c语言代码实现图像统计特性计算

以下是使用C语言实现图像统计特性计算的示例代码：

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

#define IMAGE_WIDTH 256
#define IMAGE_HEIGHT 256
#define IMAGE_SIZE (IMAGE_WIDTH * IMAGE_HEIGHT)

// 读取PGM格式图像文件，并返回图像的像素值数组
unsigned char* read_pgm_image(const char* filename)
{
    FILE* fp = fopen(filename, "rb");
    if (!fp) {
        printf("无法打开文件： %s\n", filename);
        return NULL;
    }
    char buf[16];
    fgets(buf, sizeof(buf), fp);
    if (strcmp(buf, "P5\n")) {
        printf("不是PGM格式图像文件\n");
        fclose(fp);
        return NULL;
    }
    fgets(buf, sizeof(buf), fp);
    int width, height;
    sscanf(buf, "%d %d", &width, &height);
    if (width != IMAGE_WIDTH || height != IMAGE_HEIGHT) {
        printf("图像尺寸不正确\n");
        fclose(fp);
        return NULL;
    }
    fgets(buf, sizeof(buf), fp);
    int maxval;
    sscanf(buf, "%d", &maxval);
    if (maxval != 255) {
        printf("不支持的像素深度\n");
        fclose(fp);
        return NULL;
    }
    unsigned char* data = (unsigned char*)malloc(IMAGE_SIZE);
    fread(data, 1, IMAGE_SIZE, fp);
    fclose(fp);
    return data;
}

// 计算灰度直方图
void calc_histogram(const unsigned char* data, int* hist)
{
    memset(hist, 0, sizeof(int) * 256);
    for (int i = 0; i < IMAGE_SIZE; i++) {
        hist[data[i]]++;
    }
}

// 计算均值、方差、标准差
void calc_mean_var_stddev(const unsigned char* data, double* mean, double* var, double* stddev)
{
    *mean = 0.0;
    for (int i = 0; i < IMAGE_SIZE; i++) {
        *mean += data[i];
    }
    *mean /= IMAGE_SIZE;

    *var = 0.0;
    for (int i = 0; i < IMAGE_SIZE; i++) {
        *var += (data[i] - *mean) * (data[i] - *mean);
    }
    *var /= IMAGE_SIZE;

    *stddev = sqrt(*var);
}

// 计算熵
void calc_entropy(const unsigned char* data, double* entropy)
{
    int hist[256];
    calc_histogram(data, hist);

    *entropy = 0.0;
    for (int i = 0; i < 256; i++) {
        double p = (double)hist[i] / IMAGE_SIZE;
        if (p > 0.0) {
            *entropy -= p * log2(p);
        }
    }
}

// 计算对比度
void calc_contrast(const unsigned char* data, double* contrast)
{
    double mean, var, stddev;
    calc_mean_var_stddev(data, &mean, &var, &stddev);
    *contrast = stddev / mean;
}

// 计算相关性
void calc_correlation(const unsigned char* data, double* correlation)
{
    double mean, var, stddev;
    calc_mean_var_stddev(data, &mean, &var, &stddev);

    double sum_xy = 0.0, sum_x = 0.0, sum_y = 0.0;
    for (int i = 0; i < IMAGE_SIZE; i++) {
        double x = data[i] - mean;
        double y = (double)i / IMAGE_SIZE - 0.5;
        sum_xy += x * y;
        sum_x += x;
        sum_y += y;
    }
    *correlation = sum_xy / sqrt(sum_x * sum_y * var * IMAGE_SIZE);
}

// 主函数
int main(int argc, char** argv)
{
    if (argc < 2) {
        printf("用法： %s filename.pgm\n", argv[0]);
        return 0;
    }
    const char* filename = argv[1];
    unsigned char* data = read_pgm_image(filename);
    if (!data) {
        return 0;
    }

    int hist[256];
    calc_histogram(data, hist);

    double mean, var, stddev;
    calc_mean_var_stddev(data, &mean, &var, &stddev);

    double entropy;
    calc_entropy(data, &entropy);

    double contrast;
    calc_contrast(data, &contrast);

    double correlation;
    calc_correlation(data, &correlation);

    printf("图像文件： %s\n", filename);
    printf("像素均值： %.2f\n", mean);
    printf("像素方差： %.2f\n", var);
    printf("像素标准差： %.2f\n", stddev);
    printf("像素熵： %.2f\n", entropy);
    printf("像素对比度： %.2f\n", contrast);
    printf("像素相关性： %.2f\n", correlation);

    free(data);
    return 0;
}

以上代码实现了读取PGM格式图像文件，并计算了图像的均值、方差、标准差、熵、对比度和相关性等统计特性。需要注意的是，该示例代码仅仅是一个基本的框架，具体的实现方式和细节需要根据具体的应用场景进行调整和改进。