用python实现单目标、多目标、多尺度、自定义特征的KCF跟踪算法_多尺度跟踪

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)