Update app.py

app.py

@@ -138,19 +138,18 @@ def inference(image_url, image, min_score):
     measurements = {}
     
     for ind,item_mask in enumerate(masks):
-      # box=bbox[ind]
-      # Get the true bounding box of the mask (not the same as the bbox prediction)
-      segmentation = np.where(item_mask == True)
-      x_min = int(np.min(segmentation[1]))
-      x_max = int(np.max(segmentation[1]))
-      y_min = int(np.min(segmentation[0]))
-      y_max = int(np.max(segmentation[0]))
-      measurement = int(0.5+len(segmentation[0])/600)
-      measurements[ind] = {'measurement': measurement, 'x_min': x_min, 'x_max': x_max, 'y_min': y_min, 'y_max': y_max}
-      # cv2.putText(img=img, text=str(int(0.5+len( segmentation[0])/600)), org=(x_min+20,y_min-10), fontFace=cv2.FONT_HERSHEY_TRIPLEX, fontScale=0.8, color=(0, 255, 0),thickness=2)
-    # cv2_imshow(img)
-    
-    
+        if  segmentation[1].any() segmentation[0].any()
+          # box=bbox[ind]
+          # Get the true bounding box of the mask (not the same as the bbox prediction)
+          segmentation = np.where(item_mask == True)
+          x_min = int(np.min(segmentation[1]))
+          x_max = int(np.max(segmentation[1]))
+          y_min = int(np.min(segmentation[0]))
+          y_max = int(np.max(segmentation[0]))
+          measurement = int(0.5+len(segmentation[0])/600)
+          measurements[ind] = {'measurement': measurement, 'x_min': x_min, 'x_max': x_max, 'y_min': y_min, 'y_max': y_max}
+          # cv2.putText(img=img, text=str(int(0.5+len( segmentation[0])/600)), org=(x_min+20,y_min-10), fontFace=cv2.FONT_HERSHEY_TRIPLEX, fontScale=0.8, color=(0, 255, 0),thickness=2)        
+        
     # Loop over the masks
     # return outputs
     for ind, item_mask in enumerate(masks):