简单用Python+OpenCv实现AI人脸识别--(4)-训练人脸识别模型

最后主要讲述神经网络模型的训练过程，使用OpenCV模块中的LBPH（LOCAL BINARY PATTERNS HISTOGRAMS）人脸识别器算法

人脸识别的过程也非常简单，通过使用OpenCV模块读取“.yml”后缀的识别模型文件，实现人脸识别。(ps:图来自原作者)

主要代码如下:

#!/usr/bin/env python3#CSDN：/BC_COM/#邮箱：botron.campus@import telegramimport cv2import dlibfrom PyQt5.QtCore import QTimer, QThread, pyqtSignal, QRegExp, Qtfrom PyQt5.QtGui import QImage, QPixmap, QIcon, QTextCursor, QRegExpValidatorfrom PyQt5.QtWidgets import QDialog, QApplication, QMainWindow, QMessageBoxfrom PyQt5.uic import loadUi###############from configparser import ConfigParserfrom datetime import datetime# 找不到已训练的人脸数据文件class TrainingDataNotFoundError(FileNotFoundError):pass# 找不到数据库文件class DatabaseNotFoundError(FileNotFoundError):passclass CoreUI(QMainWindow):database = './Facedata/FaceBase.db'trainingData = './recognizer/trainingData.yml'cap = cv2.VideoCapture()captureQueue = queue.Queue() # 图像队列alarmQueue = queue.LifoQueue() # 报警队列，后进先出logQueue = multiprocessing.Queue() # 日志队列receiveLogSignal = pyqtSignal(str) # LOG信号def __init__(self):super(CoreUI, self).__init__()loadUi('./ui/GUI/Core.ui', self)self.setWindowIcon(QIcon('./icons/icon.png'))self.setFixedSize(1161, 621)# 图像捕获self.isExternalCameraUsed = Falseself.useExternalCameraCheckBox.stateChanged.connect(lambda: self.useExternalCamera(self.useExternalCameraCheckBox))self.faceProcessingThread = FaceProcessingThread()self.startWebcamButton.clicked.connect(self.startWebcam)# 数据库self.initDbButton.setIcon(QIcon('./icons/warning.png'))self.initDbButton.clicked.connect(self.initDb)self.timer = QTimer(self) # 初始化一个定时器self.timer.timeout.connect(self.updateFrame)# 功能开关self.faceTrackerCheckBox.stateChanged.connect(lambda: self.faceProcessingThread.enableFaceTracker(self))self.faceRecognizerCheckBox.stateChanged.connect(lambda: self.faceProcessingThread.enableFaceRecognizer(self))self.panalarmCheckBox.stateChanged.connect(lambda: self.faceProcessingThread.enablePanalarm(self))# 直方图均衡化self.equalizeHistCheckBox.stateChanged.connect(lambda: self.faceProcessingThread.enableEqualizeHist(self))# 调试模式self.debugCheckBox.stateChanged.connect(lambda: self.faceProcessingThread.enableDebug(self))self.confidenceThresholdSlider.valueChanged.connect(lambda: self.faceProcessingThread.setConfidenceThreshold(self))self.autoAlarmThresholdSlider.valueChanged.connect(lambda: self.faceProcessingThread.setAutoAlarmThreshold(self))# 报警系统self.alarmSignalThreshold = 10self.panalarmThread = threading.Thread(target=self.recieveAlarm, daemon=True)self.isBellEnabled = Trueself.bellCheckBox.stateChanged.connect(lambda: self.enableBell(self.bellCheckBox))self.isTelegramBotPushEnabled = Falseself.telegramBotPushCheckBox.stateChanged.connect(lambda: self.enableTelegramBotPush(self.telegramBotPushCheckBox))self.telegramBotSettingsButton.clicked.connect(self.telegramBotSettings)# 帮助与支持self.viewGithubRepoButton.clicked.connect(lambda: webbrowser.open(''))self.contactDeveloperButton.clicked.connect(lambda: webbrowser.open(''))# 日志系统self.receiveLogSignal.connect(lambda log: self.logOutput(log))self.logOutputThread = threading.Thread(target=self.receiveLog, daemon=True)self.logOutputThread.start()# 检查数据库状态def initDb(self):try:if not os.path.isfile(self.database):raise DatabaseNotFoundErrorif not os.path.isfile(self.trainingData):raise TrainingDataNotFoundErrorconn = sqlite3.connect(self.database)cursor = conn.cursor()cursor.execute('SELECT Count(*) FROM users')result = cursor.fetchone()dbUserCount = result[0]except DatabaseNotFoundError:logging.error('系统找不到数据库文件{}'.format(self.database))self.initDbButton.setIcon(QIcon('./icons/error.png'))self.logQueue.put('Error：未发现数据库文件，你可能未进行人脸采集')except TrainingDataNotFoundError:logging.error('系统找不到已训练的人脸数据{}'.format(self.trainingData))self.initDbButton.setIcon(QIcon('./icons/error.png'))self.logQueue.put('Error：未发现已训练的人脸数据文件，请完成训练后继续')except Exception as e:logging.error('读取数据库异常，无法完成数据库初始化')self.initDbButton.setIcon(QIcon('./icons/error.png'))self.logQueue.put('Error：读取数据库异常，初始化数据库失败')else:cursor.close()conn.close()if not dbUserCount > 0:logging.warning('数据库为空')self.logQueue.put('warning：数据库为空，人脸识别功能不可用')self.initDbButton.setIcon(QIcon('./icons/warning.png'))else:self.logQueue.put('Success：数据库状态正常，发现用户数：{}'.format(dbUserCount))self.initDbButton.setIcon(QIcon('./icons/success.png'))self.initDbButton.setEnabled(False)self.faceRecognizerCheckBox.setToolTip('须先开启人脸跟踪')self.faceRecognizerCheckBox.setEnabled(True)# 是否使用外接摄像头def useExternalCamera(self, useExternalCameraCheckBox):if useExternalCameraCheckBox.isChecked():self.isExternalCameraUsed = Trueelse:self.isExternalCameraUsed = False# 打开/关闭摄像头def startWebcam(self):if not self.cap.isOpened():if self.isExternalCameraUsed:camID = 1else:camID = 0self.cap.open(camID)self.cap.set(cv2.CAP_PROP_FRAME_WIDTH, 640)self.cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 480)ret, frame = self.cap.read()if not ret:logging.error('无法调用电脑摄像头{}'.format(camID))self.logQueue.put('Error：初始化摄像头失败')self.cap.release()self.startWebcamButton.setIcon(QIcon('./icons/error.png'))else:self.faceProcessingThread.start() # 启动OpenCV图像处理线程self.timer.start(5) # 启动定时器self.panalarmThread.start() # 启动报警系统线程self.startWebcamButton.setIcon(QIcon('./icons/success.png'))self.startWebcamButton.setText('关闭摄像头')else:text = '如果关闭摄像头，须重启程序才能再次打开。'informativeText = '<b>是否继续？</b>'ret = CoreUI.callDialog(QMessageBox.Warning, text, informativeText, QMessageBox.Yes | QMessageBox.No,QMessageBox.No)if ret == QMessageBox.Yes:self.faceProcessingThread.stop()if self.cap.isOpened():if self.timer.isActive():self.timer.stop()self.cap.release()self.realTimeCaptureLabel.clear()self.realTimeCaptureLabel.setText('<font color=red>摄像头未开启</font>')self.startWebcamButton.setText('摄像头已关闭')self.startWebcamButton.setEnabled(False)self.startWebcamButton.setIcon(QIcon())# 