import argparse
import time
import cv2
import numpy as np# 配置參數
ap = argparse.ArgumentParser()
ap.add_argument("-v", "--video", type=str,help="path to input video file")
ap.add_argument("-t", "--tracker", type=str, default="kcf",help="OpenCV object tracker type")
args = vars(ap.parse_args())# opencv已經實現了的追蹤算法
OPENCV_OBJECT_TRACKERS = {"csrt": cv2.TrackerCSRT_create,"kcf": cv2.TrackerKCF_create,"boosting": cv2.TrackerBoosting_create,"mil": cv2.TrackerMIL_create,"tld": cv2.TrackerTLD_create,"medianflow": cv2.TrackerMedianFlow_create,"mosse": cv2.TrackerMOSSE_create
}# 實例化OpenCV's multi-object tracker
trackers = cv2.MultiTracker_create()
vs = cv2.VideoCapture(args["video"])# 視頻流
while True:# 取當前幀frame = vs.read()# (true, data)frame = frame[1]# 到頭了就結束if frame is None:break# resize每一幀(h, w) = frame.shape[:2]width=600r = width / float(w)dim = (width, int(h * r))frame = cv2.resize(frame, dim, interpolation=cv2.INTER_AREA)# 追蹤結果(success, boxes) = trackers.update(frame)# 繪制區域for box in boxes:(x, y, w, h) = [int(v) for v in box]cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)# 顯示cv2.imshow("Frame", frame)key = cv2.waitKey(100) & 0xFFif key == ord("s"):# 選擇一個區域，按sbox = cv2.selectROI("Frame", frame, fromCenter=False,showCrosshair=True)# 創建一個新的追蹤器tracker = OPENCV_OBJECT_TRACKERS[args["tracker"]]()trackers.add(tracker, frame, box)# 退出elif key == 27:break
vs.release()
cv2.destroyAllWindows()

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