init

app.py

@@ -78,8 +78,8 @@ def get_segmentation_pipeline(
 @spaces.GPU
 def segment_image(
         image: Image,
-        #image_processor: AutoImageProcessor,
-        #image_segmentor: UperNetForSemanticSegmentation
+        image_processor: AutoImageProcessor,
+        image_segmentor: UperNetForSemanticSegmentation
 ) -> Image:
     """
     Segments an image using a semantic segmentation model.
@@ -209,9 +209,9 @@ class ControlNetDepthDesignModelMulti:
         orig_w, orig_h = empty_room_image.size
         new_width, new_height = resize_dimensions(empty_room_image.size, img_size)
         input_image = empty_room_image.resize((new_width, new_height))
-        real_seg = np.array(segment_image(input_image))#,
-                                          #seg_image_processor,
-                                          #image_segmentor))
+        real_seg = np.array(segment_image(input_image,
+                                          seg_image_processor,
+                                          image_segmentor))
         unique_colors = np.unique(real_seg.reshape(-1, real_seg.shape[2]), axis=0)
         unique_colors = [tuple(color) for color in unique_colors]
         segment_items = [map_colors_rgb(i) for i in unique_colors]