人脸融合特惠

import cv2
import numpy as np

# 加载人脸检测器和特征点检测器
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")

def detect_faces(img):
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray, 1.3, 5)
    return faces

def extract_features(img, face):
    shape = predictor(img, face)
    features = np.array([[p.x, p.y] for p in shape.parts()])
    return features

# 主程序
img1 = cv2.imread('face1.jpg')
img2 = cv2.imread('face2.jpg')

faces1 = detect_faces(img1)
faces2 = detect_faces(img2)

if len(faces1) > 0 and len(faces2) > 0:
    features1 = extract_features(img1, faces1[0])
    features2 = extract_features(img2, faces2[0])
    
    # 这里可以添加融合算法，例如简单的平均融合
    blended_features = (features1 + features2) / 2
    
    # 根据融合后的特征点重建人脸
    # 这部分代码较为复杂，通常需要使用专门的库或算法实现
    
    cv2.imshow('Blended Face', blended_img)
    cv2.waitKey(0)
    cv2.destroyAllWindows()