添加什么代码，让其只在川行输出最高分？

# Edge Impulse - OpenMV Image Classification Example
       
       
       
       import sensor, image, time, os, tf, uos, gc
       
       
       
       sensor.reset()                         # Reset and initialize the sensor.
       
       sensor.set_pixformat(sensor.RGB565)    # Set pixel format to RGB565 (or GRAYSCALE)
       
       sensor.set_framesize(sensor.QVGA)      # Set frame size to QVGA (320x240)
       
       sensor.set_windowing((240, 240))       # Set 240x240 window.
       
       sensor.skip_frames(time=2000)          # Let the camera adjust.
       
       
       
       net = None
       
       labels = None
       
       
       
       try:
       
           # load the model, alloc the model file on the heap if we have at least 64K free after loading
       
           net = tf.load("trained.tflite", load_to_fb=uos.stat('trained.tflite')[6] > (gc.mem_free() - (64*1024)))
       
       except Exception as e:
       
           print(e)
       
           raise Exception('Failed to load "trained.tflite", did you copy the .tflite and labels.txt file onto the mass-storage device? (' + str(e) + ')')
       
       
       
       try:
       
           labels = [line.rstrip('\n') for line in open("labels.txt")]
       
       except Exception as e:
       
           raise Exception('Failed to load "labels.txt", did you copy the .tflite and labels.txt file onto the mass-storage device? (' + str(e) + ')')
       
       
       
       clock = time.clock()
       
       while(True):
       
           clock.tick()
       
       
       
           img = sensor.snapshot()
       
       
       
           # default settings just do one detection... change them to search the image...
       
           for obj in net.classify(img, min_scale=1.0, scale_mul=0.8, x_overlap=0.5, y_overlap=0.5):
       
               print("**********\nPredictions at [x=%d,y=%d,w=%d,h=%d]" % obj.rect())
       
               img.draw_rectangle(obj.rect())
       
               # This combines the labels and confidence values into a list of tuples
       
               predictions_list = list(zip(labels, obj.output()))
       
       
       
               for i in range(len(predictions_list)):
       
                   print("%s = %f" % (predictions_list[i][0], predictions_list[i][1]))
       
       
       
           print(clock.fps(), "fps")