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@ -256,7 +256,7 @@ class Eynollah:
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##self.model_region_dir_fully_new = dir_models + "/model_2_full_layout_new_trans"
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self.model_region_dir_fully = dir_models + "/modelens_full_layout_24_till_28"#"/model_2_full_layout_new_trans"
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if self.textline_light:
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self.model_textline_dir = dir_models + "/model_textline_ens_3_4_5_6_artificial"#"/modelens_textline_9_12_13_14_15"#"/eynollah-textline_light_20210425"#
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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"#
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else:
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self.model_textline_dir = dir_models + "/eynollah-textline_20210425"
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if self.ocr:
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@ -796,7 +796,7 @@ class Eynollah:
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return model, None
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def do_prediction(self, patches, img, model, n_batch_inference=1, marginal_of_patch_percent=0.1, thresholding_for_some_classes_in_light_version=False):
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def do_prediction(self, patches, img, model, n_batch_inference=1, marginal_of_patch_percent=0.1, thresholding_for_some_classes_in_light_version=False, thresholding_for_artificial_class_in_light_version=False):
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self.logger.debug("enter do_prediction")
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img_height_model = model.layers[len(model.layers) - 1].output_shape[1]
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@ -903,6 +903,13 @@ class Eynollah:
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seg[seg_not_base==1]=4
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seg[seg_background==1]=0
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seg[(seg_line==1) & (seg==0)]=3
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if thresholding_for_artificial_class_in_light_version:
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seg_art = label_p_pred[:,:,:,2]
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seg_art[seg_art<0.2] = 0
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seg_art[seg_art>0] =1
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seg[seg_art==1]=2
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indexer_inside_batch = 0
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for i_batch, j_batch in zip(list_i_s, list_j_s):
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@ -977,6 +984,14 @@ class Eynollah:
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seg[seg_not_base==1]=4
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seg[seg_background==1]=0
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seg[(seg_line==1) & (seg==0)]=3
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if thresholding_for_artificial_class_in_light_version:
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seg_art = label_p_pred[:,:,:,2]
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seg_art[seg_art<0.2] = 0
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seg_art[seg_art>0] =1
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seg[seg_art==1]=2
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indexer_inside_batch = 0
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for i_batch, j_batch in zip(list_i_s, list_j_s):
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@ -1845,42 +1860,50 @@ class Eynollah:
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def textline_contours(self, img, patches, scaler_h, scaler_w, num_col_classifier=None):
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self.logger.debug('enter textline_contours')
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thresholding_for_artificial_class_in_light_version = True#False
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if not self.dir_in:
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model_textline, session_textline = self.start_new_session_and_model(self.model_textline_dir if patches else self.model_textline_dir_np)
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img = img.astype(np.uint8)
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#img = img.astype(np.uint8)
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img_org = np.copy(img)
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img_h = img_org.shape[0]
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img_w = img_org.shape[1]
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img = resize_image(img_org, int(img_org.shape[0] * scaler_h), int(img_org.shape[1] * scaler_w))
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#print(img.shape,'bin shape textline')
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if not self.dir_in:
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prediction_textline = self.do_prediction(patches, img, model_textline, marginal_of_patch_percent=0.2, n_batch_inference=3)
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if num_col_classifier==1:
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prediction_textline_nopatch = self.do_prediction(False, img, model_textline)
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prediction_textline[:,:][prediction_textline_nopatch[:,:]==0] = 0
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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)
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#if not thresholding_for_artificial_class_in_light_version:
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#if num_col_classifier==1:
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#prediction_textline_nopatch = self.do_prediction(False, img, model_textline)
