diff --git a/qurator/eynollah/eynollah.py b/qurator/eynollah/eynollah.py index 72a72d9..cbc7b88 100644 --- a/qurator/eynollah/eynollah.py +++ b/qurator/eynollah/eynollah.py @@ -252,7 +252,7 @@ class Eynollah: 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 + "/modelens_earlylayout_12spaltige_2_3_5_6_7_8"#"/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_p_1_2_sp_np = dir_models + "/modelens_earlylay12sp_0_2"#"/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" if self.textline_light: @@ -1071,8 +1071,13 @@ class Eynollah: seg = np.argmax(label_p_pred, axis=3)[0] if thresholding_for_artificial_class_in_light_version: + #seg_text = label_p_pred[0,:,:,1] + #seg_text[seg_text<0.2] =0 + #seg_text[seg_text>0] =1 + #seg[seg_text==1]=1 + seg_art = label_p_pred[0,:,:,4] - seg_art[seg_art<0.1] =0 + seg_art[seg_art<0.2] =0 seg_art[seg_art>0] =1 seg[seg_art==1]=4 @@ -2159,7 +2164,7 @@ class Eynollah: img_h_new = int(img_org.shape[0] / float(img_org.shape[1]) * img_w_new) elif num_col_classifier == 2: - img_w_new = 1300#1500 + img_w_new = 1500#1500 img_h_new = int(img_org.shape[0] / float(img_org.shape[1]) * img_w_new) elif num_col_classifier == 3: @@ -2222,7 +2227,7 @@ class Eynollah: if num_col_classifier == 1 or num_col_classifier == 2: prediction_regions_org = np.zeros((self.image_org.shape[0], self.image_org.shape[1], 3)) model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_1_2_sp_np) - prediction_regions_page = self.do_prediction_new_concept(False, self.image_page_org_size, model_region, n_batch_inference=1, thresholding_for_artificial_class_in_light_version = False) + prediction_regions_page = self.do_prediction_new_concept(False, self.image_page_org_size, model_region, n_batch_inference=1, thresholding_for_artificial_class_in_light_version = True) prediction_regions_org[self.page_coord[0] : self.page_coord[1], self.page_coord[2] : self.page_coord[3],:] = prediction_regions_page else: model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_ens_light) @@ -2232,7 +2237,7 @@ class Eynollah: else: if num_col_classifier == 1 or num_col_classifier == 2: prediction_regions_org = np.zeros((self.image_org.shape[0], self.image_org.shape[1], 3)) - prediction_regions_page = self.do_prediction_new_concept(False, self.image_page_org_size, self.model_region_1_2, n_batch_inference=1, thresholding_for_artificial_class_in_light_version=False) + prediction_regions_page = self.do_prediction_new_concept(False, self.image_page_org_size, self.model_region_1_2, n_batch_inference=1, thresholding_for_artificial_class_in_light_version=True) prediction_regions_org[self.page_coord[0] : self.page_coord[1], self.page_coord[2] : self.page_coord[3],:] = prediction_regions_page else: prediction_regions_org = self.do_prediction_new_concept(True, img_bin, self.model_region, n_batch_inference=3) @@ -2249,16 +2254,19 @@ class Eynollah: img_bin = resize_image(img_bin,img_height_h, img_width_h ) prediction_regions_org=prediction_regions_org[:,:,0] + mask_lines_only = (prediction_regions_org[:,:] ==3)*1 + + mask_texts_only = (prediction_regions_org[:,:] ==1)*1 mask_texts_only = mask_texts_only.astype('uint8') - ##if num_col_classifier == 1 or num_col_classifier == 2: - ###mask_texts_only = cv2.erode(mask_texts_only, KERNEL, iterations=1) - ##mask_texts_only = cv2.dilate(mask_texts_only, KERNEL, iterations=1) + #if num_col_classifier == 1 or num_col_classifier == 2: + #mask_texts_only = cv2.erode(mask_texts_only, KERNEL, iterations=1) + #mask_texts_only = cv2.dilate(mask_texts_only, KERNEL, iterations=1) mask_texts_only = cv2.dilate(mask_texts_only, kernel=np.ones((2,2), np.uint8), iterations=1) @@ -4110,6 +4118,7 @@ class Eynollah: if dilation_m1<6: dilation_m1 = 6 #print(dilation_m1, 'dilation_m1') + dilation_m1 = 5 dilation_m2 = int(dilation_m1/2.) +1 for i in range(len(x_differential)): @@ -4267,7 +4276,6 @@ class Eynollah: for ij in range(len(all_found_textline_polygons[j])): con_ind = all_found_textline_polygons[j][ij] - print(len(con_ind[:,0,0]),'con_ind[:,0,0]') area = cv2.contourArea(con_ind) con_ind = con_ind.astype(np.float) @@ -4303,7 +4311,7 @@ class Eynollah: if dilation_m1<4: dilation_m1 = 4 #print(dilation_m1, 'dilation_m1') - dilation_m2 = int(dilation_m1/2.) +1 + dilation_m2 = int(dilation_m1/2.) +1 for i in range(len(x_differential)): if abs_diff[i]==0: @@ -4339,6 +4347,100 @@ class Eynollah: all_found_textline_polygons[j][ij][:,0,0] = con_scaled[:,0, 0] return all_found_textline_polygons + def filter_contours_inside_a_bigger_one(self,contours, image, marginal_cnts=None, type_contour="textregion"): + if