Resolve merge conflict of main and machine based reading order branch

.circleci/config.yml

@@ -1,51 +0,0 @@
-version: 2
-
-jobs:
-
-  build-python37:
-    machine:
-      - image: ubuntu-2004:2023.02.1
-
-    steps:
-      - checkout
-      - restore_cache:
-          keys:
-            - model-cache
-      - run: make models
-      - save_cache:
-          key: model-cache
-          paths:
-            models_eynollah.tar.gz
-            models_eynollah
-      - run:
-          name: "Set Python Version"
-          command: pyenv install -s 3.7.16 && pyenv global 3.7.16
-      - run: make install
-      - run: make smoke-test
-
-  build-python38:
-    machine:
-      - image: ubuntu-2004:2023.02.1
-    steps:
-      - checkout
-      - restore_cache:
-          keys:
-            - model-cache
-      - run: make models
-      - save_cache:
-          key: model-cache
-          paths:
-            models_eynollah.tar.gz
-            models_eynollah
-      - run:
-          name: "Set Python Version"
-          command: pyenv install -s 3.8.16 && pyenv global 3.8.16           
-      - run: make install
-      - run: make smoke-test
-
-workflows:
-  version: 2
-  build:
-    jobs:
-      # - build-python37
-      - build-python38

.github/workflows/test-eynollah.yml

@@ -1,7 +1,7 @@
 # This workflow will install Python dependencies, run tests and lint with a variety of Python versions
 # For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions
 
-name: Python package
+name: Test
 
 on: [push]
 
@@ -14,8 +14,8 @@ jobs:
         python-version: ['3.8', '3.9', '3.10', '3.11']
 
     steps:
-    - uses: actions/checkout@v2
+    - uses: actions/checkout@v4
-    - uses: actions/cache@v2
+    - uses: actions/cache@v4
       id: model_cache
       with:
         path: models_eynollah
@@ -24,7 +24,7 @@ jobs:
       if: steps.model_cache.outputs.cache-hit != 'true'
       run: make models
     - name: Set up Python ${{ matrix.python-version }}
-      uses: actions/setup-python@v2
+      uses: actions/setup-python@v5
       with:
         python-version: ${{ matrix.python-version }}
     - name: Install dependencies
@@ -34,3 +34,5 @@ jobs:
         pip install -r requirements-test.txt
     - name: Test with pytest
       run: make test
+    - name: Test docker build
+      run: make docker

CHANGELOG.md

@@ -5,6 +5,14 @@ Versioned according to [Semantic Versioning](http://semver.org/).
 
 ## Unreleased
 
+## [0.3.1] - 2024-08-27
+
+Fixed:
+
+  * regression in OCR-D processor, #106
+  * Expected Ptrcv::UMat for argument 'contour', #110
+  * Memory usage explosion with very narrow images (e.g. book spine), #67
+
 ## [0.3.0] - 2023-05-13
 
 Changed:
@@ -117,6 +125,8 @@ Fixed:
 Initial release
 
 <!-- link-labels -->
+[0.3.1]: ../../compare/v0.3.1...v0.3.0
+[0.3.0]: ../../compare/v0.3.0...v0.2.0
 [0.2.0]: ../../compare/v0.2.0...v0.1.0
 [0.1.0]: ../../compare/v0.1.0...v0.0.11
 [0.0.11]: ../../compare/v0.0.11...v0.0.10

Dockerfile

@@ -0,0 +1,26 @@
+ARG DOCKER_BASE_IMAGE
+FROM $DOCKER_BASE_IMAGE
+
+ARG VCS_REF
+ARG BUILD_DATE
+LABEL \
+    maintainer="https://ocr-d.de/kontakt" \
+    org.label-schema.vcs-ref=$VCS_REF \
+    org.label-schema.vcs-url="https://github.com/qurator-spk/eynollah" \
+    org.label-schema.build-date=$BUILD_DATE
+
+ENV DEBIAN_FRONTEND=noninteractive
+ENV PYTHONIOENCODING=utf8
+ENV XDG_DATA_HOME=/usr/local/share
+
+WORKDIR /build-eynollah
+COPY src/ ./src
+COPY pyproject.toml .
+COPY requirements.txt .
+COPY README.md .
+COPY Makefile .
+RUN apt-get install -y --no-install-recommends g++
+RUN make install
+
+WORKDIR /data
+VOLUME /data

Makefile

@@ -1,6 +1,11 @@
 EYNOLLAH_MODELS ?= $(PWD)/models_eynollah
 export EYNOLLAH_MODELS
 
+# DOCKER_BASE_IMAGE = artefakt.dev.sbb.berlin:5000/sbb/ocrd_core:v2.68.0
+DOCKER_BASE_IMAGE = docker.io/ocrd/core:v2.68.0
+DOCKER_TAG = ocrd/eynollah
+
+
 # BEGIN-EVAL makefile-parser --make-help Makefile
 
 help:
@@ -22,16 +27,14 @@ help:
 models: models_eynollah
 
 models_eynollah: models_eynollah.tar.gz
-	# tar xf models_eynollah_renamed.tar.gz --transform 's/models_eynollah_renamed/models_eynollah/'
-	# tar xf models_eynollah_renamed.tar.gz
-	# tar xf 2022-04-05.SavedModel.tar.gz --transform 's/models_eynollah_renamed/models_eynollah/'
 	tar xf models_eynollah.tar.gz
 
