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vahid
parent
cf5ef8f5ae
commit
c606391c31
4 changed files with 767 additions and 437 deletions
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@ -10,7 +10,6 @@ from qurator.eynollah.eynollah import Eynollah
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"-i",
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help="image filename",
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type=click.Path(exists=True, dir_okay=False),
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required=True,
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)
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@click.option(
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"--out",
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@ -19,6 +18,12 @@ from qurator.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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@click.option(
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"--model",
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"-m",
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@ -112,6 +117,7 @@ from qurator.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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model,
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save_images,
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save_layout,
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@ -140,6 +146,7 @@ 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_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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@ -155,8 +162,9 @@ def main(
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headers_off=headers_off,
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light_version=light_version,
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)
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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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main()
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@ -29,6 +29,7 @@ warnings.filterwarnings("ignore")
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from scipy.signal import find_peaks
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import matplotlib.pyplot as plt
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from scipy.ndimage import gaussian_filter1d
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from keras.backend import set_session
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from .utils.contour import (
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filter_contours_area_of_image,
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@ -38,6 +39,7 @@ from .utils.contour import (
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find_features_of_contours,
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get_text_region_boxes_by_given_contours,
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get_textregion_contours_in_org_image,
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get_textregion_contours_in_org_image_light,
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return_contours_of_image,
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return_contours_of_interested_region,
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return_contours_of_interested_region_by_min_size,
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@ -65,6 +67,7 @@ from .utils import (
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put_drop_out_from_only_drop_model,
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putt_bb_of_drop_capitals_of_model_in_patches_in_layout,
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check_any_text_region_in_model_one_is_main_or_header,
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check_any_text_region_in_model_one_is_main_or_header_light,
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small_textlines_to_parent_adherence2,
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order_of_regions,
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find_number_of_columns_in_document,
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@ -84,10 +87,11 @@ 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,
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image_filename=None,
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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_of_cropped_images=None,
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dir_of_layout=None,
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dir_of_deskewed=None,
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@ -105,6 +109,7 @@ 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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@ -113,6 +118,7 @@ class Eynollah:
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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.allow_enhancement = allow_enhancement
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self.curved_line = curved_line
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self.full_layout = full_layout
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@ -121,6 +127,7 @@ class Eynollah:
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self.allow_scaling = allow_scaling
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self.headers_off = headers_off
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self.light_version = light_version
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self.pcgts = pcgts
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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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@ -128,6 +135,7 @@ class Eynollah:
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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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if not dir_in:
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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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@ -149,6 +157,41 @@ class Eynollah:
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self.model_textline_dir = dir_models + "/model_textline_newspapers.h5"
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self.model_tables = dir_models + "/model_tables_ens_mixed_new_2.h5"
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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.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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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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def _cache_images(self, image_filename=None, image_pil=None):
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ret = {}
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if image_filename:
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@ -161,7 +204,15 @@ class Eynollah:
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for prefix in ('', '_grayscale'):
