OpenCV-Python實戰（16）——人臉追蹤詳解

import cv2

import dlib

def draw_text_info():

     # 繪製文字的位置

     menu_pos_1 = (10, 20)

     menu_pos_2 = (10, 40)

     menu_pos_3 = (10, 60)

     # 選單項

     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"

     # 繪製選單資訊

     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))

# 用於儲存要跟蹤的物件座標的結構

points = []

def mouse_event_handler(event, x, y, flags, param):

     # 對全域性變數的引用

     global points

     # 新增要跟蹤的物件的左上角座標

     if event == cv2.EVENT_LBUTTONDOWN:

         points = [(x, y)]

     # 新增要跟蹤的物件的右下角座標：

     elif event == cv2.EVENT_LBUTTONUP:

         points.append((x, y))

# 建立影片捕獲物件

capture = cv2.VideoCapture(0)

# 視窗名

window_name = "Object tracking using dlib correlation filter algorithm"

# 建立視窗

cv2.namedWindow(window_name)

# 繫結滑鼠事件

cv2.setMouseCallback(window_name, mouse_event_handler)

# 初始化跟蹤器

tracker = dlib.correlation_tracker()

tracking_state = False

while True:

     # 捕獲影片幀

     ret, frame = capture.read()

     # 繪製選單項

     draw_text_info()

     # 設定並繪製一個矩形，跟蹤矩形框內的物件

     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:

         # 更新跟蹤器並列印測量跟蹤器的置信度

         print(tracker.update(frame))

         # 獲取被跟蹤物件的位置

         pos = 外匯跟單gendan5.comtracker.get_position()

         # 繪製被跟蹤物件的位置

         cv2.rectangle(frame, (int(pos.left()), int(pos.top())), (int(pos.right()), int(pos.bottom())), (0, 255, 0), 3)

     # 捕獲鍵盤事件

     key = 0xFF & cv2.waitKey(1)

     # 按下 1 鍵，開始追蹤

     if key == ord("1"):

         if len(points) == 2:

             # Start tracking:

             tracker.start_track(frame, dlib_rectangle)

             tracking_state = True

             points = []

     # 按下 2 鍵，停止跟蹤

     if key == ord("2"):

         points = []

         tracking_state = False

     # 按下 q 鍵，返回

     if key == ord('q'):

         break

     # 展示結果影像

     cv2.imshow(window_name, frame)

# 釋放資源

capture.release()

cv2.destroyAllWindows()