├── .gitignore ├── LICENSE ├── README.md ├── 仿真程序 ├── 串口助手 │ ├── UartAssist.cfg │ └── UartAssist.exe └── 仿真M0_smartHome.exe ├── 客户端 └── FaceRecognitionAttendanceSystem │ ├── .vs │ └── FaceRecognitionAttendanceSystem │ │ └── v16 │ │ └── .suo │ ├── FaceDateSet.py │ ├── FaceImageDate │ ├── others │ │ ├── 1_100.jpg │ │ ├── 1_101.jpg │ │ ├── 1_102.jpg │ │ ├── 1_103.jpg │ │ ├── 1_60.jpg │ │ ├── 1_61.jpg │ │ ├── 1_62.jpg │ │ ├── 1_63.jpg │ │ ├── 1_64.jpg │ │ ├── 1_65.jpg │ │ ├── 1_66.jpg │ │ ├── 1_67.jpg │ │ ├── 1_68.jpg │ │ ├── 1_69.jpg │ │ ├── 1_70.jpg │ │ ├── 1_71.jpg │ │ ├── 1_72.jpg │ │ ├── 1_73.jpg │ │ ├── 1_74.jpg │ │ ├── 1_75.jpg │ │ ├── 1_76.jpg │ │ ├── 1_77.jpg │ │ ├── 1_78.jpg │ │ ├── 1_79.jpg │ │ ├── 1_80.jpg │ │ ├── 1_81.jpg │ │ ├── 1_82.jpg │ │ ├── 1_83.jpg │ │ ├── 1_84.jpg │ │ ├── 1_85.jpg │ │ ├── 1_86.jpg │ │ ├── 1_87.jpg │ │ ├── 1_88.jpg │ │ ├── 1_89.jpg │ │ ├── 1_90.jpg │ │ ├── 1_91.jpg │ │ ├── 1_92.jpg │ │ ├── 1_93.jpg │ │ ├── 1_94.jpg │ │ ├── 1_95.jpg │ │ ├── 1_96.jpg │ │ ├── 1_97.jpg │ │ ├── 1_98.jpg │ │ └── 1_99.jpg │ └── wengfeilong │ │ ├── 1_1.jpg │ │ ├── 1_10.jpg │ │ ├── 1_11.jpg │ │ ├── 1_12.jpg │ │ ├── 1_13.jpg │ │ ├── 1_14.jpg │ │ ├── 1_15.jpg │ │ ├── 1_16.jpg │ │ ├── 1_17.jpg │ │ ├── 1_18.jpg │ │ ├── 1_19.jpg │ │ ├── 1_2.jpg │ │ ├── 1_20.jpg │ │ ├── 1_21.jpg │ │ ├── 1_22.jpg │ │ ├── 1_23.jpg │ │ ├── 1_24.jpg │ │ ├── 1_25.jpg │ │ ├── 1_26.jpg │ │ ├── 1_27.jpg │ │ ├── 1_28.jpg │ │ ├── 1_29.jpg │ │ ├── 1_3.jpg │ │ ├── 1_30.jpg │ │ ├── 1_4.jpg │ │ ├── 1_5.jpg │ │ ├── 1_6.jpg │ │ ├── 1_7.jpg │ │ ├── 1_8.jpg │ │ └── 1_9.jpg │ ├── FaceRecognition.py │ ├── FaceRecognitionAttendanceSystem.py │ ├── FaceRecognitionAttendanceSystem.pyproj │ ├── FaceRecognitionAttendanceSystem.sln │ ├── FaceRecognitionAttendanceSystem.ui │ ├── ImageAcquisition.py │ ├── Model │ └── me.face.model.h5 │ ├── ModelTraining.py │ ├── __pycache__ │ ├── FaceDateSet.cpython-37.pyc │ ├── FaceRecognitionAttendanceSystem.cpython-37.pyc │ ├── ImageAcquisition.cpython-37.pyc │ └── ModelTraining.cpython-37.pyc │ └── main.py └── 服务端 ├── a.out └── server.c /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | pip-wheel-metadata/ 24 | share/python-wheels/ 25 | *.egg-info/ 26 | .installed.cfg 27 | *.egg 28 | MANIFEST 29 | 30 | # PyInstaller 31 | # Usually these files are written by a python script from a template 32 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 33 | *.manifest 34 | *.spec 35 | 36 | # Installer logs 37 | pip-log.txt 38 | pip-delete-this-directory.txt 39 | 40 | # Unit test / coverage reports 41 | htmlcov/ 42 | .tox/ 43 | .nox/ 44 | .coverage 45 | .coverage.