mirror of
https://github.com/qurator-spk/eynollah.git
synced 2025-10-31 09:44:17 +01:00
attempting partial revert of dir_in mode from c606391c31
This commit is contained in:
Clemens Neudecker
parent
482511adc0
commit
ca6090fef1
3 changed files with 270 additions and 268 deletions
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@ -19,12 +19,13 @@ from eynollah.eynollah.eynollah import Eynollah
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type=click.Path(exists=True, file_okay=False),
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required=True,
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)
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@click.option(
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"--dir_in",
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"-di",
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help="directory of images",
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type=click.Path(exists=True, file_okay=False),
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)
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# temporary disable directory input
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# @click.option(
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# "--dir_in",
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# "-di",
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# help="directory of images",
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# type=click.Path(exists=True, file_okay=False),
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# )
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@click.option(
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"--model",
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"-m",
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@ -143,7 +144,7 @@ from eynollah.eynollah.eynollah import Eynollah
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def main(
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image,
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out,
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dir_in,
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# dir_in,
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model,
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save_images,
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save_layout,
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@ -179,7 +180,7 @@ def main(
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eynollah = Eynollah(
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image_filename=image,
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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_of_cropped_images=save_images,
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dir_of_layout=save_layout,
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@ -199,9 +200,9 @@ def main(
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light_version=light_version,
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ignore_page_extraction=ignore_page_extraction,
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)
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eynollah.run()
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# pcgts = eynollah.run()
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# eynollah.writer.write_pagexml(pcgts)
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# eynollah.run()
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pcgts = eynollah.run()
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eynollah.writer.write_pagexml(pcgts)
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if __name__ == "__main__":
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@ -145,11 +145,11 @@ class Eynollah:
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def __init__(
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self,
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dir_models,
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image_filename=None,
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image_filename,
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image_pil=None,
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image_filename_stem=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_layout=None,
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dir_of_deskewed=None,
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@ -171,16 +171,16 @@ class Eynollah:
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logger=None,
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pcgts=None,
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):
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if not dir_in:
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if image_pil:
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self._imgs = self._cache_images(image_pil=image_pil)
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else:
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self._imgs = self._cache_images(image_filename=image_filename)
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if override_dpi:
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self.dpi = override_dpi
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self.image_filename = image_filename
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# if not dir_in:
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# if image_pil:
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# self._imgs = self._cache_images(image_pil=image_pil)
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# else:
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# self._imgs = self._cache_images(image_filename=image_filename)
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# if override_dpi:
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# self.dpi = override_dpi
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# self.image_filename = image_filename