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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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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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pcgts=self.pcgts)
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def imread(self, grayscale=False, uint8=True):
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key = 'img'
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if grayscale:
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@ -335,6 +386,7 @@ 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(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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@ -357,17 +409,18 @@ class Eynollah:
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img_in[0, :, :, 0] = img_1ch[:, :]
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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)
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else:
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label_p_pred = self.model_classifier.predict(img_in)
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num_col = np.argmax(label_p_pred[0]) + 1
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self.logger.info("Found %s columns (%s)", num_col, label_p_pred)
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if not self.dir_in:
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session_col_classifier.close()
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del model_num_classifier
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del session_col_classifier
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K.clear_session()
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gc.collect()
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@ -383,13 +436,14 @@ class Eynollah:
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def resize_and_enhance_image_with_column_classifier(self,light_version):
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self.logger.debug("enter resize_and_enhance_image_with_column_classifier")
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if light_version:
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dpi = 300
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else:
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dpi = self.dpi
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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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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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@ -399,6 +453,7 @@ class Eynollah:
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prediction_bin =np.repeat(prediction_bin[:, :, np.newaxis], 3, axis=2)
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if not self.dir_in:
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session_bin.close()
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del model_bin
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del session_bin
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@ -412,6 +467,7 @@ class Eynollah:
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img_bin = None
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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(self.model_dir_of_col_classifier)
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if self.input_binary:
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@ -433,22 +489,28 @@ class Eynollah:
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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)
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else:
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label_p_pred = model_num_classifier.predict(img_in)
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num_col = np.argmax(label_p_pred[0]) + 1
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self.logger.info("Found %s columns (%s)", num_col, label_p_pred)
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if not self.dir_in:
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session_col_classifier.close()
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K.clear_session()
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if dpi < DPI_THRESHOLD:
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img_new, num_column_is_classified = self.calculate_width_height_by_columns(img, num_col, width_early, label_p_pred)
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if light_version:
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image_res = np.copy(img_new)
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else:
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image_res = self.predict_enhancement(img_new)
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is_image_enhanced = True
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else:
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num_column_is_classified = True
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image_res = np.copy(img)
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is_image_enhanced = False
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if not self.dir_in:
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session_col_classifier.close()
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@ -595,48 +657,48 @@ class Eynollah:
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if i == 0 and j == 0:
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seg_color = seg_color[0 : seg_color.shape[0] - margin, 0 : seg_color.shape[1] - margin, :]
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seg = seg[0 : seg.shape[0] - margin, 0 : seg.shape[1] - margin]
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mask_true[index_y_d + 0 : index_y_u - margin, index_x_d + 0 : index_x_u - margin] = seg
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#seg = seg[0 : seg.shape[0] - margin, 0 : seg.shape[1] - margin]
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#mask_true[index_y_d + 0 : index_y_u - margin, index_x_d + 0 : index_x_u - margin] = seg
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prediction_true[index_y_d + 0 : index_y_u - margin, index_x_d + 0 : index_x_u - margin, :] = seg_color
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elif i == nxf - 1 and j == nyf - 1:
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seg_color = seg_color[margin : seg_color.shape[0] - 0, margin : seg_color.shape[1] - 0, :]
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seg = seg[margin : seg.shape[0] - 0, margin : seg.shape[1] - 0]
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mask_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - 0] = seg
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#seg = seg[margin : seg.shape[0] - 0, margin : seg.shape[1] - 0]
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#mask_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - 0] = seg
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prediction_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - 0, :] = seg_color
