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 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 * 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 *
 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):
 '''
  * 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(" * 未初始化")
 status = self.tracker.init(frame,box)
 if not status:
  raise Exception(" * 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。

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跟踪算法网站的支持！如果你觉得本文对你有帮助，欢迎转载，烦请注明出处，谢谢！

来源：https://blog.csdn.net/lyxleft/article/details/103847454