写一个基于三点映射变换的人脸矫正代码

对于一个人脸图像，三点映射变换可以将其矫正成正脸，代码如下：

1. 导入必要的库和模块

import cv2
import numpy as np
from scipy.spatial import distance

2. 定义三个关键点位置

keypoints = {
    'left_eye': (36, 37, 38, 39, 40, 41),
    'right_eye': (42, 43, 44, 45, 46, 47),
    'nose': (27, 28, 29, 30),
}

3. 定义三个目标位置

target_landmarks = {
    'left_eye': (0.3, 0.35),
    'right_eye': (0.7, 0.35),
    'nose': (0.5, 0.6),
}

4. 定义计算变换矩阵的函数

def get_transform_matrix(src_points, dst_points):
    src_matrix = np.zeros((len(src_points), 2))
    dst_matrix = np.zeros((len(dst_points), 2))
    for i, point in enumerate(src_points):
        src_matrix[i] = point
    for i, point in enumerate(dst_points):
        dst_matrix[i] = point
    transform_matrix = cv2.getAffineTransform(src_matrix, dst_matrix)
    return transform_matrix

5. 定义人脸矫正函数

def face_alignment(image, landmarks):
    left_eye = landmarks[keypoints['left_eye']]
    right_eye = landmarks[keypoints['right_eye']]
    nose = landmarks[keypoints['nose']]
    distance_left = distance.euclidean(left_eye[0], left_eye[3])
    distance_right = distance.euclidean(right_eye[0], right_eye[3])
    distance_nose = distance.euclidean(nose[0], nose[3])
    scale_left = distance_left / distance_nose
    scale_right = distance_right / distance_nose
    target_left_eye = np.array([image.shape[1] * target_landmarks['left_eye'][0], image.shape[0] * target_landmarks['left_eye'][1]])
    target_right_eye = np.array([image.shape[1] * target_landmarks['right_eye'][0], image.shape[0] * target_landmarks['right_eye'][1]])
    target_nose = np.array([image.shape[1] * target_landmarks['nose'][0], image.shape[0] * target_landmarks['nose'][1]])
    target_left_eye[0] *= scale_left
    target_left_eye[1] *= scale_left
    target_right_eye[0] *= scale_right
    target_right_eye[1] *= scale_right
    src_points = np.array([left_eye[0], right_eye[0], nose[0]])
    dst_points = np.array([target_left_eye, target_right_eye, target_nose])
    transform_matrix = get_transform_matrix(src_points, dst_points)
    aligned_image = cv2.warpAffine(image, transform_matrix, (image.shape[1], image.shape[0]), flags=cv2.INTER_LINEAR)
    return aligned_image

6. 加载一张人脸图像并进行矫正

image = cv2.imread('face.jpg')
landmarks = {
    36: (934, 542), 37: (951, 527), 38: (972, 524),
    39: (992, 531), 40: (1008, 548), 41: (996, 567),
    42: (905, 545), 43: (888, 529), 44: (866, 525),
    45: (846, 534), 46: (832, 553), 47: (843, 572),
    27: (952, 585), 28: (947, 607), 29: (944, 630),
    30: (940, 653),
}
aligned_image = face_alignment(image, landmarks)

经过矫正后，图像中的人脸将会变为正脸。