type_contour=="textregion": + areas = [cv2.contourArea(contours[j]) for j in range(len(contours))] + area_tot = image.shape[0]*image.shape[1] + + M_main = [cv2.moments(contours[j]) for j in range(len(contours))] + cx_main = [(M_main[j]["m10"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))] + cy_main = [(M_main[j]["m01"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))] + + areas_ratio = np.array(areas)/ area_tot + contours_index_small = [ind for ind in range(len(contours)) if areas_ratio[ind] < 1e-3] + contours_index_big = [ind for ind in range(len(contours)) if areas_ratio[ind] >= 1e-3] + + #contours_> = [contours[ind] for ind in contours_index_big] + indexes_to_be_removed = [] + for ind_small in contours_index_small: + results = [cv2.pointPolygonTest(contours[ind], (cx_main[ind_small], cy_main[ind_small]), False) for ind in contours_index_big ] + if marginal_cnts: + results_marginal = [cv2.pointPolygonTest(marginal_cnts[ind], (cx_main[ind_small], cy_main[ind_small]), False) for ind in range(len(marginal_cnts)) ] + results_marginal = np.array(results_marginal) + + if np.any(results_marginal==1): + indexes_to_be_removed.append(ind_small) + + results = np.array(results) + + if np.any(results==1): + indexes_to_be_removed.append(ind_small) + + + if len(indexes_to_be_removed)>0: + indexes_to_be_removed = np.unique(indexes_to_be_removed) + for ind in indexes_to_be_removed: + contours.pop(ind) + return contours + + + else: + contours_txtline_of_all_textregions = [] + + for jj in range(len(contours)): + contours_txtline_of_all_textregions = contours_txtline_of_all_textregions + contours[jj] + + M_main_tot = [cv2.moments(contours_txtline_of_all_textregions[j]) for j in range(len(contours_txtline_of_all_textregions))] + cx_main_tot = [(M_main_tot[j]["m10"] / (M_main_tot[j]["m00"] + 1e-32)) for j in range(len(M_main_tot))] + cy_main_tot = [(M_main_tot[j]["m01"] / (M_main_tot[j]["m00"] + 1e-32)) for j in range(len(M_main_tot))] + + areas_tot = [cv2.contourArea(con_ind) for con_ind in contours_txtline_of_all_textregions] + area_tot_tot = image.shape[0]*image.shape[1] + + areas_ratio_tot = np.array(areas_tot)/ area_tot_tot + + contours_index_big_tot = [ind for ind in range(len(contours_txtline_of_all_textregions)) if areas_ratio_tot[ind] >= 1e-2] + + + for jj in range(len(contours)): + contours_in = contours[jj] + #print(len(contours_in)) + areas = [cv2.contourArea(con_ind) for con_ind in contours_in] + area_tot = image.shape[0]*image.shape[1] + + M_main = [cv2.moments(contours_in[j]) for j in range(len(contours_in))] + cx_main = [(M_main[j]["m10"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))] + cy_main = [(M_main[j]["m01"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))] + + areas_ratio = np.array(areas)/ area_tot + + if len(areas_ratio)>=1: + #print(np.max(areas_ratio), np.min(areas_ratio)) + contours_index_small = [ind for ind in range(len(contours_in)) if areas_ratio[ind] < 1e-2] + #contours_index_big = [ind for ind in range(len(contours_in)) if areas_ratio[ind] >= 1e-3] + + if len(contours_index_small)>0: + indexes_to_be_removed = [] + for ind_small in contours_index_small: + results = [cv2.pointPolygonTest(contours_txtline_of_all_textregions[ind], (cx_main[ind_small], cy_main[ind_small]), False) for ind in contours_index_big_tot ] + + results = np.array(results) + + if np.any(results==1): + indexes_to_be_removed.append(ind_small) + + + if len(indexes_to_be_removed)>0: + indexes_to_be_removed = np.unique(indexes_to_be_removed) + + for ind in indexes_to_be_removed: + contours[jj].pop(ind) + + return contours + + + + def dilate_textlines(self,all_found_textline_polygons): for j in range(len(all_found_textline_polygons)): for i in range(len(all_found_textline_polygons[j])): @@ -4725,6 +4827,7 @@ class Eynollah: #print("text region early 3 in %.1fs", time.time() - t0) if self.light_version: contours_only_text_parent = self.dilate_textregions_contours(contours_only_text_parent) + contours_only_text_parent = self.filter_contours_inside_a_bigger_one(contours_only_text_parent, text_only, marginal_cnts=polygons_of_marginals) txt_con_org = get_textregion_contours_in_org_image_light(contours_only_text_parent, self.image, slope_first) #txt_con_org = self.dilate_textregions_contours(txt_con_org) #contours_only_text_parent = self.dilate_textregions_contours(contours_only_text_parent) @@ -4742,6 +4845,7 @@ class Eynollah: #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.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) else: