resolving error for inputs whcih have smaller scale than model patch

sbb_binarize/sbb_binarize.py

@@ -56,6 +56,44 @@ class SbbBinarizer:
 
     def predict(self, model_in, img, use_patches):
         model, model_height, model_width, n_classes = model_in
+        
+        img_org_h = img.shape[0]
+        img_org_w = img.shape[1]
+        
+        if img.shape[0] < model_height and img.shape[1] >= model_width:
+            img_padded = np.zeros(( model_height, img.shape[1], img.shape[2] ))
+            
+            index_start_h =  int( abs( img.shape[0] - model_height) /2.)
+            index_start_w = 0
+            
+            img_padded [ index_start_h: index_start_h+img.shape[0], :, : ] = img[:,:,:]
+            
+        elif img.shape[0] >= model_height and img.shape[1] < model_width:
+            img_padded = np.zeros(( img.shape[0], model_width, img.shape[2] ))
+            
+            index_start_h =  0 
+            index_start_w = int( abs( img.shape[1] - model_width) /2.)
+            
+            img_padded [ :, index_start_w: index_start_w+img.shape[1], : ] = img[:,:,:]
+            
+            
+        elif img.shape[0] < model_height and img.shape[1] < model_width:
+            img_padded = np.zeros(( model_height, model_width, img.shape[2] ))
+            
+            index_start_h =  int( abs( img.shape[0] - model_height) /2.)
+            index_start_w = int( abs( img.shape[1] - model_width) /2.)
+            
+            img_padded [ index_start_h: index_start_h+img.shape[0], index_start_w: index_start_w+img.shape[1], : ] = img[:,:,:]
+            
+        else:
+            index_start_h = 0
+            index_start_w  = 0
+            img_padded = np.copy(img)
+            
+            
+        img = np.copy(img_padded)
+        
+            
 
         if use_patches:
 
@@ -179,7 +217,10 @@ class SbbBinarizer:
 
                         mask_true[index_y_d + margin:index_y_u - margin, index_x_d + margin:index_x_u - margin] = seg
                         prediction_true[index_y_d + margin:index_y_u - margin, index_x_d + margin:index_x_u - margin, :] = seg_color
-
+            
+            
+            
+            prediction_true = prediction_true[index_start_h: index_start_h+img_org_h, index_start_w: index_start_w+img_org_w,:]
             prediction_true = prediction_true.astype(np.uint8)
 
         else: