Update app.py

app.py

@@ -120,7 +120,7 @@ def merge_segment(pred_segm):
     return pred_segm[new_indexes]
 
     
-def inference(image
+def inference(image):
     img = np.array(image)
     outputs_damage = predictor_damage(img)
     outputs_parts = predictor_parts(img)
@@ -134,6 +134,8 @@ def inference(image
     parts_data = outputs_parts["instances"].get_fields()
     parts_masks = parts_data['pred_masks']
     parts_classes = parts_data['pred_classes']
+    new_inst = detectron2.structures.Instances((1024,1024))
+    new_inst.set('pred_masks',merge_segment(out_dict['pred_masks']))
     
     parts_damage_dict = {}
     parts_list_damages = []
@@ -151,12 +153,36 @@ def inference(image
                 parts_damage_dict[metadata_parts.thing_classes[parts_classes[i]]].append('damage')
                 parts_list_damages.append(f'{metadata_parts.thing_classes[parts_classes[i]]} has damage')
                 print(f'{metadata_parts.thing_classes[parts_classes[i]]} has damage')
-                
-    img1 = np.array(np.random.rand(240,240))
-    img2 = np.array(np.random.rand(240,240))
-    img3 = np.array(np.random.rand(240,240))
-    parts_list_damage = 'test'
-    return img1, img2, img3, parts_list_damage
+
+    v_d = Visualizer(img[:, :, ::-1],
+                   metadata=metadata_damage, 
+                   scale=0.5, 
+                   instance_mode=ColorMode.SEGMENTATION   # remove the colors of unsegmented pixels. This option is only available for segmentation models
+    )
+    #v_d = Visualizer(img,scale=1.2)
+    #print(outputs["instances"].to('cpu'))
+    out_d = v_d.draw_instance_predictions(new_inst)
+    img1 = out_d.get_image()[:, :, ::-1]
+
+    v_s = Visualizer(img[:, :, ::-1],
+                   metadata=metadata_scratch, 
+                   scale=0.5, 
+                   instance_mode=ColorMode.SEGMENTATION   # remove the colors of unsegmented pixels. This option is only available for segmentation models
+    )
+    #v_s = Visualizer(img,scale=1.2)
+    out_s = v_s.draw_instance_predictions(outputs_scratch["instances"])
+    img2 = out_s.get_image()[:, :, ::-1]
+
+    v_p = Visualizer(img[:, :, ::-1],
+                   metadata=metadata_parts, 
+                   scale=0.5, 
+                   instance_mode=ColorMode.SEGMENTATION   # remove the colors of unsegmented pixels. This option is only available for segmentation models
+    )
+    #v_p = Visualizer(img,scale=1.2)
+    out_p = v_p.draw_instance_predictions(outputs_parts["instances"])
+    img3 = out_p.get_image()[:, :, ::-1]
+    
+    return img1, img2, img3, parts_list_damages
 
 with gr.Blocks() as demo:
     with gr.Row():