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Merge branch 'main' into integrate-training-from-sbb_pixelwise_segmentation

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kba 2025-10-16 20:50:16 +02:00
commit af5abb77fd
24 changed files with 2778 additions and 3122 deletions
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36
.github/workflows/test-eynollah.yml vendored
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@ -24,24 +24,39 @@ jobs:
sudo rm -rf "$AGENT_TOOLSDIRECTORY"
df -h
- uses: actions/checkout@v4
- uses: actions/cache@v4
- uses: actions/cache/restore@v4
id: seg_model_cache
with:
path: models_layout_v0_5_0
key: ${{ runner.os }}-models
- uses: actions/cache@v4
key: seg-models
- uses: actions/cache/restore@v4
id: ocr_model_cache
with:
path: models_ocr_v0_5_0
key: ${{ runner.os }}-models
- uses: actions/cache@v4
path: models_ocr_v0_5_1
key: ocr-models
- uses: actions/cache/restore@v4
id: bin_model_cache
with:
path: default-2021-03-09
key: ${{ runner.os }}-modelbin
key: bin-models
- name: Download models
if: steps.seg_model_cache.outputs.cache-hit != 'true' || steps.bin_model_cache.outputs.cache-hit != 'true' || steps.ocr_model_cache.outputs.cache-hit != true
run: make models
- uses: actions/cache/save@v4
if: steps.seg_model_cache.outputs.cache-hit != 'true'
with:
path: models_layout_v0_5_0
key: seg-models
- uses: actions/cache/save@v4
if: steps.ocr_model_cache.outputs.cache-hit != 'true'
with:
path: models_ocr_v0_5_1
key: ocr-models
- uses: actions/cache/save@v4
if: steps.bin_model_cache.outputs.cache-hit != 'true'
with:
path: default-2021-03-09
key: bin-models
- name: Set up Python ${{ matrix.python-version }}
uses: actions/setup-python@v5
with:
@ -50,7 +65,12 @@ jobs:
run: |
python -m pip install --upgrade pip
make install-dev EXTRAS=OCR,plotting
make deps-test
make deps-test EXTRAS=OCR,plotting
ls -l models_*
- name: Lint with ruff
uses: astral-sh/ruff-action@v3
with:
src: "./src"
- name: Test with pytest
run: make coverage PYTEST_ARGS="-vv --junitxml=pytest.xml"
- name: Get coverage results

4
.gitignore vendored
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@ -2,8 +2,12 @@
__pycache__
sbb_newspapers_org_image/pylint.log
models_eynollah*
models_ocr*
models_layout*
default-2021-03-09
output.html
/build
/dist
*.tif
*.sw?
TAGS

108
CHANGELOG.md
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@ -5,17 +5,123 @@ Versioned according to [Semantic Versioning](http://semver.org/).
## Unreleased
Fixed:
* `join_polygons` always returning Polygon, not MultiPolygon, #203
## [0.6.0rc2] - 2025-10-14
Fixed:
* Prevent OOM GPU error by avoiding loading the `region_fl` model, #199
* XML output: encoding should be `utf-8`, not `utf8`, #196, #197
## [0.6.0rc1] - 2025-10-10
Fixed:
* continue processing when no columns detected but text regions exist
* convert marginalia to main text if no main text is present
* reset deskewing angle to 0° when text covers <30% image area and detected angle >45°
* :fire: polygons: avoid invalid paths (use `Polygon.buffer()` instead of dilation etc.)
* `return_boxes_of_images_by_order_of_reading_new`: avoid Numpy.dtype mismatch, simplify
* `return_boxes_of_images_by_order_of_reading_new`: log any exceptions instead of ignoring
* `filter_contours_without_textline_inside`: avoid removing from duplicate lists twice
* `get_marginals`: exit early if no peaks found to avoid spurious overlap mask
* `get_smallest_skew`: after shifting search range of rotation angle, use overall best result
* Dockerfile: fix CUDA installation (cuDNN contested between Torch and TF due to extra OCR)
* OCR: re-instate missing methods and fix `utils_ocr` function calls
* mbreorder/enhancement CLIs: missing imports
* :fire: writer: `SeparatorRegion` needs `SeparatorRegionType` (not `ImageRegionType`), f458e3e
* tests: switch from `pytest-subtests` to `parametrize` so we can use `pytest-isolate`
(so CUDA memory gets freed between tests if running on GPU)
Added:
* :fire: `layout` CLI: new option `--model_version` to override default choices
* test coverage for OCR options in `layout`
* test coverage for table detection in `layout`
* CI linting with ruff
Changed:
* polygons: slightly widen for regions and lines, increase for separators
* various refactorings, some code style and identifier improvements
* deskewing/multiprocessing: switch back to ProcessPoolExecutor (faster),
but use shared memory if necessary, and switch back from `loky` to stdlib,
and shutdown in `del()` instead of `atexit`
* :fire: OCR: switch CNN-RNN model to `20250930` version compatible with TF 2.12 on CPU, too
* OCR: allow running `-tr` without `-fl`, too
* :fire: writer: use `@type='heading'` instead of `'header'` for headings
* :fire: performance gains via refactoring (simplification, less copy-code, vectorization,
avoiding unused calculations, avoiding unnecessary 3-channel image operations)
* :fire: heuristic reading order detection: many improvements
- contour vs splitter box matching:
* contour must be contained in box exactly instead of heuristics
* make fallback center matching, center must be contained in box
- original vs deskewed contour matching:
* same min-area filter on both sides
* similar area score in addition to center proximity
* avoid duplicate and missing mappings by allowing N:M
matches and splitting+joining where necessary
* CI: update+improve model caching
## [0.5.0] - 2025-09-26
Fixed:
* restoring the contour in the original image caused an error due to an empty tuple, #154
* removed NumPy warnings calculating sigma, mean, (fixed issue #158)
* fixed bug in `separate_lines.py`, #124
* Drop capitals are now handled separately from their corresponding textline
* Marginals are now divided into left and right. Their reading order is written first for left marginals, then for right marginals, and within each side from top to bottom
* Added a new page extraction model. Instead of bounding boxes, it outputs page contours in the XML file, improving results for skewed pages
* Improved reading order for cases where a textline is segmented into multiple smaller textlines
Changed
* CLIs: read only allowed filename suffixes (image or XML) with `--dir_in`
* CLIs: make all output option required, and `-i` / `-di` required but mutually exclusive
* ocr CLI: drop redundant `-brb` in favour of just `-dib`
* APIs: move all input/output path options from class (kwarg and attribute) ro `run` kwarg
* layout textlines: polygonal also without `-cl`
Added:
* `eynollah machine-based-reading-order` CLI to run reading order detection, #175
* `eynollah enhancement` CLI to run image enhancement, #175
* Improved models for page extraction and reading order detection, #175
* For the lightweight version (layout and textline detection), thresholds are now assigned to the artificial class. Users can apply these thresholds to improve detection of isolated textlines and regions. To counteract the drawback of thresholding, the skeleton of the artificial class is used to keep lines as thin as possible (resolved issues #163 and #161)
* Added and integrated a trained CNN-RNN OCR models
* Added and integrated a trained TrOCR model
* Improved OCR detection to support vertical and curved textlines
* Introduced a new machine-based reading order model with rotation augmentation
* Optimized reading order speed by clustering text regions that belong to the same block, maintaining top-to-bottom order
* Implemented text merging across textlines based on hyphenation when a line ends with a hyphen
* Integrated image enhancement as a separate use case
* Added reading order functionality on the layout level as a separate use case
* CNN-RNN OCR models provide confidence scores for predictions
* Added OCR visualization: predicted OCR can be overlaid on an image of the same size as the input
* Introduced a threshold value for CNN-RNN OCR models, allowing users to filter out low-confidence textline predictions
* For OCR, users can specify a single model by name instead of always using the default model
* Under the OCR use case, if Ground Truth XMLs and images are available, textline image and corresponding text extraction can now be performed
Merged PRs:
* better machine based reading order + layout and textline + ocr by @vahidrezanezhad in https://github.com/qurator-spk/eynollah/pull/175
* CI: pypi by @kba in https://github.com/qurator-spk/eynollah/pull/154
* CI: Use most recent actions/setup-python@v5 by @kba in https://github.com/qurator-spk/eynollah/pull/157
* update docker by @bertsky in https://github.com/qurator-spk/eynollah/pull/159
* Ocrd fixes by @kba in https://github.com/qurator-spk/eynollah/pull/167
* Updating readme for eynollah use cases cli by @kba in https://github.com/qurator-spk/eynollah/pull/166
* OCR-D processor: expose reading_order_machine_based by @bertsky in https://github.com/qurator-spk/eynollah/pull/171
* prepare release v0.5.0: fix logging by @bertsky in https://github.com/qurator-spk/eynollah/pull/180
* mb_ro_on_layout: remove copy-pasta code not actually used by @kba in https://github.com/qurator-spk/eynollah/pull/181
* prepare release v0.5.0: improve CLI docstring, refactor I/O path options from class to run kwargs, increase test coverage @bertsky in #182
* prepare release v0.5.0: fix for OCR doit subtest by @bertsky in https://github.com/qurator-spk/eynollah/pull/183
* Prepare release v0.5.0 by @kba in https://github.com/qurator-spk/eynollah/pull/178
* updating eynollah README, how to use it for use cases by @vahidrezanezhad in https://github.com/qurator-spk/eynollah/pull/156
* add feedback to command line interface by @michalbubula in https://github.com/qurator-spk/eynollah/pull/170
## [0.4.0] - 2025-04-07
@ -195,6 +301,8 @@ Fixed:
Initial release
<!-- link-labels -->
[0.6.0rc2]: ../../compare/v0.6.0rc2...v0.6.0rc1
[0.6.0rc1]: ../../compare/v0.6.0rc1...v0.5.0
[0.5.0]: ../../compare/v0.5.0...v0.4.0
[0.4.0]: ../../compare/v0.4.0...v0.3.1
[0.3.1]: ../../compare/v0.3.1...v0.3.0

2
Dockerfile
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@ -40,6 +40,8 @@ RUN ocrd ocrd-tool ocrd-tool.json dump-tools > $(dirname $(ocrd bashlib filename
RUN ocrd ocrd-tool ocrd-tool.json dump-module-dirs > $(dirname $(ocrd bashlib filename))/ocrd-all-module-dir.json
# install everything and reduce image size
RUN make install EXTRAS=OCR && rm -rf /build/eynollah
# fixup for broken cuDNN installation (Torch pulls in 8.5.0, which is incompatible with Tensorflow)
RUN pip install nvidia-cudnn-cu11==8.6.0.163
# smoke test
RUN eynollah --help

72
Makefile
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@ -13,12 +13,18 @@ DOCKER ?= docker
#SEG_MODEL := https://github.com/qurator-spk/eynollah/releases/download/v0.3.0/models_eynollah.tar.gz
#SEG_MODEL := https://github.com/qurator-spk/eynollah/releases/download/v0.3.1/models_eynollah.tar.gz
SEG_MODEL := https://zenodo.org/records/17194824/files/models_layout_v0_5_0.tar.gz?download=1
SEG_MODELFILE = $(notdir $(patsubst %?download=1,%,$(SEG_MODEL)))
SEG_MODELNAME = $(SEG_MODELFILE:%.tar.gz=%)
BIN_MODEL := https://github.com/qurator-spk/sbb_binarization/releases/download/v0.0.11/saved_model_2021_03_09.zip
BIN_MODELFILE = $(notdir $(BIN_MODEL))
BIN_MODELNAME := default-2021-03-09
OCR_MODEL := https://zenodo.org/records/17194824/files/models_ocr_v0_5_0.tar.gz?download=1
OCR_MODEL := https://zenodo.org/records/17236998/files/models_ocr_v0_5_1.tar.gz?download=1
OCR_MODELFILE = $(notdir $(patsubst %?download=1,%,$(OCR_MODEL)))
OCR_MODELNAME = $(OCR_MODELFILE:%.tar.gz=%)
PYTEST_ARGS ?= -vv
PYTEST_ARGS ?= -vv --isolate
# BEGIN-EVAL makefile-parser --make-help Makefile
@ -31,7 +37,8 @@ help:
@echo " install Install package with pip"
@echo " install-dev Install editable with pip"
@echo " deps-test Install test dependencies with pip"
@echo " models Download and extract models to $(CURDIR)/models_layout_v0_5_0"
@echo " models Download and extract models to $(CURDIR):"
@echo " $(BIN_MODELNAME) $(SEG_MODELNAME) $(OCR_MODELNAME)"
@echo " smoke-test Run simple CLI check"
@echo " ocrd-test Run OCR-D CLI check"
@echo " test Run unit tests"
@ -42,33 +49,32 @@ help:
@echo " PYTEST_ARGS pytest args for 'test' (Set to '-s' to see log output during test execution, '-vv' to see individual tests. [$(PYTEST_ARGS)]"
@echo " SEG_MODEL URL of 'models' archive to download for segmentation 'test' [$(SEG_MODEL)]"
@echo " BIN_MODEL URL of 'models' archive to download for binarization 'test' [$(BIN_MODEL)]"
@echo " OCR_MODEL URL of 'models' archive to download for binarization 'test' [$(OCR_MODEL)]"
@echo ""
# END-EVAL
# 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
models: $(BIN_MODELNAME) $(SEG_MODELNAME) $(OCR_MODELNAME)
models_layout_v0_5_0: models_layout_v0_5_0.tar.gz
tar zxf models_layout_v0_5_0.tar.gz
# do not download these files if we already have the directories
.INTERMEDIATE: $(BIN_MODELFILE) $(SEG_MODELFILE) $(OCR_MODELFILE)
models_layout_v0_5_0.tar.gz:
$(BIN_MODELFILE):
wget -O $@ $(BIN_MODEL)
$(SEG_MODELFILE):
wget -O $@ $(SEG_MODEL)
models_ocr_v0_5_0: models_ocr_v0_5_0.tar.gz
tar zxf models_ocr_v0_5_0.tar.gz
models_ocr_v0_5_0.tar.gz:
$(OCR_MODELFILE):
wget -O $@ $(OCR_MODEL)
default-2021-03-09: $(notdir $(BIN_MODEL))
unzip $(notdir $(BIN_MODEL))
$(BIN_MODELNAME): $(BIN_MODELFILE)
mkdir $@
mv $(basename $(notdir $(BIN_MODEL))) $@
$(notdir $(BIN_MODEL)):
wget $(BIN_MODEL)
unzip -d $@ $<
$(SEG_MODELNAME): $(SEG_MODELFILE)
tar zxf $<
$(OCR_MODELNAME): $(OCR_MODELFILE)
tar zxf $<
build:
$(PIP) install build
@ -82,28 +88,34 @@ install:
install-dev:
$(PIP) install -e .$(and $(EXTRAS),[$(EXTRAS)])
deps-test: models_layout_v0_5_0
ifeq (OCR,$(findstring OCR, $(EXTRAS)))
deps-test: $(OCR_MODELNAME)
endif
deps-test: $(BIN_MODELNAME) $(SEG_MODELNAME)
$(PIP) install -r requirements-test.txt
ifeq (OCR,$(findstring OCR, $(EXTRAS)))
ln -rs $(OCR_MODELNAME)/* $(SEG_MODELNAME)/
endif
smoke-test: TMPDIR != mktemp -d
smoke-test: tests/resources/kant_aufklaerung_1784_0020.tif
# layout analysis:
eynollah layout -i $< -o $(TMPDIR) -m $(CURDIR)/models_layout_v0_5_0
eynollah layout -i $< -o $(TMPDIR) -m $(CURDIR)/$(SEG_MODELNAME)
fgrep -q http://schema.primaresearch.org/PAGE/gts/pagecontent/2019-07-15 $(TMPDIR)/$(basename $(<F)).xml
fgrep -c -e TextRegion -e ImageRegion -e SeparatorRegion $(TMPDIR)/$(basename $(<F)).xml
# layout, directory mode (skip one, add one):
eynollah layout -di $(<D) -o $(TMPDIR) -m $(CURDIR)/models_layout_v0_5_0
eynollah layout -di $(<D) -o $(TMPDIR) -m $(CURDIR)/$(SEG_MODELNAME)
test -s $(TMPDIR)/euler_rechenkunst01_1738_0025.xml
# mbreorder, directory mode (overwrite):
eynollah machine-based-reading-order -di $(<D) -o $(TMPDIR) -m $(CURDIR)/models_layout_v0_5_0
eynollah machine-based-reading-order -di $(<D) -o $(TMPDIR) -m $(CURDIR)/$(SEG_MODELNAME)
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:
eynollah binarization -m $(CURDIR)/default-2021-03-09 -i $< -o $(TMPDIR)/$(<F)
eynollah binarization -m $(CURDIR)/$(BIN_MODELNAME) -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
eynollah enhancement -m $(CURDIR)/$(SEG_MODELNAME) -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)
@ -114,18 +126,18 @@ 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_layout_v0_5_0
ocrd-eynollah-segment -w $(TMPDIR) -I OCR-D-IMG -O OCR-D-SEG -P models $(CURDIR)/$(SEG_MODELNAME)
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
ocrd-sbb-binarize -w $(TMPDIR) -I OCR-D-IMG -O OCR-D-BIN -P model $(CURDIR)/default-2021-03-09
ocrd-sbb-binarize -w $(TMPDIR) -I OCR-D-SEG -O OCR-D-SEG-BIN -P model $(CURDIR)/default-2021-03-09 -P operation_level region
ocrd-sbb-binarize -w $(TMPDIR) -I OCR-D-IMG -O OCR-D-BIN -P model $(CURDIR)/$(BIN_MODELNAME)
ocrd-sbb-binarize -w $(TMPDIR) -I OCR-D-SEG -O OCR-D-SEG-BIN -P model $(CURDIR)/$(BIN_MODELNAME) -P operation_level region
$(RM) -r $(TMPDIR)
# Run unit tests
test: export MODELS_LAYOUT=$(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: export MODELS_LAYOUT=$(CURDIR)/$(SEG_MODELNAME)
test: export MODELS_OCR=$(CURDIR)/$(OCR_MODELNAME)
test: export MODELS_BIN=$(CURDIR)/$(BIN_MODELNAME)
test:
$(PYTHON) -m pytest tests --durations=0 --continue-on-collection-errors $(PYTEST_ARGS)

