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eynollah
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fixing a bug occuring with reading order + Slro option with no patch textline model and thresholding artificial class

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pull/138/head^2
vahidrezanezhad 2 months ago
parent 70772d4104
commit 82281bd6cf
3 changed files with 58 additions and 44 deletions
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69
qurator/eynollah/eynollah.py
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@ -245,7 +245,7 @@ class Eynollah:
self.model_dir_of_col_classifier = dir_models + "/eynollah-column-classifier_20210425" 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_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_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_region_dir_fully = dir_models + "/eynollah-full-regions-3+column_20210425"
self.model_page_dir = dir_models + "/eynollah-page-extraction_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 = 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_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_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_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: 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: 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: if self.ocr:
self.model_ocr_dir = dir_models + "/checkpoint-166692_printed_trocr" self.model_ocr_dir = dir_models + "/checkpoint-166692_printed_trocr"
@ -816,6 +816,14 @@ class Eynollah:
verbose=0) verbose=0)
seg = np.argmax(label_p_pred, axis=3)[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) seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2)
prediction_true = resize_image(seg_color, img_h_page, img_w_page) prediction_true = resize_image(seg_color, img_h_page, img_w_page)
prediction_true = prediction_true.astype(np.uint8) prediction_true = prediction_true.astype(np.uint8)
@ -1546,7 +1554,7 @@ class Eynollah:
pass pass
else: else:
img = otsu_copy_binary(img) img = otsu_copy_binary(img)
img = img.astype(np.uint8) #img = img.astype(np.uint8)
prediction_regions2 = None prediction_regions2 = None
else: else:
if cols == 1: if cols == 1:
@ -1608,6 +1616,9 @@ class Eynollah:
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(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) prediction_regions = resize_image(prediction_regions, img_height_h, img_width_h)
self.logger.debug("exit extract_text_regions") self.logger.debug("exit extract_text_regions")
return prediction_regions, prediction_regions return prediction_regions, prediction_regions
@ -2148,7 +2159,7 @@ class Eynollah:
if not thresholding_for_artificial_class_in_light_version: 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 = 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 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 (not self.input_binary) or self.full_layout:
#if self.input_binary: #if self.input_binary:
#img_bin = np.copy(img_resized) #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.input_binary and self.full_layout) or (not self.input_binary and num_col_classifier >= 30):
if not self.dir_in: ###if not self.dir_in:
model_bin, session_bin = self.start_new_session_and_model(self.model_dir_of_binarization) ###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) ###prediction_bin = self.do_prediction(True, img_resized, model_bin, n_batch_inference=5)
else: ###else:
prediction_bin = self.do_prediction(True, img_resized, self.model_bin, n_batch_inference=5) ###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]
prediction_bin = (prediction_bin[:,:]==0)*1 ###prediction_bin = (prediction_bin[:,:]==0)*1
prediction_bin = prediction_bin*255 ###prediction_bin = prediction_bin*255
prediction_bin =np.repeat(prediction_bin[:, :, np.newaxis], 3, axis=2) ###prediction_bin =np.repeat(prediction_bin[:, :, np.newaxis], 3, axis=2)
prediction_bin = prediction_bin.astype(np.uint16) ###prediction_bin = prediction_bin.astype(np.uint16)
#img= np.copy(prediction_bin) ####img= np.copy(prediction_bin)
img_bin = np.copy(prediction_bin) ###img_bin = np.copy(prediction_bin)
else: ###else:
img_bin = np.copy(img_resized) ###img_bin = np.copy(img_resized)
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_bin)
@ -3311,7 +3323,8 @@ class Eynollah:
scaler_h_textline = 1#1.3 # 1.2#1.2 scaler_h_textline = 1#1.3 # 1.2#1.2
scaler_w_textline = 1#1.3 # 0.9#1 scaler_w_textline = 1#1.3 # 0.9#1
#print(image_page.shape) #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: if self.textline_light:
textline_mask_tot_ea = textline_mask_tot_ea.astype(np.int16) textline_mask_tot_ea = textline_mask_tot_ea.astype(np.int16)
@ -3564,9 +3577,9 @@ class Eynollah:
image_page = image_page.astype(np.uint8) image_page = image_page.astype(np.uint8)
#print("full inside 1", time.time()- t_full0) #print("full inside 1", time.time()- t_full0)
if self.light_version: 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: 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) #print("full inside 2", time.time()- t_full0)
# 6 is the separators lable in old full layout model # 6 is the separators lable in old full layout model
# 4 is the drop capital class 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[:,:,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) ##regions_fully_np, _ = self.extract_text_regions(image_page, False, cols=num_col_classifier)
##if num_col_classifier > 2: ##if num_col_classifier > 2:
##regions_fully_np[:, :, 0][regions_fully_np[:, :, 0] == 4] = 0 ##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 ) 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: 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) #print("text region early -2,5 in %.1fs", time.time() - t0)
#self.logger.info("Textregion detection took %.1fs ", time.time() - t1t) #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 = \ 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 = \

19
qurator/eynollah/utils/__init__.py
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@ -1204,17 +1204,12 @@ def order_of_regions(textline_mask, contours_main, contours_header, y_ref):
top = peaks_neg_new[i] top = peaks_neg_new[i]
down = peaks_neg_new[i + 1] 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))] 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))] 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))] 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))] 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))] 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) sorted_inside = np.argsort(cxs_in)
ind_in_int = indexes_in[sorted_inside] ind_in_int = indexes_in[sorted_inside]
@ -1228,10 +1223,16 @@ def order_of_regions(textline_mask, contours_main, contours_header, y_ref):
##matrix_of_orders[:len_main,4]=final_indexers_sorted[:] ##matrix_of_orders[:len_main,4]=final_indexers_sorted[:]
# print(peaks_neg_new,'peaks') # 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..
# print(final_indexers_sorted,'indexsorted') if (len(cy_main)+len(cy_header) ) == len(final_index_type):
# print(final_types,'types') pass
# print(final_index_type,'final_index_type') 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 return final_indexers_sorted, matrix_of_orders, final_types, final_index_type

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