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self.dir_out = dir_out
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self.dir_in = dir_in
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# self.dir_in = dir_in
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self.dir_of_all = dir_of_all
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self.dir_save_page = dir_save_page
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self.dir_of_deskewed = dir_of_deskewed
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@ -200,21 +200,21 @@ class Eynollah:
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self.light_version = light_version
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self.ignore_page_extraction = ignore_page_extraction
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self.pcgts = pcgts
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if not dir_in:
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self.plotter = None if not enable_plotting else EynollahPlotter(
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dir_out=self.dir_out,
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dir_of_all=dir_of_all,
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dir_save_page=dir_save_page,
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dir_of_deskewed=dir_of_deskewed,
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dir_of_cropped_images=dir_of_cropped_images,
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dir_of_layout=dir_of_layout,
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image_filename_stem=Path(Path(image_filename).name).stem)
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self.writer = EynollahXmlWriter(
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dir_out=self.dir_out,
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image_filename=self.image_filename,
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curved_line=self.curved_line,
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textline_light=self.textline_light,
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pcgts=pcgts)
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# if not dir_in:
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self.plotter = None if not enable_plotting else EynollahPlotter(
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dir_out=self.dir_out,
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dir_of_all=dir_of_all,
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dir_save_page=dir_save_page,
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dir_of_deskewed=dir_of_deskewed,
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dir_of_cropped_images=dir_of_cropped_images,
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dir_of_layout=dir_of_layout,
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image_filename_stem=Path(Path(image_filename).name).stem)
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self.writer = EynollahXmlWriter(
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dir_out=self.dir_out,
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image_filename=self.image_filename,
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curved_line=self.curved_line,
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textline_light=self.textline_light,
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pcgts=pcgts)
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self.logger = logger if logger else getLogger('eynollah')
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self.dir_models = dir_models
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@ -236,39 +236,39 @@ class Eynollah:
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self.models = {}
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if dir_in and light_version:
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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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# if dir_in and light_version:
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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)
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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.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)
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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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# self.ls_imgs = os.listdir(self.dir_in)
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if dir_in and not light_version:
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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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# if dir_in and not light_version:
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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)
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self.model_region_p2 = self.our_load_model(self.model_region_dir_p2)
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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.model_enhancement = self.our_load_model(self.model_dir_of_enhancement)
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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)
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# self.model_region_p2 = self.our_load_model(self.model_region_dir_p2)
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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.model_enhancement = self.our_load_model(self.model_dir_of_enhancement)
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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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def _cache_images(self, image_filename=None, image_pil=None):
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ret = {}
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@ -283,9 +283,9 @@ class Eynollah:
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ret[f'img{prefix}_uint8'] = ret[f'img{prefix}'].astype(np.uint8)
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return ret
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def reset_file_name_dir(self, image_filename):
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self._imgs = self._cache_images(image_filename=image_filename)
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self.image_filename = image_filename
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# def reset_file_name_dir(self, image_filename):
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# self._imgs = self._cache_images(image_filename=image_filename)
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# self.image_filename = image_filename
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self.plotter = None if not self.enable_plotting else EynollahPlotter(
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dir_out=self.dir_out,
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@ -476,9 +476,9 @@ class Eynollah:
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img = self.imread()
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_, page_coord = self.early_page_for_num_of_column_classification(img)
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if not self.dir_in:
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model_num_classifier, session_col_classifier = self.start_new_session_and_model(
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self.model_dir_of_col_classifier)
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# if not self.dir_in:
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model_num_classifier, session_col_classifier = self.start_new_session_and_model(
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self.model_dir_of_col_classifier)
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if self.input_binary:
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img_in = np.copy(img)
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img_in = img_in / 255.0
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@ -501,10 +501,10 @@ class Eynollah:
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img_in[0, :, :, 1] = img_1ch[:, :]
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img_in[0, :, :, 2] = img_1ch[:, :]
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if not self.dir_in:
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label_p_pred = model_num_classifier.predict(img_in, verbose=0)
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else:
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label_p_pred = self.model_classifier.predict(img_in, verbose=0)
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# if not self.dir_in:
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label_p_pred = model_num_classifier.predict(img_in, verbose=0)
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# else:
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# label_p_pred = self.model_classifier.predict(img_in, verbose=0)
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num_col = np.argmax(label_p_pred[0]) + 1
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@ -524,12 +524,12 @@ class Eynollah:
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self.logger.info("Detected %s DPI", dpi)
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if self.input_binary:
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img = self.imread()
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if self.dir_in:
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prediction_bin = self.do_prediction(True, img, self.model_bin)
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else:
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# if self.dir_in:
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# prediction_bin = self.do_prediction(True, img, self.model_bin)
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# else:
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model_bin, session_bin = self.start_new_session_and_model(self.model_dir_of_binarization)
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prediction_bin = self.do_prediction(True, img, model_bin)
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model_bin, session_bin = self.start_new_session_and_model(self.model_dir_of_binarization)
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prediction_bin = self.do_prediction(True, img, model_bin)
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prediction_bin = prediction_bin[:, :, 0]
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prediction_bin = (prediction_bin[:, :] == 0) * 1
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@ -546,9 +546,9 @@ class Eynollah:
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t1 = time.time()
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_, page_coord = self.early_page_for_num_of_column_classification(img_bin)
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if not self.dir_in:
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model_num_classifier, session_col_classifier = self.start_new_session_and_model(
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self.model_dir_of_col_classifier)
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# if not self.dir_in:
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model_num_classifier, session_col_classifier = self.start_new_session_and_model(
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self.model_dir_of_col_classifier)
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if self.input_binary:
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img_in = np.copy(img)
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@ -568,10 +568,11 @@ class Eynollah:
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img_in[0, :, :, 1] = img_1ch[:, :]
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img_in[0, :, :, 2] = img_1ch[:, :]
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if self.dir_in:
