Mnist数字识别

本例程利用mnist数字数据集，自行训练神经网络得到手写数字识别神经网络模型，性能和准确率很高。 在OpenMV4 H7 Plus上面运行大概每秒45帧，在OpenMV4 H7上面运行大概每秒25帧左右。

运行目录前，请将链接中的trained.tflite下载到电脑，并复制到OpenMV的存储中。

https://github.com/SingTown/openmv_tensorflow_training_scripts/tree/main/mnist

# This code run in OpenMV4 H7 or OpenMV4 H7 Plus

import sensor, image, time, os, tf

sensor.reset()                         # Reset and initialize the sensor.
sensor.set_pixformat(sensor.GRAYSCALE)    # 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.

clock = time.clock()
while(True):
    clock.tick()
    img = sensor.snapshot().binary([(0,64)])
    for obj in tf.classify("trained.tflite", img, min_scale=1.0, scale_mul=0.5, x_overlap=0.0, y_overlap=0.0):
        output = obj.output()
        number = output.index(max(output))
        print(number)
    print(clock.fps(), "fps")

运行结果：