Set 4 images as input.

app.py

@@ -34,8 +34,8 @@ if os.path.exists(state_dict):
 model.eval()
 
 
-def pred_maps(dr):
-    images = [cv2.imread(image_path) for image_path in glob(os.path.join(dr, '*'))]
+def pred_maps(image_1, image_2, image_3, image_4):
+    images = [image_1, image_2, image_3, image_4]
     image_shapes = [image.shape[:2] for image in images]
     images = [Image.fromarray(image) for image in images]
 
@@ -55,19 +55,18 @@ def pred_maps(dr):
     image_preds = []
     for image, pred in zip(images, preds):
         image_preds.append(
-            cv2.cvtColor(
+            # cv2.cvtColor(
                 np.hstack([np.array(image.convert('RGB')), cv2.cvtColor((pred*255).astype(np.uint8), cv2.COLOR_GRAY2RGB)]),
-                cv2.COLOR_BGR2RGB
-        ))
-    # for image_pred in image_preds:
-    #     cv2.imwrite('a.png', cv2.cvtColor(image_pred, cv2.COLOR_RGB2BGR))
+                # cv2.COLOR_BGR2RGB)
+        )
     return image_preds[:]
 
 
-examples = glob('example_images/butterfly/*')
+N = 4
+examples = [[_] for _ in glob('example_images/butterfly/*')][:N]
 
-ipt = ['image' for _ in range(5)]
-opt = ipt.copy()
+ipt = [gr.Image().style(width=600, height=150) for _ in range(N)]
+opt = [gr.Image().style(width=600, height=150) for _ in range(N)]
 demo = gr.Interface(
     fn=pred_maps,
     inputs=ipt,
@@ -75,6 +74,6 @@ demo = gr.Interface(
     examples=examples,
     interpretation='default',
     title='Online demo for `GCoNet+: A Stronger Group Collaborative Co-Salient Object Detector (T-PAMI 2023)`',
-    description='Upload pictures, most of which contain salient objects of the same class.\nOur demo will give you the binary maps of these co-salient objects :)'
+    description='Upload pictures, most of which contain salient objects of the same class. Our demo will give you the binary maps of these co-salient objects :)\n**********Example images need to be dropped into each block, instead of click.**********'
 )
 demo.launch(debug=True)