4. 神经网络中的文件类名怎么打印出来？判断对于识别率为90%时打印出来
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    • 神经网络中的文件类名怎么打印出来？判断对于识别率为90%时打印出来

    • I
       

      # Edge Impulse - OpenMV Image Classification Example

import sensor, image, time, os, tf, pyb

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")]

clock = time.clock()
while(True):
    clock.tick()

    img = sensor.snapshot()
    for i in range(10):
        x = (pyb.rng() % (2*img.width())) - (img.width()//2)
        y = (pyb.rng() % (2*img.height())) - (img.height()//2)
        w = (pyb.rng() % (img.width()//2))
        h = (pyb.rng() % (img.height()//2))
        r = (pyb.rng() % 127) + 128
        g = (pyb.rng() % 127) + 128
        b = (pyb.rng() % 127) + 128
    img.draw_rectangle(x, y, w, h, color = (r, g, b), thickness = 2, fill = False)
    # 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]))

    print(clock.fps(), "fps")
    #目标判断及框取
   # img.draw_rectangle(blob.rect())#如果识别到颜色就框起来
    #img.draw_cross(blob.cx(), blob.cy())#draw_cross:在中心画十字架；blob.cx(), blob.cy：色块中心坐标+
      0

    • predictions_list[i][0]就是名字

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