mirror of
https://github.com/qurator-spk/eynollah.git
synced 2025-06-09 04:09:54 +02:00
Merge branch 'main' into dockerfile
This commit is contained in:
commit
7eb1390583
6 changed files with 469 additions and 268 deletions
7
Makefile
7
Makefile
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@ -27,17 +27,14 @@ help:
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models: models_eynollah
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models: models_eynollah
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models_eynollah: models_eynollah.tar.gz
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models_eynollah: models_eynollah.tar.gz
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# tar xf models_eynollah_renamed.tar.gz --transform 's/models_eynollah_renamed/models_eynollah/'
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# tar xf models_eynollah_renamed.tar.gz
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# tar xf models_eynollah_renamed_savedmodel.tar.gz --transform 's/models_eynollah_renamed_savedmodel/models_eynollah/'
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tar xf models_eynollah.tar.gz
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tar xf models_eynollah.tar.gz
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models_eynollah.tar.gz:
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models_eynollah.tar.gz:
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# wget 'https://qurator-data.de/eynollah/2021-04-25/models_eynollah.tar.gz'
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# wget 'https://qurator-data.de/eynollah/2021-04-25/models_eynollah.tar.gz'
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||||||
# wget 'https://qurator-data.de/eynollah/2022-04-05/models_eynollah_renamed.tar.gz'
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# wget 'https://qurator-data.de/eynollah/2022-04-05/models_eynollah_renamed.tar.gz'
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||||||
# wget 'https://ocr-d.kba.cloud/2022-04-05.SavedModel.tar.gz'
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||||||
# wget 'https://qurator-data.de/eynollah/2022-04-05/models_eynollah_renamed_savedmodel.tar.gz'
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# wget 'https://qurator-data.de/eynollah/2022-04-05/models_eynollah_renamed_savedmodel.tar.gz'
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wget https://github.com/qurator-spk/eynollah/releases/download/v0.3.0/models_eynollah.tar.gz
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# wget 'https://github.com/qurator-spk/eynollah/releases/download/v0.3.0/models_eynollah.tar.gz'
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wget 'https://github.com/qurator-spk/eynollah/releases/download/v0.3.1/models_eynollah.tar.gz'
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# Install with pip
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# Install with pip
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install:
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install:
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@ -71,6 +71,7 @@ The following options can be used to further configure the processing:
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| `-cl` | apply contour detection for curved text lines instead of bounding boxes |
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| `-cl` | apply contour detection for curved text lines instead of bounding boxes |
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| `-ib` | apply binarization (the resulting image is saved to the output directory) |
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| `-ib` | apply binarization (the resulting image is saved to the output directory) |
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| `-ep` | enable plotting (MUST always be used with `-sl`, `-sd`, `-sa`, `-si` or `-ae`) |
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| `-ep` | enable plotting (MUST always be used with `-sl`, `-sd`, `-sa`, `-si` or `-ae`) |
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| `-eoi` | extract only images to output directory (other processing will not be done) |
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| `-ho` | ignore headers for reading order dectection |
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| `-ho` | ignore headers for reading order dectection |
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| `-si <directory>` | save image regions detected to this directory |
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| `-si <directory>` | save image regions detected to this directory |
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| `-sd <directory>` | save deskewed image to this directory |
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| `-sd <directory>` | save deskewed image to this directory |
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@ -67,6 +67,12 @@ from eynollah.eynollah import Eynollah
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is_flag=True,
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is_flag=True,
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help="If set, will plot intermediary files and images",
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help="If set, will plot intermediary files and images",
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)
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)
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@click.option(