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label_p_pred = self.model_classifier.predict(img_in, verbose=0)
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else:
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label_p_pred = model_num_classifier.predict(img_in, verbose=0)
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# if self.dir_in:
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# label_p_pred = self.model_classifier.predict(img_in, verbose=0)
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# else:
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# label_p_pred = model_num_classifier.predict(img_in, verbose=0)
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label_p_pred = model_num_classifier.predict(img_in, verbose=0)
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num_col = np.argmax(label_p_pred[0]) + 1
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self.logger.info("Found %d columns (%s)", num_col, np.around(label_p_pred, decimals=5))
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@ -984,13 +985,13 @@ class Eynollah:
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if not self.ignore_page_extraction:
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img = cv2.GaussianBlur(self.image, (5, 5), 0)
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if not self.dir_in:
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model_page, session_page = self.start_new_session_and_model(self.model_page_dir)
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# if not self.dir_in:
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model_page, session_page = self.start_new_session_and_model(self.model_page_dir)
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if not self.dir_in:
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img_page_prediction = self.do_prediction(False, img, model_page)
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else:
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img_page_prediction = self.do_prediction(False, img, self.model_page)
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# if not self.dir_in:
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img_page_prediction = self.do_prediction(False, img, model_page)
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# else:
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# img_page_prediction = self.do_prediction(False, img, self.model_page)
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imgray = cv2.cvtColor(img_page_prediction, cv2.COLOR_BGR2GRAY)
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_, thresh = cv2.threshold(imgray, 0, 255, 0)
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thresh = cv2.dilate(thresh, KERNEL, iterations=3)
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@ -1036,14 +1037,14 @@ class Eynollah:
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img = img.astype(np.uint8)
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else:
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img = self.imread()
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if not self.dir_in:
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model_page, session_page = self.start_new_session_and_model(self.model_page_dir)
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# if not self.dir_in:
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model_page, session_page = self.start_new_session_and_model(self.model_page_dir)
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img = cv2.GaussianBlur(img, (5, 5), 0)
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if self.dir_in:
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img_page_prediction = self.do_prediction(False, img, self.model_page)
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else:
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img_page_prediction = self.do_prediction(False, img, model_page)
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# if self.dir_in:
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# img_page_prediction = self.do_prediction(False, img, self.model_page)
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# else:
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img_page_prediction = self.do_prediction(False, img, model_page)
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imgray = cv2.cvtColor(img_page_prediction, cv2.COLOR_BGR2GRAY)
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_, thresh = cv2.threshold(imgray, 0, 255, 0)
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@ -1069,11 +1070,11 @@ class Eynollah:
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self.logger.debug("enter extract_text_regions")
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img_height_h = img.shape[0]
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img_width_h = img.shape[1]
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if not self.dir_in:
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model_region, session_region = self.start_new_session_and_model(
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self.model_region_dir_fully if patches else self.model_region_dir_fully_np)
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else:
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model_region = self.model_region_fl if patches else self.model_region_fl_np
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# if not self.dir_in:
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model_region, session_region = self.start_new_session_and_model(
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self.model_region_dir_fully if patches else self.model_region_dir_fully_np)
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# else:
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# model_region = self.model_region_fl if patches else self.model_region_fl_np
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if not patches:
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img = otsu_copy_binary(img)
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@ -1588,23 +1589,23 @@ class Eynollah:
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def textline_contours(self, img, patches, scaler_h, scaler_w):
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self.logger.debug('enter textline_contours')
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if not self.dir_in:
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model_textline, session_textline = self.start_new_session_and_model(
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self.model_textline_dir if patches else self.model_textline_dir_np)
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# if not self.dir_in:
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model_textline, session_textline = self.start_new_session_and_model(
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self.model_textline_dir if patches else self.model_textline_dir_np)
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img = img.astype(np.uint8)
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img_org = np.copy(img)
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img_h = img_org.shape[0]
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img_w = img_org.shape[1]
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img = resize_image(img_org, int(img_org.shape[0] * scaler_h), int(img_org.shape[1] * scaler_w))
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if not self.dir_in:
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prediction_textline = self.do_prediction(patches, img, model_textline)
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else:
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prediction_textline = self.do_prediction(patches, img, self.model_textline)
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# if not self.dir_in:
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prediction_textline = self.do_prediction(patches, img, model_textline)
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# else:
|
||||
# prediction_textline = self.do_prediction(patches, img, self.model_textline)
|
||||
prediction_textline = resize_image(prediction_textline, img_h, img_w)
|
||||
if not self.dir_in:
|
||||
prediction_textline_longshot = self.do_prediction(False, img, model_textline)
|
||||
else:
|
||||
prediction_textline_longshot = self.do_prediction(False, img, self.model_textline)
|
||||
# if not self.dir_in:
|
||||
prediction_textline_longshot = self.do_prediction(False, img, model_textline)
|
||||
# else:
|
||||
# prediction_textline_longshot = self.do_prediction(False, img, self.model_textline)
|
||||
prediction_textline_longshot_true_size = resize_image(prediction_textline_longshot, img_h, img_w)
|
||||
|
||||
if self.textline_light:
|
||||
|
|
@ -1681,11 +1682,11 @@ class Eynollah:
|
|||
img_h_new = int(img_org.shape[0] / float(img_org.shape[1]) * img_w_new)
|
||||
img_resized = resize_image(img, img_h_new, img_w_new)
|
||||
|
||||
if not self.dir_in:
|
||||
model_bin, session_bin = self.start_new_session_and_model(self.model_dir_of_binarization)
|
||||
prediction_bin = self.do_prediction(True, img_resized, model_bin)
|
||||
else:
|
||||
prediction_bin = self.do_prediction(True, img_resized, self.model_bin)
|
||||
# if not self.dir_in:
|
||||
model_bin, session_bin = self.start_new_session_and_model(self.model_dir_of_binarization)
|
||||
prediction_bin = self.do_prediction(True, img_resized, model_bin)
|
||||
# else:
|
||||
# prediction_bin = self.do_prediction(True, img_resized, self.model_bin)
|
||||
prediction_bin = prediction_bin[:, :, 0]
|
||||
prediction_bin = (prediction_bin[:, :] == 0) * 1
|
||||
prediction_bin = prediction_bin * 255
|
||||
|
|
@ -1698,11 +1699,11 @@ class Eynollah:
|
|||
|
||||
textline_mask_tot_ea = self.run_textline(img_bin)
|
||||
|
||||
if not self.dir_in:
|
||||
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_ens_light)
|
||||
prediction_regions_org = self.do_prediction_new_concept(True, img_bin, model_region)
|
||||
else:
|
||||
prediction_regions_org = self.do_prediction_new_concept(True, img_bin, self.model_region)
|
||||
# if not self.dir_in:
|
||||
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_ens_light)
|
||||
prediction_regions_org = self.do_prediction_new_concept(True, img_bin, model_region)
|
||||
# else:
|
||||
# prediction_regions_org = self.do_prediction_new_concept(True, img_bin, self.model_region)
|
||||
|
||||
# plt.imshow(prediction_regions_org[:,:,0])
|
||||
# plt.show()
|
||||
|
|
@ -1744,17 +1745,17 @@ class Eynollah:
|
|||
img_height_h = img_org.shape[0]
|
||||
img_width_h = img_org.shape[1]
|
||||
|
||||
if not self.dir_in:
|
||||
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_ens)
|
||||
# if not self.dir_in:
|
||||
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_ens)
|
||||
|
||||
ratio_y = 1.3
|
||||
ratio_x = 1
|
||||
|
||||
img = resize_image(img_org, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x))
|
||||
if not self.dir_in:
|
||||
prediction_regions_org_y = self.do_prediction(True, img, model_region)
|
||||
else:
|
||||
prediction_regions_org_y = self.do_prediction(True, img, self.model_region)
|
||||
# if not self.dir_in:
|
||||
prediction_regions_org_y = self.do_prediction(True, img, model_region)
|
||||
# else:
|
||||
# prediction_regions_org_y = self.do_prediction(True, img, self.model_region)
|
||||
prediction_regions_org_y = resize_image(prediction_regions_org_y, img_height_h, img_width_h)
|
||||
|
||||
# plt.imshow(prediction_regions_org_y[:,:,0])
|
||||
|
|
@ -1774,24 +1775,24 @@ class Eynollah:
|
|||
img = resize_image(img_org, int(img_org.shape[0]),
|
||||
int(img_org.shape[1] * (1.2 if is_image_enhanced else 1)))
|
||||
|
||||
if self.dir_in:
|
||||
prediction_regions_org = self.do_prediction(True, img, self.model_region)
|
||||
else:
|
||||
prediction_regions_org = self.do_prediction(True, img, model_region)
|
||||
# if self.dir_in:
|
||||
# prediction_regions_org = self.do_prediction(True, img, self.model_region)
|
||||
# else:
|
||||
prediction_regions_org = self.do_prediction(True, img, model_region)
|
||||
prediction_regions_org = resize_image(prediction_regions_org, img_height_h, img_width_h)
|
||||
|
||||
prediction_regions_org = prediction_regions_org[:, :, 0]
|
||||
prediction_regions_org[(prediction_regions_org[:, :] == 1) & (mask_zeros_y[:, :] == 1)] = 0
|
||||
|
||||
if not self.dir_in:
|
||||
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p2)
|
||||
# if not self.dir_in:
|
||||
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p2)
|
||||
|
||||
img = resize_image(img_org, int(img_org.shape[0]), int(img_org.shape[1]))
|
||||
|
||||
if self.dir_in:
|
||||
prediction_regions_org2 = self.do_prediction(True, img, self.model_region_p2, 0.2)
|
||||
else:
|
||||
prediction_regions_org2 = self.do_prediction(True, img, model_region, 0.2)
|
||||
# if self.dir_in:
|
||||
# prediction_regions_org2 = self.do_prediction(True, img, self.model_region_p2, 0.2)
|
||||
# else:
|
||||
prediction_regions_org2 = self.do_prediction(True, img, model_region, 0.2)
|
||||
prediction_regions_org2 = resize_image(prediction_regions_org2, img_height_h, img_width_h)
|
||||
|
||||
mask_zeros2 = (prediction_regions_org2[:, :, 0] == 0)
|
||||
|
|
@ -1817,11 +1818,11 @@ class Eynollah:
|
|||
if self.input_binary:
|
||||
prediction_bin = np.copy(img_org)
|
||||
else:
|
||||
if not self.dir_in:
|
||||
model_bin, session_bin = self.start_new_session_and_model(self.model_dir_of_binarization)
|
||||
prediction_bin = self.do_prediction(True, img_org, model_bin)
|
||||
else:
|
||||
prediction_bin = self.do_prediction(True, img_org, self.model_bin)
|
||||
# if not self.dir_in:
|
||||
model_bin, session_bin = self.start_new_session_and_model(self.model_dir_of_binarization)
|
||||
prediction_bin = self.do_prediction(True, img_org, model_bin)
|
||||
# else:
|
||||
# prediction_bin = self.do_prediction(True, img_org, self.model_bin)
|
||||
prediction_bin = resize_image(prediction_bin, img_height_h, img_width_h)
|
||||
|
||||
prediction_bin = prediction_bin[:, :, 0]
|
||||
|
|
@ -1830,17 +1831,17 @@ class Eynollah:
|
|||
|
||||
prediction_bin = np.repeat(prediction_bin[:, :, np.newaxis], 3, axis=2)
|
||||
|
||||
if not self.dir_in:
|
||||
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_ens)
|
||||
# if not self.dir_in:
|
||||
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_ens)
|
||||
ratio_y = 1
|
||||
ratio_x = 1
|
||||
|
||||
img = resize_image(prediction_bin, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x))
|
||||
|
||||
if not self.dir_in:
|
||||
prediction_regions_org = self.do_prediction(True, img, model_region)
|
||||
else:
|
||||
prediction_regions_org = self.do_prediction(True, img, self.model_region)
|
||||
# if not self.dir_in:
|
||||
prediction_regions_org = self.do_prediction(True, img, model_region)
|
||||
# else:
|
||||
# prediction_regions_org = self.do_prediction(True, img, self.model_region)
|
||||
prediction_regions_org = resize_image(prediction_regions_org, img_height_h, img_width_h)
|
||||
prediction_regions_org = prediction_regions_org[:, :, 0]
|
||||
|
||||
|
|
@ -1869,11 +1870,11 @@ class Eynollah:
|
|||
if self.input_binary:
|
||||
prediction_bin = np.copy(img_org)
|
||||
|
||||
if not self.dir_in:
|
||||
model_bin, session_bin = self.start_new_session_and_model(self.model_dir_of_binarization)
|
||||
prediction_bin = self.do_prediction(True, img_org, model_bin)
|
||||
else:
|
||||
prediction_bin = self.do_prediction(True, img_org, self.model_bin)
|
||||
# if not self.dir_in:
|
||||
model_bin, session_bin = self.start_new_session_and_model(self.model_dir_of_binarization)
|
||||
prediction_bin = self.do_prediction(True, img_org, model_bin)
|
||||
# else:
|
||||
# prediction_bin = self.do_prediction(True, img_org, self.model_bin)
|
||||
prediction_bin = resize_image(prediction_bin, img_height_h, img_width_h)
|
||||
prediction_bin = prediction_bin[:, :, 0]
|
||||
|
||||
|
|
@ -1883,8 +1884,8 @@ class Eynollah:
|
|||
|
||||
prediction_bin = np.repeat(prediction_bin[:, :, np.newaxis], 3, axis=2)
|
||||
|
||||
if not self.dir_in:
|
||||
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_ens)
|
||||
# if not self.dir_in:
|
||||
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_ens)
|
||||
|
||||
else:
|
||||
prediction_bin = np.copy(img_org)
|
||||
|
|
@ -1892,10 +1893,10 @@ class Eynollah:
|
|||
ratio_x = 1
|
||||
|
||||
img = resize_image(prediction_bin, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x))
|
||||
if not self.dir_in:
|
||||
prediction_regions_org = self.do_prediction(True, img, model_region)
|
||||
else:
|
||||
prediction_regions_org = self.do_prediction(True, img, self.model_region)
|
||||
# if not self.dir_in:
|
||||
prediction_regions_org = self.do_prediction(True, img, model_region)
|
||||
# else:
|
||||
# prediction_regions_org = self.do_prediction(True, img, self.model_region)
|
||||
prediction_regions_org = resize_image(prediction_regions_org, img_height_h, img_width_h)
|
||||
prediction_regions_org = prediction_regions_org[:, :, 0]
|
||||
|
||||
|
|
@ -3036,13 +3037,13 @@ class Eynollah:
|
|||
|
||||
t0_tot = time.time()
|
||||
|
||||
if not self.dir_in:
|
||||
self.ls_imgs = [1]
|
||||
# if not self.dir_in:
|
||||
self.ls_imgs = [1]
|
||||
|
||||
for img_name in self.ls_imgs:
|
||||
t0 = time.time()
|
||||
if self.dir_in:
|
||||
self.reset_file_name_dir(os.path.join(self.dir_in, img_name))
|
||||
# if self.dir_in:
|
||||
# self.reset_file_name_dir(os.path.join(self.dir_in, img_name))
|
||||
|
||||
img_res, is_image_enhanced, num_col_classifier, num_column_is_classified = self.run_enhancement(
|
||||
self.light_version)
|
||||
|
|
@ -3077,10 +3078,10 @@ class Eynollah:
|
|||
pcgts = self.writer.build_pagexml_no_full_layout([], page_coord, [], [], [], [], [], [], [], [], [], [],
|
||||
cont_page, [], [])
|
||||
self.logger.info("Job done in %.1fs", time.time() - t1)
|
||||
if self.dir_in:
|
||||
self.writer.write_pagexml(pcgts)
|
||||
continue
|
||||
else:
|
||||
# if self.dir_in:
|
||||
# self.writer.write_pagexml(pcgts)
|
||||
# continue
|
||||
# else:
|
||||
return pcgts
|
||||
|
||||
t1 = time.time()
|
||||
|
|
@ -3419,5 +3420,5 @@ class Eynollah:
|
|||
# return pcgts
|
||||
self.writer.write_pagexml(pcgts)
|
||||
# self.logger.info("Job done in %.1fs", time.time() - t0)
|
||||
if self.dir_in:
|
||||
self.logger.info("All jobs done in %.1fs", time.time() - t0_tot)
|
||||
# if self.dir_in:
|
||||
# self.logger.info("All jobs done in %.1fs", time.time() - t0_tot)
|
||||
|
|
|
|||
|
|
@ -3,8 +3,8 @@ import numpy as np
|
|||
from shapely import geometry
|
||||
|
||||
from .rotate import rotate_image, rotation_image_new
|
||||
from multiprocessing import Process, Queue, cpu_count
|
||||
from multiprocessing import Pool
|
||||
# from multiprocessing import Process, Queue, cpu_count
|
||||
# from multiprocessing import Pool
|
||||
|
||||
|
||||
def contours_in_same_horizon(cy_main_hor):
|
||||
|
|
@ -154,95 +154,95 @@ def return_contours_of_interested_region(region_pre_p, pixel, min_area=0.0002):
|
|||
return contours_imgs
|
||||
|
||||
|
||||
def do_work_of_contours_in_image(queue_of_all_params, contours_per_process, indexes_r_con_per_pro, img, slope_first):
|
||||
cnts_org_per_each_subprocess = []
|
||||
index_by_text_region_contours = []
|
||||
for mv in range(len(contours_per_process)):
|
||||
index_by_text_region_contours.append(indexes_r_con_per_pro[mv])
|
||||
# def do_work_of_contours_in_image(queue_of_all_params, contours_per_process, indexes_r_con_per_pro, img, slope_first):
|
||||
# cnts_org_per_each_subprocess = []
|
||||
# index_by_text_region_contours = []
|
||||
# for mv in range(len(contours_per_process)):
|
||||
# index_by_text_region_contours.append(indexes_r_con_per_pro[mv])
|
||||
|
||||
img_copy = np.zeros(img.shape)
|
||||
img_copy = cv2.fillPoly(img_copy, pts=[contours_per_process[mv]], color=(1, 1, 1))
|
||||
# img_copy = np.zeros(img.shape)
|
||||
# img_copy = cv2.fillPoly(img_copy, pts=[contours_per_process[mv]], color=(1, 1, 1))
|
||||
|
||||
img_copy = rotation_image_new(img_copy, -slope_first)
|
||||
# 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)
|
||||
# img_copy = img_copy.astype(np.uint8)
|
||||
# imgray = cv2.cvtColor(img_copy, cv2.COLOR_BGR2GRAY)
|
||||
# ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
||||
|
||||
cont_int, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
||||
# 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])
|
||||
cont_int[0][:, 0, 1] = cont_int[0][:, 0, 1] + np.abs(img_copy.shape[0] - img.shape[0])
|
||||
# cont_int[0][:, 0, 0] = cont_int[0][:, 0, 0] + np.abs(img_copy.shape[1] - img.shape[1])
|
||||
# cont_int[0][:, 0, 1] = cont_int[0][:, 0, 1] + np.abs(img_copy.shape[0] - img.shape[0])
|
||||
|
||||
cnts_org_per_each_subprocess.append(cont_int[0])
|
||||
# cnts_org_per_each_subprocess.append(cont_int[0])
|
||||
|
||||
queue_of_all_params.put([cnts_org_per_each_subprocess, index_by_text_region_contours])
|
||||
# queue_of_all_params.put([cnts_org_per_each_subprocess, index_by_text_region_contours])
|
||||
|
||||
|
||||
def get_textregion_contours_in_org_image_multi(cnts, img, slope_first):
|
||||
# def get_textregion_contours_in_org_image_multi(cnts, img, slope_first):
|
||||
|
||||
num_cores = cpu_count()
|
||||
queue_of_all_params = Queue()
|
||||
# num_cores = cpu_count()
|
||||
# queue_of_all_params = Queue()
|
||||
|
||||
processes = []
|
||||
nh = np.linspace(0, len(cnts), num_cores + 1)
|
||||
indexes_by_text_con = np.array(range(len(cnts)))
|
||||
for i in range(num_cores):
|
||||
contours_per_process = cnts[int(nh[i]): int(nh[i + 1])]
|
||||
indexes_text_con_per_process = indexes_by_text_con[int(nh[i]): int(nh[i + 1])]
|
||||
# processes = []
|
||||
# nh = np.linspace(0, len(cnts), num_cores + 1)
|
||||
# indexes_by_text_con = np.array(range(len(cnts)))
|
||||
# for i in range(num_cores):
|
||||
# contours_per_process = cnts[int(nh[i]): int(nh[i + 1])]
|
||||
# indexes_text_con_per_process = indexes_by_text_con[int(nh[i]): int(nh[i + 1])]
|
||||
|
||||
processes.append(Process(target=do_work_of_contours_in_image, args=(queue_of_all_params, contours_per_process, indexes_text_con_per_process, img, slope_first)))
|
||||
for i in range(num_cores):
|
||||
processes[i].start()
|
||||
cnts_org = []
|
||||
all_index_text_con = []
|
||||
for i in range(num_cores):
|
||||
list_all_par = queue_of_all_params.get(True)
|
||||
contours_for_sub_process = list_all_par[0]
|
||||
indexes_for_sub_process = list_all_par[1]
|
||||
for j in range(len(contours_for_sub_process)):
|
||||
cnts_org.append(contours_for_sub_process[j])
|
||||
all_index_text_con.append(indexes_for_sub_process[j])
|
||||
for i in range(num_cores):
|
||||
processes[i].join()
|
||||
# processes.append(Process(target=do_work_of_contours_in_image, args=(queue_of_all_params, contours_per_process, indexes_text_con_per_process, img, slope_first)))
|
||||
# for i in range(num_cores):
|
||||
# processes[i].start()
|
||||
# cnts_org = []
|
||||
# all_index_text_con = []
|
||||
# for i in range(num_cores):
|
||||
# list_all_par = queue_of_all_params.get(True)
|
||||
# contours_for_sub_process = list_all_par[0]
|
||||
# indexes_for_sub_process = list_all_par[1]
|
||||
# for j in range(len(contours_for_sub_process)):
|
||||
# cnts_org.append(contours_for_sub_process[j])
|
||||
# all_index_text_con.append(indexes_for_sub_process[j])
|
||||
# for i in range(num_cores):
|
||||
# processes[i].join()
|
||||
|
||||
print(all_index_text_con)
|
||||
return cnts_org
|
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# print(all_index_text_con)
|
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# return cnts_org
|
||||
|
||||
|
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def loop_contour_image(index_l, cnts, img, slope_first):
|
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img_copy = np.zeros(img.shape)
|
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img_copy = cv2.fillPoly(img_copy, pts=[cnts[index_l]], color=(1, 1, 1))
|
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# def loop_contour_image(index_l, cnts, img, slope_first):
|
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# img_copy = np.zeros(img.shape)
|
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# img_copy = cv2.fillPoly(img_copy, pts=[cnts[index_l]], color=(1, 1, 1))
|
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|
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# plt.imshow(img_copy)
|
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# plt.show()
|
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# # plt.imshow(img_copy)
|
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# # plt.show()
|
||||
|
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# print(img.shape,'img')
|
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img_copy = rotation_image_new(img_copy, -slope_first)
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# print(img_copy.shape,'img_copy')
|
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# plt.imshow(img_copy)
|
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# plt.show()
|
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# # print(img.shape,'img')
|
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# img_copy = rotation_image_new(img_copy, -slope_first)
|
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# # print(img_copy.shape,'img_copy')
|
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# # plt.imshow(img_copy)
|
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# # plt.show()
|
||||
|
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img_copy = img_copy.astype(np.uint8)
|
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imgray = cv2.cvtColor(img_copy, cv2.COLOR_BGR2GRAY)
|
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ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
||||
# img_copy = img_copy.astype(np.uint8)
|
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# imgray = cv2.cvtColor(img_copy, cv2.COLOR_BGR2GRAY)
|
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# ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
||||
|
||||
cont_int, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
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# 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])
|
||||
cont_int[0][:, 0, 1] = cont_int[0][:, 0, 1] + np.abs(img_copy.shape[0] - img.shape[0])
|
||||
# print(np.shape(cont_int[0]))
|
||||
return cont_int[0]
|
||||
# cont_int[0][:, 0, 0] = cont_int[0][:, 0, 0] + np.abs(img_copy.shape[1] - img.shape[1])
|
||||
# cont_int[0][:, 0, 1] = cont_int[0][:, 0, 1] + np.abs(img_copy.shape[0] - img.shape[0])
|
||||
# # print(np.shape(cont_int[0]))
|
||||
# return cont_int[0]
|
||||
|
||||
|
||||
def get_textregion_contours_in_org_image_multi2(cnts, img, slope_first):
|
||||
# def get_textregion_contours_in_org_image_multi2(cnts, img, slope_first):
|
||||
|
||||
cnts_org = []
|
||||
# print(cnts,'cnts')
|
||||
with Pool(cpu_count()) as p:
|
||||
cnts_org = p.starmap(loop_contour_image, [(index_l, cnts, img, slope_first) for index_l in range(len(cnts))])
|
||||
# cnts_org = []
|
||||
# # print(cnts,'cnts')
|
||||
# with Pool(cpu_count()) as p:
|
||||
# cnts_org = p.starmap(loop_contour_image, [(index_l, cnts, img, slope_first) for index_l in range(len(cnts))])
|
||||
|
||||
return cnts_org
|
||||
# return cnts_org
|
||||
|
||||
|
||||
def get_textregion_contours_in_org_image(cnts, img, slope_first):
|
||||
|
|
@ -276,42 +276,42 @@ def get_textregion_contours_in_org_image(cnts, img, slope_first):
|
|||
return cnts_org
|
||||
|
||||
|
||||
def get_textregion_contours_in_org_image_light(cnts, img, slope_first):
|
||||
# def get_textregion_contours_in_org_image_light(cnts, img, slope_first):
|
||||
|
||||
h_o = img.shape[0]
|
||||
w_o = img.shape[1]
|
||||
# h_o = img.shape[0]
|
||||
# w_o = img.shape[1]
|
||||
|
||||
img = cv2.resize(img, (int(img.shape[1]/3.), int(img.shape[0]/3.)), interpolation=cv2.INTER_NEAREST)
|
||||
# cnts = list( (np.array(cnts)/2).astype(np.int16) )
|
||||
# cnts = cnts/2
|
||||
cnts = [(i / 3).astype(np.int32) for i in cnts]
|
||||
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 = cv2.resize(img, (int(img.shape[1]/3.), int(img.shape[0]/3.)), interpolation=cv2.INTER_NEAREST)
|
||||
# # cnts = list( (np.array(cnts)/2).astype(np.int16) )
|
||||
# # cnts = cnts/2
|
||||
# cnts = [(i / 3).astype(np.int32) for i in cnts]
|
||||
# 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))
|
||||
|
||||
# plt.imshow(img_copy)
|
||||
# plt.show()
|
||||
# # plt.imshow(img_copy)
|
||||
# # plt.show()
|
||||
|
||||
# print(img.shape,'img')
|
||||
img_copy = rotation_image_new(img_copy, -slope_first)
|
||||
# print(img_copy.shape,'img_copy')
|
||||
# plt.imshow(img_copy)
|
||||
# plt.show()
|
||||
# # print(img.shape,'img')
|
||||
# img_copy = rotation_image_new(img_copy, -slope_first)
|
||||
# # print(img_copy.shape,'img_copy')
|
||||
# # 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)
|
||||
# img_copy = img_copy.astype(np.uint8)
|
||||
# imgray = cv2.cvtColor(img_copy, cv2.COLOR_BGR2GRAY)
|
||||
# ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
||||
|
||||
cont_int, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
||||
# 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])
|
||||
cont_int[0][:, 0, 1] = cont_int[0][:, 0, 1] + np.abs(img_copy.shape[0] - img.shape[0])
|
||||
# print(np.shape(cont_int[0]))
|
||||
cnts_org.append(cont_int[0]*3)
|
||||
# cont_int[0][:, 0, 0] = cont_int[0][:, 0, 0] + np.abs(img_copy.shape[1] - img.shape[1])
|
||||
# cont_int[0][:, 0, 1] = cont_int[0][:, 0, 1] + np.abs(img_copy.shape[0] - img.shape[0])
|
||||
# # print(np.shape(cont_int[0]))
|
||||
# cnts_org.append(cont_int[0]*3)
|
||||
|
||||
return cnts_org
|
||||
# return cnts_org
|
||||
|
||||
|
||||
def return_contours_of_interested_textline(region_pre_p, pixel):
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue