From 90ee2d61dc1d2ce05724d6d0f11c200ba1709108 Mon Sep 17 00:00:00 2001 From: vahidrezanezhad Date: Mon, 28 Oct 2024 20:56:06 +0100 Subject: [PATCH] textline segmentation is masked with drop capitals --- qurator/eynollah/eynollah.py | 223 +++++++++++++++++++++-------------- 1 file changed, 135 insertions(+), 88 deletions(-) diff --git a/qurator/eynollah/eynollah.py b/qurator/eynollah/eynollah.py index 1cb00c7..d0a8299 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 + "/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_np = dir_models + "/modelens_full_lay_13__3_19_241024"#"/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 + "/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" + self.model_region_dir_fully = dir_models + "/modelens_full_lay_13__3_19_241024"#"/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 + "/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"# + 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"# else: - 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" + self.model_textline_dir = dir_models + "/modelens_textline_0_1__2_4_16092024"#"/eynollah-textline_20210425" if self.ocr: self.model_ocr_dir = dir_models + "/checkpoint-166692_printed_trocr" @@ -502,7 +502,8 @@ class Eynollah: if label_p_pred[0][int(num_col - 1)] < 0.9 and img_w_new < width_early: img_new = np.copy(img) num_column_is_classified = False - elif label_p_pred[0][int(num_col - 1)] < 0.8 and img_h_new >= 8000: + #elif label_p_pred[0][int(num_col - 1)] < 0.8 and img_h_new >= 8000: + elif img_h_new >= 8000: img_new = np.copy(img) num_column_is_classified = False else: @@ -523,7 +524,8 @@ class Eynollah: if label_p_pred[0][int(num_col - 1)] < 0.9 and img_w_new < width_early: img_new = np.copy(img) num_column_is_classified = False - elif label_p_pred[0][int(num_col - 1)] < 0.8 and img_h_new >= 8000: + #elif label_p_pred[0][int(num_col - 1)] < 0.8 and img_h_new >= 8000: + elif img_h_new >= 8000: img_new = np.copy(img) num_column_is_classified = False else: @@ -3323,7 +3325,7 @@ 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) - patches = False + patches = True 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) @@ -3634,6 +3636,7 @@ class Eynollah: regions_without_separators = (text_regions_p[:, :] == 1) * 1 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') #print("full inside 3", time.time()- t_full0) 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 @@ -4169,7 +4172,123 @@ class Eynollah: x_differential_new[split_masked[i]:split_masked[i+1]] = -1*np.array(x_differential)[split_masked[i]:split_masked[i+1]] return x_differential_new - + def dilate_textregions_contours_textline_version(self,all_found_textline_polygons): + #print(all_found_textline_polygons) + + for j in range(len(all_found_textline_polygons)): + for ij in range(len(all_found_textline_polygons[j])): + + con_ind = all_found_textline_polygons[j][ij] + area = cv2.contourArea(con_ind) + con_ind = con_ind.astype(np.float) + + x_differential = np.diff( con_ind[:,0,0]) + y_differential = np.diff( con_ind[:,0,1]) + + + x_differential = gaussian_filter1d(x_differential, 0.1) + y_differential = gaussian_filter1d(y_differential, 0.1) + + x_min = float(np.min( con_ind[:,0,0] )) + y_min = float(np.min( con_ind[:,0,1] )) + + x_max = float(np.max( con_ind[:,0,0] )) + y_max = float(np.max( con_ind[:,0,1] )) + + x_differential_mask_nonzeros = [ ind/abs(ind) if ind!=0 else ind for ind in x_differential] + y_differential_mask_nonzeros = [ ind/abs(ind) if ind!=0 else ind for ind in y_differential] + + abs_diff=abs(abs(x_differential)- abs(y_differential) ) + + inc_x = np.zeros(len(x_differential)+1) + inc_y = np.zeros(len(x_differential)+1) + + + if (y_max-y_min) <= (x_max-x_min): + dilation_m1 = round(area / (x_max-x_min) * 0.12) + else: + dilation_m1 = round(area / (y_max-y_min) * 0.12) + + if dilation_m1>8: + dilation_m1 = 8 + if dilation_m1<6: + dilation_m1 = 6 + #print(dilation_m1, 'dilation_m1') + dilation_m1 = 6 + dilation_m2 = int(dilation_m1/2.) +1 + + for i in range(len(x_differential)): + if abs_diff[i]==0: + inc_x[i+1] = dilation_m2*(-1*y_differential_mask_nonzeros[i]) + inc_y[i+1] = dilation_m2*(x_differential_mask_nonzeros[i]) + elif abs_diff[i]!