python opencv實現目標跟蹤
這裡根據官網示例寫了一個追蹤器類
python opencv實現目標跟蹤python-opencv3.0新增了一些比較有用的追蹤器算法
這裡根據官網示例寫了一個追蹤器類程序只能運行在安裝有opencv3.0以上版本和對應的contrib模塊的python解釋器
#encoding=utf-8import cv2from items import MessageItemimport timeimport numpy as np'''監視者模塊,負責入侵檢測,目標跟蹤'''class WatchDog(object): #入侵檢測者模塊,用於入侵檢測 def __init__(self,frame=None): #運動檢測器構造函數 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, cv2.COLOR_BGR2GRAY), (21, 21), 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,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): ''' 追蹤者模塊,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, (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 frameif __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#encoding=utf-8import jsonfrom utils import IOUtil'''信息封裝類'''class MessageItem(object): #用於封裝信息的類,包含圖片和其他信息 def __init__(self,frame,message): self._frame = frame self._message = message def getFrame(self): #圖片信息 return self._frame def getMessage(self): #文字信息,json格式 return self._message def getBase64Frame(self): #返回base64格式的圖片,將BGR圖像轉化為RGB圖像 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數據格式 dicdata = {"frame":self.getBase64Frame().decode(),"message":self.getMessage()} return json.dumps(dicdata) def getBinaryFrame(self): return IOUtil.array_to_bytes(self._frame[...,::-1])運行之後在第一幀圖像上選擇要追蹤的部分,這裡測試了一下使用KCF算法的追蹤器
更新：忘記放utils，給大家造成的困擾深表歉意
#encoding=utf-8import timeimport numpyimport base64import osimport loggingimport sysfrom settings import *from PIL import Imagefrom 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數組轉化二進制流 :param pic: numpy數組 :param format: 圖片格式 :param quality:壓縮比,壓縮比越高,產生的二進制數據越短 :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圖像轉化為rgb圖像,或者將rgb圖像轉化為bgr圖像 ''' return frame[...,::-1] @staticmethod def byte_to_package(bytes,cmd,var=1): ''' 將每一幀的圖片流的二進制數據進行分包 :param byte: 二進制文件 :param cmd:命令 :return: ''' head = [ver,len(byte),cmd] headPack = struct.pack("!3I", *head) senddata = headPack+byte return senddata @staticmethod def mkdir(filePath): ''' 創建文件夾 ''' 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,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'#構造日志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)以上為個人經驗，希望能給大家一個參考，也希望大家多多支持軟件開發網。
