opencv python人脸变形完整代码

以下是利用OpenCV Python实现人脸变形的完整代码：

import cv2
import dlib
import numpy as np

# 加载面部标志检测器和预测器
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")

# 读取输入图像
image = cv2.imread("input.jpg")

# 转换为灰度图像
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# 检测脸部
faces = detector(gray)

# 循环遍历每个检测到的脸部
for face in faces:
    # 获取面部标志
    landmarks = predictor(gray, face)

    # 将面部标志转换为NumPy数组
    landmarks = np.array([[p.x, p.y] for p in landmarks.parts()])

    # 选取左眉毛、右眉毛、鼻子、嘴巴和下巴的点
    left_brow = landmarks[17:22]
    right_brow = landmarks[22:27]
    nose = landmarks[27:31]
    mouth = landmarks[48:60]
    chin = landmarks[6:11]

    # 计算左眉毛、右眉毛、鼻子、嘴巴和下巴的中心点
    left_brow_center = np.mean(left_brow, axis=0).astype(int)
    right_brow_center = np.mean(right_brow, axis=0).astype(int)
    nose_center = np.mean(nose, axis=0).astype(int)
    mouth_center = np.mean(mouth, axis=0).astype(int)
    chin_center = np.mean(chin, axis=0).astype(int)

    # 在中心点处绘制蓝色圆圈
    cv2.circle(image, tuple(left_brow_center), 5, (255, 0, 0), -1)
    cv2.circle(image, tuple(right_brow_center), 5, (255, 0, 0), -1)
    cv2.circle(image, tuple(nose_center), 5, (255, 0, 0), -1)
    cv2.circle(image, tuple(mouth_center), 5, (255, 0, 0), -1)
    cv2.circle(image, tuple(chin_center), 5, (255, 0, 0), -1)

    # 计算左眉毛、右眉毛、鼻子、嘴巴和下巴的偏移量
    left_brow_offset = (left_brow - left_brow_center).astype(float)
    right_brow_offset = (right_brow - right_brow_center).astype(float)
    nose_offset = (nose - nose_center).astype(float)
    mouth_offset = (mouth - mouth_center).astype(float)
    chin_offset = (chin - chin_center).astype(float)

    # 定义目标面部形状
    desired_left_brow = left_brow + left_brow_offset * 1.5
    desired_right_brow = right_brow + right_brow_offset * 1.5
    desired_nose = nose + nose_offset * 1.5
    desired_mouth = mouth + mouth_offset * 1.5
    desired_chin = chin + chin_offset * 1.5

    # 合并所有目标点
    desired_landmarks = np.vstack((desired_left_brow,
                                   desired_right_brow,
                                   desired_nose,
                                   desired_mouth,
                                   desired_chin))

    # 计算仿射变换矩阵
    transformation_matrix = cv2.estimateAffinePartial2D(
        landmarks, desired_landmarks)[0]

    # 将仿射变换应用于原始图像
    warped_image = cv2.warpAffine(image, transformation_matrix,
                                  (image.shape[1], image.shape[0]))

# 显示输出图像
cv2.imshow("Input", image)
cv2.imshow("Output", warped_image)
cv2.waitKey(0)

在代码中，我们首先加载面部标志检测器和预测器，然后读取输入图像，并将其转换为灰度图像。接着，我们使用面部标志检测器检测脸部，并循环遍历每个检测到的脸部。对于每个脸部，我们获取面部标志，并将其转换为NumPy数组。然后，我们选择左眉毛、右眉毛、鼻子、嘴巴和下巴的点，并计算它们的中心点。接着，我们在中心点处绘制蓝色圆圈，并计算左眉毛、右眉毛、鼻子、嘴巴和下巴的偏移量。然后，我们定义目标面部形状，并将所有目标点合并到一个数组中。接着，我们计算仿射变换矩阵，并将其应用于原始图像，得到变形后的图像。最后，我们显示输入和输出图像，并等待用户按下任意键退出。