Update app.py

app.py

@@ -35,6 +35,42 @@ predictor = DefaultPredictor(cfg)
 my_metadata = MetadataCatalog.get("car_dataset_val")
 my_metadata.thing_classes = ["damage"]
 
+def merge_segment(pred_segm):
+    merge_dict = {}
+    for i in range(len(pred_segm)):
+        merge_dict[i] = []
+        for j in range(i+1,len(pred_segm)):
+            if torch.sum(pred_segm[i]*pred_segm[j])>0:
+                merge_dict[i].append(j)
+    
+    to_delete = []
+    for key in merge_dict:
+        for element in merge_dict[key]:
+            to_delete.append(element)
+    
+    for element in to_delete:
+        merge_dict.pop(element,None)
+        
+    empty_delete = []
+    for key in merge_dict:
+        if merge_dict[key] == []:
+            empty_delete.append(key)
+    
+    for element in empty_delete:
+        merge_dict.pop(element,None)
+        
+    for key in merge_dict:
+        for element in merge_dict[key]:
+            pred_segm[key]+=pred_segm[element]
+            
+    except_elem = list(set(to_delete))
+    
+    new_indexes = list(range(len(pred_segm)))
+    for elem in except_elem:
+        new_indexes.remove(elem)
+        
+    return pred_segm[new_indexes]
+
 def inference(image):
     print(image.height)
 
@@ -43,14 +79,17 @@ def inference(image):
     # img = np.array(image.resize((500, height)))
     img = np.array(image)
     outputs = predictor(img)
+    out_dict = outputs["instances"].to("cpu").get_fields()
+    new_inst = detectron2.structures.Instances((1024,1024))
+    new_inst.set('pred_masks',merge_segment(out_dict['pred_masks']))
     v = Visualizer(img[:, :, ::-1],
                    metadata=my_metadata, 
                    scale=0.5, 
                    instance_mode=ColorMode.SEGMENTATION   # remove the colors of unsegmented pixels. This option is only available for segmentation models
     )
     # v = Visualizer(img,scale=1.2)
-    print(outputs["instances"].to('cpu'))
-    out = v.draw_instance_predictions(outputs["instances"].to("cpu"))
+    #print(outputs["instances"].to('cpu'))
+    out = v.draw_instance_predictions(new_inst)
     
     return out.get_image()[:, :, ::-1]