From 3ef4eac24ca5d876243c62860ad9d4fa05110081 Mon Sep 17 00:00:00 2001 From: vahidrezanezhad Date: Thu, 17 Oct 2024 19:12:28 +0200 Subject: [PATCH] textlines of textregions are extracted in a faster way + early layout for all documents is done with no patches model and on rgb input --- qurator/eynollah/eynollah.py | 120 +++++++++++++++++++--------- qurator/eynollah/utils/marginals.py | 65 ++------------- 2 files changed, 89 insertions(+), 96 deletions(-) diff --git a/qurator/eynollah/eynollah.py b/qurator/eynollah/eynollah.py index 2c14ab9..fd66b81 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_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_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" if self.textline_light: @@ -1710,6 +1710,36 @@ class Eynollah: self.logger.debug("exit extract_text_regions") return prediction_regions, prediction_regions2 + def get_slopes_and_deskew_new_light2(self, contours, contours_par, textline_mask_tot, image_page_rotated, boxes, slope_deskew): + + polygons_of_textlines = return_contours_of_interested_region(textline_mask_tot,1,0.00001) + + M_main_tot = [cv2.moments(polygons_of_textlines[j]) for j in range(len(polygons_of_textlines))] + 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))] + + args_textlines = np.array(range(len(polygons_of_textlines))) + all_found_textline_polygons = [] + slopes = [] + all_box_coord =[] + + for index, con_region_ind in enumerate(contours_par): + results = [cv2.pointPolygonTest(con_region_ind, (cx_main_tot[ind], cy_main_tot[ind]), False) for ind in args_textlines ] + results = np.array(results) + + indexes_in = args_textlines[results==1] + + textlines_ins = [polygons_of_textlines[ind] for ind in indexes_in] + + all_found_textline_polygons.append(textlines_ins) + slopes.append(0) + + _, crop_coor = crop_image_inside_box(boxes[index],image_page_rotated) + + all_box_coord.append(crop_coor) + + return slopes, all_found_textline_polygons, boxes, contours, contours_par, all_box_coord, np.array(range(len(contours_par))) + def get_slopes_and_deskew_new_light(self, contours, contours_par, textline_mask_tot, image_page_rotated, boxes, slope_deskew): self.logger.debug("enter get_slopes_and_deskew_new") if len(contours)>15: @@ -2099,14 +2129,14 @@ class Eynollah: img = resize_image(img_org, int(img_org.shape[0] * scaler_h), int(img_org.shape[1] * scaler_w)) if not self.dir_in: - prediction_textline = self.do_prediction(patches, img, model_textline, marginal_of_patch_percent=0.2, n_batch_inference=3, thresholding_for_artificial_class_in_light_version=thresholding_for_artificial_class_in_light_version) + prediction_textline = self.do_prediction(patches, img, model_textline, marginal_of_patch_percent=0.15, n_batch_inference=3, thresholding_for_artificial_class_in_light_version=thresholding_for_artificial_class_in_light_version) #if not thresholding_for_artificial_class_in_light_version: #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, marginal_of_patch_percent=0.2, n_batch_inference=3,thresholding_for_artificial_class_in_light_version=thresholding_for_artificial_class_in_light_version) + prediction_textline = self.do_prediction(patches, img, self.model_textline, marginal_of_patch_percent=0.15, n_batch_inference=3,thresholding_for_artificial_class_in_light_version=thresholding_for_artificial_class_in_light_version) #if not thresholding_for_artificial_class_in_light_version: #if num_col_classifier==1: #prediction_textline_nopatch = self.do_prediction(False, img, model_textline) @@ -2216,14 +2246,14 @@ 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 >= 3): + 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) + print("inside bin ", time.time()-t_bin) prediction_bin=prediction_bin[:,:,0] prediction_bin = (prediction_bin[:,:]==0)*1 prediction_bin = prediction_bin*255 @@ -2236,7 +2266,7 @@ class Eynollah: else: img_bin = np.copy(img_resized) - #print("inside 1 ", time.time()-t_in) + print("inside 1 ", time.time()-t_in) ###textline_mask_tot_ea = self.run_textline(img_bin) textline_mask_tot_ea = self.run_textline(img_resized, num_col_classifier) @@ -2246,14 +2276,15 @@ class Eynollah: #print(self.image_org.shape) + #cv2.imwrite('out_13.png', self.image_page_org_size) #plt.imshwo(self.image_page_org_size) #plt.show() if not skip_layout_and_reading_order: - #print("inside 2 ", time.time()-t_in) + print("inside 2 ", time.time()-t_in) if not self.dir_in: - if num_col_classifier == 1 or num_col_classifier == 2: + 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) if self.image_org.shape[0]/self.image_org.shape[1] > 2.5: prediction_regions_org = self.do_prediction_new_concept(True, img_resized, model_region, n_batch_inference=1, thresholding_for_artificial_class_in_light_version = True) @@ -2267,7 +2298,7 @@ class Eynollah: ##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: + if num_col_classifier == 1 or num_col_classifier >= 2: if self.image_org.shape[0]/self.image_org.shape[1] > 2.5: prediction_regions_org = self.do_prediction_new_concept(True, img_resized, self.model_region_1_2, n_batch_inference=1, thresholding_for_artificial_class_in_light_version=True) else: @@ -2278,7 +2309,7 @@ class Eynollah: prediction_regions_org = self.do_prediction_new_concept(True, img_bin, self.model_region, n_batch_inference=3) ###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) + print("inside 3 ", time.time()-t_in) #plt.imshow(prediction_regions_org[:,:,0]) #plt.show() @@ -2356,7 +2387,15 @@ class Eynollah: text_regions_p_true[:,:][mask_images_only[:,:] == 1] = 2 text_regions_p_true = cv2.fillPoly(text_regions_p_true, pts = polygons_of_only_texts, color=(1,1,1)) - #print("inside 4 ", time.time()-t_in) + + #plt.imshow(textline_mask_tot_ea) + #plt.show() + + textline_mask_tot_ea[(text_regions_p_true==0) | (text_regions_p_true==4) ] = 0 + + #plt.imshow(textline_mask_tot_ea) + #plt.show() + print("inside 4 ", time.time()-t_in) return text_regions_p_true, erosion_hurts, polygons_lines_xml, textline_mask_tot_ea, img_bin else: img_bin = resize_image(img_bin,img_height_h, img_width_h ) @@ -3308,7 +3347,7 @@ class Eynollah: if self.tables: regions_without_separators[table_prediction==1] = 1 regions_without_separators = regions_without_separators.astype(np.uint8) - text_regions_p = get_marginals(rotate_image(regions_without_separators, slope_deskew), text_regions_p, num_col_classifier, slope_deskew, kernel=KERNEL) + text_regions_p = get_marginals(rotate_image(regions_without_separators, slope_deskew), text_regions_p, num_col_classifier, slope_deskew, light_version=self.light_version, kernel=KERNEL) except Exception as e: self.logger.error("exception %s", e) @@ -3319,6 +3358,7 @@ class Eynollah: def run_boxes_no_full_layout(self, image_page, textline_mask_tot, text_regions_p, slope_deskew, num_col_classifier, table_prediction, erosion_hurts): self.logger.debug('enter run_boxes_no_full_layout') + t_0_box = time.time() if np.abs(slope_deskew) >= SLOPE_THRESHOLD: _, textline_mask_tot_d, text_regions_p_1_n, table_prediction_n = rotation_not_90_func(image_page, textline_mask_tot, text_regions_p, table_prediction, slope_deskew) text_regions_p_1_n = resize_image(text_regions_p_1_n, text_regions_p.shape[0], text_regions_p.shape[1]) @@ -3328,6 +3368,7 @@ class Eynollah: if self.tables: regions_without_separators_d[table_prediction_n[:,:] == 1] = 1 regions_without_separators = (text_regions_p[:, :] == 1) * 1 # ( (text_regions_p[:,:]==1) | (text_regions_p[:,:]==2) )*1 #self.return_regions_without_separators_new(text_regions_p[:,:,0],img_only_regions) + print(time.time()-t_0_box,'time box in 1') if self.tables: regions_without_separators[table_prediction ==1 ] = 1 if np.abs(slope_deskew) < SLOPE_THRESHOLD: @@ -3340,7 +3381,7 @@ class Eynollah: if np.abs(slope_deskew) >= SLOPE_THRESHOLD: _, _, matrix_of_lines_ch_d, splitter_y_new_d, _ = find_number_of_columns_in_document(np.repeat(text_regions_p_1_n[:, :, np.newaxis], 3, axis=2), num_col_classifier, self.tables, pixel_lines) - + print(time.time()-t_0_box,'time box in 2') self.logger.info("num_col_classifier: %s", num_col_classifier) if num_col_classifier >= 3: @@ -3350,6 +3391,7 @@ class Eynollah: else: regions_without_separators_d = regions_without_separators_d.astype(np.uint8) regions_without_separators_d = cv2.erode(regions_without_separators_d[:, :], KERNEL, iterations=6) + print(time.time()-t_0_box,'time box in 3') t1 = time.time() if np.abs(slope_deskew) < SLOPE_THRESHOLD: boxes, peaks_neg_tot_tables = return_boxes_of_images_by_order_of_reading_new(splitter_y_new, regions_without_separators, matrix_of_lines_ch, num_col_classifier, erosion_hurts, self.tables, self.right2left) @@ -3378,7 +3420,7 @@ class Eynollah: img_revised_tab2_d_rotated = np.round(img_revised_tab2_d_rotated) img_revised_tab2_d_rotated = img_revised_tab2_d_rotated.astype(np.int8) img_revised_tab2_d_rotated = resize_image(img_revised_tab2_d_rotated, text_regions_p.shape[0], text_regions_p.shape[1]) - + print(time.time()-t_0_box,'time box in 4') self.logger.info("detecting boxes took %.1fs", time.time() - t1) if self.tables: @@ -3410,7 +3452,7 @@ class Eynollah: pixel_img = 10 contours_tables = return_contours_of_interested_region(text_regions_p, pixel_img, min_area_mar) - + print(time.time()-t_0_box,'time box in 5') self.logger.debug('exit run_boxes_no_full_layout') 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 @@ -3751,8 +3793,10 @@ class Eynollah: img_poly[text_regions_p[:,:]==3] = 4 img_poly[text_regions_p[:,:]==6] = 5 - - model_ro_machine, _ = self.start_new_session_and_model(self.model_reading_order_machine_dir) + if self.dir_in: + pass + else: + self.model_reading_order_machine, _ = self.start_new_session_and_model(self.model_reading_order_machine_dir) height1 =672#448 width1 = 448#224 @@ -3793,7 +3837,7 @@ class Eynollah: img3 = img3.astype(np.uint16) - inference_bs = 4 + inference_bs = 3 input_1= np.zeros( (inference_bs, height1, width1,3)) starting_list_of_regions = [] starting_list_of_regions.append( list(range(labels_con.shape[2])) ) @@ -3835,7 +3879,7 @@ class Eynollah: batch_counter = batch_counter+1 if batch_counter==inference_bs or ( (tot_counter//inference_bs)==full_bs_ite and tot_counter%inference_bs==last_bs): - y_pr=model_ro_machine.predict(input_1 , verbose=0) + y_pr=self.model_reading_order_machine.predict(input_1 , verbose=0) if batch_counter==inference_bs: iteration_batches = inference_bs @@ -4698,16 +4742,16 @@ class Eynollah: t0 = time.time() if self.dir_in: self.reset_file_name_dir(os.path.join(self.dir_in,img_name)) - #print("text region early -11 in %.1fs", time.time() - t0) + print("text region early -11 in %.1fs", time.time() - t0) img_res, is_image_enhanced, num_col_classifier, num_column_is_classified = self.run_enhancement(self.light_version) self.logger.info("Enhancing took %.1fs ", time.time() - t0) - #print("text region early -1 in %.1fs", time.time() - t0) + print("text region early -1 in %.1fs", time.time() - t0) t1 = time.time() if not self.skip_layout_and_reading_order: if self.light_version: 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) - #print("text region early -2 in %.1fs", time.time() - t0) + print("text region early -2 in %.1fs", time.time() - t0) if num_col_classifier == 1 or num_col_classifier ==2: if num_col_classifier == 1: @@ -4720,17 +4764,17 @@ class Eynollah: textline_mask_tot_ea_deskew = resize_image(textline_mask_tot_ea,img_h_new, img_w_new ) - slope_deskew, slope_first = self.run_deskew(textline_mask_tot_ea_deskew) + slope_deskew, slope_first = 0, 0#self.run_deskew(textline_mask_tot_ea_deskew) else: - slope_deskew, slope_first = self.run_deskew(textline_mask_tot_ea) - #print("text region early -2,5 in %.1fs", time.time() - t0) + slope_deskew, slope_first = 0, 0#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 = \ 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) #self.logger.info("run graphics %.1fs ", time.time() - t1t) - #print("text region early -3 in %.1fs", time.time() - t0) + print("text region early -3 in %.1fs", time.time() - t0) textline_mask_tot_ea_org = np.copy(textline_mask_tot_ea) - #print("text region early -4 in %.1fs", time.time() - t0) + print("text region early -4 in %.1fs", time.time() - t0) else: text_regions_p_1 ,erosion_hurts, polygons_lines_xml = self.get_regions_from_xy_2models(img_res, is_image_enhanced, num_col_classifier) self.logger.info("Textregion