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@ -1862,7 +1862,10 @@ class Eynollah:
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def textline_contours(self, img, patches, scaler_h, scaler_w, num_col_classifier=None):
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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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self.logger.debug('enter textline_contours')
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thresholding_for_artificial_class_in_light_version = True#False
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if self.textline_light:
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thresholding_for_artificial_class_in_light_version = True#False
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else:
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thresholding_for_artificial_class_in_light_version = False
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if not self.dir_in:
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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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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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@ -2016,7 +2019,7 @@ class Eynollah:
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#print("inside 1 ", time.time()-t_in)
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#print("inside 1 ", time.time()-t_in)
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###textline_mask_tot_ea = self.run_textline(img_bin)
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###textline_mask_tot_ea = self.run_textline(img_bin)
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textline_mask_tot_ea = self.run_textline(img_bin, num_col_classifier)
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textline_mask_tot_ea = self.run_textline(img_resized, num_col_classifier)
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textline_mask_tot_ea = resize_image(textline_mask_tot_ea,img_height_h, img_width_h )
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textline_mask_tot_ea = resize_image(textline_mask_tot_ea,img_height_h, img_width_h )
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@ -2057,7 +2060,8 @@ class Eynollah:
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mask_texts_only = mask_texts_only.astype('uint8')
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mask_texts_only = mask_texts_only.astype('uint8')
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mask_texts_only = cv2.dilate(mask_texts_only, KERNEL, iterations=2)
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#mask_texts_only = cv2.erode(mask_texts_only, KERNEL, iterations=1)
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#mask_texts_only = cv2.dilate(mask_texts_only, KERNEL, iterations=1)
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mask_images_only=(prediction_regions_org[:,:] ==2)*1
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mask_images_only=(prediction_regions_org[:,:] ==2)*1
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@ -2097,6 +2101,7 @@ class Eynollah:
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polygons_of_only_texts = return_contours_of_interested_region(mask_texts_only,1,0.00001)
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polygons_of_only_texts = return_contours_of_interested_region(mask_texts_only,1,0.00001)
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polygons_of_only_lines = return_contours_of_interested_region(mask_lines_only,1,0.00001)
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polygons_of_only_lines = return_contours_of_interested_region(mask_lines_only,1,0.00001)
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text_regions_p_true = np.zeros(prediction_regions_org.shape)
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text_regions_p_true = np.zeros(prediction_regions_org.shape)
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@ -3845,132 +3850,139 @@ class Eynollah:
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return x_differential_new
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return x_differential_new
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def dilate_textlines(self,all_found_textline_polygons):
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def dilate_textlines(self,all_found_textline_polygons):
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for i in range(len(all_found_textline_polygons[0])):
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for j in range(len(all_found_textline_polygons)):
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con_ind = all_found_textline_polygons[0][i]
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for i in range(len(all_found_textline_polygons[j])):
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con_ind = all_found_textline_polygons[j][i]
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con_ind = con_ind.astype(np.float)
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x_differential = np.diff( con_ind[:,0,0])
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y_differential = np.diff( con_ind[:,0,1])
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x_min = float(np.min( con_ind[:,0,0] ))
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y_min = float(np.min( con_ind[:,0,1] ))
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x_max = float(np.max( con_ind[:,0,0] ))
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y_max = float(np.max( con_ind[:,0,1] ))
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if (y_max - y_min) > (x_max - x_min) and (x_max - x_min)<70:
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x_biger_than_x = np.abs(x_differential) > np.abs(y_differential)
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con_ind = con_ind.astype(np.float)
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mult = x_biger_than_x*x_differential
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x_differential = np.diff( con_ind[:,0,0])
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y_differential = np.diff( con_ind[:,0,1])
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arg_min_mult = np.argmin(mult)
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x_min = float(np.min( con_ind[:,0,0] ))
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arg_max_mult = np.argmax(mult)
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y_min = float(np.min( con_ind[:,0,1] ))
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if y_differential[0]==0:
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x_max = float(np.max( con_ind[:,0,0] ))
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y_differential[0] = 0.1
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y_max = float(np.max( con_ind[:,0,1] ))
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if y_differential[-1]==0:
