mirror of
https://github.com/qurator-spk/eynollah.git
synced 2025-06-09 12:19:54 +02:00
updating 1&2 columns images + full layout
This commit is contained in:
vahidrezanezhad
parent
a62ae370c3
commit
be144db9f8
2 changed files with 115 additions and 42 deletions
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@ -1083,43 +1083,64 @@ class Eynollah:
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model_region = self.model_region_fl_new if patches else self.model_region_fl_np
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if not patches:
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img = otsu_copy_binary(img)
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if self.light_version:
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pass
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else:
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img = otsu_copy_binary(img)
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img = img.astype(np.uint8)
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prediction_regions2 = None
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else:
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if cols == 1:
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img = otsu_copy_binary(img)
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if self.light_version:
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pass
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else:
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img = otsu_copy_binary(img)
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img = img.astype(np.uint8)
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img = resize_image(img, int(img_height_h * 1000 / float(img_width_h)), 1000)
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img = img.astype(np.uint8)
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if cols == 2:
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img = otsu_copy_binary(img)
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if self.light_version:
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pass
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else:
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img = otsu_copy_binary(img)
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img = img.astype(np.uint8)
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img = resize_image(img, int(img_height_h * 1300 / float(img_width_h)), 1300)
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img = img.astype(np.uint8)
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if cols == 3:
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img = otsu_copy_binary(img)
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if self.light_version:
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pass
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else:
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img = otsu_copy_binary(img)
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img = img.astype(np.uint8)
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img = resize_image(img, int(img_height_h * 1600 / float(img_width_h)), 1600)
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img = img.astype(np.uint8)
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if cols == 4:
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img = otsu_copy_binary(img)
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if self.light_version:
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pass
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else:
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img = otsu_copy_binary(img)
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img = img.astype(np.uint8)
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img = resize_image(img, int(img_height_h * 1900 / float(img_width_h)), 1900)
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img = img.astype(np.uint8)
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if cols == 5:
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img = otsu_copy_binary(img)
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if self.light_version:
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pass
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else:
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img = otsu_copy_binary(img)
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img = img.astype(np.uint8)
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img = resize_image(img, int(img_height_h * 2200 / float(img_width_h)), 2200)
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img = img.astype(np.uint8)
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if cols >= 6:
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img = otsu_copy_binary(img)
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if self.light_version:
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pass
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else:
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img = otsu_copy_binary(img)
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img = img.astype(np.uint8)
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img = resize_image(img, int(img_height_h * 2500 / float(img_width_h)), 2500)
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img = img.astype(np.uint8)
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@ -1611,6 +1632,7 @@ class Eynollah:
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img_h = img_org.shape[0]
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img_w = img_org.shape[1]
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img = resize_image(img_org, int(img_org.shape[0] * scaler_h), int(img_org.shape[1] * scaler_w))
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#print(img.shape,'bin shape')
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if not self.dir_in:
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prediction_textline = self.do_prediction(patches, img, model_textline)
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else:
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@ -1664,6 +1686,7 @@ class Eynollah:
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box_sub.put(boxes_sub_new)
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def get_regions_light_v(self,img,is_image_enhanced, num_col_classifier):
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self.logger.debug("enter get_regions_light_v")
