人脸美化新年活动

import cv2
import dlib
from skimage import transform as tf

# 加载人脸检测器和关键点预测器
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")

# 读取图像
image = cv2.imread('input.jpg')

# 检测人脸
faces = detector(image)

for face in faces:
    landmarks = predictor(image, face)
    
    # 对关键点进行变换以实现美化效果
    tform = tf.SimilarityTransform(translation=(0, -10), scale=1.1)
    new_landmarks = tf.warp(landmarks, tform.inverse, output_shape=(image.shape[0], image.shape[1]))
    
    # 应用变换到原图像
    for (x, y), (new_x, new_y) in zip(landmarks.parts(), new_landmarks):
        cv2.circle(image, (x, y), 2, (0, 255, 0), -1)
        cv2.circle(image, (int(new_x), int(new_y)), 2, (255, 0, 0), -1)

cv2.imshow('Output', image)
cv2.waitKey(0)
cv2.destroyAllWindows()