18
pyproject.toml
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@ -54,3 +54,21 @@ where = ["src"]
[tool.coverage.run]
branch = true
source = ["eynollah"]
[tool.ruff]
line-length = 120
[tool.ruff.lint]
ignore = [
# disable unused imports
"F401",
# disable import order
"E402",
# disable unused variables
"F841",
# disable bare except
"E722",
]
[tool.ruff.format]
quote-style = "preserve"

2
requirements-test.txt
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@ -1,4 +1,4 @@
pytest
pytest-subtests
pytest-isolate
coverage[toml]
black

1
requirements.txt
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@ -5,5 +5,4 @@ scikit-learn >= 0.23.2
tensorflow < 2.13
numba <= 0.58.1
scikit-image
loky
biopython

10
src/eynollah/cli.py
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@ -202,6 +202,13 @@ def enhancement(image, out, overwrite, dir_in, model, num_col_upper, num_col_low
type=click.Path(exists=True, file_okay=False),
required=True,
)
@click.option(
"--model_version",
"-mv",
help="override default versions of model categories",
type=(str, str),
multiple=True,
)
@click.option(
"--save_images",
"-si",
@ -373,7 +380,7 @@ def enhancement(image, out, overwrite, dir_in, model, num_col_upper, num_col_low
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, 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):
def layout(image, out, overwrite, dir_in, model, model_version, save_images, save_layout, save_deskewed, save_all, extract_only_images, save_page, enable_plotting, allow_enhancement, curved_line, textline_light, full_layout, tables, right2left, input_binary, allow_scaling, headers_off, light_version, reading_order_machine_based, do_ocr, transformer_ocr, batch_size_ocr, num_col_upper, num_col_lower, threshold_art_class_textline, threshold_art_class_layout, skip_layout_and_reading_order, ignore_page_extraction, log_level, setup_logging):
if setup_logging:
console_handler = logging.StreamHandler(sys.stdout)
console_handler.setLevel(logging.INFO)
@ -404,6 +411,7 @@ def layout(image, out, overwrite, dir_in, model, save_images, save_layout, save_
assert bool(image) != bool(dir_in), "Either -i (single input) or -di (directory) must be provided, but not both."
eynollah = Eynollah(
model,
model_versions=model_version,
extract_only_images=extract_only_images,
enable_plotting=enable_plotting,
allow_enhancement=allow_enhancement,

3084
src/eynollah/eynollah.py
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6
src/eynollah/image_enhancer.py
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@ -6,23 +6,23 @@ 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
)
from .eynollah import PatchEncoder, Patches
DPI_THRESHOLD = 298
KERNEL = np.ones((5, 5), np.uint8)

9
src/eynollah/mb_ro_on_layout.py
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@ -6,25 +6,24 @@ 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.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
from .eynollah import PatchEncoder, Patches
DPI_THRESHOLD = 298
KERNEL = np.ones((5, 5), np.uint8)
@ -806,7 +805,7 @@ class machine_based_reading_order_on_layout:
tree_xml.write(os.path.join(dir_out, file_name+'.xml'),
xml_declaration=True,
method='xml',
encoding="utf8",
encoding="utf-8",
default_namespace=None)
#sys.exit()

2
src/eynollah/ocrd-tool.json
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@ -1,5 +1,5 @@
{
"version": "0.5.0",
"version": "0.6.0rc2",
"git_url": "https://github.com/qurator-spk/eynollah",
"dockerhub": "ocrd/eynollah",
"tools": {