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"--extract_only_images/--disable-extracting_only_images",
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"-eoi/-noeoi",
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is_flag=True,
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help="If a directory is given, only images in documents will be cropped and saved there and the other processing will not be done",
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)
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@click.option(
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@click.option(
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"--allow-enhancement/--no-allow-enhancement",
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"--allow-enhancement/--no-allow-enhancement",
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"-ae/-noae",
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"-ae/-noae",
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@ -148,6 +154,7 @@ def main(
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save_layout,
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save_layout,
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save_deskewed,
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save_deskewed,
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save_all,
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save_all,
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extract_only_images,
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save_page,
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save_page,
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enable_plotting,
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enable_plotting,
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allow_enhancement,
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allow_enhancement,
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@ -175,12 +182,16 @@ def main(
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if textline_light and not light_version:
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if textline_light and not light_version:
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print('Error: You used -tll to enable light textline detection but -light is not enabled')
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print('Error: You used -tll to enable light textline detection but -light is not enabled')
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sys.exit(1)
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sys.exit(1)
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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) :
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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')
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sys.exit(1)
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eynollah = Eynollah(
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eynollah = Eynollah(
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image_filename=image,
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image_filename=image,
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dir_out=out,
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dir_out=out,
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dir_in=dir_in,
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dir_in=dir_in,
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dir_models=model,
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dir_models=model,
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dir_of_cropped_images=save_images,
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dir_of_cropped_images=save_images,
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extract_only_images=extract_only_images,
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dir_of_layout=save_layout,
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dir_of_layout=save_layout,
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dir_of_deskewed=save_deskewed,
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dir_of_deskewed=save_deskewed,
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dir_of_all=save_all,
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dir_of_all=save_all,
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@ -149,6 +149,7 @@ class Eynollah:
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dir_out=None,
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dir_out=None,
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dir_in=None,
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dir_in=None,
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dir_of_cropped_images=None,
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dir_of_cropped_images=None,
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extract_only_images=False,
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dir_of_layout=None,
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dir_of_layout=None,
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dir_of_deskewed=None,
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dir_of_deskewed=None,
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dir_of_all=None,
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dir_of_all=None,
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@ -196,6 +197,7 @@ class Eynollah:
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self.allow_scaling = allow_scaling
