||||
|
||||
if not len(co_text_all):
|
||||
return [], []
|
||||
|
||||
labels_con = np.zeros((int(y_len /6.), int(x_len/6.), len(co_text_all)), dtype=bool)
|
||||
|
||||
co_text_all = [(i/6).astype(int) for i in co_text_all]
|
||||
for i in range(len(co_text_all)):
|
||||
img = labels_con[:,:,i].astype(np.uint8)
|
||||
|
||||
#img = cv2.resize(img, (int(img.shape[1]/6), int(img.shape[0]/6)), interpolation=cv2.INTER_NEAREST)
|
||||
|
||||
cv2.fillPoly(img, pts=[co_text_all[i]], color=(1,))
|
||||
labels_con[:,:,i] = img
|
||||
|
||||
|
||||
labels_con = resize_image(labels_con.astype(np.uint8), height1, width1).astype(bool)
|
||||
img_header_and_sep = resize_image(img_header_and_sep, height1, width1)
|
||||
img_poly = resize_image(img_poly, height3, width3)
|
||||
|
||||
|
||||
|
||||
input_1 = np.zeros((inference_bs, height1, width1, 3))
|
||||
ordered = [list(range(len(co_text_all)))]
|
||||
index_update = 0
|
||||
#print(labels_con.shape[2],"number of regions for reading order")
|
||||
while index_update>=0:
|
||||
ij_list = ordered.pop(index_update)
|
||||
i = ij_list.pop(0)
|
||||
|
||||
ante_list = []
|
||||
post_list = []
|
||||
tot_counter = 0
|
||||
batch = []
|
||||
for j in ij_list:
|
||||
img1 = labels_con[:,:,i].astype(float)
|
||||
img2 = labels_con[:,:,j].astype(float)
|
||||
img1[img_poly==5] = 2
|
||||
img2[img_poly==5] = 2
|
||||
img1[img_header_and_sep==1] = 3
|
||||
img2[img_header_and_sep==1] = 3
|
||||
|
||||
input_1[len(batch), :, :, 0] = img1 / 3.
|
||||
input_1[len(batch), :, :, 2] = img2 / 3.
|
||||
input_1[len(batch), :, :, 1] = img_poly / 5.
|
||||
|
||||
tot_counter += 1
|
||||
batch.append(j)
|
||||
if tot_counter % inference_bs == 0 or tot_counter == len(ij_list):
|
||||
y_pr = self.model_reading_order.predict(input_1 , verbose=0)
|
||||
for jb, j in enumerate(batch):
|
||||
if y_pr[jb][0]>=0.5:
|
||||
post_list.append(j)
|
||||
else:
|
||||
ante_list.append(j)
|
||||
batch = []
|
||||
|
||||
if len(ante_list):
|
||||
ordered.insert(index_update, ante_list)
|
||||
index_update += 1
|
||||
ordered.insert(index_update, [i])
|
||||
if len(post_list):
|
||||
ordered.insert(index_update + 1, post_list)
|
||||
|
||||
index_update = -1
|
||||
for index_next, ij_list in enumerate(ordered):
|
||||
if len(ij_list) > 1:
|
||||
index_update = index_next
|
||||
break
|
||||
|
||||
ordered = [i[0] for i in ordered]
|
||||
|
||||
##id_all_text = np.array(id_all_text)[index_sort]
|
||||
|
||||
|
||||
if len(contours_only_text_parent)>min_cont_size_to_be_dilated and self.light_version:
|
||||
org_contours_indexes = []
|
||||
for ind in range(len(ordered)):
|
||||
region_with_curr_order = ordered[ind]
|
||||
if region_with_curr_order < len(contours_only_dilated):
|
||||
if np.isscalar(indexes_of_located_cont[region_with_curr_order]):
|
||||
org_contours_indexes = org_contours_indexes + [indexes_of_located_cont[region_with_curr_order]]
|
||||
else:
|
||||
arg_sort_located_cont = np.argsort(center_y_coordinates_of_located[region_with_curr_order])
|
||||
org_contours_indexes = org_contours_indexes + list(np.array(indexes_of_located_cont[region_with_curr_order])[arg_sort_located_cont]) ##org_contours_indexes + list (
|
||||
else:
|
||||
org_contours_indexes = org_contours_indexes + [indexes_of_located_cont[region_with_curr_order]]
|
||||
|
||||
region_ids = ['region_%04d' % i for i in range(len(co_text_all_org))]
|
||||
return org_contours_indexes, region_ids
|
||||
else:
|
||||
region_ids = ['region_%04d' % i for i in range(len(co_text_all_org))]
|
||||
return ordered, region_ids
|
||||
|
||||
|
||||
|
||||
|
||||
def run(self,
|
||||
overwrite: bool = False,
|
||||
xml_filename: Optional[str] = None,
|
||||
dir_in: Optional[str] = None,
|
||||
dir_out: Optional[str] = None,
|
||||
):
|
||||
"""
|
||||
Get image and scales, then extract the page of scanned image
|
||||
"""
|
||||
self.logger.debug("enter run")
|
||||
t0_tot = time.time()
|
||||
|
||||
if dir_in:
|
||||
ls_xmls = [os.path.join(dir_in, xml_filename)
|
||||
for xml_filename in filter(is_xml_filename,
|
||||
os.listdir(dir_in))]
|
||||
elif xml_filename:
|
||||
ls_xmls = [xml_filename]
|
||||
else:
|
||||
raise ValueError("run requires either a single image filename or a directory")
|
||||
|
||||
for xml_filename in ls_xmls:
|
||||
self.logger.info(xml_filename)
|
||||
t0 = time.time()
|
||||
|
||||
file_name = Path(xml_filename).stem
|
||||
(tree_xml, root_xml, bb_coord_printspace, id_paragraph, id_header,
|
||||
co_text_paragraph, co_text_header, tot_region_ref,
|
||||
x_len, y_len, index_tot_regions, img_poly) = self.read_xml(xml_filename)
|
||||
|
||||
id_all_text = id_paragraph + id_header
|
||||
|
||||
order_text_new, id_of_texts_tot = self.do_order_of_regions_with_model(co_text_paragraph, co_text_header, img_poly[:,:,0])
|
||||
|
||||
id_all_text = np.array(id_all_text)[order_text_new]
|
||||
|
||||
alltags=[elem.tag for elem in root_xml.iter()]
|
||||
|
||||
|
||||
|
||||
link=alltags[0].split('}')[0]+'}'
|
||||
name_space = alltags[0].split('}')[0]
|
||||
name_space = name_space.split('{')[1]
|
||||
|
||||
page_element = root_xml.find(link+'Page')
|
||||
|
||||
|
||||
old_ro = root_xml.find(".//{*}ReadingOrder")
|
||||
|
||||
if old_ro is not None:
|
||||
page_element.remove(old_ro)
|
||||
|
||||
#print(old_ro, 'old_ro')
|
||||
ro_subelement = ET.Element('ReadingOrder')
|
||||
|
||||
ro_subelement2 = ET.SubElement(ro_subelement, 'OrderedGroup')
|
||||
ro_subelement2.set('id', "ro357564684568544579089")
|
||||
|
||||
for index, id_text in enumerate(id_all_text):
|
||||
new_element_2 = ET.SubElement(ro_subelement2, 'RegionRefIndexed')
|
||||
new_element_2.set('regionRef', id_all_text[index])
|
||||
new_element_2.set('index', str(index))
|
||||
|
||||
if (link+'PrintSpace' in alltags) or (link+'Border' in alltags):
|
||||
page_element.insert(1, ro_subelement)
|
||||
else:
|
||||
page_element.insert(0, ro_subelement)
|
||||
|
||||
alltags=[elem.tag for elem in root_xml.iter()]
|
||||
|
||||
ET.register_namespace("",name_space)
|
||||
tree_xml.write(os.path.join(dir_out, file_name+'.xml'),
|
||||
xml_declaration=True,
|
||||
method='xml',
|
||||
encoding="utf8",
|
||||
default_namespace=None)
|
||||
|
||||
#sys.exit()
|
||||
|
||||
|
|
@ -82,13 +82,23 @@
|
|||
}
|
||||
},
|
||||
"resources": [
|
||||
{
|
||||
"url": "https://zenodo.org/records/17194824/files/models_layout_v0_5_0.tar.gz?download=1",
|
||||
"name": "models_layout_v0_5_0",
|
||||
"type": "archive",
|
||||
"path_in_archive": "models_layout_v0_5_0",
|
||||
"size": 3525684179,
|
||||
"description": "Models for layout detection, reading order detection, textline detection, page extraction, column classification, table detection, binarization, image enhancement",
|
||||
"version_range": ">= v0.5.0"
|
||||
},
|
||||
{
|
||||
"description": "models for eynollah (TensorFlow SavedModel format)",
|
||||
"url": "https://github.com/qurator-spk/eynollah/releases/download/v0.3.1/models_eynollah.tar.gz",
|
||||
"name": "default",
|
||||
"size": 1894627041,
|
||||
"type": "archive",
|
||||
"path_in_archive": "models_eynollah"
|
||||
"path_in_archive": "models_eynollah",
|
||||
"version_range": ">= v0.3.0, < v0.5.0"
|
||||
}
|
||||
]
|
||||
},
|
||||
|
|
|
|||
|
|
@ -1,6 +1,7 @@
|
|||
from functools import cached_property
|
||||
from typing import Optional
|
||||
from ocrd_models import OcrdPage
|
||||
from ocrd import Processor, OcrdPageResult
|
||||
from ocrd import OcrdPageResultImage, Processor, OcrdPageResult
|
||||
|
||||
from .eynollah import Eynollah, EynollahXmlWriter
|
||||
|
||||
|
|
@ -9,8 +10,8 @@ class EynollahProcessor(Processor):
|
|||
# already employs GPU (without singleton process atm)
|
||||
max_workers = 1
|
||||
|
||||
@property
|
||||
def executable(self):
|
||||
@cached_property
|
||||
def executable(self) -> str:
|
||||
return 'ocrd-eynollah-segment'
|
||||
|
||||
def setup(self) -> None:
|
||||
|
|
@ -20,7 +21,6 @@ class EynollahProcessor(Processor):
|
|||
"and parameter 'light_version' (faster+simpler method for main region detection and deskewing)")
|
||||
self.eynollah = Eynollah(
|
||||
self.resolve_resource(self.parameter['models']),
|
||||
logger=self.logger,
