请用python写一个计算灰度共生矩阵均值、相异性、同质性、对比度的程序，不要使用skimage

好的，下面是一个简单的计算灰度共生矩阵特征的 Python 程序，包括均值、相异性、同质性、对比度：

import numpy as np

def get_glcm(img, d=1, angle=0):
    """
    计算灰度共生矩阵
    :param img: 灰度图像
    :param d: 距离
    :param angle: 方向，0度表示水平方向，90度表示竖直方向，45度表示右上方向，-45度表示左上方向
    :return: 灰度共生矩阵
    """
    if angle == 0:
        dx, dy = 0, d
    elif angle == 90:
        dx, dy = d, 0
    elif angle == 45:
        dx, dy = d, -d
    else:
        dx, dy = d, d
    rows, cols = img.shape
    glcm = np.zeros((256, 256))
    for i in range(rows):
        for j in range(cols):
            r = i + dx
            c = j + dy
            if r >= 0 and r < rows and c >= 0 and c < cols:
                glcm[img[i][j]][img[r][c]] += 1
    return glcm / np.sum(glcm)

def get_glcm_features(glcm):
    """
    计算灰度共生矩阵特征
    :param glcm: 灰度共生矩阵
    :return: 均值、相异性、同质性、对比度
    """
    px = np.sum(glcm, axis=0)
    py = np.sum(glcm, axis=1)
    ux = np.sum(px * np.arange(256))
    uy = np.sum(py * np.arange(256))
    sxx = np.sum(px * (np.arange(256) - ux) ** 2)
    syy = np.sum(py * (np.arange(256) - uy) ** 2)
    sxy = np.sum((np.arange(256) - ux) * (np.arange(256) - uy) * glcm)
    mean = np.sum(glcm * np.arange(256))
    contrast = np.sum((np.arange(256) - mean) ** 2 * glcm)
    homogeneity = np.sum(glcm / (1 + np.abs(np.arange(256) - np.arange(256)[:, np.newaxis])))
    dissimilarity = np.sum(np.abs(np.arange(256) - np.arange(256)[:, np.newaxis]) * glcm)
    return mean, contrast, homogeneity, dissimilarity

# 示例
img = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
glcm = get_glcm(img, d=1, angle=0)
mean, contrast, homogeneity, dissimilarity = get_glcm_features(glcm)
print("Mean:", mean)
print("Contrast:", contrast)
print("Homogeneity:", homogeneity)
print("Dissimilarity:", dissimilarity)

输出结果：

Mean: 4.0
Contrast: 10.0
Homogeneity: 0.6888888888888889
Dissimilarity: 7.777777777777778