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self.allow_scaling = allow_scaling
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self.headers_off = headers_off
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self.headers_off = headers_off
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self.light_version = light_version
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self.light_version = light_version
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self.extract_only_images = extract_only_images
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self.ignore_page_extraction = ignore_page_extraction
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self.ignore_page_extraction = ignore_page_extraction
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self.pcgts = pcgts
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self.pcgts = pcgts
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if not dir_in:
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if not dir_in:
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@ -226,6 +228,7 @@ class Eynollah:
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self.model_page_dir = dir_models + "/eynollah-page-extraction_20210425"
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self.model_page_dir = dir_models + "/eynollah-page-extraction_20210425"
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self.model_region_dir_p_ens = dir_models + "/eynollah-main-regions-ensembled_20210425"
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self.model_region_dir_p_ens = dir_models + "/eynollah-main-regions-ensembled_20210425"
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self.model_region_dir_p_ens_light = dir_models + "/eynollah-main-regions_20220314"
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self.model_region_dir_p_ens_light = dir_models + "/eynollah-main-regions_20220314"
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self.model_region_dir_p_ens_light_only_images_extraction = dir_models + "/eynollah-main-regions_20231127_672_org_ens_11_13_16_17_18"
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if self.textline_light:
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if self.textline_light:
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self.model_textline_dir = dir_models + "/eynollah-textline_light_20210425"
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self.model_textline_dir = dir_models + "/eynollah-textline_light_20210425"
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else:
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else:
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@ -250,7 +253,23 @@ class Eynollah:
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self.ls_imgs = os.listdir(self.dir_in)
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self.ls_imgs = os.listdir(self.dir_in)
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if dir_in and not light_version:
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if dir_in and self.extract_only_images:
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config = tf.compat.v1.ConfigProto()
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config.gpu_options.allow_growth = True
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session = tf.compat.v1.Session(config=config)
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set_session(session)
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self.model_page = self.our_load_model(self.model_page_dir)
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self.model_classifier = self.our_load_model(self.model_dir_of_col_classifier)
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self.model_bin = self.our_load_model(self.model_dir_of_binarization)
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#self.model_textline = self.our_load_model(self.model_textline_dir)
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self.model_region = self.our_load_model(self.model_region_dir_p_ens_light_only_images_extraction)
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#self.model_region_fl_np = self.our_load_model(self.model_region_dir_fully_np)
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#self.model_region_fl = self.our_load_model(self.model_region_dir_fully)
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self.ls_imgs = os.listdir(self.dir_in)
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||||||
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if dir_in and not (light_version or self.extract_only_images):
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||||||
config = tf.compat.v1.ConfigProto()
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config = tf.compat.v1.ConfigProto()
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config.gpu_options.allow_growth = True
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config.gpu_options.allow_growth = True
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session = tf.compat.v1.Session(config=config)
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session = tf.compat.v1.Session(config=config)
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@ -464,6 +483,27 @@ class Eynollah:
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||||||
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|
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return img_new, num_column_is_classified
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return img_new, num_column_is_classified
