单脸融合怎么搭建

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

# 加载预训练模型
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")
model = load_model("face_fusion_model.h5")

def align_faces(img1, img2):
    # 人脸检测和关键点定位
    faces1 = detector(img1)
    faces2 = detector(img2)
    
    if len(faces1) == 0 or len(faces2) == 0:
        raise ValueError("No face detected")
    
    shape1 = predictor(img1, faces1[0])
    shape2 = predictor(img2, faces2[0])
    
    # 对齐人脸
    aligned_img1 = dlib.get_face_chip(img1, shape1)
    aligned_img2 = dlib.get_face_chip(img2, shape2)
    
    return aligned_img1, aligned_img2

def fuse_faces(aligned_img1, aligned_img2):
    # 特征融合
    input_data = np.concatenate([aligned_img1, aligned_img2], axis=-1)
    fused_image = model.predict(np.expand_dims(input_data, axis=0))
    return fused_image[0]

# 读取图像
img1 = cv2.imread("face1.jpg")
img2 = cv2.imread("face2.jpg")

# 对齐人脸
aligned_img1, aligned_img2 = align_faces(img1, img2)

# 融合人脸
fused_image = fuse_faces(aligned_img1, aligned_img2)

# 显示结果
cv2.imshow("Fused Image", fused_image)
cv2.waitKey(0)
cv2.destroyAllWindows()