python实现单目标、多目标、多尺度、自定义特征的KCF跟踪算法(实例代码)

概述python实现单目标、多目标、多尺度、自定义特征的KCF跟踪算法(实例代码) 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 ...

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 cv2from items import MessageItemimport timeimport 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,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,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),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,p2,1)     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,h) in faces:   cv2.rectangle(frame,(x,y+h),(0,255),2)  return frameif __name__ == '__main__' :# tracker_types = ['BOOSTING','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 Jsonfrom 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,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 timeimport numpyimport base64import osimport loggingimport sysfrom PIL import Imagefrom io import BytesIO#工具类class IoUtil(object): #流 *** 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,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 npimport cv2import 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_cinit_once = Falseok,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( ),bBox2)  ok = tracker.add(cv2.Trackerkcf_create(),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,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。

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