* 46 | .cache 47 | nosetests.xml 48 | coverage.xml 49 | *.cover 50 | *.py,cover 51 | .hypothesis/ 52 | .pytest_cache/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | target/ 76 | 77 | # Jupyter Notebook 78 | .ipynb_checkpoints 79 | 80 | # IPython 81 | profile_default/ 82 | ipython_config.py 83 | 84 | # pyenv 85 | .python-version 86 | 87 | # pipenv 88 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 89 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 90 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 91 | # install all needed dependencies. 92 | #Pipfile.lock 93 | 94 | # PEP 582; 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But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # FaceRecognitionAttendanceSystem 2 | 基于Python+opencv+keras+numpy+sklearn的人脸识别门禁系统 3 | 4 | 欢迎访问个人博客：https://www.ikunl.com 5 | 项目地址：https://www.ikunl.com/1.html 6 | 7 | FaceRecognitionAttendanceSystem 8 | Python+opencv+keras+numpy+sklearn的人脸识别门禁系统 9 | 本项目为实习期间做的一款基于opencv的人脸识别门禁系统，作者某双非二本院校，写于2020/7/23。 10 | 11 | 12 | 作者 翁飞龙 13 | QQ交流群 692695467(点击跳转) 14 | 博客地址 https://www.ikunl.com 15 | 16 | 使用环境 17 | windows/Linux,支持Python3.6以上版本和GCC的编辑器 18 | 19 | 开发环境：Microsoft Visio Studio 2019（Python3.6） 20 | 21 | 准备材料 22 | 23 | 1、python环境所需要的包：numpyen、sorflow、keras、opencv、scikit-learn 24 | 2、Ubuntu虚拟机 25 | 3、M0_smartHome仿真软件 26 | 4、串口助手 27 | 5、vspd虚拟端口配置工具 28 | 29 | 30 | 注：本文用到的所有工具和源码均在文章末提供下载 31 | 32 | 33 | 一、项目结构 34 | 35 | 1、客户端 36 | 37 | 38 | 39 | 40 | main.py：主界面启动文件（暂未完成） 41 | ImageAcquisition.py：图像采集模块 42 | FaceDateSet.py：图像数据处理模块 43 | ModelTraining.py：模型训练模块 44 | FaceRecognition.py：人脸识别模块 45 | FaceImageDate：文件夹存放采集得到的灰度图片 46 | Model：文件夹存放训练好的人脸模型 47 | 48 | 49 | 2、服务端 50 | 51 | server.c服务端源代码 52 | a.out可执行文件 53 | 54 | 55 | 二、运行效果 56 | 57 | 识别到自己门打开，关闭报警 58 | 59 | 60 | 61 | 62 | 识别到其他人，关闭门，开始报警 63 | 64 | 65 | 66 | 67 | 三、源代码(客户端) 68 | 1、图像采集 69 | ImageAcquisition.py 70 | 71 | 72 | # -*- coding: utf-8 -*- 73 | __author__ = '翁飞龙' 74 | import cv2 75 | import numpy as np 76 | import os 77 | 78 | def path(): 79 | name=input('\n enter user name:') 80 | path="./FaceImageDate/" + str(name) 81 | path=path.strip() 82 | path=path.rstrip("\\") 83 | isExists=os.path.exists(path) 84 | if not isExists: 85 | os.makedirs(path) 86 | print ( '\n ' + path + ' 创建成功') 87 | else: 88 | print ( '\n ' + path+ ' 名称已存在') 89 | 90 | return path 91 | def CatchPICFromVideo(path,catch_num): 92 | faceCascade = cv2.CascadeClassifier(r'D:\Program Files (x86)\Microsoft Visual Studio\Shared\Python37_64\Lib\site-packages\cv2\data\haarcascade_frontalface_default.xml') 93 | eyeCascade = cv2.CascadeClassifier(r'D:\Program Files (x86)\Microsoft Visual Studio\Shared\Python37_64\Lib\site-packages\cv2\data\haarcascade_eye.xml') 94 | 95 | # 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 96 | camera = cv2.VideoCapture(0, cv2.CAP_DSHOW) 97 | face_id = input('\n enter user id:') 98 | print('\n 采集数据前请摘下您的眼镜、口罩等遮蔽物，请保持光线良好 ... ') 99 | print('\n 正在采集人脸数据，请稍后 ...') 