 models_eynollah.tar.gz:
 	# wget 'https://qurator-data.de/eynollah/2021-04-25/models_eynollah.tar.gz'
 	# wget 'https://qurator-data.de/eynollah/2022-04-05/models_eynollah_renamed.tar.gz'
-	# wget 'https://ocr-d.kba.cloud/2022-04-05.SavedModel.tar.gz'
+	# wget 'https://qurator-data.de/eynollah/2022-04-05/models_eynollah_renamed_savedmodel.tar.gz'
-	wget https://github.com/qurator-spk/eynollah/releases/download/v0.3.0/models_eynollah.tar.gz
+	# wget 'https://github.com/qurator-spk/eynollah/releases/download/v0.3.0/models_eynollah.tar.gz'
+	wget 'https://github.com/qurator-spk/eynollah/releases/download/v0.3.1/models_eynollah.tar.gz'
 
 # Install with pip
 install:
@@ -47,3 +50,12 @@ smoke-test:
 # Run unit tests
 test:
 	pytest tests
+
+# Build docker image
+docker:
+	docker build \
+	--build-arg DOCKER_BASE_IMAGE=$(DOCKER_BASE_IMAGE) \
+	--build-arg VCS_REF=$$(git rev-parse --short HEAD) \
+	--build-arg BUILD_DATE=$$(date -u +"%Y-%m-%dT%H:%M:%SZ") \
+	-t $(DOCKER_TAG) .
+

README.md

@@ -1,10 +1,10 @@
 # Eynollah
-> Document Layout Analysis (segmentation) using pre-trained models and heuristics
+> Document Layout Analysis with Deep Learning and Heuristics
 
 [![PyPI Version](https://img.shields.io/pypi/v/eynollah)](https://pypi.org/project/eynollah/)
-[![CircleCI Build Status](https://circleci.com/gh/qurator-spk/eynollah.svg?style=shield)](https://circleci.com/gh/qurator-spk/eynollah)
 [![GH Actions Test](https://github.com/qurator-spk/eynollah/actions/workflows/test-eynollah.yml/badge.svg)](https://github.com/qurator-spk/eynollah/actions/workflows/test-eynollah.yml)
 [![License: ASL](https://img.shields.io/github/license/qurator-spk/eynollah)](https://opensource.org/license/apache-2-0/)
+[![DOI](https://img.shields.io/badge/DOI-10.1145%2F3604951.3605513-red)](https://doi.org/10.1145/3604951.3605513)
 
 ![](https://user-images.githubusercontent.com/952378/102350683-8a74db80-3fa5-11eb-8c7e-f743f7d6eae2.jpg)
 
@@ -14,16 +14,18 @@
 * Support for various image optimization operations:
   * cropping (border detection), binarization, deskewing, dewarping, scaling, enhancing, resizing
 * Text line segmentation to bounding boxes or polygons (contours) including for curved lines and vertical text
-* Detection of reading order
+* Detection of reading order (left-to-right or right-to-left)
 * Output in [PAGE-XML](https://github.com/PRImA-Research-Lab/PAGE-XML)
 * [OCR-D](https://github.com/qurator-spk/eynollah#use-as-ocr-d-processor) interface
 
+:warning: Development is currently focused on achieving the best possible quality of results for a wide variety of historical documents and therefore processing can be very slow. We aim to improve this, but contributions are welcome.
+
 ## Installation
-Python versions `3.8-3.11` with Tensorflow versions >=`2.12` are currently supported. 
+Python `3.8-3.11` with Tensorflow `2.12-2.15` on Linux are currently supported.
 
 For (limited) GPU support the CUDA toolkit needs to be installed.
 
-You can either install via 
+You can either install from PyPI
 
 ```
 pip install eynollah
@@ -39,25 +41,28 @@ cd eynollah; pip install -e .
 Alternatively, you can run `make install` or `make install-dev` for editable installation.
 
 ## Models
-Pre-trained models can be downloaded from [qurator-data.de](https://qurator-data.de/eynollah/).
+Pre-trained models can be downloaded from [qurator-data.de](https://qurator-data.de/eynollah/) or [huggingface](https://huggingface.co/SBB?search_models=eynollah). 
+
+## Train
+🚧 **Work in progress**  
   
-In case you want to train your own model to use with Eynollah, have a look at [sbb_pixelwise_segmentation](https://github.com/qurator-spk/sbb_pixelwise_segmentation). 
+In case you want to train your own model, have a look at [`sbb_pixelwise_segmentation`](https://github.com/qurator-spk/sbb_pixelwise_segmentation). 
 
 ## Usage
 The command-line interface can be called like this:
 
