diff --git a/qurator/eynollah/eynollah.py b/qurator/eynollah/eynollah.py index e587ff3..03252fb 100644 --- a/qurator/eynollah/eynollah.py +++ b/qurator/eynollah/eynollah.py @@ -245,7 +245,7 @@ class Eynollah: self.model_dir_of_col_classifier = dir_models + "/eynollah-column-classifier_20210425" self.model_region_dir_p = dir_models + "/eynollah-main-regions-aug-scaling_20210425" self.model_region_dir_p2 = dir_models + "/eynollah-main-regions-aug-rotation_20210425" - self.model_region_dir_fully_np = dir_models + "/eynollah-full-regions-1column_20210425" + self.model_region_dir_fully_np = dir_models + "/model_full_lay_13_241024"#"/modelens_full_lay_13_17_231024"#"/modelens_full_lay_1_2_221024"#"/eynollah-full-regions-1column_20210425" #self.model_region_dir_fully = dir_models + "/eynollah-full-regions-3+column_20210425" self.model_page_dir = dir_models + "/eynollah-page-extraction_20210425" self.model_region_dir_p_ens = dir_models + "/eynollah-main-regions-ensembled_20210425" @@ -253,11 +253,11 @@ class Eynollah: self.model_reading_order_machine_dir = dir_models + "/model_ens_reading_order_machine_based" self.model_region_dir_p_1_2_sp_np = dir_models + "/modelens_e_l_all_sp_0_1_2_3_4_171024"#"/modelens_12sp_elay_0_3_4__3_6_n"#"/modelens_earlylayout_12spaltige_2_3_5_6_7_8"#"/modelens_early12_sp_2_3_5_6_7_8_9_10_12_14_15_16_18"#"/modelens_1_2_4_5_early_lay_1_2_spaltige"#"/model_3_eraly_layout_no_patches_1_2_spaltige" ##self.model_region_dir_fully_new = dir_models + "/model_2_full_layout_new_trans" - self.model_region_dir_fully = dir_models + "/modelens_full_layout_24_till_28"#"/model_2_full_layout_new_trans" + self.model_region_dir_fully = dir_models + "/model_full_lay_13_241024"#"/modelens_full_lay_13_17_231024"#"/modelens_full_lay_1_2_221024"#"/modelens_full_layout_24_till_28"#"/model_2_full_layout_new_trans" if self.textline_light: - self.model_textline_dir = dir_models + "/modelens_textline_0_1__2_4_16092024"#"/modelens_textline_1_4_16092024"#"/model_textline_ens_3_4_5_6_artificial"#"/modelens_textline_1_3_4_20240915"#"/model_textline_ens_3_4_5_6_artificial"#"/modelens_textline_9_12_13_14_15"#"/eynollah-textline_light_20210425"# + self.model_textline_dir = dir_models + "/model_textline_ens_5_6_7_8_10_11_nopatch"#"/modelens_textline_0_1__2_4_16092024"#"/modelens_textline_1_4_16092024"#"/model_textline_ens_3_4_5_6_artificial"#"/modelens_textline_1_3_4_20240915"#"/model_textline_ens_3_4_5_6_artificial"#"/modelens_textline_9_12_13_14_15"#"/eynollah-textline_light_20210425"# else: - self.model_textline_dir = dir_models + "/modelens_textline_0_1__2_4_16092024"#"/eynollah-textline_20210425" + self.model_textline_dir = dir_models + "/model_textline_ens_5_6_7_8_10_11_nopatch"#"/modelens_textline_0_1__2_4_16092024"#"/eynollah-textline_20210425" if self.ocr: self.model_ocr_dir = dir_models + "/checkpoint-166692_printed_trocr" @@ -816,6 +816,14 @@ class Eynollah: verbose=0) seg = np.argmax(label_p_pred, axis=3)[0] + + if thresholding_for_artificial_class_in_light_version: + seg_art = label_p_pred[0,:,:,2] + + seg_art[seg_art<0.2] = 0 + seg_art[seg_art>0] =1 + + seg[seg_art==1]=2 seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2) prediction_true = resize_image(seg_color, img_h_page, img_w_page) prediction_true = prediction_true.astype(np.uint8) @@ -1546,7 +1554,7 @@ class Eynollah: pass else: img = otsu_copy_binary(img) - img = img.astype(np.uint8) + #img = img.astype(np.uint8) prediction_regions2 = None else: if cols == 1: @@ -1605,9 +1613,12 @@ class Eynollah: img = img.astype(np.uint8) marginal_of_patch_percent = 0.1 - + prediction_regions = self.do_prediction(patches, img, model_region, marginal_of_patch_percent=marginal_of_patch_percent, n_batch_inference=3) + + ##prediction_regions = self.do_prediction(False, img, model_region, marginal_of_patch_percent=marginal_of_patch_percent, n_batch_inference=3) + prediction_regions = resize_image(prediction_regions, img_height_h, img_width_h) self.logger.debug("exit extract_text_regions") return prediction_regions, prediction_regions @@ -2148,7 +2159,7 @@ class Eynollah: if not thresholding_for_artificial_class_in_light_version: textline_mask_tot_ea_art = textline_mask_tot_ea_art.astype('uint8') - textline_mask_tot_ea_art = cv2.dilate(textline_mask_tot_ea_art, KERNEL, iterations=1) + #textline_mask_tot_ea_art = cv2.dilate(textline_mask_tot_ea_art, KERNEL, iterations=1) prediction_textline[:,:][textline_mask_tot_ea_art[:,:]==1]=2 @@ -2245,26 +2256,27 @@ class Eynollah: #if (not self.input_binary) or self.full_layout: #if self.input_binary: #img_bin = np.copy(img_resized) - if (not self.input_binary and self.full_layout) or (not self.input_binary and num_col_classifier >= 30): - if not self.dir_in: - model_bin, session_bin = self.start_new_session_and_model(self.model_dir_of_binarization) - prediction_bin = self.do_prediction(True, img_resized, model_bin, n_batch_inference=5) - else: - prediction_bin = self.do_prediction(True, img_resized, self.model_bin, n_batch_inference=5) - - #print("inside bin ", time.time()-t_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) - - prediction_bin = prediction_bin.astype(np.uint16) - #img= np.copy(prediction_bin) - img_bin = np.copy(prediction_bin) - else: - img_bin = np.copy(img_resized) - + ###if (not self.input_binary and self.full_layout) or (not self.input_binary and num_col_classifier >= 30): + ###if not self.dir_in: + ###model_bin, session_bin = self.start_new_session_and_model(self.model_dir_of_binarization) + ###prediction_bin = self.do_prediction(True, img_resized, model_bin, n_batch_inference=5) + ###else: + ###prediction_bin = self.do_prediction(True, img_resized, self.model_bin, n_batch_inference=5) + + ####print("inside bin ", time.time()-t_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) + + ###prediction_bin = prediction_bin.astype(np.uint16) + ####img= np.copy(prediction_bin) + ###img_bin = np.copy(prediction_bin) + ###else: + ###img_bin = np.copy(img_resized) + + img_bin = np.copy(img_resized) #print("inside 1 ", time.time()-t_in) ###textline_mask_tot_ea = self.run_textline(img_bin) @@ -3311,7 +3323,8 @@ class Eynollah: scaler_h_textline = 1#1.3 # 1.2#1.2 scaler_w_textline = 1#1.3 # 0.9#1 #print(image_page.shape) - textline_mask_tot_ea, _ = self.textline_contours(image_page, True, scaler_h_textline, scaler_w_textline, num_col_classifier) + patches = False + textline_mask_tot_ea, _ = self.textline_contours(image_page, patches, scaler_h_textline, scaler_w_textline, num_col_classifier) if self.textline_light: textline_mask_tot_ea = textline_mask_tot_ea.astype(np.int16) @@ -3564,9 +3577,9 @@ class Eynollah: image_page = image_page.astype(np.uint8) #print("full inside 1", time.time()- t_full0) if self.light_version: - regions_fully, regions_fully_only_drop = self.extract_text_regions_new(img_bin_light, True, cols=num_col_classifier) + regions_fully, regions_fully_only_drop = self.extract_text_regions_new(img_bin_light, False, cols=num_col_classifier) else: - regions_fully, regions_fully_only_drop = self.extract_text_regions_new(image_page, True, cols=num_col_classifier) + regions_fully, regions_fully_only_drop = self.extract_text_regions_new(image_page, False, cols=num_col_classifier) #print("full inside 2", time.time()- t_full0) # 6 is the separators lable in old full layout model # 4 is the drop capital class in old full layout model @@ -3590,7 +3603,7 @@ class Eynollah: regions_fully[:,:,0][drops[:,:]==1] = drop_capital_label_in_full_layout_model - regions_fully = putt_bb_of_drop_capitals_of_model_in_patches_in_layout(regions_fully, drop_capital_label_in_full_layout_model) + ##regions_fully = putt_bb_of_drop_capitals_of_model_in_patches_in_layout(regions_fully, drop_capital_label_in_full_layout_model) ##regions_fully_np, _ = self.extract_text_regions(image_page, False, cols=num_col_classifier) ##if num_col_classifier > 2: ##regions_fully_np[:, :, 0][regions_fully_np[:, :, 0] == 4] = 0 @@ -4768,9 +4781,9 @@ class Eynollah: textline_mask_tot_ea_deskew = resize_image(textline_mask_tot_ea,img_h_new, img_w_new ) - slope_deskew, slope_first = 0, 0#self.run_deskew(textline_mask_tot_ea_deskew) + slope_deskew, slope_first = self.run_deskew(textline_mask_tot_ea_deskew) else: - slope_deskew, slope_first = 0, 0#self.run_deskew(textline_mask_tot_ea) + slope_deskew, slope_first = self.run_deskew(textline_mask_tot_ea) #print("text region early -2,5 in %.1fs", time.time() - t0) #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, img_bin_light = \ diff --git a/qurator/eynollah/utils/__init__.py b/qurator/eynollah/utils/__init__.py index 6219df2..e7cbbea 100644 --- a/qurator/eynollah/utils/__init__.py +++ b/qurator/eynollah/utils/__init__.py @@ -1204,17 +1204,12 @@ def order_of_regions(textline_mask, contours_main, contours_header, y_ref): top = peaks_neg_new[i] down = peaks_neg_new[i + 1] - # print(top,down,'topdown') - indexes_in = matrix_of_orders[:, 0][(matrix_of_orders[:, 3] >= top) & ((matrix_of_orders[:, 3] < down))] cxs_in = matrix_of_orders[:, 2][(matrix_of_orders[:, 3] >= top) & ((matrix_of_orders[:, 3] < down))] cys_in = matrix_of_orders[:, 3][(matrix_of_orders[:, 3] >= top) & ((matrix_of_orders[:, 3] < down))] types_of_text = matrix_of_orders[:, 1][(matrix_of_orders[:, 3] >= top) & ((matrix_of_orders[:, 3] < down))] index_types_of_text = matrix_of_orders[:, 4][(matrix_of_orders[:, 3] >= top) & ((matrix_of_orders[:, 3] < down))] - # print(top,down) - # print(cys_in,'cyyyins') - # print(indexes_in,'indexes') sorted_inside = np.argsort(cxs_in) ind_in_int = indexes_in[sorted_inside] @@ -1228,11 +1223,17 @@ def order_of_regions(textline_mask, contours_main, contours_header, y_ref): ##matrix_of_orders[:len_main,4]=final_indexers_sorted[:] - # print(peaks_neg_new,'peaks') - # print(final_indexers_sorted,'indexsorted') - # print(final_types,'types') - # print(final_index_type,'final_index_type') - + # This fix is applied if the sum of the lengths of contours and contours_h does not match final_indexers_sorted. However, this is not the optimal solution.. + if (len(cy_main)+len(cy_header) ) == len(final_index_type): + pass + else: + indexes_missed = set(list( np.array( range((len(cy_main)+len(cy_header) ) )) )) - set(final_indexers_sorted) + for ind_missed in indexes_missed: + final_indexers_sorted.append(ind_missed) + final_types.append(1) + final_index_type.append(ind_missed) + + return final_indexers_sorted, matrix_of_orders, final_types, final_index_type def combine_hor_lines_and_delete_cross_points_and_get_lines_features_back_new(img_p_in_ver, img_in_hor,num_col_classifier): diff --git a/qurator/eynollah/utils/xml.py b/qurator/eynollah/utils/xml.py index 0386b25..bd95702 100644 --- a/qurator/eynollah/utils/xml.py +++ b/qurator/eynollah/utils/xml.py @@ -72,7 +72,7 @@ def order_and_id_of_texts(found_polygons_text_region, found_polygons_text_region index_of_types_2 = index_of_types[kind_of_texts == 2] indexes_sorted_2 = indexes_sorted[kind_of_texts == 2] - + counter = EynollahIdCounter(region_idx=ref_point) for idx_textregion, _ in enumerate(found_polygons_text_region): id_of_texts.append(counter.next_region_id)