micropython实现最大类间方差法(Otsu)

Micropython中实现最大类间方差法（Otsu）的方法如下：

import utime
import sensor
import image

def otsu_threshold(img):
    hist = img.get_histogram()
    total_pixels = img.width() * img.height()
    sum_total = 0

    for i in range(256):
        sum_total += i * hist[i]

    sum_back = 0
    w_back = 0
    w_fore = 0
    var_max = 0
    threshold = 0

    for i in range(256):
        w_back += hist[i]
        if w_back == 0:
            continue
        w_fore = total_pixels - w_back
        if w_fore == 0:
            break

        sum_back += i * hist[i]
        mean_back = sum_back / w_back
        mean_fore = (sum_total - sum_back) / w_fore

        var_between = w_back * w_fore * (mean_back - mean_fore) ** 2

        if var_between > var_max:
            var_max = var_between
            threshold = i

    return threshold

sensor.reset()
sensor.set_pixformat(sensor.GRAYSCALE)
sensor.set_framesize(sensor.QQVGA)
sensor.skip_frames(time=2000)

while True:
    img = sensor.snapshot()
    threshold_value = otsu_threshold(img)
    img.binary([threshold_value])
    img.show()
    utime.sleep(100)

这段代码实现了使用Otsu算法进行图像二值化。在`otsu_threshold`函数中，首先计算图像的直方图，然后根据直方图计算类间方差。通过遍历所有可能的阈值，找到最大的类间方差对应的阈值，作为最终的二值化阈值。在主循环中，读取图像并调用`otsu_threshold`函数得到阈值，然后使用该阈值对图像进行二值化并显示。

请注意，这段代码是基于MicroPython语言编写的，并假设您已经正确配置了摄像头和传感器。确保在运行之前进行适当的硬件和软件设置。