多线程处理客户端连接

客户端负责人脸采集和人脸注册功能；

服务器负责人脸数据集训练和人脸识别功能。

信息交互采用字节形式。

【后期可加入数据库、客户端收发线程实现多平台操作】

fs_server.py

import cv2
import json
import time
import os,sys
import socket
import pyttsx3
import threading
import numpy as np
from PIL import Image#服务器端负责实际的训练人脸集以及识别任务# 创建一个socket:
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
#绑定端口号
s.bind(('127.0.0.1', 3333))#首次启动环境创建
def makeDir(engine):flag= 0if 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=1if 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=1return flag
#语音模块
def say(engine,str):engine.say(str)engine.runAndWait()
#初始化
names = []
if os.path.exists("name.txt"):with open("name.txt") as f:names = json.loads(f.read())# print(names)
engine = pyttsx3.init()
rate = engine.getProperty('rate')
engine.setProperty('rate', rate - 20)
#是否首次启动，若首次启动则直接提示录入人脸或退出
flag = makeDir(engine)# 监听端口:
s.listen(5)
print('Waiting for connection...')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 grayscaleimg_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, idsdef trainFace():# 人脸数据路径path = 'Facedata'recognizer = cv2.face.LBPHFaceRecognizer_create()detector = cv2.CascadeClassifier(r"F:\npyWorkspace\venv\Lib\site-packages\cv2\data\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 tcplink(sock, addr):#连接客户端print('Accept new connection from %s:%s...' % addr)sock.send(b'Welcome!')while True:recvbuf = sock.recv(1024)if not recvbuf or recvbuf.decode('utf-8') == 'exit':breakif recvbuf.decode('utf-8') == 'flag':global flag# print(flag)if flag == 1:sendbuf = "1"sock.send(sendbuf.encode('utf-8'))flag = 0else:sendbuf = "0"sock.send(sendbuf.encode('utf-8'))if recvbuf.decode('utf-8') == 'train':trainFace()sock.send(b'train success')sock.close()print('Connection from %s:%s closed.' % addr)while True:# 接受一个新连接:sock, addr = s.accept()# 创建新线程来处理TCP连接:t = threading.Thread(target=tcplink, args=(sock, addr))t.start()

fs_client.py

import os
import cv2
import sys
import json
import time
import socket
import pyttsx3
import threading
import numpy as np
from PIL import Image#客户端承担人脸采集识别和人脸注册两大职责#创建socket
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
# 建立本地连接:
s.connect(('127.0.0.1', 3333))#语音模块
def say(engine,str):engine.say(str)engine.runAndWait()
#初始化
names = []
if os.path.exists("name.txt"):with open("name.txt") as f:names = json.loads(f.read())# print(names)
engine = pyttsx3.init()
rate = engine.getProperty('rate')
engine.setProperty('rate', rate - 20)# 接收欢迎消息:
print(s.recv(1024).decode('utf-8'))
say(engine,"欢迎进入人脸识别系统")def getFace(cap,face_id):face_detector = cv2.CascadeClassifier(r'F:\npyWorkspace\venv\Lib\site-packages\cv2\data\haarcascade_frontalface_default.xml')print('\n Initializing face capture. Look at the camera and wait ...')count = 0while 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(face_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breakelif count >= 100:  # 得到1000个样本后退出摄像breakcam.release()cv2.destroyAllWindows()def checkFace(cam,names,engine):recognizer = cv2.face.LBPHFaceRecognizer_create()recognizer.read('face_trainer/trainer.yml')cascadePath = r"F:\npyWorkspace\venv\Lib\site-packages\cv2\data\haarcascade_frontalface_default.xml"faceCascade = cv2.CascadeClassifier(cascadePath)font = cv2.FONT_HERSHEY_SIMPLEXidnum = 0#names = ['zongyong', 'zhangmin', 'shanglanqing']#cam = cv2.VideoCapture(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:idnum = names[idnum]confidence = "{0}%".format(round(100 - confidence))print("欢迎  "+idnum+"签到成功！\n")say(engine, "欢迎    "+idnum+"签到成功！")# cv2.imshow('camera', img)# time.sleep(2)# os.system("pause")cam.release()returnelse:idnum = "unknown"confidence = "{0}%".format(round(100 - confidence))print("对不起，未识别到！\n");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:breakcam.release()cv2.destroyAllWindows()while True:#是否具备训练环境sendbuf = "flag"s.send(sendbuf.encode('utf-8'))time.sleep(1)recvbuf = s.recv(1024).decode('utf-8')time.sleep(1)# print(recvbuf)if recvbuf == '1':#首次say(engine,"新人脸信息录入 或者 退出")value = input("1:录入  or other：退出\n")if value == '1':say(engine, "请输入您的姓名，注意要写成拼音形式")name = input("请输入姓名：")names.append(name)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, len(names) - 1)say(engine,"采集完毕，成功上传系统训练")s.send((b'train'))print(s.recv(1024).decode('utf-8'))else:# 将姓名保存到文件with open("name.txt", 'w') as f:f.write(json.dumps(names))say(engine, "信息已保存")say(engine, "再见")s.send(b'exit')break# sys.exit(0)else:say(engine,"请选择系统功能")value = input("1:录入新的人脸信息 2：人脸识别签到 0：退出\n")if value == '1':say(engine, "请输入您的姓名，注意要写成拼音形式")name = input("请输入姓名：")names.append(name)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, len(names) - 1)say(engine,"采集完毕，成功上传系统训练")s.send((b'train'))print(s.recv(1024).decode('utf-8'))elif value == '2':say(engine, "开始人脸识别")say(engine, "正在打开摄像头")cam = cv2.VideoCapture(0)checkFace(cam, names, engine)else:# 将姓名保存到文件with open("name.txt", 'w') as f:f.write(json.dumps(names))say(engine, "信息已保存")say(engine, "再见")s.send(b'exit')break# sys.exit(0)
# 关闭网络连接
s.close()