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#prediction_textline[:,:][prediction_textline_nopatch[:,:]==0] = 0
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else:
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prediction_textline = self.do_prediction(patches, img, self.model_textline, marginal_of_patch_percent=0.2, n_batch_inference=3)
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if num_col_classifier==1:
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prediction_textline_nopatch = self.do_prediction(False, img, model_textline)
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prediction_textline[:,:][prediction_textline_nopatch[:,:]==0] = 0
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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)
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#if not thresholding_for_artificial_class_in_light_version:
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#if num_col_classifier==1:
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#prediction_textline_nopatch = self.do_prediction(False, img, model_textline)
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#prediction_textline[:,:][prediction_textline_nopatch[:,:]==0] = 0
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prediction_textline = resize_image(prediction_textline, img_h, img_w)
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textline_mask_tot_ea_art = (prediction_textline[:,:]==2)*1
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old_art = np.copy(textline_mask_tot_ea_art)
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textline_mask_tot_ea_art = textline_mask_tot_ea_art.astype('uint8')
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textline_mask_tot_ea_art = cv2.dilate(textline_mask_tot_ea_art, KERNEL, iterations=1)
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prediction_textline[:,:][textline_mask_tot_ea_art[:,:]==1]=2
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if not thresholding_for_artificial_class_in_light_version:
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textline_mask_tot_ea_art = textline_mask_tot_ea_art.astype('uint8')
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textline_mask_tot_ea_art = cv2.dilate(textline_mask_tot_ea_art, KERNEL, iterations=1)
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prediction_textline[:,:][textline_mask_tot_ea_art[:,:]==1]=2
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textline_mask_tot_ea_lines = (prediction_textline[:,:]==1)*1
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textline_mask_tot_ea_lines = textline_mask_tot_ea_lines.astype('uint8')
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textline_mask_tot_ea_lines = cv2.dilate(textline_mask_tot_ea_lines, KERNEL, iterations=1)
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if not thresholding_for_artificial_class_in_light_version:
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textline_mask_tot_ea_lines = cv2.dilate(textline_mask_tot_ea_lines, KERNEL, iterations=1)
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prediction_textline[:,:][textline_mask_tot_ea_lines[:,:]==1]=1
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prediction_textline[:,:][old_art[:,:]==1]=2
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if not thresholding_for_artificial_class_in_light_version:
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prediction_textline[:,:][old_art[:,:]==1]=2
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if not self.dir_in:
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prediction_textline_longshot = self.do_prediction(False, img, model_textline)
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@ -1959,7 +1982,7 @@ class Eynollah:
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img_w_new = 2300#3000
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img_h_new = int(img_org.shape[0] / float(img_org.shape[1]) * img_w_new)
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else:
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img_w_new = 3300#4000
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img_w_new = 3000#4000
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img_h_new = int(img_org.shape[0] / float(img_org.shape[1]) * img_w_new)
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img_resized = resize_image(img,img_h_new, img_w_new )
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@ -1968,7 +1991,7 @@ class Eynollah:
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#if (not self.input_binary) or self.full_layout:
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#if self.input_binary:
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#img_bin = np.copy(img_resized)
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if (not self.input_binary and self.full_layout) or (not self.input_binary and num_col_classifier >= 3):
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if (not self.input_binary and self.full_layout):# or (not self.input_binary and num_col_classifier >= 3):
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if not self.dir_in:
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model_bin, session_bin = self.start_new_session_and_model(self.model_dir_of_binarization)
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prediction_bin = self.do_prediction(True, img_resized, model_bin, n_batch_inference=5)
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@ -3794,15 +3817,146 @@ class Eynollah:
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return textline_contour
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def return_list_of_contours_with_desired_order(self, ls_cons, sorted_indexes):
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return [ls_cons[sorted_indexes[index]] for index in range(len(sorted_indexes))]
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def scale_contours(self,all_found_textline_polygons):
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for i in range(len(all_found_textline_polygons[0])):
