用python实现单目标、多目标、多尺度、自定义特征的KCF跟踪算法

目录

单目标跟踪：

多目标跟踪：

多尺度检测的KCF、自定义所用特征的KCF

值得参考 

---

单目标跟踪：

直接调用opencv中封装的tracker即可。

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun Jan  5 17:50:47 2020
第四章 kcf跟踪
@author: youxinlin
"""
 
import cv2
from items import MessageItem
import time
import numpy as np
'''
监视者模块,负责入侵检测,目标跟踪
'''
class WatchDog(object):
  #入侵检测者模块,用于入侵检测
    def __init__(self,frame=None):
        #运动检测器构造函数
        self._background = None
        if frame is not None:
            self._background = cv2.GaussianBlur(cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY),(21,21),0)
        self.es = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (10, 10))
    def isWorking(self):
        #运动检测器是否工作
        return self._background is not None
    def startWorking(self,frame):
        #运动检测器开始工作
        if frame is not None:
            self._background = cv2.GaussianBlur(cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY), (21, 21), 0)
    def stopWorking(self):
        #运动检测器结束工作
        self._background = None
    def analyze(self,frame):
        #运动检测
        if frame is None or self._background is None:
            return
        sample_frame = cv2.GaussianBlur(cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY),(21,21),0)
        diff = cv2.absdiff(self._background,sample_frame)
        diff = cv2.threshold(diff, 25, 255, cv2.THRESH_BINARY)[1]
        diff = cv2.dilate(diff, self.es, iterations=2)
        image, cnts, hierarchy = cv2.findContours(diff.copy(),cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
        coordinate = []
        bigC = None
        bigMulti = 0
        for c in cnts:
            if cv2.contourArea(c) < 1500:
                continue
            (x,y,w,h) = cv2.boundingRect(c)
            if w * h > bigMulti:
                bigMulti = w * h
                bigC = ((x,y),(x+w,y+h))
        if bigC:
            cv2.rectangle(frame, bigC[0],bigC[1], (255,0,0), 2, 1)
        coordinate.append(bigC)
        message = {"coord":coordinate}
        message['msg'] = None
        return MessageItem(frame,message)
 
class Tracker(object):
    '''
    追踪者模块,用于追踪指定目标
    '''
    def __init__(self,tracker_type = "BOOSTING",draw_coord = True):
        '''
        初始化追踪器种类
        '''
        #获得opencv版本
        (major_ver, minor_ver, subminor_ver) = (cv2.__version__).split('.')
        self.tracker_types = ['BOOSTING', 'MIL','KCF', 'TLD', 'MEDIANFLOW', 'GOTURN']
        self.tracker_type = tracker_type
        self.isWorking = False
        self.draw_coord = draw_coord
        #构造追踪器
        if int(minor_ver) < 3:
            self.tracker = cv2.Tracker_create(tracker_type)
        else:
            if tracker_type == 'BOOSTING':
                self.tracker = cv2.TrackerBoosting_create()
            if tracker_type == 'MIL':
                self.tracker = cv2.TrackerMIL_create()
            if tracker_type == 'KCF':
                self.tracker = cv2.TrackerKCF_create()
            if tracker_type == 'TLD':
                self.tracker = cv2.TrackerTLD_create()
            if tracker_type == 'MEDIANFLOW':
                self.tracker = cv2.TrackerMedianFlow_create()
            if tracker_type == 'GOTURN':
                self.tracker = cv2.TrackerGOTURN_create()
    def initWorking(self,frame,box):
        '''
        追踪器工作初始化
        frame:初始化追踪画面
        box:追踪的区域
        '''
        if not self.tracker:
            raise Exception("追踪器未初始化")
        status = self.tracker.init(frame,box)
        if not status:
            raise Exception("追踪器工作初始化失败")
        self.coord = box
        self.isWorking = True
 
    def track(self,frame):
        '''
        开启追踪
        '''
        message = None
        if self.isWorking:
            status,self.coord = self.tracker.update(frame)
            if status:
                message = {"coord":[((int(self.coord[0]), int(self.coord[1])),(int(self.coord[0] + self.coord[2]), int(self.coord[1] + self.coord[3])))]}
                if self.draw_coord:
                    p1 = (int(self.coord[0]), int(self.coord[1]))
                    p2 = (int(self.coord[0] + self.coord[2]), int(self.coord[1] + self.coord[3]))
                    cv2.rectangle(frame, p1, p2, (255,0,0), 2, 1)
                    message['msg'] = "is tracking"
        return MessageItem(frame,message)
 
class ObjectTracker(object):
    def __init__(self,dataSet):
        self.cascade = cv2.CascadeClassifier(dataSet)
    def track(self,frame):
        gray = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)
        faces = self.cascade.detectMultiScale(gray,1.03,5)
        for (x,y,w,h) in faces:
            cv2.rectangle(frame,(x,y),(x+w,y+h),(0,0,255),2)
        return frame
 
if __name__ == '__main__' :
#    tracker_types = ['BOOSTING', 'MIL','KCF', 'TLD', 'MEDIANFLOW', 'GOTURN']
    tracker = Tracker(tracker_type="KCF")
#    video = cv2.VideoCapture(0)
#    video = cv2.VideoCapture("complex1.mov")
    video = cv2.VideoCapture(r"/Users/youxinlin/Desktop/video_data/complex1.MOV")   

    ok, frame = video.read()
    bbox = cv2.selectROI(frame, False)
    tracker.initWorking(frame,bbox)
    while True:
        _,frame = video.read();
        if(_):
            item = tracker.track(frame);
            cv2.imshow("track",item.getFrame())
            k = cv2.waitKey(1) & 0xff
            if k == 27:
                break

附带items.py，放在同个文件夹下：

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun Jan  5 17:51:04 2020

@author: youxinlin
"""

import json
from utils import IOUtil
'''
信息封装类
'''
class MessageItem(object):
    #用于封装信息的类,包含图片和其他信息
    def __init__(self,frame,message):
        self._frame = frame
        self._message = message
    def getFrame(self):
        #图片信息
        return self._frame
    def getMessage(self):
        #文字信息,json格式
        return self._message
    def getBase64Frame(self):
        #返回base64格式的图片,将BGR图像转化为RGB图像
        jepg = IOUtil.array_to_bytes(self._frame[...,::-1])
        return IOUtil.bytes_to_base64(jepg)
    def getBase64FrameByte(self):
        #返回base64格式图片的bytes
        return bytes(self.getBase64Frame())
    def getJson(self):
        #获得json数据格式
        dicdata = {"frame":self.getBase64Frame().decode(),"message":self.getMessage()}
        return json.dumps(dicdata)
    def getBinaryFrame(self):
        return IOUtil.array_to_bytes(self._frame[...,::-1])

utils.py：也放在同一个文件夹下。

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun Jan  5 17:51:40 2020

@author: youxinlin
"""
import time
import numpy
import base64
import os
import logging
import sys
from PIL import Image
from io import BytesIO
 
#工具类
class IOUtil(object):
    #流操作工具类
    @staticmethod
    def array_to_bytes(pic,formatter="jpeg",quality=70):
        '''
        静态方法,将numpy数组转化二进制流
        :param pic: numpy数组
        :param format: 图片格式
        :param quality:压缩比,压缩比越高,产生的二进制数据越短
        :return: 
        '''
        stream = BytesIO()
        picture = Image.fromarray(pic)
        picture.save(stream,format=formatter,quality=quality)
        jepg = stream.getvalue()
        stream.close()
        return jepg
    @staticmethod
    def bytes_to_base64(byte):
        '''
        静态方法,bytes转base64编码
        :param byte: 
        :return: 
        '''
        return base64.b64encode(byte)
    @staticmethod
    def transport_rgb(frame):
        '''
        将bgr图像转化为rgb图像,或者将rgb图像转化为bgr图像
        '''
        return frame[...,::-1]
    @staticmethod
    def byte_to_package(bytes,cmd,var=1):
        '''
        将每一帧的图片流的二进制数据进行分包
        :param byte: 二进制文件
        :param cmd:命令
        :return: 
        '''
        head = [ver,len(byte),cmd]
        headPack = struct.pack("!3I", *head)
        senddata = headPack+byte
        return senddata
    @staticmethod
    def mkdir(filePath):
        '''
        创建文件夹
        '''
        if not os.path.exists(filePath):
            os.mkdir(filePath)
    @staticmethod
    def countCenter(box):
        '''
        计算一个矩形的中心
        '''
        return (int(abs(box[0][0] - box[1][0])*0.5) + box[0][0],int(abs(box[0][1] - box[1][1])*0.5) +box[0][1])
    @staticmethod
    def countBox(center):
        '''
        根据两个点计算出,x,y,c,r
        '''
        return (center[0][0],center[0][1],center[1][0]-center[0][0],center[1][1]-center[0][1])
    @staticmethod
    def getImageFileName():
        return time.strftime("%Y_%m_%d_%H_%M_%S", time.localtime())+'.png'

多目标跟踪：

和单目标差不多，改用MultiTracker_create()

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun Jan  5 18:02:33 2020
多目标跟踪
@author: youxinlin
"""

import numpy as np
import cv2
import sys
'''
if len(sys.argv) != 2:
    print('Input video name is missing')
    exit()
'''

print('Select multiple tracking targets') 

cv2.namedWindow("tracking")

camera = cv2.VideoCapture(r"/Users/youxinlin/Desktop/video_data/complex6.MOV")   

#camera = cv2.VideoCapture(0)

tracker = cv2.MultiTracker_create()  #多目标跟踪
a= cv2.Tracker_c

init_once = False

ok, image=camera.read()
if not ok:
    print('Failed to read video')
    exit()

bbox1 = cv2.selectROI('tracking', image)
bbox2 = cv2.selectROI('tracking', image)
bbox3 = cv2.selectROI('tracking', image)

while camera.isOpened():
    ok, image=camera.read()
    if not ok:
        print ('no image to read')
        break

    if not init_once:
        ok = tracker.add(cv2.TrackerKCF_create(),image,bbox1)
        
        ok = tracker.add(cv2.TrackerKCF_create( ),image, bbox2)
        ok = tracker.add(cv2.TrackerKCF_create(),image, bbox3)
        init_once = True

    ok, boxes = tracker.update(image)

    for newbox in boxes:
        p1 = (int(newbox[0]), int(newbox[1]))
        p2 = (int(newbox[0] + newbox[2]), int(newbox[1] + newbox[3]))
        cv2.rectangle(image, p1, p2, (0,0,255))

    cv2.imshow('tracking', image)
    k = cv2.waitKey(1)
    if k == 27 : break # esc pressed

多尺度检测的KCF、自定义所用特征的KCF

在一些场景下，不想使用默认的hog特征跟踪，或需要对比不同特征的跟踪效果，那么封装好的方法似乎不可用，需要可以自己撸一波kcf的代码，从而使用自己设定的特征。

相关代码我会上传到CSDN资源上。（拒绝白嫖-0-）

值得参考 ：

1、OpenCV中KCF跟踪算法的参数自定义https://blog.csdn.net/u013832707/article/details/95941568

2、目标跟踪: opencv::TrackerKCF 参数详解https://blog.csdn.net/Augurlee/article/details/102684533

3、网上有人自己撸出来的kcf python代码https://github.com/uoip/KCFpy 但不能直接运行 还没时间研究、

4、OpenCV官方的文档《tracker.hpp File Reference》：https://docs.opencv.org/3.4.1/df/ddd/tracker_8hpp.html、https://docs.opencv.org/3.4.1/d2/dff/classcv_1_1TrackerKCF.html#a2a97c5b832651524b8e782496b933d45

5、Adaptive Color Attributes for Real-Time Visual Trackinghttp://www.cvl.isy.liu.se/research/objrec/visualtracking/colvistrack/index.html