 ```sh
 eynollah \
-  -i <image file> \
+  -i <single image file> | -di <directory containing image files> \
   -o <output directory> \
-  -m <path to directory containing model files> \
+  -m <directory containing model files> \
      [OPTIONS]
 ```
 
 The following options can be used to further configure the processing:
 
 | option            | description                                                                    |
-|----------|:-------------|
+|-------------------|:-------------------------------------------------------------------------------|
 | `-fl`             | full layout analysis including all steps and segmentation classes              |
 | `-light`          | lighter and faster but simpler method for main region detection and deskewing  |
 | `-tab`            | apply table detection                                                          |
@@ -66,18 +71,19 @@ The following options can be used to further configure the processing:
 | `-cl`             | apply contour detection for curved text lines instead of bounding boxes        |
 | `-ib`             | apply binarization (the resulting image is saved to the output directory)      |
 | `-ep`             | enable plotting (MUST always be used with `-sl`, `-sd`, `-sa`, `-si` or `-ae`) |
+| `-eoi`            | extract only images to output directory (other processing will not be done)    |
 | `-ho`             | ignore headers for reading order dectection                                    |
-| `-di <directory>`  | process all images in a directory in batch mode |
 | `-si <directory>` | save image regions detected to this directory                                  |
 | `-sd <directory>` | save deskewed image to this directory                                          |
 | `-sl <directory>` | save layout prediction as plot to this directory                               |
 | `-sp <directory>` | save cropped page image to this directory                                      |
 | `-sa <directory>` | save all (plot, enhanced/binary image, layout) to this directory               |
 
-If no option is set, the tool will perform layout detection of main regions (background, text, images, separators and marginals).
+If no option is set, the tool performs layout detection of main regions (background, text, images, separators and marginals).
-The tool produces better quality output when RGB images are used as input than greyscale or binarized images.
+The best output quality is produced when RGB images are used as input rather than greyscale or binarized images.
 
 #### Use as OCR-D processor
+🚧 **Work in progress** 
 
 Eynollah ships with a CLI interface to be used as [OCR-D](https://ocr-d.de) processor. 
 
@@ -95,11 +101,14 @@ ocrd-eynollah-segment -I OCR-D-IMG-BIN -O SEG-LINE -P models
     
 uses the original (RGB) image despite any binarization that may have occured in previous OCR-D processing steps
 
+#### Additional documentation
+Please check the [wiki](https://github.com/qurator-spk/eynollah/wiki).
+
 ## How to cite
 If you find this tool useful in your work, please consider citing our paper:
 
 ```bibtex
-@inproceedings{rezanezhad2023eynollah,
+@inproceedings{hip23rezanezhad,
   title     = {Document Layout Analysis with Deep Learning and Heuristics},
   author    = {Rezanezhad, Vahid and Baierer, Konstantin and Gerber, Mike and Labusch, Kai and Neudecker, Clemens},
   booktitle = {Proceedings of the 7th International Workshop on Historical Document Imaging and Processing {HIP} 2023,

ocrd-tool.json

@@ -1 +1 @@
-qurator/eynollah/ocrd-tool.json
+src/eynollah/ocrd-tool.json

pyproject.toml

@@ -1,34 +1,43 @@
 [build-system]
-requires = ["setuptools>=61.0"]
+requires = ["setuptools>=61.0", "wheel", "setuptools-ocrd"]
-build-backend = "setuptools.build_meta"
 
 [project]
 name = "eynollah"
-version = "0.1.0"
+authors = [
-
+    {name = "Vahid Rezanezhad"},
-
+    {name = "Staatsbibliothek zu Berlin - Preußischer Kulturbesitz"},
-
+]
-
+description = "Document Layout Analysis"
-dependencies = [
+readme = "README.md"
-    "ocrd >= 2.23.3",
+license.file = "LICENSE"
-    "tensorflow == 2.12.1",
+requires-python = ">=3.8"
-    "scikit-learn >= 0.23.2",
+keywords = ["document layout analysis", "image segmentation"]
-    "imutils >= 0.5.3",
+
-    "numpy < 1.24.0",
+dynamic = ["dependencies", "version"]
-    "matplotlib",
+
-    "torch == 2.0.1",
+classifiers = [
-    "transformers == 4.30.2",
+    "Development Status :: 4 - Beta",
-    "numba == 0.58.1",
+    "Environment :: Console",
+    "Intended Audience :: Science/Research",
+    "License :: OSI Approved :: Apache Software License",
+    "Programming Language :: Python :: 3",
+    "Programming Language :: Python :: 3 :: Only",
+    "Topic :: Scientific/Engineering :: Image Processing",
 ]
 
 [project.scripts]
-eynollah = "qurator.eynollah.cli:main"
+eynollah = "eynollah.cli:main"
-ocrd-eynollah-segment="qurator.eynollah.ocrd_cli:main"
+ocrd-eynollah-segment = "eynollah.ocrd_cli:main"
+
+[project.urls]
+Homepage = "https://github.com/qurator-spk/eynollah"
+Repository = "https://github.com/qurator-spk/eynollah.git"
 
+[tool.setuptools.dynamic]
+dependencies = {file = ["requirements.txt"]}
 
 [tool.setuptools.packages.find]
-where = ["."]
+where = ["src"]
-include = ["qurator"]
 
 [tool.setuptools.package-data]
 "*" = ["*.json", '*.yml', '*.xml', '*.xsd']

qurator/__init__.py

@@ -1 +0,0 @@
-__import__("pkg_resources").declare_namespace(__name__)

qurator/eynollah/__init__.py

@@ -1 +0,0 @@
-

requirements.txt

@@ -0,0 +1,8 @@
+# ocrd includes opencv, numpy, shapely, click
+ocrd >= 2.23.3
+numpy <1.24.0
+scikit-learn >= 0.23.2
+tensorflow == 2.12.1
+imutils >= 0.5.3
+matplotlib
+setuptools >= 50

src/eynollah/cli.py

@@ -1,8 +1,8 @@
 import sys
 import click
 from ocrd_utils import initLogging, setOverrideLogLevel
-from qurator.eynollah.eynollah import Eynollah
+from eynollah.eynollah import Eynollah
-from qurator.eynollah.sbb_binarize import SbbBinarizer
+from eynollah.sbb_binarize import SbbBinarizer
 
