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)

Python  /  管理员 发布于 7年前   241

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, 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 frameif __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 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,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 timeimport numpyimport base64import osimport loggingimport sysfrom PIL import Imagefrom 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 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( ),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。

总结

以上所述是小编给大家介绍的python实现单目标、多目标、多尺度、自定义特征的KCF跟踪算法，希望对大家有所帮助，如果大家有任何疑问请给我留言，小编会及时回复大家的。在此也非常感谢大家对站的支持！如果你觉得本文对你有帮助，欢迎转载，烦请注明出处，谢谢！