622
src/eynollah/utils/__init__.py
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352
src/eynollah/utils/contour.py
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@ -1,7 +1,15 @@
from typing import Sequence, Union
from numbers import Number
from functools import partial
import itertools
import cv2
import numpy as np
from shapely import geometry
from scipy.sparse.csgraph import minimum_spanning_tree
from shapely.geometry import Polygon, LineString
from shapely.geometry.polygon import orient
from shapely import set_precision
from shapely.ops import unary_union, nearest_points
from .rotate import rotate_image, rotation_image_new
@ -28,38 +36,31 @@ def find_contours_mean_y_diff(contours_main):
return np.mean(np.diff(np.sort(np.array(cy_main))))
def get_text_region_boxes_by_given_contours(contours):
boxes = []
contours_new = []
for jj in range(len(contours)):
box = cv2.boundingRect(contours[jj])
boxes.append(box)
contours_new.append(contours[jj])
return [cv2.boundingRect(contour)
for contour in contours]
return boxes, contours_new
def filter_contours_area_of_image(image, contours, hierarchy, max_area, min_area):
def filter_contours_area_of_image(image, contours, hierarchy, max_area=1.0, min_area=0.0, dilate=0):
found_polygons_early = []
for jv,c in enumerate(contours):
if len(c) < 3: # A polygon cannot have less than 3 points
for jv, contour in enumerate(contours):
if len(contour) < 3: # A polygon cannot have less than 3 points
continue
polygon = geometry.Polygon([point[0] for point in c])
polygon = contour2polygon(contour, dilate=dilate)
area = polygon.area
if (area >= min_area * np.prod(image.shape[:2]) and
area <= max_area * np.prod(image.shape[:2]) and
hierarchy[0][jv][3] == -1):
found_polygons_early.append(np.array([[point]
for point in polygon.exterior.coords], dtype=np.uint))
found_polygons_early.append(polygon2contour(polygon))
return found_polygons_early
def filter_contours_area_of_image_tables(image, contours, hierarchy, max_area, min_area):
def filter_contours_area_of_image_tables(image, contours, hierarchy, max_area=1.0, min_area=0.0, dilate=0):
found_polygons_early = []
for jv,c in enumerate(contours):
if len(c) < 3: # A polygon cannot have less than 3 points
for jv, contour in enumerate(contours):
if len(contour) < 3: # A polygon cannot have less than 3 points
continue
polygon = geometry.Polygon([point[0] for point in c])
# area = cv2.contourArea(c)
polygon = contour2polygon(contour, dilate=dilate)
# area = cv2.contourArea(contour)
area = polygon.area
##print(np.prod(thresh.shape[:2]))
# Check that polygon has area greater than minimal area
@ -68,66 +69,41 @@ def filter_contours_area_of_image_tables(image, contours, hierarchy, max_area, m
area <= max_area * np.prod(image.shape[:2]) and
# hierarchy[0][jv][3]==-1
True):
# print(c[0][0][1])
found_polygons_early.append(np.array([[point]
for point in polygon.exterior.coords], dtype=np.int32))
# print(contour[0][0][1])
found_polygons_early.append(polygon2contour(polygon))
return found_polygons_early
def find_new_features_of_contours(contours_main):
areas_main = np.array([cv2.contourArea(contours_main[j])
for j in range(len(contours_main))])
M_main = [cv2.moments(contours_main[j])
for j in range(len(contours_main))]
cx_main = [(M_main[j]["m10"] / (M_main[j]["m00"] + 1e-32))
for j in range(len(M_main))]
cy_main = [(M_main[j]["m01"] / (M_main[j]["m00"] + 1e-32))
for j in range(len(M_main))]
try:
x_min_main = np.array([np.min(contours_main[j][:, 0, 0])
for j in range(len(contours_main))])
argmin_x_main = np.array([np.argmin(contours_main[j][:, 0, 0])
for j in range(len(contours_main))])
x_min_from_argmin = np.array([contours_main[j][argmin_x_main[j], 0, 0]
for j in range(len(contours_main))])
y_corr_x_min_from_argmin = np.array([contours_main[j][argmin_x_main[j], 0, 1]
for j in range(len(contours_main))])
x_max_main = np.array([np.max(contours_main[j][:, 0, 0])
for j in range(len(contours_main))])
y_min_main = np.array([np.min(contours_main[j][:, 0, 1])
for j in range(len(contours_main))])
y_max_main = np.array([np.max(contours_main[j][:, 0, 1])
for j in range(len(contours_main))])
except:
x_min_main = np.array([np.min(contours_main[j][:, 0])
for j in range(len(contours_main))])
argmin_x_main = np.array([np.argmin(contours_main[j][:, 0])
for j in range(len(contours_main))])
x_min_from_argmin = np.array([contours_main[j][argmin_x_main[j], 0]
for j in range(len(contours_main))])
y_corr_x_min_from_argmin = np.array([contours_main[j][argmin_x_main[j], 1]
for j in range(len(contours_main))])
x_max_main = np.array([np.max(contours_main[j][:, 0])
for j in range(len(contours_main))])
y_min_main = np.array([np.min(contours_main[j][:, 1])
for j in range(len(contours_main))])
y_max_main = np.array([np.max(contours_main[j][:, 1])
for j in range(len(contours_main))])
# dis_x=np.abs(x_max_main-x_min_main)
def find_center_of_contours(contours):
moments = [cv2.moments(contour) for contour in contours]
cx = [feat["m10"] / (feat["m00"] + 1e-32)
for feat in moments]
cy = [feat["m01"] / (feat["m00"] + 1e-32)
for feat in moments]
return cx, cy
return cx_main, cy_main, x_min_main, x_max_main, y_min_main, y_max_main, y_corr_x_min_from_argmin
def find_new_features_of_contours(contours):
# areas = np.array([cv2.contourArea(contour) for contour in contours])
cx, cy = find_center_of_contours(contours)
slice_x = np.index_exp[:, 0, 0]
slice_y = np.index_exp[:, 0, 1]
if any(contour.ndim < 3 for contour in contours):
slice_x = np.index_exp[:, 0]
slice_y = np.index_exp[:, 1]
x_min = np.array([np.min(contour[slice_x]) for contour in contours])
x_max = np.array([np.max(contour[slice_x]) for contour in contours])
y_min = np.array([np.min(contour[slice_y]) for contour in contours])
y_max = np.array([np.max(contour[slice_y]) for contour in contours])
# dis_x=np.abs(x_max-x_min)
y_corr_x_min = np.array([contour[np.argmin(contour[slice_x])][slice_y[1:]]
for contour in contours])
def find_features_of_contours(contours_main):
areas_main=np.array([cv2.contourArea(contours_main[j]) for j in range(len(contours_main))])
M_main=[cv2.moments(contours_main[j]) for j in range(len(contours_main))]
cx_main=[(M_main[j]['m10']/(M_main[j]['m00']+1e-32)) for j in range(len(M_main))]
cy_main=[(M_main[j]['m01']/(M_main[j]['m00']+1e-32)) for j in range(len(M_main))]
x_min_main=np.array([np.min(contours_main[j][:,0,0]) for j in range(len(contours_main))])
x_max_main=np.array([np.max(contours_main[j][:,0,0]) for j in range(len(contours_main))])
return cx, cy, x_min, x_max, y_min, y_max, y_corr_x_min
y_min_main=np.array([np.min(contours_main[j][:,0,1]) for j in range(len(contours_main))])
y_max_main=np.array([np.max(contours_main[j][:,0,1]) for j in range(len(contours_main))])
def find_features_of_contours(contours):
y_min = np.array([np.min(contour[:,0,1]) for contour in contours])
y_max = np.array([np.max(contour[:,0,1]) for contour in contours])
return y_min_main, y_max_main
return y_min, y_max
def return_parent_contours(contours, hierarchy):
contours_parent = [contours[i]
@ -135,16 +111,13 @@ def return_parent_contours(contours, hierarchy):
if hierarchy[0][i][3] == -1]
return contours_parent
def return_contours_of_interested_region(region_pre_p, pixel, min_area=0.0002):
def return_contours_of_interested_region(region_pre_p, label, min_area=0.0002):
# pixels of images are identified by 5
if len(region_pre_p.shape) == 3:
cnts_images = (region_pre_p[:, :, 0] == pixel) * 1
if region_pre_p.ndim == 3:
cnts_images = (region_pre_p[:, :, 0] == label) * 1
else:
cnts_images = (region_pre_p[:, :] == pixel) * 1
cnts_images = cnts_images.astype(np.uint8)
cnts_images = np.repeat(cnts_images[:, :, np.newaxis], 3, axis=2)
imgray = cv2.cvtColor(cnts_images, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
cnts_images = (region_pre_p[:, :] == label) * 1
_, thresh = cv2.threshold(cnts_images.astype(np.uint8), 0, 255, 0)
contours_imgs, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
contours_imgs = return_parent_contours(contours_imgs, hierarchy)
@ -153,13 +126,11 @@ def return_contours_of_interested_region(region_pre_p, pixel, min_area=0.0002):
return contours_imgs
def do_work_of_contours_in_image(contour, index_r_con, img, slope_first):
img_copy = np.zeros(img.shape)
img_copy = cv2.fillPoly(img_copy, pts=[contour], color=(1, 1, 1))
img_copy = np.zeros(img.shape[:2], dtype=np.uint8)
img_copy = cv2.fillPoly(img_copy, pts=[contour], color=1)
img_copy = rotation_image_new(img_copy, -slope_first)
img_copy = img_copy.astype(np.uint8)
imgray = cv2.cvtColor(img_copy, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
_, thresh = cv2.threshold(img_copy, 0, 255, 0)
cont_int, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
@ -182,8 +153,8 @@ def get_textregion_contours_in_org_image(cnts, img, slope_first):
cnts_org = []
# print(cnts,'cnts')
for i in range(len(cnts)):
img_copy = np.zeros(img.shape)
img_copy = cv2.fillPoly(img_copy, pts=[cnts[i]], color=(1, 1, 1))
img_copy = np.zeros(img.shape[:2], dtype=np.uint8)
img_copy = cv2.fillPoly(img_copy, pts=[cnts[i]], color=1)
# plt.imshow(img_copy)
# plt.show()
@ -194,9 +165,7 @@ def get_textregion_contours_in_org_image(cnts, img, slope_first):
# plt.imshow(img_copy)
# plt.show()
img_copy = img_copy.astype(np.uint8)
imgray = cv2.cvtColor(img_copy, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
_, thresh = cv2.threshold(img_copy, 0, 255, 0)
cont_int, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
cont_int[0][:, 0, 0] = cont_int[0][:, 0, 0] + np.abs(img_copy.shape[1] - img.shape[1])
@ -213,12 +182,11 @@ def get_textregion_contours_in_org_image_light_old(cnts, img, slope_first):
interpolation=cv2.INTER_NEAREST)
cnts_org = []
for cnt in cnts:
img_copy = np.zeros(img.shape)
img_copy = cv2.fillPoly(img_copy, pts=[(cnt / zoom).astype(int)], color=(1, 1, 1))
img_copy = np.zeros(img.shape[:2], dtype=np.uint8)
img_copy = cv2.fillPoly(img_copy, pts=[cnt // zoom], color=1)
img_copy = rotation_image_new(img_copy, -slope_first).astype(np.uint8)
imgray = cv2.cvtColor(img_copy, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
_, thresh = cv2.threshold(img_copy, 0, 255, 0)
cont_int, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
cont_int[0][:, 0, 0] = cont_int[0][:, 0, 0] + np.abs(img_copy.shape[1] - img.shape[1])
@ -228,14 +196,13 @@ def get_textregion_contours_in_org_image_light_old(cnts, img, slope_first):
return cnts_org
def do_back_rotation_and_get_cnt_back(contour_par, index_r_con, img, slope_first, confidence_matrix):
img_copy = np.zeros(img.shape)
img_copy = cv2.fillPoly(img_copy, pts=[contour_par], color=(1, 1, 1))
confidence_matrix_mapped_with_contour = confidence_matrix * img_copy[:,:,0]
confidence_contour = np.sum(confidence_matrix_mapped_with_contour) / float(np.sum(img_copy[:,:,0]))
img_copy = np.zeros(img.shape[:2], dtype=np.uint8)
img_copy = cv2.fillPoly(img_copy, pts=[contour_par], color=1)
confidence_matrix_mapped_with_contour = confidence_matrix * img_copy
confidence_contour = np.sum(confidence_matrix_mapped_with_contour) / float(np.sum(img_copy))
img_copy = rotation_image_new(img_copy, -slope_first).astype(np.uint8)
imgray = cv2.cvtColor(img_copy, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
_, thresh = cv2.threshold(img_copy, 0, 255, 0)
cont_int, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
if len(cont_int)==0:
@ -246,34 +213,27 @@ def do_back_rotation_and_get_cnt_back(contour_par, index_r_con, img, slope_first
cont_int[0][:, 0, 1] = cont_int[0][:, 0, 1] + np.abs(img_copy.shape[0] - img.shape[0])
return cont_int[0], index_r_con, confidence_contour
def get_textregion_contours_in_org_image_light(cnts, img, slope_first, confidence_matrix, map=map):
def get_textregion_contours_in_org_image_light(cnts, img, confidence_matrix):
if not len(cnts):
return [], []
return []
confidence_matrix = cv2.resize(confidence_matrix, (int(img.shape[1]/6), int(img.shape[0]/6)), interpolation=cv2.INTER_NEAREST)
img = cv2.resize(img, (int(img.shape[1]/6), int(img.shape[0]/6)), interpolation=cv2.INTER_NEAREST)
##cnts = list( (np.array(cnts)/2).astype(np.int16) )
#cnts = cnts/2
cnts = [(i/6).astype(int) for i in cnts]
results = map(partial(do_back_rotation_and_get_cnt_back,
img=img,
slope_first=slope_first,
confidence_matrix=confidence_matrix,
),
cnts, range(len(cnts)))
contours, indexes, conf_contours = tuple(zip(*results))
return [i*6 for i in contours], list(conf_contours)
confidence_matrix = cv2.resize(confidence_matrix,
(img.shape[1] // 6, img.shape[0] // 6),
interpolation=cv2.INTER_NEAREST)
confs = []
for cnt in cnts:
cnt_mask = np.zeros(confidence_matrix.shape)
cnt_mask = cv2.fillPoly(cnt_mask, pts=[cnt // 6], color=1.0)
confs.append(np.sum(confidence_matrix * cnt_mask) / np.sum(cnt_mask))
return confs
def return_contours_of_interested_textline(region_pre_p, pixel):
def return_contours_of_interested_textline(region_pre_p, label):
# pixels of images are identified by 5
if len(region_pre_p.shape) == 3:
cnts_images = (region_pre_p[:, :, 0] == pixel) * 1
if region_pre_p.ndim == 3:
cnts_images = (region_pre_p[:, :, 0] == label) * 1
else:
cnts_images = (region_pre_p[:, :] == pixel) * 1
cnts_images = cnts_images.astype(np.uint8)
cnts_images = np.repeat(cnts_images[:, :, np.newaxis], 3, axis=2)
imgray = cv2.cvtColor(cnts_images, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
cnts_images = (region_pre_p[:, :] == label) * 1
_, thresh = cv2.threshold(cnts_images.astype(np.uint8), 0, 255, 0)
contours_imgs, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
contours_imgs = return_parent_contours(contours_imgs, hierarchy)
@ -283,51 +243,123 @@ def return_contours_of_interested_textline(region_pre_p, pixel):
def return_contours_of_image(image):
if len(image.shape) == 2:
image = np.repeat(image[:, :, np.newaxis], 3, axis=2)
image = image.astype(np.uint8)
imgray = image
else:
image = image.astype(np.uint8)
imgray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
_, thresh = cv2.threshold(imgray, 0, 255, 0)
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
return contours, hierarchy
def return_contours_of_interested_region_by_min_size(region_pre_p, pixel, min_size=0.00003):
# pixels of images are identified by 5
if len(region_pre_p.shape) == 3:
cnts_images = (region_pre_p[:, :, 0] == pixel) * 1
else:
cnts_images = (region_pre_p[:, :] == pixel) * 1
cnts_images = cnts_images.astype(np.uint8)
cnts_images = np.repeat(cnts_images[:, :, np.newaxis], 3, axis=2)
imgray = cv2.cvtColor(cnts_images, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
def dilate_textline_contours(all_found_textline_polygons):
return [[polygon2contour(contour2polygon(contour, dilate=6))
for contour in region]
for region in all_found_textline_polygons]
contours_imgs, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
contours_imgs = return_parent_contours(contours_imgs, hierarchy)
contours_imgs = filter_contours_area_of_image_tables(
thresh, contours_imgs, hierarchy, max_area=1, min_area=min_size)
def dilate_textregion_contours(all_found_textline_polygons):
return [polygon2contour(contour2polygon(contour, dilate=6))
for contour in all_found_textline_polygons]
return contours_imgs
def contour2polygon(contour: Union[np.ndarray, Sequence[Sequence[Sequence[Number]]]], dilate=0):
polygon = Polygon([point[0] for point in contour])
if dilate:
polygon = polygon.buffer(dilate)
if polygon.geom_type == 'GeometryCollection':
# heterogeneous result: filter zero-area shapes (LineString, Point)
polygon = unary_union([geom for geom in polygon.geoms if geom.area > 0])
if polygon.geom_type == 'MultiPolygon':
# homogeneous result: construct convex hull to connect
polygon = join_polygons(polygon.geoms)
return make_valid(polygon)
def return_contours_of_interested_region_by_size(region_pre_p, pixel, min_area, max_area):
# pixels of images are identified by 5
if len(region_pre_p.shape) == 3:
cnts_images = (region_pre_p[:, :, 0] == pixel) * 1
else:
cnts_images = (region_pre_p[:, :] == pixel) * 1
cnts_images = cnts_images.astype(np.uint8)
cnts_images = np.repeat(cnts_images[:, :, np.newaxis], 3, axis=2)
imgray = cv2.cvtColor(cnts_images, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
contours_imgs, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
def polygon2contour(polygon: Polygon) -> np.ndarray:
polygon = np.array(polygon.exterior.coords[:-1], dtype=int)
return np.maximum(0, polygon).astype(int)[:, np.newaxis]
contours_imgs = return_parent_contours(contours_imgs, hierarchy)
contours_imgs = filter_contours_area_of_image_tables(
thresh, contours_imgs, hierarchy, max_area=max_area, min_area=min_area)
def make_intersection(poly1, poly2):
interp = poly1.intersection(poly2)
# post-process
if interp.is_empty or interp.area == 0.0:
return None
if interp.geom_type == 'GeometryCollection':
# heterogeneous result: filter zero-area shapes (LineString, Point)
interp = unary_union([geom for geom in interp.geoms if geom.area > 0])
if interp.geom_type == 'MultiPolygon':
# homogeneous result: construct convex hull to connect
interp = join_polygons(interp.geoms)
assert interp.geom_type == 'Polygon', interp.wkt
interp = make_valid(interp)
return interp
img_ret = np.zeros((region_pre_p.shape[0], region_pre_p.shape[1], 3))
img_ret = cv2.fillPoly(img_ret, pts=contours_imgs, color=(1, 1, 1))
return img_ret[:, :, 0]
def make_valid(polygon: Polygon) -> Polygon:
"""Ensures shapely.geometry.Polygon object is valid by repeated rearrangement/simplification/enlargement."""
def isint(x):
return isinstance(x, int) or int(x) == x
# make sure rounding does not invalidate
if not all(map(isint, np.array(polygon.exterior.coords).flat)) and polygon.minimum_clearance < 1.0:
polygon = Polygon(np.round(polygon.exterior.coords))
points = list(polygon.exterior.coords[:-1])
# try by re-arranging points
for split in range(1, len(points)):
if polygon.is_valid or polygon.simplify(polygon.area).is_valid:
break
# simplification may not be possible (at all) due to ordering
# in that case, try another starting point
polygon = Polygon(points[-split:]+points[:-split])
# try by simplification
for tolerance in range(int(polygon.area + 1.5)):
if polygon.is_valid:
break
# simplification may require a larger tolerance
polygon = polygon.simplify(tolerance + 1)
# try by enlarging
for tolerance in range(1, int(polygon.area + 2.5)):
if polygon.is_valid:
break
# enlargement may require a larger tolerance
polygon = polygon.buffer(tolerance)
assert polygon.is_valid, polygon.wkt
return polygon
def join_polygons(polygons: Sequence[Polygon], scale=20) -> Polygon:
"""construct concave hull (alpha shape) from input polygons by connecting their pairwise nearest points"""
# ensure input polygons are simply typed and all oriented equally
polygons = [orient(poly)
for poly in itertools.chain.from_iterable(
[poly.geoms
if poly.geom_type in ['MultiPolygon', 'GeometryCollection']
else [poly]
for poly in polygons])]
npoly = len(polygons)
if npoly == 1:
return polygons[0]
# find min-dist path through all polygons (travelling salesman)
pairs = itertools.combinations(range(npoly), 2)
dists = np.zeros((npoly, npoly), dtype=float)
for i, j in pairs:
dist = polygons[i].distance(polygons[j])
if dist < 1e-5:
dist = 1e-5 # if pair merely touches, we still need to get an edge
dists[i, j] = dist
dists[j, i] = dist
dists = minimum_spanning_tree(dists, overwrite=True)
# add bridge polygons (where necessary)
for prevp, nextp in zip(*dists.nonzero()):
prevp = polygons[prevp]
nextp = polygons[nextp]
nearest = nearest_points(prevp, nextp)
bridgep = orient(LineString(nearest).buffer(max(1, scale/5), resolution=1), -1)
polygons.append(bridgep)
jointp = unary_union(polygons)
if jointp.geom_type == 'MultiPolygon':
jointp = unary_union(jointp.geoms)
assert jointp.geom_type == 'Polygon', jointp.wkt
# follow-up calculations will necessarily be integer;
# so anticipate rounding here and then ensure validity
jointp2 = set_precision(jointp, 1.0)
if jointp2.geom_type != 'Polygon' or not jointp2.is_valid:
jointp2 = Polygon(np.round(jointp.exterior.coords))
jointp2 = make_valid(jointp2)
assert jointp2.geom_type == 'Polygon', jointp2.wkt
return jointp2

27
src/eynollah/utils/drop_capitals.py
View file

@ -1,6 +1,7 @@
import numpy as np
import cv2
from .contour import (
find_center_of_contours,
find_new_features_of_contours,
return_contours_of_image,
return_parent_contours,
@ -22,8 +23,8 @@ def adhere_drop_capital_region_into_corresponding_textline(
):
# print(np.shape(all_found_textline_polygons),np.shape(all_found_textline_polygons[3]),'all_found_textline_polygonsshape')
# print(all_found_textline_polygons[3])
cx_m, cy_m, _, _, _, _, _ = find_new_features_of_contours(contours_only_text_parent)
cx_h, cy_h, _, _, _, _, _ = find_new_features_of_contours(contours_only_text_parent_h)
cx_m, cy_m = find_center_of_contours(contours_only_text_parent)
cx_h, cy_h = find_center_of_contours(contours_only_text_parent_h)
cx_d, cy_d, _, _, y_min_d, y_max_d, _ = find_new_features_of_contours(polygons_of_drop_capitals)
img_con_all = np.zeros((text_regions_p.shape[0], text_regions_p.shape[1], 3))
@ -89,9 +90,9 @@ def adhere_drop_capital_region_into_corresponding_textline(
region_final = region_with_intersected_drop[np.argmax(sum_pixels_of_intersection)] - 1
# print(region_final,'region_final')
# cx_t,cy_t ,_, _, _ ,_,_= find_new_features_of_contours(all_found_textline_polygons[int(region_final)])
# cx_t, cy_t = find_center_of_contours(all_found_textline_polygons[int(region_final)])
try:
cx_t, cy_t, _, _, _, _, _ = find_new_features_of_contours(all_found_textline_polygons[int(region_final)])
cx_t, cy_t = find_center_of_contours(all_found_textline_polygons[int(region_final)])
# print(all_box_coord[j_cont])
# print(cx_t)
# print(cy_t)
@ -153,9 +154,9 @@ def adhere_drop_capital_region_into_corresponding_textline(
# areas_main=np.array([cv2.contourArea(all_found_textline_polygons[int(region_final)][0][j] ) for j in range(len(all_found_textline_polygons[int(region_final)]))])
# cx_t,cy_t ,_, _, _ ,_,_= find_new_features_of_contours(all_found_textline_polygons[int(region_final)])
# cx_t, cy_t = find_center_of_contours(all_found_textline_polygons[int(region_final)])
try:
cx_t, cy_t, _, _, _, _, _ = find_new_features_of_contours(all_found_textline_polygons[int(region_final)])
cx_t, cy_t = find_center_of_contours(all_found_textline_polygons[int(region_final)])
# print(all_box_coord[j_cont])
# print(cx_t)
# print(cy_t)
@ -208,7 +209,7 @@ def adhere_drop_capital_region_into_corresponding_textline(
try:
# print(all_found_textline_polygons[j_cont][0])
cx_t, cy_t, _, _, _, _, _ = find_new_features_of_contours(all_found_textline_polygons[int(region_final)])
cx_t, cy_t = find_center_of_contours(all_found_textline_polygons[int(region_final)])
# print(all_box_coord[j_cont])
# print(cx_t)
# print(cy_t)
@ -261,7 +262,7 @@ def adhere_drop_capital_region_into_corresponding_textline(
else:
pass
##cx_t,cy_t ,_, _, _ ,_,_= find_new_features_of_contours(all_found_textline_polygons[int(region_final)])
##cx_t, cy_t = find_center_of_contours(all_found_textline_polygons[int(region_final)])
###print(all_box_coord[j_cont])
###print(cx_t)
###print(cy_t)
@ -315,9 +316,9 @@ def adhere_drop_capital_region_into_corresponding_textline(
region_final = region_with_intersected_drop[np.argmax(sum_pixels_of_intersection)] - 1
# print(region_final,'region_final')
# cx_t,cy_t ,_, _, _ ,_,_= find_new_features_of_contours(all_found_textline_polygons[int(region_final)])
# cx_t, cy_t = find_center_of_contours(all_found_textline_polygons[int(region_final)])
try:
cx_t, cy_t, _, _, _, _, _ = find_new_features_of_contours(all_found_textline_polygons[int(region_final)])
cx_t, cy_t = find_center_of_contours(all_found_textline_polygons[int(region_final)])
# print(all_box_coord[j_cont])
# print(cx_t)
# print(cy_t)
@ -375,12 +376,12 @@ def adhere_drop_capital_region_into_corresponding_textline(
# areas_main=np.array([cv2.contourArea(all_found_textline_polygons[int(region_final)][0][j] ) for j in range(len(all_found_textline_polygons[int(region_final)]))])
# cx_t,cy_t ,_, _, _ ,_,_= find_new_features_of_contours(all_found_textline_polygons[int(region_final)])
# cx_t, cy_t = find_center_of_contours(all_found_textline_polygons[int(region_final)])
# print(cx_t,'print')
try:
# print(all_found_textline_polygons[j_cont][0])
cx_t, cy_t, _, _, _, _, _ = find_new_features_of_contours(all_found_textline_polygons[int(region_final)])
cx_t, cy_t = find_center_of_contours(all_found_textline_polygons[int(region_final)])
# print(all_box_coord[j_cont])
# print(cx_t)
# print(cy_t)
@ -453,7 +454,7 @@ def adhere_drop_capital_region_into_corresponding_textline(
#####try:
#####if len(contours_new_parent)==1:
######print(all_found_textline_polygons[j_cont][0])
#####cx_t,cy_t ,_, _, _ ,_,_= find_new_features_of_contours(all_found_textline_polygons[j_cont])
#####cx_t, cy_t = find_center_of_contours(all_found_textline_polygons[j_cont])
######print(all_box_coord[j_cont])
######print(cx_t)
######print(cy_t)