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elif i == 0 and j == nyf - 1:
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seg_color = seg_color[margin : seg_color.shape[0] - 0, 0 : seg_color.shape[1] - margin, :]
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seg = seg[margin : seg.shape[0] - 0, 0 : seg.shape[1] - margin]
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mask_true[index_y_d + margin : index_y_u - 0, index_x_d + 0 : index_x_u - margin] = seg
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#seg = seg[margin : seg.shape[0] - 0, 0 : seg.shape[1] - margin]
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#mask_true[index_y_d + margin : index_y_u - 0, index_x_d + 0 : index_x_u - margin] = seg
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prediction_true[index_y_d + margin : index_y_u - 0, index_x_d + 0 : index_x_u - margin, :] = seg_color
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elif i == nxf - 1 and j == 0:
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seg_color = seg_color[0 : seg_color.shape[0] - margin, margin : seg_color.shape[1] - 0, :]
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seg = seg[0 : seg.shape[0] - margin, margin : seg.shape[1] - 0]
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mask_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - 0] = seg
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#seg = seg[0 : seg.shape[0] - margin, margin : seg.shape[1] - 0]
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#mask_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - 0] = seg
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prediction_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - 0, :] = seg_color
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elif i == 0 and j != 0 and j != nyf - 1:
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seg_color = seg_color[margin : seg_color.shape[0] - margin, 0 : seg_color.shape[1] - margin, :]
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seg = seg[margin : seg.shape[0] - margin, 0 : seg.shape[1] - margin]
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mask_true[index_y_d + margin : index_y_u - margin, index_x_d + 0 : index_x_u - margin] = seg
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#seg = seg[margin : seg.shape[0] - margin, 0 : seg.shape[1] - margin]
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#mask_true[index_y_d + margin : index_y_u - margin, index_x_d + 0 : index_x_u - margin] = seg
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prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + 0 : index_x_u - margin, :] = seg_color
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elif i == nxf - 1 and j != 0 and j != nyf - 1:
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seg_color = seg_color[margin : seg_color.shape[0] - margin, margin : seg_color.shape[1] - 0, :]
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seg = seg[margin : seg.shape[0] - margin, margin : seg.shape[1] - 0]
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mask_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - 0] = seg
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#seg = seg[margin : seg.shape[0] - margin, margin : seg.shape[1] - 0]
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#mask_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - 0] = seg
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prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - 0, :] = seg_color
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elif i != 0 and i != nxf - 1 and j == 0:
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seg_color = seg_color[0 : seg_color.shape[0] - margin, margin : seg_color.shape[1] - margin, :]
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seg = seg[0 : seg.shape[0] - margin, margin : seg.shape[1] - margin]
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mask_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - margin] = seg
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#seg = seg[0 : seg.shape[0] - margin, margin : seg.shape[1] - margin]
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#mask_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - margin] = seg
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prediction_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - margin, :] = seg_color
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elif i != 0 and i != nxf - 1 and j == nyf - 1:
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seg_color = seg_color[margin : seg_color.shape[0] - 0, margin : seg_color.shape[1] - margin, :]
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seg = seg[margin : seg.shape[0] - 0, margin : seg.shape[1] - margin]
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mask_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - margin] = seg
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#seg = seg[margin : seg.shape[0] - 0, margin : seg.shape[1] - margin]
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#mask_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - margin] = seg
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prediction_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - margin, :] = seg_color
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else:
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seg_color = seg_color[margin : seg_color.shape[0] - margin, margin : seg_color.shape[1] - margin, :]
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seg = seg[margin : seg.shape[0] - margin, margin : seg.shape[1] - margin]
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mask_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - margin] = seg
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#seg = seg[margin : seg.shape[0] - margin, margin : seg.shape[1] - margin]
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#mask_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - margin] = seg
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prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - margin, :] = seg_color
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prediction_true = prediction_true.astype(np.uint8)
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|
|
@ -817,9 +879,12 @@ class Eynollah:
|
|||
img = img.astype(np.uint8)
|
||||
else:
|
||||
img = self.imread()
|
||||
if not self.dir_in:
|
||||
model_page, session_page = self.start_new_session_and_model(self.model_page_dir)
|
||||
img = cv2.GaussianBlur(img, (5, 5), 0)
|
||||
|
||||
if self.dir_in:
|
||||
img_page_prediction = self.do_prediction(False, img, self.model_page)
|
||||
else:
|
||||
img_page_prediction = self.do_prediction(False, img, model_page)
|
||||
|
||||
imgray = cv2.cvtColor(img_page_prediction, cv2.COLOR_BGR2GRAY)
|
||||
|
|
@ -834,20 +899,25 @@ class Eynollah:
|
|||
else:
|
||||
box = [0, 0, img.shape[1], img.shape[0]]
|
||||
croped_page, page_coord = crop_image_inside_box(box, img)
|
||||
if not self.dir_in:
|
||||
session_page.close()
|
||||
del model_page
|
||||
del session_page
|
||||
gc.collect()
|
||||
K.clear_session()
