如何使用基于 GLCM 的 Haralick 推导从超像素中提取特征？

问题描述

1 - 我正在为超像素提取特征，我需要实施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。从这个意义上讲，我明白首先我必须提取Haralick，然后将其应用到GLCM中才能找到我需要的特征的对比度，能量等，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,dissimilarity = get_texture_glcm (ht_mean)
    
    return contrast,dissimilarity

2 - 除了了解我是否正确思考了上面的代码之外，我还需要基于 GLCM 计算 Haralick 的熵，为此，我明白我可以使用 SCIPY 函数或实现 SHANNON 熵，如下所述：

Calculating entropy from GLCM of an image https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.entropy.html

对于我想要实现的 Haralick 来说，考虑这个熵是否合适？

entropy = scipy.stats.entropy (ma_hist)

感谢您愿意提供帮助。

解决方法

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