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con_ind = all_found_textline_polygons[0][i]
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x_min = np.min( con_ind[:,0,0] )
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y_min = np.min( con_ind[:,0,1] )
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x_max = np.max( con_ind[:,0,0] )
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y_max = np.max( con_ind[:,0,1] )
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x_mean = np.mean( con_ind[:,0,0] )
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y_mean = np.mean( con_ind[:,0,1] )
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arg_y_max = np.argmax( con_ind[:,0,1] )
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arg_y_min = np.argmin( con_ind[:,0,1] )
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x_cor_y_max = con_ind[arg_y_max,0,0]
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x_cor_y_min = con_ind[arg_y_min,0,0]
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m_con = (y_max - y_min) / float(x_cor_y_max - x_cor_y_min)
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con_scaled = con_ind*1
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con_scaled = con_scaled.astype(np.float)
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con_scaled[:,0,0] = con_scaled[:,0,0] - int(x_mean)
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con_scaled[:,0,1] = con_scaled[:,0,1] - int(y_mean)
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if (x_max - x_min) > (y_max - y_min):
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if (y_max-y_min)<=15:
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con_scaled[:,0,1] = con_ind[:,0,1]*1.8
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y_max_scaled = np.max(con_scaled[:,0,1])
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y_min_scaled = np.min(con_scaled[:,0,1])
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y_max_expected = ( m_con*1.8*(x_cor_y_max-x_cor_y_min) + y_min_scaled )
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elif (y_max-y_min)<=30 and (y_max-y_min)>15:
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con_scaled[:,0,1] = con_ind[:,0,1]*1.6
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y_max_scaled = np.max(con_scaled[:,0,1])
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y_min_scaled = np.min(con_scaled[:,0,1])
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y_max_expected = ( m_con*1.6*(x_cor_y_max-x_cor_y_min) + y_min_scaled )
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elif (y_max-y_min)>30 and (y_max-y_min)<100:
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con_scaled[:,0,1] = con_ind[:,0,1]*1.35
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y_max_scaled = np.max(con_scaled[:,0,1])
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y_min_scaled = np.min(con_scaled[:,0,1])
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y_max_expected = ( m_con*1.35*(x_cor_y_max-x_cor_y_min) + y_min_scaled )
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else:
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con_scaled[:,0,1] = con_ind[:,0,1]*1.2
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y_max_scaled = np.max(con_scaled[:,0,1])
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y_min_scaled = np.min(con_scaled[:,0,1])
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y_max_expected = ( m_con*1.2*(x_cor_y_max-x_cor_y_min) + y_min_scaled )
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con_scaled[:,0,0] = con_ind[:,0,0]*1.03
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if y_max_expected<=y_max_scaled:
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con_scaled[:,0,1] = con_scaled[:,0,1] - y_min_scaled
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con_scaled[:,0,1] = con_scaled[:,0,1]*(y_max_expected - y_min_scaled)/ (y_max_scaled - y_min_scaled)
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con_scaled[:,0,1] = con_scaled[:,0,1] + y_min_scaled
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else:
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if (x_max-x_min)<=15:
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con_scaled[:,0,0] = con_ind[:,0,0]*1.8
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elif (x_max-x_min)<=30 and (x_max-x_min)>15:
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con_scaled[:,0,0] = con_ind[:,0,0]*1.6
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elif (x_max-x_min)>30 and (x_max-x_min)<100:
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con_scaled[:,0,0] = con_ind[:,0,0]*1.35
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else:
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con_scaled[:,0,0] = con_ind[:,0,0]*1.2
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con_scaled[:,0,1] = con_ind[:,0,1]*1.03
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x_min_n = np.min( con_scaled[:,0,0] )
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y_min_n = np.min( con_scaled[:,0,1] )
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x_mean_n = np.mean( con_scaled[:,0,0] )
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y_mean_n = np.mean( con_scaled[:,0,1] )
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##diff_x = (x_min_n - x_min)*1
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##diff_y = (y_min_n - y_min)*1
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diff_x = (x_mean_n - x_mean)*1
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diff_y = (y_mean_n - y_mean)*1
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con_scaled[:,0,0] = (con_scaled[:,0,0] - diff_x)
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con_scaled[:,0,1] = (con_scaled[:,0,1] - diff_y)
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x_max_n = np.max( con_scaled[:,0,0] )
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y_max_n = np.max( con_scaled[:,0,1] )
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diff_disp_x = (x_max_n - x_max) / 2.
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diff_disp_y = (y_max_n - y_max) / 2.
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x_vals = np.array( np.abs(con_scaled[:,0,0] - diff_disp_x) ).astype(np.int16)
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y_vals = np.array( np.abs(con_scaled[:,0,1] - diff_disp_y) ).astype(np.int16)
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all_found_textline_polygons[0][i][:,0,0] = x_vals[:]
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all_found_textline_polygons[0][i][:,0,1] = y_vals[:]
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return all_found_textline_polygons
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def scale_contours_new(self, textline_mask_tot_ea):
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cnt_clean_rot_raw, hir_on_cnt_clean_rot = return_contours_of_image(textline_mask_tot_ea)
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all_found_textline_polygons1 = filter_contours_area_of_image(textline_mask_tot_ea, cnt_clean_rot_raw, hir_on_cnt_clean_rot, max_area=1, min_area=0.00001)
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textline_mask_tot_ea_res = resize_image(textline_mask_tot_ea, int( textline_mask_tot_ea.shape[0]*1.6), textline_mask_tot_ea.shape[1])
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cnt_clean_rot_raw, hir_on_cnt_clean_rot = return_contours_of_image(textline_mask_tot_ea_res)
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##all_found_textline_polygons = filter_contours_area_of_image(textline_mask_tot_ea_res, cnt_clean_rot_raw, hir_on_cnt_clean_rot, max_area=1, min_area=0.00001)
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all_found_textline_polygons = filter_contours_area_of_image(textline_mask_tot_ea_res, cnt_clean_rot_raw, hir_on_cnt_clean_rot, max_area=1, min_area=0.00001)
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for i in range(len(all_found_textline_polygons)):
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#x_mean_1 = np.mean( all_found_textline_polygons1[i][:,0,0] )
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y_mean_1 = np.mean( all_found_textline_polygons1[i][:,0,1] )
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#x_mean = np.mean( all_found_textline_polygons[i][:,0,0] )
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y_mean = np.mean( all_found_textline_polygons[i][:,0,1] )
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ydiff = y_mean - y_mean_1
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all_found_textline_polygons[i][:,0,1] = all_found_textline_polygons[i][:,0,1] - ydiff
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return all_found_textline_polygons
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def run(self):
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"""
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Get image and scales, then extract the page of scanned image
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"""
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self.logger.debug("enter run")
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skip_layout_ro = False#True
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skip_layout_ro = True
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t0_tot = time.time()
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@ -3820,7 +3974,6 @@ class Eynollah:
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self.logger.info("Enhancing took %.1fs ", time.time() - t0)
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#print("text region early -1 in %.1fs", time.time() - t0)
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t1 = time.time()
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if not skip_layout_ro:
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if self.light_version:
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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)
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@ -4032,6 +4185,7 @@ class Eynollah:
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if self.textline_light:
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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)
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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)
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else:
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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, image_page_rotated, boxes_text, slope_deskew)
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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, image_page_rotated, boxes_marginals, slope_deskew)
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@ -4212,10 +4366,17 @@ class Eynollah:
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page_coord, image_page, textline_mask_tot_ea, img_bin_light, cont_page = self.run_graphics_and_columns_without_layout(textline_mask_tot_ea, img_bin_light)
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##all_found_textline_polygons =self.scale_contours_new(textline_mask_tot_ea)
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cnt_clean_rot_raw, hir_on_cnt_clean_rot = return_contours_of_image(textline_mask_tot_ea)
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all_found_textline_polygons = filter_contours_area_of_image(textline_mask_tot_ea, cnt_clean_rot_raw, hir_on_cnt_clean_rot, max_area=1, min_area=0.00001)
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all_found_textline_polygons=[ all_found_textline_polygons ]
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all_found_textline_polygons = self.scale_contours(all_found_textline_polygons)
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order_text_new = [0]
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slopes =[0]
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id_of_texts_tot =['region_0001']
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vahidrezanezhad
3 months ago