python目标检测基于opencv实现目标追踪示例 python目标检测基于opencv实现目

主要代码
信息封装类
更新utils

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 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 = Noneif 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 Nonedef 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 = Nonedef analyze(self,frame):#运动检测if frame is None or self._background is None:returnsample_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 = NonebigMulti = 0for c in cnts:if cv2.contourArea(c) < 1500:continue(x,y,w,h) = cv2.boundingRect(c)if w * h > bigMulti:bigMulti = w * hbigC = ((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'] = Nonereturn 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_typeself.isWorking = Falseself.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 = boxself.isWorking = True def track(self,frame):'''开启追踪'''message = Noneif 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) & 0xffif k == 27:break

信息封装类

#encoding=utf-8import 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 = frameself._message = messagedef getFrame(self):#图片信息return self._framedef getMessage(self):#文字信息,json格式return self._messagedef 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bytesreturn bytes(self.getBase64Frame())def getJson(self):#获得json数据格式dicdata = https://www.it610.com/article/{"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-8import timeimport numpyimport base64import osimport loggingimport sysfrom settings import *from PIL import Imagefrom io import BytesIO #工具类class IOUtil(object):#流操作工具类@staticmethoddef 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@staticmethoddef bytes_to_base64(byte):'''静态方法,bytes转base64编码:param byte: :return: '''return base64.b64encode(byte)@staticmethoddef 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]@staticmethoddef 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 = https://www.it610.com/article/headPack+bytereturn senddata@staticmethoddef mkdir(filePath):'''创建文件夹'''if not os.path.exists(filePath):os.mkdir(filePath)@staticmethoddef 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])@staticmethoddef 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])@staticmethoddef 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)

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