python實現單目標、多目標、多尺度、自定義特徵的KCF跟蹤演算法(例項程式碼)
阿新 • • 發佈：2020-01-09
單目標跟蹤：
直接呼叫opencv中封裝的tracker即可。
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun Jan 5 17:50:47 2020
第四章 kcf跟蹤
@author: youxinlin
"""
import cv2
from items import MessageItem
import time
import numpy as np
'''
監視者模組,負責入侵檢測,目標跟蹤
'''
class WatchDog(object):
#入侵檢測者模組,用於入侵檢測
def __init__(self,frame=None):
#運動檢測器建構函式
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):
#運動檢測器是否工作
return self._background is not None
def startWorking(self,frame):
#運動檢測器開始工作
if frame is not None:
self._background = cv2.GaussianBlur(cv2.cvtColor(frame,0)
def stopWorking(self):
#運動檢測器結束工作
self._background = None
def analyze(self,frame):
#運動檢測
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):
'''
追蹤者模組,用於追蹤指定目標
'''
def __init__(self,tracker_type = "BOOSTING",draw_coord = True):
'''
初始化追蹤器種類
'''
#獲得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
#構造追蹤器
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):
'''
追蹤器工作初始化
frame:初始化追蹤畫面
box:追蹤的區域
'''
if not self.tracker:
raise Exception("追蹤器未初始化")
status = self.tracker.init(frame,box)
if not status:
raise Exception("追蹤器工作初始化失敗")
self.coord = box
self.isWorking = True
def track(self,frame):
'''
開啟追蹤
'''
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 frame
if __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附帶items.py，放在同個資料夾下：
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun Jan 5 17:51:04 2020
@author: youxinlin
"""
import json
from utils import IOUtil
'''
資訊封裝類
'''
class MessageItem(object):
#用於封裝資訊的類,包含圖片和其他資訊
def __init__(self,message):
self._frame = frame
self._message = message
def getFrame(self):
#圖片資訊
return self._frame
def getMessage(self):
#文字資訊,json格式
return self._message
def getBase64Frame(self):
#返回base64格式的圖片,將BGR影象轉化為RGB影象
jepg = IOUtil.array_to_bytes(self._frame[...,::-1])
return IOUtil.bytes_to_base64(jepg)
def getBase64FrameByte(self):
#返回base64格式圖片的bytes
return bytes(self.getBase64Frame())
def getJson(self):
#獲得json資料格式
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：也放在同一個資料夾下。
#!/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
#工具類
class IOUtil(object):
#流操作工具類
@staticmethod
def array_to_bytes(pic,formatter="jpeg",quality=70):
'''
靜態方法,將numpy陣列轉化二進位制流
:param pic: numpy陣列
:param format: 圖片格式
:param quality:壓縮比,壓縮比越高,產生的二進位制資料越短
: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):
'''
靜態方法,bytes轉base64編碼
:param byte:
:return:
'''
return base64.b64encode(byte)
@staticmethod
def transport_rgb(frame):
'''
將bgr影象轉化為rgb影象,或者將rgb影象轉化為bgr影象
'''
return frame[...,::-1]
@staticmethod
def byte_to_package(bytes,cmd,var=1):
'''
將每一幀的圖片流的二進位制資料進行分包
:param byte: 二進位制檔案
:param cmd:命令
:return:
'''
head = [ver,len(byte),cmd]
headPack = struct.pack("!3I",*head)
senddata = headPack+byte
return senddata
@staticmethod
def mkdir(filePath):
'''
建立資料夾
'''
if not os.path.exists(filePath):
os.mkdir(filePath)
@staticmethod
def countCenter(box):
'''
計算一個矩形的中心
'''
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):
'''
根據兩個點計算出,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'多目標跟蹤：
和單目標差不多，改用MultiTracker_create()
#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Sun Jan 5 18:02:33 2020
多目標跟蹤
@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() #多目標跟蹤
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( ),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
多尺度檢測的KCF、自定義所用特徵的KCF
在一些場景下，不想使用預設的hog特徵跟蹤，或需要對比不同特徵的跟蹤效果，那麼封裝好的方法似乎不可用，需要可以自己擼一波kcf的程式碼，從而使用自己設定的特徵。
總結
以上所述是小編給大家介紹的python實現單目標、多目標、多尺度、自定義特徵的KCF跟蹤演算法，希望對大家有所幫助，如果大家有任何疑問請給我留言，小編會及時回覆大家的。在此也非常感謝大家對我們網站的支援！如果你覺得本文對你有幫助，歡迎轉載，煩請註明出處，謝謝！