2
src/eynollah/utils/marginals.py
View file

@ -99,6 +99,8 @@ def get_marginals(text_with_lines, text_regions, num_col, slope_deskew, light_ve
except:
point_left=first_nonzero
if point_left == first_nonzero and point_right == last_nonzero:
return text_regions
if point_right>=mask_marginals.shape[1]:

438
src/eynollah/utils/separate_lines.py
View file

@ -17,9 +17,12 @@ from .contour import (
return_contours_of_interested_textline,
find_contours_mean_y_diff,
)
from .shm import share_ndarray, wrap_ndarray_shared
from . import (
find_num_col_deskew,
crop_image_inside_box,
box2rect,
box2slice,
)
def dedup_separate_lines(img_patch, contour_text_interest, thetha, axis):
@ -64,7 +67,8 @@ def dedup_separate_lines(img_patch, contour_text_interest, thetha, axis):
peaks_neg_e, _ = find_peaks(y_padded_up_to_down_padded_e, height=0)
neg_peaks_max = np.max(y_padded_up_to_down_padded_e[peaks_neg_e])
arg_neg_must_be_deleted = np.arange(len(peaks_neg_e))[y_padded_up_to_down_padded_e[peaks_neg_e] / float(neg_peaks_max) < 0.3]
arg_neg_must_be_deleted = np.arange(len(peaks_neg_e))[
y_padded_up_to_down_padded_e[peaks_neg_e] / float(neg_peaks_max) < 0.3]
diff_arg_neg_must_be_deleted = np.diff(arg_neg_must_be_deleted)
arg_diff = np.array(range(len(diff_arg_neg_must_be_deleted)))
@ -75,11 +79,14 @@ def dedup_separate_lines(img_patch, contour_text_interest, thetha, axis):
clusters_to_be_deleted = []
if len(arg_diff_cluster) > 0:
clusters_to_be_deleted.append(arg_neg_must_be_deleted[0 : arg_diff_cluster[0] + 1])
clusters_to_be_deleted.append(
arg_neg_must_be_deleted[0 : arg_diff_cluster[0] + 1])
for i in range(len(arg_diff_cluster) - 1):
clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[i] + 1 :
clusters_to_be_deleted.append(
arg_neg_must_be_deleted[arg_diff_cluster[i] + 1 :
arg_diff_cluster[i + 1] + 1])
clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[len(arg_diff_cluster) - 1] + 1 :])
clusters_to_be_deleted.append(
arg_neg_must_be_deleted[arg_diff_cluster[len(arg_diff_cluster) - 1] + 1 :])
if len(clusters_to_be_deleted) > 0:
peaks_new_extra = []
for m in range(len(clusters_to_be_deleted)):
@ -135,13 +142,12 @@ def dedup_separate_lines(img_patch, contour_text_interest, thetha, axis):
rotation_matrix)
def separate_lines(img_patch, contour_text_interest, thetha, x_help, y_help):
(h, w) = img_patch.shape[:2]
h, w = img_patch.shape[:2]
center = (w // 2, h // 2)
M = cv2.getRotationMatrix2D(center, -thetha, 1.0)
x_d = M[0, 2]
y_d = M[1, 2]
thetha = thetha / 180. * np.pi
rotation_matrix = np.array([[np.cos(thetha), -np.sin(thetha)], [np.sin(thetha), np.cos(thetha)]])
rotation_matrix = M[:2, :2]
contour_text_interest_copy = contour_text_interest.copy()
x_cont = contour_text_interest[:, 0, 0]
@ -176,7 +182,8 @@ def separate_lines(img_patch, contour_text_interest, thetha, x_help, y_help):
peaks_neg_e, _ = find_peaks(y_padded_up_to_down_padded_e, height=0)
neg_peaks_max=np.max(y_padded_up_to_down_padded_e[peaks_neg_e])
arg_neg_must_be_deleted= np.arange(len(peaks_neg_e))[y_padded_up_to_down_padded_e[peaks_neg_e]/float(neg_peaks_max)<0.3]
arg_neg_must_be_deleted = np.arange(len(peaks_neg_e))[
y_padded_up_to_down_padded_e[peaks_neg_e]/float(neg_peaks_max)<0.3]
diff_arg_neg_must_be_deleted=np.diff(arg_neg_must_be_deleted)
arg_diff=np.array(range(len(diff_arg_neg_must_be_deleted)))
@ -236,7 +243,8 @@ def separate_lines(img_patch, contour_text_interest, thetha, x_help, y_help):
try:
neg_peaks_max=np.max(y_padded_smoothed[peaks])
arg_neg_must_be_deleted= np.arange(len(peaks_neg))[y_padded_up_to_down_padded[peaks_neg]/float(neg_peaks_max)<0.42]
arg_neg_must_be_deleted = np.arange(len(peaks_neg))[
y_padded_up_to_down_padded[peaks_neg]/float(neg_peaks_max)<0.42]
diff_arg_neg_must_be_deleted=np.diff(arg_neg_must_be_deleted)
arg_diff=np.array(range(len(diff_arg_neg_must_be_deleted)))
@ -313,23 +321,36 @@ def separate_lines(img_patch, contour_text_interest, thetha, x_help, y_help):
if peaks_values[jj]>mean_value_of_peaks-std_value_of_peaks/2.:
point_up = peaks[jj] + first_nonzero - int(1.3 * dis_to_next_up) ##+int(dis_to_next_up*1./4.0)
point_down =y_max_cont-1##peaks[jj] + first_nonzero + int(1.3 * dis_to_next_down) #point_up# np.max(y_cont)#peaks[jj] + first_nonzero + int(1.4 * dis_to_next_down) ###-int(dis_to_next_down*1./4.0)
point_down =y_max_cont-1
##peaks[jj] + first_nonzero + int(1.3 * dis_to_next_down)
#point_up
# np.max(y_cont)#peaks[jj] + first_nonzero + int(1.4 * dis_to_next_down)
###-int(dis_to_next_down*1./4.0)
else:
point_up = peaks[jj] + first_nonzero - int(1.4 * dis_to_next_up) ##+int(dis_to_next_up*1./4.0)
point_down =y_max_cont-1##peaks[jj] + first_nonzero + int(1.6 * dis_to_next_down) #point_up# np.max(y_cont)#peaks[jj] + first_nonzero + int(1.4 * dis_to_next_down) ###-int(dis_to_next_down*1./4.0)
point_down =y_max_cont-1
##peaks[jj] + first_nonzero + int(1.6 * dis_to_next_down)
#point_up
# np.max(y_cont)#peaks[jj] + first_nonzero + int(1.4 * dis_to_next_down)
###-int(dis_to_next_down*1./4.0)
point_down_narrow = peaks[jj] + first_nonzero + int(
1.4 * dis_to_next_down) ###-int(dis_to_next_down*1./2)
1.4 * dis_to_next_down)
###-int(dis_to_next_down*1./2)
else:
dis_to_next_up = abs(peaks[jj] - peaks_neg[jj])
dis_to_next_down = abs(peaks[jj] - peaks_neg[jj + 1])
if peaks_values[jj]>mean_value_of_peaks-std_value_of_peaks/2.:
point_up = peaks[jj] + first_nonzero - int(1.1 * dis_to_next_up) ##+int(dis_to_next_up*1./4.0)
point_down = peaks[jj] + first_nonzero + int(1.1 * dis_to_next_down) ###-int(dis_to_next_down*1./4.0)
point_up = peaks[jj] + first_nonzero - int(1.1 * dis_to_next_up)
##+int(dis_to_next_up*1./4.0)
point_down = peaks[jj] + first_nonzero + int(1.1 * dis_to_next_down)
###-int(dis_to_next_down*1./4.0)
else:
point_up = peaks[jj] + first_nonzero - int(1.23 * dis_to_next_up) ##+int(dis_to_next_up*1./4.0)
point_down = peaks[jj] + first_nonzero + int(1.33 * dis_to_next_down) ###-int(dis_to_next_down*1./4.0)
point_up = peaks[jj] + first_nonzero - int(1.23 * dis_to_next_up)
##+int(dis_to_next_up*1./4.0)
point_down = peaks[jj] + first_nonzero + int(1.33 * dis_to_next_down)
###-int(dis_to_next_down*1./4.0)
point_down_narrow = peaks[jj] + first_nonzero + int(
1.1 * dis_to_next_down) ###-int(dis_to_next_down*1./2)
@ -338,7 +359,9 @@ def separate_lines(img_patch, contour_text_interest, thetha, x_help, y_help):
point_down_narrow = img_patch.shape[0] - 2
distances = [cv2.pointPolygonTest(contour_text_interest_copy, tuple(int(x) for x in np.array([xv[mj], peaks[jj] + first_nonzero])), True)
distances = [cv2.pointPolygonTest(contour_text_interest_copy,
tuple(int(x) for x in np.array([xv[mj], peaks[jj] + first_nonzero])),
True)
for mj in range(len(xv))]
distances = np.array(distances)
@ -465,7 +488,8 @@ def separate_lines(img_patch, contour_text_interest, thetha, x_help, y_help):
point_up =peaks[jj] + first_nonzero - int(1. / 1.8 * dis_to_next)
distances = [cv2.pointPolygonTest(contour_text_interest_copy,
tuple(int(x) for x in np.array([xv[mj], peaks[jj] + first_nonzero])), True)
tuple(int(x) for x in np.array([xv[mj], peaks[jj] + first_nonzero])),
True)
for mj in range(len(xv))]
distances = np.array(distances)
@ -540,7 +564,8 @@ def separate_lines(img_patch, contour_text_interest, thetha, x_help, y_help):
point_down = peaks[jj] + first_nonzero + int(1. / 1.9 * dis_to_next_down)
distances = [cv2.pointPolygonTest(contour_text_interest_copy,
tuple(int(x) for x in np.array([xv[mj], peaks[jj] + first_nonzero])), True)
tuple(int(x) for x in np.array([xv[mj], peaks[jj] + first_nonzero])),
True)
for mj in range(len(xv))]
distances = np.array(distances)
@ -610,7 +635,8 @@ def separate_lines_vertical(img_patch, contour_text_interest, thetha):
neg_peaks_max = np.max(y_padded_up_to_down_padded[peaks_neg])
arg_neg_must_be_deleted = np.arange(len(peaks_neg))[y_padded_up_to_down_padded[peaks_neg] / float(neg_peaks_max) < 0.42]
arg_neg_must_be_deleted = np.arange(len(peaks_neg))[
y_padded_up_to_down_padded[peaks_neg] / float(neg_peaks_max) < 0.42]
diff_arg_neg_must_be_deleted = np.diff(arg_neg_must_be_deleted)
arg_diff = np.array(range(len(diff_arg_neg_must_be_deleted)))
@ -686,30 +712,50 @@ def separate_lines_vertical(img_patch, contour_text_interest, thetha):
dis_to_next_down = abs(peaks[jj] - peaks_neg[jj + 1])
if peaks_values[jj] > mean_value_of_peaks - std_value_of_peaks / 2.0:
point_up = peaks[jj] + first_nonzero - int(1.3 * dis_to_next_up) ##+int(dis_to_next_up*1./4.0)
point_down = x_max_cont - 1 ##peaks[jj] + first_nonzero + int(1.3 * dis_to_next_down) #point_up# np.max(y_cont)#peaks[jj] + first_nonzero + int(1.4 * dis_to_next_down) ###-int(dis_to_next_down*1./4.0)
point_up = peaks[jj] + first_nonzero - int(1.3 * dis_to_next_up)
##+int(dis_to_next_up*1./4.0)
point_down = x_max_cont - 1
##peaks[jj] + first_nonzero + int(1.3 * dis_to_next_down)
#point_up
# np.max(y_cont)#peaks[jj] + first_nonzero + int(1.4 * dis_to_next_down)
###-int(dis_to_next_down*1./4.0)
else:
point_up = peaks[jj] + first_nonzero - int(1.4 * dis_to_next_up) ##+int(dis_to_next_up*1./4.0)
point_down = x_max_cont - 1 ##peaks[jj] + first_nonzero + int(1.6 * dis_to_next_down) #point_up# np.max(y_cont)#peaks[jj] + first_nonzero + int(1.4 * dis_to_next_down) ###-int(dis_to_next_down*1./4.0)
point_up = peaks[jj] + first_nonzero - int(1.4 * dis_to_next_up)
##+int(dis_to_next_up*1./4.0)
point_down = x_max_cont - 1
##peaks[jj] + first_nonzero + int(1.6 * dis_to_next_down)
#point_up
# np.max(y_cont)
#peaks[jj] + first_nonzero + int(1.4 * dis_to_next_down)
###-int(dis_to_next_down*1./4.0)
point_down_narrow = peaks[jj] + first_nonzero + int(1.4 * dis_to_next_down) ###-int(dis_to_next_down*1./2)
point_down_narrow = peaks[jj] + first_nonzero + int(1.4 * dis_to_next_down)
###-int(dis_to_next_down*1./2)
else:
dis_to_next_up = abs(peaks[jj] - peaks_neg[jj])
dis_to_next_down = abs(peaks[jj] - peaks_neg[jj + 1])
if peaks_values[jj] > mean_value_of_peaks - std_value_of_peaks / 2.0:
point_up = peaks[jj] + first_nonzero - int(1.1 * dis_to_next_up) ##+int(dis_to_next_up*1./4.0)
point_down = peaks[jj] + first_nonzero + int(1.1 * dis_to_next_down) ###-int(dis_to_next_down*1./4.0)
point_up = peaks[jj] + first_nonzero - int(1.1 * dis_to_next_up)
##+int(dis_to_next_up*1./4.0)
point_down = peaks[jj] + first_nonzero + int(1.1 * dis_to_next_down)
###-int(dis_to_next_down*1./4.0)
else:
point_up = peaks[jj] + first_nonzero - int(1.23 * dis_to_next_up) ##+int(dis_to_next_up*1./4.0)
point_down = peaks[jj] + first_nonzero + int(1.33 * dis_to_next_down) ###-int(dis_to_next_down*1./4.0)
point_up = peaks[jj] + first_nonzero - int(1.23 * dis_to_next_up)
##+int(dis_to_next_up*1./4.0)
point_down = peaks[jj] + first_nonzero + int(1.33 * dis_to_next_down)
###-int(dis_to_next_down*1./4.0)
point_down_narrow = peaks[jj] + first_nonzero + int(1.1 * dis_to_next_down) ###-int(dis_to_next_down*1./2)
point_down_narrow = peaks[jj] + first_nonzero + int(1.1 * dis_to_next_down)
###-int(dis_to_next_down*1./2)
if point_down_narrow >= img_patch.shape[0]:
point_down_narrow = img_patch.shape[0] - 2
distances = [cv2.pointPolygonTest(contour_text_interest_copy, tuple(int(x) for x in np.array([xv[mj], peaks[jj] + first_nonzero])), True) for mj in range(len(xv))]
distances = [cv2.pointPolygonTest(contour_text_interest_copy,
tuple(int(x) for x in np.array([xv[mj], peaks[jj] + first_nonzero])),
True)
for mj in range(len(xv))]
distances = np.array(distances)
xvinside = xv[distances >= 0]
@ -798,7 +844,8 @@ def separate_lines_vertical(img_patch, contour_text_interest, thetha):
point_up = peaks[jj] + first_nonzero - int(1.0 / 1.8 * dis_to_next)
distances = [cv2.pointPolygonTest(contour_text_interest_copy,
tuple(int(x) for x in np.array([xv[mj], peaks[jj] + first_nonzero])), True)
tuple(int(x) for x in np.array([xv[mj], peaks[jj] + first_nonzero])),
True)
for mj in range(len(xv))]
distances = np.array(distances)
@ -863,7 +910,8 @@ def separate_lines_vertical(img_patch, contour_text_interest, thetha):
point_down = peaks[jj] + first_nonzero + int(1.0 / 1.9 * dis_to_next_down)
distances = [cv2.pointPolygonTest(contour_text_interest_copy,
tuple(int(x) for x in np.array([xv[mj], peaks[jj] + first_nonzero])), True)
tuple(int(x) for x in np.array([xv[mj], peaks[jj] + first_nonzero])),
True)
for mj in range(len(xv))]
distances = np.array(distances)
@ -947,7 +995,8 @@ def separate_lines_new_inside_tiles2(img_patch, thetha):
peaks_neg_e, _ = find_peaks(y_padded_up_to_down_padded_e, height=0)
neg_peaks_max = np.max(y_padded_up_to_down_padded_e[peaks_neg_e])
arg_neg_must_be_deleted = np.arange(len(peaks_neg_e))[y_padded_up_to_down_padded_e[peaks_neg_e] / float(neg_peaks_max) < 0.3]
arg_neg_must_be_deleted = np.arange(len(peaks_neg_e))[
y_padded_up_to_down_padded_e[peaks_neg_e] / float(neg_peaks_max) < 0.3]
diff_arg_neg_must_be_deleted = np.diff(arg_neg_must_be_deleted)
arg_diff = np.array(range(len(diff_arg_neg_must_be_deleted)))
@ -960,8 +1009,11 @@ def separate_lines_new_inside_tiles2(img_patch, thetha):
if len(arg_diff_cluster) > 0:
clusters_to_be_deleted.append(arg_neg_must_be_deleted[0 : arg_diff_cluster[0] + 1])
for i in range(len(arg_diff_cluster) - 1):
clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[i] + 1 : arg_diff_cluster[i + 1] + 1])
clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[len(arg_diff_cluster) - 1] + 1 :])
clusters_to_be_deleted.append(
arg_neg_must_be_deleted[arg_diff_cluster[i] + 1:
arg_diff_cluster[i + 1] + 1])
clusters_to_be_deleted.append(
arg_neg_must_be_deleted[arg_diff_cluster[len(arg_diff_cluster) - 1] + 1 :])
if len(clusters_to_be_deleted) > 0:
peaks_new_extra = []
for m in range(len(clusters_to_be_deleted)):
@ -1011,7 +1063,8 @@ def separate_lines_new_inside_tiles2(img_patch, thetha):
try:
neg_peaks_max = np.max(y_padded_smoothed[peaks])
arg_neg_must_be_deleted = np.arange(len(peaks_neg))[y_padded_up_to_down_padded[peaks_neg] / float(neg_peaks_max) < 0.24]
arg_neg_must_be_deleted = np.arange(len(peaks_neg))[
y_padded_up_to_down_padded[peaks_neg] / float(neg_peaks_max) < 0.24]
diff_arg_neg_must_be_deleted = np.diff(arg_neg_must_be_deleted)
arg_diff = np.array(range(len(diff_arg_neg_must_be_deleted)))
@ -1174,7 +1227,8 @@ def separate_lines_new_inside_tiles(img_path, thetha):
if diff_peaks[i] > cut_off:
if not np.isnan(forest[np.argmin(z[forest])]):
peaks_neg_true.append(forest[np.argmin(z[forest])])
forest = [peaks_neg[i + 1]]
forest = []
forest.append(peaks_neg[i + 1])
if i == (len(peaks_neg) - 1):
if not np.isnan(forest[np.argmin(z[forest])]):
peaks_neg_true.append(forest[np.argmin(z[forest])])
@ -1194,7 +1248,8 @@ def separate_lines_new_inside_tiles(img_path, thetha):
if diff_peaks_pos[i] > cut_off:
if not np.isnan(forest[np.argmax(z[forest])]):
peaks_pos_true.append(forest[np.argmax(z[forest])])
forest = [peaks[i + 1]]
forest = []
forest.append(peaks[i + 1])
if i == (len(peaks) - 1):
if not np.isnan(forest[np.argmax(z[forest])]):
peaks_pos_true.append(forest[np.argmax(z[forest])])
@ -1246,19 +1301,16 @@ def separate_lines_new_inside_tiles(img_path, thetha):
def separate_lines_vertical_cont(img_patch, contour_text_interest, thetha, box_ind, add_boxes_coor_into_textlines):
kernel = np.ones((5, 5), np.uint8)
pixel = 255
label = 255
min_area = 0
max_area = 1
if len(img_patch.shape) == 3:
cnts_images = (img_patch[:, :, 0] == pixel) * 1
if img_patch.ndim == 3:
cnts_images = (img_patch[:, :, 0] == label) * 1
else:
cnts_images = (img_patch[:, :] == pixel) * 1
cnts_images = cnts_images.astype(np.uint8)
cnts_images = np.repeat(cnts_images[:, :, np.newaxis], 3, axis=2)
imgray = cv2.cvtColor(cnts_images, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
contours_imgs, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
cnts_images = (img_patch[:, :] == label) * 1
_, thresh = cv2.threshold(cnts_images.astype(np.uint8), 0, 255, 0)
contours_imgs, hierarchy = cv2.findContours(thresh.astype(np.uint8), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
contours_imgs = return_parent_contours(contours_imgs, hierarchy)
contours_imgs = filter_contours_area_of_image_tables(thresh,
@ -1266,14 +1318,12 @@ def separate_lines_vertical_cont(img_patch, contour_text_interest, thetha, box_i
max_area=max_area, min_area=min_area)
cont_final = []
for i in range(len(contours_imgs)):
img_contour = np.zeros((cnts_images.shape[0], cnts_images.shape[1], 3))
img_contour = cv2.fillPoly(img_contour, pts=[contours_imgs[i]], color=(255, 255, 255))
img_contour = img_contour.astype(np.uint8)
img_contour = np.zeros(cnts_images.shape[:2], dtype=np.uint8)
img_contour = cv2.fillPoly(img_contour, pts=[contours_imgs[i]], color=255)
img_contour = cv2.dilate(img_contour, kernel, iterations=4)
imgrayrot = cv2.cvtColor(img_contour, cv2.COLOR_BGR2GRAY)
_, threshrot = cv2.threshold(imgrayrot, 0, 255, 0)
contours_text_rot, _ = cv2.findContours(threshrot.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
_, threshrot = cv2.threshold(img_contour, 0, 255, 0)
contours_text_rot, _ = cv2.findContours(threshrot.astype(np.uint8), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
