mirror of
https://github.com/qurator-spk/eynollah.git
synced 2025-09-01 13:29:58 +02:00
ocr model renamed - image text font for ocr result is now using Charis-7.000 font (downloaded from here https://software.sil.org/charis/download/)
This commit is contained in:
vahidrezanezhad
parent
e54ebaa23e
commit
e0f4a007e4
1 changed files with 78 additions and 70 deletions
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@ -318,7 +318,7 @@ class Eynollah:
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if self.ocr and self.tr:
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if self.ocr and self.tr:
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self.model_ocr_dir = dir_models + "/trocr_model_ens_of_3_checkpoints_201124"
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self.model_ocr_dir = dir_models + "/trocr_model_ens_of_3_checkpoints_201124"
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elif self.ocr and not self.tr:
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elif self.ocr and not self.tr:
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self.model_ocr_dir = dir_models + "/model_step_750000_ocr"#"/model_step_125000_ocr"#"/model_step_25000_ocr"#"/model_step_1050000_ocr"#"/model_0_ocr_cnnrnn"#"/model_23_ocr_cnnrnn"
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self.model_ocr_dir = dir_models + "/model_eynollah_ocr_cnnrnn_20250716"
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if self.tables:
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if self.tables:
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if self.light_version:
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if self.light_version:
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self.model_table_dir = dir_models + "/modelens_table_0t4_201124"
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self.model_table_dir = dir_models + "/modelens_table_0t4_201124"
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@ -5129,7 +5129,7 @@ class Eynollah_ocr:
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self.b_s = int(batch_size)
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self.b_s = int(batch_size)
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else:
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else:
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self.model_ocr_dir = dir_models + "/model_step_900000_ocr"#"/model_step_25000_ocr"#"/model_step_1050000_ocr"#"/model_0_ocr_cnnrnn"#"/model_23_ocr_cnnrnn"
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self.model_ocr_dir = dir_models + "/model_eynollah_ocr_cnnrnn_20250716"
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model_ocr = load_model(self.model_ocr_dir , compile=False)
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model_ocr = load_model(self.model_ocr_dir , compile=False)
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self.prediction_model = tf.keras.models.Model(
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self.prediction_model = tf.keras.models.Model(
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@ -5276,7 +5276,7 @@ class Eynollah_ocr:
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if self.draw_texts_on_image:
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if self.draw_texts_on_image:
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font_path = "NotoSans-Regular.ttf" # Make sure this file exists!
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font_path = "Charis-7.000/Charis-Regular.ttf" # Make sure this file exists!
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font = ImageFont.truetype(font_path, 40)
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font = ImageFont.truetype(font_path, 40)
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for indexer_text, bb_ind in enumerate(total_bb_coordinates):
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for indexer_text, bb_ind in enumerate(total_bb_coordinates):
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@ -5340,8 +5340,8 @@ class Eynollah_ocr:
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tree1.write(out_file_ocr,xml_declaration=True,method='xml',encoding="utf8",default_namespace=None)
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tree1.write(out_file_ocr,xml_declaration=True,method='xml',encoding="utf8",default_namespace=None)
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#print("Job done in %.1fs", time.time() - t0)
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#print("Job done in %.1fs", time.time() - t0)
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else:
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else:
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max_len = 512
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max_len = 512#280#512
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padding_token = 299
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padding_token = 299#1500#299
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image_width = 512#max_len * 4
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image_width = 512#max_len * 4
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image_height = 32
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image_height = 32
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@ -5435,52 +5435,57 @@ class Eynollah_ocr:
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mask_poly = mask_poly[y:y+h, x:x+w, :]
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mask_poly = mask_poly[y:y+h, x:x+w, :]
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img_crop = img_poly_on_img[y:y+h, x:x+w, :]
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img_crop = img_poly_on_img[y:y+h, x:x+w, :]
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#print(file_name, angle_degrees,w*h , mask_poly[:,:,0].sum(), mask_poly[:,:,0].sum() /float(w*h) , 'didi')
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if self.export_textline_images_and_text:
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if not self.do_not_mask_with_textline_contour:
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if not self.do_not_mask_with_textline_contour:
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if angle_degrees > 3:
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better_des_slope = get_orientation_moments(textline_coords)
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img_crop = rotate_image_with_padding(img_crop, better_des_slope )
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if self.prediction_with_both_of_rgb_and_bin:
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img_crop_bin = rotate_image_with_padding(img_crop_bin, better_des_slope )
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mask_poly = rotate_image_with_padding(mask_poly, better_des_slope )
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mask_poly = mask_poly.astype('uint8')
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#new bounding box
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x_n, y_n, w_n, h_n = get_contours_and_bounding_boxes(mask_poly[:,:,0])
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mask_poly = mask_poly[y_n:y_n+h_n, x_n:x_n+w_n, :]
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img_crop = img_crop[y_n:y_n+h_n, x_n:x_n+w_n, :]
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img_crop[mask_poly==0] = 255
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img_crop[mask_poly==0] = 255
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if self.prediction_with_both_of_rgb_and_bin:
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else:
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img_crop_bin = img_crop_bin[y_n:y_n+h_n, x_n:x_n+w_n, :]
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#print(file_name, angle_degrees,w*h , mask_poly[:,:,0].sum(), mask_poly[:,:,0].sum() /float(w*h) , 'didi')
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img_crop_bin[mask_poly==0] = 255
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if not self.do_not_mask_with_textline_contour:
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if angle_degrees > 3:
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if mask_poly[:,:,0].sum() /float(w_n*h_n) < 0.50 and w_scaled > 90:
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better_des_slope = get_orientation_moments(textline_coords)
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img_crop = rotate_image_with_padding(img_crop, better_des_slope )
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if self.prediction_with_both_of_rgb_and_bin:
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if self.prediction_with_both_of_rgb_and_bin:
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img_crop, img_crop_bin = break_curved_line_into_small_pieces_and_then_merge(img_crop, mask_poly, img_crop_bin)
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img_crop_bin = rotate_image_with_padding(img_crop_bin, better_des_slope )
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else:
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img_crop, _ = break_curved_line_into_small_pieces_and_then_merge(img_crop, mask_poly)
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mask_poly = rotate_image_with_padding(mask_poly, better_des_slope )
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mask_poly = mask_poly.astype('uint8')
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else:
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#new bounding box
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better_des_slope = 0
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x_n, y_n, w_n, h_n = get_contours_and_bounding_boxes(mask_poly[:,:,0])
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img_crop[mask_poly==0] = 255
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if self.prediction_with_both_of_rgb_and_bin:
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mask_poly = mask_poly[y_n:y_n+h_n, x_n:x_n+w_n, :]
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img_crop_bin[mask_poly==0] = 255
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img_crop = img_crop[y_n:y_n+h_n, x_n:x_n+w_n, :]
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if type_textregion=='drop-capital':
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pass
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img_crop[mask_poly==0] = 255
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else:
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if mask_poly[:,:,0].sum() /float(w*h) < 0.50 and w_scaled > 90:
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if self.prediction_with_both_of_rgb_and_bin:
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img_crop_bin = img_crop_bin[y_n:y_n+h_n, x_n:x_n+w_n, :]
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img_crop_bin[mask_poly==0] = 255
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if mask_poly[:,:,0].sum() /float(w_n*h_n) < 0.50 and w_scaled > 90:
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if self.prediction_with_both_of_rgb_and_bin:
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if self.prediction_with_both_of_rgb_and_bin:
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img_crop, img_crop_bin = break_curved_line_into_small_pieces_and_then_merge(img_crop, mask_poly, img_crop_bin)
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img_crop, img_crop_bin = break_curved_line_into_small_pieces_and_then_merge(img_crop, mask_poly, img_crop_bin)
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else:
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else:
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img_crop, _ = break_curved_line_into_small_pieces_and_then_merge(img_crop, mask_poly)
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img_crop, _ = break_curved_line_into_small_pieces_and_then_merge(img_crop, mask_poly)
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else:
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better_des_slope = 0
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img_crop[mask_poly==0] = 255
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if self.prediction_with_both_of_rgb_and_bin:
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img_crop_bin[mask_poly==0] = 255
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if type_textregion=='drop-capital':
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pass
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else:
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if mask_poly[:,:,0].sum() /float(w*h) < 0.50 and w_scaled > 90:
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if self.prediction_with_both_of_rgb_and_bin:
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img_crop, img_crop_bin = break_curved_line_into_small_pieces_and_then_merge(img_crop, mask_poly, img_crop_bin)
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else:
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img_crop, _ = break_curved_line_into_small_pieces_and_then_merge(img_crop, mask_poly)
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if not self.export_textline_images_and_text:
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if not self.export_textline_images_and_text:
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if w_scaled < 750:#1.5*image_width:
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if w_scaled < 750:#1.5*image_width:
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@ -5541,35 +5546,38 @@ class Eynollah_ocr:
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cropped_lines_bin.append(img_fin)
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cropped_lines_bin.append(img_fin)
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if self.export_textline_images_and_text:
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if self.export_textline_images_and_text:
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if child_textlines.tag.endswith("TextEquiv"):
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if img_crop.shape[0]==0 or img_crop.shape[1]==0:
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for cheild_text in child_textlines:
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pass
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if cheild_text.tag.endswith("Unicode"):
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else:
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textline_text = cheild_text.text
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if child_textlines.tag.endswith("TextEquiv"):
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if textline_text:
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for cheild_text in child_textlines:
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if self.do_not_mask_with_textline_contour:
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if cheild_text.tag.endswith("Unicode"):
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if self.pref_of_dataset:
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textline_text = cheild_text.text
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with open(os.path.join(self.dir_out, file_name+'_line_'+str(indexer_textlines)+'_'+self.pref_of_dataset+'.txt'), 'w') as text_file:
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if textline_text:
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text_file.write(textline_text)
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if self.do_not_mask_with_textline_contour:
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if self.pref_of_dataset:
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with open(os.path.join(self.dir_out, file_name+'_line_'+str(indexer_textlines)+'_'+self.pref_of_dataset+'.txt'), 'w') as text_file:
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text_file.write(textline_text)
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cv2.imwrite(os.path.join(self.dir_out, file_name+'_line_'+str(indexer_textlines)+'_'+self.pref_of_dataset+'.png'), img_crop )
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cv2.imwrite(os.path.join(self.dir_out, file_name+'_line_'+str(indexer_textlines)+'_'+self.pref_of_dataset+'.png'), img_crop )
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else:
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with open(os.path.join(self.dir_out, file_name+'_line_'+str(indexer_textlines)+'.txt'), 'w') as text_file:
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text_file.write(textline_text)
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cv2.imwrite(os.path.join(self.dir_out, file_name+'_line_'+str(indexer_textlines)+'.png'), img_crop )
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else:
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else:
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with open(os.path.join(self.dir_out, file_name+'_line_'+str(indexer_textlines)+'.txt'), 'w') as text_file:
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if self.pref_of_dataset:
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text_file.write(textline_text)
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with open(os.path.join(self.dir_out, file_name+'_line_'+str(indexer_textlines)+'_'+self.pref_of_dataset+'_masked.txt'), 'w') as text_file:
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text_file.write(textline_text)
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cv2.imwrite(os.path.join(self.dir_out, file_name+'_line_'+str(indexer_textlines)+'.png'), img_crop )
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cv2.imwrite(os.path.join(self.dir_out, file_name+'_line_'+str(indexer_textlines)+'_'+self.pref_of_dataset+'_masked.png'), img_crop )
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else:
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else:
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if self.pref_of_dataset:
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with open(os.path.join(self.dir_out, file_name+'_line_'+str(indexer_textlines)+'_masked.txt'), 'w') as text_file:
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with open(os.path.join(self.dir_out, file_name+'_line_'+str(indexer_textlines)+'_'+self.pref_of_dataset+'_masked.txt'), 'w') as text_file:
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text_file.write(textline_text)
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text_file.write(textline_text)
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cv2.imwrite(os.path.join(self.dir_out, file_name+'_line_'+str(indexer_textlines)+'_'+self.pref_of_dataset+'_masked.png'), img_crop )
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cv2.imwrite(os.path.join(self.dir_out, file_name+'_line_'+str(indexer_textlines)+'_masked.png'), img_crop )
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else:
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with open(os.path.join(self.dir_out, file_name+'_line_'+str(indexer_textlines)+'_masked.txt'), 'w') as text_file:
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indexer_textlines+=1
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text_file.write(textline_text)
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cv2.imwrite(os.path.join(self.dir_out, file_name+'_line_'+str(indexer_textlines)+'_masked.png'), img_crop )
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indexer_textlines+=1
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if not self.export_textline_images_and_text:
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if not self.export_textline_images_and_text:
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indexer_text_region = indexer_text_region +1
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indexer_text_region = indexer_text_region +1
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@ -5727,7 +5735,7 @@ class Eynollah_ocr:
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if self.draw_texts_on_image:
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if self.draw_texts_on_image:
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font_path = "NotoSans-Regular.ttf" # Make sure this file exists!
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font_path = "Charis-7.000/Charis-Regular.ttf" # Make sure this file exists!
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font = ImageFont.truetype(font_path, 40)
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font = ImageFont.truetype(font_path, 40)
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for indexer_text, bb_ind in enumerate(total_bb_coordinates):
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for indexer_text, bb_ind in enumerate(total_bb_coordinates):
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