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eynollah
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updating early layout inference for light version

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pull/138/head^2
vahidrezanezhad 2 months ago
parent e796a99c5c
commit 751b0102f7
1 changed files with 21 additions and 16 deletions
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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 + "/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_np = dir_models + "/modelens_full_lay_1_3_031124"#"/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_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"
@ -253,7 +253,7 @@ 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_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" self.model_region_dir_fully = dir_models + "/modelens_full_lay_1_3_031124"#"/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: 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 + "/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:
@ -743,7 +743,7 @@ class Eynollah:
def get_image_and_scales_after_enhancing(self, img_org, img_res): def get_image_and_scales_after_enhancing(self, img_org, img_res):
self.logger.debug("enter get_image_and_scales_after_enhancing") self.logger.debug("enter get_image_and_scales_after_enhancing")
self.image = np.copy(img_res) self.image = np.copy(img_res)
#self.image = self.image.astype(np.uint8) self.image = self.image.astype(np.uint8)
self.image_org = np.copy(img_org) self.image_org = np.copy(img_org)
self.height_org = self.image_org.shape[0] self.height_org = self.image_org.shape[0]
self.width_org = self.image_org.shape[1] self.width_org = self.image_org.shape[1]
@ -1298,20 +1298,25 @@ class Eynollah:
seg = np.argmax(label_p_pred, axis=3) seg = np.argmax(label_p_pred, axis=3)
if thresholding_for_some_classes_in_light_version: if thresholding_for_some_classes_in_light_version:
seg_not_base = label_p_pred[:,:,:,4]
seg_not_base[seg_not_base>0.03] =1 seg_art = label_p_pred[:,:,:,4]
seg_not_base[seg_not_base<1] =0 seg_art[seg_art<0.2] =0
seg_art[seg_art>0] =1
###seg[seg_art==1]=4
##seg_not_base = label_p_pred[:,:,:,4]
##seg_not_base[seg_not_base>0.03] =1
##seg_not_base[seg_not_base<1] =0
seg_line = label_p_pred[:,:,:,3] seg_line = label_p_pred[:,:,:,3]
seg_line[seg_line>0.1] =1 seg_line[seg_line>0.1] =1
seg_line[seg_line<1] =0 seg_line[seg_line<1] =0
seg_background = label_p_pred[:,:,:,0] ##seg_background = label_p_pred[:,:,:,0]
seg_background[seg_background>0.25] =1 ##seg_background[seg_background>0.25] =1
seg_background[seg_background<1] =0 ##seg_background[seg_background<1] =0
seg[seg_not_base==1]=4 seg[seg_art==1]=4
seg[seg_background==1]=0 ##seg[seg_background==1]=0
seg[(seg_line==1) & (seg==0)]=3 seg[(seg_line==1) & (seg==0)]=3
if thresholding_for_artificial_class_in_light_version: if thresholding_for_artificial_class_in_light_version:
seg_art = label_p_pred[:,:,:,2] seg_art = label_p_pred[:,:,:,2]
@ -2300,26 +2305,26 @@ class Eynollah:
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) 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: 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) prediction_regions_org = self.do_prediction_new_concept(True, img_resized, model_region, n_batch_inference=1, thresholding_for_some_classes_in_light_version = True)
else: else:
prediction_regions_org = np.zeros((self.image_org.shape[0], self.image_org.shape[1], 3)) 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, model_region, n_batch_inference=1, thresholding_for_artificial_class_in_light_version = True) 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 prediction_regions_org[self.page_coord[0] : self.page_coord[1], self.page_coord[2] : self.page_coord[3],:] = prediction_regions_page
else: else:
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_1_2_sp_np) 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(True, resize_image(img_bin, int( (900+ (num_col_classifier-3)*100) *(img_bin.shape[0]/img_bin.shape[1]) ), 900+ (num_col_classifier-3)*100), model_region) prediction_regions_org = self.do_prediction_new_concept(True, resize_image(img_bin, int( (900+ (num_col_classifier-3)*100) *(img_bin.shape[0]/img_bin.shape[1]) ), 900+ (num_col_classifier-3)*100), model_region, n_batch_inference=2, thresholding_for_some_classes_in_light_version=True)
##model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_ens_light) ##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) ##prediction_regions_org = self.do_prediction(True, img_bin, model_region, n_batch_inference=3, thresholding_for_some_classes_in_light_version=True)
else: 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: 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) prediction_regions_org = self.do_prediction_new_concept(True, img_resized, self.model_region_1_2, n_batch_inference=1, thresholding_for_some_classes_in_light_version=True)
else: else:
prediction_regions_org = np.zeros((self.image_org.shape[0], self.image_org.shape[1], 3)) 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=True) 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 prediction_regions_org[self.page_coord[0] : self.page_coord[1], self.page_coord[2] : self.page_coord[3],:] = prediction_regions_page
else: else:
prediction_regions_org = self.do_prediction_new_concept(True, resize_image(img_bin, int( (900+ (num_col_classifier-3)*100) *(img_bin.shape[0]/img_bin.shape[1]) ), 900+ (num_col_classifier-3)*100), self.model_region_1_2, n_batch_inference=2) prediction_regions_org = self.do_prediction_new_concept(True, resize_image(img_bin, int( (900+ (num_col_classifier-3)*100) *(img_bin.shape[0]/img_bin.shape[1]) ), 900+ (num_col_classifier-3)*100), self.model_region_1_2, n_batch_inference=2, thresholding_for_some_classes_in_light_version=True)
###prediction_regions_org = self.do_prediction(True, img_bin, self.model_region, n_batch_inference=3, thresholding_for_some_classes_in_light_version=True) ###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)
@ -4595,7 +4600,7 @@ class Eynollah:
areas_without = np.array(areas_tot)[args_all] areas_without = np.array(areas_tot)[args_all]
area_of_con_interest = areas_tot[ij] area_of_con_interest = areas_tot[ij]
args_with_bigger_area = np.array(args_all)[areas_without > area_of_con_interest] args_with_bigger_area = np.array(args_all)[areas_without > 1.5*area_of_con_interest]
if len(args_with_bigger_area)>0: if len(args_with_bigger_area)>0:
results = [cv2.pointPolygonTest(contours_txtline_of_all_textregions[ind], (cx_main_tot[ij], cy_main_tot[ij]), False) for ind in args_with_bigger_area ] results = [cv2.pointPolygonTest(contours_txtline_of_all_textregions[ind], (cx_main_tot[ij], cy_main_tot[ij]), False) for ind in args_with_bigger_area ]

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