##contour_text_copy[:, 0, 0] = contour_text_copy[:, 0, 0] - box_ind[
##0]
@ -1285,25 +1335,25 @@ def separate_lines_vertical_cont(img_patch, contour_text_interest, thetha, box_i
return None, cont_final
def textline_contours_postprocessing(textline_mask, slope, contour_text_interest, box_ind, add_boxes_coor_into_textlines=False):
textline_mask = np.repeat(textline_mask[:, :, np.newaxis], 3, axis=2) * 255
textline_mask = textline_mask.astype(np.uint8)
def textline_contours_postprocessing(textline_mask, slope,
contour_text_interest, box_ind,
add_boxes_coor_into_textlines=False):
textline_mask = textline_mask * 255
kernel = np.ones((5, 5), np.uint8)
textline_mask = cv2.morphologyEx(textline_mask, cv2.MORPH_OPEN, kernel)
textline_mask = cv2.morphologyEx(textline_mask, cv2.MORPH_CLOSE, kernel)
textline_mask = cv2.erode(textline_mask, kernel, iterations=2)
# textline_mask = cv2.erode(textline_mask, kernel, iterations=1)
try:
x_help = 30
y_help = 2
textline_mask_help = np.zeros((textline_mask.shape[0] + int(2 * y_help),
textline_mask.shape[1] + int(2 * x_help), 3))
textline_mask.shape[1] + int(2 * x_help)))
textline_mask_help[y_help : y_help + textline_mask.shape[0],
x_help : x_help + textline_mask.shape[1], :] = np.copy(textline_mask[:, :, :])
x_help : x_help + textline_mask.shape[1]] = np.copy(textline_mask[:, :])
dst = rotate_image(textline_mask_help, slope)
dst = dst[:, :, 0]
dst[dst != 0] = 1
# if np.abs(slope)>.5 and textline_mask.shape[0]/float(textline_mask.shape[1])>3:
@ -1314,21 +1364,18 @@ def textline_contours_postprocessing(textline_mask, slope, contour_text_interest
contour_text_copy[:, 0, 0] = contour_text_copy[:, 0, 0] - box_ind[0]
contour_text_copy[:, 0, 1] = contour_text_copy[:, 0, 1] - box_ind[1]
img_contour = np.zeros((box_ind[3], box_ind[2], 3))
img_contour = cv2.fillPoly(img_contour, pts=[contour_text_copy], color=(255, 255, 255))
img_contour = np.zeros((box_ind[3], box_ind[2]))
img_contour = cv2.fillPoly(img_contour, pts=[contour_text_copy], color=255)
img_contour_help = np.zeros((img_contour.shape[0] + int(2 * y_help),
img_contour.shape[1] + int(2 * x_help), 3))
img_contour.shape[1] + int(2 * x_help)))
img_contour_help[y_help : y_help + img_contour.shape[0],
x_help : x_help + img_contour.shape[1], :] = np.copy(img_contour[:, :, :])
x_help : x_help + img_contour.shape[1]] = np.copy(img_contour[:, :])
img_contour_rot = rotate_image(img_contour_help, slope)
img_contour_rot = img_contour_rot.astype(np.uint8)
# dst_help = dst_help.astype(np.uint8)
imgrayrot = cv2.cvtColor(img_contour_rot, cv2.COLOR_BGR2GRAY)
_, threshrot = cv2.threshold(imgrayrot, 0, 255, 0)
contours_text_rot, _ = cv2.findContours(threshrot.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
_, threshrot = cv2.threshold(img_contour_rot, 0, 255, 0)
contours_text_rot, _ = cv2.findContours(threshrot.astype(np.uint8), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
len_con_text_rot = [len(contours_text_rot[ib]) for ib in range(len(contours_text_rot))]
ind_big_con = np.argmax(len_con_text_rot)
@ -1340,29 +1387,29 @@ def textline_contours_postprocessing(textline_mask, slope, contour_text_interest
else:
_, contours_rotated_clean = separate_lines(
dst, contours_text_rot[ind_big_con], slope, x_help, y_help)
except:
contours_rotated_clean = []
return contours_rotated_clean
def separate_lines_new2(img_path, thetha, num_col, slope_region, logger=None, plotter=None):
def separate_lines_new2(img_crop, thetha, num_col, slope_region, logger=None, plotter=None):
if logger is None:
logger = getLogger(__package__)
if not np.prod(img_crop.shape):
return img_crop
if num_col == 1:
num_patches = int(img_path.shape[1] / 200.0)
num_patches = int(img_crop.shape[1] / 200.0)
else:
num_patches = int(img_path.shape[1] / 140.0)
# num_patches=int(img_path.shape[1]/200.)
num_patches = int(img_crop.shape[1] / 140.0)
# num_patches=int(img_crop.shape[1]/200.)
if num_patches == 0:
num_patches = 1
img_patch_ineterst = img_path[:, :] # [peaks_neg_true[14]-dis_up:peaks_neg_true[15]+dis_down ,:]
img_patch_interest = img_crop[:, :] # [peaks_neg_true[14]-dis_up:peaks_neg_true[15]+dis_down ,:]
# plt.imshow(img_patch_ineterst)
# plt.imshow(img_patch_interest)
# plt.show()
length_x = int(img_path.shape[1] / float(num_patches))
length_x = int(img_crop.shape[1] / float(num_patches))
# margin = int(0.04 * length_x) just recently this was changed because it break lines into 2
margin = int(0.04 * length_x)
# if margin<=4:
@ -1370,7 +1417,7 @@ def separate_lines_new2(img_path, thetha, num_col, slope_region, logger=None, pl
# margin=0
width_mid = length_x - 2 * margin
nxf = img_path.shape[1] / float(width_mid)
nxf = img_crop.shape[1] / float(width_mid)
if nxf > int(nxf):
nxf = int(nxf) + 1
@ -1386,12 +1433,12 @@ def separate_lines_new2(img_path, thetha, num_col, slope_region, logger=None, pl
index_x_d = i * width_mid
index_x_u = index_x_d + length_x
if index_x_u > img_path.shape[1]:
index_x_u = img_path.shape[1]
index_x_d = img_path.shape[1] - length_x
if index_x_u > img_crop.shape[1]:
index_x_u = img_crop.shape[1]
index_x_d = img_crop.shape[1] - length_x
# img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
img_xline = img_patch_ineterst[:, index_x_d:index_x_u]
img_xline = img_patch_interest[:, index_x_d:index_x_u]
try:
assert img_xline.any()
@ -1407,9 +1454,9 @@ def separate_lines_new2(img_path, thetha, num_col, slope_region, logger=None, pl
img_line_rotated = rotate_image(img_xline, slope_xline)
img_line_rotated[:, :][img_line_rotated[:, :] != 0] = 1
img_patch_ineterst = img_path[:, :] # [peaks_neg_true[14]-dis_up:peaks_neg_true[14]+dis_down ,:]
img_patch_interest = img_crop[:, :] # [peaks_neg_true[14]-dis_up:peaks_neg_true[14]+dis_down ,:]
img_patch_ineterst_revised = np.zeros(img_patch_ineterst.shape)
img_patch_interest_revised = np.zeros(img_patch_interest.shape)
for i in range(nxf):
if i == 0:
@ -1419,18 +1466,18 @@ def separate_lines_new2(img_path, thetha, num_col, slope_region, logger=None, pl
index_x_d = i * width_mid
index_x_u = index_x_d + length_x
if index_x_u > img_path.shape[1]:
index_x_u = img_path.shape[1]
index_x_d = img_path.shape[1] - length_x
if index_x_u > img_crop.shape[1]:
index_x_u = img_crop.shape[1]
index_x_d = img_crop.shape[1] - length_x
img_xline = img_patch_ineterst[:, index_x_d:index_x_u]
img_xline = img_patch_interest[:, index_x_d:index_x_u]
img_int = np.zeros((img_xline.shape[0], img_xline.shape[1]))
img_int[:, :] = img_xline[:, :] # img_patch_org[:,:,0]
img_resized = np.zeros((int(img_int.shape[0] * 1.2), int(img_int.shape[1] * 3)))
img_resized[int(img_int.shape[0] * 0.1): int(img_int.shape[0] * 0.1) + img_int.shape[0],
int(img_int.shape[1] * 1.0): int(img_int.shape[1] * 1.0) + img_int.shape[1]] = img_int[:, :]
img_resized = np.zeros((int(img_int.shape[0] * (1.2)), int(img_int.shape[1] * (3))))
img_resized[int(img_int.shape[0] * (0.1)) : int(img_int.shape[0] * (0.1)) + img_int.shape[0],
int(img_int.shape[1] * (1.0)) : int(img_int.shape[1] * (1.0)) + img_int.shape[1]] = img_int[:, :]
# plt.imshow(img_xline)
# plt.show()
img_line_rotated = rotate_image(img_resized, slopes_tile_wise[i])
@ -1442,15 +1489,16 @@ def separate_lines_new2(img_path, thetha, num_col, slope_region, logger=None, pl
img_patch_separated_returned[:, :][img_patch_separated_returned[:, :] != 0] = 1
img_patch_separated_returned_true_size = img_patch_separated_returned[
int(img_int.shape[0] * 0.1): int(img_int.shape[0] * 0.1) + img_int.shape[0],
int(img_int.shape[1] * 1.0): int(img_int.shape[1] * 1.0) + img_int.shape[1]]
int(img_int.shape[0] * (0.1)) : int(img_int.shape[0] * (0.1)) + img_int.shape[0],
int(img_int.shape[1] * (1.0)) : int(img_int.shape[1] * (1.0)) + img_int.shape[1]]
img_patch_separated_returned_true_size = img_patch_separated_returned_true_size[:, margin : length_x - margin]
img_patch_ineterst_revised[:, index_x_d + margin : index_x_u - margin] = img_patch_separated_returned_true_size
img_patch_interest_revised[:, index_x_d + margin : index_x_u - margin] = img_patch_separated_returned_true_size
return img_patch_ineterst_revised
return img_patch_interest_revised
def do_image_rotation(angle, img, sigma_des, logger=None):
@wrap_ndarray_shared(kw='img')
def do_image_rotation(angle, img=None, sigma_des=1.0, logger=None):
if logger is None:
logger = getLogger(__package__)
img_rot = rotate_image(img, angle)
@ -1463,7 +1511,7 @@ def do_image_rotation(angle, img, sigma_des, logger=None):
return var
def return_deskew_slop(img_patch_org, sigma_des,n_tot_angles=100,
main_page=False, logger=None, plotter=None, map=map):
main_page=False, logger=None, plotter=None, map=None):
if main_page and plotter:
plotter.save_plot_of_textline_density(img_patch_org)
@ -1471,165 +1519,81 @@ def return_deskew_slop(img_patch_org, sigma_des,n_tot_angles=100,
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)))
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=np.zeros((int( img_int.shape[0]*(1.8) ) , int( img_int.shape[1]*(2.6) ) ))
#img_resized[ int( img_int.shape[0]*(.4)):int( img_int.shape[0]*(.4))+img_int.shape[0] , int( img_int.shape[1]*(.8)):int( img_int.shape[1]*(.8))+img_int.shape[1] ]=img_int[:,:]
#img_resized[ int( img_int.shape[0]*(.4)):int( img_int.shape[0]*(.4))+img_int.shape[0],
# int( img_int.shape[1]*(.8)):int( img_int.shape[1]*(.8))+img_int.shape[1] ]=img_int[:,:]
img_resized[ onset_y:onset_y+img_int.shape[0] , onset_x:onset_x+img_int.shape[1] ]=img_int[:,:]
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(img_resized, sigma_des, angles, map=map, logger=logger, plotter=plotter)
angle, _ = get_smallest_skew(img_resized, sigma_des, angles, map=map, logger=logger, plotter=plotter)
angles = np.linspace(angle - 22.5, angle + 22.5, n_tot_angles)
angle = get_smallest_skew(img_resized, sigma_des, angles, map=map, logger=logger, plotter=plotter)
angle, _ = get_smallest_skew(img_resized, sigma_des, angles, map=map, logger=logger, plotter=plotter)
elif main_page:
angles = np.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)
#angles = np.linspace(-12, 12, n_tot_angles)#np.array([0 , 45 , 90 , -45])
angles = np.concatenate((np.linspace(-12, -7, n_tot_angles // 4),
np.linspace(-6, 6, n_tot_angles // 2),
np.linspace(7, 12, n_tot_angles // 4)))
angle, var = get_smallest_skew(img_resized, sigma_des, angles, map=map, logger=logger, plotter=plotter)
early_slope_edge=11
if abs(angle) > early_slope_edge:
if angle < 0:
angles = np.linspace(-90, -12, n_tot_angles)
angles2 = np.linspace(-90, -12, n_tot_angles)
else:
angles = np.linspace(90, 12, n_tot_angles)
angle = get_smallest_skew(img_resized, sigma_des, angles, map=map, logger=logger, plotter=plotter)
angles2 = np.linspace(90, 12, n_tot_angles)
angle2, var2 = get_smallest_skew(img_resized, sigma_des, angles2, map=map, logger=logger, plotter=plotter)
if var2 > var:
angle = angle2
else:
angles = np.linspace(-25, 25, int(0.5 * n_tot_angles) + 10)
angle = get_smallest_skew(img_resized, sigma_des, angles, map=map, logger=logger, plotter=plotter)
angle, var = get_smallest_skew(img_resized, sigma_des, angles, map=map, logger=logger, plotter=plotter)
early_slope_edge=22
if abs(angle) > early_slope_edge:
if angle < 0:
angles = np.linspace(-90, -25, int(0.5 * n_tot_angles) + 10)
angles2 = 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(img_resized, sigma_des, angles, map=map, logger=logger, plotter=plotter)
angles2 = np.linspace(90, 25, int(0.5 * n_tot_angles) + 10)
angle2, var2 = get_smallest_skew(img_resized, sigma_des, angles2, map=map, logger=logger, plotter=plotter)
if var2 > var:
angle = angle2
return angle
def get_smallest_skew(img, sigma_des, angles, logger=None, plotter=None, map=map):
if logger is None:
logger = getLogger(__package__)
results = list(map(partial(do_image_rotation, img=img, sigma_des=sigma_des, logger=logger), angles))
if map is None:
results = [do_image_rotation.__wrapped__(angle, img=img, sigma_des=sigma_des, logger=logger)
for angle in angles]
else:
with share_ndarray(img) as img_shared:
results = list(map(partial(do_image_rotation, img=img_shared, sigma_des=sigma_des, logger=None),
angles))
if plotter:
plotter.save_plot_of_rotation_angle(angles, results)
try:
var_res = np.array(results)
assert var_res.any()
angle = angles[np.argmax(var_res)]
idx = np.argmax(var_res)
angle = angles[idx]
var = var_res[idx]
except:
logger.exception("cannot determine best angle among %s", str(angles))
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
var = 0
return angle, var
@wrap_ndarray_shared(kw='textline_mask_tot_ea')
def do_work_of_slopes_new(
box_text, contour, contour_par, index_r_con,
textline_mask_tot_ea, image_page_rotated, slope_deskew,
box_text, contour, contour_par,
textline_mask_tot_ea=None, slope_deskew=0.0,
logger=None, MAX_SLOPE=999, KERNEL=None, plotter=None
):
if KERNEL is None:
@ -1639,7 +1603,7 @@ def do_work_of_slopes_new(
logger.debug('enter do_work_of_slopes_new')
x, y, w, h = box_text
_, crop_coor = crop_image_inside_box(box_text, image_page_rotated)
crop_coor = box2rect(box_text)
mask_textline = np.zeros(textline_mask_tot_ea.shape)
mask_textline = cv2.fillPoly(mask_textline, pts=[contour], color=(1,1,1))
all_text_region_raw = textline_mask_tot_ea * mask_textline
@ -1647,7 +1611,7 @@ def do_work_of_slopes_new(
img_int_p = all_text_region_raw[:,:]
img_int_p = cv2.erode(img_int_p, KERNEL, iterations=2)
if img_int_p.shape[0] /img_int_p.shape[1] < 0.1:
if not np.prod(img_int_p.shape) or img_int_p.shape[0] /img_int_p.shape[1] < 0.1:
slope = 0
slope_for_all = slope_deskew
all_text_region_raw = textline_mask_tot_ea[y: y + h, x: x + w]
@ -1683,11 +1647,14 @@ def do_work_of_slopes_new(
all_text_region_raw[mask_only_con_region == 0] = 0
cnt_clean_rot = textline_contours_postprocessing(all_text_region_raw, slope_for_all, contour_par, box_text)
return cnt_clean_rot, box_text, contour, contour_par, crop_coor, index_r_con, slope
return cnt_clean_rot, crop_coor, slope
@wrap_ndarray_shared(kw='textline_mask_tot_ea')
@wrap_ndarray_shared(kw='mask_texts_only')
def do_work_of_slopes_new_curved(
box_text, contour, contour_par, index_r_con,
textline_mask_tot_ea, image_page_rotated, mask_texts_only, num_col, scale_par, slope_deskew,
box_text, contour_par,
textline_mask_tot_ea=None, mask_texts_only=None,
num_col=1, scale_par=1.0, slope_deskew=0.0,
logger=None, MAX_SLOPE=999, KERNEL=None, plotter=None
):
if KERNEL is None:
@ -1704,7 +1671,7 @@ def do_work_of_slopes_new_curved(
# plt.imshow(img_int_p)
# plt.show()
if img_int_p.shape[0] / img_int_p.shape[1] < 0.1:
if not np.prod(img_int_p.shape) or img_int_p.shape[0] / img_int_p.shape[1] < 0.1:
slope = 0
slope_for_all = slope_deskew
else:
@ -1730,7 +1697,7 @@ def do_work_of_slopes_new_curved(
slope_for_all = slope_deskew
slope = slope_for_all
_, crop_coor = crop_image_inside_box(box_text, image_page_rotated)
crop_coor = box2rect(box_text)
if abs(slope_for_all) < 45:
textline_region_in_image = np.zeros(textline_mask_tot_ea.shape)
@ -1763,20 +1730,25 @@ def do_work_of_slopes_new_curved(
mask_biggest2 = cv2.dilate(mask_biggest2, KERNEL, iterations=4)
pixel_img = 1
mask_biggest2 = resize_image(mask_biggest2, int(mask_biggest2.shape[0] * scale_par), int(mask_biggest2.shape[1] * scale_par))
mask_biggest2 = resize_image(mask_biggest2,
int(mask_biggest2.shape[0] * scale_par),
int(mask_biggest2.shape[1] * scale_par))
cnt_textlines_in_image_ind = return_contours_of_interested_textline(mask_biggest2, pixel_img)
try:
textlines_cnt_per_region.append(cnt_textlines_in_image_ind[0])
except Exception as why:
logger.error(why)
else:
textlines_cnt_per_region = textline_contours_postprocessing(all_text_region_raw, slope_for_all, contour_par, box_text, True)
textlines_cnt_per_region = textline_contours_postprocessing(all_text_region_raw,
slope_for_all, contour_par,
box_text, True)
return textlines_cnt_per_region[::-1], box_text, contour, contour_par, crop_coor, index_r_con, slope
return textlines_cnt_per_region[::-1], crop_coor, slope
@wrap_ndarray_shared(kw='textline_mask_tot_ea')
def do_work_of_slopes_new_light(
box_text, contour, contour_par, index_r_con,
textline_mask_tot_ea, image_page_rotated, slope_deskew, textline_light,
box_text, contour, contour_par,
textline_mask_tot_ea=None, slope_deskew=0, textline_light=True,
logger=None
):
if logger is None:
@ -1784,7 +1756,7 @@ def do_work_of_slopes_new_light(
logger.debug('enter do_work_of_slopes_new_light')
x, y, w, h = box_text
_, crop_coor = crop_image_inside_box(box_text, image_page_rotated)
crop_coor = box2rect(box_text)
mask_textline = np.zeros(textline_mask_tot_ea.shape)
mask_textline = cv2.fillPoly(mask_textline, pts=[contour], color=(1,1,1))
all_text_region_raw = textline_mask_tot_ea * mask_textline
@ -1805,4 +1777,4 @@ def do_work_of_slopes_new_light(
all_text_region_raw[mask_only_con_region == 0] = 0
cnt_clean_rot = textline_contours_postprocessing(all_text_region_raw, slope_deskew, contour_par, box_text)
return cnt_clean_rot, box_text, contour, contour_par, crop_coor, index_r_con, slope_deskew
return cnt_clean_rot, crop_coor, slope_deskew

