基于英飞凌单片机和MT9V034摄像头的c语言sauvola算法

Sauvola算法是一种局部自适应二值化算法，它可以根据局部像素的灰度值和方差来自适应地确定二值化的阈值。下面是基于英飞凌单片机和MT9V034摄像头的C语言Sauvola算法的参考代码。

#include <math.h>

#define W 80
#define H 60
#define R 10

unsigned char image[H][W], binary[H][W];

void sauvola(unsigned char* src, unsigned char* dst, int width, int height, int r, double k)
{
    double sum, sum2, mean, stdev, threshold;
    int count, x, y, i, j, sx, sy, ex, ey;
    for (y = 0; y < height; y++) {
        for (x = 0; x < width; x++) {
            sum = sum2 = 0;
            count = 0;
            sx = x - r;
            sy = y - r;
            ex = x + r;
            ey = y + r;
            if (sx < 0) sx = 0;
            if (sy < 0) sy = 0;
            if (ex >= width) ex = width - 1;
            if (ey >= height) ey = height - 1;
            for (j = sy; j <= ey; j++) {
                for (i = sx; i <= ex; i++) {
                    sum += src[j * width + i];
                    sum2 += src[j * width + i] * src[j * width + i];
                    count++;
                }
            }
            mean = sum / count;
            stdev = sqrt((sum2 - sum * sum / count) / count);
            threshold = mean * (1 + k * (stdev / 128 - 1));
            if (src[y * width + x] > threshold)
                dst[y * width + x] = 255;
            else
                dst[y * width + x] = 0;
        }
    }
}

void main()
{
    int i, j;
    while (1) {
        // Read image from MT9V034 camera
        // ...

        // Convert to grayscale
        for (j = 0; j < H; j++) {
            for (i = 0; i < W; i++) {
                unsigned char r = image[j][i * 2];
                unsigned char g = image[j][i * 2 + 1];
                unsigned char b = image[j][i * 2 + 2];
                image[j][i] = (r * 19595 + g * 38469 + b * 7472) >> 16;
            }
        }

        // Apply Sauvola algorithm
        sauvola(image, binary, W, H, R, 0.5);

        // Output binary image
        // ...
    }
}

在上面的代码中，首先从MT9V034摄像头读取图像，并将其转换为灰度图像。然后，通过调用sauvola函数对图像进行二值化处理。最后，将二值化图像输出。在sauvola函数中，使用一个正方形的局部窗口来计算每个像素的阈值。计算阈值时，根据局部像素的灰度值和方差来计算阈值。根据阈值将像素二值化为0或255。