From 2c939049854c73c7dc27e4b04863c8498d654129 Mon Sep 17 00:00:00 2001 From: vahidrezanezhad Date: Thu, 12 Sep 2024 17:35:28 +0200 Subject: [PATCH] avoiding double binarization --- qurator/eynollah/eynollah.py | 155 +++++++++++++++++++---------- qurator/eynollah/utils/__init__.py | 4 +- 2 files changed, 106 insertions(+), 53 deletions(-) diff --git a/qurator/eynollah/eynollah.py b/qurator/eynollah/eynollah.py index 533e2a0..569aec5 100644 --- a/qurator/eynollah/eynollah.py +++ b/qurator/eynollah/eynollah.py @@ -89,7 +89,7 @@ from .utils.xml import order_and_id_of_texts from .plot import EynollahPlotter from .writer import EynollahXmlWriter -MIN_AREA_REGION = 0.00001 +MIN_AREA_REGION = 0.000001 SLOPE_THRESHOLD = 0.13 RATIO_OF_TWO_MODEL_THRESHOLD = 95.50 #98.45: DPI_THRESHOLD = 298 @@ -237,15 +237,16 @@ class Eynollah: 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 = dir_models + "/eynollah-full-regions-3+column_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" self.model_region_dir_p_ens_light = dir_models + "/eynollah-main-regions_20220314" 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 + "/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_p_1_2_sp_np = dir_models + "/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" if self.textline_light: - self.model_textline_dir = dir_models + "/eynollah-textline_light_20210425" + self.model_textline_dir = dir_models + "/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 + "/eynollah-textline_20210425" if self.ocr: @@ -267,7 +268,7 @@ class Eynollah: self.model_textline = self.our_load_model(self.model_textline_dir) self.model_region = self.our_load_model(self.model_region_dir_p_ens_light) self.model_region_1_2 = self.our_load_model(self.model_region_dir_p_1_2_sp_np) - self.model_region_fl_new = self.our_load_model(self.model_region_dir_fully_new) + ###self.model_region_fl_new = self.our_load_model(self.model_region_dir_fully_new) self.model_region_fl_np = self.our_load_model(self.model_region_dir_fully_np) self.model_region_fl = self.our_load_model(self.model_region_dir_fully) self.model_reading_order_machine = self.our_load_model(self.model_reading_order_machine_dir) @@ -993,9 +994,16 @@ class Eynollah: img = resize_image(img, img_height_model, img_width_model) label_p_pred = model.predict(img.reshape(1, img.shape[0], img.shape[1], img.shape[2]), verbose=0) - + + seg_not_base = label_p_pred[0,:,:,4] + + seg_not_base[seg_not_base>0.4] =1 + seg_not_base[seg_not_base<1] =0 seg = np.argmax(label_p_pred, axis=3)[0] + + seg[seg_not_base==1]=4 + 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) @@ -1781,7 +1789,7 @@ class Eynollah: all_box_coord_per_process.append(crop_coor) queue_of_all_params.put([slopes_per_each_subprocess, textlines_rectangles_per_each_subprocess, bounding_box_of_textregion_per_each_subprocess, contours_textregion_per_each_subprocess, contours_textregion_par_per_each_subprocess, all_box_coord_per_process, index_by_text_region_contours]) - def textline_contours(self, img, patches, scaler_h, scaler_w): + def textline_contours(self, img, patches, scaler_h, scaler_w, num_col_classifier=None): 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) @@ -1792,10 +1800,34 @@ class Eynollah: img = resize_image(img_org, int(img_org.shape[0] * scaler_h), int(img_org.shape[1] * scaler_w)) #print(img.shape,'bin shape textline') if not self.dir_in: - prediction_textline = self.do_prediction(patches, img, model_textline, n_batch_inference=3) + prediction_textline = self.do_prediction(patches, img, model_textline, marginal_of_patch_percent=0.2, n_batch_inference=3) + if num_col_classifier==1: + prediction_textline_nopatch = self.do_prediction(False, img, model_textline) + prediction_textline[:,:][prediction_textline_nopatch[:,:]==0] = 0 else: - prediction_textline = self.do_prediction(patches, img, self.model_textline, n_batch_inference=3) + prediction_textline = self.do_prediction(patches, img, self.model_textline, marginal_of_patch_percent=0.2, n_batch_inference=3) + if num_col_classifier==1: + prediction_textline_nopatch = self.do_prediction(False, img, model_textline) + prediction_textline[:,:][prediction_textline_nopatch[:,:]==0] = 0 prediction_textline = resize_image(prediction_textline, img_h, img_w) + + textline_mask_tot_ea_art = (prediction_textline[:,:]==2)*1 + + old_art = np.copy(textline_mask_tot_ea_art) + + 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) + + prediction_textline[:,:][textline_mask_tot_ea_art[:,:]==1]=2 + + textline_mask_tot_ea_lines = (prediction_textline[:,:]==1)*1 + textline_mask_tot_ea_lines = textline_mask_tot_ea_lines.astype('uint8') + textline_mask_tot_ea_lines = cv2.dilate(textline_mask_tot_ea_lines, KERNEL, iterations=1) + + prediction_textline[:,:][textline_mask_tot_ea_lines[:,:]==1]=1 + + prediction_textline[:,:][old_art[:,:]==1]=2 + if not self.dir_in: prediction_textline_longshot = self.do_prediction(False, img, model_textline) else: @@ -1855,49 +1887,58 @@ class Eynollah: #print(num_col_classifier,'num_col_classifier') if num_col_classifier == 1: - img_w_new = 1000 + img_w_new = 900#1000 img_h_new = int(img_org.shape[0] / float(img_org.shape[1]) * img_w_new) elif num_col_classifier == 2: - img_w_new = 1500 + img_w_new = 1300#1500 img_h_new = int(img_org.shape[0] / float(img_org.shape[1]) * img_w_new) elif num_col_classifier == 3: - img_w_new = 2000 + img_w_new = 1600#2000 img_h_new = int(img_org.shape[0] / float(img_org.shape[1]) * img_w_new) elif num_col_classifier == 4: - img_w_new = 2500 + img_w_new = 1900#2500 img_h_new = int(img_org.shape[0] / float(img_org.shape[1]) * img_w_new) elif num_col_classifier == 5: - img_w_new = 3000 + img_w_new = 2300#3000 img_h_new = int(img_org.shape[0] / float(img_org.shape[1]) * img_w_new) else: - img_w_new = 4000 + img_w_new = 3300#4000 img_h_new = int(img_org.shape[0] / float(img_org.shape[1]) * img_w_new) img_resized = resize_image(img,img_h_new, img_w_new ) t_bin = time.time() - 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) + #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 >= 3): + 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) #print("inside 1 ", time.time()-t_in) - textline_mask_tot_ea = self.run_textline(img_bin) + ###textline_mask_tot_ea = self.run_textline(img_bin) + textline_mask_tot_ea = self.run_textline(img_bin, num_col_classifier) + textline_mask_tot_ea = resize_image(textline_mask_tot_ea,img_height_h, img_width_h ) @@ -1906,20 +1947,20 @@ class Eynollah: #print(img_resized.shape, num_col_classifier, "num_col_classifier") if not self.dir_in: - ###if num_col_classifier == 1 or num_col_classifier == 2: - ###model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_1_2_sp_np) - ###prediction_regions_org = self.do_prediction_new_concept(False, img_resized, model_region) - ###else: - ###model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_ens_light) - ###prediction_regions_org = self.do_prediction_new_concept(True, img_bin, model_region) - model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_ens_light) - prediction_regions_org = self.do_prediction(True, img_bin, model_region, n_batch_inference=3, thresholding_for_some_classes_in_light_version=True) + if num_col_classifier == 1 or num_col_classifier == 2: + model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_1_2_sp_np) + prediction_regions_org = self.do_prediction_new_concept(False, img_resized, model_region) + else: + model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_ens_light) + prediction_regions_org = self.do_prediction_new_concept(True, img_bin, model_region) + ##model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_ens_light) + ##prediction_regions_org = self.do_prediction(True, img_bin, model_region, n_batch_inference=3, thresholding_for_some_classes_in_light_version=True) else: - ##if num_col_classifier == 1 or num_col_classifier == 2: - ##prediction_regions_org = self.do_prediction_new_concept(False, img_resized, self.model_region_1_2) - ##else: - ##prediction_regions_org = self.do_prediction_new_concept(True, img_bin, self.model_region) - prediction_regions_org = self.do_prediction(True, img_bin, self.model_region, n_batch_inference=3, thresholding_for_some_classes_in_light_version=True) + if num_col_classifier == 1 or num_col_classifier == 2: + prediction_regions_org = self.do_prediction_new_concept(False, img_resized, self.model_region_1_2) + else: + prediction_regions_org = self.do_prediction_new_concept(True, img_bin, self.model_region) + ###prediction_regions_org = self.do_prediction(True, img_bin, self.model_region, n_batch_inference=3, thresholding_for_some_classes_in_light_version=True) #print("inside 3 ", time.time()-t_in) #plt.imshow(prediction_regions_org[:,:,0]) @@ -1937,7 +1978,7 @@ class Eynollah: mask_texts_only = mask_texts_only.astype('uint8') - mask_texts_only = cv2.dilate(mask_texts_only, KERNEL, iterations=3) + mask_texts_only = cv2.dilate(mask_texts_only, KERNEL, iterations=2) mask_images_only=(prediction_regions_org[:,:] ==2)*1 @@ -2899,10 +2940,11 @@ class Eynollah: #print("enhancement in ", time.time()-t_in) return img_res, is_image_enhanced, num_col_classifier, num_column_is_classified - def run_textline(self, image_page): - 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) + def run_textline(self, image_page, num_col_classifier=None): + 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) if self.textline_light: textline_mask_tot_ea = textline_mask_tot_ea.astype(np.int16) @@ -3147,6 +3189,17 @@ class Eynollah: ##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 drop_capital_label_in_full_layout_model = 3 + + drops = (regions_fully[:,:,0]==drop_capital_label_in_full_layout_model)*1 + + drops= drops.astype(np.uint8) + + regions_fully[:,:,0][regions_fully[:,:,0]==drop_capital_label_in_full_layout_model] = 1 + + drops = cv2.erode(drops[:,:], KERNEL, iterations=1) + 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_np, _ = self.extract_text_regions(image_page, False, cols=num_col_classifier) ##if num_col_classifier > 2: @@ -3695,7 +3748,7 @@ class Eynollah: """ self.logger.debug("enter run") - skip_layout_ro = True + skip_layout_ro = False#True t0_tot = time.time() diff --git a/qurator/eynollah/utils/__init__.py b/qurator/eynollah/utils/__init__.py index 929669f..8705ecf 100644 --- a/qurator/eynollah/utils/__init__.py +++ b/qurator/eynollah/utils/__init__.py @@ -792,11 +792,11 @@ def putt_bb_of_drop_capitals_of_model_in_patches_in_layout(layout_in_patch, drop for jj in range(len(contours_drop_parent)): x, y, w, h = cv2.boundingRect(contours_drop_parent[jj]) - if ( ( areas_cnt_text[jj] * float(drop_only.shape[0] * drop_only.shape[1]) ) / float(w*h) ) > 0.4: + if ( ( areas_cnt_text[jj] * float(drop_only.shape[0] * drop_only.shape[1]) ) / float(w*h) ) > 0.8: 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 + layout_in_patch[y : y + h, x : x + w, 0][layout_in_patch[y : y + h, x : x + w, 0] == drop_capital_label] = 1#drop_capital_label return layout_in_patch