高光谱的灰度共生矩阵获取代码

# coding: utf-8 # The code is written by Linghui import numpy as np import cv2 from math import floor from skimage.feature import greycomatrix def main(): pass def image_patch(img2, slide_window, h, w): patch = np.zeros((slide_window, slide_window, h, w), dtype=np.uint8) for i in range(patch.shape[2]): for j in range(patch.shape[3]): patch[:, :, i, j] = img2[i : i + slide_window, j : j + slide_window] return patch def calcu_glcm(img, vmin=0, vmax=255, nbit=64, slide_window=5, step=[2], angle=[0]): mi, ma = vmin, vmax h, w = img.shape # Compressed gray range：vmin: 0-->0, vmax: 256-1 -->nbit-1 bins = np.linspace(mi, ma+1, nbit+1) img1 = np.digitize(img, bins) - 1 # (512, 512) --> (slide_window, slide_window, 512, 512) img2 = cv2.copyMakeBorder(img1, floor(slide_window/2), floor(slide_window/2) , floor(slide_window/2), floor(slide_window/2), cv2.BORDER_REPLICATE) # 图像扩充 patch = image_patch(img2, slide_window, h, w) # Calculate GLCM (5, 5, 512, 512) --> (64, 64, 512, 512) # greycomatrix(image, distances, angles, levels=None, symmetric=False, normed=False) glcm = np.zeros((nbit, nbit, len(step), len(angle), h, w), dtype=np.uint8) for i in range(patch.shape[2]): for j in range(patch.shape[3]): glcm[:, :, :, :, i, j]= greycomatrix(patch[:, :, i, j], step, angle, levels=nbit) return glcm def calcu_glcm_mean(glcm, nbit=64): ''' calc glcm mean ''' mean = np.zeros((glcm.shape[2], glcm.shape[3]), dtype=np.float32) for i in range(nbit): for j in range(nbit): mean += glcm[i,j] * i / (nbit)**2 return mean def calcu_glcm_variance(glcm, nbit=64): ''' calc glcm variance ''' mean = np.zeros((glcm.shape[2], glcm.shape[3]), dtype=np.float32) for i in range(nbit): for j in range(nbit): mean += glcm[i, j] * i / (nbit)**2 variance = np.zeros((glcm.shape[2], glcm.shape[3]), dtype=np.float32) for i in range(nbit): for j in range(nbit): variance += glcm[i, j] * (i - mean)**2 return variance def calcu_glcm_homogeneity(glcm, nbit=64): ''' calc glcm Homogeneity ''' Homogeneity = np.zeros((glcm.shape[2], glcm.shape[3]), dtype=np.float32) for i in range(nbit): for j in range(nbit): Homogeneity += glcm[i,j] / (1.+(i-j)**2) return Homogeneity def calcu_glcm_contrast(glcm, nbit=64): ''' calc glcm contrast ''' contrast = np.zeros((glcm.shape[2], glcm.shape[3]), dtype=np.float32) for i in range(nbit): for j in range(nbit): contrast += glcm[i, j] * (i-j)**2 return contrast def calcu_glcm_dissimilarity(glcm, nbit=64): ''' calc glcm dissimilarity ''' dissimilarity = np.zeros((glcm.shape[2], glcm.shape[3]), dtype=np.float32) for i in range(nbit): for j in range(nbit): dissimilarity += glcm[i, j] * np.abs(i-j) return dissimilarity def calcu_glcm_entropy(glcm, nbit=64): ''' calc glcm entropy ''' eps = 0.00001 entropy = np.zeros((glcm.shape[2], glcm.shape[3]), dtype=np.float32) for i in range(nbit): for j in range(nbit): entropy -= glcm[i, j] * np.log10(glcm[i, j] + eps) return entropy def calcu_glcm_energy(glcm, nbit=64): ''' calc glcm energy or second moment ''' energy = np.zeros((glcm.shape[2], glcm.shape[3]), dtype=np.float32) for i in range(nbit): for j in range(nbit): energy += glcm[i, j]**2 return energy def calcu_glcm_correlation(glcm, nbit=64): ''' calc glcm correlation (Unverified result) ''' mean = np.zeros((glcm.shape[2], glcm.shape[3]), dtype=np.float32) for i in range(nbit): for j in range(nbit): mean += glcm[i, j] * i / (nbit)**2 variance = np.zeros((glcm.shape[2], glcm.shape[3]), dtype=np.float32) for i in range(nbit): for j in range(nbit): variance += glcm[i, j] * (i - mean)**2 correlation = np.zeros((glcm.shape[2], glcm.shape[3]), dtype=np.float32) for i in range(nbit): for j in range(nbit): correlation += ((i - mean) * (j - mean) * (glcm[i, j]**2))/variance return correlation def calcu_glcm_Auto_correlation(glcm, nbit=64): ''' calc glcm auto correlation ''' Auto_correlation = np.zeros((glcm.shape[2], glcm.shape[3]), dtype=np.float32) for i in range(nbit): for j in range(nbit): Auto_correlation += glcm[i, j] * i * j return Auto_correlation if __name__ == '__main__': main()