|
||||
gc.collect()
|
||||
self.logger.debug("exit early_page_for_num_of_column_classification")
|
||||
return croped_page, page_coord
|
||||
|
||||
def extract_page(self):
|
||||
self.logger.debug("enter extract_page")
|
||||
cont_page = []
|
||||
if not self.dir_in:
|
||||
model_page, session_page = self.start_new_session_and_model(self.model_page_dir)
|
||||
img = cv2.GaussianBlur(self.image, (5, 5), 0)
|
||||
if not self.dir_in:
|
||||
img_page_prediction = self.do_prediction(False, img, model_page)
|
||||
else:
|
||||
img_page_prediction = self.do_prediction(False, img, self.model_page)
|
||||
imgray = cv2.cvtColor(img_page_prediction, cv2.COLOR_BGR2GRAY)
|
||||
_, thresh = cv2.threshold(imgray, 0, 255, 0)
|
||||
thresh = cv2.dilate(thresh, KERNEL, iterations=3)
|
||||
|
|
@ -873,11 +943,12 @@ class Eynollah:
|
|||
box = [0, 0, img.shape[1], img.shape[0]]
|
||||
croped_page, page_coord = crop_image_inside_box(box, self.image)
|
||||
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]]]))
|
||||
if not self.dir_in:
|
||||
session_page.close()
|
||||
del model_page
|
||||
del session_page
|
||||
gc.collect()
|
||||
K.clear_session()
|
||||
gc.collect()
|
||||
self.logger.debug("exit extract_page")
|
||||
return croped_page, page_coord, cont_page
|
||||
|
||||
|
|
@ -888,9 +959,13 @@ class Eynollah:
|
|||
img = img.astype(np.uint8)
|
||||
else:
|
||||
img = self.imread()
|
||||
if not self.dir_in:
|
||||
model_page, session_page = self.start_new_session_and_model(self.model_page_dir)
|
||||
img = cv2.GaussianBlur(img, (5, 5), 0)
|
||||
|
||||
if self.dir_in:
|
||||
img_page_prediction = self.do_prediction(False, img, self.model_page)
|
||||
else:
|
||||
img_page_prediction = self.do_prediction(False, img, model_page)
|
||||
|
||||
imgray = cv2.cvtColor(img_page_prediction, cv2.COLOR_BGR2GRAY)
|
||||
|
|
@ -905,20 +980,28 @@ class Eynollah:
|
|||
else:
|
||||
box = [0, 0, img.shape[1], img.shape[0]]
|
||||
croped_page, page_coord = crop_image_inside_box(box, img)
|
||||
|
||||
if not self.dir_in:
|
||||
session_page.close()
|
||||
del model_page
|
||||
del session_page
|
||||
gc.collect()
|
||||
K.clear_session()
|
||||
|
||||
gc.collect()
|
||||
|
||||
self.logger.debug("exit early_page_for_num_of_column_classification")
|
||||
return croped_page, page_coord
|
||||
|
||||
def extract_page(self):
|
||||
self.logger.debug("enter extract_page")
|
||||
cont_page = []
|
||||
if not self.dir_in:
|
||||
model_page, session_page = self.start_new_session_and_model(self.model_page_dir)
|
||||
img = cv2.GaussianBlur(self.image, (5, 5), 0)
|
||||
if not self.dir_in:
|
||||
img_page_prediction = self.do_prediction(False, img, model_page)
|
||||
else:
|
||||
img_page_prediction = self.do_prediction(False, img, self.model_page)
|
||||
imgray = cv2.cvtColor(img_page_prediction, cv2.COLOR_BGR2GRAY)
|
||||
_, thresh = cv2.threshold(imgray, 0, 255, 0)
|
||||
thresh = cv2.dilate(thresh, KERNEL, iterations=3)
|
||||
|
|
@ -944,11 +1027,12 @@ class Eynollah:
|
|||
box = [0, 0, img.shape[1], img.shape[0]]
|
||||
croped_page, page_coord = crop_image_inside_box(box, self.image)
|
||||
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]]]))
|
||||
if not self.dir_in:
|
||||
session_page.close()
|
||||
del model_page
|
||||
del session_page
|
||||
gc.collect()
|
||||
K.clear_session()
|
||||
gc.collect()
|
||||
self.logger.debug("exit extract_page")
|
||||
return croped_page, page_coord, cont_page
|
||||
|
||||
|
|
@ -956,8 +1040,10 @@ class Eynollah:
|
|||
self.logger.debug("enter extract_text_regions")
|
||||
img_height_h = img.shape[0]
|
||||
img_width_h = img.shape[1]
|
||||
|
||||
if not self.dir_in:
|
||||
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_fully if patches else self.model_region_dir_fully_np)
|
||||
else:
|
||||
model_region = self.model_region_fl if patches else self.model_region_fl_np
|
||||
|
||||
if not patches:
|
||||
img = otsu_copy_binary(img)
|
||||
|
|
@ -1044,6 +1130,7 @@ class Eynollah:
|
|||
prediction_regions = self.do_prediction(patches, img, model_region, marginal_of_patch_percent)
|
||||
prediction_regions = resize_image(prediction_regions, img_height_h, img_width_h)
|
||||
|
||||
if not self.dir_in:
|
||||
session_region.close()
|
||||
del model_region
|
||||
del session_region
|
||||
|
|
@ -1422,18 +1509,25 @@ class Eynollah:
|
|||
|
||||
def textline_contours(self, img, patches, scaler_h, scaler_w):
|
||||
self.logger.debug('enter textline_contours')
|
||||
|
||||
if not self.dir_in:
|
||||
model_textline, session_textline = self.start_new_session_and_model(self.model_textline_dir if patches else self.model_textline_dir_np)
|
||||
img = img.astype(np.uint8)
|
||||
img_org = np.copy(img)
|
||||
img_h = img_org.shape[0]
|
||||
img_w = img_org.shape[1]
|
||||
img = resize_image(img_org, int(img_org.shape[0] * scaler_h), int(img_org.shape[1] * scaler_w))
|
||||
if not self.dir_in:
|
||||
prediction_textline = self.do_prediction(patches, img, model_textline)
|
||||
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)
|
||||
prediction_textline_longshot_true_size = resize_image(prediction_textline_longshot, img_h, img_w)
|
||||
|
||||
if not self.dir_in:
|
||||
session_textline.close()
|
||||
|
||||
|
||||
|
|
@ -1508,17 +1602,17 @@ class Eynollah:
|
|||
##img_resized = resize_image(img_bin,img_height_h, img_width_h )
|
||||
img_resized = resize_image(img,img_h_new, img_w_new )
|
||||
|
||||
tbin = time.time()
|
||||
if not self.dir_in:
|
||||
model_bin, session_bin = self.start_new_session_and_model(self.model_dir_of_binarization)
|
||||
print("time bin session", time.time()-tbin)
|
||||
prediction_bin = self.do_prediction(True, img_resized, model_bin)
|
||||
print("time bin all ", time.time()-tbin)
|
||||
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
|
||||
|
||||
prediction_bin =np.repeat(prediction_bin[:, :, np.newaxis], 3, axis=2)
|
||||
|
||||
if not self.dir_in:
|
||||
session_bin.close()
|
||||
del model_bin
|
||||
del session_bin
|
||||
|
|
@ -1530,17 +1624,13 @@ class Eynollah:
|
|||
|
||||
|
||||
|
||||
|
||||
tline = time.time()
|
||||
textline_mask_tot_ea = self.run_textline(img_bin)
|
||||
print("time line all ", time.time()-tline)
|
||||
|
||||
if not self.dir_in:
|
||||
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_ens_light)
|
||||
|
||||
|
||||
#plt.imshow(img_bin)
|
||||
#plt.show()
|
||||
|
||||
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()
|
||||
|
|
@ -1564,7 +1654,6 @@ class Eynollah:
|
|||
|
||||
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))
|
||||
|
|
@ -1574,6 +1663,7 @@ class Eynollah:
|
|||
text_regions_p_true = cv2.fillPoly(text_regions_p_true, pts = polygons_of_only_texts, color=(1,1,1))
|
||||
|
||||
#erosion_hurts = True
|
||||
if not self.dir_in:
|
||||
K.clear_session()
|
||||
return text_regions_p_true, erosion_hurts, polygons_lines_xml, textline_mask_tot_ea
|
||||
|
||||
|
|
@ -1584,14 +1674,17 @@ 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)
|
||||
|
||||
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)
|
||||
prediction_regions_org_y = resize_image(prediction_regions_org_y, img_height_h, img_width_h )
|
||||
|
||||
#plt.imshow(prediction_regions_org_y[:,:,0])
|
||||
|
|
@ -1610,6 +1703,9 @@ 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)
|
||||
prediction_regions_org = resize_image(prediction_regions_org, img_height_h, img_width_h )
|
||||
|
||||
|
|
@ -1618,17 +1714,23 @@ class Eynollah:
|
|||
prediction_regions_org=prediction_regions_org[:,:,0]
|
||||
prediction_regions_org[(prediction_regions_org[:,:]==1) & (mask_zeros_y[:,:]==1)]=0
|
||||
|
||||
if not self.dir_in:
|
||||
session_region.close()
|
||||
del model_region
|
||||
del session_region
|
||||
gc.collect()
|
||||
|
||||
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)
|
||||
prediction_regions_org2=resize_image(prediction_regions_org2, img_height_h, img_width_h )
|
||||
|
||||
|
||||
if not self.dir_in:
|
||||
session_region.close()
|
||||
del model_region
|
||||
del session_region
|
||||
|
|
@ -1663,8 +1765,11 @@ 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)
|
||||
prediction_bin = resize_image(prediction_bin, img_height_h, img_width_h )
|
||||
|
||||
prediction_bin=prediction_bin[:,:,0]
|
||||
|
|
@ -1673,13 +1778,14 @@ class Eynollah:
|
|||
|
||||
prediction_bin =np.repeat(prediction_bin[:, :, np.newaxis], 3, axis=2)
|
||||
|
||||
if not self.dir_in:
|
||||
session_bin.close()
|
||||
del model_bin
|
||||
del session_bin
|
||||
gc.collect()
|
||||
|
||||
|
||||
|
||||
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
|
||||
|
|
@ -1687,11 +1793,15 @@ class Eynollah:
|
|||
|
||||
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)
|
||||
prediction_regions_org = resize_image(prediction_regions_org, img_height_h, img_width_h )
|
||||
prediction_regions_org=prediction_regions_org[:,:,0]
|
||||
|
||||
mask_lines_only=(prediction_regions_org[:,:]==3)*1
|
||||
if not self.dir_in:
|
||||
session_region.close()
|
||||
del model_region
|
||||
del session_region
|
||||
|
|
@ -1716,7 +1826,7 @@ class Eynollah:
|
|||
text_regions_p_true=cv2.fillPoly(text_regions_p_true,pts=polygons_of_only_texts, color=(1,1,1))
|
||||
|
||||
|
||||
|
||||
if not self.dir_in:
|
||||
K.clear_session()
|
||||
return text_regions_p_true, erosion_hurts, polygons_lines_xml
|
||||
except:
|
||||
|
|
@ -1724,13 +1834,17 @@ class Eynollah:
|
|||
if self.input_binary:
|
||||
prediction_bin = np.copy(img_org)
|
||||
else:
|
||||
if not self.dir_in:
|
||||
session_region.close()
|
||||
del model_region
|
||||
del session_region
|
||||
gc.collect()
|
||||
|
||||
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]
|
||||
|
||||
|
|
@ -1741,26 +1855,29 @@ class Eynollah:
|
|||
prediction_bin =np.repeat(prediction_bin[:, :, np.newaxis], 3, axis=2)
|
||||
|
||||
|
||||
|
||||
if not self.dir_in:
|
||||
session_bin.close()
|
||||
del model_bin
|
||||
del session_bin
|
||||
gc.collect()
|
||||
|
||||
|
||||
|
||||
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)
|
||||
prediction_regions_org = resize_image(prediction_regions_org, img_height_h, img_width_h )
|
||||
prediction_regions_org=prediction_regions_org[:,:,0]
|
||||
|
||||
#mask_lines_only=(prediction_regions_org[:,:]==3)*1
|
||||
if not self.dir_in:
|
||||
session_region.close()
|
||||
del model_region
|
||||
del session_region
|
||||
|
|
@ -1807,6 +1924,7 @@ class Eynollah:
|
|||
text_regions_p_true = cv2.fillPoly(text_regions_p_true, pts = polygons_of_only_texts, color=(1,1,1))
|
||||
|
||||
erosion_hurts = True
|
||||
if not self.dir_in:
|
||||
K.clear_session()
|
||||
return text_regions_p_true, erosion_hurts, polygons_lines_xml
|
||||
|
||||
|
|
@ -2409,7 +2527,7 @@ class Eynollah:
|
|||
##print(img_only_regions.shape,'img_only_regions')
|
||||
##plt.imshow(img_only_regions[:,:])
|
||||
##plt.show()
|
||||
num_col, _ = find_num_col(img_only_regions, num_col_classifier, self.tables, multiplier=6.0)
|
||||
##num_col, _ = find_num_col(img_only_regions, num_col_classifier, self.tables, multiplier=6.0)
|
||||
try:
|
||||
num_col, _ = find_num_col(img_only_regions, num_col_classifier, self.tables, multiplier=6.0)
|
||||
num_col = num_col + 1
|
||||
|
|
@ -2468,6 +2586,7 @@ class Eynollah:
|
|||
self.logger.info("Resizing and enhancing image...")
|
||||
is_image_enhanced, img_org, img_res, num_col_classifier, num_column_is_classified, img_bin = self.resize_and_enhance_image_with_column_classifier(light_version)
|
||||
self.logger.info("Image was %senhanced.", '' if is_image_enhanced else 'not ')
|
||||
if not self.dir_in:
|
||||
K.clear_session()
|
||||
scale = 1
|
||||
if is_image_enhanced:
|
||||
|
|
@ -2492,6 +2611,7 @@ class Eynollah:
|
|||
scaler_h_textline = 1 # 1.2#1.2
|
||||
scaler_w_textline = 1 # 0.9#1
|
||||
textline_mask_tot_ea, _ = self.textline_contours(image_page, True, scaler_h_textline, scaler_w_textline)
|
||||
if not self.dir_in:
|
||||
K.clear_session()
|
||||
if self.plotter:
|
||||
self.plotter.save_plot_of_textlines(textline_mask_tot_ea, image_page)
|
||||
|
|
@ -2554,6 +2674,7 @@ class Eynollah:
|
|||
|
||||
if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
|
||||
_, _, matrix_of_lines_ch_d, splitter_y_new_d, _ = find_number_of_columns_in_document(np.repeat(text_regions_p_1_n[:, :, np.newaxis], 3, axis=2), num_col_classifier, self.tables, pixel_lines)
|
||||
if not self.dir_in:
|
||||
K.clear_session()
|
||||
|
||||
self.logger.info("num_col_classifier: %s", num_col_classifier)
|
||||
|
|
@ -2619,7 +2740,7 @@ class Eynollah:
|
|||
pixel_img = 10
|
||||
contours_tables = return_contours_of_interested_region(text_regions_p, pixel_img, min_area_mar)
|
||||
|
||||
|
||||
if not self.dir_in:
|
||||
K.clear_session()
|
||||
self.logger.debug('exit run_boxes_no_full_layout')
|
||||
return polygons_of_images, img_revised_tab, text_regions_p_1_n, textline_mask_tot_d, regions_without_separators_d, boxes, boxes_d, polygons_of_marginals, contours_tables
|
||||
|
|
@ -2651,6 +2772,7 @@ class Eynollah:
|
|||
|
||||
if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
|
||||
num_col_d, peaks_neg_fin_d, matrix_of_lines_ch_d, splitter_y_new_d, seperators_closeup_n_d = find_number_of_columns_in_document(np.repeat(text_regions_p_1_n[:, :, np.newaxis], 3, axis=2),num_col_classifier, self.tables, pixel_lines)
|
||||
if not self.dir_in:
|
||||
K.clear_session()
|
||||
gc.collect()
|
||||
|
||||
|
|
@ -2718,7 +2840,7 @@ class Eynollah:
|
|||
text_regions_p[:, :][text_regions_p[:, :] == 2] = 5
|
||||
text_regions_p[:, :][text_regions_p[:, :] == 3] = 6
|
||||
text_regions_p[:, :][text_regions_p[:, :] == 4] = 8
|
||||
|
||||
if not self.dir_in:
|
||||
K.clear_session()
|
||||
image_page = image_page.astype(np.uint8)
|
||||
|
||||
|
|
@ -2726,6 +2848,7 @@ class Eynollah:
|
|||
text_regions_p[:,:][regions_fully[:,:,0]==6]=6
|
||||
regions_fully_only_drop = put_drop_out_from_only_drop_model(regions_fully_only_drop, text_regions_p)
|
||||
regions_fully[:, :, 0][regions_fully_only_drop[:, :, 0] == 4] = 4
|
||||
if not self.dir_in:
|
||||
K.clear_session()
|
||||
|
||||
# plt.imshow(regions_fully[:,:,0])
|
||||
|
|
@ -2733,6 +2856,7 @@ class Eynollah:
|
|||
regions_fully = putt_bb_of_drop_capitals_of_model_in_patches_in_layout(regions_fully)
|
||||
# plt.imshow(regions_fully[:,:,0])
|
||||
# plt.show()
|
||||
if not self.dir_in:
|
||||
K.clear_session()
|
||||
regions_fully_np, _ = self.extract_text_regions(image_page, False, cols=num_col_classifier)
|
||||
# plt.imshow(regions_fully_np[:,:,0])
|
||||
|
|
@ -2744,6 +2868,7 @@ class Eynollah:
|
|||
|
||||
# plt.imshow(regions_fully_np[:,:,0])
|
||||
# plt.show()
|
||||
if not self.dir_in:
|
||||
K.clear_session()
|
||||
# plt.imshow(regions_fully[:,:,0])
|
||||
# plt.show()
|
||||
|
|
@ -2769,20 +2894,35 @@ class Eynollah:
|
|||
regions_without_separators_d = None
|
||||
if not self.tables:
|
||||
regions_without_separators = (text_regions_p[:, :] == 1) * 1
|
||||
|
||||
if not self.dir_in:
|
||||
K.clear_session()
|
||||
img_revised_tab = np.copy(text_regions_p[:, :])
|
||||
polygons_of_images = return_contours_of_interested_region(img_revised_tab, 5)
|
||||
self.logger.debug('exit run_boxes_full_layout')
|
||||
return polygons_of_images, img_revised_tab, text_regions_p_1_n, textline_mask_tot_d, regions_without_separators_d, regions_fully, regions_without_separators, polygons_of_marginals, contours_tables
|
||||
|
||||
def our_load_model(self, model_file):
|
||||
|
||||
model = load_model(model_file, compile=False)
|
||||
|
||||
return model
|
||||
|
||||
def run(self):
|
||||
"""
|
||||
Get image and scales, then extract the page of scanned image
|
||||
"""
|
||||
self.logger.debug("enter run")
|
||||
|
||||
t0_tot = time.time()
|
||||
|
||||
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))
|
||||
|
||||
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)
|
||||
|
|
@ -2790,11 +2930,11 @@ class Eynollah:
|
|||
t1 = time.time()
|
||||
if self.light_version:
|
||||
text_regions_p_1 ,erosion_hurts, polygons_lines_xml, textline_mask_tot_ea = self.get_regions_from_xy_2models_light(img_res, is_image_enhanced, num_col_classifier)
|
||||
|
||||
slope_deskew, slope_first = self.run_deskew(textline_mask_tot_ea)
|
||||
|
||||
#self.logger.info("Textregion detection took %.1fs ", time.time() - t1t)
|
||||
num_col, num_col_classifier, img_only_regions, page_coord, image_page, mask_images, mask_lines, text_regions_p_1, cont_page, table_prediction, textline_mask_tot_ea = \
|
||||
self.run_graphics_and_columns_light(text_regions_p_1, textline_mask_tot_ea, num_col_classifier, num_column_is_classified, erosion_hurts)
|
||||
#self.logger.info("run graphics %.1fs ", time.time() - t1t)
|
||||
else:
|
||||
text_regions_p_1 ,erosion_hurts, polygons_lines_xml = self.get_regions_from_xy_2models(img_res, is_image_enhanced, num_col_classifier)
|
||||
self.logger.info("Textregion detection took %.1fs ", time.time() - t1)
|
||||
|
|
@ -2803,7 +2943,7 @@ class Eynollah:
|
|||
num_col, num_col_classifier, img_only_regions, page_coord, image_page, mask_images, mask_lines, text_regions_p_1, cont_page, table_prediction = \
|
||||
self.run_graphics_and_columns(text_regions_p_1, num_col_classifier, num_column_is_classified, erosion_hurts)
|
||||
self.logger.info("Graphics detection took %.1fs ", time.time() - t1)
|
||||
self.logger.info('cont_page %s', cont_page)
|
||||
#self.logger.info('cont_page %s', cont_page)
|
||||
|
||||
if not num_col:
|
||||
self.logger.info("No columns detected, outputting an empty PAGE-XML")
|
||||
|
|
@ -2835,6 +2975,7 @@ class Eynollah:
|
|||
if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
|
||||
text_only_d = ((text_regions_p_1_n[:, :] == 1)) * 1
|
||||
|
||||
|
||||
min_con_area = 0.000005
|
||||
if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
|
||||
contours_only_text, hir_on_text = return_contours_of_image(text_only)
|
||||
|
|
@ -2843,7 +2984,7 @@ class Eynollah:
|
|||
if len(contours_only_text_parent) > 0:
|
||||
areas_cnt_text = np.array([cv2.contourArea(contours_only_text_parent[j]) for j in range(len(contours_only_text_parent))])
|
||||
areas_cnt_text = areas_cnt_text / float(text_only.shape[0] * text_only.shape[1])
|
||||
self.logger.info('areas_cnt_text %s', areas_cnt_text)
|
||||
#self.logger.info('areas_cnt_text %s', areas_cnt_text)
|
||||
contours_biggest = contours_only_text_parent[np.argmax(areas_cnt_text)]
|
||||
contours_only_text_parent = [contours_only_text_parent[jz] for jz in range(len(contours_only_text_parent)) if areas_cnt_text[jz] > min_con_area]
|
||||
areas_cnt_text_parent = [areas_cnt_text[jz] for jz in range(len(areas_cnt_text)) if areas_cnt_text[jz] > min_con_area]
|
||||
|
|
@ -2914,7 +3055,6 @@ class Eynollah:
|
|||
contours_only_text_parent_d_ordered = []
|
||||
contours_only_text_parent_d = []
|
||||
contours_only_text_parent = []
|
||||
|
||||
else:
|
||||
contours_only_text, hir_on_text = return_contours_of_image(text_only)
|
||||
contours_only_text_parent = return_parent_contours(contours_only_text, hir_on_text)
|
||||
|
|
@ -2933,11 +3073,14 @@ class Eynollah:
|
|||
|
||||
cx_bigest_big, cy_biggest_big, _, _, _, _, _ = find_new_features_of_contours([contours_biggest])
|
||||
cx_bigest, cy_biggest, _, _, _, _, _ = find_new_features_of_contours(contours_only_text_parent)
|
||||
self.logger.debug('areas_cnt_text_parent %s', areas_cnt_text_parent)
|
||||
#self.logger.debug('areas_cnt_text_parent %s', areas_cnt_text_parent)
|
||||
# self.logger.debug('areas_cnt_text_parent_d %s', areas_cnt_text_parent_d)
|
||||
# self.logger.debug('len(contours_only_text_parent) %s', len(contours_only_text_parent_d))
|
||||
else:
|
||||
pass
|
||||
if self.light_version:
|
||||
txt_con_org = get_textregion_contours_in_org_image_light(contours_only_text_parent, self.image, slope_first)
|
||||
else:
|
||||
txt_con_org = get_textregion_contours_in_org_image(contours_only_text_parent, self.image, slope_first)
|
||||
boxes_text, _ = get_text_region_boxes_by_given_contours(contours_only_text_parent)
|
||||
boxes_marginals, _ = get_text_region_boxes_by_given_contours(polygons_of_marginals)
|
||||
|
|
@ -2956,26 +3099,33 @@ class Eynollah:
|
|||
all_found_texline_polygons = small_textlines_to_parent_adherence2(all_found_texline_polygons, textline_mask_tot_ea, num_col_classifier)
|
||||
all_found_texline_polygons_marginals, boxes_marginals, _, polygons_of_marginals, all_box_coord_marginals, _, slopes_marginals = self.get_slopes_and_deskew_new_curved(polygons_of_marginals, polygons_of_marginals, cv2.erode(textline_mask_tot_ea, kernel=KERNEL, iterations=1), image_page_rotated, boxes_marginals, text_only, num_col_classifier, scale_param, slope_deskew)
|
||||
all_found_texline_polygons_marginals = small_textlines_to_parent_adherence2(all_found_texline_polygons_marginals, textline_mask_tot_ea, num_col_classifier)
|
||||
if not self.dir_in:
|
||||
K.clear_session()
|
||||
|
||||
if self.full_layout:
|
||||
if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
|
||||
contours_only_text_parent_d_ordered = list(np.array(contours_only_text_parent_d_ordered)[index_by_text_par_con])
|
||||
if self.light_version:
|
||||
text_regions_p, contours_only_text_parent, contours_only_text_parent_h, all_box_coord, all_box_coord_h, all_found_texline_polygons, all_found_texline_polygons_h, slopes, slopes_h, contours_only_text_parent_d_ordered, contours_only_text_parent_h_d_ordered = check_any_text_region_in_model_one_is_main_or_header_light(text_regions_p, regions_fully, contours_only_text_parent, all_box_coord, all_found_texline_polygons, slopes, contours_only_text_parent_d_ordered)
|
||||
else:
|
||||
text_regions_p, contours_only_text_parent, contours_only_text_parent_h, all_box_coord, all_box_coord_h, all_found_texline_polygons, all_found_texline_polygons_h, slopes, slopes_h, contours_only_text_parent_d_ordered, contours_only_text_parent_h_d_ordered = check_any_text_region_in_model_one_is_main_or_header(text_regions_p, regions_fully, contours_only_text_parent, all_box_coord, all_found_texline_polygons, slopes, contours_only_text_parent_d_ordered)
|
||||
else:
|
||||
#takes long timee
|
||||
contours_only_text_parent_d_ordered = None
|
||||
if self.light_version:
|
||||
text_regions_p, contours_only_text_parent, contours_only_text_parent_h, all_box_coord, all_box_coord_h, all_found_texline_polygons, all_found_texline_polygons_h, slopes, slopes_h, contours_only_text_parent_d_ordered, contours_only_text_parent_h_d_ordered = check_any_text_region_in_model_one_is_main_or_header_light(text_regions_p, regions_fully, contours_only_text_parent, all_box_coord, all_found_texline_polygons, slopes, contours_only_text_parent_d_ordered)
|
||||
else:
|
||||
text_regions_p, contours_only_text_parent, contours_only_text_parent_h, all_box_coord, all_box_coord_h, all_found_texline_polygons, all_found_texline_polygons_h, slopes, slopes_h, contours_only_text_parent_d_ordered, contours_only_text_parent_h_d_ordered = check_any_text_region_in_model_one_is_main_or_header(text_regions_p, regions_fully, contours_only_text_parent, all_box_coord, all_found_texline_polygons, slopes, contours_only_text_parent_d_ordered)
|
||||
|
||||
if self.plotter:
|
||||
self.plotter.save_plot_of_layout(text_regions_p, image_page)
|
||||
self.plotter.save_plot_of_layout_all(text_regions_p, image_page)
|
||||
|
||||
if not self.dir_in:
|
||||
K.clear_session()
|
||||
|
||||
pixel_img = 4
|
||||
polygons_of_drop_capitals = return_contours_of_interested_region_by_min_size(text_regions_p, pixel_img)
|
||||
all_found_texline_polygons = adhere_drop_capital_region_into_corresponding_textline(text_regions_p, polygons_of_drop_capitals, contours_only_text_parent, contours_only_text_parent_h, all_box_coord, all_box_coord_h, all_found_texline_polygons, all_found_texline_polygons_h, kernel=KERNEL, curved_line=self.curved_line)
|
||||
|
||||
# print(len(contours_only_text_parent_h),len(contours_only_text_parent_h_d_ordered),'contours_only_text_parent_h')
|
||||
pixel_lines = 6
|
||||
|
||||
|
||||
|
|
@ -2990,45 +3140,15 @@ class Eynollah:
|
|||
else:
|
||||
_, _, matrix_of_lines_ch_d, splitter_y_new_d, _ = find_number_of_columns_in_document(np.repeat(text_regions_p_1_n[:, :, np.newaxis], 3, axis=2), num_col_classifier, self.tables, pixel_lines)
|
||||
|
||||
# print(peaks_neg_fin,peaks_neg_fin_d,'num_col2')
|
||||
# print(splitter_y_new,splitter_y_new_d,'num_col_classifier')
|
||||
# print(matrix_of_lines_ch.shape,matrix_of_lines_ch_d.shape,'matrix_of_lines_ch')
|
||||
|
||||
if num_col_classifier >= 3:
|
||||
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
|
||||
regions_without_separators = regions_without_separators.astype(np.uint8)
|
||||
regions_without_separators = cv2.erode(regions_without_separators[:, :], KERNEL, iterations=6)
|
||||
|
||||
#regions_without_separators_0 = regions_without_separators[:, :].sum(axis=0)
|
||||
#meda_n_updown = regions_without_separators_0[len(regions_without_separators_0) :: -1]
|
||||
#first_nonzero = next((i for i, x in enumerate(regions_without_separators_0) if x), 0)
|
||||
#last_nonzero = next((i for i, x in enumerate(meda_n_updown) if x), 0)
|
||||
#last_nonzero = len(regions_without_separators_0) - last_nonzero
|
||||
|
||||
#random_pixels_for_image = np.random.randn(regions_without_separators.shape[0], regions_without_separators.shape[1])
|
||||
#random_pixels_for_image[random_pixels_for_image < -0.5] = 0
|
||||
#random_pixels_for_image[random_pixels_for_image != 0] = 1
|
||||
#regions_without_separators[(random_pixels_for_image[:, :] == 1) & (text_regions_p[:, :] == 5)] = 1
|
||||
|
||||
#regions_without_separators[:, 0:first_nonzero] = 0
|
||||
#regions_without_separators[:, last_nonzero:] = 0
|
||||
else:
|
||||
regions_without_separators_d = regions_without_separators_d.astype(np.uint8)
|
||||
regions_without_separators_d = cv2.erode(regions_without_separators_d[:, :], KERNEL, iterations=6)
|
||||
|
||||
#regions_without_separators_0 = regions_without_separators_d[:, :].sum(axis=0)
|
||||
#meda_n_updown = regions_without_separators_0[len(regions_without_separators_0) :: -1]
|
||||
#first_nonzero = next((i for i, x in enumerate(regions_without_separators_0) if x), 0)
|
||||
#last_nonzero = next((i for i, x in enumerate(meda_n_updown) if x), 0)
|
||||
#last_nonzero = len(regions_without_separators_0) - last_nonzero
|
||||
|
||||
#random_pixels_for_image = np.random.randn(regions_without_separators_d.shape[0], regions_without_separators_d.shape[1])
|
||||
#random_pixels_for_image[random_pixels_for_image < -0.5] = 0
|
||||
#random_pixels_for_image[random_pixels_for_image != 0] = 1
|
||||
##regions_without_separators_d[(random_pixels_for_image[:, :] == 1) & (text_regions_p_1_n[:, :] == 5)] = 1
|
||||
|
||||
#regions_without_separators_d[:, 0:first_nonzero] = 0
|
||||
#regions_without_separators_d[:, last_nonzero:] = 0
|
||||
|
||||
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
|
||||
boxes, peaks_neg_tot_tables = return_boxes_of_images_by_order_of_reading_new(splitter_y_new, regions_without_separators, matrix_of_lines_ch, num_col_classifier, erosion_hurts, self.tables)
|
||||
|
|
@ -3037,7 +3157,7 @@ class Eynollah:
|
|||
|
||||
if self.plotter:
|
||||
self.plotter.write_images_into_directory(polygons_of_images, image_page)
|
||||
|
||||
t_order = time.time()
|
||||
if self.full_layout:
|
||||
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
|
||||
order_text_new, id_of_texts_tot = self.do_order_of_regions(contours_only_text_parent, contours_only_text_parent_h, boxes, textline_mask_tot)
|
||||
|
|
@ -3046,7 +3166,7 @@ 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_texline_polygons, all_found_texline_polygons_h, all_box_coord, all_box_coord_h, polygons_of_images, contours_tables, polygons_of_drop_capitals, polygons_of_marginals, all_found_texline_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)
|
||||
return pcgts
|
||||
##return pcgts
|
||||
else:
|
||||
contours_only_text_parent_h = None
|
||||
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
|
||||
|
|
@ -3056,4 +3176,7 @@ class Eynollah:
|
|||
order_text_new, id_of_texts_tot = self.do_order_of_regions(contours_only_text_parent_d_ordered, contours_only_text_parent_h, boxes_d, textline_mask_tot_d)
|
||||
pcgts = self.writer.build_pagexml_no_full_layout(txt_con_org, page_coord, order_text_new, id_of_texts_tot, all_found_texline_polygons, all_box_coord, polygons_of_images, polygons_of_marginals, all_found_texline_polygons_marginals, all_box_coord_marginals, slopes, slopes_marginals, cont_page, polygons_lines_xml, contours_tables)
|
||||
self.logger.info("Job done in %.1fs", time.time() - t0)
|
||||
return pcgts
|
||||
##return pcgts
|
||||
self.writer.write_pagexml(pcgts)
|
||||
#self.logger.info("Job done in %.1fs", time.time() - t0)
|
||||
self.logger.info("All jobs done in %.1fs", time.time() - t0_tot)
|
||||
|
|
|
|||
|
|
@ -797,6 +797,76 @@ def putt_bb_of_drop_capitals_of_model_in_patches_in_layout(layout_in_patch):
|
|||
return layout_in_patch
|
||||
|
||||
def check_any_text_region_in_model_one_is_main_or_header(regions_model_1,regions_model_full,contours_only_text_parent,all_box_coord,all_found_texline_polygons,slopes,contours_only_text_parent_d_ordered):
|
||||
|
||||
cx_main,cy_main ,x_min_main , x_max_main, y_min_main ,y_max_main,y_corr_x_min_from_argmin=find_new_features_of_contours(contours_only_text_parent)
|
||||
|
||||
length_con=x_max_main-x_min_main
|
||||
height_con=y_max_main-y_min_main
|
||||
|
||||
|
||||
|
||||
all_found_texline_polygons_main=[]
|
||||
all_found_texline_polygons_head=[]
|
||||
|
||||
all_box_coord_main=[]
|
||||
all_box_coord_head=[]
|
||||
|
||||
slopes_main=[]
|
||||
slopes_head=[]
|
||||
|
||||
contours_only_text_parent_main=[]
|
||||
contours_only_text_parent_head=[]
|
||||
|
||||
contours_only_text_parent_main_d=[]
|
||||
contours_only_text_parent_head_d=[]
|
||||
|
||||
for ii in range(len(contours_only_text_parent)):
|
||||
con=contours_only_text_parent[ii]
|
||||
img=np.zeros((regions_model_1.shape[0],regions_model_1.shape[1],3))
|
||||
img = cv2.fillPoly(img, pts=[con], color=(255, 255, 255))
|
||||
|
||||
|
||||
|
||||
all_pixels=((img[:,:,0]==255)*1).sum()
|
||||
|
||||
pixels_header=( ( (img[:,:,0]==255) & (regions_model_full[:,:,0]==2) )*1 ).sum()
|
||||
pixels_main=all_pixels-pixels_header
|
||||
|
||||
|
||||
if (pixels_header>=pixels_main) and ( (length_con[ii]/float(height_con[ii]) )>=1.3 ):
|
||||
regions_model_1[:,:][(regions_model_1[:,:]==1) & (img[:,:,0]==255) ]=2
|
||||
contours_only_text_parent_head.append(con)
|
||||
if contours_only_text_parent_d_ordered is not None:
|
||||
contours_only_text_parent_head_d.append(contours_only_text_parent_d_ordered[ii])
|
||||
all_box_coord_head.append(all_box_coord[ii])
|
||||
slopes_head.append(slopes[ii])
|
||||
all_found_texline_polygons_head.append(all_found_texline_polygons[ii])
|
||||
else:
|
||||
regions_model_1[:,:][(regions_model_1[:,:]==1) & (img[:,:,0]==255) ]=1
|
||||
contours_only_text_parent_main.append(con)
|
||||
if contours_only_text_parent_d_ordered is not None:
|
||||
contours_only_text_parent_main_d.append(contours_only_text_parent_d_ordered[ii])
|
||||
all_box_coord_main.append(all_box_coord[ii])
|
||||
slopes_main.append(slopes[ii])
|
||||
all_found_texline_polygons_main.append(all_found_texline_polygons[ii])
|
||||
|
||||
#print(all_pixels,pixels_main,pixels_header)
|
||||
|
||||
return regions_model_1,contours_only_text_parent_main,contours_only_text_parent_head,all_box_coord_main,all_box_coord_head,all_found_texline_polygons_main,all_found_texline_polygons_head,slopes_main,slopes_head,contours_only_text_parent_main_d,contours_only_text_parent_head_d
|
||||
|
||||
|
||||
def check_any_text_region_in_model_one_is_main_or_header_light(regions_model_1,regions_model_full,contours_only_text_parent,all_box_coord,all_found_texline_polygons,slopes,contours_only_text_parent_d_ordered):
|
||||
|
||||
### to make it faster
|
||||
h_o = regions_model_1.shape[0]
|
||||
w_o = regions_model_1.shape[1]
|
||||
|
||||
regions_model_1 = cv2.resize(regions_model_1, (int(regions_model_1.shape[1]/3.), int(regions_model_1.shape[0]/3.)), interpolation=cv2.INTER_NEAREST)
|
||||
regions_model_full = cv2.resize(regions_model_full, (int(regions_model_full.shape[1]/3.), int(regions_model_full.shape[0]/3.)), interpolation=cv2.INTER_NEAREST)
|
||||
contours_only_text_parent = [ (i/3.).astype(np.int32) for i in contours_only_text_parent]
|
||||
|
||||
###
|
||||
|
||||
cx_main,cy_main ,x_min_main , x_max_main, y_min_main ,y_max_main,y_corr_x_min_from_argmin=find_new_features_of_contours(contours_only_text_parent)
|
||||
|
||||
length_con=x_max_main-x_min_main
|
||||
|
|
@ -853,8 +923,14 @@ def check_any_text_region_in_model_one_is_main_or_header(regions_model_1,regions
|
|||
|
||||
|
||||
|
||||
#plt.imshow(img[:,:,0])
|
||||
#plt.show()
|
||||
### to make it faster
|
||||
|
||||
regions_model_1 = cv2.resize(regions_model_1, (w_o, h_o), interpolation=cv2.INTER_NEAREST)
|
||||
#regions_model_full = cv2.resize(img, (int(regions_model_full.shape[1]/3.), int(regions_model_full.shape[0]/3.)), interpolation=cv2.INTER_NEAREST)
|
||||
contours_only_text_parent_head = [ (i*3.).astype(np.int32) for i in contours_only_text_parent_head]
|
||||
contours_only_text_parent_main = [ (i*3.).astype(np.int32) for i in contours_only_text_parent_main]
|
||||
###
|
||||
|
||||
return regions_model_1,contours_only_text_parent_main,contours_only_text_parent_head,all_box_coord_main,all_box_coord_head,all_found_texline_polygons_main,all_found_texline_polygons_head,slopes_main,slopes_head,contours_only_text_parent_main_d,contours_only_text_parent_head_d
|
||||
|
||||
def small_textlines_to_parent_adherence2(textlines_con, textline_iamge, num_col):
|
||||
|
|
|
|||
|
|
@ -3,7 +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
|
||||
def contours_in_same_horizon(cy_main_hor):
|
||||
X1 = np.zeros((len(cy_main_hor), len(cy_main_hor)))
|
||||
X2 = np.zeros((len(cy_main_hor), len(cy_main_hor)))
|
||||
|
|
@ -147,6 +148,96 @@ 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])
|
||||
|
||||
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 = 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[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])
|
||||
|
||||
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):
|
||||
|
||||
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.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
|
||||
def loop_contour_image(index_l, cnts,img, slope_first):
|
||||
img_copy = np.zeros(img.shape)
|
||||
img_copy = cv2.fillPoly(img_copy, pts=[cnts[index_l]], color=(1, 1, 1))
|
||||
|
||||
# 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)
|
||||
|
||||
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]
|
||||
|
||||
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))])
|
||||
|
||||
print(len(cnts_org),'lendiha')
|
||||
|
||||
return cnts_org
|
||||
|
||||
def get_textregion_contours_in_org_image(cnts, img, slope_first):
|
||||
|
||||
cnts_org = []
|
||||
|
|
@ -175,11 +266,43 @@ def get_textregion_contours_in_org_image(cnts, img, slope_first):
|
|||
# print(np.shape(cont_int[0]))
|
||||
cnts_org.append(cont_int[0])
|
||||
|
||||
# print(cnts_org,'cnts_org')
|
||||
return cnts_org
|
||||
|
||||
def get_textregion_contours_in_org_image_light(cnts, img, slope_first):
|
||||
|
||||
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))
|
||||
|
||||
# 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)
|
||||
|
||||
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)
|
||||
|
||||
# sys.exit()
|
||||
# self.y_shift = np.abs(img_copy.shape[0] - img.shape[0])
|
||||
# self.x_shift = np.abs(img_copy.shape[1] - img.shape[1])
|
||||
return cnts_org
|
||||
|
||||
def return_contours_of_interested_textline(region_pre_p, pixel):
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue