import cv2
import dlib
def draw_text_info():
# 绘制文本的地位
menu_pos_1 = (10, 20)
menu_pos_2 = (10, 40)
menu_pos_3 = (10, 60)
# 菜单项
info_1 = "Use left click of the mouse to select the object to track"
info_2 = "Use'1'to start tracking,'2'to reset tracking and'q'to exit"
# 绘制菜单信息
cv2.putText(frame, "Use'1'to re-initialize tracking", menu_pos_1, cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255))
cv2.putText(frame, info_2, menu_pos_2, cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255))
if tracking_state:
cv2.putText(frame, "tracking", menu_pos_3, cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0))
else:
cv2.putText(frame, "not tracking", menu_pos_3, cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255))
用于保留要跟踪的对象坐标的构造
points = []
def mouse_event_handler(event, x, y, flags, param):
# 对全局变量的援用
global points
# 增加要跟踪的对象的左上角坐标
if event == cv2.EVENT_LBUTTONDOWN:
points = [(x, y)]
# 增加要跟踪的对象的右下角坐标:elif event == cv2.EVENT_LBUTTONUP:
points.append((x, y))
创立视频捕捉对象
capture = cv2.VideoCapture(0)
窗口名
window_name = “Object tracking using dlib correlation filter algorithm”
创立窗口
cv2.namedWindow(window_name)
绑定鼠标事件
cv2.setMouseCallback(window_name, mouse_event_handler)
初始化跟踪器
tracker = dlib.correlation_tracker()
tracking_state = False
while True:
# 捕捉视频帧
ret, frame = capture.read()
# 绘制菜单项
draw_text_info()
# 设置并绘制一个矩形,跟踪矩形框内的对象
if len(points) == 2:
cv2.rectangle(frame, points[0], points[1], (0, 0, 255), 3)
dlib_rectangle = dlib.rectangle(points[0][0], points[0][1], points[1][0], points[1][1])
if tracking_face is True:
# 更新跟踪器并打印测量跟踪器的置信度
print(tracker.update(frame))
# 获取被跟踪对象的地位
pos = tracker.get_position()
# 绘制被跟踪对象的地位
cv2.rectangle(frame, (int(pos.left()), int(pos.top())), (int(pos.right()), int(pos.bottom())), (0, 255, 0), 3)
# 捕捉键盘事件
key = 0xFF & cv2.waitKey(1)
# 按下 1 键,[能源期货](https://www.gendan5.com/cf/ef.html) 开始追踪
if key == ord("1"):
if len(points) == 2:
# Start tracking:
tracker.start_track(frame, dlib_rectangle)
tracking_state = True
points = []
# 按下 2 键,进行跟踪
if key == ord("2"):
points = []
tracking_state = False
# 按下 q 键,返回
if key == ord('q'):
break
# 展现后果图像
cv2.imshow(window_name, frame)
开释资源
capture.release()
cv2.destroyAllWindows()