人脸融合新年活动

import cv2
import dlib
from skimage import transform as tf

# 加载人脸检测器和特征点预测器
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")

# 读取两张图像
img1 = cv2.imread('face1.jpg')
img2 = cv2.imread('face2.jpg')

# 检测人脸并获取特征点
def get_landmarks(image):
    faces = detector(image)
    if len(faces) > 0:
        return np.matrix([[p.x, p.y] for p in predictor(image, faces[0]).parts()])
    return None

landmarks1 = get_landmarks(img1)
landmarks2 = get_landmarks(img2)

# 使用薄板样条插值进行图像变形
tform = tf.SimilarityTransform()
tform.estimate(landmarks1, landmarks2)
img1_warped = tf.warp(img1, tform.inverse, output_shape=img2.shape)

# 融合图像
alpha = 0.5
blended_img = cv2.addWeighted(img1_warped, alpha, img2, 1 - alpha, 0)

cv2.imshow('Blended Image', blended_img)
cv2.waitKey(0)
cv2.destroyAllWindows()