定时器，实时更新画面def updateFrame(self):if self.cap.isOpened():# ret, frame = self.cap.read()# if ret:#self.showImg(frame, self.realTimeCaptureLabel)if not self.captureQueue.empty():captureData = self.captureQueue.get()realTimeFrame = captureData.get('realTimeFrame')self.displayImage(realTimeFrame, self.realTimeCaptureLabel)# 显示图片def displayImage(self, img, qlabel):# BGR -> RGBimg = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)# default：The image is stored using 8-bit indexes into a colormap， for example：a gray imageqformat = QImage.Format_Indexed8if len(img.shape) == 3: # rows[0], cols[1], channels[2]if img.shape[2] == 4:# The image is stored using a 32-bit byte-ordered RGBA format (8-8-8-8)# A: alpha channel，不透明度参数。如果一个像素的alpha通道数值为0%，那它就是完全透明的qformat = QImage.Format_RGBA8888else:qformat = QImage.Format_RGB888# img.shape[1]：图像宽度width，img.shape[0]：图像高度height，img.shape[2]：图像通道数# QImage.__init__ (self, bytes data, int width, int height, int bytesPerLine, Format format)# 从内存缓冲流获取img数据构造QImage类# img.strides[0]：每行的字节数（width*3）,rgb为3，rgba为4# strides[0]为最外层(即一个二维数组所占的字节长度)，strides[1]为次外层（即一维数组所占字节长度），strides[2]为最内层（即一个元素所占字节长度）# 从里往外看，strides[2]为1个字节长度（uint8），strides[1]为3*1个字节长度（3即rgb 3个通道）# strides[0]为width*3个字节长度，width代表一行有几个像素outImage = QImage(img, img.shape[1], img.shape[0], img.strides[0], qformat)qlabel.setPixmap(QPixmap.fromImage(outImage))qlabel.setScaledContents(True) # 图片自适应大小# 报警系统：是否允许设备响铃def enableBell(self, bellCheckBox):if bellCheckBox.isChecked():self.isBellEnabled = Trueself.statusBar().showMessage('设备发声：开启')else:if self.isTelegramBotPushEnabled:self.isBellEnabled = Falseself.statusBar().showMessage('设备发声：关闭')else:self.logQueue.put('Error：操作失败，至少选择一种报警方式')self.bellCheckBox.setCheckState(Qt.Unchecked)self.bellCheckBox.setChecked(True)# print('isBellEnabled：', self.isBellEnabled)# 报警系统：是否允许TelegramBot推送def enableTelegramBotPush(self, telegramBotPushCheckBox):if telegramBotPushCheckBox.isChecked():self.isTelegramBotPushEnabled = Trueself.statusBar().showMessage('TelegramBot推送：开启')else:if self.isBellEnabled:self.isTelegramBotPushEnabled = Falseself.statusBar().showMessage('TelegramBot推送：关闭')else:self.logQueue.put('Error：操作失败，至少选择一种报警方式')self.telegramBotPushCheckBox.setCheckState(Qt.Unchecked)self.telegramBotPushCheckBox.setChecked(True)# print('isTelegramBotPushEnabled：', self.isTelegramBotPushEnabled)# TelegramBot设置def telegramBotSettings(self):cfg = ConfigParser()cfg.read('./config/telegramBot.cfg', encoding='utf-8-sig')read_only = cfg.getboolean('telegramBot', 'read_only')# read_only = Falseif read_only:text = '基于安全考虑，系统拒绝了本次请求。'informativeText = '<b>请联系设备管理员。</b>'CoreUI.callDialog(QMessageBox.Critical, text, informativeText, QMessageBox.Ok)else:token = cfg.get('telegramBot', 'token')chat_id = cfg.get('telegramBot', 'chat_id')proxy_url = cfg.get('telegramBot', 'proxy_url')message = cfg.get('telegramBot', 'message')self.telegramBotDialog = TelegramBotDialog()self.telegramBotDialog.tokenLineEdit.setText(token)self.telegramBotDialog.telegramIDLineEdit.setText(chat_id)self.telegramBotDialog.socksLineEdit.setText(proxy_url)self.telegramBotDialog.messagePlainTextEdit.setPlainText(message)self.telegramBotDialog.exec()# 设备响铃进程@staticmethoddef bellProcess(queue):logQueue = queuelogQueue.put('Info：设备正在响铃...')winsound.PlaySound('./icon/alarm.wav', winsound.SND_FILENAME)# TelegramBot推送进程@staticmethoddef telegramBotPushProcess(queue, img=None):logQueue = queuecfg = ConfigParser()try:cfg.read('./config/telegramBot.cfg', encoding='utf-8-sig')# 读取TelegramBot配置token = cfg.get('telegramBot', 'token')chat_id = cfg.getint('telegramBot', 'chat_id')proxy_url = cfg.get('telegramBot', 'proxy_url')message = cfg.get('telegramBot', 'message')# 是否使用代理if proxy_url:proxy = telegram.utils.request.Request(proxy_url=proxy_url)bot = telegram.Bot(token=token, request=proxy)else:bot = telegram.Bot(token=token)bot.send_message(chat_id=chat_id, text=message)# 发送疑似陌生人脸截屏到Telegramif img:bot.send_photo(chat_id=chat_id, photo=open(img, 'rb'), timeout=10)except Exception as e:logQueue.put('Error：TelegramBot推送失败')else:logQueue.put('Success：TelegramBot推送成功')# 报警系统服务常驻，接收并处理报警信号def recieveAlarm(self):while True:jobs = []# print(self.alarmQueue.qsize())if self.alarmQueue.qsize() > self.alarmSignalThreshold: # 若报警信号触发超出既定计数，进行报警if not os.path.isdir('./unknown'):os.makedirs('./unknown')lastAlarmSignal = self.alarmQueue.get()timestamp = lastAlarmSignal.get('timestamp')img = lastAlarmSignal.get('img')# 疑似陌生人脸，截屏存档cv2.imwrite('./unknown/{}.jpg'.format(timestamp), img)logging.info('报警信号触发超出预设计数，自动报警系统已被激活')self.logQueue.put('Info：报警信号触发超出预设计数，自动报警系统已被激活')# 是否进行响铃if self.isBellEnabled:p1 = multiprocessing.Process(target=CoreUI.bellProcess, args=(self.logQueue,))p1.start()jobs.append(p1)# 是否进行TelegramBot推送if self.isTelegramBotPushEnabled:if os.path.isfile('./unknown/{}.jpg'.format(timestamp)):img = './unknown/{}.jpg'.format(timestamp)else:img = Nonep2 = multiprocessing.Process(target=CoreUI.telegramBotPushProcess, args=(self.logQueue, img))p2.start()jobs.append(p2)# 等待本轮报警结束for p in jobs:p.join()# 重置报警信号with self.alarmQueue.mutex:self.alarmQueue.queue.clear()else:continue# 系统日志服务常驻，接收并处理系统日志def receiveLog(self):while True:data = self.logQueue.get()if data:self.receiveLogSignal.emit(data)else:continue# LOG输出def logOutput(self, log):# 获取当前系统时间time = datetime.now().strftime('[%Y/%m/%d %H:%M:%S]')log = time + ' ' + log + '\n'self.logTextEdit.moveCursor(QTextCursor.End)self.logTextEdit.insertPlainText(log)self.logTextEdit.ensureCursorVisible() # 自动滚屏# 系统对话框@staticmethoddef callDialog(icon, text, informativeText, standardButtons, defaultButton=None):msg = QMessageBox()msg.setWindowIcon(QIcon('./icons/icon.png'))msg.setWindowTitle('OpenCV Face Recognition System - Core')msg.setIcon(icon)msg.setText(text)msg.setInformativeText(informativeText)msg.setStandardButtons(standardButtons)if defaultButton:msg.setDefaultButton(defaultButton)return msg.exec()# 窗口关闭事件，关闭OpenCV线程、定时器、摄像头def closeEvent(self, event):if self.faceProcessingThread.isRunning:self.faceProcessingThread.stop()if self.timer.isActive():self.timer.stop()if self.cap.isOpened():self.cap.release()event.accept()# TelegramBot设置对话框class TelegramBotDialog(QDialog):def __init__(self):super(TelegramBotDialog, self).