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t_in = time.time()
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erosion_hurts = False
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img_org = np.copy(img)
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img_height_h = img_org.shape[0]
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@ -1671,7 +1694,7 @@ class Eynollah:
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#model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_ens)
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#print(num_col_classifier,'num_col_classifier')
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if num_col_classifier == 1:
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img_w_new = 1000
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@ -1711,9 +1734,12 @@ class Eynollah:
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#img= np.copy(prediction_bin)
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img_bin = np.copy(prediction_bin)
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#print("inside 1 ", time.time()-t_in)
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textline_mask_tot_ea = self.run_textline(img_bin)
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#print("inside 2 ", time.time()-t_in)
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if not self.dir_in:
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if num_col_classifier == 1 or num_col_classifier == 2:
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@ -1727,12 +1753,14 @@ class Eynollah:
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prediction_regions_org = self.do_prediction_new_concept(False, img_resized, self.model_region_1_2)
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else:
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prediction_regions_org = self.do_prediction_new_concept(True, img_bin, self.model_region)
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#print("inside 3 ", time.time()-t_in)
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#plt.imshow(prediction_regions_org[:,:,0])
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#plt.show()
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prediction_regions_org = resize_image(prediction_regions_org,img_height_h, img_width_h )
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textline_mask_tot_ea = resize_image(textline_mask_tot_ea,img_height_h, img_width_h )
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img_bin = resize_image(img_bin,img_height_h, img_width_h )
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prediction_regions_org=prediction_regions_org[:,:,0]
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@ -1787,8 +1815,8 @@ class Eynollah:
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text_regions_p_true[:,:][mask_images_only[:,:] == 1] = 2
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text_regions_p_true = cv2.fillPoly(text_regions_p_true, pts = polygons_of_only_texts, color=(1,1,1))
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return text_regions_p_true, erosion_hurts, polygons_lines_xml, textline_mask_tot_ea
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#print("inside 4 ", time.time()-t_in)
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return text_regions_p_true, erosion_hurts, polygons_lines_xml, textline_mask_tot_ea, img_bin
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def get_regions_from_xy_2models(self,img,is_image_enhanced, num_col_classifier):
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self.logger.debug("enter get_regions_from_xy_2models")
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@ -2553,7 +2581,11 @@ class Eynollah:
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prediction_table_erode = cv2.erode(prediction_table[:,:,0], KERNEL, iterations=20)
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prediction_table_erode = cv2.dilate(prediction_table_erode, KERNEL, iterations=20)
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return prediction_table_erode.astype(np.int16)
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def run_graphics_and_columns_light(self, text_regions_p_1, textline_mask_tot_ea, num_col_classifier, num_column_is_classified, erosion_hurts):
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def run_graphics_and_columns_light(self, text_regions_p_1, textline_mask_tot_ea, num_col_classifier, num_column_is_classified, erosion_hurts, img_bin_light):
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#print(text_regions_p_1.shape, 'text_regions_p_1 shape run graphics')
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#print(erosion_hurts, 'erosion_hurts')
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t_in_gr = time.time()
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img_g = self.imread(grayscale=True, uint8=True)
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img_g3 = np.zeros((img_g.shape[0], img_g.shape[1], 3))
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@ -2563,7 +2595,7 @@ class Eynollah:
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img_g3[:, :, 2] = img_g[:, :]
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image_page, page_coord, cont_page = self.extract_page()
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#print("inside graphics 1 ", time.time() - t_in_gr)
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if self.tables:
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table_prediction = self.get_tables_from_model(image_page, num_col_classifier)
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else:
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@ -2574,6 +2606,9 @@ class Eynollah:
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text_regions_p_1 = text_regions_p_1[page_coord[0] : page_coord[1], page_coord[2] : page_coord[3]]
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textline_mask_tot_ea = textline_mask_tot_ea[page_coord[0] : page_coord[1], page_coord[2] : page_coord[3]]
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img_bin_light = img_bin_light[page_coord[0] : page_coord[1], page_coord[2] : page_coord[3]]
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mask_images = (text_regions_p_1[:, :] == 2) * 1
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mask_images = mask_images.astype(np.uint8)
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mask_images = cv2.erode(mask_images[:, :], KERNEL, iterations=10)
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@ -2582,7 +2617,7 @@ class Eynollah:
