用lbp特征提取人脸python代码

下面是用Python实现LBP特征提取的人脸识别代码：

import numpy as np
import cv2

# 计算LBP值
def calc_lbp(img, x, y):
    c = img[x][y]
    code = 0
    code |= (img[x-1][y-1] >= c) << 7
    code |= (img[x-1][y] >= c) << 6
    code |= (img[x-1][y+1] >= c) << 5
    code |= (img[x][y+1] >= c) << 4
    code |= (img[x+1][y+1] >= c) << 3
    code |= (img[x+1][y] >= c) << 2
    code |= (img[x+1][y-1] >= c) << 1
    code |= (img[x][y-1] >= c) << 0
    return code

# 计算LBP特征
def calc_lbp_feature(img):
    rows, cols = img.shape
    feature = np.zeros((rows-2)*(cols-2), dtype=np.uint8)
    cnt = 0
    for i in range(1, rows-1):
        for j in range(1, cols-1):
            feature[cnt] = calc_lbp(img, i, j)
            cnt += 1
    return feature

# 加载人脸数据集
def load_face_dataset(path):
    images = []
    labels = []
    label = 0
    for dirpath, dirnames, filenames in os.walk(path):
        for filename in filenames:
            if filename.endswith('.jpg'):
                img = cv2.imread(os.path.join(dirpath, filename), cv2.IMREAD_GRAYSCALE)
                img = cv2.resize(img, (64, 64))
                feature = calc_lbp_feature(img)
                images.append(feature)
                labels.append(label)
        label += 1
    return images, labels

# 训练分类器
def train_classifier(images, labels):
    model = cv2.face.LBPHFaceRecognizer_create()
    model.train(images, np.array(labels))
    return model

# 测试分类器
def test_classifier(model, img):
    img = cv2.resize(img, (64, 64))
    feature = calc_lbp_feature(img)
    label, confidence = model.predict(feature)
    return label, confidence

# 加载人脸数据集
images, labels = load_face_dataset('face_dataset')

# 训练分类器
model = train_classifier(images, labels)

# 加载测试图片并进行识别
img = cv2.imread('test.jpg', cv2.IMREAD_GRAYSCALE)
label, confidence = test_classifier(model, img)

# 显示识别结果
cv2.imshow('test', img)
cv2.waitKey()
cv2.destroyAllWindows()
print(f'label: {label}, confidence: {confidence}')

这段代码用到了OpenCV库，需要先安装。代码中的calc_lbp函数实现了LBP值的计算，calc_lbp_feature函数实现了LBP特征的计算，load_face_dataset函数用于加载人脸数据集，train_classifier函数用于训练分类器，test_classifier函数用于测试分类器。最后，加载测试图片并进行识别，输出识别结果。