 @click.group()
 def main():
@@ -146,6 +146,12 @@ def binarization(patches, model_dir, input_image, output_image, dir_in, dir_out)
     is_flag=True,
     help="If set, will plot intermediary files and images",
 )
+@click.option(
+    "--extract_only_images/--disable-extracting_only_images",
+    "-eoi/-noeoi",
+    is_flag=True,
+    help="If a directory is given, only images in documents will be cropped and saved there and the other processing will not be done",
+)
 @click.option(
     "--allow-enhancement/--no-allow-enhancement",
     "-ae/-noae",
@@ -262,6 +268,8 @@ def layout(image, out, dir_in, model, save_images, save_layout, save_deskewed, s
         sys.exit(1)
     if light_version and not textline_light:
         print('Error: You used -light without -tll. Light version need light textline to be enabled.')
+    if extract_only_images and  (allow_enhancement or allow_scaling or light_version or curved_line or textline_light or full_layout or tables or right2left or headers_off) :
+        print('Error: You used -eoi which can not be enabled alongside light_version -light or allow_scaling -as or allow_enhancement -ae or curved_line -cl or textline_light -tll or full_layout -fl or tables -tab or right2left -r2l or headers_off -ho')
         sys.exit(1)
     eynollah = Eynollah(
         image_filename=image,
@@ -269,6 +277,7 @@ def layout(image, out, dir_in, model, save_images, save_layout, save_deskewed, s
         dir_in=dir_in,
         dir_models=model,
         dir_of_cropped_images=save_images,
+        extract_only_images=extract_only_images,
         dir_of_layout=save_layout,
         dir_of_deskewed=save_deskewed,
         dir_of_all=save_all,

src/eynollah/eynollah.py

@@ -158,6 +158,7 @@ class Eynollah:
         dir_out=None,
         dir_in=None,
         dir_of_cropped_images=None,
+        extract_only_images=False,
         dir_of_layout=None,
         dir_of_deskewed=None,
         dir_of_all=None,
@@ -211,6 +212,8 @@ class Eynollah:
         self.input_binary = input_binary
         self.allow_scaling = allow_scaling
         self.headers_off = headers_off
+        self.light_version = light_version
+        self.extract_only_images = extract_only_images
         self.ignore_page_extraction = ignore_page_extraction
         self.skip_layout_and_reading_order = skip_layout_and_reading_order
         self.ocr = do_ocr
@@ -254,6 +257,7 @@ class Eynollah:
         self.model_region_dir_p_1_2_sp_np = dir_models + "/modelens_e_l_all_sp_0_1_2_3_4_171024"#"/modelens_12sp_elay_0_3_4__3_6_n"#"/modelens_earlylayout_12spaltige_2_3_5_6_7_8"#"/modelens_early12_sp_2_3_5_6_7_8_9_10_12_14_15_16_18"#"/modelens_1_2_4_5_early_lay_1_2_spaltige"#"/model_3_eraly_layout_no_patches_1_2_spaltige"
         ##self.model_region_dir_fully_new = dir_models + "/model_2_full_layout_new_trans"
         self.model_region_dir_fully = dir_models + "/modelens_full_lay_1_3_031124"#"/modelens_full_lay_13__3_19_241024"#"/model_full_lay_13_241024"#"/modelens_full_lay_13_17_231024"#"/modelens_full_lay_1_2_221024"#"/modelens_full_layout_24_till_28"#"/model_2_full_layout_new_trans"
+        self.model_region_dir_p_ens_light_only_images_extraction = dir_models + "/eynollah-main-regions_20231127_672_org_ens_11_13_16_17_18"
         if self.textline_light:
             self.model_textline_dir = dir_models + "/modelens_textline_0_1__2_4_16092024"#"/modelens_textline_1_4_16092024"#"/model_textline_ens_3_4_5_6_artificial"#"/modelens_textline_1_3_4_20240915"#"/model_textline_ens_3_4_5_6_artificial"#"/modelens_textline_9_12_13_14_15"#"/eynollah-textline_light_20210425"#
         else:
@@ -288,7 +292,23 @@ class Eynollah:
             
             self.ls_imgs  = os.listdir(self.dir_in)
             
-        if dir_in and not light_version:
+        if dir_in and self.extract_only_images:
+            config = tf.compat.v1.ConfigProto()
+            config.gpu_options.allow_growth = True
+            session = tf.compat.v1.Session(config=config)
+            set_session(session)
+
+            self.model_page = self.our_load_model(self.model_page_dir)
+            self.model_classifier = self.our_load_model(self.model_dir_of_col_classifier)
+            self.model_bin = self.our_load_model(self.model_dir_of_binarization)
+            #self.model_textline = self.our_load_model(self.model_textline_dir)
+            self.model_region = self.our_load_model(self.model_region_dir_p_ens_light_only_images_extraction)
+            #self.model_region_fl_np = self.our_load_model(self.model_region_dir_fully_np)
+            #self.model_region_fl = self.our_load_model(self.model_region_dir_fully)
+
+            self.ls_imgs  = os.listdir(self.dir_in)
+
+        if dir_in and not (light_version or self.extract_only_images):
             config = tf.compat.v1.ConfigProto()
             config.gpu_options.allow_growth = True
             session = tf.compat.v1.Session(config=config)
@@ -534,6 +554,27 @@ class Eynollah:
 