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img_only_regions_with_sep = ((text_regions_p_1[:, :] != 3) & (text_regions_p_1[:, :] != 0)) * 1
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img_only_regions_with_sep = img_only_regions_with_sep.astype(np.uint8)
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#print("inside graphics 2 ", time.time() - t_in_gr)
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if erosion_hurts:
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img_only_regions = np.copy(img_only_regions_with_sep[:,:])
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else:
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@ -2600,8 +2635,10 @@ class Eynollah:
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except Exception as why:
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self.logger.error(why)
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num_col = None
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return 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
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#print("inside graphics 3 ", time.time() - t_in_gr)
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return 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, img_bin_light
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def run_graphics_and_columns(self, text_regions_p_1, num_col_classifier, num_column_is_classified, erosion_hurts):
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t_in_gr = time.time()
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img_g = self.imread(grayscale=True, uint8=True)
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img_g3 = np.zeros((img_g.shape[0], img_g.shape[1], 3))
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@ -2629,13 +2666,11 @@ class Eynollah:
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img_only_regions_with_sep = ((text_regions_p_1[:, :] != 3) & (text_regions_p_1[:, :] != 0)) * 1
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img_only_regions_with_sep = img_only_regions_with_sep.astype(np.uint8)
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if erosion_hurts:
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img_only_regions = np.copy(img_only_regions_with_sep[:,:])
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else:
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img_only_regions = cv2.erode(img_only_regions_with_sep[:,:], KERNEL, iterations=6)
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try:
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num_col, _ = find_num_col(img_only_regions, num_col_classifier, self.tables, multiplier=6.0)
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num_col = num_col + 1
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@ -2682,6 +2717,7 @@ class Eynollah:
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return textline_mask_tot_ea
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def run_deskew(self, textline_mask_tot_ea):
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#print(textline_mask_tot_ea.shape, 'textline_mask_tot_ea deskew')
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sigma = 2
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main_page_deskew = True
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slope_deskew = return_deskew_slop(cv2.erode(textline_mask_tot_ea, KERNEL, iterations=2), sigma, main_page_deskew, plotter=self.plotter)
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@ -2805,7 +2841,7 @@ class Eynollah:
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self.logger.debug('exit run_boxes_no_full_layout')
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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
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def run_boxes_full_layout(self, image_page, textline_mask_tot, text_regions_p, slope_deskew, num_col_classifier, img_only_regions, table_prediction, erosion_hurts):
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def run_boxes_full_layout(self, image_page, textline_mask_tot, text_regions_p, slope_deskew, num_col_classifier, img_only_regions, table_prediction, erosion_hurts, img_bin_light):
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self.logger.debug('enter run_boxes_full_layout')
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if self.tables:
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@ -2900,20 +2936,23 @@ class Eynollah:
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image_page = image_page.astype(np.uint8)
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regions_fully, regions_fully_only_drop = self.extract_text_regions_new(image_page, True, cols=num_col_classifier)
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if self.light_version:
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regions_fully, regions_fully_only_drop = self.extract_text_regions_new(img_bin_light, True, cols=num_col_classifier)
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else:
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regions_fully, regions_fully_only_drop = self.extract_text_regions_new(image_page, True, cols=num_col_classifier)
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# 6 is the separators lable in old full layout model
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# 4 is the drop capital class in old full layout model
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# in the new full layout drop capital is 3 and separators are 5
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text_regions_p[:,:][regions_fully[:,:,0]==5]=6
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regions_fully[:, :, 0][regions_fully_only_drop[:, :, 0] == 3] = 4
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###regions_fully[:, :, 0][regions_fully_only_drop[:, :, 0] == 3] = 4
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#text_regions_p[:,:][regions_fully[:,:,0]==6]=6
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#regions_fully_only_drop = put_drop_out_from_only_drop_model(regions_fully_only_drop, text_regions_p)
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#regions_fully[:, :, 0][regions_fully_only_drop[:, :, 0] == 4] = 4
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regions_fully = putt_bb_of_drop_capitals_of_model_in_patches_in_layout(regions_fully)
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##regions_fully_only_drop = put_drop_out_from_only_drop_model(regions_fully_only_drop, text_regions_p)
