人脸识别|面向佩戴口罩的人脸识别系统【4】-人脸识别 conda|目标检测|目标跟踪|深度学

数据标记
本项目采用labelimg标记佩戴口罩和不配戴口罩的人脸图片共2000张，训练12小时。
labelimg使用介绍
数据训练
将数据划分为测试集合训练集，配置yolov5环境进行训练
测试集和训练集划分以及训练
相关介绍
项目经历从dlib人脸识别---->优化的dlib人脸识别（利用质心追踪算法优化速度）---->加上pyside2界面的dlib人脸识别（此时无法识别是否佩戴口罩）---->训练yolov5后的人脸识别（此时只能输出是否佩戴口罩，无法识别佩戴口罩的人脸）---->最终版人脸识别（可以识别是否佩戴口罩以及识别佩戴口罩的人脸）
相关代码

# 人脸识别界面程序 import datetime import timeimport cv2 import dlib from PIL import Image, ImageDraw, ImageFont from PySide2.QtGui import Qt from PySide2.QtWidgets import QApplication, QMessageBox from PySide2 import QtWidgets from PySide2.QtUiTools import QUiLoader from PySide2 import QtGui, QtCore from lib.share import SI import lib.share as sa import lib.dataDB as db import traceback from PyCameraList.camera_device import test_list_cameras, list_video_devices, list_audio_devices import numpy as np import face as fa# Dlib 正向人脸检测器 detector = dlib.get_frontal_face_detector() # Dlib 人脸 landmark 特征点检测器 predictor = dlib.shape_predictor('data/dlib/shape_predictor_68_face_landmarks.dat') # Dlib Resnet 人脸识别模型，提取 128D 的特征矢量 face_reco_model = dlib.face_recognition_model_v1("data/dlib/dlib_face_recognition_resnet_model_v1.dat")class Client: def __init__(self): self.ui = QUiLoader().load('ui/face_recognition.ui') # self.ui.setWindowFlags(Qt.WindowCloseButtonHint)"""加载人脸识别各种信息""" self.current_face_position = []# 加载已录入人脸特征和人员姓名下标对应 self.features = SI.features self.features_names = SI.f_uid# 帧数 self.frame_cnt = 0# 用来存储上一帧和当前帧的质心坐标 self.last_centriod = [] self.current_centroid = []# 当前帧和上一帧检测出的目标的名字 self.current_face_name = [] self.last_face_name = []# 当前帧和上一帧的人脸数 self.current_face_cnt = 0 self.last_face_cnt = 0# 存储当前摄像头中捕获到的所有人脸的坐标名称 self.current_face_position = [] # 存放当前帧捕获到的人脸坐标和人脸特征 self.current_face_feature = []# 当前帧和上一帧的质心欧式距离 self.last_current_distance = 0 # 存放识别的距离 self.current_face_distance = []# 控制再识别的后续帧，如果识别出unknow的人脸，将reclassify_cnt计数到reclassify_interval后，对人脸进行重新识别 self.reclassify_cnt = 0 self.reclassify_interval = 20self.current_face = None"""视频相关操作""" # 定义定时器，用于控制显示视频的帧率 self.timer_camera = QtCore.QTimer() # 视频流 self.cap = cv2.VideoCapture() # 为0时表示视频流来自笔记本内置摄像头 self.CAM_NUM = 0"""操作""" # 若定时器结束，则调用show_camera() self.timer_camera.timeout.connect(self.show_camera) # 选择打开的摄像头是内置摄像头还是外置摄像头 self.ui.select_camera.clicked.connect(self.select_camera_num) # 开始识别 self.ui.begin_btn.setEnabled(False) self.ui.begin_btn.clicked.connect(self.run_rec) # 获取可用摄像头列表 self.get_camera()"""获取可用摄像头列表"""def get_camera(self): cameras = list_video_devices() camera_dict = dict(cameras) print(camera_dict) camera_name = [] # if len(camera_dict) <= 0: #print("the serial port can't find！") #return False # else: #for items in range(len(camera_dict)): #camera_name.append(str(items)) # print("camera:", camera_name) camera_name.append(str(0)) camera_name.append(str(1)) self.ui.cameraList.addItems(camera_name)"""摄像头画面展示"""def show_camera(self):try: start_time = time.time() # 从视频流中读取画面 flag, img_rd = self.cap.read() # 如果可以读取到画面 if flag: # 将图片进行对称 img_rd = cv2.flip(img_rd, 