         return img_new, num_column_is_classified
 
+    def calculate_width_height_by_columns_extract_only_images(self, img, num_col, width_early, label_p_pred):
+        self.logger.debug("enter calculate_width_height_by_columns")
+        if num_col == 1:
+            img_w_new = 700
+        elif num_col == 2:
+            img_w_new = 900
+        elif num_col == 3:
+            img_w_new = 1500
+        elif num_col == 4:
+            img_w_new = 1800
+        elif num_col == 5:
+            img_w_new = 2200
+        elif num_col == 6:
+            img_w_new = 2500
+        img_h_new = int(img.shape[0] / float(img.shape[1]) * img_w_new)
+
+        img_new = resize_image(img, img_h_new, img_w_new)
+        num_column_is_classified = True
+
+        return img_new, num_column_is_classified
+
     def resize_image_with_column_classifier(self, is_image_enhanced, img_bin):
         self.logger.debug("enter resize_image_with_column_classifier")
         if self.input_binary:
@@ -690,6 +731,7 @@ class Eynollah:
         
         self.logger.info("Found %d columns (%s)", num_col, np.around(label_p_pred, decimals=5))
 
+        if not self.extract_only_images:
             if dpi < DPI_THRESHOLD:
                 if light_version and num_col in (1,2):
                     img_new, num_column_is_classified = self.calculate_width_height_by_columns_1_2(img, num_col, width_early, label_p_pred)
@@ -709,6 +751,10 @@ class Eynollah:
                     num_column_is_classified = True
                     image_res = np.copy(img)
                     is_image_enhanced = False
+        else:
+            num_column_is_classified = True
+            image_res = np.copy(img)
+            is_image_enhanced = False
 
         self.logger.debug("exit resize_and_enhance_image_with_column_classifier")
         return is_image_enhanced, img, image_res, num_col, num_column_is_classified, img_bin
@@ -1191,106 +1237,6 @@ class Eynollah:
                     
                     batch_indexer = batch_indexer + 1
                         
-                    #img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
-                    #label_p_pred = model.predict(img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2]),
-                                                 #verbose=0)
-                    #seg = np.argmax(label_p_pred, axis=3)[0]
-                    
-                    
-                    ######seg_not_base = label_p_pred[0,:,:,4]
-                    ########seg2 = -label_p_pred[0,:,:,2]
-                    
-                    
-                    ######seg_not_base[seg_not_base>0.03] =1
-                    ######seg_not_base[seg_not_base<1] =0
-                    
-                    
-                    
-                    ######seg_test = label_p_pred[0,:,:,1]
-                    ########seg2 = -label_p_pred[0,:,:,2]
-                    
-                    
-                    ######seg_test[seg_test>0.75] =1
-                    ######seg_test[seg_test<1] =0
-                    
-                    
-                    ######seg_line = label_p_pred[0,:,:,3]
-                    ########seg2 = -label_p_pred[0,:,:,2]
-                    
-                    
-                    ######seg_line[seg_line>0.1] =1
-                    ######seg_line[seg_line<1] =0
-                    
-                    
-                    ######seg_background = label_p_pred[0,:,:,0]
-                    ########seg2 = -label_p_pred[0,:,:,2]
-                    
-                    
-                    ######seg_background[seg_background>0.25] =1
-                    ######seg_background[seg_background<1] =0
-                    ##seg = seg+seg2
-                    #seg = label_p_pred[0,:,:,2]
-                    #seg[seg>0.4] =1
-                    #seg[seg<1] =0
-                    
-                    ##plt.imshow(seg_test)
-                    ##plt.show()
-                    
-                    ##plt.imshow(seg_background)
-                    ##plt.show()
-                    #seg[seg==1]=0
-                    #seg[seg_test==1]=1
-                    ######seg[seg_not_base==1]=4
-                    ######seg[seg_background==1]=0
-                    ######seg[(seg_line==1) & (seg==0)]=3
-                    #seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2)
-
-                    #if i == 0 and j == 0:
-                        #seg_color = seg_color[0 : seg_color.shape[0] - margin, 0 : seg_color.shape[1] - margin, :]
-                        #seg = seg[0 : seg.shape[0] - margin, 0 : seg.shape[1] - margin]
-                        #mask_true[index_y_d + 0 : index_y_u - margin, index_x_d + 0 : index_x_u - margin] = seg
-                        #prediction_true[index_y_d + 0 : index_y_u - margin, index_x_d + 0 : index_x_u - margin, :] = seg_color
-                    #elif i == nxf - 1 and j == nyf - 1:
-                        #seg_color = seg_color[margin : seg_color.shape[0] - 0, margin : seg_color.shape[1] - 0, :]
-                        #seg = seg[margin : seg.shape[0] - 0, margin : seg.shape[1] - 0]
-                        #mask_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - 0] = seg
-                        #prediction_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - 0, :] = seg_color
-                    #elif i == 0 and j == nyf - 1:
-                        #seg_color = seg_color[margin : seg_color.shape[0] - 0, 0 : seg_color.shape[1] - margin, :]
-                        #seg = seg[margin : seg.shape[0] - 0, 0 : seg.shape[1] - margin]
-                        #mask_true[index_y_d + margin : index_y_u - 0, index_x_d + 0 : index_x_u - margin] = seg
-                        #prediction_true[index_y_d + margin : index_y_u - 0, index_x_d + 0 : index_x_u - margin, :] = seg_color
-                    #elif i == nxf - 1 and j == 0:
-                        #seg_color = seg_color[0 : seg_color.shape[0] - margin, margin : seg_color.shape[1] - 0, :]
-                        #seg = seg[0 : seg.shape[0] - margin, margin : seg.shape[1] - 0]
-                        #mask_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - 0] = seg
-                        #prediction_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - 0, :] = seg_color
-                    #elif i == 0 and j != 0 and j != nyf - 1:
-                        #seg_color = seg_color[margin : seg_color.shape[0] - margin, 0 : seg_color.shape[1] - margin, :]
-                        #seg = seg[margin : seg.shape[0] - margin, 0 : seg.shape[1] - margin]
-                        #mask_true[index_y_d + margin : index_y_u - margin, index_x_d + 0 : index_x_u - margin] = seg
-                        #prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + 0 : index_x_u - margin, :] = seg_color
-                    #elif i == nxf - 1 and j != 0 and j != nyf - 1:
-                        #seg_color = seg_color[margin : seg_color.shape[0] - margin, margin : seg_color.shape[1] - 0, :]
-                        #seg = seg[margin : seg.shape[0] - margin, margin : seg.shape[1] - 0]
-                        #mask_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - 0] = seg
-                        #prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - 0, :] = seg_color
-                    #elif i != 0 and i != nxf - 1 and j == 0:
-                        #seg_color = seg_color[0 : seg_color.shape[0] - margin, margin : seg_color.shape[1] - margin, :]
-                        #seg = seg[0 : seg.shape[0] - margin, margin : seg.shape[1] - margin]
-                        #mask_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - margin] = seg
-                        #prediction_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - margin, :] = seg_color
-                    #elif i != 0 and i != nxf - 1 and j == nyf - 1:
-                        #seg_color = seg_color[margin : seg_color.shape[0] - 0, margin : seg_color.shape[1] - margin, :]
-                        #seg = seg[margin : seg.shape[0] - 0, margin : seg.shape[1] - margin]
-                        #mask_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - margin] = seg
-                        #prediction_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - margin, :] = seg_color
-                    #else:
-                        #seg_color = seg_color[margin : seg_color.shape[0] - margin, margin : seg_color.shape[1] - margin, :]
-                        #seg = seg[margin : seg.shape[0] - margin, margin : seg.shape[1] - margin]
-                        #mask_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - margin] = seg
-                        #prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - margin, :] = seg_color
-                        
                         
                     if batch_indexer == n_batch_inference:
                         label_p_pred = model.predict(img_patch,verbose=0)
@@ -1302,18 +1248,11 @@ class Eynollah:
                             seg_art = label_p_pred[:,:,:,4]
                             seg_art[seg_art<0.2] =0
                             seg_art[seg_art>0] =1
-                            ###seg[seg_art==1]=4
-                            ##seg_not_base = label_p_pred[:,:,:,4]
-                            ##seg_not_base[seg_not_base>0.03] =1
-                            ##seg_not_base[seg_not_base<1] =0
                             
                             seg_line = label_p_pred[:,:,:,3]
                             seg_line[seg_line>0.1] =1
                             seg_line[seg_line<1] =0
                             
-                            ##seg_background = label_p_pred[:,:,:,0]
-                            ##seg_background[seg_background>0.25] =1
-                            ##seg_background[seg_background<1] =0
                             
                             seg[seg_art==1]=4
                             ##seg[seg_background==1]=0
@@ -1384,20 +1323,15 @@ class Eynollah:
                         
                         seg = np.argmax(label_p_pred, axis=3)
                         if thresholding_for_some_classes_in_light_version:
-                            seg_not_base = label_p_pred[:,:,:,4]
+                            seg_art = label_p_pred[:,:,:,4]
-                            seg_not_base[seg_not_base>0.03] =1
+                            seg_art[seg_art<0.2] =0
-                            seg_not_base[seg_not_base<1] =0
+                            seg_art[seg_art>0] =1
                             
                             seg_line = label_p_pred[:,:,:,3]
                             seg_line[seg_line>0.1] =1
                             seg_line[seg_line<1] =0
                             
-                            seg_background = label_p_pred[:,:,:,0]
+                            seg[seg_art==1]=4
-                            seg_background[seg_background>0.25] =1
-                            seg_background[seg_background<1] =0
-                            
-                            seg[seg_not_base==1]=4
-                            seg[seg_background==1]=0
                             seg[(seg_line==1) & (seg==0)]=3
                             
