opencvkcf算法改进_OpenCV上八种不同的目标追踪算法

拼搏现实的明天。 2022-10-31 13:41 16阅读 0赞

在运行代码的时候遇到的问题

module 'cv2.cv2' has no attribute 'TrackerKCF\_create'

opencv上的这八种算法是否开源，我能否对其进行修改，适合自己的特定场景

最近想了解opencv的跟踪模块，可却出现一下情况的报错：

![e0b12a5e9f71][]

what ,为什么opencv中没有这个模块，找到一个解决方案

原文链接

![e0b12a5e9f71][]

可是安装opencv-contrib-python 速度极慢，不可等待，只能通过镜像源安装：

原文链接

![e0b12a5e9f71][]

可是问题依旧：

这时候就要看我的tracking module有没有安装上

原文链接

![e0b12a5e9f71][]

结果是：

![e0b12a5e9f71][]

卧槽，问题的解决方式竟然在原帖的评论区当中，枉我在这里苦苦寻觅一整天：

原文链接

![e0b12a5e9f71][]

安装opencv-contrib之前要卸载opencv，竟然是这个原因，崩溃。

代码如下：

\# USAGE

\# python opencv\_object\_tracking.py

\# python opencv\_object\_tracking.py --video dashcam\_boston.mp4 --tracker csrt

\# import the necessary packages

from imutils.video import VideoStream

from imutils.video import FPS

import argparse

import imutils

import time

import cv2

\# construct the argument parser and parse the arguments

ap = argparse.ArgumentParser()

ap.add\_argument("-v", "--video", type=str, default="./race.mp4", 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())

\# extract the OpenCV version info

(major, minor) = cv2.\_\_version\_\_.split(".")\[:2\]

\# if we are using OpenCV s3.2 OR BEFORE, we can use a special factory

\# function to create our object tracker

if int(minor) < 3:

tracker = cv2.Tracker\_create(args\["tracker"\].upper())

\# otherwise, for OpenCV 3.3 OR NEWER, we need to explicity call the

\# approrpiate object tracker constructor:

else:

\# initialize a dictionary that maps strings to their corresponding

\# OpenCV object tracker implementations

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

\# grab the appropriate object tracker using our dictionary of

\# OpenCV object tracker objects

tracker = OPENCV\_OBJECT\_TRACKERS\[args\["tracker"\]\]()

\# initialize the bounding box coordinates of the object we are going

\# to track

initBB = None

\# if a video path was not supplied, grab the reference to the web cam

if not args.get("video", False):

print("\[INFO\] starting video stream...")

vs = VideoStream(src=0).start()

time.sleep(1.0)

\# otherwise, grab a reference to the video file

else:

vs = cv2.VideoCapture(args\["video"\])

\# initialize the FPS throughput estimator

fps = None

\# loop over frames from the video stream

while True:

\# grab the current frame, then handle if we are using a

\# VideoStream or VideoCapture object

frame = vs.read()

frame = frame\[1\] if args.get("video", False) else frame

\# check to see if we have reached the end of the stream

if frame is None:

break

\# resize the frame (so we can process it faster) and grab the

\# frame dimensions

frame = imutils.resize(frame, width=500)

(H, W) = frame.shape\[:2\]

\# check to see if we are currently tracking an object

if initBB is not None:

\# grab the new bounding box coordinates of the object

(success, box) = tracker.update(frame)

\# check to see if the tracking was a success

if success:

(x, y, w, h) = \[int(v) for v in box\]

cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)

\# update the FPS counter

fps.update()

fps.stop()

\# initialize the set of information we'll be displaying on

\# the frame

info = \[

("Tracker", args\["tracker"\]),

("Success", "Yes" if success else "No"),

("FPS", "\{:.2f\}".format(fps.fps())),

\# loop over the info tuples and draw them on our frame

for (i, (k, v)) in enumerate(info):

text = "\{\}: \{\}".format(k, v)

cv2.putText(frame, text, (10, H - ((i \* 20) + 20)),

cv2.FONT\_HERSHEY\_SIMPLEX, 0.6, (0, 0, 255), 2)

\# show the output frame

cv2.imshow("Frame", frame)

key = cv2.waitKey(20) & 0xFF

\# if the 's' key is selected, we are going to "select" a bounding

\# box to track

if key == ord("s"):

\# select the bounding box of the object we want to track (make

\# sure you press ENTER or SPACE after selecting the ROI)

initBB = cv2.selectROI("Frame", frame, fromCenter=False,

showCrosshair=True)

\# start OpenCV object tracker using the supplied bounding box

\# coordinates, then start the FPS throughput estimator as well

tracker.init(frame, initBB)

fps = FPS().start()

\# if the \`q\` key was pressed, break from the loop

elif key == ord("q"):

break

\# if we are using a webcam, release the pointer

if not args.get("video", False):

vs.stop()

\# otherwise, release the file pointer

else:

vs.release()

\# close all windows

cv2.destroyAllWindows()

[e0b12a5e9f71]: /images/20221024/b878645d704d44e5bd457afa4db4963a.png

opencvkcf算法改进_OpenCV上八种不同的目标追踪算法

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