100 | 101 | count = 0 102 | 103 | while True: 104 | 105 | # 从摄像头读取图片 106 | 107 | sucess, img = camera.read() 108 | 109 | # 转为灰度图片 110 | 111 | gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) 112 | 113 | # 人脸检测 114 | faces = faceCascade.detectMultiScale( 115 | gray, 116 | scaleFactor=1.1, 117 | minNeighbors=5, 118 | minSize=(64, 64) 119 | ) 120 | # 在检测人脸的基础上检测眼睛 121 | result = [] 122 | for (x, y, w, h) in faces: 123 | fac_gray = gray[y: (y+h), x: (x+w)] 124 | eyes = eyeCascade.detectMultiScale(fac_gray, 1.3, 2) 125 | 126 | # 眼睛坐标的换算，将相对位置换成绝对位置 127 | for (ex, ey, ew, eh) in eyes: 128 | result.append((x+ex, y+ey, ew, eh)) 129 | 130 | for (x, y, w, h) in faces: 131 | cv2.rectangle(img, (x, y), (x+w, y+w), (255, 0, 0),2) 132 | for (ex, ey, ew, eh) in result: 133 | cv2.rectangle(img, (ex, ey), (ex+ew, ey+eh), (0, 255, 0), 2) 134 | # 显示捕捉了多少张人脸 135 | font = cv2.FONT_HERSHEY_SIMPLEX 136 | cv2.putText(img, f'count:{str(count)}', (x + 30, y + 30), font, 1, (255, 0, 255), 4) 137 | 138 | count += 1 139 | 140 | img_name = path + '/' + str(face_id) + '_' + str(count) + '.jpg' 141 | # 保存图像（路径不能包含中文） 142 | #cv2.imwrite(img_name, gray[y: y + h, x: x + w]) 143 | 144 | #保存图像 145 | cv2.imencode('.jpg', gray[y: y + h, x: x + w])[1].tofile(img_name) 146 | 147 | cv2.imshow('image', img) 148 | 149 | # 保持画面的持续。1ms 150 | 151 | k = cv2.waitKey(1) 152 | 153 | if k == 27: # 通过esc键退出摄像 154 | break 155 | elif count >= catch_num: 156 | break; 157 | 158 | print("\n 人脸信息采集完成") 159 | # 关闭摄像头 160 | camera.release() 161 | cv2.destroyAllWindows() 162 | 163 | if __name__ == '__main__': 164 | path=path() 165 | CatchPICFromVideo(path,100) 166 | 全选代码复制 167 | 2、数据处理 168 | FaceDateSet.py 169 | 170 | # -*- coding: utf-8 -*- 171 | __author__ = '翁飞龙' 172 | import os 173 | import numpy as np 174 | import cv2 175 | # 定义图片尺寸 176 | IMAGE_SIZE = 64 177 | 178 | 179 | # 按照定义图像大小进行尺度调整 180 | def resize_image(image, height=IMAGE_SIZE, width=IMAGE_SIZE): 181 | top, bottom, left, right = 0, 0, 0, 0 182 | # 获取图像尺寸 183 | h, w, _ = image.shape 184 | # 找到图片最长的一边 185 | longest_edge = max(h, w) 186 | # 计算短边需要填充多少使其与长边等长 187 | if h < longest_edge: 188 | d = longest_edge - h 189 | top = d // 2 190 | bottom = d // 2 191 | elif w < longest_edge: 192 | d = longest_edge - w 193 | left = d // 2 194 | right = d // 2 195 | else: 196 | pass 197 | 198 | # 设置填充颜色 199 | BLACK = [0, 0, 0] 200 | # 对原始图片进行填充操作 201 | constant = cv2.copyMakeBorder(image, top, bottom, left, right, cv2.BORDER_CONSTANT, value=BLACK) 202 | # 调整图像大小并返回 203 | return cv2.resize(constant, (height, width)) 204 | 205 | images, labels = list(), list() 206 | # 读取训练数据 207 | def read_path(path): 208 | for dir_item in os.listdir(path): 209 | # 合并成可识别的操作路径 210 | full_path = os.path.abspath(os.path.join(path, dir_item)) 211 | # 如果是文件夹，则继续递归调用 212 | if os.path.isdir(full_path): 213 | read_path(full_path) 214 | else: 215 | if dir_item.endswith('.jpg'): 216 | # print(dir_item) 217 | image = cv2.imread(full_path) 218 | image = resize_image(image, IMAGE_SIZE, IMAGE_SIZE) 219 | images.append(image) 220 | labels.append(path) 221 | #print(labels) 222 | return images, labels 223 | 224 | 225 | # 从指定路径读取训练数据 226 | def load_dataset(path): 227 | images, labels = read_path(path) 228 | # 由于图片是基于矩阵计算的， 将其转为矩阵 229 | images = np.array(images) 230 | print(images.shape) 231 | labels = np.array([0 if label.endswith('wengfeilong') else 1 for label in labels]) 232 | return images, labels 233 | 234 | 235 | if __name__ == '__main__': 236 | images, labels = load_dataset(os.getcwd()+'/FaceImageDate') 237 | print('\n 读取结束，数据处理完成......') 238 | 全选代码复制 239 | 240 | 3、模型训练 241 | ModelTraining.py 242 | 243 | 244 | # -*- coding: utf-8 -*- 245 | __author__ = '翁飞龙' 246 | import random 247 | import numpy as np 248 | from sklearn.model_selection import train_test_split 249 | from keras.preprocessing.image import ImageDataGenerator 250 | from keras.models import Sequential 251 | from keras.layers import Dense, Activation, Flatten, Dropout 252 | from keras.layers import Conv2D, MaxPool2D 253 | from keras.optimizers import SGD 254 | from keras.utils import np_utils 255 | from keras.models import load_model 256 | from keras import backend as K 257 | from FaceDateSet import load_dataset, resize_image, IMAGE_SIZE 258 | import warnings 259 | warnings.filterwarnings('ignore') 260 | 261 | 262 | class Dataset: 263 | def __init__(self, path_name): 264 | # 训练集 265 | self.train_images = None 266 | self.train_labels = None 267 | # 验证集 268 | # self.valid_images = None 269 | # self.valid_labels = None 270 | # 测试集 271 | self.test_images = None 272 | self.test_labels = None 273 | # 数据加载路径 274 | self.path_name = path_name 275 | # 当前库采用的维度顺序 276 | self.input_shape = None 277 | 278 | def load(self, img_rows=IMAGE_SIZE, img_cols=IMAGE_SIZE, img_channels=3, nb_classes=2): 279 | # 加载数据集至内存 280 | images, labels = load_dataset(self.path_name) 281 | train_images, test_images, train_labels, test_labels = train_test_split(images, labels, test_size=0.3, 282 | random_state=random.randint(0, 10)) 283 | #if K.image_dim_ordering() == 'th': 284 | if K.image_data_format() == 'channels_first': 285 | train_images = train_images.reshape(train_images.shape[0], img_channels, img_rows, img_cols) 286 | test_images = test_images.reshape(test_images.shape[0], img_channels, img_rows, img_cols) 287 | self.input_shape = (img_channels, img_rows, img_cols) 288 | else: 289 | train_images = train_images.reshape(train_images.shape[0], img_rows, img_cols, img_channels) 290 | test_images = test_images.reshape(test_images.shape[0], img_rows, img_cols, img_channels) 291 | self.input_shape = (img_rows, img_cols, img_channels) 292 | 293 | # 输出训练集、测试集的数量 294 | print(train_images.shape[0], 'train samples') 295 | print(test_images.shape[0], 'test samples') 296 | # 我们的模型使用categorical_crossentropy作为损失函数，因此需要根据类别数量nb_classes将 297 | # 类别标签进行one-hot编码使其向量化，在这里我们的类别只有两种，经过转化后标签数据变为二维 298 | train_labels = np_utils.to_categorical(train_labels, nb_classes) 299 | test_labels = np_utils.to_categorical(test_labels, nb_classes) 300 | # 像素数据浮点化以便归一化 301 | train_images = train_images.astype('float32') 302 | test_images = test_images.astype('float32') 303 | # 将其归一化,图像的各像素值归一化到0~1区间 304 | train_images /= 255.0 305 | test_images /= 255.0 306 | self.train_images = train_images 307 | self.test_images = test_images 308 | self.train_labels = train_labels 309 | self.test_labels = test_labels 310 | 311 | 312 | # CNN网络模型类 313 | class Model: 314 | def __init__(self): 315 | self.model = None 316 | 317 | # 建立模型 318 | def build_model(self, dataset, nb_classes=2): 319 | # 构建一个空的网络模型，它是一个线性堆叠模型，各神经网络层会被顺序添加，专业名称为序贯模型或线性堆叠模型 320 | self.model = Sequential() 321 | 322 | # 以下代码将顺序添加CNN网络需要的各层，一个add就是一个网络层 323 | self.model.add(Conv2D(32, 3, 3, border_mode='same', 324 | input_shape=dataset.input_shape)) # 1 2维卷积层 325 | self.model.add(Activation('relu')) # 2 激活函数层 326 | 327 | self.model.add(Conv2D(32, 3, 3)) # 3 2维卷积层 328 | self.model.add(Activation('relu')) # 4 激活函数层 329 | 330 | self.model.add(MaxPool2D(pool_size=(2, 2))) # 5 池化层 331 | self.model.add(Dropout(0.25)) # 6 Dropout层 332 | 333 | self.model.add(Conv2D(64, 3, 3, border_mode='same')) # 7 2维卷积层 334 | self.model.add(Activation('relu')) # 8 激活函数层 335 | 336 | self.model.add(Conv2D(64, 3, 3)) # 9 2维卷积层 337 | self.model.add(Activation('relu')) # 10 激活函数层 338 | 339 | self.model.add(MaxPool2D(pool_size=(2, 2))) # 11 池化层 340 | self.model.add(Dropout(0.25)) # 12 Dropout层 341 | 342 | self.model.add(Flatten()) # 13 Flatten层 343 | self.model.add(Dense(512)) # 14 Dense层,又被称作全连接层 344 | self.model.add(Activation('relu')) # 15 激活函数层 345 | self.model.add(Dropout(0.5)) # 16 Dropout层 346 | self.model.add(Dense(nb_classes)) # 17 Dense层 347 | self.model.add(Activation('softmax')) # 18 分类层，输出最终结果 348 | 349 | # 输出模型概况 350 | self.model.summary() 351 | 352 | # 训练模型 353 | def train(self, dataset, batch_size=20, nb_epoch=100, data_augmentation=True): 354 | sgd = SGD(lr=0.01, decay=1e-6, 355 | momentum=0.9, nesterov=True) # 采用SGD+momentum的优化器进行训练，首先生成一个优化器对象 356 | self.model.compile(loss='categorical_crossentropy', 357 | optimizer=sgd, 358 | metrics=['accuracy']) # 完成实际的模型配置工作 359 | 360 | # 不使用数据提升，所谓的提升就是从我们提供的训练数据中利用旋转、翻转、加噪声等方法创造新的 361 | # 训练数据，有意识的提升训练数据规模，增加模型训练量 362 | if not data_augmentation: 363 | self.model.fit(dataset.train_images, 364 | dataset.train_labels, 365 | batch_size=batch_size, 366 | nb_epoch=nb_epoch, 367 | validation_data=(dataset.test_images, dataset.test_labels), 368 | shuffle=True) 369 | # 使用实时数据提升 370 | else: 371 | # 定义数据生成器用于数据提升，其返回一个生成器对象datagen，datagen每被调用一 372 | # 次其生成一组数据（顺序生成），节省内存，其实就是python的数据生成器 373 | datagen = ImageDataGenerator( 374 | featurewise_center=False, # 是否使输入数据去中心化（均值为0）， 375 | samplewise_center=False, # 是否使输入数据的每个样本均值为0 376 | featurewise_std_normalization=False, # 是否数据标准化（输入数据除以数据集的标准差） 377 | samplewise_std_normalization=False, # 是否将每个样本数据除以自身的标准差 378 | zca_whitening=False, # 是否对输入数据施以ZCA白化 379 | rotation_range=20, # 数据提升时图片随机转动的角度(范围为0～180) 380 | width_shift_range=0.2, # 数据提升时图片水平偏移的幅度（单位为图片宽度的占比，0~1之间的浮点数） 381 | height_shift_range=0.2, # 同上，只不过这里是垂直 382 | horizontal_flip=True, # 是否进行随机水平翻转 383 | vertical_flip=False) # 是否进行随机垂直翻转 384 | 385 | # 计算整个训练样本集的数量以用于特征值归一化、ZCA白化等处理 386 | datagen.fit(dataset.train_images) 387 | 388 | # 利用生成器开始训练模型 389 | self.model.fit_generator(datagen.flow(dataset.train_images, dataset.train_labels, 390 | batch_size=batch_size), 391 | samples_per_epoch=dataset.train_images.shape[0], 392 | nb_epoch=nb_epoch, 393 | validation_data=(dataset.test_images, dataset.test_labels)) 394 | 395 | MODEL_PATH = './Model/face.model.h5' 396 | 397 | def save_model(self, file_path=MODEL_PATH): 398 | self.model.save(file_path) 399 | 400 | def load_model(self, file_path=MODEL_PATH): 401 | self.model = load_model(file_path) 402 | 403 | def evaluate(self, dataset): 404 | score = self.model.evaluate(dataset.test_images, dataset.test_labels, verbose=1) 405 | # print("%s: %.2f%%" % (self.model.metrics_names[1], score[1] * 100)) 406 | print(f'{self.model.metrics_names[1]}:{score[1] * 100}%') 407 | 408 | # 识别人脸 409 | def face_predict(self, image): 410 | # 依然是根据后端系统确定维度顺序 411 | #if K.image_dim_ordering() == 'th' 412 | if K.image_data_format() == 'channels_first'and image.shape != (1, 3, IMAGE_SIZE, IMAGE_SIZE): 413 | image = resize_image(image) # 尺寸必须与训练集一致都应该是IMAGE_SIZE x IMAGE_SIZE 414 | image = image.reshape((1, 3, IMAGE_SIZE, IMAGE_SIZE)) # 与模型训练不同，这次只是针对1张图片进行预测 415 | #elif K.image_dim_ordering() == 'tf' 416 | elif K.image_data_format() == 'channels_last'and image.shape != (1, IMAGE_SIZE, IMAGE_SIZE, 3): 417 | image = resize_image(image) 418 | image = image.reshape((1, IMAGE_SIZE, IMAGE_SIZE, 3)) 419 | 420 | # 浮点并归一化 421 | image = image.astype('float32') 422 | image /= 255.0 423 | 424 | # 给出输入属于各个类别的概率，我们是二值类别，则该函数会给出输入图像属于0和1的概率各为多少 425 | result = self.model.predict_proba(image) 426 | print('result:', result) 427 | 428 | # 给出类别预测：0或者1 429 | result = self.model.predict_classes(image) 430 | 431 | # 返回类别预测结果 432 | return result[0] 433 | 434 | 435 | if __name__ == '__main__': 436 | dataset = Dataset('./FaceImageDate/') 437 | dataset.load() 438 | 439 | # 训练模型 440 | model = Model() 441 | model.build_model(dataset) 442 | # 测试训练函数的代码 443 | model.train(dataset) 444 | model.save_model(file_path='./Model/me.face.model.h5') 445 | # 评估模型 446 | model = Model() 447 | model.load_model(file_path='./Model/me.face.model.h5') 448 | model.evaluate(dataset) 449 | 全选代码复制 450 | 451 | 4、人脸识别 452 | FaceRecognition.py 453 | 454 | 455 | # -*- coding: utf-8 -*- 456 | __author__ = '翁飞龙' 457 | import cv2 458 | from ModelTraining import Model 459 | import socket 460 | 461 | if __name__ == '__main__': 462 | # 加载模型 463 | model = Model() 464 | model.load_model(file_path='./Model/me.face.model.h5') 465 | 466 | # 框住人脸的矩形边框颜色 467 | color = (0, 255, 0) 468 | 469 | # 捕获指定摄像头的实时视频流 470 | camera = cv2.VideoCapture(0, cv2.CAP_DSHOW) 471 | 472 | # 人脸识别分类器本地存储路径 473 | cascade_path = "D:\Program Files (x86)\Microsoft Visual Studio\Shared\Python37_64\Lib\site-packages\cv2\data\haarcascade_frontalface_default.xml" 474 | 475 | # 循环检测识别人脸 476 | while True: 477 | ret, img = camera.read() # 读取一帧视频 478 | 479 | # 图像灰化，降低计算复杂度 480 | gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) 481 | 482 | # 使用人脸识别分类器，读入分类器 483 | cascade = cv2.CascadeClassifier(cascade_path) 484 | 485 | # 利用分类器识别出哪个区域为人脸 486 | fac_gray = cascade.detectMultiScale(gray, 1.1, 5) 487 | if len(fac_gray) > 0: 488 | for (x, y, w, h) in fac_gray: 489 | # 截取脸部图像提交给模型识别这是谁 490 | image = img[y: y + h, x: x + w] 491 | faceID = model.face_predict(image) 492 | print(faceID) 493 | 494 | # 如果是“我” 495 | if faceID == 0: 496 | cv2.rectangle(img, (x, y), (x + w, y + h), color, thickness=2) 497 | 498 | # 文字提示是谁 499 | cv2.putText(img, 'wengfeilong', 500 | (x + 30, y + 30), # 坐标 501 | cv2.FONT_HERSHEY_SIMPLEX, # 字体 502 | 1, # 字号 503 | (255, 0, 255), # 颜色 504 | 2) # 字的线宽 505 | client = socket.socket(socket.AF_INET,socket.SOCK_STREAM) 506 | client.connect(('192.168.174.128',6666)) 507 | print('\n Client is running') 508 | client.send(str(faceID).encode('utf-8')) 509 | #data = client.recv(1024).decode('utf-8') 510 | #client.send("yes".encode("utf-8")) #响应服务器端发送请求，为防止粘包的产生 511 | #print(data) 512 | 513 | else: 514 | cv2.rectangle(img, (x, y), (x + w, y + h), color, thickness=2) 515 | 516 | # 文字提示是谁 517 | cv2.putText(img, 'others', 518 | (x + 30, y + 30), # 坐标 519 | cv2.FONT_HERSHEY_SIMPLEX, # 字体 520 | 1, # 字号 521 | (255, 0, 255), # 颜色 522 | 2) 523 | client = socket.socket(socket.AF_INET,socket.SOCK_STREAM) 524 | client.connect(('192.168.174.128',6666)) 525 | print('\nClient is running...') 