=0 and x_differential_mask_nonzeros[i]==0 and y_differential_mask_nonzeros[i]!=0: + inc_x[i+1]= dilation_m1*(-1*y_differential_mask_nonzeros[i]) + elif abs_diff[i]!=0 and x_differential_mask_nonzeros[i]!=0 and y_differential_mask_nonzeros[i]==0: + inc_y[i+1] = dilation_m1*(x_differential_mask_nonzeros[i]) + + elif abs_diff[i]!=0 and abs_diff[i]>=3: + if abs(x_differential[i])>abs(y_differential[i]): + inc_y[i+1] = dilation_m1*(x_differential_mask_nonzeros[i]) + else: + inc_x[i+1]= dilation_m1*(-1*y_differential_mask_nonzeros[i]) + else: + inc_x[i+1] = dilation_m2*(-1*y_differential_mask_nonzeros[i]) + inc_y[i+1] = dilation_m2*(x_differential_mask_nonzeros[i]) + + + inc_x[0] = inc_x[-1] + inc_y[0] = inc_y[-1] + + con_scaled = con_ind*1 + + con_scaled[:,0, 0] = con_ind[:,0,0] + np.array(inc_x)[:] + con_scaled[:,0, 1] = con_ind[:,0,1] + np.array(inc_y)[:] + + con_scaled[:,0, 1][con_scaled[:,0, 1]<0] = 0 + con_scaled[:,0, 0][con_scaled[:,0, 0]<0] = 0 + + area_scaled = cv2.contourArea(con_scaled.astype(np.int32)) + + con_ind = con_ind.astype(np.int32) + + results = [cv2.pointPolygonTest(con_ind, (con_scaled[ind,0, 0], con_scaled[ind,0, 1]), False) for ind in range(len(con_scaled[:,0, 1])) ] + + results = np.array(results) + + #print(results,'results') + + results[results==0] = 1 + + + diff_result = np.diff(results) + + indices_2 = [ind for ind in range(len(diff_result)) if diff_result[ind]==2] + indices_m2 = [ind for ind in range(len(diff_result)) if diff_result[ind]==-2] + + + if results[0]==1: + con_scaled[:indices_m2[0]+1,0, 1] = con_ind[:indices_m2[0]+1,0,1] + con_scaled[:indices_m2[0]+1,0, 0] = con_ind[:indices_m2[0]+1,0,0] + #indices_2 = indices_2[1:] + indices_m2 = indices_m2[1:] + + + + if len(indices_2)>len(indices_m2): + con_scaled[indices_2[-1]+1:,0, 1] = con_ind[indices_2[-1]+1:,0,1] + con_scaled[indices_2[-1]+1:,0, 0] = con_ind[indices_2[-1]+1:,0,0] + + indices_2 = indices_2[:-1] + + + for ii in range(len(indices_2)): + con_scaled[indices_2[ii]+1:indices_m2[ii]+1,0, 1] = con_scaled[indices_2[ii],0, 1] + con_scaled[indices_2[ii]+1:indices_m2[ii]+1,0, 0] = con_scaled[indices_2[ii],0, 0] + + + all_found_textline_polygons[j][ij][:,0,1] = con_scaled[:,0, 1] + all_found_textline_polygons[j][ij][:,0,0] = con_scaled[:,0, 0] + return all_found_textline_polygons def dilate_textregions_contours(self,all_found_textline_polygons): #print(all_found_textline_polygons) for j in range(len(all_found_textline_polygons)): @@ -4179,9 +4298,6 @@ class Eynollah: area = cv2.contourArea(con_ind) con_ind = con_ind.astype(np.float) - #con_ind[:,0,0] = gaussian_filter1d(con_ind[:,0,0], 0.5) - #con_ind[:,0,1] = gaussian_filter1d(con_ind[:,0,1], 0.5) - x_differential = np.diff( con_ind[:,0,0]) y_differential = np.diff( con_ind[:,0,1]) @@ -4235,29 +4351,6 @@ class Eynollah: inc_x[i+1] = dilation_m2*(-1*y_differential_mask_nonzeros[i]) inc_y[i+1] = dilation_m2*(x_differential_mask_nonzeros[i]) - ###for i in range(len(x_differential)): - ###if abs_diff[i]==0: - ###inc_x[i+1] = 7*(-1*y_differential_mask_nonzeros[i]) - ###inc_y[i+1] = 7*(x_differential_mask_nonzeros[i]) - ###elif abs_diff[i]!=0 and x_differential_mask_nonzeros[i]==0 and y_differential_mask_nonzeros[i]!=0: - ###inc_x[i+1]= 12*(-1*y_differential_mask_nonzeros[i]) - ###elif abs_diff[i]!=0 and x_differential_mask_nonzeros[i]!=0 and y_differential_mask_nonzeros[i]==0: - ###inc_y[i+1] = 12*(x_differential_mask_nonzeros[i]) - - ###elif abs_diff[i]!=0 and abs_diff[i]>=3: - ###if abs(x_differential[i])>abs(y_differential[i]): - ###inc_y[i+1] = 12*(x_differential_mask_nonzeros[i]) - ###else: - ###inc_x[i+1]= 12*(-1*y_differential_mask_nonzeros[i]) - ###else: - ###inc_x[i+1] = 7*(-1*y_differential_mask_nonzeros[i]) - ###inc_y[i+1] = 7*(x_differential_mask_nonzeros[i]) - - ###inc_x =list(inc_x) - ###inc_x.append(inc_x[0]) - - ###inc_y =list(inc_y) - ###inc_y.append(inc_y[0]) inc_x[0] = inc_x[-1] inc_y[0] = inc_y[-1] @@ -4288,21 +4381,6 @@ class Eynollah: indices_2 = [ind for ind in range(len(diff_result)) if diff_result[ind]==2] indices_m2 = [ind for ind in range(len(diff_result)) if diff_result[ind]==-2] - #print(area_scaled / area, "ratio") - #print(results,'results') - #if results[0]==1 and diff_result[-1]==-2: - ##indices_2 = indices_2[1:] - ##indices_m2 = indices_m2[1:] - - #con_scaled[:indices_m2[0]+1,0, 1] = con_scaled[indices_m2[-1],0, 1] - #con_scaled[:indices_m2[0]+1,0, 0] = con_scaled[indices_m2[-1],0, 0] - - - #con_scaled[indices_2[-1]+1:,0, 1] = con_scaled[indices_m2[-1],0, 1] - #con_scaled[indices_2[-1]+1:,0, 0] = con_scaled[indices_m2[-1],0, 0] - - #indices_2 = indices_2[:-1] - #indices_m2 = indices_m2[1:-1] if results[0]==1: con_scaled[:indices_m2[0]+1,0, 1] = con_ind[:indices_m2[0]+1,0,1] @@ -4318,50 +4396,12 @@ class Eynollah: indices_2 = indices_2[:-1] - - - #diff_neg_pos = np.array(indices_m2) - np.array(indices_2) - - - #print(diff_neg_pos,'diff') - ##print(indices_2, 'indices_2') - #indices_2 = np.array(indices_2)[diff_neg_pos>1] - #indices_m2 = np.array(indices_m2)[diff_neg_pos>1] for ii in range(len(indices_2)): - #x_inner = con_ind[indices_2[ii]+1:indices_m2[ii]+1,0, 0] - #y_inner = con_ind[indices_2[ii]+1:indices_m2[ii]+1,0, 1] - - #if x_inner[-1]>=x_inner[0]: - #x_interest = np.min(x_inner) - #else: - #x_interest = np.max(x_inner) - - #if y_inner[-1]>=y_inner[0]: - #y_interest = np.min(y_inner) - #else: - #y_interest = np.max(y_inner) - con_scaled[indices_2[ii]+1:indices_m2[ii]+1,0, 1] = con_scaled[indices_2[ii],0, 1] con_scaled[indices_2[ii]+1:indices_m2[ii]+1,0, 0] = con_scaled[indices_2[ii],0, 0] - - #con_scaled[:,0, 1][results[:]>0] = con_ind[:,0,1][results[:]>0] - #con_scaled[:,0, 0][results[:]>0] = con_ind[:,0,0][results[:]>0] - - #print(list(results), 'results') - #print(list(diff_result), 'diff_result') - #print(indices_2,'2') - #print(indices_m2,'-2') - #print(diff_neg_pos,'diff_neg_pos') - - ##con_scaled[:,0, 1] = gaussian_filter1d(con_scaled[:,0, 1], 0.1) - ##con_scaled[:,0, 0] = gaussian_filter1d(con_scaled[:,0, 0], 0.1) - - #con_scaled[-1,0, 1] = con_scaled[0,0, 1] - #con_scaled[-1,0, 0] = con_scaled[0,0, 0] - ##print(len(con_scaled[:,0,0]),'con_scaled[:,0,0]') all_found_textline_polygons[j][:,0,1] = con_scaled[:,0, 1] all_found_textline_polygons[j][:,0,0] = con_scaled[:,0, 0] return all_found_textline_polygons @@ -4865,6 +4905,12 @@ class Eynollah: 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) ###polygons_of_marginals = self.dilate_textregions_contours(polygons_of_marginals) + + if self.light_version: + drop_label_in_full_layout = 4 + textline_mask_tot_ea_org[img_revised_tab==drop_label_in_full_layout] = 0 + + text_only = ((img_revised_tab[:, :] == 1)) * 1 if np.abs(slope_deskew) >= SLOPE_THRESHOLD: text_only_d = ((text_regions_p_1_n[:, :] == 1)) * 1 @@ -5018,7 +5064,8 @@ class Eynollah: #slopes_marginals, all_found_textline_polygons_marginals, boxes_marginals, polygons_of_marginals, polygons_of_marginals, _ = self.delete_regions_without_textlines(slopes_marginals, all_found_textline_polygons_marginals, boxes_marginals, polygons_of_marginals, polygons_of_marginals, np.array(range(len(polygons_of_marginals)))) #all_found_textline_polygons = self.dilate_textlines(all_found_textline_polygons) - all_found_textline_polygons = self.dilate_textline_contours(all_found_textline_polygons) + #####all_found_textline_polygons = self.dilate_textline_contours(all_found_textline_polygons) + all_found_textline_polygons = self.dilate_textregions_contours_textline_version(all_found_textline_polygons) all_found_textline_polygons = self.filter_contours_inside_a_bigger_one(all_found_textline_polygons, textline_mask_tot_ea_org, type_contour="textline") all_found_textline_polygons_marginals = self.dilate_textline_contours(all_found_textline_polygons_marginals)