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前言
如何使用skimage计算GLCM和纹理因子可以参考利用python的skimage计算灰度共生矩阵。但是Skimage库只提供了常用的8中纹理因子（均值、方差、同质性、对比度、差异性、熵、角二阶矩、相关性、）中的5种，缺少均值、方差和熵的计算。这里讲解如何修改源码添加这三个因子的计算。添加后调用方式和其他因子的计算相同。
修改/添加部分
找到 skimage库greycoprops源码。Spyder可以直接右键该函数，转到定义处。
需要添加/修改的部分，保持了与源码相同的编码风格，全是numpy向量化计算。
...............elif prop in ['ASM', 'energy', 'correlation', 'mean', 'variance', 'entroy']:
...............elif prop == 'mean':results = np.apply_over_axes(np.mean, P, axes=(0, 1))[0, 0]elif prop == 'variance':results = np.apply_over_axes(np.sum,(P - np.apply_over_axes(np.mean, P, axes=(0, 1)))**2,axes=(0, 1))[0, 0]elif prop == 'entroy':with np.errstate(divide='ignore'):condition_list = [P != 0, P == 0]choice_list = [-np.log10(P), 0]P = np.select(condition_list, choice_list)results = np.apply_over_axes(np.sum, P, axes=(0, 1))[0, 0]
..............
完整源码
def greycoprops(P, prop='contrast'):"""Calculate texture properties of a GLCM.Compute a feature of a grey level co-occurrence matrix to serve asa compact summary of the matrix. The properties are computed asfollows:- 'contrast': :math:`\\sum_{i,j=0}^{levels-1} P_{i,j}(i-j)^2`- 'dissimilarity': :math:`\\sum_{i,j=0}^{levels-1}P_{i,j}|i-j|`- 'homogeneity': :math:`\\sum_{i,j=0}^{levels-1}\\frac{P_{i,j}}{1+(i-j)^2}`- 'ASM': :math:`\\sum_{i,j=0}^{levels-1} P_{i,j}^2`- 'energy': :math:`\\sqrt{ASM}`- 'correlation':.. math:: \\sum_{i,j=0}^{levels-1} P_{i,j}\\left[\\frac{(i-\\mu_i) \\(j-\\mu_j)}{\\sqrt{(\\sigma_i^2)(\\sigma_j^2)}}\\right]Each GLCM is normalized to have a sum of 1 before the computation of textureproperties.Parameters----------P : ndarrayInput array. `P` is the grey-level co-occurrence histogramfor which to compute the specified property. The value`P[i,j,d,theta]` is the number of times that grey-level joccurs at a distance d and at an angle theta fromgrey-level i.prop : {'contrast', 'dissimilarity', 'homogeneity', 'energy', \'correlation', 'ASM'}, optionalThe property of the GLCM to compute. The default is 'contrast'.Returns-------results : 2-D ndarray2-dimensional array. `results[d, a]` is the property 'prop' forthe d'th distance and the a'th angle.References----------.. [1] The GLCM Tutorial Home Page,http://www.fp.ucalgary.ca/mhallbey/tutorial.htmExamples--------Compute the contrast for GLCMs with distances [1, 2] and angles[0 degrees, 90 degrees]>>> image = np.array([[0, 0, 1, 1],... [0, 0, 1, 1],... [0, 2, 2, 2],... [2, 2, 3, 3]], dtype=np.uint8)>>> g = greycomatrix(image, [1, 2], [0, np.pi/2], levels=4,... normed=True, symmetric=True)>>> contrast = greycoprops(g, 'contrast')>>> contrastarray([[0.58333333, 1. ],[1.25 , 2.75 ]])"""check_nD(P, 4, 'P')(num_level, num_level2, num_dist, num_angle) = P.shapeif num_level != num_level2:raise ValueError('num_level and num_level2 must be equal.')if num_dist <= 0:raise ValueError('num_dist must be positive.')if num_angle <= 0:raise ValueError('num_angle must be positive.')# normalize each GLCMP = P.astype(np.float64)glcm_sums = np.apply_over_axes(np.sum, P, axes=(0, 1))glcm_sums[glcm_sums == 0] = 1P /= glcm_sums# create weights for specified propertyI, J = np.ogrid[0:num_level, 0:num_level]if prop == 'contrast':weights = (I - J) ** 2elif prop == 'dissimilarity':weights = np.abs(I - J)elif prop == 'homogeneity':weights = 1. / (1. + (I - J) ** 2)elif prop in ['ASM', 'energy', 'correlation', 'mean', 'variance', 'entroy']:passelse:raise ValueError('%s is an invalid property' % (prop))# compute property for each GLCMif prop == 'energy':asm = np.apply_over_axes(np.sum, (P ** 2), axes=(0, 1))[0, 0]results = np.sqrt(asm)elif prop == 'ASM':results = np.apply_over_axes(np.sum, (P ** 2), axes=(0, 1))[0, 0]elif prop == 'correlation':results = np.zeros((num_dist, num_angle), dtype=np.float64)I = np.array(range(num_level)).reshape((num_level, 1, 1, 1))J = np.array(range(num_level)).reshape((1, num_level, 1, 1))diff_i = I - np.apply_over_axes(np.sum, (I * P), axes=(0, 1))[0, 0]diff_j = J - np.apply_over_axes(np.sum, (J * P), axes=(0, 1))[0, 0]std_i = np.sqrt(np.apply_over_axes(np.sum, (P * (diff_i) ** 2),axes=(0, 1))[0, 0])std_j = np.sqrt(np.apply_over_axes(np.sum, (P * (diff_j) ** 2),axes=(0, 1))[0, 0])cov = np.apply_over_axes(np.sum, (P * (diff_i * diff_j)),axes=(0, 1))[0, 0]# handle the special case of standard deviations near zeromask_0 = std_i < 1e-15mask_0[std_j < 1e-15] = Trueresults[mask_0] = 1# handle the standard casemask_1 = mask_0 == Falseresults[mask_1] = cov[mask_1] / (std_i[mask_1] * std_j[mask_1])elif prop in ['contrast', 'dissimilarity', 'homogeneity']:weights = weights.reshape((num_level, num_level, 1, 1))results = np.apply_over_axes(np.sum, (P * weights), axes=(0, 1))[0, 0]elif prop == 'mean':results = np.apply_over_axes(np.mean, P, axes=(0, 1))[0, 0]elif prop == 'variance':results = np.apply_over_axes(np.sum, (P - np.apply_over_axes(np.mean, P, axes=(0, 1)))**2, axes=(0, 1))[0, 0]elif prop == 'entroy':with np.errstate(divide='ignore'):condition_list = [P != 0, P == 0]choice_list = [-np.log10(P), 0]P = np.select(condition_list, choice_list)results = np.apply_over_axes(np.sum, P, axes=(0, 1))[0, 0]return results
使用示例
分别为方差、均值、熵的计算。
greycoprops(glcm, 'variance')
greycoprops(glcm, 'mean')
greycoprops(glcm, 'entroy')
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