视频人脸真伪鉴别创建

import cv2
import numpy as np
from tensorflow.keras.models import load_model

# 加载预训练模型
model = load_model('face_authenticity_model.h5')

# 打开摄像头
cap = cv2.VideoCapture(0)

while True:
    ret, frame = cap.read()
    if not ret:
        break

    # 转换为灰度图像
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    # 使用OpenCV的人脸检测器
    face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
    faces = face_cascade.detectMultiScale(gray, 1.3, 5)

    for (x, y, w, h) in faces:
        face_roi = gray[y:y+h, x:x+w]
        face_roi = cv2.resize(face_roi, (160, 160))  # 调整大小以匹配模型输入
        face_roi = np.expand_dims(face_roi, axis=-1)
        face_roi = np.expand_dims(face_roi, axis=0)
        face_roi = face_roi / 255.0  # 归一化

        # 预测人脸真伪
        prediction = model.predict(face_roi)
        if prediction[0][0] > 0.5:
            label = "Real"
        else:
            label = "Fake"

        cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 2)
        cv2.putText(frame, label, (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (255, 0, 0), 2)

    cv2.imshow('Face Authenticity Detection', frame)

    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()