__init__()loadUi('./ui/GUI/TelegramBotDialog.ui', self)self.setWindowIcon(QIcon('./icons/icon.png'))self.setFixedSize(550, 358)chat_id_regx = QRegExp('^\d+$')chat_id_validator = QRegExpValidator(chat_id_regx, self.telegramIDLineEdit)self.telegramIDLineEdit.setValidator(chat_id_validator)self.okButton.clicked.connect(self.telegramBotSettings)def telegramBotSettings(self):# 获取用户输入token = self.tokenLineEdit.text().strip()chat_id = self.telegramIDLineEdit.text().strip()proxy_url = self.socksLineEdit.text().strip()message = self.messagePlainTextEdit.toPlainText().strip()# 校验并处理用户输入if not (token and chat_id and message):self.okButton.setIcon(QIcon('./icons/error.png'))CoreUI.logQueue.put('Error：API Token、Telegram ID和消息内容为必填项')else:ret = self.telegramBotTest(token, proxy_url)if ret:cfg_file = './config/telegramBot.cfg'cfg = ConfigParser()cfg.read(cfg_file, encoding='utf-8-sig')cfg.set('telegramBot', 'token', token)cfg.set('telegramBot', 'chat_id', chat_id)cfg.set('telegramBot', 'proxy_url', proxy_url)cfg.set('telegramBot', 'message', message)try:with open(cfg_file, 'w', encoding='utf-8') as file:cfg.write(file)except:logging.error('写入telegramBot配置文件发生异常')CoreUI.logQueue.put('Error：写入配置文件时发生异常，更新失败')else:CoreUI.logQueue.put('Success：测试通过，系统已更新TelegramBot配置')self.close()else:CoreUI.logQueue.put('Error：测试失败，无法更新TelegramBot配置')# TelegramBot 测试def telegramBotTest(self, token, proxy_url):try:# 是否使用代理if proxy_url:proxy = telegram.utils.request.Request(proxy_url=proxy_url)bot = telegram.Bot(token=token, request=proxy)else:bot = telegram.Bot(token=token)bot.get_me()except Exception as e:return Falseelse:return True# OpenCV线程class FaceProcessingThread(QThread):def __init__(self):super(FaceProcessingThread, self).__init__()self.isRunning = Trueself.isFaceTrackerEnabled = Trueself.isFaceRecognizerEnabled = Falseself.isPanalarmEnabled = Trueself.isDebugMode = Falseself.confidenceThreshold = 50self.autoAlarmThreshold = 65self.isEqualizeHistEnabled = False# 是否开启人脸跟踪def enableFaceTracker(self, coreUI):if coreUI.faceTrackerCheckBox.isChecked():self.isFaceTrackerEnabled = TruecoreUI.statusBar().showMessage('人脸跟踪：开启')else:self.isFaceTrackerEnabled = FalsecoreUI.statusBar().showMessage('人脸跟踪：关闭')# 是否开启人脸识别def enableFaceRecognizer(self, coreUI):if coreUI.faceRecognizerCheckBox.isChecked():if self.isFaceTrackerEnabled:self.isFaceRecognizerEnabled = TruecoreUI.statusBar().showMessage('人脸识别：开启')else:CoreUI.logQueue.put('Error：操作失败，请先开启人脸跟踪')coreUI.faceRecognizerCheckBox.setCheckState(Qt.Unchecked)coreUI.faceRecognizerCheckBox.setChecked(False)else:self.isFaceRecognizerEnabled = FalsecoreUI.statusBar().showMessage('人脸识别：关闭')# 是否开启报警系统def enablePanalarm(self, coreUI):if coreUI.panalarmCheckBox.isChecked():self.isPanalarmEnabled = TruecoreUI.statusBar().showMessage('报警系统：开启')else:self.isPanalarmEnabled = FalsecoreUI.statusBar().showMessage('报警系统：关闭')# 是否开启调试模式def enableDebug(self, coreUI):if coreUI.debugCheckBox.isChecked():self.isDebugMode = TruecoreUI.statusBar().showMessage('调试模式：开启')else:self.isDebugMode = FalsecoreUI.statusBar().showMessage('调试模式：关闭')# 设置置信度阈值def setConfidenceThreshold(self, coreUI):if self.isDebugMode:self.confidenceThreshold = coreUI.confidenceThresholdSlider.value()coreUI.statusBar().showMessage('置信度阈值：{}'.format(self.confidenceThreshold))# 设置自动报警阈值def setAutoAlarmThreshold(self, coreUI):if self.isDebugMode:self.autoAlarmThreshold = coreUI.autoAlarmThresholdSlider.value()coreUI.statusBar().showMessage('自动报警阈值：{}'.format(self.autoAlarmThreshold))# 直方图均衡化def enableEqualizeHist(self, coreUI):if coreUI.equalizeHistCheckBox.isChecked():self.isEqualizeHistEnabled = TruecoreUI.statusBar().showMessage('直方图均衡化：开启')else:self.isEqualizeHistEnabled = FalsecoreUI.statusBar().showMessage('直方图均衡化：关闭')def run(self):faceCascade = cv2.CascadeClassifier('./haarcascades/haarcascade_frontalface_default.xml')# 帧数、人脸ID初始化frameCounter = 0currentFaceID = 0# 人脸跟踪器字典初始化faceTrackers = {}isTrainingDataLoaded = FalseisDbConnected = Falsewhile self.isRunning:if CoreUI.cap.isOpened():ret, frame = CoreUI.cap.read()gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)# 是否执行直方图均衡化if self.isEqualizeHistEnabled:gray = cv2.equalizeHist(gray)faces = faceCascade.detectMultiScale(gray, 1.3, 5, minSize=(90, 90))# 预加载数据文件if not isTrainingDataLoaded and os.path.isfile(CoreUI.trainingData):recognizer = cv2.face.LBPHFaceRecognizer_create()recognizer.read(CoreUI.trainingData)isTrainingDataLoaded = Trueif not isDbConnected and os.path.isfile(CoreUI.database):conn = sqlite3.connect(CoreUI.database)cursor = conn.cursor()isDbConnected = TruecaptureData = {}realTimeFrame = frame.copy()alarmSignal = {}# 人脸跟踪if self.isFaceTrackerEnabled:# 要删除的人脸跟踪器列表初始化fidsToDelete = []for fid in faceTrackers.keys():# 实时跟踪trackingQuality = faceTrackers[fid].update(realTimeFrame)# 如果跟踪质量过低，删除该人脸跟踪器if trackingQuality < 7:fidsToDelete.append(fid)# 删除跟踪质量过低的人脸跟踪器for fid in fidsToDelete:faceTrackers.pop(fid, None)for (_x, _y, _w, _h) in faces:isKnown = Falseif self.isFaceRecognizerEnabled:cv2.rectangle(realTimeFrame, (_x, _y), (_x + _w, _y + _h), (232, 138, 30), 2)face_id, confidence = recognizer.predict(gray[_y:_y + _h, _x:_x + _w])logging.debug('face_id：{}，confidence：{}'.format(face_id, confidence))if self.isDebugMode:CoreUI.logQueue.put('Debug -> face_id：{}，confidence：{}'.format(face_id, confidence))# 从数据库中获取识别人脸的身份信息try:cursor.execute("SELECT * FROM users WHERE face_id=?", (face_id,))result = cursor.fetchall()if result:en_name = result[0][3]else:raise Exceptionexcept Exception as e:logging.error('读取数据库异常，系统无法获取Face ID为{}的身份信息'.format(face_id))CoreUI.logQueue.put('Error：读取数据库异常，系统无法获取Face ID为{}的身份信息'.format(face_id))en_name = ''# 若置信度评分小于置信度阈值，认为是可靠识别if confidence < self.confidenceThreshold:isKnown = Truecv2.putText(realTimeFrame, en_name, (_x - 5, _y - 10), cv2.FONT_HERSHEY_SIMPLEX, 1,(0, 97, 255), 2)else:# 若置信度评分大于置信度阈值，该人脸可能是陌生人cv2.putText(realTimeFrame, 'unknown', (_x - 5, _y - 10), cv2.FONT_HERSHEY_SIMPLEX, 1,(0, 0, 255), 2)# 若置信度评分超出自动报警阈值，触发报警信号if confidence > self.autoAlarmThreshold:# 检测报警系统是否开启if self.isPanalarmEnabled:alarmSignal['timestamp'] = datetime.now().strftime('%Y%m%d%H%M%S')alarmSignal['img'] = realTimeFrameCoreUI.alarmQueue.put(alarmSignal)logging.info('系统发出了报警信号')# 帧数自增frameCounter += 1# 每读取10帧，检测跟踪器的人脸是否还在当前画面内if frameCounter % 10 == 0:# 这里必须转换成int类型，因为OpenCV人脸检测返回的是numpy.int32类型，# 而dlib人脸跟踪器要求的是int类型x = int(_x)y = int(_y)w = int(_w)h = int(_h)# 计算中心点x_bar = x + 0.5 * wy_bar = y + 0.5 * h# matchedFid表征当前检测到的人脸是否已被跟踪matchedFid = Nonefor fid in faceTrackers.keys():# 获取人脸跟踪器的位置# tracked_position 是 dlib.drectangle 类型，用来表征图像的矩形区域，坐标是浮点数tracked_position = faceTrackers[fid].get_position()# 浮点数取整t_x = int(tracked_position.left())t_y = int(tracked_position.top())t_w = int(tracked_position.width())t_h = int(tracked_position.height())# 计算人脸跟踪器的中心点t_x_bar = t_x + 0.5 * t_wt_y_bar = t_y + 0.5 * t_h# 如果当前检测到的人脸中心点落在人脸跟踪器内，且人脸跟踪器的中心点也落在当前检测到的人脸内# 说明当前人脸已被跟踪if ((t_x <= x_bar <= (t_x + t_w)) and (t_y <= y_bar <= (t_y + t_h)) and(x <= t_x_bar <= (x + w)) and (y <= t_y_bar <= (y + h))):matchedFid = fid# 如果当前检测到的人脸是陌生人脸且未被跟踪if not isKnown and matchedFid is None:# 创建一个人脸跟踪器tracker = dlib.correlation_tracker()# 锁定跟踪范围tracker.start_track(realTimeFrame, dlib.rectangle(x - 5, y - 10, x + w + 5, y + h + 10))# 将该人脸跟踪器分配给当前检测到的人脸faceTrackers[currentFaceID] = tracker# 人脸ID自增currentFaceID += 1# 使用当前的人脸跟踪器，更新画面，输出跟踪结果for fid in faceTrackers.keys():tracked_position = faceTrackers[fid].get_position()t_x = int(tracked_position.left())t_y = int(tracked_position.top())t_w = int(tracked_position.width())t_h = int(tracked_position.height())# 在跟踪帧中圈出人脸cv2.rectangle(realTimeFrame, (t_x, t_y), (t_x + t_w, t_y + t_h), (0, 0, 255), 2)cv2.putText(realTimeFrame, 'tracking...', (15, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 0, 255),2)captureData['originFrame'] = framecaptureData['realTimeFrame'] = realTimeFrameCoreUI.captureQueue.put(captureData)else:continue# 停止OpenCV线程def stop(self):self.isRunning = Falseself.quit()self.wait()if __name__ == '__main__':logging.config.fileConfig('./config/logging.cfg')app = QApplication(sys.argv)window = CoreUI()window.show()sys.exit(app.exec())

最后得说的是 非常感谢原作者 :不脱发的程序猿

参考文章:

1./m0_38106923/article/details/96108803

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