detection took %.1fs ", time.time() - t1) @@ -4751,7 +4795,7 @@ class Eynollah: continue else: return pcgts - #print("text region early in %.1fs", time.time() - t0) + print("text region early in %.1fs", time.time() - t0) t1 = time.time() if not self.light_version: textline_mask_tot_ea = self.run_textline(image_page) @@ -4793,7 +4837,8 @@ class Eynollah: image_page_rotated = resize_image(image_page_rotated,org_h_l_m, org_w_l_m ) self.logger.info("detection of marginals took %.1fs", time.time() - t1) - #print("text region early 2 marginal in %.1fs", time.time() - t0) + print("text region early 2 marginal in %.1fs", time.time() - t0) + ## birdan sora chock chakir t1 = time.time() if not self.full_layout: 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) @@ -4807,7 +4852,7 @@ class Eynollah: 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) + 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) @@ -4929,7 +4974,7 @@ class Eynollah: else: pass - #print("text region early 3 in %.1fs", time.time() - t0) + 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) @@ -4938,14 +4983,17 @@ class Eynollah: #contours_only_text_parent = self.dilate_textregions_contours(contours_only_text_parent) 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) + 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) + print("text region early 5 in %.1fs", time.time() - t0) + ## birdan sora chock chakir if not self.curved_line: if self.light_version: if self.textline_light: - slopes, all_found_textline_polygons, boxes_text, txt_con_org, contours_only_text_parent, all_box_coord, index_by_text_par_con = self.get_slopes_and_deskew_new_light(txt_con_org, contours_only_text_parent, textline_mask_tot_ea_org, image_page_rotated, boxes_text, slope_deskew) + #slopes, all_found_textline_polygons, boxes_text, txt_con_org, contours_only_text_parent, all_box_coord, index_by_text_par_con = self.get_slopes_and_deskew_new_light(txt_con_org, contours_only_text_parent, textline_mask_tot_ea_org, image_page_rotated, boxes_text, slope_deskew) + + slopes, all_found_textline_polygons, boxes_text, txt_con_org, contours_only_text_parent, all_box_coord, index_by_text_par_con = self.get_slopes_and_deskew_new_light2(txt_con_org, contours_only_text_parent, textline_mask_tot_ea_org, image_page_rotated, boxes_text, slope_deskew) slopes_marginals, all_found_textline_polygons_marginals, boxes_marginals, _, polygons_of_marginals, all_box_coord_marginals, _ = self.get_slopes_and_deskew_new_light(polygons_of_marginals, polygons_of_marginals, textline_mask_tot_ea_org, image_page_rotated, boxes_marginals, slope_deskew) #slopes, all_found_textline_polygons, boxes_text, txt_con_org, contours_only_text_parent, index_by_text_par_con = self.delete_regions_without_textlines(slopes, all_found_textline_polygons, boxes_text, txt_con_org, contours_only_text_parent, index_by_text_par_con) @@ -4974,7 +5022,7 @@ 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=2), 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) + 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 = self.return_list_of_contours_with_desired_order(contours_only_text_parent_d_ordered, index_by_text_par_con) @@ -5134,7 +5182,7 @@ class Eynollah: self.logger.info("Job done in %.1fs", time.time() - t0) if not self.dir_in: return pcgts - #print("text region early 7 in %.1fs", time.time() - t0) + print("text region early 7 in %.1fs", time.time() - t0) else: _ ,_, _, textline_mask_tot_ea, img_bin_light = self.get_regions_light_v(img_res, is_image_enhanced, num_col_classifier, skip_layout_and_reading_order=self.skip_layout_and_reading_order) diff --git a/qurator/eynollah/utils/marginals.py b/qurator/eynollah/utils/marginals.py index 7c43de6..984156f 100644 --- a/qurator/eynollah/utils/marginals.py +++ b/qurator/eynollah/utils/marginals.py @@ -8,7 +8,7 @@ from .contour import find_new_features_of_contours, return_contours_of_intereste from .resize import resize_image from .rotate import rotate_image -def get_marginals(text_with_lines, text_regions, num_col, slope_deskew, kernel=None): +def