目标追踪

• 配置参数

--video videos/soccer_01.mp4
--tracker kcf

• 代码案例

import argparse
import time
import cv2
import numpy as np
 
# 配置参数
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已经实现了的追踪算法
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
}
 
# 实例化OpenCV's multi-object tracker
trackers = cv2.MultiTracker_create()
vs = cv2.VideoCapture(args["video"])
 
# 视频流
while True:
	# 取当前帧
	frame = vs.read()
	# (true, data)
	frame = frame[1]
	# 到头了就结束
	if frame is None:
		break
 
	# resize每一帧
	(h, w) = frame.shape[:2]
	width=600
	r = width / float(w)
	dim = (width, int(h * r))
	frame = cv2.resize(frame, dim, interpolation=cv2.INTER_AREA)
 
	# 追踪结果
	(success, boxes) = trackers.update(frame)
 
	# 绘制区域
	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)
 
	# 显示
	cv2.imshow("Frame", frame)
	key = cv2.waitKey(100) & 0xFF
 
	if key == ord("s"):
		# 选择一个区域，按s
		box = cv2.selectROI("Frame", frame, fromCenter=False,
			showCrosshair=True)
 
		# 创建一个新的追踪器
		tracker = OPENCV_OBJECT_TRACKERS[args["tracker"]]()
		trackers.add(tracker, frame, box)
 
	# 退出
	elif key == 27:
		break
vs.release()
cv2.destroyAllWindows()

• 执行：s + Enter -> 框选后 -> Enter
• 效果

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