基于opencv的人脸识别 python 代码

以下是一段基于OpenCV的人脸识别的Python代码：

import cv2
import numpy as np

# 加载已经训练好的人脸识别模型
recognizer = cv2.face.LBPHFaceRecognizer_create()
recognizer.read('ykc.yml')

# 加载人脸检测模型
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_alt2.xml')

def predict_faces(image_path):
    # 读取图像并转为灰度图像
    image = cv2.imread(image_path)
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

    # 检测人脸
    faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))

    # 对检测到的每张人脸进行预测
    for (x, y, w, h) in faces:
        # 根据检测到的人脸区域提取出人脸
        face_roi = gray\[y:y + h, x:x + w\]
        # 进行人脸识别
        label, confidence = recognizer.predict(face_roi)

        # 在图像上绘制人脸区域和识别结果
        cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
        cv2.putText(image, f'Label: {label}', (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)

    # 显示结果图像
    cv2.imshow('Face Recognition', image)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

# 调用函数进行人脸识别
image_path = 'test.jpg'
predict_faces(image_path)

这段代码首先加载已经训练好的人脸识别模型，并使用人脸检测模型对输入图像进行人脸检测。然后，对每一个检测到的人脸区域进行预测，并在图像上标注出人脸区域和识别结果。最后，显示结果图像。

请确保在运行代码之前，已经安装了OpenCV库，并将人脸检测模型（haarcascade_frontalface_alt2.xml）和训练好的人脸识别模型（ykc.yml）放置在相应的路径下。
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