汉字特征的灰度共生矩阵法的具体算法c语言如何实现

以下是汉字特征的灰度共生矩阵法的具体算法c语言实现的示例代码：

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

#define N 256 // 灰度级数
#define D 4 // 灰度共生矩阵方向数

// 计算灰度共生矩阵
void calcGLCM(int** img, int height, int width, int** glcm) {
    int i, j, k, di, dj, max;
    int** count = (int**)malloc(N * sizeof(int*));
    for (i = 0; i < N; i++) {
        count[i] = (int*)malloc(N * sizeof(int));
        for (j = 0; j < N; j++) {
            count[i][j] = 0;
        }
    }
    for (i = 0; i < height; i++) {
        for (j = 0; j < width; j++) {
            for (k = 0; k < D; k++) {
                di = i + (int)round(sin(k * M_PI / D));
                dj = j + (int)round(cos(k * M_PI / D));
                if (di >= 0 && di < height && dj >= 0 && dj < width) {
                    count[img[i][j]][img[di][dj]]++;
                }
            }
        }
    }
    // 归一化
    max = 0;
    for (i = 0; i < N; i++) {
        for (j = 0; j < N; j++) {
            if (count[i][j] > max) {
                max = count[i][j];
            }
        }
    }
    for (i = 0; i < N; i++) {
        for (j = 0; j < N; j++) {
            glcm[i][j] = (int)round(count[i][j] * 255.0 / max);
        }
    }
    // 释放内存
    for (i = 0; i < N; i++) {
        free(count[i]);
    }
    free(count);
}

// 计算灰度共生矩阵特征
void calcGLCMFeatures(int** glcm, int** features) {
    int i, j, k;
    double u1, u2, s1, s2, c, e, h;
    int* sum_r = (int*)malloc(N * sizeof(int));
    int* sum_c = (int*)malloc(N * sizeof(int));
    for (i = 0; i < N; i++) {
        sum_r[i] = 0;
        sum_c[i] = 0;
        for (j = 0; j < N; j++) {
            sum_r[i] += glcm[i][j];
            sum_c[j] += glcm[i][j];
        }
    }
    u1 = u2 = s1 = s2 = c = e = h = 0.0;
    for (i = 0; i < N; i++) {
        for (j = 0; j < N; j++) {
            u1 += i * glcm[i][j];
            u2 += j * glcm[i][j];
            s1 += pow(i - u1, 2) * glcm[i][j];
            s2 += pow(j - u2, 2) * glcm[i][j];
            c += pow(i - u1, 1) * pow(j - u2, 1) * glcm[i][j];
            e += pow(glcm[i][j], 2);
            if (glcm[i][j] > 0) {
                h -= glcm[i][j] * log2(glcm[i][j]);
            }
        }
    }
    features[0][0] = (int)round(s1);
    features[0][1] = (int)round(s2);
    features[0][2] = (int)round(c);
    features[0][3] = (int)round(e);
    features[0][4] = (int)round(h);
    // 释放内存
    free(sum_r);
    free(sum_c);
}

int main() {
    // 读取图像
    int height, width, i, j;
    int** img = (int**)malloc(height * sizeof(int*));
    for (i = 0; i < height; i++) {
        img[i] = (int*)malloc(width * sizeof(int));
        for (j = 0; j < width; j++) {
            // 读取像素值
        }
    }
    // 计算灰度共生矩阵
    int** glcm = (int**)malloc(N * sizeof(int*));
    for (i = 0; i < N; i++) {
        glcm[i] = (int*)malloc(N * sizeof(int));
        for (j = 0; j < N; j++) {
            glcm[i][j] = 0;
        }
    }
    calcGLCM(img, height, width, glcm);
    // 计算灰度共生矩阵特征
    int** features = (int**)malloc(1 * sizeof(int*));
    features[0] = (int*)malloc(5 * sizeof(int));
    calcGLCMFeatures(glcm, features);
    // 输出特征向量
    for (i = 0; i < 5; i++) {
        printf("%d ", features[0][i]);
    }
    printf("\n");
    // 释放内存
    for (i = 0; i < height; i++) {
        free(img[i]);
    }
    free(img);
    for (i = 0; i < N; i++) {
        free(glcm[i]);
    }
    free(glcm);
    free(features[0]);
    free(features);
    return 0;
}

其中，calcGLCM()函数用于计算灰度共生矩阵，calcGLCMFeatures()函数用于计算灰度共生矩阵的特征，例如对比度、能量、熵等。在主函数中，首先读取汉字图像，然后调用calcGLCM()函数计算灰度共生矩阵，最后调用calcGLCMFeatures()函数计算灰度共生矩阵的特征向量。