基于opencv的人脸识别 python 代码

如果你想基于OpenCV进行人脸识别的Python代码，你可以使用以下代码作为一个参考：

import cv2
import numpy as np

# 加载分类器
faceCascade = cv2.CascadeClassifier('path_to_haarcascade_xml')

# 加载人脸图像和标签的函数
def load_images(path):
    facesSamples = \[\]
    labels = \[\]
    
    imagePaths = \[os.path.join(path, f) for f in os.listdir(path)\]
    for imagePath in imagePaths:
        # 读取灰度图像
        image = cv2.imread(imagePath, cv2.IMREAD_GRAYSCALE)
        # 检测人脸
        faces = faceCascade.detectMultiScale(image)
        
        for (x, y, w, h) in faces:
            # 提取人脸区域并调整大小
            face = cv2.resize(image\[y:y+h, x:x+w\], (100, 100))
            # 添加到人脸样本列表中
            facesSamples.append(face)
            # 获取图片的标签（假设文件名为"标签.jpg"）
            label = int(os.path.basename(imagePath).split(".")\[0\])
            labels.append(label)
    
    return facesSamples, labels

# 加载训练数据
path = 'path_to_dataset_folder'
faces, labels = load_images(path)

# 训练人脸识别模型
recognizer = cv2.face.LBPHFaceRecognizer_create()
recognizer.train(faces, np.array(labels))

# 保存训练好的模型
recognizer.save('path_to_save_model')

这段代码使用OpenCV库实现基于Haar特征分类器的人脸检测和识别。首先，你需要将'Harrcascades'文件夹中的所有XML文件复制到你的项目子文件夹中。然后，使用`load_images`函数加载训练数据，并使用`LBPHFaceRecognizer`训练人脸识别模型。最后，你可以将训练好的模型保存到一个文件中。

请注意，你需要将`path_to_haarcascade_xml`替换为你实际的Haar级联分类器XML文件的路径，将`path_to_dataset_folder`替换为包含训练图像的文件夹的路径，将`path_to_save_model`替换为保存训练模型的路径。此外，还需要安装OpenCV和numpy库。
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