                         if thresholding_for_artificial_class_in_light_version:
@@ -2224,6 +2158,119 @@ class Eynollah:
         q.put(slopes_sub)
         poly.put(poly_sub)
         box_sub.put(boxes_sub_new)
+
+    def get_regions_light_v_extract_only_images(self,img,is_image_enhanced, num_col_classifier):
+        self.logger.debug("enter get_regions_extract_images_only")
+        erosion_hurts = False
+        img_org = np.copy(img)
+        img_height_h = img_org.shape[0]
+        img_width_h = img_org.shape[1]
+
+        if num_col_classifier == 1:
+            img_w_new = 700
+        elif num_col_classifier == 2:
+            img_w_new = 900
+        elif num_col_classifier == 3:
+            img_w_new = 1500
+        elif num_col_classifier == 4:
+            img_w_new = 1800
+        elif num_col_classifier == 5:
+            img_w_new = 2200
+        elif num_col_classifier == 6:
+            img_w_new = 2500
+        img_h_new = int(img.shape[0] / float(img.shape[1]) * img_w_new)
+
+        img_resized = resize_image(img,img_h_new, img_w_new )
+
+
+
+        if not self.dir_in:
+            model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_ens_light_only_images_extraction)
+            prediction_regions_org = self.do_prediction_new_concept(True, img_resized, model_region)
+        else:
+            prediction_regions_org = self.do_prediction_new_concept(True, img_resized, self.model_region)
+
+        prediction_regions_org = resize_image(prediction_regions_org,img_height_h, img_width_h )
+
+        image_page, page_coord, cont_page = self.extract_page()
+
+
+        prediction_regions_org = prediction_regions_org[page_coord[0] : page_coord[1], page_coord[2] : page_coord[3]]
+
+
+        prediction_regions_org=prediction_regions_org[:,:,0]
+
+        mask_lines_only = (prediction_regions_org[:,:] ==3)*1
+
+        mask_texts_only = (prediction_regions_org[:,:] ==1)*1
+
+        mask_images_only=(prediction_regions_org[:,:] ==2)*1
+
+        polygons_lines_xml, hir_lines_xml = return_contours_of_image(mask_lines_only)
+        polygons_lines_xml = textline_con_fil = filter_contours_area_of_image(mask_lines_only, polygons_lines_xml, hir_lines_xml, max_area=1, min_area=0.00001)
+
+
+        polygons_of_only_texts = return_contours_of_interested_region(mask_texts_only,1,0.00001)
+
+        polygons_of_only_lines = return_contours_of_interested_region(mask_lines_only,1,0.00001)
+
+        text_regions_p_true = np.zeros(prediction_regions_org.shape)
+
+        text_regions_p_true = cv2.fillPoly(text_regions_p_true, pts = polygons_of_only_lines, color=(3,3,3))
+
+        text_regions_p_true[:,:][mask_images_only[:,:] == 1] = 2
+
+        text_regions_p_true = cv2.fillPoly(text_regions_p_true, pts = polygons_of_only_texts, color=(1,1,1))
+
+
+
+        text_regions_p_true[text_regions_p_true.shape[0]-15:text_regions_p_true.shape[0], :] = 0
+        text_regions_p_true[:, text_regions_p_true.shape[1]-15:text_regions_p_true.shape[1]] = 0
+
+        ##polygons_of_images = return_contours_of_interested_region(text_regions_p_true, 2, 0.0001)
+        polygons_of_images = return_contours_of_interested_region(text_regions_p_true, 2, 0.001)
+
+        image_boundary_of_doc = np.zeros((text_regions_p_true.shape[0], text_regions_p_true.shape[1]))
+
+        ###image_boundary_of_doc[:6, :] = 1
+        ###image_boundary_of_doc[text_regions_p_true.shape[0]-6:text_regions_p_true.shape[0], :] = 1
+
+        ###image_boundary_of_doc[:, :6] = 1
+        ###image_boundary_of_doc[:, text_regions_p_true.shape[1]-6:text_regions_p_true.shape[1]] = 1
+
+
+        polygons_of_images_fin = []
+        for ploy_img_ind in polygons_of_images:
+            """
+            test_poly_image = np.zeros((text_regions_p_true.shape[0], text_regions_p_true.shape[1]))
+            test_poly_image = cv2.fillPoly(test_poly_image, pts = [ploy_img_ind], color=(1,1,1))
+            
+            test_poly_image = test_poly_image[:,:] + image_boundary_of_doc[:,:]
+            test_poly_image_intersected_area = ( test_poly_image[:,:]==2 )*1
+            
+            test_poly_image_intersected_area = test_poly_image_intersected_area.sum()
+            
+            if test_poly_image_intersected_area==0:
+                ##polygons_of_images_fin.append(ploy_img_ind)
+                
+                x, y, w, h = cv2.boundingRect(ploy_img_ind)
+                box = [x, y, w, h]
+                _, page_coord_img = crop_image_inside_box(box, text_regions_p_true)
+                #cont_page.append(np.array([[page_coord[2], page_coord[0]], [page_coord[3], page_coord[0]], [page_coord[3], page_coord[1]], [page_coord[2], page_coord[1]]]))
+                
+                polygons_of_images_fin.append(np.array([[page_coord_img[2], page_coord_img[0]], [page_coord_img[3], page_coord_img[0]], [page_coord_img[3], page_coord_img[1]], [page_coord_img[2], page_coord_img[1]]]) )
+            """
+            x, y, w, h = cv2.boundingRect(ploy_img_ind)
+            if h < 150 or w < 150:
+                pass
+            else:
+                box = [x, y, w, h]
+                _, page_coord_img = crop_image_inside_box(box, text_regions_p_true)
+                #cont_page.append(np.array([[page_coord[2], page_coord[0]], [page_coord[3], page_coord[0]], [page_coord[3], page_coord[1]], [page_coord[2], page_coord[1]]]))
+
+                polygons_of_images_fin.append(np.array([[page_coord_img[2], page_coord_img[0]], [page_coord_img[3], page_coord_img[0]], [page_coord_img[3], page_coord_img[1]], [page_coord_img[2], page_coord_img[1]]]) )
+
+        return text_regions_p_true, erosion_hurts, polygons_lines_xml, polygons_of_images_fin, image_page, page_coord, cont_page
     def get_regions_light_v(self,img,is_image_enhanced, num_col_classifier, skip_layout_and_reading_order=False):
         self.logger.debug("enter get_regions_light_v")
         t_in = time.time()
@@ -3179,11 +3226,13 @@ class Eynollah:
         prediction_table_erode = cv2.erode(prediction_table[:,:,0], KERNEL, iterations=20)
         prediction_table_erode = cv2.dilate(prediction_table_erode, KERNEL, iterations=20)
         return prediction_table_erode.astype(np.int16)
+
     def run_graphics_and_columns_light(self, text_regions_p_1, textline_mask_tot_ea, num_col_classifier, num_column_is_classified, erosion_hurts, img_bin_light):
         