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##regions_fully[:, :, 0][regions_fully_only_drop[:, :, 0] == 4] = 4
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drop_capital_label_in_full_layout_model = 3
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regions_fully = putt_bb_of_drop_capitals_of_model_in_patches_in_layout(regions_fully, drop_capital_label_in_full_layout_model)
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##regions_fully_np, _ = self.extract_text_regions(image_page, False, cols=num_col_classifier)
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##if num_col_classifier > 2:
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##regions_fully_np[:, :, 0][regions_fully_np[:, :, 0] == 4] = 0
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@ -2923,7 +2962,7 @@ class Eynollah:
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###regions_fully = boosting_headers_by_longshot_region_segmentation(regions_fully, regions_fully_np, img_only_regions)
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# plt.imshow(regions_fully[:,:,0])
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# plt.show()
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text_regions_p[:, :][regions_fully[:, :, 0] == 4] = 4
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text_regions_p[:, :][regions_fully[:, :, 0] == drop_capital_label_in_full_layout_model] = 4
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####text_regions_p[:, :][regions_fully_np[:, :, 0] == 4] = 4
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#plt.imshow(text_regions_p)
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#plt.show()
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@ -3463,22 +3502,41 @@ class Eynollah:
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self.ls_imgs = [1]
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for img_name in self.ls_imgs:
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print(img_name)
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t0 = time.time()
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if self.dir_in:
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self.reset_file_name_dir(os.path.join(self.dir_in,img_name))
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img_res, is_image_enhanced, num_col_classifier, num_column_is_classified = self.run_enhancement(self.light_version)
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self.logger.info("Enhancing took %.1fs ", time.time() - t0)
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#print("text region early -1 in %.1fs", time.time() - t0)
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t1 = time.time()
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if self.light_version:
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text_regions_p_1 ,erosion_hurts, polygons_lines_xml, textline_mask_tot_ea = self.get_regions_light_v(img_res, is_image_enhanced, num_col_classifier)
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slope_deskew, slope_first = self.run_deskew(textline_mask_tot_ea)
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text_regions_p_1 ,erosion_hurts, polygons_lines_xml, textline_mask_tot_ea, img_bin_light = self.get_regions_light_v(img_res, is_image_enhanced, num_col_classifier)
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#print("text region early -2 in %.1fs", time.time() - t0)
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if num_col_classifier == 1 or num_col_classifier ==2:
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if num_col_classifier == 1:
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img_w_new = 1000
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img_h_new = int(textline_mask_tot_ea.shape[0] / float(textline_mask_tot_ea.shape[1]) * img_w_new)
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elif num_col_classifier == 2:
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img_w_new = 1300
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img_h_new = int(textline_mask_tot_ea.shape[0] / float(textline_mask_tot_ea.shape[1]) * img_w_new)
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textline_mask_tot_ea_deskew = resize_image(textline_mask_tot_ea,img_h_new, img_w_new )
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slope_deskew, slope_first = self.run_deskew(textline_mask_tot_ea_deskew)
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else:
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slope_deskew, slope_first = self.run_deskew(textline_mask_tot_ea)
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#print("text region early -2,5 in %.1fs", time.time() - t0)
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#self.logger.info("Textregion detection took %.1fs ", time.time() - t1t)
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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 = \
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self.run_graphics_and_columns_light(text_regions_p_1, textline_mask_tot_ea, num_col_classifier, num_column_is_classified, erosion_hurts)
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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, img_bin_light = \
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self.run_graphics_and_columns_light(text_regions_p_1, textline_mask_tot_ea, num_col_classifier, num_column_is_classified, erosion_hurts, img_bin_light)
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#self.logger.info("run graphics %.1fs ", time.time() - t1t)
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#print("text region early -3 in %.1fs", time.time() - t0)
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textline_mask_tot_ea_org = np.copy(textline_mask_tot_ea)
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#print("text region early -4 in %.1fs", time.time() - t0)
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else:
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text_regions_p_1 ,erosion_hurts, polygons_lines_xml = self.get_regions_from_xy_2models(img_res, is_image_enhanced, num_col_classifier)
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self.logger.info("Textregion detection took %.1fs ", time.time() - t1)
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@ -3498,7 +3556,7 @@ class Eynollah:
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continue
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else:
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return pcgts