|
||||
allow_enhancement=self.parameter['allow_enhancement'],
|
||||
curved_line=self.parameter['curved_line'],
|
||||
right2left=self.parameter['right_to_left'],
|
||||
|
|
@ -33,6 +33,7 @@ class EynollahProcessor(Processor):
|
|||
headers_off=self.parameter['headers_off'],
|
||||
tables=self.parameter['tables'],
|
||||
)
|
||||
self.eynollah.logger = self.logger
|
||||
self.eynollah.plotter = None
|
||||
|
||||
def shutdown(self):
|
||||
|
|
|
|||
|
|
@ -16,6 +16,7 @@ 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
|
||||
|
||||
def resize_image(img_in, input_height, input_width):
|
||||
return cv2.resize(img_in, (input_width, input_height), interpolation=cv2.INTER_NEAREST)
|
||||
|
|
@ -314,8 +315,8 @@ class SbbBinarizer:
|
|||
prediction_true = prediction_true.astype(np.uint8)
|
||||
return prediction_true[:,:,0]
|
||||
|
||||
def run(self, image=None, image_path=None, save=None, use_patches=False, dir_in=None, dir_out=None):
|
||||
print(dir_in,'dir_in')
|
||||
def run(self, image=None, image_path=None, output=None, use_patches=False, dir_in=None):
|
||||
# print(dir_in,'dir_in')
|
||||
if not dir_in:
|
||||
if (image is not None and image_path is not None) or \
|
||||
(image is None and image_path is None):
|
||||
|
|
@ -343,11 +344,11 @@ class SbbBinarizer:
|
|||
kernel = np.ones((5, 5), np.uint8)
|
||||
img_last[:, :][img_last[:, :] > 0] = 255
|
||||
img_last = (img_last[:, :] == 0) * 255
|
||||
if save:
|
||||
cv2.imwrite(save, img_last)
|
||||
if output:
|
||||
cv2.imwrite(output, img_last)
|
||||
return img_last
|
||||
else:
|
||||
ls_imgs = os.listdir(dir_in)
|
||||
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')
|
||||
|
|
@ -374,4 +375,4 @@ class SbbBinarizer:
|
|||
img_last[:, :][img_last[:, :] > 0] = 255
|
||||
img_last = (img_last[:, :] == 0) * 255
|
||||
|
||||
cv2.imwrite(os.path.join(dir_out,image_stem+'.png'), img_last)
|
||||
cv2.imwrite(os.path.join(output, image_stem + '.png'), img_last)
|
||||
|
|
|
|||
|
|
@ -992,7 +992,7 @@ def check_any_text_region_in_model_one_is_main_or_header_light(
|
|||
(regions_model_full[:,:,0]==2)).sum()
|
||||
pixels_main = all_pixels - pixels_header
|
||||
|
||||
if (pixels_header/float(pixels_main)>=0.3) and ( (length_con[ii]/float(height_con[ii]) )>=1.3 ):
|
||||
if ( (pixels_header/float(pixels_main)>=0.6) and ( (length_con[ii]/float(height_con[ii]) )>=1.3 ) and ( (length_con[ii]/float(height_con[ii]) )<=3 )) or ( (pixels_header/float(pixels_main)>=0.3) and ( (length_con[ii]/float(height_con[ii]) )>=3 ) ):
|
||||
regions_model_1[:,:][(regions_model_1[:,:]==1) & (img[:,:,0]==255) ]=2
|
||||
contours_only_text_parent_head.append(con)
|
||||
if contours_only_text_parent_d_ordered is not None:
|
||||
|
|
@ -1801,8 +1801,8 @@ def return_boxes_of_images_by_order_of_reading_new(
|
|||
#print(y_type_2_up,x_starting_up,x_ending_up,'didid')
|
||||
nodes_in = []
|
||||
for ij in range(len(x_starting_up)):
|
||||
nodes_in = nodes_in + list(range(x_starting_up[ij],
|
||||
x_ending_up[ij]))
|
||||
nodes_in = nodes_in + list(range(int(x_starting_up[ij]),
|
||||
int(x_ending_up[ij])))
|
||||
nodes_in = np.unique(nodes_in)
|
||||
#print(nodes_in,'nodes_in')
|
||||
|
||||
|
|
@ -1825,8 +1825,8 @@ def return_boxes_of_images_by_order_of_reading_new(
|
|||
elif len(y_diff_main_separator_up)==0:
|
||||
nodes_in = []
|
||||
for ij in range(len(x_starting_up)):
|
||||
nodes_in = nodes_in + list(range(x_starting_up[ij],
|
||||
x_ending_up[ij]))
|
||||
nodes_in = nodes_in + list(range(int(x_starting_up[ij]),
|
||||
int(x_ending_up[ij])))
|
||||
nodes_in = np.unique(nodes_in)
|
||||
#print(nodes_in,'nodes_in2')
|
||||
#print(np.array(range(len(peaks_neg_tot)-1)),'np.array(range(len(peaks_neg_tot)-1))')
|
||||
|
|
@ -1866,8 +1866,8 @@ def return_boxes_of_images_by_order_of_reading_new(
|
|||
columns_covered_by_mothers = []
|
||||
for dj in range(len(x_start_without_mother)):
|
||||
columns_covered_by_mothers = columns_covered_by_mothers + \
|
||||
list(range(x_start_without_mother[dj],
|
||||
x_end_without_mother[dj]))
|
||||
list(range(int(x_start_without_mother[dj]),
|
||||
int(x_end_without_mother[dj])))
|
||||
columns_covered_by_mothers = list(set(columns_covered_by_mothers))
|
||||
|
||||
all_columns=np.arange(len(peaks_neg_tot)-1)
|
||||
|
|
@ -1909,8 +1909,8 @@ def return_boxes_of_images_by_order_of_reading_new(
|
|||
columns_covered_by_mothers = []
|
||||
for dj in range(len(x_start_without_mother)):
|
||||
columns_covered_by_mothers = columns_covered_by_mothers + \
|
||||
list(range(x_start_without_mother[dj],
|
||||
x_end_without_mother[dj]))
|
||||
list(range(int(x_start_without_mother[dj]),
|
||||
int(x_end_without_mother[dj])))
|
||||
columns_covered_by_mothers = list(set(columns_covered_by_mothers))
|
||||
|
||||
all_columns=np.arange(len(peaks_neg_tot)-1)
|
||||
|
|
@ -1926,8 +1926,8 @@ def return_boxes_of_images_by_order_of_reading_new(
|
|||
columns_covered_by_with_child_no_mothers = []
|
||||
for dj in range(len(x_end_with_child_without_mother)):
|
||||
columns_covered_by_with_child_no_mothers = columns_covered_by_with_child_no_mothers + \
|
||||
list(range(x_start_with_child_without_mother[dj],
|
||||
x_end_with_child_without_mother[dj]))
|
||||
list(range(int(x_start_with_child_without_mother[dj]),
|
||||
int(x_end_with_child_without_mother[dj])))
|
||||
columns_covered_by_with_child_no_mothers = list(set(columns_covered_by_with_child_no_mothers))
|
||||
|
||||
all_columns = np.arange(len(peaks_neg_tot)-1)
|
||||
|
|
@ -1970,8 +1970,8 @@ def return_boxes_of_images_by_order_of_reading_new(
|
|||
columns_covered_by_mothers = []
|
||||
for dj in range(len(x_starting_all_between_nm_wc)):
|
||||
columns_covered_by_mothers = columns_covered_by_mothers + \
|
||||
list(range(x_starting_all_between_nm_wc[dj],
|
||||
x_ending_all_between_nm_wc[dj]))
|
||||
list(range(int(x_starting_all_between_nm_wc[dj]),
|
||||
int(x_ending_all_between_nm_wc[dj])))
|
||||
columns_covered_by_mothers = list(set(columns_covered_by_mothers))
|
||||
|
||||
all_columns=np.arange(i_s_nc, x_end_biggest_column)
|
||||
|
|
@ -1979,8 +1979,8 @@ def return_boxes_of_images_by_order_of_reading_new(
|
|||
|
||||
should_longest_line_be_extended=0
|
||||
if (len(x_diff_all_between_nm_wc) > 0 and
|
||||
set(list(range(x_starting_all_between_nm_wc[biggest],
|
||||
x_ending_all_between_nm_wc[biggest])) +
|
||||
set(list(range(int(x_starting_all_between_nm_wc[biggest]),
|
||||
int(x_ending_all_between_nm_wc[biggest]))) +
|
||||
list(columns_not_covered)) != set(all_columns)):
|
||||
should_longest_line_be_extended=1
|
||||
index_lines_so_close_to_top_separator = \
|
||||
|
|
@ -2012,7 +2012,7 @@ def return_boxes_of_images_by_order_of_reading_new(
|
|||
x_ending_all_between_nm_wc = np.append(x_ending_all_between_nm_wc, np.array(columns_not_covered) + 1)
|
||||
|
||||
ind_args_between=np.arange(len(x_ending_all_between_nm_wc))
|
||||
for column in range(i_s_nc, x_end_biggest_column):
|
||||
for column in range(int(i_s_nc), int(x_end_biggest_column)):
|
||||
ind_args_in_col=ind_args_between[x_starting_all_between_nm_wc==column]
|
||||
#print('babali2')
|
||||
#print(ind_args_in_col,'ind_args_in_col')
|
||||
|
|
@ -2064,7 +2064,7 @@ def return_boxes_of_images_by_order_of_reading_new(
|
|||
x_end_itself=x_end_copy.pop(il)
|
||||
|
||||
#print(y_copy,'y_copy2')
|
||||
for column in range(x_start_itself, x_end_itself+1):
|
||||
for column in range(int(x_start_itself), int(x_end_itself)+1):
|
||||
#print(column,'cols')
|
||||
y_in_cols=[]
|
||||
for yic in range(len(y_copy)):
|
||||
|
|
@ -2095,11 +2095,11 @@ def return_boxes_of_images_by_order_of_reading_new(
|
|||
all_columns = np.arange(len(peaks_neg_tot)-1)
|
||||
columns_covered_by_lines_covered_more_than_2col = []
|
||||
for dj in range(len(x_starting)):
|
||||
if set(list(range(x_starting[dj],x_ending[dj]))) == set(all_columns):
|
||||
if set(list(range(int(x_starting[dj]),int(x_ending[dj]) ))) == set(all_columns):
|
||||
pass
|
||||
else:
|
||||
columns_covered_by_lines_covered_more_than_2col = columns_covered_by_lines_covered_more_than_2col + \
|
||||
list(range(x_starting[dj],x_ending[dj]))
|
||||
list(range(int(x_starting[dj]),int(x_ending[dj]) ))
|
||||
columns_covered_by_lines_covered_more_than_2col = list(set(columns_covered_by_lines_covered_more_than_2col))
|
||||
columns_not_covered = list(set(all_columns) - set(columns_covered_by_lines_covered_more_than_2col))
|
||||
|
||||
|
|
@ -2124,7 +2124,7 @@ def return_boxes_of_images_by_order_of_reading_new(
|
|||
x_ending = np.append(x_ending, np.array(columns_not_covered) + 1)
|
||||
|
||||
ind_args=np.array(range(len(y_type_2)))
|
||||
#ind_args=np.array(ind_args)
|
||||
|
||||
for column in range(len(peaks_neg_tot)-1):
|
||||
#print(column,'column')
|
||||
ind_args_in_col=ind_args[x_starting==column]
|
||||
|
|
@ -2155,8 +2155,7 @@ def return_boxes_of_images_by_order_of_reading_new(
|
|||
x_start_itself=x_start_copy.pop(il)
|
||||
x_end_itself=x_end_copy.pop(il)
|
||||
|
||||
#print(y_copy,'y_copy2')
|
||||
for column in range(x_start_itself, x_end_itself+1):
|
||||
for column in range(int(x_start_itself), int(x_end_itself)+1):
|
||||
#print(column,'cols')
|
||||
y_in_cols=[]
|
||||
for yic in range(len(y_copy)):
|
||||
|
|
@ -2195,3 +2194,14 @@ def return_boxes_of_images_by_order_of_reading_new(
|
|||
return boxes, peaks_neg_tot_tables_new
|
||||
else:
|
||||
return boxes, peaks_neg_tot_tables
|
||||
|
||||
def is_image_filename(fname: str) -> bool:
|
||||
return fname.lower().endswith(('.jpg',
|
||||
'.jpeg',
|
||||
'.png',
|
||||
'.tif',
|
||||
'.tiff',
|
||||
))
|
||||
|
||||
def is_xml_filename(fname: str) -> bool:
|
||||
return fname.lower().endswith('.xml')
|
||||
|
|
|
|||
|
|
@ -10,7 +10,6 @@ def get_marginals(text_with_lines, text_regions, num_col, slope_deskew, light_ve
|
|||
mask_marginals=np.zeros((text_with_lines.shape[0],text_with_lines.shape[1]))
|
||||
mask_marginals=mask_marginals.astype(np.uint8)
|
||||
|
||||
|
||||
text_with_lines=text_with_lines.astype(np.uint8)
|
||||
##text_with_lines=cv2.erode(text_with_lines,self.kernel,iterations=3)
|
||||
|
||||
|
|
@ -28,6 +27,10 @@ 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)
|
||||
|
||||
text_with_lines_y=text_with_lines.sum(axis=0)
|
||||
text_with_lines_y_eroded=text_with_lines_eroded.sum(axis=0)
|
||||
|
||||
|
|
@ -40,8 +43,10 @@ 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:
|
||||
min_textline_thickness=40
|
||||
|
||||
if light_version:
|
||||
min_textline_thickness=45
|
||||
else:
|
||||
min_textline_thickness=40
|
||||
|
||||
|
||||
if thickness_along_y_percent>=14:
|
||||
|
|
|
|||
|
|
@ -5,6 +5,8 @@ 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 (
|
||||
|
|
@ -1487,7 +1489,7 @@ def return_deskew_slop(img_patch_org, sigma_des,n_tot_angles=100,
|
|||
angles = np.linspace(angle - 22.5, angle + 22.5, n_tot_angles)
|
||||
angle = get_smallest_skew(img_resized, sigma_des, angles, map=map, logger=logger, plotter=plotter)
|
||||
elif main_page:
|
||||
angles = np.linspace(-12, 12, n_tot_angles)#np.array([0 , 45 , 90 , -45])
|
||||
angles = np.array (list(np.linspace(-12, -7, int(n_tot_angles/4))) + list(np.linspace(-6, 6, n_tot_angles- 2* int(n_tot_angles/4))) + list(np.linspace(7, 12, int(n_tot_angles/4))))#np.linspace(-12, 12, n_tot_angles)#np.array([0 , 45 , 90 , -45])
|
||||
angle = get_smallest_skew(img_resized, sigma_des, angles, map=map, logger=logger, plotter=plotter)
|
||||
|
||||
early_slope_edge=11
|
||||
|
|
@ -1526,6 +1528,107 @@ def get_smallest_skew(img, sigma_des, angles, logger=None, plotter=None, map=map
|
|||
angle = 0
|
||||
return angle
|
||||
|
||||
|
||||
def return_deskew_slop_old_mp(img_patch_org, sigma_des,n_tot_angles=100,
|
||||
main_page=False, logger=None, plotter=None):
|
||||
if main_page and plotter:
|
||||
plotter.save_plot_of_textline_density(img_patch_org)
|
||||
|
||||
img_int=np.zeros((img_patch_org.shape[0],img_patch_org.shape[1]))
|
||||
img_int[:,:]=img_patch_org[:,:]#img_patch_org[:,:,0]
|
||||
|
||||
max_shape=np.max(img_int.shape)
|
||||
img_resized=np.zeros((int( max_shape*(1.1) ) , int( max_shape*(1.1) ) ))
|
||||
|
||||
onset_x=int((img_resized.shape[1]-img_int.shape[1])/2.)
|
||||
onset_y=int((img_resized.shape[0]-img_int.shape[0])/2.)
|
||||
|
||||
img_resized[ onset_y:onset_y+img_int.shape[0] , onset_x:onset_x+img_int.shape[1] ]=img_int[:,:]
|
||||
|
||||
if main_page and img_patch_org.shape[1] > img_patch_org.shape[0]:
|
||||
angles = np.array([-45, 0, 45, 90,])
|
||||
angle = get_smallest_skew_omp(img_resized, sigma_des, angles, plotter=plotter)
|
||||
|
||||
angles = np.linspace(angle - 22.5, angle + 22.5, n_tot_angles)
|
||||
angle = get_smallest_skew_omp(img_resized, sigma_des, angles, plotter=plotter)
|
||||
elif main_page:
|
||||
angles = np.linspace(-12, 12, n_tot_angles)#np.array([0 , 45 , 90 , -45])
|
||||
angle = get_smallest_skew_omp(img_resized, sigma_des, angles, plotter=plotter)
|
||||
|
||||
early_slope_edge=11
|
||||
if abs(angle) > early_slope_edge:
|
||||
if angle < 0:
|
||||
angles = np.linspace(-90, -12, n_tot_angles)
|
||||
else:
|
||||
angles = np.linspace(90, 12, n_tot_angles)
|
||||
angle = get_smallest_skew_omp(img_resized, sigma_des, angles, plotter=plotter)
|
||||
else:
|
||||
angles = np.linspace(-25, 25, int(0.5 * n_tot_angles) + 10)
|
||||
angle = get_smallest_skew_omp(img_resized, sigma_des, angles, plotter=plotter)
|
||||
|
||||
early_slope_edge=22
|
||||
if abs(angle) > early_slope_edge:
|
||||
if angle < 0:
|
||||
angles = np.linspace(-90, -25, int(0.5 * n_tot_angles) + 10)
|
||||
else:
|
||||
angles = np.linspace(90, 25, int(0.5 * n_tot_angles) + 10)
|
||||
angle = get_smallest_skew_omp(img_resized, sigma_des, angles, plotter=plotter)
|
||||
|
||||
return angle
|
||||
|
||||
def do_image_rotation_omp(queue_of_all_params,angles_per_process, img_resized, sigma_des):
|
||||
vars_per_each_subprocess = []
|
||||
angles_per_each_subprocess = []
|
||||
for mv in range(len(angles_per_process)):
|
||||
img_rot=rotate_image(img_resized,angles_per_process[mv])
|
||||
img_rot[img_rot!=0]=1
|
||||
try:
|
||||
var_spectrum=find_num_col_deskew(img_rot,sigma_des,20.3 )
|
||||
except:
|
||||
var_spectrum=0
|
||||
vars_per_each_subprocess.append(var_spectrum)
|
||||
angles_per_each_subprocess.append(angles_per_process[mv])
|
||||
|
||||
queue_of_all_params.put([vars_per_each_subprocess, angles_per_each_subprocess])
|
||||
|
||||
def get_smallest_skew_omp(img_resized, sigma_des, angles, plotter=None):
|
||||
num_cores = cpu_count()
|
||||
|
||||
queue_of_all_params = Queue()
|
||||
processes = []
|
||||
nh = np.linspace(0, len(angles), num_cores + 1)
|
||||
|
||||
for i in range(num_cores):
|
||||
angles_per_process = angles[int(nh[i]) : int(nh[i + 1])]
|
||||
processes.append(Process(target=do_image_rotation_omp, args=(queue_of_all_params, angles_per_process, img_resized, sigma_des)))
|
||||
|
||||
for i in range(num_cores):
|
||||
processes[i].start()
|
||||
|
||||
var_res=[]
|
||||
all_angles = []
|
||||
for i in range(num_cores):
|
||||
list_all_par = queue_of_all_params.get(True)
|
||||
vars_for_subprocess = list_all_par[0]
|
||||
angles_sub_process = list_all_par[1]
|
||||
for j in range(len(vars_for_subprocess)):
|
||||
var_res.append(vars_for_subprocess[j])
|
||||
all_angles.append(angles_sub_process[j])
|
||||
|
||||
for i in range(num_cores):
|
||||
processes[i].join()
|
||||
|
||||
if plotter:
|
||||
plotter.save_plot_of_rotation_angle(all_angles, var_res)
|
||||
|
||||
|
||||
try:
|
||||
var_res=np.array(var_res)
|
||||
ang_int=all_angles[np.argmax(var_res)]#angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
|
||||
except:
|
||||
ang_int=0
|
||||
return ang_int
|
||||
|
||||
def do_work_of_slopes_new(
|
||||
box_text, contour, contour_par, index_r_con,
|
||||
textline_mask_tot_ea, image_page_rotated, slope_deskew,
|
||||
|
|
|
|||
488
src/eynollah/utils/utils_ocr.py
Normal file
488
src/eynollah/utils/utils_ocr.py
Normal file
|
|
@ -0,0 +1,488 @@
|
|||
import numpy as np
|
||||
import cv2
|
||||
import tensorflow as tf
|
||||
from scipy.signal import find_peaks
|
||||
from scipy.ndimage import gaussian_filter1d
|
||||
import math
|
||||
from PIL import Image, ImageDraw, ImageFont
|
||||
from Bio import pairwise2
|
||||
from .resize import resize_image
|
||||
|
||||
def decode_batch_predictions(pred, num_to_char, max_len = 128):
|
||||
# input_len is the product of the batch size and the
|
||||
# number of time steps.
|
||||
input_len = np.ones(pred.shape[0]) * pred.shape[1]
|
||||
|
||||
# Decode CTC predictions using greedy search.
|
||||
# decoded is a tuple with 2 elements.
|
||||
decoded = tf.keras.backend.ctc_decode(pred,
|
||||
input_length = input_len,
|
||||
beam_width = 100)
|
||||
# The outputs are in the first element of the tuple.
|
||||
# Additionally, the first element is actually a list,
|
||||
# therefore we take the first element of that list as well.
|
||||
#print(decoded,'decoded')
|
||||
decoded = decoded[0][0][:, :max_len]
|
||||
|
||||
#print(decoded, decoded.shape,'decoded')
|
||||
|
||||
output = []
|
||||
for d in decoded:
|
||||
# Convert the predicted indices to the corresponding chars.
|
||||
d = tf.strings.reduce_join(num_to_char(d))
|
||||
d = d.numpy().decode("utf-8")
|
||||
output.append(d)
|
||||
return output
|
||||
|
||||
|
||||
def distortion_free_resize(image, img_size):
|
||||
w, h = img_size
|
||||
image = tf.image.resize(image, size=(h, w), preserve_aspect_ratio=True)
|
||||
|
||||
# Check tha amount of padding needed to be done.
|
||||
pad_height = h - tf.shape(image)[0]
|
||||
pad_width = w - tf.shape(image)[1]
|
||||
|
||||
# Only necessary if you want to do same amount of padding on both sides.
|
||||
if pad_height % 2 != 0:
|
||||
height = pad_height // 2
|
||||
pad_height_top = height + 1
|
||||
pad_height_bottom = height
|
||||
else:
|
||||
pad_height_top = pad_height_bottom = pad_height // 2
|
||||
|
||||
if pad_width % 2 != 0:
|
||||
width = pad_width // 2
|
||||
pad_width_left = width + 1
|
||||
pad_width_right = width
|
||||
else:
|
||||
pad_width_left = pad_width_right = pad_width // 2
|
||||
|
||||
image = tf.pad(
|
||||
image,
|
||||
paddings=[
|
||||
[pad_height_top, pad_height_bottom],
|
||||
[pad_width_left, pad_width_right],
|
||||
[0, 0],
|
||||
],
|
||||
)
|
||||
|
||||
image = tf.transpose(image, (1, 0, 2))
|
||||
image = tf.image.flip_left_right(image)
|
||||
return image
|
||||
|
||||
def return_start_and_end_of_common_text_of_textline_ocr_without_common_section(textline_image):
|
||||
width = np.shape(textline_image)[1]
|
||||
height = np.shape(textline_image)[0]
|
||||
common_window = int(0.06*width)
|
||||
|
||||
width1 = int ( width/2. - common_window )
|
||||
width2 = int ( width/2. + common_window )
|
||||
|
||||
img_sum = np.sum(textline_image[:,:,0], axis=0)
|
||||
sum_smoothed = gaussian_filter1d(img_sum, 3)
|
||||
|
||||
peaks_real, _ = find_peaks(sum_smoothed, height=0)
|
||||
if len(peaks_real)>70:
|
||||
|
||||
peaks_real = peaks_real[(peaks_real<width2) & (peaks_real>width1)]
|
||||
|
||||
arg_max = np.argmax(sum_smoothed[peaks_real])
|
||||
peaks_final = peaks_real[arg_max]
|
||||
return peaks_final
|
||||
else:
|
||||
return None
|
||||
# Function to fit text inside the given area
|
||||
def fit_text_single_line(draw, text, font_path, max_width, max_height):
|
||||
initial_font_size = 50
|
||||
font_size = initial_font_size
|
||||
while font_size > 10: # Minimum font size
|
||||
font = ImageFont.truetype(font_path, font_size)
|
||||
text_bbox = draw.textbbox((0, 0), text, font=font) # Get text bounding box
|
||||
text_width = text_bbox[2] - text_bbox[0]
|
||||
text_height = text_bbox[3] - text_bbox[1]
|
||||
|
||||
if text_width <= max_width and text_height <= max_height:
|
||||
return font # Return the best-fitting font
|
||||
|
||||
font_size -= 2 # Reduce font size and retry
|
||||
|
||||
return ImageFont.truetype(font_path, 10) # Smallest font fallback
|
||||
|
||||
def return_textlines_split_if_needed(textline_image, textline_image_bin=None):
|
||||
|
||||
split_point = return_start_and_end_of_common_text_of_textline_ocr_without_common_section(textline_image)
|
||||
if split_point:
|
||||
image1 = textline_image[:, :split_point,:]# image.crop((0, 0, width2, height))
|
||||
image2 = textline_image[:, split_point:,:]#image.crop((width1, 0, width, height))
|
||||
if textline_image_bin is not None:
|
||||
image1_bin = textline_image_bin[:, :split_point,:]# image.crop((0, 0, width2, height))
|
||||
image2_bin = textline_image_bin[:, split_point:,:]#image.crop((width1, 0, width, height))
|
||||
return [image1, image2], [image1_bin, image2_bin]
|
||||
else:
|
||||
return [image1, image2], None
|
||||
else:
|
||||
return None, None
|
||||
def preprocess_and_resize_image_for_ocrcnn_model(img, image_height, image_width):
|
||||
if img.shape[0]==0 or img.shape[1]==0:
|
||||
img_fin = np.ones((image_height, image_width, 3))
|
||||
else:
|
||||
ratio = image_height /float(img.shape[0])
|
||||
w_ratio = int(ratio * img.shape[1])
|
||||
|
||||
if w_ratio <= image_width:
|
||||
width_new = w_ratio
|
||||
else:
|
||||
width_new = image_width
|
||||
|
||||
if width_new == 0:
|
||||
width_new = img.shape[1]
|
||||
|
||||
|
||||
img = resize_image(img, image_height, width_new)
|
||||
img_fin = np.ones((image_height, image_width, 3))*255
|
||||
|
||||
img_fin[:,:width_new,:] = img[:,:,:]
|
||||
img_fin = img_fin / 255.
|
||||
return img_fin
|
||||
|
||||
def get_deskewed_contour_and_bb_and_image(contour, image, deskew_angle):
|
||||
(h_in, w_in) = image.shape[:2]
|
||||
center = (w_in // 2, h_in // 2)
|
||||
|
||||
rotation_matrix = cv2.getRotationMatrix2D(center, deskew_angle, 1.0)
|
||||
|
||||
cos_angle = abs(rotation_matrix[0, 0])
|
||||
sin_angle = abs(rotation_matrix[0, 1])
|
||||
new_w = int((h_in * sin_angle) + (w_in * cos_angle))
|
||||
new_h = int((h_in * cos_angle) + (w_in * sin_angle))
|
||||
|
||||
rotation_matrix[0, 2] += (new_w / 2) - center[0]
|
||||
rotation_matrix[1, 2] += (new_h / 2) - center[1]
|
||||
|
||||
deskewed_image = cv2.warpAffine(image, rotation_matrix, (new_w, new_h))
|
||||
|
||||
contour_points = np.array(contour, dtype=np.float32)
|
||||
transformed_points = cv2.transform(np.array([contour_points]), rotation_matrix)[0]
|
||||
|
||||
x, y, w, h = cv2.boundingRect(np.array(transformed_points, dtype=np.int32))
|
||||
cropped_textline = deskewed_image[y:y+h, x:x+w]
|
||||
|
||||
return cropped_textline
|
||||
|
||||
def rotate_image_with_padding(image, angle, border_value=(0,0,0)):
|
||||
# Get image dimensions
|
||||
(h, w) = image.shape[:2]
|
||||
|
||||
# Calculate the center of the image
|
||||
center = (w // 2, h // 2)
|
||||
|
||||
# Get the rotation matrix
|
||||
rotation_matrix = cv2.getRotationMatrix2D(center, angle, 1.0)
|
||||
|
||||
# Compute the new bounding dimensions
|
||||
cos = abs(rotation_matrix[0, 0])
|
||||
sin = abs(rotation_matrix[0, 1])
|
||||
new_w = int((h * sin) + (w * cos))
|
||||
new_h = int((h * cos) + (w * sin))
|
||||
|
||||
# Adjust the rotation matrix to account for translation
|
||||
rotation_matrix[0, 2] += (new_w / 2) - center[0]
|
||||
rotation_matrix[1, 2] += (new_h / 2) - center[1]
|
||||
|
||||
# Perform the rotation
|
||||
try:
|
||||
rotated_image = cv2.warpAffine(image, rotation_matrix, (new_w, new_h), borderValue=border_value)
|
||||
except:
|
||||
rotated_image = np.copy(image)
|
||||
|
||||
return rotated_image
|
||||
|
||||
def get_orientation_moments(contour):
|
||||
moments = cv2.moments(contour)
|
||||
if moments["mu20"] - moments["mu02"] == 0: # Avoid division by zero
|
||||
return 90 if moments["mu11"] > 0 else -90
|
||||
else:
|
||||
angle = 0.5 * np.arctan2(2 * moments["mu11"], moments["mu20"] - moments["mu02"])
|
||||
return np.degrees(angle) # Convert radians to degrees
|
||||
|
||||
|
||||
def get_orientation_moments_of_mask(mask):
|
||||
mask=mask.astype('uint8')
|
||||
contours, _ = cv2.findContours(mask[:,:,0], cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
|
||||
|
||||
largest_contour = max(contours, key=cv2.contourArea) if contours else None
|
||||
|
||||
moments = cv2.moments(largest_contour)
|
||||
if moments["mu20"] - moments["mu02"] == 0: # Avoid division by zero
|
||||
return 90 if moments["mu11"] > 0 else -90
|
||||
else:
|
||||
angle = 0.5 * np.arctan2(2 * moments["mu11"], moments["mu20"] - moments["mu02"])
|
||||
return np.degrees(angle) # Convert radians to degrees
|
||||
|
||||
def get_contours_and_bounding_boxes(mask):
|
||||
# Find contours in the binary mask
|
||||
contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
|
||||
|
||||
largest_contour = max(contours, key=cv2.contourArea) if contours else None
|
||||
|
||||
# Get the bounding rectangle for the contour
|
||||
x, y, w, h = cv2.boundingRect(largest_contour)
|
||||
#bounding_boxes.append((x, y, w, h))
|
||||
|
||||
return x, y, w, h
|
||||
|
||||
def return_splitting_point_of_image(image_to_spliited):
|
||||
width = np.shape(image_to_spliited)[1]
|
||||
height = np.shape(image_to_spliited)[0]
|
||||
common_window = int(0.03*width)
|
||||
|
||||
width1 = int ( common_window)
|
||||
width2 = int ( width - common_window )
|
||||
|
||||
img_sum = np.sum(image_to_spliited[:,:,0], axis=0)
|
||||
sum_smoothed = gaussian_filter1d(img_sum, 1)
|
||||
|
||||
peaks_real, _ = find_peaks(sum_smoothed, height=0)
|
||||
peaks_real = peaks_real[(peaks_real<width2) & (peaks_real>width1)]
|
||||
|
||||
arg_sort = np.argsort(sum_smoothed[peaks_real])
|
||||
peaks_sort_4 = peaks_real[arg_sort][::-1][:3]
|
||||
|
||||
return np.sort(peaks_sort_4)
|
||||
|
||||
def break_curved_line_into_small_pieces_and_then_merge(img_curved, mask_curved, img_bin_curved=None):
|
||||
peaks_4 = return_splitting_point_of_image(img_curved)
|
||||
if len(peaks_4)>0:
|
||||
imgs_tot = []
|
||||
|
||||
for ind in range(len(peaks_4)+1):
|
||||
if ind==0:
|
||||
img = img_curved[:, :peaks_4[ind], :]
|
||||
if img_bin_curved is not None:
|
||||
img_bin = img_bin_curved[:, :peaks_4[ind], :]
|
||||
mask = mask_curved[:, :peaks_4[ind], :]
|
||||
elif ind==len(peaks_4):
|
||||
img = img_curved[:, peaks_4[ind-1]:, :]
|
||||
if img_bin_curved is not None:
|
||||
img_bin = img_bin_curved[:, peaks_4[ind-1]:, :]
|
||||
mask = mask_curved[:, peaks_4[ind-1]:, :]
|
||||
else:
|
||||
img = img_curved[:, peaks_4[ind-1]:peaks_4[ind], :]
|
||||
if img_bin_curved is not None:
|
||||
img_bin = img_bin_curved[:, peaks_4[ind-1]:peaks_4[ind], :]
|
||||
mask = mask_curved[:, peaks_4[ind-1]:peaks_4[ind], :]
|
||||
|
||||
or_ma = get_orientation_moments_of_mask(mask)
|
||||
|
||||
if img_bin_curved is not None:
|
||||
imgs_tot.append([img, mask, or_ma, img_bin] )
|
||||
else:
|
||||
imgs_tot.append([img, mask, or_ma] )
|
||||
|
||||
|
||||
w_tot_des_list = []
|
||||
w_tot_des = 0
|
||||
imgs_deskewed_list = []
|
||||
imgs_bin_deskewed_list = []
|
||||
|
||||
for ind in range(len(imgs_tot)):
|
||||
img_in = imgs_tot[ind][0]
|
||||
mask_in = imgs_tot[ind][1]
|
||||
ori_in = imgs_tot[ind][2]
|
||||
if img_bin_curved is not None:
|
||||
img_bin_in = imgs_tot[ind][3]
|
||||
|
||||
if abs(ori_in)<45:
|
||||
img_in_des = rotate_image_with_padding(img_in, ori_in, border_value=(255,255,255) )
|
||||
if img_bin_curved is not None:
|
||||
img_bin_in_des = rotate_image_with_padding(img_bin_in, ori_in, border_value=(255,255,255) )
|
||||
mask_in_des = rotate_image_with_padding(mask_in, ori_in)
|
||||
mask_in_des = mask_in_des.astype('uint8')
|
||||
|
||||
#new bounding box
|
||||
x_n, y_n, w_n, h_n = get_contours_and_bounding_boxes(mask_in_des[:,:,0])
|
||||
|
||||
if w_n==0 or h_n==0:
|
||||
img_in_des = np.copy(img_in)
|
||||
if img_bin_curved is not None:
|
||||
img_bin_in_des = np.copy(img_bin_in)
|
||||
w_relative = int(32 * img_in_des.shape[1]/float(img_in_des.shape[0]) )
|
||||
if w_relative==0:
|
||||
w_relative = img_in_des.shape[1]
|
||||
img_in_des = resize_image(img_in_des, 32, w_relative)
|
||||
if img_bin_curved is not None:
|
||||
img_bin_in_des = resize_image(img_bin_in_des, 32, w_relative)
|
||||
else:
|
||||
mask_in_des = mask_in_des[y_n:y_n+h_n, x_n:x_n+w_n, :]
|
||||
img_in_des = img_in_des[y_n:y_n+h_n, x_n:x_n+w_n, :]
|
||||
if img_bin_curved is not None:
|
||||
img_bin_in_des = img_bin_in_des[y_n:y_n+h_n, x_n:x_n+w_n, :]
|
||||
|
||||
w_relative = int(32 * img_in_des.shape[1]/float(img_in_des.shape[0]) )
|
||||
if w_relative==0:
|
||||
w_relative = img_in_des.shape[1]
|
||||
img_in_des = resize_image(img_in_des, 32, w_relative)
|
||||
if img_bin_curved is not None:
|
||||
img_bin_in_des = resize_image(img_bin_in_des, 32, w_relative)
|
||||
|
||||
|
||||
else:
|
||||
img_in_des = np.copy(img_in)
|
||||
if img_bin_curved is not None:
|
||||
img_bin_in_des = np.copy(img_bin_in)
|
||||
w_relative = int(32 * img_in_des.shape[1]/float(img_in_des.shape[0]) )
|
||||
if w_relative==0:
|
||||
w_relative = img_in_des.shape[1]
|
||||
img_in_des = resize_image(img_in_des, 32, w_relative)
|
||||
if img_bin_curved is not None:
|
||||
img_bin_in_des = resize_image(img_bin_in_des, 32, w_relative)
|
||||
|
||||
w_tot_des+=img_in_des.shape[1]
|
||||
w_tot_des_list.append(img_in_des.shape[1])
|
||||
imgs_deskewed_list.append(img_in_des)
|
||||
if img_bin_curved is not None:
|
||||
imgs_bin_deskewed_list.append(img_bin_in_des)
|
||||
|
||||
|
||||
|
||||
|
||||
img_final_deskewed = np.zeros((32, w_tot_des, 3))+255
|
||||
if img_bin_curved is not None:
|
||||
img_bin_final_deskewed = np.zeros((32, w_tot_des, 3))+255
|
||||
else:
|
||||
img_bin_final_deskewed = None
|
||||
|
||||
w_indexer = 0
|
||||
for ind in range(len(w_tot_des_list)):
|
||||
img_final_deskewed[:,w_indexer:w_indexer+w_tot_des_list[ind],:] = imgs_deskewed_list[ind][:,:,:]
|
||||
if img_bin_curved is not None:
|
||||
img_bin_final_deskewed[:,w_indexer:w_indexer+w_tot_des_list[ind],:] = imgs_bin_deskewed_list[ind][:,:,:]
|
||||
w_indexer = w_indexer+w_tot_des_list[ind]
|
||||
return img_final_deskewed, img_bin_final_deskewed
|
||||
else:
|
||||
return img_curved, img_bin_curved
|
||||
|
||||
def return_textline_contour_with_added_box_coordinate(textline_contour, box_ind):
|
||||
textline_contour[:,0] = textline_contour[:,0] + box_ind[2]
|
||||
textline_contour[:,1] = textline_contour[:,1] + box_ind[0]
|
||||
return textline_contour
|
||||
|
||||
|
||||
def return_rnn_cnn_ocr_of_given_textlines(image, all_found_textline_polygons, prediction_model, b_s_ocr, num_to_char, textline_light=False, curved_line=False):
|
||||
max_len = 512
|
||||
padding_token = 299
|
||||
image_width = 512#max_len * 4
|
||||
image_height = 32
|
||||
ind_tot = 0
|
||||
#cv2.imwrite('./img_out.png', image_page)
|
||||
ocr_all_textlines = []
|
||||
cropped_lines_region_indexer = []
|
||||
cropped_lines_meging_indexing = []
|
||||
cropped_lines = []
|
||||
indexer_text_region = 0
|
||||
|
||||
for indexing, ind_poly_first in enumerate(all_found_textline_polygons):
|
||||
#ocr_textline_in_textregion = []
|
||||
if len(ind_poly_first)==0:
|
||||
cropped_lines_region_indexer.append(indexer_text_region)
|
||||
cropped_lines_meging_indexing.append(0)
|
||||
img_fin = np.ones((image_height, image_width, 3))*1
|
||||
cropped_lines.append(img_fin)
|
||||
|
||||
else:
|
||||
for indexing2, ind_poly in enumerate(ind_poly_first):
|
||||
cropped_lines_region_indexer.append(indexer_text_region)
|
||||
if not (textline_light or curved_line):
|
||||
ind_poly = copy.deepcopy(ind_poly)
|
||||
box_ind = all_box_coord[indexing]
|
||||
|
||||
ind_poly = return_textline_contour_with_added_box_coordinate(ind_poly, box_ind)
|
||||
#print(ind_poly_copy)
|
||||
ind_poly[ind_poly<0] = 0
|
||||
x, y, w, h = cv2.boundingRect(ind_poly)
|
||||
|
||||
w_scaled = w * image_height/float(h)
|
||||
|
||||
mask_poly = np.zeros(image.shape)
|
||||
|
||||
img_poly_on_img = np.copy(image)
|
||||
|
||||
mask_poly = cv2.fillPoly(mask_poly, pts=[ind_poly], color=(1, 1, 1))
|
||||
|
||||
|
||||
|
||||
mask_poly = mask_poly[y:y+h, x:x+w, :]
|
||||
img_crop = img_poly_on_img[y:y+h, x:x+w, :]
|
||||
|
||||
img_crop[mask_poly==0] = 255
|
||||
|
||||
if w_scaled < 640:#1.5*image_width:
|
||||
img_fin = preprocess_and_resize_image_for_ocrcnn_model(img_crop, image_height, image_width)
|
||||
cropped_lines.append(img_fin)
|
||||
cropped_lines_meging_indexing.append(0)
|
||||
else:
|
||||
splited_images, splited_images_bin = return_textlines_split_if_needed(img_crop, None)
|
||||
|
||||
if splited_images:
|
||||
img_fin = preprocess_and_resize_image_for_ocrcnn_model(splited_images[0], image_height, image_width)
|
||||
cropped_lines.append(img_fin)
|
||||
cropped_lines_meging_indexing.append(1)
|
||||
|
||||
img_fin = preprocess_and_resize_image_for_ocrcnn_model(splited_images[1], image_height, image_width)
|
||||
|
||||
cropped_lines.append(img_fin)
|
||||
cropped_lines_meging_indexing.append(-1)
|
||||
|
||||
else:
|
||||
img_fin = preprocess_and_resize_image_for_ocrcnn_model(img_crop, image_height, image_width)
|
||||
cropped_lines.append(img_fin)
|
||||
cropped_lines_meging_indexing.append(0)
|
||||
|
||||
indexer_text_region+=1
|
||||
|
||||
extracted_texts = []
|
||||
|
||||
n_iterations = math.ceil(len(cropped_lines) / b_s_ocr)
|
||||
|
||||
for i in range(n_iterations):
|
||||
if i==(n_iterations-1):
|
||||
n_start = i*b_s_ocr
|
||||
imgs = cropped_lines[n_start:]
|
||||
imgs = np.array(imgs)
|
||||
imgs = imgs.reshape(imgs.shape[0], image_height, image_width, 3)
|
||||
|
||||
|
||||
else:
|
||||
n_start = i*b_s_ocr
|
||||
n_end = (i+1)*b_s_ocr
|
||||
imgs = cropped_lines[n_start:n_end]
|
||||
imgs = np.array(imgs).reshape(b_s_ocr, image_height, image_width, 3)
|
||||
|
||||
|
||||
preds = prediction_model.predict(imgs, verbose=0)
|
||||
|
||||
pred_texts = decode_batch_predictions(preds, num_to_char)
|
||||
|
||||
for ib in range(imgs.shape[0]):
|
||||
pred_texts_ib = pred_texts[ib].replace("[UNK]", "")
|
||||
extracted_texts.append(pred_texts_ib)
|
||||
|
||||
extracted_texts_merged = [extracted_texts[ind] if cropped_lines_meging_indexing[ind]==0 else extracted_texts[ind]+" "+extracted_texts[ind+1] if cropped_lines_meging_indexing[ind]==1 else None for ind in range(len(cropped_lines_meging_indexing))]
|
||||
|
||||
extracted_texts_merged = [ind for ind in extracted_texts_merged if ind is not None]
|
||||
unique_cropped_lines_region_indexer = np.unique(cropped_lines_region_indexer)
|
||||
|
||||
ocr_all_textlines = []
|
||||
for ind in unique_cropped_lines_region_indexer:
|
||||
ocr_textline_in_textregion = []
|
||||
extracted_texts_merged_un = np.array(extracted_texts_merged)[np.array(cropped_lines_region_indexer)==ind]
|
||||
for it_ind, text_textline in enumerate(extracted_texts_merged_un):
|
||||
ocr_textline_in_textregion.append(text_textline)
|
||||
ocr_all_textlines.append(ocr_textline_in_textregion)
|
||||
return ocr_all_textlines
|
||||
|
||||
def biopython_align(str1, str2):
|
||||
alignments = pairwise2.align.globalms(str1, str2, 2, -1, -2, -2)
|
||||
best_alignment = alignments[0] # Get the best alignment
|
||||
return best_alignment.seqA, best_alignment.seqB
|
||||
|
|
@ -46,16 +46,22 @@ def create_page_xml(imageFilename, height, width):
|
|||
))
|
||||
return pcgts
|
||||
|
||||
def xml_reading_order(page, order_of_texts, id_of_marginalia):
|
||||
def xml_reading_order(page, order_of_texts, id_of_marginalia_left, id_of_marginalia_right):
|
||||
region_order = ReadingOrderType()
|
||||
og = OrderedGroupType(id="ro357564684568544579089")
|
||||
page.set_ReadingOrder(region_order)
|
||||
region_order.set_OrderedGroup(og)
|
||||
region_counter = EynollahIdCounter()
|
||||
|
||||
for id_marginal in id_of_marginalia_left:
|
||||
og.add_RegionRefIndexed(RegionRefIndexedType(index=str(region_counter.get('region')), regionRef=id_marginal))
|
||||
region_counter.inc('region')
|
||||
|
||||
for idx_textregion, _ in enumerate(order_of_texts):
|
||||
og.add_RegionRefIndexed(RegionRefIndexedType(index=str(region_counter.get('region')), regionRef=region_counter.region_id(order_of_texts[idx_textregion] + 1)))
|
||||
region_counter.inc('region')
|
||||
for id_marginal in id_of_marginalia:
|
||||
|
||||
for id_marginal in id_of_marginalia_right:
|
||||
og.add_RegionRefIndexed(RegionRefIndexedType(index=str(region_counter.get('region')), regionRef=id_marginal))
|
||||
region_counter.inc('region')
|
||||
|
||||
|
|
|
|||
|
|
@ -56,10 +56,12 @@ class EynollahXmlWriter():
|
|||
points_page_print = points_page_print + ' '
|
||||
return points_page_print[:-1]
|
||||
|
||||
def serialize_lines_in_marginal(self, marginal_region, all_found_textline_polygons_marginals, marginal_idx, page_coord, all_box_coord_marginals, slopes_marginals, counter):
|
||||
def serialize_lines_in_marginal(self, marginal_region, all_found_textline_polygons_marginals, marginal_idx, page_coord, all_box_coord_marginals, slopes_marginals, counter, ocr_all_textlines_textregion):
|
||||
for j in range(len(all_found_textline_polygons_marginals[marginal_idx])):
|
||||
coords = CoordsType()
|
||||
textline = TextLineType(id=counter.next_line_id, Coords=coords)
|
||||
if ocr_all_textlines_textregion:
|
||||
textline.set_TextEquiv( [ TextEquivType(Unicode=ocr_all_textlines_textregion[j]) ] )
|
||||
marginal_region.add_TextLine(textline)
|
||||
marginal_region.set_orientation(-slopes_marginals[marginal_idx])
|
||||
points_co = ''
|
||||
|
|
@ -119,7 +121,7 @@ class EynollahXmlWriter():
|
|||
points_co += ','
|
||||
points_co += str(textline_y_coord)
|
||||
|
||||
if (self.curved_line or self.textline_light) and np.abs(slopes[region_idx]) <= 45:
|
||||
if self.textline_light or (self.curved_line and np.abs(slopes[region_idx]) <= 45):
|
||||
if len(contour_textline) == 2:
|
||||
points_co += str(int((contour_textline[0] + page_coord[2]) / self.scale_x))
|
||||
points_co += ','
|
||||
|
|
@ -128,7 +130,7 @@ class EynollahXmlWriter():
|
|||
points_co += str(int((contour_textline[0][0] + page_coord[2]) / self.scale_x))
|
||||
points_co += ','
|
||||
points_co += str(int((contour_textline[0][1] + page_coord[0])/self.scale_y))
|
||||
elif (self.curved_line or self.textline_light) and np.abs(slopes[region_idx]) > 45:
|
||||
elif self.curved_line and np.abs(slopes[region_idx]) > 45:
|
||||
if len(contour_textline)==2:
|
||||
points_co += str(int((contour_textline[0] + region_bboxes[2] + page_coord[2])/self.scale_x))
|
||||
points_co += ','
|
||||
|
|
@ -168,7 +170,7 @@ class EynollahXmlWriter():
|
|||
with open(self.output_filename, 'w') as f:
|
||||
f.write(to_xml(pcgts))
|
||||
|
||||
def build_pagexml_no_full_layout(self, found_polygons_text_region, page_coord, order_of_texts, id_of_texts, all_found_textline_polygons, all_box_coord, found_polygons_text_region_img, found_polygons_marginals, all_found_textline_polygons_marginals, all_box_coord_marginals, slopes, slopes_marginals, cont_page, polygons_lines_to_be_written_in_xml, found_polygons_tables, ocr_all_textlines, conf_contours_textregion):
|
||||
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_lines_to_be_written_in_xml, found_polygons_tables, ocr_all_textlines=None, ocr_all_textlines_marginals_left=None, ocr_all_textlines_marginals_right=None, conf_contours_textregion=None, skip_layout_reading_order=False):
|
||||
self.logger.debug('enter build_pagexml_no_full_layout')
|
||||
|
||||
# create the file structure
|
||||
|
|
@ -179,12 +181,13 @@ class EynollahXmlWriter():
|
|||
counter = EynollahIdCounter()
|
||||
if len(found_polygons_text_region) > 0:
|
||||
_counter_marginals = EynollahIdCounter(region_idx=len(order_of_texts))
|
||||
id_of_marginalia = [_counter_marginals.next_region_id for _ in found_polygons_marginals]
|
||||
xml_reading_order(page, order_of_texts, id_of_marginalia)
|
||||
id_of_marginalia_left = [_counter_marginals.next_region_id for _ in found_polygons_marginals_left]
|
||||
id_of_marginalia_right = [_counter_marginals.next_region_id for _ in found_polygons_marginals_right]
|
||||
xml_reading_order(page, order_of_texts, id_of_marginalia_left, id_of_marginalia_right)
|
||||
|
||||
for mm in range(len(found_polygons_text_region)):
|
||||
textregion = TextRegionType(id=counter.next_region_id, type_='paragraph',
|
||||
Coords=CoordsType(points=self.calculate_polygon_coords(found_polygons_text_region[mm], page_coord), conf=conf_contours_textregion[mm]),
|
||||
Coords=CoordsType(points=self.calculate_polygon_coords(found_polygons_text_region[mm], page_coord, skip_layout_reading_order), conf=conf_contours_textregion[mm]),
|
||||
)
|
||||
#textregion.set_conf(conf_contours_textregion[mm])
|
||||
page.add_TextRegion(textregion)
|
||||
|
|
@ -194,11 +197,28 @@ class EynollahXmlWriter():
|
|||
ocr_textlines = None
|
||||
self.serialize_lines_in_region(textregion, all_found_textline_polygons, mm, page_coord, all_box_coord, slopes, counter, ocr_textlines)
|
||||
|
||||
for mm in range(len(found_polygons_marginals)):
|
||||
for mm in range(len(found_polygons_marginals_left)):
|
||||
marginal = TextRegionType(id=counter.next_region_id, type_='marginalia',
|
||||
Coords=CoordsType(points=self.calculate_polygon_coords(found_polygons_marginals[mm], page_coord)))
|
||||
Coords=CoordsType(points=self.calculate_polygon_coords(found_polygons_marginals_left[mm], page_coord)))
|
||||
page.add_TextRegion(marginal)
|
||||
self.serialize_lines_in_marginal(marginal, all_found_textline_polygons_marginals, mm, page_coord, all_box_coord_marginals, slopes_marginals, counter)
|
||||
if ocr_all_textlines_marginals_left:
|
||||
ocr_textlines = ocr_all_textlines_marginals_left[mm]
|
||||
else:
|
||||
ocr_textlines = None
|
||||
|
||||
#print(ocr_textlines, mm, len(all_found_textline_polygons_marginals_left[mm]) )
|
||||
self.serialize_lines_in_marginal(marginal, all_found_textline_polygons_marginals_left, mm, page_coord, all_box_coord_marginals_left, slopes_marginals_left, counter, ocr_textlines)
|
||||
|
||||
for mm in range(len(found_polygons_marginals_right)):
|
||||
marginal = TextRegionType(id=counter.next_region_id, type_='marginalia',
|
||||
Coords=CoordsType(points=self.calculate_polygon_coords(found_polygons_marginals_right[mm], page_coord)))
|
||||
page.add_TextRegion(marginal)
|
||||
if ocr_all_textlines_marginals_right:
|
||||
ocr_textlines = ocr_all_textlines_marginals_right[mm]
|
||||
else:
|
||||
ocr_textlines = None
|
||||
|
||||
self.serialize_lines_in_marginal(marginal, all_found_textline_polygons_marginals_right, mm, page_coord, all_box_coord_marginals_right, slopes_marginals_right, counter, ocr_textlines)
|
||||
|
||||
for mm in range(len(found_polygons_text_region_img)):
|
||||
img_region = ImageRegionType(id=counter.next_region_id, Coords=CoordsType())
|
||||
|
|
@ -242,7 +262,7 @@ class EynollahXmlWriter():
|
|||
|
||||
return pcgts
|
||||
|
||||
def build_pagexml_full_layout(self, found_polygons_text_region, found_polygons_text_region_h, page_coord, order_of_texts, id_of_texts, all_found_textline_polygons, all_found_textline_polygons_h, all_box_coord, all_box_coord_h, found_polygons_text_region_img, found_polygons_tables, found_polygons_drop_capitals, found_polygons_marginals, all_found_textline_polygons_marginals, all_box_coord_marginals, slopes, slopes_h, slopes_marginals, cont_page, polygons_lines_to_be_written_in_xml, ocr_all_textlines, conf_contours_textregion, conf_contours_textregion_h):
|
||||
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_lines_to_be_written_in_xml, 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_textregion=None, conf_contours_textregion_h=None):
|
||||
self.logger.debug('enter build_pagexml_full_layout')
|
||||
|
||||
# create the file structure
|
||||
|
|
@ -252,8 +272,9 @@ class EynollahXmlWriter():
|
|||
|
||||
counter = EynollahIdCounter()
|
||||
_counter_marginals = EynollahIdCounter(region_idx=len(order_of_texts))
|
||||
id_of_marginalia = [_counter_marginals.next_region_id for _ in found_polygons_marginals]
|
||||
xml_reading_order(page, order_of_texts, id_of_marginalia)
|
||||
id_of_marginalia_left = [_counter_marginals.next_region_id for _ in found_polygons_marginals_left]
|
||||
id_of_marginalia_right = [_counter_marginals.next_region_id for _ in found_polygons_marginals_right]
|
||||
xml_reading_order(page, order_of_texts, id_of_marginalia_left, id_of_marginalia_right)
|
||||
|
||||
for mm in range(len(found_polygons_text_region)):
|
||||
textregion = TextRegionType(id=counter.next_region_id, type_='paragraph',
|
||||
|
|
@ -272,25 +293,43 @@ class EynollahXmlWriter():
|
|||
Coords=CoordsType(points=self.calculate_polygon_coords(found_polygons_text_region_h[mm], page_coord)))
|
||||
page.add_TextRegion(textregion)
|
||||
|
||||
if ocr_all_textlines:
|
||||
ocr_textlines = ocr_all_textlines[mm]
|
||||
if ocr_all_textlines_h:
|
||||
ocr_textlines = ocr_all_textlines_h[mm]
|
||||
else:
|
||||
ocr_textlines = None
|
||||
self.serialize_lines_in_region(textregion, all_found_textline_polygons_h, mm, page_coord, all_box_coord_h, slopes_h, counter, ocr_textlines)
|
||||
|
||||
for mm in range(len(found_polygons_marginals)):
|
||||
for mm in range(len(found_polygons_marginals_left)):
|
||||
marginal = TextRegionType(id=counter.next_region_id, type_='marginalia',
|
||||
Coords=CoordsType(points=self.calculate_polygon_coords(found_polygons_marginals[mm], page_coord)))
|
||||
Coords=CoordsType(points=self.calculate_polygon_coords(found_polygons_marginals_left[mm], page_coord)))
|
||||
page.add_TextRegion(marginal)
|
||||
self.serialize_lines_in_marginal(marginal, all_found_textline_polygons_marginals, mm, page_coord, all_box_coord_marginals, slopes_marginals, counter)
|
||||
if ocr_all_textlines_marginals_left:
|
||||
ocr_textlines = ocr_all_textlines_marginals_left[mm]
|
||||
else:
|
||||
ocr_textlines = None
|
||||
self.serialize_lines_in_marginal(marginal, all_found_textline_polygons_marginals_left, mm, page_coord, all_box_coord_marginals_left, slopes_marginals_left, counter, ocr_textlines)
|
||||
|
||||
for mm in range(len(found_polygons_marginals_right)):
|
||||
marginal = TextRegionType(id=counter.next_region_id, type_='marginalia',
|
||||
Coords=CoordsType(points=self.calculate_polygon_coords(found_polygons_marginals_right[mm], page_coord)))
|
||||
page.add_TextRegion(marginal)
|
||||
if ocr_all_textlines_marginals_right:
|
||||
ocr_textlines = ocr_all_textlines_marginals_right[mm]
|
||||
else:
|
||||
ocr_textlines = None
|
||||
self.serialize_lines_in_marginal(marginal, all_found_textline_polygons_marginals_right, mm, page_coord, all_box_coord_marginals_right, slopes_marginals_right, counter, ocr_textlines)
|
||||
|
||||
for mm in range(len(found_polygons_drop_capitals)):
|
||||
dropcapital = TextRegionType(id=counter.next_region_id, type_='drop-capital',
|
||||
Coords=CoordsType(points=self.calculate_polygon_coords(found_polygons_drop_capitals[mm], page_coord)))
|
||||
page.add_TextRegion(dropcapital)
|
||||
###all_box_coord_drop = None
|
||||
###slopes_drop = None
|
||||
###self.serialize_lines_in_dropcapital(dropcapital, [found_polygons_drop_capitals[mm]], mm, page_coord, all_box_coord_drop, slopes_drop, counter, ocr_all_textlines_textregion=None)
|
||||
all_box_coord_drop = None
|
||||
slopes_drop = None
|
||||
if ocr_all_textlines_drop:
|
||||
ocr_textlines = ocr_all_textlines_drop[mm]
|
||||
else:
|
||||
ocr_textlines = None
|
||||
self.serialize_lines_in_dropcapital(dropcapital, [found_polygons_drop_capitals[mm]], mm, page_coord, all_box_coord_drop, slopes_drop, counter, ocr_all_textlines_textregion=ocr_textlines)
|
||||
|
||||
for mm in range(len(found_polygons_text_region_img)):
|
||||
page.add_ImageRegion(ImageRegionType(id=counter.next_region_id, Coords=CoordsType(points=self.calculate_polygon_coords(found_polygons_text_region_img[mm], page_coord))))
|
||||
|
|
@ -303,18 +342,28 @@ class EynollahXmlWriter():
|
|||
|
||||
return pcgts
|
||||
|
||||
def calculate_polygon_coords(self, contour, page_coord):
|
||||
def calculate_polygon_coords(self, contour, page_coord, skip_layout_reading_order=False):
|
||||
self.logger.debug('enter calculate_polygon_coords')
|
||||
coords = ''
|
||||
for value_bbox in contour:
|
||||
if len(value_bbox) == 2:
|
||||
coords += str(int((value_bbox[0] + page_coord[2]) / self.scale_x))
|
||||
coords += ','
|
||||
coords += str(int((value_bbox[1] + page_coord[0]) / self.scale_y))
|
||||
if skip_layout_reading_order:
|
||||
if len(value_bbox) == 2:
|
||||
coords += str(int((value_bbox[0]) / self.scale_x))
|
||||
coords += ','
|
||||
coords += str(int((value_bbox[1]) / self.scale_y))
|
||||
else:
|
||||
coords += str(int((value_bbox[0][0]) / self.scale_x))
|
||||
coords += ','
|
||||
coords += str(int((value_bbox[0][1]) / self.scale_y))
|
||||
else:
|
||||
coords += str(int((value_bbox[0][0] + page_coord[2]) / self.scale_x))
|
||||
coords += ','
|
||||
coords += str(int((value_bbox[0][1] + page_coord[0]) / self.scale_y))
|
||||
if len(value_bbox) == 2:
|
||||
coords += str(int((value_bbox[0] + page_coord[2]) / self.scale_x))
|
||||
coords += ','
|
||||
coords += str(int((value_bbox[1] + page_coord[0]) / self.scale_y))
|
||||
else:
|
||||
coords += str(int((value_bbox[0][0] + page_coord[2]) / self.scale_x))
|
||||
coords += ','
|
||||
coords += str(int((value_bbox[0][1] + page_coord[0]) / self.scale_y))
|
||||
coords=coords + ' '
|
||||
return coords[:-1]
|
||||
|
||||
|
|
|
|||
1626
tests/resources/euler_rechenkunst01_1738_0025.xml
Normal file
1626
tests/resources/euler_rechenkunst01_1738_0025.xml
Normal file
File diff suppressed because it is too large
Load diff
2129
tests/resources/kant_aufklaerung_1784_0020.xml
Normal file
2129
tests/resources/kant_aufklaerung_1784_0020.xml
Normal file
File diff suppressed because it is too large
Load diff
|
|
@ -1,22 +1,30 @@
|
|||
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
|
||||
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()
|
||||
|
||||
EYNOLLAH_MODELS = environ.get('EYNOLLAH_MODELS', str(testdir.joinpath('..', 'models_eynollah').resolve()))
|
||||
SBBBIN_MODELS = environ.get('SBBBIN_MODELS', str(testdir.joinpath('..', 'default-2021-03-09').resolve()))
|
||||
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_0').resolve()))
|
||||
MODELS_BIN = environ.get('MODELS_BIN', str(testdir.joinpath('..', 'default-2021-03-09').resolve()))
|
||||
|
||||
def test_run_eynollah_layout_filename(tmp_path, subtests, pytestconfig, caplog):
|
||||
infile = testdir.joinpath('resources/kant_aufklaerung_1784_0020.tif')
|
||||
outfile = tmp_path / 'kant_aufklaerung_1784_0020.xml'
|
||||
args = [
|
||||
'-m', EYNOLLAH_MODELS,
|
||||
'-m', MODELS_LAYOUT,
|
||||
'-i', str(infile),
|
||||
'-o', str(outfile.parent),
|
||||
# subtests write to same location
|
||||
|
|
@ -44,8 +52,7 @@ def test_run_eynollah_layout_filename(tmp_path, subtests, pytestconfig, caplog):
|
|||
options=options):
|
||||
with caplog.filtering(only_eynollah):
|
||||
result = runner.invoke(layout_cli, args + options, catch_exceptions=False)
|
||||
print(result)
|
||||
assert result.exit_code == 0
|
||||
assert result.exit_code == 0, result.stdout
|
||||
logmsgs = [logrec.message for logrec in caplog.records]
|
||||
assert str(infile) in logmsgs
|
||||
assert outfile.exists()
|
||||
|
|
@ -61,7 +68,7 @@ def test_run_eynollah_layout_directory(tmp_path, pytestconfig, caplog):
|
|||
indir = testdir.joinpath('resources')
|
||||
outdir = tmp_path
|
||||
args = [
|
||||
'-m', EYNOLLAH_MODELS,
|
||||
'-m', MODELS_LAYOUT,
|
||||
'-di', str(indir),
|
||||
'-o', str(outdir),
|
||||
]
|
||||
|
|
@ -72,9 +79,8 @@ def test_run_eynollah_layout_directory(tmp_path, pytestconfig, caplog):
|
|||
return logrec.name == 'eynollah'
|
||||
runner = CliRunner()
|
||||
with caplog.filtering(only_eynollah):
|
||||
result = runner.invoke(layout_cli, args)
|
||||
print(result)
|
||||
assert result.exit_code == 0
|
||||
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'))
|
||||
|
|
@ -84,10 +90,12 @@ def test_run_eynollah_binarization_filename(tmp_path, subtests, pytestconfig, ca
|
|||
infile = testdir.joinpath('resources/kant_aufklaerung_1784_0020.tif')
|
||||
outfile = tmp_path.joinpath('kant_aufklaerung_1784_0020.png')
|
||||
args = [
|
||||
'-m', SBBBIN_MODELS,
|
||||
str(infile),
|
||||
str(outfile),
|
||||
'-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'
|
||||
|
|
@ -99,9 +107,8 @@ def test_run_eynollah_binarization_filename(tmp_path, subtests, pytestconfig, ca
|
|||
with subtests.test(#msg="test CLI",
|
||||
options=options):
|
||||
with caplog.filtering(only_eynollah):
|
||||
result = runner.invoke(binarization_cli, args + options)
|
||||
print(result)
|
||||
assert result.exit_code == 0
|
||||
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()
|
||||
|
|
@ -115,18 +122,193 @@ def test_run_eynollah_binarization_directory(tmp_path, subtests, pytestconfig, c
|
|||
indir = testdir.joinpath('resources')
|
||||
outdir = tmp_path
|
||||
args = [
|
||||
'-m', SBBBIN_MODELS,
|
||||
'-m', MODELS_BIN,
|
||||
'-di', str(indir),
|
||||
'-do', str(outdir),
|
||||
'-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)
|
||||
print(result)
|
||||
assert result.exit_code == 0
|
||||
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
|
||||
|
||||
def test_run_eynollah_enhancement_filename(tmp_path, subtests, pytestconfig, caplog):
|
||||
infile = testdir.joinpath('resources/kant_aufklaerung_1784_0020.tif')
|
||||
outfile = tmp_path.joinpath('kant_aufklaerung_1784_0020.png')
|
||||
args = [
|
||||
'-m', MODELS_LAYOUT,
|
||||
'-i', str(infile),
|
||||
'-o', str(outfile.parent),
|
||||
# subtests write to same location
|
||||
'--overwrite',
|
||||
]
|
||||
if pytestconfig.getoption('verbose') > 0:
|
||||
args.extend(['-l', 'DEBUG'])
|
||||
caplog.set_level(logging.INFO)
|
||||
def only_eynollah(logrec):
|
||||
return logrec.name == 'enhancement'
|
||||
runner = CliRunner()
|
||||
for options in [
|
||||
[], # defaults
|
||||
["-sos"],
|
||||
]:
|
||||
with subtests.test(#msg="test CLI",
|
||||
options=options):
|
||||
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, subtests, 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, subtests, 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, subtests, 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
|
||||
|
||||
def test_run_eynollah_ocr_filename(tmp_path, subtests, pytestconfig, caplog):
|
||||
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),
|
||||
# subtests write to same location
|
||||
'--overwrite',
|
||||
]
|
||||
if pytestconfig.getoption('verbose') > 0:
|
||||
args.extend(['-l', 'DEBUG'])
|
||||
caplog.set_level(logging.DEBUG)
|
||||
def only_eynollah(logrec):
|
||||
return logrec.name == 'eynollah'
|
||||
runner = CliRunner()
|
||||
for options in [
|
||||
# kba Fri Sep 26 12:53:49 CEST 2025
|
||||
# Disabled until NHWC/NCHW error in https://github.com/qurator-spk/eynollah/actions/runs/18019655200/job/51273541895 debugged
|
||||
# [], # defaults
|
||||
# ["-doit", str(outrenderfile.parent)],
|
||||
["-trocr"],
|
||||
]:
|
||||
with subtests.test(#msg="test CLI",
|
||||
options=options):
|
||||
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)
|
||||
|
||||
@pytest.mark.skip("Disabled until NHWC/NCHW error in https://github.com/qurator-spk/eynollah/actions/runs/18019655200/job/51273541895 debugged")
|
||||
def test_run_eynollah_ocr_directory(tmp_path, subtests, 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
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue