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1 - 我正在提取超像素的特征，我需要实现的提取之一是从灰度共生矩阵 GLCM（灰度共生矩阵）中提取 Haralick 特征。在这个意义上，我明白了，首先我必须提取 Haralick，然后将其应用到 GLCM 以找到我需要的对比度、能量等特征，但我不知道我是否真的正确。如果有道理，你能帮我吗？

# GLCM
def get_texture_glcm (img):

    # convert to gray
    gray = cv2.cvtColor (img, cv2.COLOR_BGR2GRAY)
  
    distances = [1, 2, 3]
    angles = [0, np.pi / 4, np.pi / 2, 3 * np.pi / 4]


    glcm = greycomatrix (gray,
                    distances = distances,
                    angles = angles,
                    symmetric = True,
                    normed = True)

    properties = ['contrast', 'energy', 'homogeneity', 'correlation', 'dissimilarity']
    contrast = greycoprops (glcm, properties [0])
    energy = greycoprops (glcm, properties [1])
    homogeneity = greycoprops (glcm, properties [2])
    correlation = greycoprops (glcm, properties [3])
    dissimilarity = greycoprops (glcm, properties [4])
    contrast = greycoprops (glcm, properties [0]). mean (axis = 1)

    return contrast, energy, homogeneity, correlation, dissimilarity

# HARALICK
def get_texture_haralick (image):
    print ("[STATUS] Started extracting haralick textures ...")
    # function to extract haralick textures from an image
    # calculate haralick texture features for 4 types of adjacency
    textures = mh.features.haralick (image)

    # take the mean of it and return it
    ht_mean = textures.mean (axis = 0)

    contrast, energy, homogeneity, correlation, dissimilarity = get_texture_glcm (ht_mean)
    
    return contrast, energy, homogeneity, correlation, dissimilarity

2 - 除了了解我是否正确考虑了上面的代码之外，我还需要根据 GLCM 计算 Haralick 的熵，为此，我知道我可以使用 SCIPY 函数或实现 SHANNON ENTROPY，如下所述：

从图像的 GLCM 计算熵 https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.entropy.html

我想实现的 Haralick 考虑这个熵是否合适？

entropy = scipy.stats.entropy (ma_hist)

感谢您愿意提供帮助。

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