opencv-python dlib 人脸关键点 截取眼睛和嘴巴 代码

下面是使用 OpenCV 和 dlib 库实现截取眼睛和嘴巴的 Python 代码：

import cv2
import dlib

# 加载模型
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor('shape_predictor_68_face_landmarks.dat')

# 加载图像
img = cv2.imread('test.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# 检测人脸
faces = detector(gray)

# 遍历每张脸并截取眼睛和嘴巴
for face in faces:
    # 获取关键点
    landmarks = predictor(gray, face)
    
    # 截取左眼
    left_eye_pts = [(landmarks.part(36).x, landmarks.part(36).y),
                    (landmarks.part(37).x, landmarks.part(37).y),
                    (landmarks.part(38).x, landmarks.part(38).y),
                    (landmarks.part(39).x, landmarks.part(39).y),
                    (landmarks.part(40).x, landmarks.part(40).y),
                    (landmarks.part(41).x, landmarks.part(41).y)]
    left_eye_mask = np.zeros(img.shape[:2], dtype=np.uint8)
    cv2.drawContours(left_eye_mask, [np.array(left_eye_pts)], -1, (255, 255, 255), -1, cv2.LINE_AA)
    left_eye = cv2.bitwise_and(img, img, mask=left_eye_mask)

    # 截取右眼
    right_eye_pts = [(landmarks.part(42).x, landmarks.part(42).y),
                     (landmarks.part(43).x, landmarks.part(43).y),
                     (landmarks.part(44).x, landmarks.part(44).y),
                     (landmarks.part(45).x, landmarks.part(45).y),
                     (landmarks.part(46).x, landmarks.part(46).y),
                     (landmarks.part(47).x, landmarks.part(47).y)]
    right_eye_mask = np.zeros(img.shape[:2], dtype=np.uint8)
    cv2.drawContours(right_eye_mask, [np.array(right_eye_pts)], -1, (255, 255, 255), -1, cv2.LINE_AA)
    right_eye = cv2.bitwise_and(img, img, mask=right_eye_mask)

    # 截取嘴巴
    mouth_pts = [(landmarks.part(48).x, landmarks.part(48).y),
                 (landmarks.part(49).x, landmarks.part(49).y),
                 (landmarks.part(50).x, landmarks.part(50).y),
                 (landmarks.part(51).x, landmarks.part(51).y),
                 (landmarks.part(52).x, landmarks.part(52).y),
                 (landmarks.part(53).x, landmarks.part(53).y),
                 (landmarks.part(54).x, landmarks.part(54).y),
                 (landmarks.part(55).x, landmarks.part(55).y),
                 (landmarks.part(56).x, landmarks.part(56).y),
                 (landmarks.part(57).x, landmarks.part(57).y),
                 (landmarks.part(58).x, landmarks.part(58).y),
                 (landmarks.part(59).x, landmarks.part(59).y),
                 (landmarks.part(60).x, landmarks.part(60).y),
                 (landmarks.part(61).x, landmarks.part(61).y),
                 (landmarks.part(62).x, landmarks.part(62).y),
                 (landmarks.part(63).x, landmarks.part(63).y),
                 (landmarks.part(64).x, landmarks.part(64).y),
                 (landmarks.part(65).x, landmarks.part(65).y),
                 (landmarks.part(66).x, landmarks.part(66).y),
                 (landmarks.part(67).x, landmarks.part(67).y)]
    mouth_mask = np.zeros(img.shape[:2], dtype=np.uint8)
    cv2.drawContours(mouth_mask, [np.array(mouth_pts)], -1, (255, 255, 255), -1, cv2.LINE_AA)
    mouth = cv2.bitwise_and(img, img, mask=mouth_mask)

# 显示结果
cv2.imshow('Left Eye', left_eye)
cv2.imshow('Right Eye', right_eye)
cv2.imshow('Mouth', mouth)
cv2.waitKey(0)
cv2.destroyAllWindows()

在上面的代码中，我们首先加载了训练好的人脸检测器和面部关键点检测器。然后，我们加载了一张图像，并将其转换为灰度图像。接下来，我们使用人脸检测器检测出图像中的所有人脸，并使用面部关键点检测器获取每个人脸的面部关键点。最后，我们根据面部关键点截取出眼睛和嘴巴的部分，并在窗口中显示结果。