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= 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
return 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 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"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':
break
if recvbuf.decode('utf-8') == 'flag':
global flag
# print(flag)
if flag == 1:
sendbuf = "1"
sock.send(sendbuf.encode('utf-8'))
flag = 0
else:
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 = 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(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
break
elif count >= 100: # 得到1000个样本后退出摄像
break
cam.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_SIMPLEX
idnum = 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()
return
else:
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:
break
cam.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()