用C语言写一个SURF程序，不依赖第三方库

写一个完整的SURF程序需要涉及很多细节和算法，如果你想要自己实现一个SURF程序，需要具备一定的图像处理和算法基础。下面是一个简单的SURF特征提取程序，仅供参考。

首先需要准备一张输入图像，这里假设为lena.bmp。

#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <math.h>

#define WIDTH 512
#define HEIGHT 512

typedef struct {
    int x;
    int y;
    int s;
    float *desc;
} keypoint;

float gaussian_kernel[5][5] = {
    {0.023528, 0.033969, 0.038393, 0.033969, 0.023528},
    {0.033969, 0.049045, 0.055432, 0.049045, 0.033969},
    {0.038393, 0.055432, 0.062651, 0.055432, 0.038393},
    {0.033969, 0.049045, 0.055432, 0.049045, 0.033969},
    {0.023528, 0.033969, 0.038393, 0.033969, 0.023528}
};

float sobel_x[3][3] = {
    {-1, 0, 1},
    {-2, 0, 2},
    {-1, 0, 1}
};

float sobel_y[3][3] = {
    {-1, -2, -1},
    {0, 0, 0},
    {1, 2, 1}
};

unsigned char *read_image(char *filename, int width, int height)
{
    unsigned char *image_data = (unsigned char *)malloc(width * height * sizeof(unsigned char));
    FILE *fp = fopen(filename, "rb");
    fread(image_data, sizeof(unsigned char), width * height, fp);
    fclose(fp);
    return image_data;
}

void write_image(char *filename, unsigned char *image_data, int width, int height)
{
    FILE *fp = fopen(filename, "wb");
    fwrite(image_data, sizeof(unsigned char), width * height, fp);
    fclose(fp);
}

float *convolve(float *input, int width, int height, float kernel[][5], int kernel_size)
{
    float *output = (float *)calloc(width * height, sizeof(float));
    int k = kernel_size / 2;
    for (int y = k; y < height - k; y++) {
        for (int x = k; x < width - k; x++) {
            float sum = 0;
            for (int j = -k; j <= k; j++) {
                for (int i = -k; i <= k; i++) {
                    int offset = (y + j) * width + x + i;
                    sum += input[offset] * kernel[j + k][i + k];
                }
            }
            output[y * width + x] = sum;
        }
    }
    return output;
}

void gaussian_filter(unsigned char *input, int width, int height, float *output)
{
    float *tmp = (float *)malloc(width * height * sizeof(float));
    for (int i = 0; i < width * height; i++) {
        tmp[i] = (float)input[i];
    }
    float *filtered = convolve(tmp, width, height, gaussian_kernel, 5);
    for (int i = 0; i < width * height; i++) {
        output[i] = filtered[i];
    }
    free(tmp);
    free(filtered);
}

void sobel_filter(float *input, int width, int height, float *gradient, float *theta)
{
    float *dx = convolve(input, width, height, sobel_x, 3);
    float *dy = convolve(input, width, height, sobel_y, 3);
    for (int i = 0; i < width * height; i++) {
        gradient[i] = sqrt(dx[i] * dx[i] + dy[i] * dy[i]);
        theta[i] = atan2(dy[i], dx[i]);
    }
    free(dx);
    free(dy);
}

void find_keypoints(float *gradient, float *theta, int width, int height, keypoint *keys, int *count)
{
    int k = 4;
    float H[3][3] = {0};
    for (int y = k; y < height - k; y += k) {
        for (int x = k; x < width - k; x += k) {
            int offset = y * width + x;
            float max_grad = -1;
            int max_x = -1;
            int max_y = -1;
            for (int j = -k; j <= k; j++) {
                for (int i = -k; i <= k; i++) {
                    int tmp_offset = (y + j) * width + x + i;
                    float grad_value = gradient[tmp_offset];
                    if (grad_value > max_grad) {
                        max_grad = grad_value;
                        max_x = i;
                        max_y = j;
                    }
                }
            }
            if (max_grad > 0) {
                float tmp_theta = theta[offset + max_y * width + max_x];
                keys[*count].x = x;
                keys[*count].y = y;
                keys[*count].s = k;
                keys[*count].desc = NULL;
                (*count)++;
            }
        }
    }
}

int main(int argc, char *argv[])
{
    unsigned char *input = read_image("lena.bmp", WIDTH, HEIGHT);
    float *filtered = (float *)malloc(WIDTH * HEIGHT * sizeof(float));
    gaussian_filter(input, WIDTH, HEIGHT, filtered);
    float *gradient = (float *)malloc(WIDTH * HEIGHT * sizeof(float));
    float *theta = (float *)malloc(WIDTH * HEIGHT * sizeof(float));
    sobel_filter(filtered, WIDTH, HEIGHT, gradient, theta);
    keypoint *keys = (keypoint *)malloc(WIDTH * HEIGHT * sizeof(keypoint));
    int count = 0;
    find_keypoints(gradient, theta, WIDTH, HEIGHT, keys, &count);
    printf("Found %d keypoints\n", count);
    free(input);
    free(filtered);
    free(gradient);
    free(theta);
    free(keys);
    return 0;
}

以上代码仅包含了SURF算法的一部分，只能提取图像中的关键点，并没有进行特征描述和匹配。如果需要完整的SURF实现，可以参考相关文献或代码实现。