get_marginals(text_with_lines, text_regions, num_col, slope_deskew, light_version=False, kernel=None): mask_marginals=np.zeros((text_with_lines.shape[0],text_with_lines.shape[1])) mask_marginals=mask_marginals.astype(np.uint8) @@ -49,27 +49,14 @@ def get_marginals(text_with_lines, text_regions, num_col, slope_deskew, kernel=N if thickness_along_y_percent>=14: text_with_lines_y_rev=-1*text_with_lines_y[:] - #print(text_with_lines_y) - #print(text_with_lines_y_rev) - - - - - #plt.plot(text_with_lines_y) - #plt.show() - text_with_lines_y_rev=text_with_lines_y_rev-np.min(text_with_lines_y_rev) - #plt.plot(text_with_lines_y_rev) - #plt.show() sigma_gaus=1 region_sum_0= gaussian_filter1d(text_with_lines_y, sigma_gaus) region_sum_0_rev=gaussian_filter1d(text_with_lines_y_rev, sigma_gaus) - #plt.plot(region_sum_0_rev) - #plt.show() region_sum_0_updown=region_sum_0[len(region_sum_0)::-1] first_nonzero=(next((i for i, x in enumerate(region_sum_0) if x), None)) @@ -78,43 +65,17 @@ def get_marginals(text_with_lines, text_regions, num_col, slope_deskew, kernel=N last_nonzero=len(region_sum_0)-last_nonzero - ##img_sum_0_smooth_rev=-region_sum_0 - - mid_point=(last_nonzero+first_nonzero)/2. one_third_right=(last_nonzero-mid_point)/3.0 one_third_left=(mid_point-first_nonzero)/3.0 - #img_sum_0_smooth_rev=img_sum_0_smooth_rev-np.min(img_sum_0_smooth_rev) - - - - peaks, _ = find_peaks(text_with_lines_y_rev, height=0) - - peaks=np.array(peaks) - - - #print(region_sum_0[peaks]) - ##plt.plot(region_sum_0) - ##plt.plot(peaks,region_sum_0[peaks],'*') - ##plt.show() - #print(first_nonzero,last_nonzero,peaks) peaks=peaks[(peaks>first_nonzero) & ((peaksmid_point] @@ -137,9 +98,6 @@ def get_marginals(text_with_lines, text_regions, num_col, slope_deskew, kernel=N - - #print(point_left,point_right) - #print(text_regions.shape) if point_right>=mask_marginals.shape[1]: point_right=mask_marginals.shape[1]-1 @@ -148,10 +106,8 @@ def get_marginals(text_with_lines, text_regions, num_col, slope_deskew, kernel=N except: mask_marginals[:,:]=1 - #print(mask_marginals.shape,point_left,point_right,'nadosh') mask_marginals_rotated=rotate_image(mask_marginals,-slope_deskew) - #print(mask_marginals_rotated.shape,'nadosh') mask_marginals_rotated_sum=mask_marginals_rotated.sum(axis=0) mask_marginals_rotated_sum[mask_marginals_rotated_sum!=0]=1 @@ -168,11 +124,6 @@ def get_marginals(text_with_lines, text_regions, num_col, slope_deskew, kernel=N max_point_of_right_marginal=text_regions.shape[1]-1 - #print(np.min(index_x_interest) ,np.max(index_x_interest),'minmaxnew') - #print(mask_marginals_rotated.shape,text_regions.shape,'mask_marginals_rotated') - #plt.imshow(mask_marginals) - #plt.show() - #plt.imshow(mask_marginals_rotated) #plt.show() @@ -195,10 +146,9 @@ def get_marginals(text_with_lines, text_regions, num_col, slope_deskew, kernel=N x_min_marginals_right=[] for i in range(len(cx_text_only)): - x_width_mar=abs(x_min_text_only[i]-x_max_text_only[i]) y_height_mar=abs(y_min_text_only[i]-y_max_text_only[i]) - #print(x_width_mar,y_height_mar,y_height_mar/x_width_mar,'y_height_mar') + if x_width_mar>16 and y_height_mar/x_width_mar<18: marginlas_should_be_main_text.append(polygons_of_marginals[i]) if x_min_text_only[i]<(mid_point-one_third_left): @@ -220,18 +170,13 @@ def get_marginals(text_with_lines, text_regions, num_col, slope_deskew, kernel=N x_min_marginals_right=[text_regions.shape[1]-1] - - - #print(x_min_marginals_left[0],x_min_marginals_right[0],'margo') - - #print(marginlas_should_be_main_text,'marginlas_should_be_main_text') text_regions=cv2.fillPoly(text_regions, pts =marginlas_should_be_main_text, color=(4,4)) - #print(np.unique(text_regions)) #text_regions[:,:int(x_min_marginals_left[0])][text_regions[:,:int(x_min_marginals_left[0])]==1]=0 #text_regions[:,int(x_min_marginals_right[0]):][text_regions[:,int(x_min_marginals_right[0]):]==1]=0 - + + text_regions[:,:int(min_point_of_left_marginal)][text_regions[:,:int(min_point_of_left_marginal)]==1]=0 text_regions[:,int(max_point_of_right_marginal):][text_regions[:,int(max_point_of_right_marginal):]==1]=0