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#print("text region early in %.1fs", time.time() - t0)
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t1 = time.time()
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if not self.light_version:
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textline_mask_tot_ea = self.run_textline(image_page)
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@ -3513,17 +3571,20 @@ class Eynollah:
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textline_mask_tot, text_regions_p, image_page_rotated = self.run_marginals(image_page, textline_mask_tot_ea, mask_images, mask_lines, num_col_classifier, slope_deskew, text_regions_p_1, table_prediction)
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self.logger.info("detection of marginals took %.1fs", time.time() - t1)
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#print("text region early 2 marginal in %.1fs", time.time() - t0)
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t1 = time.time()
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if not self.full_layout:
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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 = self.run_boxes_no_full_layout(image_page, textline_mask_tot, text_regions_p, slope_deskew, num_col_classifier, table_prediction, erosion_hurts)
|
||||
|
||||
if self.full_layout:
|
||||
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 = self.run_boxes_full_layout(image_page, textline_mask_tot, text_regions_p, slope_deskew, num_col_classifier, img_only_regions, table_prediction, erosion_hurts)
|
||||
if not self.light_version:
|
||||
img_bin_light = None
|
||||
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 = self.run_boxes_full_layout(image_page, textline_mask_tot, text_regions_p, slope_deskew, num_col_classifier, img_only_regions, table_prediction, erosion_hurts, img_bin_light)
|
||||
text_only = ((img_revised_tab[:, :] == 1)) * 1
|
||||
if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
|
||||
text_only_d = ((text_regions_p_1_n[:, :] == 1)) * 1
|
||||
|
||||
|
||||
#print("text region early 2 in %.1fs", time.time() - t0)
|
||||
###min_con_area = 0.000005
|
||||
if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
|
||||
contours_only_text, hir_on_text = return_contours_of_image(text_only)
|
||||
|
|
@ -3625,13 +3686,16 @@ class Eynollah:
|
|||
# self.logger.debug('len(contours_only_text_parent) %s', len(contours_only_text_parent_d))
|
||||
else:
|
||||
pass
|
||||
|
||||
#print("text region early 3 in %.1fs", time.time() - t0)
|
||||
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)
|
||||
#print("text region early 4 in %.1fs", time.time() - t0)
|
||||
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)
|
||||
|
||||
#print("text region early 5 in %.1fs", time.time() - t0)
|
||||
if not self.curved_line:
|
||||
if self.light_version:
|
||||
if self.textline_light:
|
||||
|
|
@ -3651,7 +3715,7 @@ class Eynollah:
|
|||
all_found_textline_polygons = small_textlines_to_parent_adherence2(all_found_textline_polygons, textline_mask_tot_ea, num_col_classifier)
|
||||
all_found_textline_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_textline_polygons_marginals = small_textlines_to_parent_adherence2(all_found_textline_polygons_marginals, textline_mask_tot_ea, num_col_classifier)
|
||||
|
||||
#print("text region early 6 in %.1fs", time.time() - t0)
|
||||
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, dtype=object)[index_by_text_par_con])
|
||||
|
|
@ -3778,7 +3842,10 @@ class Eynollah:
|
|||
#print(x, y, w, h, h/float(w),'ratio')
|
||||
h2w_ratio = h/float(w)
|
||||
mask_poly = np.zeros(image_page.shape)
|
||||
img_poly_on_img = np.copy(image_page)
|
||||
if not self.light_version:
|
||||
img_poly_on_img = np.copy(image_page)
|
||||
else:
|
||||
img_poly_on_img = np.copy(img_bin_light)
|
||||
|
||||
mask_poly = cv2.fillPoly(mask_poly, pts=[ind_poly], color=(1, 1, 1))
|
||||
|
||||
|
|
@ -3805,8 +3872,10 @@ class Eynollah:
|
|||
pcgts = self.writer.build_pagexml_no_full_layout(txt_con_org, page_coord, order_text_new, id_of_texts_tot, all_found_textline_polygons, all_box_coord, polygons_of_images, polygons_of_marginals, all_found_textline_polygons_marginals, all_box_coord_marginals, slopes, slopes_marginals, cont_page, polygons_lines_xml, contours_tables, ocr_all_textlines)
|
||||
self.logger.info("Job done in %.1fs", time.time() - t0)
|
||||
##return pcgts
|
||||
#print("text region early 7 in %.1fs", time.time() - t0)
|
||||
self.writer.write_pagexml(pcgts)
|
||||
#self.logger.info("Job done in %.1fs", time.time() - t0)
|
||||
#print("Job done in %.1fs", time.time() - t0)
|
||||
|
||||
if self.dir_in:
|
||||
self.logger.info("All jobs done in %.1fs", time.time() - t0_tot)
|
||||
|
|
|
|||
|
|
@ -775,9 +775,8 @@ def put_drop_out_from_only_drop_model(layout_no_patch, layout1):
|
|||
|
||||
return layout_no_patch
|
||||
|
||||
def putt_bb_of_drop_capitals_of_model_in_patches_in_layout(layout_in_patch):
|
||||
|
||||
drop_only = (layout_in_patch[:, :, 0] == 4) * 1
|
||||
def putt_bb_of_drop_capitals_of_model_in_patches_in_layout(layout_in_patch, drop_capital_label):
|
||||
drop_only = (layout_in_patch[:, :, 0] == drop_capital_label) * 1
|
||||
contours_drop, hir_on_drop = return_contours_of_image(drop_only)
|
||||
contours_drop_parent = return_parent_contours(contours_drop, hir_on_drop)
|
||||
|
||||
|
|
@ -786,13 +785,18 @@ def putt_bb_of_drop_capitals_of_model_in_patches_in_layout(layout_in_patch):
|
|||
|
||||
contours_drop_parent = [contours_drop_parent[jz] for jz in range(len(contours_drop_parent)) if areas_cnt_text[jz] > 0.00001]
|
||||
|
||||
areas_cnt_text = [areas_cnt_text[jz] for jz in range(len(areas_cnt_text)) if areas_cnt_text[jz] > 0.001]
|
||||
areas_cnt_text = [areas_cnt_text[jz] for jz in range(len(areas_cnt_text)) if areas_cnt_text[jz] > 0.00001]
|
||||
|
||||
contours_drop_parent_final = []
|
||||
|
||||
for jj in range(len(contours_drop_parent)):
|
||||
x, y, w, h = cv2.boundingRect(contours_drop_parent[jj])
|
||||
layout_in_patch[y : y + h, x : x + w, 0] = 4
|
||||
|
||||
if ( ( areas_cnt_text[jj] * float(drop_only.shape[0] * drop_only.shape[1]) ) / float(w*h) ) > 0.4:
|
||||
|
||||
layout_in_patch[y : y + h, x : x + w, 0] = drop_capital_label
|
||||
else:
|
||||
layout_in_patch[y : y + h, x : x + w, 0][layout_in_patch[y : y + h, x : x + w, 0] == drop_capital_label] = drop_capital_label
|
||||
|
||||
return layout_in_patch
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue