# LetNet识别数字演示程序
import sensor, image, time, os, nn

sensor.reset()                         # 复位、初始化摄像头
sensor.set_contrast(3)                 # 设置对比度
sensor.set_pixformat(sensor.GRAYSCALE) # 设置像素格式 RGB565 (or GRAYSCALE)
sensor.set_framesize(sensor.QVGA)      # 设置分辨率 QVGA (320x240)
sensor.set_windowing((128, 128))       # 设置 128x128 窗口
sensor.skip_frames(time=100)           # 设置跳过时间
sensor.set_auto_gain(False)            # 禁用自动增益
sensor.set_auto_exposure(False)        # 禁用自动曝光

# 导入lenet模型
net = nn.load('/lenet.network')
labels = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']# 数字存放

clock = time.clock()                # 创建时钟跟踪帧率
while(True):
    clock.tick()                    # 更新帧率时钟
    img = sensor.snapshot()         # 拍照一张
    out = net.forward(img.copy().binary([(150, 255)], invert=True)) #转换为二进制格式
    max_idx = out.index(max(out))   # 最大准确度
    score = int(out[max_idx]*100)   # 小数*100，两位整数
    if (score < 60):                # 60%以上认为识别成功
        score_str = "Sorry!"        # 不成功提示放到字符串
    else:
        score_str = "%s:%d%% "%(labels[max_idx], score) # 识别的数字和准确度放到字符串
    img.draw_string(0, 0, score_str)# 图片上显示字符串

    print("Number:",score_str)# 终端显示

    #print(score_str)
    #print(clock.fps())             # Note: OpenMV Cam runs about half as fast when connected
                                   # to the IDE. The FPS should increase once disconnected.