45
src/eynollah/utils/shm.py Normal file
View file

@ -0,0 +1,45 @@
from multiprocessing import shared_memory
from contextlib import contextmanager
from functools import wraps
import numpy as np
@contextmanager
def share_ndarray(array: np.ndarray):
size = np.dtype(array.dtype).itemsize * np.prod(array.shape)
shm = shared_memory.SharedMemory(create=True, size=size)
try:
shared_array = np.ndarray(array.shape, dtype=array.dtype, buffer=shm.buf)
shared_array[:] = array[:]
shared_array.flags["WRITEABLE"] = False
yield dict(shape=array.shape, dtype=array.dtype, name=shm.name)
finally:
shm.close()
shm.unlink()
@contextmanager
def ndarray_shared(array: dict):
shm = shared_memory.SharedMemory(name=array['name'])
try:
array = np.ndarray(array['shape'], dtype=array['dtype'], buffer=shm.buf)
yield array
finally:
shm.close()
def wrap_ndarray_shared(kw=None):
def wrapper(f):
if kw is None:
@wraps(f)
def shared_func(array, *args, **kwargs):
with ndarray_shared(array) as ndarray:
return f(ndarray, *args, **kwargs)
return shared_func
else:
@wraps(f)
def shared_func(*args, **kwargs):
array = kwargs.pop(kw)
with ndarray_shared(array) as ndarray:
kwargs[kw] = ndarray
return f(*args, **kwargs)
return shared_func
return wrapper

34
src/eynollah/utils/utils_ocr.py
View file

@ -1,13 +1,17 @@
import math
import copy
import numpy as np
import cv2
import tensorflow as tf
from scipy.signal import find_peaks
from scipy.ndimage import gaussian_filter1d
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.
@ -92,6 +96,7 @@ def return_start_and_end_of_common_text_of_textline_ocr_without_common_section(t
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
@ -369,7 +374,13 @@ def return_textline_contour_with_added_box_coordinate(textline_contour, box_ind
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):
def return_rnn_cnn_ocr_of_given_textlines(image,
all_found_textline_polygons,
all_box_coord,
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
@ -425,17 +436,23 @@ def return_rnn_cnn_ocr_of_given_textlines(image, all_found_textline_polygons, pr
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)
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)
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)
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)
@ -468,7 +485,12 @@ def return_rnn_cnn_ocr_of_given_textlines(image, all_found_textline_polygons, pr
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 = [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)

10
src/eynollah/utils/xml.py
View file

@ -57,19 +57,15 @@ def xml_reading_order(page, order_of_texts, id_of_marginalia_left, id_of_margina
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)))
for idx_textregion in order_of_texts:
og.add_RegionRefIndexed(RegionRefIndexedType(index=str(region_counter.get('region')), regionRef=region_counter.region_id(idx_textregion + 1)))
region_counter.inc('region')
for id_marginal in id_of_marginalia_right:
og.add_RegionRefIndexed(RegionRefIndexedType(index=str(region_counter.get('region')), regionRef=id_marginal))
region_counter.inc('region')
def order_and_id_of_texts(found_polygons_text_region, found_polygons_text_region_h, matrix_of_orders, indexes_sorted, index_of_types, kind_of_texts, ref_point):
indexes_sorted = np.array(indexes_sorted)
index_of_types = np.array(index_of_types)
kind_of_texts = np.array(kind_of_texts)
def order_and_id_of_texts(found_polygons_text_region, found_polygons_text_region_h, indexes_sorted, index_of_types, kind_of_texts, ref_point):
id_of_texts = []
order_of_texts = []

368
src/eynollah/writer.py
View file

@ -56,113 +56,30 @@ 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, 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 = ''
for l in range(len(all_found_textline_polygons_marginals[marginal_idx][j])):
if not (self.curved_line or self.textline_light):
if len(all_found_textline_polygons_marginals[marginal_idx][j][l]) == 2:
textline_x_coord = max(0, int((all_found_textline_polygons_marginals[marginal_idx][j][l][0] + all_box_coord_marginals[marginal_idx][2] + page_coord[2]) / self.scale_x) )
textline_y_coord = max(0, int((all_found_textline_polygons_marginals[marginal_idx][j][l][1] + all_box_coord_marginals[marginal_idx][0] + page_coord[0]) / self.scale_y) )
else:
textline_x_coord = max(0, int((all_found_textline_polygons_marginals[marginal_idx][j][l][0][0] + all_box_coord_marginals[marginal_idx][2] + page_coord[2]) / self.scale_x) )
textline_y_coord = max(0, int((all_found_textline_polygons_marginals[marginal_idx][j][l][0][1] + all_box_coord_marginals[marginal_idx][0] + page_coord[0]) / self.scale_y) )
points_co += str(textline_x_coord)
points_co += ','
points_co += str(textline_y_coord)
if (self.curved_line or self.textline_light) and np.abs(slopes_marginals[marginal_idx]) <= 45:
if len(all_found_textline_polygons_marginals[marginal_idx][j][l]) == 2:
points_co += str(int((all_found_textline_polygons_marginals[marginal_idx][j][l][0] + page_coord[2]) / self.scale_x))
points_co += ','
points_co += str(int((all_found_textline_polygons_marginals[marginal_idx][j][l][1] + page_coord[0]) / self.scale_y))
else:
points_co += str(int((all_found_textline_polygons_marginals[marginal_idx][j][l][0][0] + page_coord[2]) / self.scale_x))
points_co += ','
points_co += str(int((all_found_textline_polygons_marginals[marginal_idx][j][l][0][1] + page_coord[0]) / self.scale_y))
elif (self.curved_line or self.textline_light) and np.abs(slopes_marginals[marginal_idx]) > 45:
if len(all_found_textline_polygons_marginals[marginal_idx][j][l]) == 2:
points_co += str(int((all_found_textline_polygons_marginals[marginal_idx][j][l][0] + all_box_coord_marginals[marginal_idx][2] + page_coord[2]) / self.scale_x))
points_co += ','
points_co += str(int((all_found_textline_polygons_marginals[marginal_idx][j][l][1] + all_box_coord_marginals[marginal_idx][0] + page_coord[0]) / self.scale_y))
else:
points_co += str(int((all_found_textline_polygons_marginals[marginal_idx][j][l][0][0] + all_box_coord_marginals[marginal_idx][2] + page_coord[2]) / self.scale_x))
points_co += ','
points_co += str(int((all_found_textline_polygons_marginals[marginal_idx][j][l][0][1] + all_box_coord_marginals[marginal_idx][0] + page_coord[0]) / self.scale_y))
points_co += ' '
coords.set_points(points_co[:-1])
def serialize_lines_in_region(self, text_region, all_found_textline_polygons, region_idx, page_coord, all_box_coord, slopes, counter, ocr_all_textlines_textregion):
self.logger.debug('enter serialize_lines_in_region')
for j in range(len(all_found_textline_polygons[region_idx])):
for j, polygon_textline in enumerate(all_found_textline_polygons[region_idx]):
coords = CoordsType()
textline = TextLineType(id=counter.next_line_id, Coords=coords)
if ocr_all_textlines_textregion:
# FIXME: add OCR confidence
textline.set_TextEquiv([TextEquivType(Unicode=ocr_all_textlines_textregion[j])])
text_region.add_TextLine(textline)
text_region.set_orientation(-slopes[region_idx])
region_bboxes = all_box_coord[region_idx]
points_co = ''
for idx_contour_textline, contour_textline in enumerate(all_found_textline_polygons[region_idx][j]):
if not (self.curved_line or self.textline_light):
if len(contour_textline) == 2:
textline_x_coord = max(0, int((contour_textline[0] + region_bboxes[2] + page_coord[2]) / self.scale_x))
textline_y_coord = max(0, int((contour_textline[1] + region_bboxes[0] + page_coord[0]) / self.scale_y))
else:
textline_x_coord = max(0, int((contour_textline[0][0] + region_bboxes[2] + page_coord[2]) / self.scale_x))
textline_y_coord = max(0, int((contour_textline[0][1] + region_bboxes[0] + page_coord[0]) / self.scale_y))
points_co += str(textline_x_coord)
points_co += ','
points_co += str(textline_y_coord)
if self.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 += ','
points_co += str(int((contour_textline[1] + page_coord[0]) / self.scale_y))
else:
points_co += str(int((contour_textline[0][0] + page_coord[2]) / self.scale_x))
points_co += ','
points_co += str(int((contour_textline[0][1] + page_coord[0])/self.scale_y))
elif self.curved_line and np.abs(slopes[region_idx]) > 45:
if len(contour_textline)==2:
points_co += str(int((contour_textline[0] + region_bboxes[2] + page_coord[2])/self.scale_x))
points_co += ','
points_co += str(int((contour_textline[1] + region_bboxes[0] + page_coord[0])/self.scale_y))
else:
points_co += str(int((contour_textline[0][0] + region_bboxes[2]+page_coord[2])/self.scale_x))
points_co += ','
points_co += str(int((contour_textline[0][1] + region_bboxes[0]+page_coord[0])/self.scale_y))
points_co += ' '
coords.set_points(points_co[:-1])
def serialize_lines_in_dropcapital(self, text_region, all_found_textline_polygons, region_idx, page_coord, all_box_coord, slopes, counter, ocr_all_textlines_textregion):
self.logger.debug('enter serialize_lines_in_region')
for j in range(1):
coords = CoordsType()
textline = TextLineType(id=counter.next_line_id, Coords=coords)
if ocr_all_textlines_textregion:
textline.set_TextEquiv( [ TextEquivType(Unicode=ocr_all_textlines_textregion[j]) ] )
text_region.add_TextLine(textline)
#region_bboxes = all_box_coord[region_idx]
points_co = ''
for idx_contour_textline, contour_textline in enumerate(all_found_textline_polygons[j]):
if len(contour_textline) == 2:
points_co += str(int((contour_textline[0] + page_coord[2]) / self.scale_x))
points_co += ','
points_co += str(int((contour_textline[1] + page_coord[0]) / self.scale_y))
else:
points_co += str(int((contour_textline[0][0] + page_coord[2]) / self.scale_x))
points_co += ','
points_co += str(int((contour_textline[0][1] + page_coord[0])/self.scale_y))
points_co += ' '
for point in polygon_textline:
if len(point) != 2:
point = point[0]
point_x = point[0] + page_coord[2]
point_y = point[1] + page_coord[0]
# FIXME: or actually... not self.textline_light and not self.curved_line or np.abs(slopes[region_idx]) > 45?
if not self.textline_light and not (self.curved_line and np.abs(slopes[region_idx]) <= 45):
point_x += region_bboxes[2]
point_y += region_bboxes[0]
point_x = max(0, int(point_x / self.scale_x))
point_y = max(0, int(point_y / self.scale_y))
points_co += str(point_x) + ',' + str(point_y) + ' '
coords.set_points(points_co[:-1])
def write_pagexml(self, pcgts):
@ -170,8 +87,50 @@ 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_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')
def build_pagexml_no_full_layout(
self, found_polygons_text_region,
page_coord, order_of_texts, id_of_texts,
all_found_textline_polygons,
all_box_coord,
found_polygons_text_region_img,
found_polygons_marginals_left, found_polygons_marginals_right,
all_found_textline_polygons_marginals_left, all_found_textline_polygons_marginals_right,
all_box_coord_marginals_left, all_box_coord_marginals_right,
slopes, slopes_marginals_left, slopes_marginals_right,
cont_page, polygons_seplines,
found_polygons_tables,
**kwargs):
return self.build_pagexml_full_layout(
found_polygons_text_region, [],
page_coord, order_of_texts, id_of_texts,
all_found_textline_polygons, [],
all_box_coord, [],
found_polygons_text_region_img, found_polygons_tables, [],
found_polygons_marginals_left, found_polygons_marginals_right,
all_found_textline_polygons_marginals_left, all_found_textline_polygons_marginals_right,
all_box_coord_marginals_left, all_box_coord_marginals_right,
slopes, [], slopes_marginals_left, slopes_marginals_right,
cont_page, polygons_seplines,
**kwargs)
def build_pagexml_full_layout(
self,
found_polygons_text_region, found_polygons_text_region_h,
page_coord, order_of_texts, id_of_texts,
all_found_textline_polygons, all_found_textline_polygons_h,
all_box_coord, all_box_coord_h,
found_polygons_text_region_img, found_polygons_tables, found_polygons_drop_capitals,
found_polygons_marginals_left,found_polygons_marginals_right,
all_found_textline_polygons_marginals_left, all_found_textline_polygons_marginals_right,
all_box_coord_marginals_left, all_box_coord_marginals_right,
slopes, slopes_h, slopes_marginals_left, slopes_marginals_right,
cont_page, polygons_seplines,
ocr_all_textlines=None, ocr_all_textlines_h=None,
ocr_all_textlines_marginals_left=None, ocr_all_textlines_marginals_right=None,
ocr_all_textlines_drop=None,
conf_contours_textregions=None, conf_contours_textregions_h=None,
skip_layout_reading_order=False):
self.logger.debug('enter build_pagexml')
# create the file structure
pcgts = self.pcgts if self.pcgts else create_page_xml(self.image_filename, self.height_org, self.width_org)
@ -179,191 +138,116 @@ class EynollahXmlWriter:
page.set_Border(BorderType(Coords=CoordsType(points=self.calculate_page_coords(cont_page))))
counter = EynollahIdCounter()
if len(found_polygons_text_region) > 0:
if len(order_of_texts):
_counter_marginals = EynollahIdCounter(region_idx=len(order_of_texts))
id_of_marginalia_left = [_counter_marginals.next_region_id for _ in found_polygons_marginals_left]
id_of_marginalia_right = [_counter_marginals.next_region_id for _ in found_polygons_marginals_right]
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, skip_layout_reading_order), conf=conf_contours_textregion[mm]),
for mm, region_contour in enumerate(found_polygons_text_region):
textregion = TextRegionType(
id=counter.next_region_id, type_='paragraph',
Coords=CoordsType(points=self.calculate_polygon_coords(region_contour, page_coord,
skip_layout_reading_order))
)
#textregion.set_conf(conf_contours_textregion[mm])
if conf_contours_textregions:
textregion.Coords.set_conf(conf_contours_textregions[mm])
page.add_TextRegion(textregion)
if ocr_all_textlines:
ocr_textlines = ocr_all_textlines[mm]
else:
ocr_textlines = None
self.serialize_lines_in_region(textregion, all_found_textline_polygons, mm, page_coord, all_box_coord, slopes, counter, ocr_textlines)
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_left[mm], page_coord)))
page.add_TextRegion(marginal)
if ocr_all_textlines_marginals_left:
ocr_textlines = ocr_all_textlines_marginals_left[mm]
else:
ocr_textlines = None
#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())
page.add_ImageRegion(img_region)
points_co = ''
for lmm in range(len(found_polygons_text_region_img[mm])):
try:
points_co += str(int((found_polygons_text_region_img[mm][lmm,0,0] + page_coord[2]) / self.scale_x))
points_co += ','
points_co += str(int((found_polygons_text_region_img[mm][lmm,0,1] + page_coord[0]) / self.scale_y))
points_co += ' '
except:
points_co += str(int((found_polygons_text_region_img[mm][lmm][0] + page_coord[2])/ self.scale_x ))
points_co += ','
points_co += str(int((found_polygons_text_region_img[mm][lmm][1] + page_coord[0])/ self.scale_y ))
points_co += ' '
img_region.get_Coords().set_points(points_co[:-1])
for mm in range(len(polygons_lines_to_be_written_in_xml)):
sep_hor = SeparatorRegionType(id=counter.next_region_id, Coords=CoordsType())
page.add_SeparatorRegion(sep_hor)
points_co = ''
for lmm in range(len(polygons_lines_to_be_written_in_xml[mm])):
points_co += str(int((polygons_lines_to_be_written_in_xml[mm][lmm,0,0] ) / self.scale_x))
points_co += ','
points_co += str(int((polygons_lines_to_be_written_in_xml[mm][lmm,0,1] ) / self.scale_y))
points_co += ' '
sep_hor.get_Coords().set_points(points_co[:-1])
for mm in range(len(found_polygons_tables)):
tab_region = TableRegionType(id=counter.next_region_id, Coords=CoordsType())
page.add_TableRegion(tab_region)
points_co = ''
for lmm in range(len(found_polygons_tables[mm])):
points_co += str(int((found_polygons_tables[mm][lmm,0,0] + page_coord[2]) / self.scale_x))
points_co += ','
points_co += str(int((found_polygons_tables[mm][lmm,0,1] + page_coord[0]) / self.scale_y))
points_co += ' '
tab_region.get_Coords().set_points(points_co[:-1])
return pcgts
def 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
pcgts = self.pcgts if self.pcgts else create_page_xml(self.image_filename, self.height_org, self.width_org)
page = pcgts.get_Page()
page.set_Border(BorderType(Coords=CoordsType(points=self.calculate_page_coords(cont_page))))
counter = EynollahIdCounter()
_counter_marginals = EynollahIdCounter(region_idx=len(order_of_texts))
id_of_marginalia_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]))
page.add_TextRegion(textregion)
if ocr_all_textlines:
ocr_textlines = ocr_all_textlines[mm]
else:
ocr_textlines = None
self.serialize_lines_in_region(textregion, all_found_textline_polygons, mm, page_coord, all_box_coord, slopes, counter, ocr_textlines)
self.serialize_lines_in_region(textregion, all_found_textline_polygons, mm, page_coord,
all_box_coord, slopes, counter, ocr_textlines)
self.logger.debug('len(found_polygons_text_region_h) %s', len(found_polygons_text_region_h))
for mm in range(len(found_polygons_text_region_h)):
textregion = TextRegionType(id=counter.next_region_id, type_='header',
Coords=CoordsType(points=self.calculate_polygon_coords(found_polygons_text_region_h[mm], page_coord)))
for mm, region_contour in enumerate(found_polygons_text_region_h):
textregion = TextRegionType(
id=counter.next_region_id, type_='heading',
Coords=CoordsType(points=self.calculate_polygon_coords(region_contour, page_coord))
)
if conf_contours_textregions_h:
textregion.Coords.set_conf(conf_contours_textregions_h[mm])
page.add_TextRegion(textregion)
if ocr_all_textlines_h:
ocr_textlines = ocr_all_textlines_h[mm]
else:
ocr_textlines = None
self.serialize_lines_in_region(textregion, all_found_textline_polygons_h, mm, page_coord, all_box_coord_h, slopes_h, counter, ocr_textlines)
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_left)):
marginal = TextRegionType(id=counter.next_region_id, type_='marginalia',
Coords=CoordsType(points=self.calculate_polygon_coords(found_polygons_marginals_left[mm], page_coord)))
for mm, region_contour in enumerate(found_polygons_marginals_left):
marginal = TextRegionType(
id=counter.next_region_id, type_='marginalia',
Coords=CoordsType(points=self.calculate_polygon_coords(region_contour, page_coord))
)
page.add_TextRegion(marginal)
if ocr_all_textlines_marginals_left:
ocr_textlines = ocr_all_textlines_marginals_left[mm]
else:
ocr_textlines = None
self.serialize_lines_in_marginal(marginal, all_found_textline_polygons_marginals_left, mm, page_coord, all_box_coord_marginals_left, slopes_marginals_left, counter, ocr_textlines)
self.serialize_lines_in_region(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)))
for mm, region_contour in enumerate(found_polygons_marginals_right):
marginal = TextRegionType(
id=counter.next_region_id, type_='marginalia',
Coords=CoordsType(points=self.calculate_polygon_coords(region_contour, page_coord))
)
page.add_TextRegion(marginal)
if ocr_all_textlines_marginals_right:
ocr_textlines = ocr_all_textlines_marginals_right[mm]
else:
ocr_textlines = None
self.serialize_lines_in_marginal(marginal, all_found_textline_polygons_marginals_right, mm, page_coord, all_box_coord_marginals_right, slopes_marginals_right, counter, ocr_textlines)
self.serialize_lines_in_region(marginal, all_found_textline_polygons_marginals_right, mm, page_coord,
all_box_coord_marginals_right, slopes_marginals_right, counter, ocr_textlines)
for mm in range(len(found_polygons_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)))
for mm, region_contour in enumerate(found_polygons_drop_capitals):
dropcapital = TextRegionType(
id=counter.next_region_id, type_='drop-capital',
Coords=CoordsType(points=self.calculate_polygon_coords(region_contour, page_coord))
)
page.add_TextRegion(dropcapital)
all_box_coord_drop = None
slopes_drop = None
all_box_coord_drop = [[0, 0, 0, 0]]
slopes_drop = [0]
if ocr_all_textlines_drop:
ocr_textlines = ocr_all_textlines_drop[mm]
else:
ocr_textlines = None
self.serialize_lines_in_dropcapital(dropcapital, [found_polygons_drop_capitals[mm]], mm, page_coord, all_box_coord_drop, slopes_drop, counter, ocr_all_textlines_textregion=ocr_textlines)
self.serialize_lines_in_region(dropcapital, [[found_polygons_drop_capitals[mm]]], 0, page_coord,
all_box_coord_drop, slopes_drop, counter, 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))))
for region_contour in found_polygons_text_region_img:
page.add_ImageRegion(
ImageRegionType(id=counter.next_region_id,
Coords=CoordsType(points=self.calculate_polygon_coords(region_contour, page_coord))))
for mm in range(len(polygons_lines_to_be_written_in_xml)):
page.add_SeparatorRegion(ImageRegionType(id=counter.next_region_id, Coords=CoordsType(points=self.calculate_polygon_coords(polygons_lines_to_be_written_in_xml[mm], [0 , 0, 0, 0]))))
for region_contour in polygons_seplines:
page.add_SeparatorRegion(
SeparatorRegionType(id=counter.next_region_id,
Coords=CoordsType(points=self.calculate_polygon_coords(region_contour, [0, 0, 0, 0]))))
for mm in range(len(found_polygons_tables)):
page.add_TableRegion(TableRegionType(id=counter.next_region_id, Coords=CoordsType(points=self.calculate_polygon_coords(found_polygons_tables[mm], page_coord))))
for region_contour in found_polygons_tables:
page.add_TableRegion(
TableRegionType(id=counter.next_region_id,
Coords=CoordsType(points=self.calculate_polygon_coords(region_contour, page_coord))))
return pcgts
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 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:
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 + ' '
for point in contour:
if len(point) != 2:
point = point[0]
point_x = point[0]
point_y = point[1]
if not skip_layout_reading_order:
point_x += page_coord[2]
point_y += page_coord[0]
point_x = int(point_x / self.scale_x)
point_y = int(point_y / self.scale_y)
coords += str(point_x) + ',' + str(point_y) + ' '
return coords[:-1]

