python中OpenCV的人脸检测功能

import os
import cv2
from PIL import Image
import numpy as np

def getImageAndLabels(path):
    facesSamples = []
    ids = []
    imagePaths = [os.path.join(path, f) for f in os.listdir(path)]

    # 检测人脸
    # 加载特征数据
    face_detector = cv2.CascadeClassifier(
        'D:/Python/opencv/sources/data/haarcascades/haarcascade_frontalface_default.xml')
    # 参数： scaleFactor(比例因子)：图片缩放多少，minNeighbors:至少检测多少次，minSize maxSize:当前检测区域的最小面积
    # scaleFactor=1.01, minNeighbors=3, maxSize=(33, 33), minSize=(28, 28)

    # 遍历列表中的图片
    for imagePath in imagePaths:
        # 打开当前图片
        PIL_img = Image.open(imagePath).convert('L')

        # 将图片转化为数组
        img_numpy = np.array(PIL_img, 'uint8')
        faces = face_detector.detectMultiScale(img_numpy)

        # 获取每张图片的id
        id = int(os.path.split(imagePath)[1].split('.')[0])

        # 获取人脸区域
        for x, y, w, h in faces:
            # 进行切片处理,获取图像数组和id
            facesSamples.append(img_numpy[y:y+h, x:x+w])
            ids.append(id)

        return facesSamples, ids


if __name__ == '__main__':
    # 图片路径
    path = './data/jm/'
    # 获取图像数组和id标签数组
    faces, ids = getImageAndLabels(path)

    # 获取循环对象
    recognizer = cv2.face.LBPHFaceRecognizer_create()
    recognizer.train(faces, np.array(ids))

    # 保存文件
    recognizer.write('trainer/trainer.yml')