python使用dlib进行稀疏光流跟踪人脸的代码

下面是使用dlib进行稀疏光流跟踪人脸的Python代码：

import dlib
import cv2

# 初始化dlib的人脸检测器和68个关键点检测器
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor('shape_predictor_68_face_landmarks.dat')

# 读取视频文件
cap = cv2.VideoCapture('video.mp4')

# 选择第一帧中的人脸作为跟踪目标
ret, frame = cap.read()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
rects = detector(gray, 0)
if len(rects) > 0:
    rect = rects[0]
    shape = predictor(gray, rect)
    points = [(shape.part(i).x, shape.part(i).y) for i in range(68)]
    prev_points = points
else:
    exit()

# 创建稀疏光流对象
lk_params = dict(winSize=(15, 15), maxLevel=2,
                 criteria=(cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03))

# 跟踪人脸关键点
while True:
    ret, frame = cap.read()
    if not ret:
        break
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    # 使用光流跟踪人脸关键点
    next_points, status, error = cv2.calcOpticalFlowPyrLK(
        cv2.GaussianBlur(gray, (5, 5), 0), cv2.GaussianBlur(prev_gray, (5, 5), 0),
        prev_points, None, **lk_params)

    # 仅保留跟踪成功的关键点
    next_points = next_points[status == 1]
    prev_points = prev_points[status == 1]

    # 更新跟踪目标
    if len(next_points) < 10:
        rects = detector(gray, 0)
        if len(rects) > 0:
            rect = rects[0]
            shape = predictor(gray, rect)
            next_points = [(shape.part(i).x, shape.part(i).y) for i in range(68)]
            prev_points = next_points

    # 绘制跟踪结果
    for i, (prev_point, next_point) in enumerate(zip(prev_points, next_points)):
        x1, y1 = prev_point
        x2, y2 = next_point
        cv2.line(frame, (x1, y1), (x2, y2), (0, 0, 255), 2, cv2.LINE_AA)
        cv2.circle(frame, (x2, y2), 3, (0, 255, 0), -1, cv2.LINE_AA)

    prev_gray = gray.copy()
    prev_points = next_points

    cv2.imshow('frame', frame)
    if cv2.waitKey(1) == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()

上述代码中，首先使用dlib的人脸检测器和68个关键点检测器选择第一帧中的人脸作为跟踪目标。然后，创建稀疏光流对象，并使用cv2.calcOpticalFlowPyrLK()函数跟踪人脸关键点。在跟踪过程中，如果跟踪成功的关键点数量小于10，则重新选择一个人脸作为跟踪目标。最后，绘制跟踪结果并显示。