python-opencv3.0新增了一些比较有用的追踪器算法,这里根据官网示例写了一个追踪器类
程序只能运行在安装有opencv3.0以上版本和对应的contrib模块的python解释器
主要代码
#encoding=utf-8
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),(255,0,0),2)
return frame
if __name__ == '__main__' :
a = ['BOOSTING', 'MIL','KCF', 'TLD', 'MEDIANFLOW', 'GOTURN']
tracker = Tracker(tracker_type="KCF")
video = cv2.VideoCapture(0)
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
信息封装类
#encoding=utf-8
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])
运行之后在第一帧图像上选择要追踪的部分,这里测试了一下使用KCF算法的追踪器
更新utils
#encoding=utf-8
import time
import numpy
import base64
import os
import logging
import sys
from settings import *
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'
#构造日志
logger = logging.getLogger(LOG_NAME)
formatter = logging.Formatter(LOG_FORMATTER)
IOUtil.mkdir(LOG_DIR);
file_handler = logging.FileHandler(LOG_DIR + LOG_FILE,encoding='utf-8')
file_handler.setFormatter(formatter)
console_handler = logging.StreamHandler(sys.stdout)
console_handler.setFormatter(formatter)
logger.addHandler(file_handler)
logger.addHandler(console_handler)
logger.setLevel(logging.INFO)
以上就是python基于opencv实现目标追踪示例的详细内容，更多关于python opencv目标追踪的资料请关注猪先飞其它相关文章！
原文出处：https://blog.csdn.net/qq_35488769/article/details/79103264