# Edge Impulse - OpenMV Image Classification Example

import sensor, image, time, os, tf
from pyb import Pin
from pyb import LED
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")]

s=0  # ����������ֵ
b=0  # ��������

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()))
        s=s+predictions_list[1][1]
        b=b+1
        if b>4:
           b=0
           if s/5 > 0.99:
              led = LED(2) # ��led
              led.toggle()
              led.on()  #��
              s=0
           else:
              led = LED(2)
              led.toggle()
              led.off()  #��
              s=0

              #p_out = Pin('P7', Pin.OUT_PP)  # ����p_out��������
              #p_out.high()  # ����p_out������

        for i in range(len(predictions_list)):
            print("%s = %f" % (predictions_list[i][0], predictions_list[i][1]))

    print(clock.fps(), "fps")