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if (y_max - y_min) > (x_max - x_min) and (x_max - x_min)<70:
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y_differential[-1]= 0.1
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x_biger_than_x = np.abs(x_differential) > np.abs(y_differential)
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mult = x_biger_than_x*x_differential
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y_differential = [y_differential[ind] if y_differential[ind]!=0 else (y_differential[ind-1] + y_differential[ind+1])/2. for ind in range(len(y_differential)) ]
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arg_min_mult = np.argmin(mult)
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arg_max_mult = np.argmax(mult)
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if y_differential[0]==0.1:
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y_differential[0] = y_differential[1]
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if y_differential[-1]==0.1:
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y_differential[-1] = y_differential[-2]
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y_differential.append(y_differential[0])
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if y_differential[0]==0:
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y_differential[0] = 0.1
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y_differential = [-1 if y_differential[ind]<0 else 1 for ind in range(len(y_differential))]
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y_differential = self.return_it_in_two_groups(y_differential)
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y_differential = np.array(y_differential)
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con_scaled = con_ind*1
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con_scaled[:,0, 0] = con_ind[:,0,0] - 8*y_differential
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con_scaled[arg_min_mult,0, 1] = con_ind[arg_min_mult,0,1] + 8
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con_scaled[arg_min_mult+1,0, 1] = con_ind[arg_min_mult+1,0,1] + 8
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try:
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con_scaled[arg_min_mult-1,0, 1] = con_ind[arg_min_mult-1,0,1] + 5
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con_scaled[arg_min_mult+2,0, 1] = con_ind[arg_min_mult+2,0,1] + 5
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except:
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pass
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con_scaled[arg_max_mult,0, 1] = con_ind[arg_max_mult,0,1] - 8
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con_scaled[arg_max_mult+1,0, 1] = con_ind[arg_max_mult+1,0,1] - 8
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try:
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con_scaled[arg_max_mult-1,0, 1] = con_ind[arg_max_mult-1,0,1] - 5
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con_scaled[arg_max_mult+2,0, 1] = con_ind[arg_max_mult+2,0,1] - 5
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except:
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pass
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else:
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y_biger_than_x = np.abs(y_differential) > np.abs(x_differential)
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mult = y_biger_than_x*y_differential
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arg_min_mult = np.argmin(mult)
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arg_max_mult = np.argmax(mult)
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if x_differential[0]==0:
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x_differential[0] = 0.1
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if x_differential[-1]==0:
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x_differential[-1]= 0.1
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if y_differential[-1]==0:
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y_differential[-1]= 0.1
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y_differential = [y_differential[ind] if y_differential[ind]!=0 else (y_differential[ind-1] + y_differential[ind+1])/2. for ind in range(len(y_differential)) ]
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x_differential = [x_differential[ind] if x_differential[ind]!=0 else (x_differential[ind-1] + x_differential[ind+1])/2. for ind in range(len(x_differential)) ]
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if y_differential[0]==0.1:
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y_differential[0] = y_differential[1]
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if y_differential[-1]==0.1:
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if x_differential[0]==0.1:
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y_differential[-1] = y_differential[-2]
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x_differential[0] = x_differential[1]
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if x_differential[-1]==0.1:
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y_differential.append(y_differential[0])
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x_differential[-1] = x_differential[-2]
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x_differential.append(x_differential[0])
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y_differential = [-1 if y_differential[ind]<0 else 1 for ind in range(len(y_differential))]
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x_differential = [-1 if x_differential[ind]<0 else 1 for ind in range(len(x_differential))]
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y_differential = self.return_it_in_two_groups(y_differential)
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x_differential = self.return_it_in_two_groups(x_differential)
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y_differential = np.array(y_differential)
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x_differential = np.array(x_differential)
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con_scaled = con_ind*1
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con_scaled = con_ind*1
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con_scaled[:,0, 0] = con_ind[:,0,0] - 8*y_differential
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con_scaled[:,0, 1] = con_ind[:,0,1] + 8*x_differential
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con_scaled[arg_min_mult,0, 1] = con_ind[arg_min_mult,0,1] + 8
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con_scaled[arg_min_mult,0, 0] = con_ind[arg_min_mult,0,0] + 8
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con_scaled[arg_min_mult+1,0, 1] = con_ind[arg_min_mult+1,0,1] + 8
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con_scaled[arg_min_mult+1,0, 0] = con_ind[arg_min_mult+1,0,0] + 8
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try:
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con_scaled[arg_min_mult-1,0, 1] = con_ind[arg_min_mult-1,0,1] + 5
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con_scaled[arg_min_mult+2,0, 1] = con_ind[arg_min_mult+2,0,1] + 5
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except:
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pass
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con_scaled[arg_max_mult,0, 1] = con_ind[arg_max_mult,0,1] - 8
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con_scaled[arg_max_mult+1,0, 1] = con_ind[arg_max_mult+1,0,1] - 8
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try:
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con_scaled[arg_max_mult-1,0, 1] = con_ind[arg_max_mult-1,0,1] - 5
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con_scaled[arg_max_mult+2,0, 1] = con_ind[arg_max_mult+2,0,1] - 5
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except:
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pass
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con_scaled[arg_min_mult-1,0, 0] = con_ind[arg_min_mult-1,0,0] + 5
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con_scaled[arg_min_mult+2,0, 0] = con_ind[arg_min_mult+2,0,0] + 5
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con_scaled[arg_max_mult,0, 0] = con_ind[arg_max_mult,0,0] - 8
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else:
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con_scaled[arg_max_mult+1,0, 0] = con_ind[arg_max_mult+1,0,0] - 8
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y_biger_than_x = np.abs(y_differential) > np.abs(x_differential)
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mult = y_biger_than_x*y_differential
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arg_min_mult = np.argmin(mult)
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arg_max_mult = np.argmax(mult)
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if x_differential[0]==0:
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x_differential[0] = 0.1
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if x_differential[-1]==0:
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x_differential[-1]= 0.1
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x_differential = [x_differential[ind] if x_differential[ind]!=0 else (x_differential[ind-1] + x_differential[ind+1])/2. for ind in range(len(x_differential)) ]
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if x_differential[0]==0.1:
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x_differential[0] = x_differential[1]
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if x_differential[-1]==0.1:
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x_differential[-1] = x_differential[-2]
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x_differential.append(x_differential[0])
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x_differential = [-1 if x_differential[ind]<0 else 1 for ind in range(len(x_differential))]
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x_differential = self.return_it_in_two_groups(x_differential)
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x_differential = np.array(x_differential)
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con_scaled = con_ind*1
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con_scaled[:,0, 1] = con_ind[:,0,1] + 8*x_differential
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con_scaled[arg_min_mult,0, 0] = con_ind[arg_min_mult,0,0] + 8
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con_scaled[arg_min_mult+1,0, 0] = con_ind[arg_min_mult+1,0,0] + 8
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try:
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con_scaled[arg_min_mult-1,0, 0] = con_ind[arg_min_mult-1,0,0] + 5
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con_scaled[arg_min_mult+2,0, 0] = con_ind[arg_min_mult+2,0,0] + 5
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except:
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pass
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con_scaled[arg_max_mult,0, 0] = con_ind[arg_max_mult,0,0] - 8
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con_scaled[arg_max_mult+1,0, 0] = con_ind[arg_max_mult+1,0,0] - 8
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try:
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con_scaled[arg_max_mult-1,0, 0] = con_ind[arg_max_mult-1,0,0] - 5
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con_scaled[arg_max_mult+2,0, 0] = con_ind[arg_max_mult+2,0,0] - 5
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except:
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pass
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con_scaled[arg_max_mult-1,0, 0] = con_ind[arg_max_mult-1,0,0] - 5
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con_scaled[:,0, 1][con_scaled[:,0, 1]<0] = 0
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con_scaled[arg_max_mult+2,0, 0] = con_ind[arg_max_mult+2,0,0] - 5
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con_scaled[:,0, 0][con_scaled[:,0, 0]<0] = 0
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all_found_textline_polygons[j][i][:,0,1] = con_scaled[:,0, 1]
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con_scaled[:,0, 1][con_scaled[:,0, 1]<0] = 0
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all_found_textline_polygons[j][i][:,0,0] = con_scaled[:,0, 0]
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con_scaled[:,0, 0][con_scaled[:,0, 0]<0] = 0
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all_found_textline_polygons[0][i][:,0,1] = con_scaled[:,0, 1]
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all_found_textline_polygons[0][i][:,0,0] = con_scaled[:,0, 0]
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return all_found_textline_polygons
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return all_found_textline_polygons
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def run(self):
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def run(self):
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@ -4207,6 +4219,8 @@ class Eynollah:
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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, 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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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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all_found_textline_polygons = self.dilate_textlines(all_found_textline_polygons)
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else:
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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, 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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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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vahidrezanezhad
3 months ago