人脸签到系统 pyQT+数据库+深度学习

一、简介

人脸签到系统是一种基于人脸识别技术的自动签到和认证系统。它利用计算机视觉和深度学习算法来检测、识别和验证个体的面部特征，以确定其身份并记录其出现的时间。这个系统通常用于各种场景，包括企业、学校、会议、活动和公共交通等，以替代传统的签到方式，如签到表或磁卡，提供更高的安全性、准确性和便捷性。

二、效果展示

*左上角是采集人脸和签到的界面显示

*左下角输入采集人脸的信息

*右侧实时展示已经存放人脸的各种属性信息

三、实现细节

1、多线程实时现实摄像头内容

采用QT多线程实时对摄像头捕捉到的画面进行显示，当点击结束签到时，程序退出摄像头线程

class SignThread(QThread):changePixmap = pyqtSignal(QImage)update_database=pyqtSignal(str)def run(self):self.is_running=Truecap = cv2.VideoCapture(0,cv2.CAP_DSHOW)while True:ret, frame = cap.read()if ret and self.is_running:cls,rate=predict(frame)if float(rate)>70:print(rate)conn = sqlite3.connect("data.db")cursor = conn.cursor()# cursor.execute("INSERT OR REPLACE INTO students VALUES (?, ?, ?, ?, ?, ?)",#                (student_id, name, age, classname, major, sign_up))cursor.execute("UPDATE students SET sign_up=? WHERE name=?", ('是', cls))conn.commit()conn.close()self.update_database.emit("签到人:"+cls+",   准确率:"+rate)rgbImage = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)h, w, ch = rgbImage.shapebytesPerLine = ch * wimage = QImage(rgbImage.data, w, h, bytesPerLine, QImage.Format_RGB888)self.changePixmap.emit(image)else:#当按下暂停键，停止键以后，界面变为白色width = 640height = 480# 创建一个全白色的 QImagewhite_color = QColor(255, 255, 255)  # 白色image = QImage(width, height, QImage.Format_RGB888)image.fill(white_color)self.changePixmap.emit(image)breakdef stop(self):self.is_running=False

2、深度学习

在train.py对录取人脸手动进行训练，训练完成后，打开main.py主界面进行预测识别，开始签到

#数据增强的方式
traintransform = transforms .Compose([transforms .RandomRotation (20),               #随机旋转角度transforms .ColorJitter(brightness=0.1),     #颜色亮度transforms .Resize([224, 224]),               #设置成224×224大小的张量transforms .ToTensor(),                        # 将图⽚数据变为tensor格式
# transforms.Normalize(mean=[0.485, 0.456, 0.406],
# std=[0.229, 0.224, 0.225]),
])valtransform = transforms .Compose([transforms .Resize([224, 224]),transforms .ToTensor(), # 将图⽚数据变为tensor格式
])trainData = dsets.ImageFolder (trainpath, transform =traintransform ) # 读取训练集，标签就是train⽬录下的⽂件夹的名字，图像保存在格⼦标签下的⽂件夹⾥
valData = dsets.ImageFolder (valpath, transform =valtransform )       #读取演正剧
trainLoader = torch.utils.data.DataLoader(dataset=trainData, batch_size=batch_size, shuffle=True)     #将数据集分批次  并打乱顺序
valLoader = torch.utils.data.DataLoader(dataset=valData, batch_size=batch_size, shuffle=False)          #将测试集分批次并打乱顺序train_sum=len(trainData)    #计算  训练集和测试集的图片总数
test_sum = len(valData)import numpy as npimport torchvision.models as models
model = models.resnet50(pretrained=True) #pretrained表⽰是否加载已经与训练好的参数
model.fc = torch.nn.Linear(2048, num_of_classes) #将最后的fc层的输出改为标签数量（如3）,512取决于原始⽹络fc层的输⼊通道
model = model.to(device) # 如果有GPU，⽽且确认使⽤则保留；如果没有GPU，请删除criterion = torch.nn.CrossEntropyLoss() # 定义损失函数optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate) # 定义优化器from torch.autograd import Variable
#定义训练的函数
def train(model, optimizer, criterion):model.train()total_loss = 0train_corrects = 0for i, (image, label) in enumerate (tqdm(trainLoader)):image = Variable(image.to(device)) # 同理label = Variable(label.to(device)) # 同理#print(i,image,label)optimizer.zero_grad ()target = model(image)loss = criterion(target, label)loss.backward()optimizer.step()total_loss += loss.item()max_value , max_index = torch.max(target, 1)pred_label = max_index.cpu().numpy()true_label = label.cpu().numpy()train_corrects += np.sum(pred_label == true_label)return total_loss / float(len(trainLoader)), train_corrects / train_sumtestLoader=valLoader
#定义测试的函数
def evaluate(model, criterion):model.eval()corrects = eval_loss = 0with torch.no_grad():for image, label in tqdm(testLoader):image = Variable(image.to(device)) # 如果不使⽤GPU，删除.cuda()label = Variable(label.to(device)) # 同理pred = model(image)loss = criterion(pred, label)eval_loss += loss.item()max_value, max_index = torch.max(pred, 1)pred_label = max_index.cpu().numpy()true_label = label.cpu().numpy()corrects += np.sum(pred_label == true_label)return eval_loss / float(len(testLoader)), corrects, corrects / test_sum

3、数据库设计

例如下：对新生信息进行插入操作

student_id = self.studentID.text()
name = self.name.text()
age = self.age.text()
classname = self.classname.text()
major = self.major.text()
sign_up='否'conn = sqlite3.connect("data.db")
cursor = conn.cursor()cursor.execute("INSERT OR REPLACE INTO students VALUES (?, ?, ?, ?, ?, ?)",(student_id, name, age, classname, major,sign_up))conn.commit()
conn.close()self.label_txt.setText(f"Saved: {name}")
self.show_data()