         #print(text_regions_p_1.shape, 'text_regions_p_1 shape run graphics')
         #print(erosion_hurts, 'erosion_hurts')
         t_in_gr = time.time()
+
         img_g = self.imread(grayscale=True, uint8=True)
 
         img_g3 = np.zeros((img_g.shape[0], img_g.shape[1], 3))
@@ -3668,11 +3717,11 @@ class Eynollah:
         img_poly[text_regions_p[:,:]==3] = 4
         img_poly[text_regions_p[:,:]==6] = 5
         
-
         model_ro_machine, _ = self.start_new_session_and_model(self.model_reading_order_machine_dir)
 
         height1 =672#448
         width1 = 448#224
+        t0_tot = time.time()
 
         height2 =672#448
         width2= 448#224
@@ -3684,7 +3733,6 @@ class Eynollah:
         
         if contours_only_text_parent_h:
             _, cy_main, x_min_main, x_max_main, y_min_main, y_max_main, _ = find_new_features_of_contours(contours_only_text_parent_h)
-            
             for j in range(len(cy_main)):
                 img_header_and_sep[int(y_max_main[j]):int(y_max_main[j])+12,int(x_min_main[j]):int(x_max_main[j]) ] = 1 
             

src/eynollah/ocrd-tool.json

@@ -1,5 +1,5 @@
 {
-  "version": "0.3.0",
+  "version": "0.3.1",
   "git_url": "https://github.com/qurator-spk/eynollah",
   "tools": {
     "ocrd-eynollah-segment": {
@@ -52,10 +52,10 @@
       },
       "resources": [
 	{
-	  "description": "models for eynollah (TensorFlow format)",
+	  "description": "models for eynollah (TensorFlow SavedModel format)",
-	  "url": "https://github.com/qurator-spk/eynollah/releases/download/v0.3.0/models_eynollah.tar.gz",
+	  "url": "https://github.com/qurator-spk/eynollah/releases/download/v0.3.1/models_eynollah.tar.gz",
 	  "name": "default",
-	  "size": 1761991295,
+	  "size": 1894627041,
 	  "type": "archive",
 	  "path_in_archive": "models_eynollah"
 	}

src/eynollah/processor.py

@@ -42,7 +42,7 @@ class EynollahProcessor(Processor):
             page = pcgts.get_Page()
             # XXX loses DPI information
             # page_image, _, _ = self.workspace.image_from_page(page, page_id, feature_filter='binarized')
-            image_filename = self.workspace.download_file(next(self.workspace.mets.find_files(url=page.imageFilename))).local_filename
+            image_filename = self.workspace.download_file(next(self.workspace.mets.find_files(local_filename=page.imageFilename))).local_filename
             eynollah_kwargs = {
                 'dir_models': self.resolve_resource(self.parameter['models']),
                 'allow_enhancement': False,

src/eynollah/writer.py

@@ -202,10 +202,18 @@ class EynollahXmlWriter():
             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)):

tests/test_counter.py

@@ -1,5 +1,5 @@
 from tests.base import main
-from qurator.eynollah.utils.counter import EynollahIdCounter
+from eynollah.utils.counter import EynollahIdCounter
 
 def test_counter_string():
     c = EynollahIdCounter()

tests/test_dpi.py

@@ -1,6 +1,6 @@
 import cv2
 from pathlib import Path
-from qurator.eynollah.utils.pil_cv2 import check_dpi
+from eynollah.utils.pil_cv2 import check_dpi
 from tests.base import main
 
 def test_dpi():

tests/test_run.py

@@ -2,7 +2,7 @@ from os import environ
 from pathlib import Path
 from ocrd_utils import pushd_popd
 from tests.base import CapturingTestCase as TestCase, main
-from qurator.eynollah.cli import main as eynollah_cli
+from eynollah.cli import main as eynollah_cli
 
 testdir = Path(__file__).parent.resolve()
 

tests/test_smoke.py

@@ -1,7 +1,7 @@
 def test_utils_import():
-    import qurator.eynollah.utils
+    import eynollah.utils
-    import qurator.eynollah.utils.contour
+    import eynollah.utils.contour
-    import qurator.eynollah.utils.drop_capitals
+    import eynollah.utils.drop_capitals
-    import qurator.eynollah.utils.drop_capitals
+    import eynollah.utils.drop_capitals
-    import qurator.eynollah.utils.is_nan
+    import eynollah.utils.is_nan
-    import qurator.eynollah.utils.rotate
+    import eynollah.utils.rotate

tests/test_xml.py

@@ -1,5 +1,5 @@
 from pytest import main
-from qurator.eynollah.utils.xml import create_page_xml
+from eynollah.utils.xml import create_page_xml
 from ocrd_models.ocrd_page import to_xml
 
 PAGE_2019 = 'http://schema.primaresearch.org/PAGE/gts/pagecontent/2019-07-15'