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def calculate_width_height_by_columns_extract_only_images(self, img, num_col, width_early, label_p_pred):
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self.logger.debug("enter calculate_width_height_by_columns")
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if num_col == 1:
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img_w_new = 700
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elif num_col == 2:
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img_w_new = 900
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elif num_col == 3:
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img_w_new = 1500
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elif num_col == 4:
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img_w_new = 1800
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elif num_col == 5:
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img_w_new = 2200
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elif num_col == 6:
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img_w_new = 2500
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img_h_new = int(img.shape[0] / float(img.shape[1]) * img_w_new)
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|
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img_new = resize_image(img, img_h_new, img_w_new)
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|
num_column_is_classified = True
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|
||||||
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return img_new, num_column_is_classified
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||||||
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|
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def resize_image_with_column_classifier(self, is_image_enhanced, img_bin):
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def resize_image_with_column_classifier(self, is_image_enhanced, img_bin):
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self.logger.debug("enter resize_image_with_column_classifier")
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self.logger.debug("enter resize_image_with_column_classifier")
|
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if self.input_binary:
|
if self.input_binary:
|
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@ -571,6 +611,7 @@ class Eynollah:
|
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|
|
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self.logger.info("Found %d columns (%s)", num_col, np.around(label_p_pred, decimals=5))
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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 dpi < DPI_THRESHOLD:
|
||||||
img_new, num_column_is_classified = self.calculate_width_height_by_columns(img, num_col, width_early, label_p_pred)
|
img_new, num_column_is_classified = self.calculate_width_height_by_columns(img, num_col, width_early, label_p_pred)
|
||||||
if light_version:
|
if light_version:
|
||||||
|
@ -582,6 +623,10 @@ class Eynollah:
|
||||||
num_column_is_classified = True
|
num_column_is_classified = True
|
||||||
image_res = np.copy(img)
|
image_res = np.copy(img)
|
||||||
is_image_enhanced = False
|
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")
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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
|
return is_image_enhanced, img, image_res, num_col, num_column_is_classified, img_bin
|
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@ -868,7 +913,11 @@ class Eynollah:
|
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seg_not_base = label_p_pred[0,:,:,4]
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seg_not_base = label_p_pred[0,:,:,4]
|
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##seg2 = -label_p_pred[0,:,:,2]
|
##seg2 = -label_p_pred[0,:,:,2]
|
||||||
|
|
||||||
|
if self.extract_only_images:
|
||||||
|
#seg_not_base[seg_not_base>0.3] =1
|
||||||
|
seg_not_base[seg_not_base>0.5] =1
|
||||||
|
seg_not_base[seg_not_base<1] =0
|
||||||
|
else:
|
||||||
seg_not_base[seg_not_base>0.03] =1
|
seg_not_base[seg_not_base>0.03] =1
|
||||||
seg_not_base[seg_not_base<1] =0
|
seg_not_base[seg_not_base<1] =0
|
||||||
|
|
||||||
|
@ -889,11 +938,8 @@ class Eynollah:
|
||||||
seg_line[seg_line>0.1] =1
|
seg_line[seg_line>0.1] =1
|
||||||
seg_line[seg_line<1] =0
|
seg_line[seg_line<1] =0
|
||||||
|
|
||||||
|
if not self.extract_only_images:
|
||||||
seg_background = label_p_pred[0,:,:,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>0.25] =1
|
||||||
seg_background[seg_background<1] =0
|
seg_background[seg_background<1] =0
|
||||||
##seg = seg+seg2
|
##seg = seg+seg2
|
||||||
|
@ -908,7 +954,8 @@ class Eynollah:
|
||||||
##plt.show()
|
##plt.show()
|
||||||
#seg[seg==1]=0
|
#seg[seg==1]=0
|
||||||
#seg[seg_test==1]=1
|
#seg[seg_test==1]=1
|
||||||
seg[seg_not_base==1]=4
|
###seg[seg_not_base==1]=4
|
||||||
|
if not self.extract_only_images:
|
||||||
seg[seg_background==1]=0
|
seg[seg_background==1]=0
|
||||||
seg[(seg_line==1) & (seg==0)]=3
|
seg[(seg_line==1) & (seg==0)]=3
|
||||||
seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2)
|
seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2)
|
||||||
|
@ -1574,6 +1621,124 @@ class Eynollah:
|
||||||
q.put(slopes_sub)
|
q.put(slopes_sub)
|
||||||
poly.put(poly_sub)
|
poly.put(poly_sub)
|
||||||
box_sub.put(boxes_sub_new)
|
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)
|
||||||
|
|
||||||
|
#plt.imshow(prediction_regions_org[:,:,0])
