updating light version

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_earlylayout_12spaltige_2_3_5_6_7_8"#"/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_earlylayout_12spaltige_2_3_5_6_7_8"#"/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:
@@ -541,6 +541,7 @@ class Eynollah:
             img = self.imread()
 
         _, page_coord = self.early_page_for_num_of_column_classification(img)
+        
         if not self.dir_in:
             model_num_classifier, session_col_classifier = self.start_new_session_and_model(self.model_dir_of_col_classifier)
         if self.input_binary:
@@ -611,6 +612,10 @@ class Eynollah:
         width_early = img.shape[1]
         t1 = time.time()
         _, page_coord = self.early_page_for_num_of_column_classification(img_bin)
+        
+        self.image_page_org_size = img[page_coord[0] : page_coord[1], page_coord[2] : page_coord[3], :]
+        self.page_coord = page_coord
+        
         if not self.dir_in:
             model_num_classifier, session_col_classifier = self.start_new_session_and_model(self.model_dir_of_col_classifier)
         
@@ -737,7 +742,7 @@ class Eynollah:
     def get_image_and_scales_after_enhancing(self, img_org, img_res):
         self.logger.debug("enter get_image_and_scales_after_enhancing")
         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.height_org = self.image_org.shape[0]
         self.width_org = self.image_org.shape[1]
@@ -1059,19 +1064,18 @@ class Eynollah:
         if not patches:
             img_h_page = img.shape[0]
             img_w_page = img.shape[1]
-            img = img / float(255.0)
+            img = img / 255.0
             img = resize_image(img, img_height_model, img_width_model)
 
             label_p_pred = model.predict(img.reshape(1, img.shape[0], img.shape[1], img.shape[2]), verbose=0)
-            
-            #seg_not_base = label_p_pred[0,:,:,4]
-            
-            #seg_not_base[seg_not_base>0.4] =1
-            #seg_not_base[seg_not_base<1] =0
-
             seg = np.argmax(label_p_pred, axis=3)[0]
             
-            #seg[seg_not_base==1]=4
+            if thresholding_for_artificial_class_in_light_version:
+                seg_art = label_p_pred[0,:,:,4]
+                seg_art[seg_art<0.1] =0
+                seg_art[seg_art>0] =1
+                seg[seg_art==1]=4
+
             
             seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2)
             prediction_true = resize_image(seg_color, img_h_page, img_w_page)
@@ -2151,7 +2155,7 @@ class Eynollah:
         #print(num_col_classifier,'num_col_classifier')
         
         if num_col_classifier == 1:
-            img_w_new = 1000
+            img_w_new = 800
             img_h_new = int(img_org.shape[0] / float(img_org.shape[1]) * img_w_new)
             
         elif num_col_classifier == 2:
@@ -2206,29 +2210,39 @@ class Eynollah:
         
         textline_mask_tot_ea = resize_image(textline_mask_tot_ea,img_height_h, img_width_h )
         
+        
+        #print(self.image_org.shape)
+        
+        #plt.imshwo(self.image_page_org_size)
+        #plt.show()
         if not skip_layout_and_reading_order:
             #print("inside 2 ", time.time()-t_in)
             
-            #print(img_resized.shape, num_col_classifier, "num_col_classifier")
             if not self.dir_in:
                 if num_col_classifier == 1 or num_col_classifier == 2:
+                    prediction_regions_org = np.zeros((self.image_org.shape[0], self.image_org.shape[1], 3))
                     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(False, img_resized, model_region, n_batch_inference=1)
+                    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 = False)
+                    prediction_regions_org[self.page_coord[0] : self.page_coord[1], self.page_coord[2] : self.page_coord[3],:] = prediction_regions_page
                 else:
-                    model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_ens_light, n_batch_inference=3)
+                    model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_ens_light)
                     prediction_regions_org = self.do_prediction_new_concept(True, img_bin, model_region)
                 ##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:
-                    prediction_regions_org = self.do_prediction_new_concept(False, img_resized, self.model_region_1_2, n_batch_inference=1)
+                    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=False)
+                    prediction_regions_org[self.page_coord[0] : self.page_coord[1], self.page_coord[2] : self.page_coord[3],:] = prediction_regions_page
                 else:
                     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)
+            
             #plt.imshow(prediction_regions_org[:,:,0])
             #plt.show()
+            
                 
             prediction_regions_org = resize_image(prediction_regions_org,img_height_h, img_width_h )
             
@@ -3195,7 +3209,7 @@ class Eynollah:
         scale = 1
         if is_image_enhanced:
             if self.allow_enhancement:
-                img_res = img_res.astype(np.uint8)
+                #img_res = img_res.astype(np.uint8)
                 self.get_image_and_scales(img_org, img_res, scale)
                 if self.plotter:
                     self.plotter.save_enhanced_image(img_res)