141
tests/test_run.py
View file

@ -17,18 +17,35 @@ from ocrd_models.constants import NAMESPACES as NS
testdir = Path(__file__).parent.resolve()
MODELS_LAYOUT = environ.get('MODELS_LAYOUT', str(testdir.joinpath('..', 'models_layout_v0_5_0').resolve()))
MODELS_OCR = environ.get('MODELS_OCR', str(testdir.joinpath('..', 'models_ocr_v0_5_0').resolve()))
MODELS_OCR = environ.get('MODELS_OCR', str(testdir.joinpath('..', 'models_ocr_v0_5_1').resolve()))
MODELS_BIN = environ.get('MODELS_BIN', str(testdir.joinpath('..', 'default-2021-03-09').resolve()))
def test_run_eynollah_layout_filename(tmp_path, subtests, pytestconfig, caplog):
@pytest.mark.parametrize(
"options",
[
[], # defaults
#["--allow_scaling", "--curved-line"],
["--allow_scaling", "--curved-line", "--full-layout"],
["--allow_scaling", "--curved-line", "--full-layout", "--reading_order_machine_based"],
["--allow_scaling", "--curved-line", "--full-layout", "--reading_order_machine_based",
"--textline_light", "--light_version"],
# -ep ...
# -eoi ...
# FIXME: find out whether OCR extra was installed, otherwise skip these
["--do_ocr"],
["--do_ocr", "--light_version", "--textline_light"],
["--do_ocr", "--transformer_ocr"],
#["--do_ocr", "--transformer_ocr", "--light_version", "--textline_light"],
["--do_ocr", "--transformer_ocr", "--light_version", "--textline_light", "--full-layout"],
# --skip_layout_and_reading_order
], ids=str)
def test_run_eynollah_layout_filename(tmp_path, pytestconfig, caplog, options):
infile = testdir.joinpath('resources/kant_aufklaerung_1784_0020.tif')
outfile = tmp_path / 'kant_aufklaerung_1784_0020.xml'
args = [
'-m', MODELS_LAYOUT,
'-i', str(infile),
'-o', str(outfile.parent),
# subtests write to same location
'--overwrite',
]
if pytestconfig.getoption('verbose') > 0:
args.extend(['-l', 'DEBUG'])
@ -36,20 +53,6 @@ def test_run_eynollah_layout_filename(tmp_path, subtests, pytestconfig, caplog):
def only_eynollah(logrec):
return logrec.name == 'eynollah'
runner = CliRunner()
for options in [
[], # defaults
["--allow_scaling", "--curved-line"],
["--allow_scaling", "--curved-line", "--full-layout"],
["--allow_scaling", "--curved-line", "--full-layout", "--reading_order_machine_based"],
["--allow_scaling", "--curved-line", "--full-layout", "--reading_order_machine_based",
"--textline_light", "--light_version"],
# -ep ...
# -eoi ...
# --do_ocr
# --skip_layout_and_reading_order
]:
with subtests.test(#msg="test CLI",
options=options):
with caplog.filtering(only_eynollah):
result = runner.invoke(layout_cli, args + options, catch_exceptions=False)
assert result.exit_code == 0, result.stdout
@ -64,6 +67,44 @@ def test_run_eynollah_layout_filename(tmp_path, subtests, pytestconfig, caplog):
lines = tree.xpath("//page:TextLine", namespaces=NS)
assert len(lines) == 31, "result is inaccurate" # 29 paragraph lines, 1 page and 1 catch-word line
@pytest.mark.parametrize(
"options",
[
["--tables"],
["--tables", "--full-layout"],
["--tables", "--full-layout", "--textline_light", "--light_version"],
], ids=str)
def test_run_eynollah_layout_filename2(tmp_path, pytestconfig, caplog, options):
infile = testdir.joinpath('resources/euler_rechenkunst01_1738_0025.tif')
outfile = tmp_path / 'euler_rechenkunst01_1738_0025.xml'
args = [
'-m', MODELS_LAYOUT,
'-i', str(infile),
'-o', str(outfile.parent),
]
if pytestconfig.getoption('verbose') > 0:
args.extend(['-l', 'DEBUG'])
caplog.set_level(logging.INFO)
def only_eynollah(logrec):
return logrec.name == 'eynollah'
runner = CliRunner()
with caplog.filtering(only_eynollah):
result = runner.invoke(layout_cli, args + options, catch_exceptions=False)
assert result.exit_code == 0, result.stdout
logmsgs = [logrec.message for logrec in caplog.records]
assert str(infile) in logmsgs
assert outfile.exists()
tree = page_from_file(str(outfile)).etree
regions = tree.xpath("//page:TextRegion", namespaces=NS)
assert len(regions) >= 2, "result is inaccurate"
regions = tree.xpath("//page:TableRegion", namespaces=NS)
# model/decoding is not very precise, so (depending on mode) we can get fractures/splits/FP
assert len(regions) >= 1, "result is inaccurate"
regions = tree.xpath("//page:SeparatorRegion", namespaces=NS)
assert len(regions) >= 2, "result is inaccurate"
lines = tree.xpath("//page:TextLine", namespaces=NS)
assert len(lines) >= 2, "result is inaccurate" # mostly table (if detected correctly), but 1 page and 1 catch-word line
def test_run_eynollah_layout_directory(tmp_path, pytestconfig, caplog):
indir = testdir.joinpath('resources')
outdir = tmp_path
@ -86,7 +127,13 @@ def test_run_eynollah_layout_directory(tmp_path, pytestconfig, caplog):
assert any(logmsg for logmsg in logmsgs if logmsg.startswith('All jobs done in'))
assert len(list(outdir.iterdir())) == 2
def test_run_eynollah_binarization_filename(tmp_path, subtests, pytestconfig, caplog):
@pytest.mark.parametrize(
"options",
[
[], # defaults
["--no-patches"],
], ids=str)
def test_run_eynollah_binarization_filename(tmp_path, pytestconfig, caplog, options):
infile = testdir.joinpath('resources/kant_aufklaerung_1784_0020.tif')
outfile = tmp_path.joinpath('kant_aufklaerung_1784_0020.png')
args = [
@ -100,12 +147,6 @@ def test_run_eynollah_binarization_filename(tmp_path, subtests, pytestconfig, ca
def only_eynollah(logrec):
return logrec.name == 'SbbBinarizer'
runner = CliRunner()
for options in [
[], # defaults
["--no-patches"],
]:
with subtests.test(#msg="test CLI",
options=options):
with caplog.filtering(only_eynollah):
result = runner.invoke(binarization_cli, args + options, catch_exceptions=False)
assert result.exit_code == 0, result.stdout
@ -118,7 +159,7 @@ def test_run_eynollah_binarization_filename(tmp_path, subtests, pytestconfig, ca
binarized_size = binarized_img.size
assert original_size == binarized_size
def test_run_eynollah_binarization_directory(tmp_path, subtests, pytestconfig, caplog):
def test_run_eynollah_binarization_directory(tmp_path, pytestconfig, caplog):
indir = testdir.joinpath('resources')
outdir = tmp_path
args = [
@ -139,15 +180,19 @@ def test_run_eynollah_binarization_directory(tmp_path, subtests, pytestconfig, c
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):
@pytest.mark.parametrize(
"options",
[
[], # defaults
["-sos"],
], ids=str)
def test_run_eynollah_enhancement_filename(tmp_path, pytestconfig, caplog, options):
infile = testdir.joinpath('resources/kant_aufklaerung_1784_0020.tif')
outfile = tmp_path.joinpath('kant_aufklaerung_1784_0020.png')
args = [
'-m', MODELS_LAYOUT,
'-i', str(infile),
'-o', str(outfile.parent),
# subtests write to same location
'--overwrite',
]
if pytestconfig.getoption('verbose') > 0:
args.extend(['-l', 'DEBUG'])
@ -155,12 +200,6 @@ def test_run_eynollah_enhancement_filename(tmp_path, subtests, pytestconfig, cap
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
@ -173,7 +212,7 @@ def test_run_eynollah_enhancement_filename(tmp_path, subtests, pytestconfig, cap
enhanced_size = enhanced_img.size
assert (original_size == enhanced_size) == ("-sos" in options)
def test_run_eynollah_enhancement_directory(tmp_path, subtests, pytestconfig, caplog):
def test_run_eynollah_enhancement_directory(tmp_path, pytestconfig, caplog):
indir = testdir.joinpath('resources')
outdir = tmp_path
args = [
@ -194,7 +233,7 @@ def test_run_eynollah_enhancement_directory(tmp_path, subtests, pytestconfig, ca
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):
def test_run_eynollah_mbreorder_filename(tmp_path, pytestconfig, caplog):
infile = testdir.joinpath('resources/kant_aufklaerung_1784_0020.xml')
outfile = tmp_path.joinpath('kant_aufklaerung_1784_0020.xml')
args = [
@ -223,7 +262,7 @@ def test_run_eynollah_mbreorder_filename(tmp_path, subtests, pytestconfig, caplo
#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):
def test_run_eynollah_mbreorder_directory(tmp_path, pytestconfig, caplog):
indir = testdir.joinpath('resources')
outdir = tmp_path
args = [
@ -245,7 +284,15 @@ def test_run_eynollah_mbreorder_directory(tmp_path, subtests, pytestconfig, capl
#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):
@pytest.mark.parametrize(
"options",
[
[], # defaults
["-doit", #str(outrenderfile.parent)],
],
["-trocr"],
], ids=str)
def test_run_eynollah_ocr_filename(tmp_path, pytestconfig, caplog, options):
infile = testdir.joinpath('resources/kant_aufklaerung_1784_0020.tif')
outfile = tmp_path.joinpath('kant_aufklaerung_1784_0020.xml')
outrenderfile = tmp_path.joinpath('render').joinpath('kant_aufklaerung_1784_0020.png')
@ -255,8 +302,6 @@ def test_run_eynollah_ocr_filename(tmp_path, subtests, pytestconfig, caplog):
'-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'])
@ -264,15 +309,8 @@ def test_run_eynollah_ocr_filename(tmp_path, subtests, pytestconfig, caplog):
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):
if "-doit" in options:
options.insert(options.index("-doit") + 1, str(outrenderfile.parent))
with caplog.filtering(only_eynollah):
result = runner.invoke(ocr_cli, args + options, catch_exceptions=False)
assert result.exit_code == 0, result.stdout
@ -289,8 +327,7 @@ def test_run_eynollah_ocr_filename(tmp_path, subtests, pytestconfig, caplog):
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):
def test_run_eynollah_ocr_directory(tmp_path, pytestconfig, caplog):
indir = testdir.joinpath('resources')
outdir = tmp_path
args = [

1
tests/test_smoke.py
View file

@ -2,6 +2,5 @@ def test_utils_import():
import eynollah.utils
import eynollah.utils.contour
import eynollah.utils.drop_capitals
import eynollah.utils.drop_capitals
import eynollah.utils.is_nan
import eynollah.utils.rotate