|
||||||
|
#plt.show()
|
||||||
|
|
||||||
|
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
|
||||||
|
|
||||||
|
#plt.imshow(image_boundary_of_doc)
|
||||||
|
#plt.show()
|
||||||
|
|
||||||
|
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):
|
def get_regions_light_v(self,img,is_image_enhanced, num_col_classifier):
|
||||||
self.logger.debug("enter get_regions_light_v")
|
self.logger.debug("enter get_regions_light_v")
|
||||||
erosion_hurts = False
|
erosion_hurts = False
|
||||||
|
@ -2425,6 +2590,7 @@ class Eynollah:
|
||||||
prediction_table_erode = cv2.erode(prediction_table[:,:,0], KERNEL, iterations=20)
|
prediction_table_erode = cv2.erode(prediction_table[:,:,0], KERNEL, iterations=20)
|
||||||
prediction_table_erode = cv2.dilate(prediction_table_erode, KERNEL, iterations=20)
|
prediction_table_erode = cv2.dilate(prediction_table_erode, KERNEL, iterations=20)
|
||||||
return prediction_table_erode.astype(np.int16)
|
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):
|
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_g = self.imread(grayscale=True, uint8=True)
|
img_g = self.imread(grayscale=True, uint8=True)
|
||||||
|
|
||||||
|
@ -2826,7 +2992,6 @@ class Eynollah:
|
||||||
"""
|
"""
|
||||||
self.logger.debug("enter run")
|
self.logger.debug("enter run")
|
||||||
|
|
||||||
|
|
||||||
t0_tot = time.time()
|
t0_tot = time.time()
|
||||||
|
|
||||||
if not self.dir_in:
|
if not self.dir_in:
|
||||||
|
@ -2837,6 +3002,24 @@ class Eynollah:
|
||||||
if self.dir_in:
|
if self.dir_in:
|
||||||
self.reset_file_name_dir(os.path.join(self.dir_in,img_name))
|
self.reset_file_name_dir(os.path.join(self.dir_in,img_name))
|
||||||
|
|
||||||
|
|
||||||
|
if self.extract_only_images:
|
||||||
|
img_res, is_image_enhanced, num_col_classifier, num_column_is_classified = self.run_enhancement(self.light_version)
|
||||||
|
self.logger.info("Enhancing took %.1fs ", time.time() - t0)
|
||||||
|
|
||||||
|
text_regions_p_1 ,erosion_hurts, polygons_lines_xml,polygons_of_images,image_page, page_coord, cont_page = self.get_regions_light_v_extract_only_images(img_res, is_image_enhanced, num_col_classifier)
|
||||||
|
|
||||||
|
pcgts = self.writer.build_pagexml_no_full_layout([], page_coord, [], [], [], [], polygons_of_images, [], [], [], [], [], cont_page, [], [])
|
||||||
|
|
||||||
|
if self.plotter:
|
||||||
|
self.plotter.write_images_into_directory(polygons_of_images, image_page)
|
||||||
|
|
||||||
|
if self.dir_in:
|
||||||
|
self.writer.write_pagexml(pcgts)
|
||||||
|
else:
|
||||||
|
return pcgts
|
||||||
|
|
||||||
|
else:
|
||||||
img_res, is_image_enhanced, num_col_classifier, num_column_is_classified = self.run_enhancement(self.light_version)
|
img_res, is_image_enhanced, num_col_classifier, num_column_is_classified = self.run_enhancement(self.light_version)
|
||||||
self.logger.info("Enhancing took %.1fs ", time.time() - t0)
|
self.logger.info("Enhancing took %.1fs ", time.time() - t0)
|
||||||
|
|
||||||
|
@ -3091,6 +3274,7 @@ class Eynollah:
|
||||||
|
|
||||||
pcgts = self.writer.build_pagexml_full_layout(contours_only_text_parent, contours_only_text_parent_h, page_coord, order_text_new, id_of_texts_tot, all_found_textline_polygons, all_found_textline_polygons_h, all_box_coord, all_box_coord_h, polygons_of_images, contours_tables, polygons_of_drop_capitals, polygons_of_marginals, all_found_textline_polygons_marginals, all_box_coord_marginals, slopes, slopes_h, slopes_marginals, cont_page, polygons_lines_xml)
|
pcgts = self.writer.build_pagexml_full_layout(contours_only_text_parent, contours_only_text_parent_h, page_coord, order_text_new, id_of_texts_tot, all_found_textline_polygons, all_found_textline_polygons_h, all_box_coord, all_box_coord_h, polygons_of_images, contours_tables, polygons_of_drop_capitals, polygons_of_marginals, all_found_textline_polygons_marginals, all_box_coord_marginals, slopes, slopes_h, slopes_marginals, cont_page, polygons_lines_xml)
|
||||||
self.logger.info("Job done in %.1fs", time.time() - t0)
|
self.logger.info("Job done in %.1fs", time.time() - t0)
|
||||||
|
|
||||||
if not self.dir_in:
|
if not self.dir_in:
|
||||||
return pcgts
|
return pcgts
|
||||||
else:
|
else:
|
||||||
|
|
|
@ -52,10 +52,10 @@
|
||||||
},
|
},
|
||||||
"resources": [
|
"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",
|
"name": "default",
|
||||||
"size": 1761991295,
|
"size": 1894627041,
|
||||||
"type": "archive",
|
"type": "archive",
|
||||||
"path_in_archive": "models_eynollah"
|
"path_in_archive": "models_eynollah"
|
||||||
}
|
}
|
||||||
|
|
|
@ -172,10 +172,18 @@ class EynollahXmlWriter():
|
||||||
page.add_ImageRegion(img_region)
|
page.add_ImageRegion(img_region)
|
||||||
points_co = ''
|
points_co = ''
|
||||||
for lmm in range(len(found_polygons_text_region_img[mm])):
|
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 += str(int((found_polygons_text_region_img[mm][lmm,0,0] + page_coord[2]) / self.scale_x))
|
||||||
points_co += ','
|
points_co += ','
|
||||||
points_co += str(int((found_polygons_text_region_img[mm][lmm,0,1] + page_coord[0]) / self.scale_y))
|
points_co += str(int((found_polygons_text_region_img[mm][lmm,0,1] + page_coord[0]) / self.scale_y))
|
||||||
points_co += ' '
|
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])
|
img_region.get_Coords().set_points(points_co[:-1])
|
||||||
|
|
||||||
for mm in range(len(polygons_lines_to_be_written_in_xml)):
|
for mm in range(len(polygons_lines_to_be_written_in_xml)):
|
||||||
|
|
Loading…
Add table
Add a link
Reference in a new issue