我在用skimage用于大多数图像分析工作的库。
我有一个 RGB 图像，我打算提取texture特征如entropy, energy, homogeneity and contrast从图像中。
以下是我正在执行的步骤：
from skimage import io, color, feature
from skimage.filters import rank
rgbImg = io.imread(imgFlNm)
grayImg = color.rgb2gray(rgbImg)
print(grayImg.shape) # (667,1000), a 2 dimensional grayscale image
glcm = feature.greycomatrix(grayImg, [1], [0, np.pi/4, np.pi/2, 3*np.pi/4])
print(glcm.shape) # (256, 256, 1, 4)
rank.entropy(glcm, disk(5)) # throws an error since entropy expects a 2-D array in its arguments
rank.entropy(grayImg, disk(5)) # given an output.
我的问题是，从灰度图像（直接）计算出的熵与从 GLCM（纹理特征）提取的熵特征相同吗？
如果不是，从图像中提取所有纹理特征的正确方法是什么？
注：我已经提到过：
熵 - skimage http://scikit-image.org/docs/dev/api/skimage.filters.rank.html#entropy
GLCM - 纹理特征 http://scikit-image.org/docs/dev/auto_examples/plot_glcm.html
从灰度图像（直接）计算出的熵与从 GLCM（纹理特征）提取的熵特征相同吗？
不，这两个熵相当不同：
skimage.filters.rank.entropy(grayImg, disk(5)) yields an array the same size as grayImg which contains the local entropy across the image computed on a circular disk with center at the the corresponding pixel and radius 5 pixels. Take a look at Entropy (information theory) https://en.wikipedia.org/wiki/Entropy_(information_theory) to find out how entropy is calculated. The values in this array are useful for segmentation (follow this link http://scikit-image.org/docs/dev/auto_examples/filters/plot_entropy.html to see an example of entropy-based object detection). If your goal is to describe the entropy of the image through a single (scalar) value you can use skimage.measure.shannon_entropy(grayImg). This function basically applies the following formula to the full image:
is the number of gray levels (256 for 8-bit images), 
is the probability of a pixel having gray level 
, and 
is the base of the logarithm function. When is set to 2 the returned value is measured in bits.
and are again the number of gray levels and the base of the logarithm function, respectively, and 
stands for the probability of two pixels separated by the specified offset having intensities and 
. Unfortunately the entropy is not one of the properties of a GLCM that you can calculate through scikit-image*. If you wish to compute this feature you need to pass the GLCM to skimage.measure.shannon_entropy http://scikit-image.org/docs/0.13.x/api/skimage.measure.html#skimage.measure.shannon_entropy.*At the time this post was last edited, the latest version of scikit-image is 0.13.1.
如果不是，从图像中提取所有纹理特征的正确方法是什么？
描述图像纹理的特征有很多种，例如局部二值模式、Gabor 滤波器、小波、Laws 掩模等等。哈拉利克的GLCM http://www.ucalgary.ca/mhallbey/tutorial-glcm-texture是最流行的纹理描述符之一。通过 GLCM 特征描述图像纹理的一种可能方法包括计算不同偏移量的 GLCM（每个偏移量通过距离和角度定义），并从每个 GLCM 中提取不同的属性。
Let us consider for example three distances (1, 2 and 3 pixels), four angles (0, 45, 90 and 135 degrees) and two properties (energy and homogeneity). This results in 
offsets (and hence 12 GLCM's) and a feature vector of dimension . Here's the code:
import numpy as np
from skimage import io, color, img_as_ubyte
from skimage.feature import greycomatrix, greycoprops
from sklearn.metrics.cluster import entropy
rgbImg = io.imread('https://i.stack.imgur.com/1xDvJ.jpg')
grayImg = img_as_ubyte(color.rgb2gray(rgbImg))
distances = [1, 2, 3]
angles = [0, np.pi/4, np.pi/2, 3*np.pi/4]
properties = ['energy', 'homogeneity']
glcm = greycomatrix(grayImg,
distances=distances,
angles=angles,
symmetric=True,
normed=True)
feats = np.hstack([greycoprops(glcm, prop).ravel() for prop in properties])
使用该图像获得的结果：
:
In [56]: entropy(grayImg)
Out[56]: 5.3864158185167534
In [57]: np.set_printoptions(precision=4)
In [58]: print(feats)
[ 0.026 0.0207 0.0237 0.0206 0.0201 0.0207 0.018 0.0206 0.0173
0.016 0.0157 0.016 0.3185 0.2433 0.2977 0.2389 0.2219 0.2433
0.1926 0.2389 0.1751 0.1598 0.1491 0.1565]