GLCM特征提取

Question

所以我正在读一篇研究论文[链接]，它有下面的Algo来获取图像的纹理。

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所以我从上面了解到的是，我需要建立4x4的网格，然后为每个4x4子矩阵找到GLCM矩阵，然后计算这些属性。

但是我对这个方法的问题是，图像的大小是256x384，它给出了64*96个网格，计算一个GLCM矩阵64*96次，计算量非常大，特别是因为我有900幅这样的图像。

守则如下：

def texture_extract(img):
    distance = [1]
    angles = [0, np.pi/4, np.pi/2, 3*np.pi/4]
    properties = ['correlation', 'homogeneity', 'contrast', 'energy', 'dissimilarity']

    gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    blur = cv2.GaussianBlur(gray_img, (5,5), 0)

    texture_features = []
    for i in range(0, blur.shape[0], 4):
        for j in range(0, blur.shape[1], 4):
            block = blur[i:i+4, j:j+4]

            glcm_mat = greycomatrix(block, distances=distance, angles=angles, symmetric=True, normed=True)
            block_glcm = np.hstack([greycoprops(glcm_mat, props).ravel() for props in properties])
            texture_features.append(block_glcm)

    return np.concatenate(texture_features)

我只想知道，我对算法的理解是正确的，还是我在某个地方犯了一个愚蠢的错误。

Answer 1

我就是这样用的，一般的方式和你的一样。

GLCM性质定义

import numpy as np
from skimage.feature import greycomatrix, greycoprops
from skimage import io, color, img_as_ubyte

# GLCM properties
def contrast_feature(matrix_coocurrence):
    contrast = greycoprops(matrix_coocurrence, 'contrast')
    return contrast

def dissimilarity_feature(matrix_coocurrence):
    dissimilarity = greycoprops(matrix_coocurrence, 'dissimilarity')    
    return dissimilarity

def homogeneity_feature(matrix_coocurrence):
    homogeneity = greycoprops(matrix_coocurrence, 'homogeneity')
    return homogeneity

def energy_feature(matrix_coocurrence):
    energy = greycoprops(matrix_coocurrence, 'energy')
    return energy

def correlation_feature(matrix_coocurrence):
    correlation = greycoprops(matrix_coocurrence, 'correlation')
    return correlation

def asm_feature(matrix_coocurrence):
    asm = greycoprops(matrix_coocurrence, 'ASM')
    return asm

bins32 = np.array([0, 8, 16, 24, 32, 40, 48, 56, 64, 72, 80, 88, 96, 104, 112, 120, 128, 136, 144, 152, 160,
            168, 176, 184, 192, 200, 208, 216, 224, 232, 240, 248, 255]) #32-bit

GLCM性质的计算

from skimage.feature import greycomatrix, greycoprops
from skimage import data
from skimage import util, exposure, data
import pywt
from skimage import io
from skimage.color import rgb2gray

lenx = x_train.shape[0] # Your image array!
X_correlationTRAIN = np.zeros((lenx, 4))
...

for i in range(lenx):
    image32 = x_train[i] # Your image array!
    image32 = rgb2gray(image32)
    np.clip(image32, 0, 255, out=image32)
    image = image32.astype('uint8')
    inds = np.digitize(image, bins32)

    max_value = inds.max()+1
    matrix_coocurrence = greycomatrix(inds, [1], [0, np.pi/4, np.pi/2, 3*np.pi/4], levels=max_value, normed=False, symmetric=False)

    X_correlationTRAIN[i] = correlation_feature(matrix_coocurrence)
    ...
    ...