359 lines
12 KiB
Python
359 lines
12 KiB
Python
# 服务端版本2，接收客户端发来的图像并返回识别结果
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# -*- coding=utf-8 -*-
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import socket
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import threading
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import sys
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import os
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import struct
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#import dlib
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import csv
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import numpy as np
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#import cv2
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import time
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class GlobalVar:
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ip = ''
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port = 8000
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# 候选人名单
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candidate = []
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# 候选人特征向量
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descriptors = []
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name_namelist = ""
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# 候选人脸文件夹
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faces_folder_path = "D:/JetBrains/FaceRecognation/picture/id/"
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mainpage_file = "mainpage.jpg" # 主页图片
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detector = []
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sp = []
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facerec = []
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descriptors = []
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class ServerThread(threading.Thread):
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def __init__(self):
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self._running = True # 定义线程状态变量
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super().__init__()
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def terminate(self):
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self._running = False
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def run(self) -> None:
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try:
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s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
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s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
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s.bind((GlobalVar.ip, GlobalVar.port))
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s.listen(10)
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print("Wait for Connection.....................")
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except socket.error as msg:
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print(msg)
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sys.exit(1)
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while self._running:
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try:
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sock, address = s.accept() # 等待客户端连接
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#deal_image(sock, address)
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except socket.error:
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print("连接失败")
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# 接收并处理图片
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def deal_image(sock, address):
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global name_namelist
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print("Accept connection from {0}".format(address)) # 查看发送端的ip和端口
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st1 = time.time()
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while True:
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fileinfo_size = struct.calcsize('128sq') # 申请相同大小的空间存放发送过来的文件名与文件大小信息
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# print('fileinfo_size is', fileinfo_size)
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buf = sock.recv(fileinfo_size) # 接收图片名
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# 判断是否接收到文件头信息
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if buf: # 如果接收到了数据就开始接下来的操作
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filename, filesize = struct.unpack('128sq', buf) # 按照客户端打包的格式进行解包，得到图片的名字和图片大小
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# print('filename :', filename.decode(), 'filesize :', filesize)
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fn = filename.strip(b'\00')
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fn = fn.decode()
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print('file new name is {0}, filesize is {1}'.format(str(fn), filesize))
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new_filename = os.path.join(str('./'), str('new_') + fn)
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# 在服务器端新建图片名(可以不用新建的，直接用原来的也行，只要客户端和服务器不是同一个系统或接收到的图片和原图片不在一个文件夹下)
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recvd_size = 0
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fp = open(new_filename, 'wb') # 二进制打开文件
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print('start receiving...')
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while not recvd_size == filesize: # 如果收到的直接总数不等于这个文件的直接总数，那么就继续接受数据
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if filesize - recvd_size > 1024: # 这个不是最后一次
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try:
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data = sock.recv(1024) # 每次从客户端接受1024个字节
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recvd_size += len(data) # 每次都记录好收到的字节数，然后叠加上去
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except socket.error as msg:
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print(msg)
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else: # 这个最后一次数据如果比1024少的话那么这一次读完就OK了，而且这次结束后循环也就结束了
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try:
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data = sock.recv(filesize - recvd_size)
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recvd_size = filesize
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except socket.error as msg:
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print(msg)
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# print('data is', data) # 输出每一次收到的数据
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fp.write(data) # 写入图片数据，因为每次都是读取到1024个字节，所以每一次读取都需要把得到的字节进行拼凑
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fp.close()
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print('end receive...')
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analysize(new_filename)
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try:
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sock.send(GlobalVar.name_namelist.encode('utf-8'))
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print("Name is sent back already: " + GlobalVar.name_namelist)
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except socket.error as msg:
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print(msg)
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st2 = time.time()
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if st2 - st1 > 2:
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break
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sock.close()
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def analysize(new_filename):
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str1 = str(new_filename).replace("b'./", "")
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str2 = str1.replace("'", "")
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features_cap_arr = []
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image = cv2.imread(str2)
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# cv2.imshow("Server", image)
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cv2.waitKey(1)
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try:
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gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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except:
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return
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faces2 = GlobalVar.detector(gray, 0)
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if len(faces2) == 0: return # 相片中不存在人脸，直接返回
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# 获取当前捕获到的图像的人脸特征，存储到 features_cap_arr
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shape = GlobalVar.sp(image, faces2[0])
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features_cap_arr.append(GlobalVar.facerec.compute_face_descriptor(image, shape))
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# 先默认不认识，是 unknown
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GlobalVar.name_namelist = "Unknown person"
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# 对于某张人脸，遍历所有存储的人脸特征
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# for every faces detected, compare the faces in the database
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e_distance_list = []
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for i in range(len(GlobalVar.descriptors)):
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# 如果 person_X 数据不为空
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if str(GlobalVar.descriptors[i][0]) != '0.0':
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# print("with person", str(i + 1), "the e distance: ", end='')
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e_distance_tmp = return_euclidean_distance(features_cap_arr[0], GlobalVar.descriptors[i])
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# print(e_distance_tmp)
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e_distance_list.append(e_distance_tmp)
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else:
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# 空数据 person_X
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e_distance_list.append(999999999)
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# Find the one with minimum e distance
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similar_person_num = e_distance_list.index(min(e_distance_list))
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# print("Minimum e distance with person:", candidate[int(similar_person_num)])
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if min(e_distance_list) < 0.45:
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GlobalVar.name_namelist = GlobalVar.candidate[int(similar_person_num)]
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print("In Server: The person may be " + GlobalVar.name_namelist)
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def init():
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# 加载正脸检测器
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# GlobalVar.detector = dlib.get_frontal_face_detector()
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# 加载人脸关键点检测器
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##GlobalVar.facerec = dlib.face_recognition_model_v1(
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# "D:/JetBrains/FaceRecognation/model/dlib_face_recognition_resnet_model_v1.dat")
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# 7. 获取本机IP地址
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hostname = socket.gethostname()
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GlobalVar.ip = socket.gethostbyname(hostname)
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print("本机地址：", GlobalVar.ip)
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def load_csv():
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with open('D:/JetBrains/FaceRecognation/feature.csv', 'r') as file:
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csv_reader = csv.reader(file)
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for row in csv_reader:
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# 转换为numpy array
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v = np.array(row)
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v_data = [float(x) for x in v] # 将数据从string形式转换为float形式
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GlobalVar.descriptors.append(v_data)
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file.close()
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def load_names():
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with open('D:/JetBrains/FaceRecognation/names.txt', 'r', encoding="utf-8") as namesfile:
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line = namesfile.readline()
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while line:
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line = line.replace('\n', '')
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GlobalVar.candidate.append(line)
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line = namesfile.readline()
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namesfile.close()
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def show_mainpage():
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#mp = cv2.imread(GlobalVar.mainpage_file)
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(h, w) = mp.shape[:2]
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#resized = cv2.resize(mp, (w // 2, h // 2), interpolation=cv2.INTER_LINEAR)
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#cv2.imshow("MainPage", resized)
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#cv2.waitKey(10)
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def predict(descriptors, img):
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# 对需识别人脸进行同样处理
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# 提取描述子
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dets = GlobalVar.detector(img, 1)
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dist = []
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for k, d in enumerate(dets):
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shape = GlobalVar.sp(img, d)
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face_descriptor = GlobalVar.facerec.compute_face_descriptor(img, shape)
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d_test = np.array(face_descriptor)
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# 计算欧式距离
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for i in descriptors:
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dist_ = np.linalg.norm(i - d_test)
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dist.append(dist_)
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return dist
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def return_euclidean_distance(feature_1, feature_2):
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feature_1 = np.array(feature_1)
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feature_2 = np.array(feature_2)
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dist = np.sqrt(np.sum(np.square(feature_1 - feature_2)))
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return dist
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def Save():
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#cap = cv2.VideoCapture(0) # 从摄像头获取图像
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flag = 0
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time1 = time.time() # 计时开始
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while flag != 2:
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ret, frame = cap.read() # if frame is read correctly ret is True
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if not ret:
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print("Can't receive frame (stream end?). Exiting ...")