1) image = img_rd.copy() # 把读到的帧的大小重新设置为 640x480 img_rd = cv2.resize(img_rd, (640, 480)) # 检测人脸可以检测多个人脸返回检测结果和人脸坐标(x,y,w,h) detections = fa.detect(img_rd) print("人脸数：", len(detections)) faces = [] mask_or_not = [] for i in detections: pos = i['position'] # 将坐标转化为dlib类型（左上，右下） face = dlib.rectangle(pos[0], pos[1], pos[0] + pos[2], pos[1] + pos[3]) faces.append(face) mask_or_not.append(i['class']) print(">>>目标检测结果>>>:", mask_or_not) print(">>>人脸坐标>>>:", faces)# 展示人脸数 self.ui.personNumber.setText(str(len(detections))) # 更新人脸计数器 self.last_face_cnt = self.current_face_cnt self.current_face_cnt = len(detections)# 更新上一帧的人脸姓名列表 self.last_face_name = self.current_face_name[:] # 更新上一帧和当前帧的质心列表 self.last_centriod = self.current_centroid self.current_centroid = []# 如果当前帧和上一帧人脸数未发生变化 if (self.current_face_cnt == self.last_face_cnt) and (self.reclassify_cnt != self.reclassify_interval): # 当前人脸坐标 self.current_face_position = [] if "unknow" in self.current_face_name: self.reclassify_cnt += 1 if self.current_face_cnt != 0: # k表示第k个人脸 for k, d in enumerate(faces): # 将每一个人脸坐标添加进当前帧人脸坐标库 self.current_face_position.append(tuple( [faces[k].left(), int(faces[k].bottom() + (faces[k].bottom() - faces[k].top()) / 4)])) # 添加当前帧质心坐标 self.current_centroid.append( [int(faces[k].left() + faces[k].right()) / 2, int(faces[k].top() + faces[k].bottom()) / 2]) # 人脸矩形框 x1 = d.top() y1 = d.left() x2 = d.bottom() y2 = d.right() # 判断人脸区域是否超出画面范围 if y2 > img_rd.shape[1]: y2 = img_rd.shape[1] elif x2 > img_rd.shape[0]: x2 = img_rd.shape[0] elif y1 < 0: y1 = 0 elif x1 < 0: x1 = 0# 剪裁人脸并在右侧区域显示此处右边人脸框在人脸数多的时候会发生变化 crop = img_rd[x1:x2, y1:y2] self.current_face = crop self.display_face(crop)# 人脸框的位置 rect = (d.left(), d.top(), d.right(), d.bottom()) # 人名字标签 name_lab = self.current_face_name[k] if self.current_face_name != [] else "" name_lab = str(name_lab) + "-" + str(mask_or_not[k]) print("current_face_name:", name_lab) # 画出人脸框 image = self.drawRectBox(image, rect, name_lab) self.ui.name.setText(str(self.current_face_name[k])) self.ui.maskMark.setText(str(mask_or_not[k]))# 在画面中显示 self.display_img(image)# 如果当前帧中不止一个人脸使用质心追踪算法 if self.current_face_cnt != 1: self.centroid_tracker()# 如果人脸数发生变化 else: self.current_face_position = [] self.current_face_distance = [] self.current_face_feature = [] self.reclassify_cnt = 0# 如果人脸数为 0 if self.current_face_cnt == 0: # 清空姓名和特征 self.current_face_name = [] self.current_face_feature = [] # 人脸数增加 else: self.current_face_name = [] for i in range(len(faces)): self.current_face_feature.append(sa.return_face_recognition_result(img_rd, faces[i])) self.current_face_name.append("unknow") print("self.current_face_name:", self.current_face_name) for k in range(len(faces)): x1 = faces[k].top() x2 = faces[k].left() y1 = faces[k].bottom() y2 = faces[k].right() self.current_face = img_rd[x1:y1, x2:y2] self.current_centroid.append([int(faces[k].left() + faces[k].right()) / 2, int(faces[k].top() + faces[k].bottom()) / 2]) self.current_face_distance = [] # 每个捕获人脸的名字坐标 