将上述代码用python表示
时间: 2024-03-31 07:37:38 浏览: 10
好的,这里给你提供一个使用dlib进行稀疏光流跟踪人脸的Python示例代码,希望能对你有所帮助:
```
import cv2
import dlib
import numpy as np
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor('shape_predictor_68_face_landmarks.dat')
cap = cv2.VideoCapture(0)
if not cap.isOpened():
print("Cannot open video capture device")
exit()
# 读取第一帧
ret, frame = cap.read()
if not ret:
print("Cannot read video stream")
exit()
# 检测人脸
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = detector(gray, 0)
if len(faces) == 0:
print("No faces found in the first frame!")
exit()
# 获取第一个人脸的关键点
shape = predictor(gray, faces[0])
points = np.zeros((68, 2), dtype=np.float32)
for i in range(68):
points[i] = (shape.part(i).x, shape.part(i).y)
# 初始化稀疏光流跟踪器
prev_gray = gray.copy()
prev_points = points.copy()
next_points = np.zeros_like(points)
status = np.zeros((68, 1), dtype=np.uint8)
error = np.zeros((68, 1), dtype=np.float32)
criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 30, 0.01)
win_size = (21, 21)
max_level = 3
flags = cv2.OPTFLOW_USE_INITIAL_FLOW
# 循环处理每一帧
while True:
ret, frame = cap.read()
if not ret:
break
# 使用稀疏光流跟踪器跟踪人脸
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
cv2.calcOpticalFlowPyrLK(prev_gray, gray, prev_points, next_points, status, error, win_size, max_level, criteria, flags)
# 更新人脸关键点位置
for i in range(68):
points[i] = next_points[i]
# 在图像上绘制人脸关键点
for i in range(68):
cv2.circle(frame, tuple(points[i].astype(int)), 2, (0, 0, 255), -1)
# 显示图像
cv2.imshow("Face tracking", frame)
# 更新上一帧图像和关键点
prev_gray = gray.copy()
prev_points = points.copy()
# 按下ESC键退出程序
if cv2.waitKey(10) == 27:
break
cap.release()
cv2.destroyAllWindows()
```
这段代码使用了dlib进行人脸检测和关键点提取,使用稀疏光流算法进行人脸跟踪,可以在摄像头捕获的视频中实时跟踪人脸。