前⾔
如何使⽤skimage计算GLCM和纹理因⼦可以参考。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
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']:
...............化尸池
elif prop =='mean':
results = np.apply_over_an, P, axes=(0,1))[0,0]
elif prop =='variance':
results = np.apply_over_axes(np.sum,
(P - np.apply_over_an, P, axes=(0,1)))**2,
axes=(0,1))[0,0]
elif prop =='entroy':
state(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 as
a compact summary of the matrix. The properties are computed as
follows:
- '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 texture
乐谱架properties.
Parameters
----------
P : ndarray
Input array. `P` is the grey-level co-occurrence histogram
for which to compute the specified property. The value
`P[i,j,d,theta]` is the number of times that grey-level j
occurs at a distance d and at an angle theta from
grey-level i.
prop : {'contrast', 'dissimilarity', 'homogeneity', 'energy', \
'correlation', 'ASM'}, optional
The property of the GLCM to compute. The default is 'contrast'.
Returns
-------
results : 2-D ndarray
2-dimensional array. `results[d, a]` is the property 'prop' for
the d'th distance and the a'th angle.
References
References
----------
.. [1] The GLCM Tutorial Home Page,
www.fp.ucalgary.ca/mhallbey/tutorial.htm
Examples
--------
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')
>>> contrast
array([[0.58333333, 1. ],
[1.25 , 2.75 ]])
"""
check_nD(P,4,'P')
(num_level, num_level2, num_dist, num_angle)= P.shape
if 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 GLCM
P = P.astype(np.float64)
glcm_sums = np.apply_over_axes(np.sum, P, axes=(0,1))
glcm_sums[glcm_sums ==0]=1
P /= glcm_sums
# create weights for specified property
I, J = np.ogrid[0:num_level,0:num_level]
if prop =='contrast':
weights =(I - J)**2
elif 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']: pass
else:
保安对讲机raise ValueError('%s is an invalid property'%(prop))
# compute property for each GLCM
if 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))
2硅酸铝纤维毡
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]
axes=(0,1))[0,0]
# handle the special case of standard deviations near zero
mask_0 = std_i <1e-15
mask_0[std_j <1e-15]=True
results[mask_0]=1
# handle the standard case
mask_1 = mask_0 ==False
results[mask_1]= cov[mask_1]/(std_i[mask_1]* std_j[mask_1])
elif prop in['contrast','dissimilarity','homogeneity']:
weights = shape((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_an, P, axes=(0,1))[0,0]
elif prop =='variance':
results = np.apply_over_axes(np.sum,
(P - np.apply_over_an, P, axes=(0,1)))**2, axes=(0,1))[0,0]
elif prop =='entroy':
state(divide='ignore'):360历史
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
高频预热机使⽤⽰例
分别为⽅差、均值、熵的计算。
greycoprops(glcm,'variance')
greycoprops(glcm,'mean')
greycoprops(glcm,'entroy')
豫公网安备41160202000603
站长QQ:729038198
关于我们
投诉建议