self.current_face_position.append(tuple( [faces[k].left(), int(faces[k].bottom() + (faces[k].bottom() - faces[k].top()) / 4)])) # 遍历人脸库 print("人脸库：", self.features) for i in range(len(self.features)): if str(self.features[i][0]) != '0.0': e_distance_tmp = sa.return_euclidean_distance(self.current_face_feature[k], self.features[i]) self.current_face_distance.append(e_distance_tmp) else: self.current_face_distance.append(99999999999) print("self.current_face_distance:", self.current_face_distance) # 寻找出最小的欧式距离匹配 min_dis = min(self.current_face_distance) similar_person_num = self.current_face_distance.index(min_dis)if min_dis < 0.4: self.current_face_name[k] = self.features_names[similar_person_num] # 获取系统当前时间 curr_time = datetime.datetime.now() curTime = datetime.datetime.strftime(curr_time, '%Y-%m-%d %H:%M:%S') print("current_Time:", curTime) # 设置图片存储路径 filePath = "record_img/" + str(curr_time.year) + "_" + str(curr_time.month) + "_" + str( curr_time.day) + "_" + str(curr_time.hour) + "_" + str( curr_time.minute) + "_" + str( curr_time.second) + ".jpg" print("filePath:", filePath) # 存储图片 cv2.imencode('.jpg', self.current_face)[1].tofile(filePath) # 存储当日通行记录 sql = "insert into record(username,time,pic) values ('%s','%s','%s')" % ( self.current_face_name[k], curTime, filePath) print("通行记录:", sql) result = db.insertDB(sql) end_time = time.time() if end_time == start_time: use_time = 1 else: use_time = end_time - start_time fps = int(1.0 / round(use_time, 3)) self.ui.fps.setText(str(fps)) except Exception as e: traceback.print_exc() QMessageBox.critical( self.ui, "人脸识别错误", str(e) )"""摄像头选择事件"""def select_camera_num(self): try: if not self.timer_camera.isActive():# 若定时器未启动 self.CAM_NUM = int(self.ui.cameraList.currentText()) print("self.CAM_NUM:", self.CAM_NUM) if self.CAM_NUM == 0: self.ui.log.setText("内置摄像头准备就绪") else: self.ui.log.setText("外置摄像头准备就绪") # 初始化数据 self.ui.name.setText("unknow") self.ui.maskMark.setText("nomask") self.ui.personNumber.setText("0") self.ui.fps.setText("0") self.ui.cameraLabel.setPixmap(QtGui.QPixmap("img/camera_label.jpg")) self.ui.personX.setPixmap(QtGui.QPixmap("img/qql.jpg")) self.ui.begin_btn.setEnabled(True)except Exception as e: traceback.print_exc() QMessageBox.critical( self.ui, "摄像头选择函数出错了", str(e) )"""画人脸框"""def drawRectBox(self, image, rect, addText): try: cv2.rectangle(image, (int(round(rect[0])), int(round(rect[1]))), (int(round(rect[2])), int(round(rect[3]))), (0, 0, 255), 2) cv2.rectangle(image, (int(rect[0] - 1), int(rect[1]) - 16), (int(rect[0] + 75), int(rect[1])), (0, 0, 255), -1, cv2.LINE_AA) img = Image.fromarray(image) draw = ImageDraw.Draw(img) font_path = "Font/platech.ttf" font = ImageFont.truetype(font_path, 14, 0) draw.text((int(rect[0] + 1), int(rect[1] - 16)), addText, (255, 255, 255), font=font) imagex = np.array(img) return imagex except Exception as e: traceback.print_exc() QMessageBox.critical( self.ui, "画人脸框错误", str(e) )"""加载视频区域"""def display_img(self, image): try: # self.label_display.clear() image = cv2.resize(image, (640, 480))# 设定图像尺寸为显示界面大小 show = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) showImage = QtGui.QImage(show.data, show.shape[1], show.shape[0], show.shape[1] * 3, QtGui.QImage.Format_RGB888) self.ui.cameraLabel.setPixmap(QtGui.QPixmap.fromImage(showImage)) self.ui.cameraLabel.setScaledContents(True) QtWidgets.QApplication.processEvents() except Exception as e: traceback.print_exc() QMessageBox.critical( self.ui, "加载视频区域错误", str(e) )"""在右边小框显示人脸"""def display_face(self, image): try: # 清空右侧显示区域 self.ui.personX.clear() if image.any(): # 图片颜色转化 show = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) showImage = QtGui.QImage(show.data, show.shape[1], show.shape[0], show.shape[1] * 3, QtGui.QImage.Format_RGB888) self.ui.personX.setFixedSize(200, 200) self.ui.personX.setPixmap(QtGui.QPixmap.fromImage(showImage)) self.ui.personX.setScaledContents(True) QtWidgets.QApplication.processEvents() except Exception as e: traceback.print_exc() QMessageBox.critical( self.ui, "显示人脸框错误", str(e) )""" 使用质心追踪来识别人脸"""def centroid_tracker(self): for i in range(len(self.current_centroid)): distance_current_person = [] # 计算不同对象间的距离 for j in range(len(self.last_centriod)): self.last_current_distance = sa.return_euclidean_distance( self.current_centroid[i], self.last_centriod[j])distance_current_person.append(self.last_current_distance)last_frame_num = distance_current_person.index(min(distance_current_person)) self.current_face_name[i] = self.last_face_name[last_frame_num]"""点击开始运行按钮执行函数"""def run_rec(self): self.features, self.features_names = sa.get_all_features() try: # 如果定时器未启动 if not self.timer_camera.isActive(): QtWidgets.QApplication.processEvents() flag = self.cap.open(self.CAM_NUM) if not flag: QMessageBox.critical( self.ui, "错误", "摄像头打开失败，请检查连接后重新打开" ) else: self.ui.log.setText("正在打开摄像头，请稍等") # 打开定时器 self.timer_camera.start(30) # 将摄像头按钮的文字置为结束识别 self.ui.begin_btn.setText("结束识别") self.ui.log.setText("摄像头打开成功") self.ui.begin_btn.setStyleSheet("background-color: red; \n" "color: rgb(255, 255, 255); \n" "font: 87 14pt \"Arial Black\"; \n" "border-radius:10px; ") self.features = SI.features self.features_names = SI.f_uid else: # 关闭定时器 self.timer_camera.stop() # 释放视频流 self.cap.release() self.ui.log.setText("摄像头已关闭") # 清空视频显示区域 self.ui.cameraLabel.clear() self.ui.personX.clear() self.ui.cameraLabel.setPixmap(QtGui.QPixmap("img/camera_label.jpg")) self.ui.personX.setPixmap(QtGui.QPixmap("img/qql.jpg")) # 重置识别按钮 self.ui.begin_btn.setText("开始识别") self.ui.begin_btn.setStyleSheet("background-color: rgb(104, 164, 253); \n" "color: rgb(255, 255, 255); \n" "font: 87 14pt \"Arial Black\"; \n" "border-radius:10px; ") # 重置区域文字 self.ui.name.setText("unknow") self.ui.maskMark.setText("nomask") self.ui.personNumber.setText("0") self.ui.fps.setText("0") QtWidgets.QApplication.processEvents() except Exception as e: traceback.print_exc() QMessageBox.critical( self.ui, "打开摄像头错误", str(e) )if __name__ == "__main__": app = QApplication([]) SI.loginWin = Client() SI.loginWin.ui.show() app.exec_()