基于python的人脸识别签到签退系统源码+项目操作说明.zip

import cv2 import os import numpy as np from PIL import Image #pillow import pyttsx3 import sys import deathymzFace.connect as connect import deathymzFace.baseConnect as baseConnect import time import json def makeDir(engine): flag= 0 if not os.path.exists("face_trainer"): print("创建预训练环境") engine.say('检测到第一次启动，未检测到环境，正在创建环境') engine.say('正在创建预训练环境') os.mkdir("face_trainer") engine.say('创建成功') engine.runAndWait() flag=1 if not os.path.exists("Facedata"): print("创建训练环境") engine.say('正在创建训练环境') os.mkdir("Facedata") engine.say('创建成功') engine.runAndWait() flag=1 if not os.path.exists("excel"): print("创建导出表环境") engine.say('正在创建导出表环境') os.mkdir("excel") engine.say('创建成功') engine.runAndWait() flag = 1 return flag def getFace(cap,path_id): # 调用笔记本内置摄像头，所以参数为0，如果有其他的摄像头可以调整参数为1，2 #cap = cv2.VideoCapture(0) face_detector = cv2.CascadeClassifier(r'C:\projects\opencv-python\opencv\modules\objdetect\src\cascadedetect\haarcascades\haarcascade_frontalface_default.xml') #face_id = input('\n enter user id:') print('\n Initializing face capture. Look at the camera and wait ...') count = 0 while True: # 从摄像头读取图片 sucess, img = cap.read() # 转为灰度图片 gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # 检测人脸 faces = face_detector.detectMultiScale(gray, 1.3, 5) for (x, y, w, h) in faces: cv2.rectangle(img, (x, y), (x+w, y+w), (255, 0, 0)) count += 1 # 保存图像 cv2.imwrite("Facedata/User." + str(path_id) + '.' + str(count) + '.jpg', gray[y: y + h, x: x + w]) cv2.imshow('image', img) # 保持画面的持续。 k = cv2.waitKey(1) if k == 27: # 通过esc键退出摄像 break elif count >= 100: # 得到1000个样本后退出摄像 break cv2.destroyAllWindows() def getImagesAndLabels(path, detector): imagePaths = [os.path.join(path, f) for f in os.listdir(path)] # join函数的作用 faceSamples = [] ids = [] for imagePath in imagePaths: PIL_img = Image.open(imagePath).convert('L') # convert it to grayscale img_numpy = np.array(PIL_img, 'uint8') id = int(os.path.split(imagePath)[-1].split(".")[1]) faces = detector.detectMultiScale(img_numpy) for (x, y, w, h) in faces: faceSamples.append(img_numpy[y:y + h, x: x + w]) ids.append(id) return faceSamples, ids def trainFace(): # 人脸数据路径 path = 'Facedata' recognizer = cv2.face.LBPHFaceRecognizer_create() detector = cv2.CascadeClassifier(r'C:\projects\opencv-python\opencv\modules\objdetect\src\cascadedetect\haarcascades\haarcascade_frontalface_default.xml') print('Training faces. It will take a few seconds. Wait ...') faces, ids = getImagesAndLabels(path, detector) recognizer.train(faces, np.array(ids)) recognizer.write(r'face_trainer\trainer.yml') print("{0} faces trained. Exiting Program".format(len(np.unique(ids)))) def checkFace(cam,names,engine,sign_flag): sex = {"female":"女士","male":"先生"} recognizer = cv2.face.LBPHFaceRecognizer_create() recognizer.read('face_trainer/trainer.yml') cascadePath = r"C:\projects\opencv-python\opencv\modules\objdetect\src\cascadedetect\haarcascades\haarcascade_frontalface_default.xml" faceCascade = cv2.CascadeClassifier(cascadePath) font = cv2.FONT_HERSHEY_SIMPLEX idnum = 0 minW = 0.1 * cam.get(3) minH = 0.1 * cam.get(4) while True: ret, img = cam.read() gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) faces = faceCascade.detectMultiScale( gray, scaleFactor=1.2, minNeighbors=5, minSize=(int(minW), int(minH)) ) for (x, y, w, h) in faces: cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2) idnum, confidence = recognizer.predict(gray[y:y + h, x:x + w]) if confidence < 100: Name =connect.readName(idnum) #connect 传入ID 学生信息表找到 返回 name Sex =connect.readSex(idnum) #connect ID 学生信息表找到 返回 Sex StudentID =connect.readStudentID(idnum) #connect ID 学生信息表找到 返回 studentID #idnum = names[idnum] #利用数据库 读取学生信息表 该id 对应的name confidence = "{0}%".format(round(100 - confidence)) if sign_flag=='0': #签到 say(engine, "欢迎 "+Name+ sex[Sex]+" 签到成功 ") baseConnect.insertd(idnum,Name,StudentID,Sex) #签到表中 插入签到信息 print("欢迎 "+Name+ sex[Sex] + "签到成功 ") else : say(engine, "欢迎 "+Name+ sex[Sex]+" 签退成功 ") baseConnect.insertt(idnum,Name,StudentID,Sex) #签到表中 插入签退信息 print("欢迎 "+Name+ sex[Sex] + "签退成功 ") # cv2.imshow("img",img) # os.system("pause") return else: idnum = "unknown" confidence = "{0}%".format(round(100 - confidence)) cv2.putText(img, str(idnum), (x + 5, y - 5), font, 1, (0, 0, 255), 1) cv2.putText(img, str(confidence), (x + 5, y + h - 5), font, 1, (0, 0, 0), 1) cv2.imshow('camera', img) k = cv2.waitKey(10) if k == 27: break cam.release() cv2.destroyAllWindows() def say(engine,str): engine.say(str) engine.runAndWait() def admission(): #录入信息模块 #names = {"yumengzhen":0,"dujuanjuan":1,"litingting":2} say(engine, "请输入您的学号 ") StudentID = input("请输入学号：") # 读取数据库信息表 取出Name 对应ID ID=connect.readIDbaseStudentID(StudentID) #connect 传入name 学生信息表找到 返回 ID if ID==-1:#没有找到该学生插入学生信息 while True: say(engine,"没有找到该学生信息 输人 0 注册 1重新输入") op=input("\n 没有找到该学生信息 输人数字 0 注册学生信息 1重新输入") if op=='0': Name,studentID,Sex=input("输入学生信息： Name studentID Sex").split() connect.insert(Name,studentID,Sex) #插入学生信息信息 else: StudentID = input("请输入学号：") ID=connect.readIDbaseStudentID(StudentID) #connect 传入name 学生信息表找到 返回 ID if ID!=-1 : break say(engine, "正在打开摄像头") cam = cv2.VideoCapture(0) say(engine, "注视摄像头，开始采集人脸数据") getFace(cam, ID) # 实际传入的是id cam.release() if __name__ == '__main__': names = {"yumengzhen":0,"dujuanjuan":1,"litingting": 2} password="123456" #密码 engine = pyttsx3.init() rate = engine.getProperty('rate') engine.setProperty('rate', rate - 20) flag=makeDir(engine) #trainFace() while True: if flag==1 : flag = 0 say(engine, "首次使用 没有人脸信息 ") say(engine, "是否要录入新的人脸信息 ") say