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exit()
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#cv2.imshow("MainPage", frame)
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#cv2.waitKey(10)
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#frame_gray = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
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#faces = GlobalVar.detector(frame_gray, 0)
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if (len(faces) == 1): # 检测到有且仅有一张人脸，并且保持静止1秒
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if (flag == 0):
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flag = 1
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frame_gray_old = frame_gray
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start = time.time() # 开始计时
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else:
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if (moving_detect(frame_gray_old, frame_gray) and flag == 1): # 画面静止
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end = time.time() # 计算时间是否达到1秒
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if (end - start >= 1): # 计时1秒，时间到
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cv2.destroyWindow("MainPage")
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SavePic(frame) # 保存图像
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flag = 2 # 退出while循环
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else: # 画面有变动
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flag = 0
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else: # 无人脸或有多张人脸
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flag = 0
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if (len(faces) > 1):
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print("Error: More than 1 person！！！")
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time2 = time.time()
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if (time2 - time1 > 10): # 超时退出
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break
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cap.release()
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def SavePic(img):
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filename = input("Please input your name (in English)：") # 输入姓名（拼音）
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confirm = input("Your name is " + filename + ". Please confirm: Y/N:")
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if (filename != "" and (confirm == "Y" or confirm == "y")):
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# 保存图片
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save_path = os.path.join(GlobalVar.faces_folder_path, filename + ".jpg") # 构建保存路径
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cv2.imwrite(save_path, img)
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Add2CSV(img) # 更新人脸特征CSV文件
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Add2TXT(filename) # 更新候选人名单
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load_names() # 重新加载候选人名单
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load_csv() # 重新加载全部人脸特征
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def Add2CSV(img):
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with open('feature.csv', "a", newline="") as csvfile:
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writer = csv.writer(csvfile)
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# 1.人脸检测
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dets = GlobalVar.detector(img, 1)
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for k, d in enumerate(dets):
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# 2.关键点检测
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shape = GlobalVar.sp(img, d)
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# 3.描述子提取，128D向量
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face_descriptor = GlobalVar.facerec.compute_face_descriptor(img, shape)
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# 转换为numpy array
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v = np.array(face_descriptor)
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writer.writerow(v) # 按行写入到Csv文件中
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csvfile.close()
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def Add2TXT(filename):
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with open('names.txt', "a", newline="", encoding="utf-8") as namesfile:
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namesfile.write(filename + '\n')
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namesfile.close()
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def moving_detect(img1, img2): # 比较两幅画面，判断是否静止
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grey_diff = cv2.absdiff(img1, img2) # 计算两幅图的像素差
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change = np.average(grey_diff)
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if (change > 10):
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return False # 画面有明显变动，返回False
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else:
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return True # 画面无明显变动，返回True
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def Reset():
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cv2.destroyAllWindows()
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init()
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if __name__ == '__main__':
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init()
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# 开启线程
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t = ServerThread()
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t.start()
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'''
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while True:
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if cv2.waitKey(1) == ord('s') or cv2.waitKey(1) == ord('S'):
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Save() # 保存图像
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show_mainpage()
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if cv2.waitKey(1) == ord('r') or cv2.waitKey(1) == ord('R'):
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Reset() # 重新启动服务器
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if cv2.waitKey(1) == ord('q') or cv2.waitKey(1) == ord('Q'):
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# 释放窗口
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cv2.destroyAllWindows()
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t.terminate() # 关闭子线程
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break
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'''
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