4. 怎么比较神经网络识别的正确率大小呢
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    • 怎么比较神经网络识别的正确率大小呢

    • A
       

      import sensor, image, time, os, tf, time
from pyb import Servo
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 = "trained.tflite"
labels = [line.rstrip('\n') for line in open("labels.txt")]
s1 = Servo(1) # P7连接舵机的PWM线
s2 = Servo(2) # P8
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 tf.classify(net, 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]))
            if predictions_list[i][0]=='battery' and predictions_list[i][1]>0.8:             #在什么概率下 进行不同的动作
                print("%s = %f" % (predictions_list[i][0], predictions_list[i][1]))
                for i in range(-180,-90):
                    s1.angle(0)
                    s2.angle(i)
                    time.sleep(10)
            if predictions_list[i][0]=='bottle' and float(predictions_list[i][1])>0.8:
                print("%s = %f" % (predictions_list[i][0], predictions_list[i][1]))
                for i in range(-180,0):
                    s1.angle(90)
                    s2.angle(i)
                    time.sleep(10)
            if predictions_list[i][0]=='can' and float(predictions_list[i][1])>0.8:
                print("%s = %f" % (predictions_list[i][0], predictions_list[i][1]))
                for i in range(-180,90):
                    s1.angle(180)
                    s2.angle(i)
                    time.sleep(10)
            if predictions_list[i][0]=='cigarette' and float(predictions_list[i][1])>0.8:
                print("%s = %f" % (predictions_list[i][0], predictions_list[i][1]))
                for i in range(-180,180):![0_1607955907770_f599f1802baf269f4b0010f9c928f49.jpg](https://fcdn.singtown.com/4c0a1418-650a-4853-9f62-6284660f5ecd.jpg) 
                    s1.angle(270)
                    s2.angle(i)
                    time.sleep(10)
      0
    • A

      if predictions_list[i][0]=='bottle' and float(predictions_list[i][1])>0.8:
      就我这句错了 说我越界了 要怎么写才是对的呢

      0
    • A

      找到原因了 把列表里的“bottle”这种的判断删去就没事了

      0