python实现LBP纹理特征提取

好的，下面是Python实现LBP纹理特征提取的代码：

import cv2
import numpy as np

#计算LBP值
def calc_lbp(img):
    rows, cols = img.shape
    lbp_img = np.zeros_like(img)
    for i in range(1, rows-1):
        for j in range(1, cols-1):
            center = img[i, j]
            code = 0
            code |= (img[i-1, j-1] >= center) << 7
            code |= (img[i-1, j] >= center) << 6
            code |= (img[i-1, j+1] >= center) << 5
            code |= (img[i, j+1] >= center) << 4
            code |= (img[i+1, j+1] >= center) << 3
            code |= (img[i+1, j] >= center) << 2
            code |= (img[i+1, j-1] >= center) << 1
            code |= (img[i, j-1] >= center) << 0
            lbp_img[i, j] = code
    return lbp_img

#计算LBP直方图特征
def calc_lbp_hist(lbp_img):
    hist, _ = np.histogram(lbp_img.ravel(), 256, [0, 256])
    return hist

#计算LBP特征向量
def calc_lbp_feature(img):
    gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    lbp_img = calc_lbp(gray_img)
    hist = calc_lbp_hist(lbp_img)
    feature = hist.astype('float32')
    feature /= (feature.sum() + 1e-7)
    return feature

#测试
img = cv2.imread('test.jpg')
feature = calc_lbp_feature(img)
print(feature)

这段代码中，calc_lbp函数用于计算LBP值，calc_lbp_hist函数用于计算LBP直方图特征，calc_lbp_feature函数用于计算LBP特征向量。最后，我们读入一张测试图片，计算其LBP特征向量并输出。