526 | client.send(str(faceID).encode('utf-8')) 527 | #data = client.recv(1024).decode('utf-8') 528 | #client.send("no".encode("utf-8")) #响应服务器端发送请求，为防止粘包的产生 529 | #print(data) 530 | 531 | cv2.imshow("camera", img) 532 | 533 | # 等待200毫秒看是否有按键输入 534 | k = cv2.waitKey(200) 535 | #如果输入q则退出循环 536 | if k & 0xFF == ord('q'): 537 | break 538 | 539 | # 释放摄像头并销毁所有窗口 540 | camera.release() 541 | cv2.destroyAllWindows() 542 | 全选代码复制 543 | 544 | 四、服务端 545 | server.c 546 | 547 | 548 | #include 549 | #include 550 | #include 551 | #include 552 | #include 553 | #include 554 | #include 555 | #include 556 | #include 557 | #include 558 | #include 559 | #include 560 | 561 | #define BAUDRATE B115200 ///Baud rate : 115200 562 | #define DEVICE "/dev/ttyS1"//设置端口号 563 | #define FALSE 0 564 | #define TRUE 1 565 | #define _POSIX_SOURCE 1 //POSIX系统兼容 566 | int SerialPort_Send(int i){ 567 | 568 | int fd,res; 569 | struct termios oldtio,newtio; 570 | 571 | fd=open(DEVICE,O_RDWR | O_NOCTTY); 572 | if(fd<0){ 573 | perror(DEVICE); 574 | exit(-1); 575 | } 576 | tcgetattr(fd,&oldtio);//保存原来的参数 577 | bzero(&newtio,sizeof(newtio)); 578 | newtio.c_cflag=BAUDRATE | CS8 | CLOCAL | CREAD | HUPCL; 579 | newtio.c_iflag=IGNBRK; 580 | newtio.c_oflag=0; 581 | newtio.c_lflag=ICANON; 582 | tcflush(fd,TCIFLUSH); 583 | tcsetattr(fd,TCSANOW,&newtio);//设置串口参数 584 | printf("%d\n",i); 585 | if(i==0){ 586 | char openbuf[255]={0xdd,0x05,0x24,0x00,0x09}; 587 | char closebj[255]={0xdd,0x05,0x24,0x00,0x03}; 588 | write(fd,openbuf,5); 589 | write(fd,closebj,5); 590 | close(fd); 591 | } 592 | else{ 593 | char closebuf[255]={0xdd,0x05,0x24,0x00,0x0a}; 594 | char baojing[255]={0xdd,0x05,0x24,0x00,0x02}; 595 | write(fd,closebuf,5); 596 | write(fd,baojing,5); 597 | close(fd); 598 | } 599 | 600 | } 601 | 602 | int main() 603 | { 604 | int sockfd, new_fd; 605 | struct sockaddr_in my_addr; 606 | struct sockaddr_in their_addr; 607 | int sin_size; 608 | //建立TCP套接口 609 | if ((sockfd = socket(AF_INET, SOCK_STREAM, 0)) == -1) 610 | { 611 | printf("create socket error"); 612 | perror("socket"); 613 | exit(1); 614 | } 615 | //初始化结构体，并绑定6666端口 616 | my_addr.sin_family = AF_INET; 617 | my_addr.sin_port = htons(6666); 618 | my_addr.sin_addr.s_addr = INADDR_ANY; 619 | bzero(&(my_addr.sin_zero), 8); 620 | int on; 621 | on = 1; 622 | setsockopt( sockfd, SOL_SOCKET, SO_REUSEADDR, &on, sizeof(on) ); 623 | //绑定套接口 624 | if (bind(sockfd, (struct sockaddr*)&my_addr, sizeof(struct sockaddr)) == -1) 625 | { 626 | perror("bind socket error"); 627 | exit(1); 628 | } 629 | //创建监听套接口 630 | if (listen(sockfd, 10) == -1) 631 | { 632 | perror("listen"); 633 | exit(1); 634 | } 635 | //等待连接 636 | while (1) 637 | { 638 | sin_size = sizeof(struct sockaddr_in); 639 | printf("server is run......\n"); 640 | //如果建立连接，将产生一个全新的套接字 641 | if ((new_fd = accept(sockfd, (struct sockaddr*)&their_addr, &sin_size)) == -1) 642 | { 643 | perror("accept"); 644 | exit(1); 645 | } 646 | printf("accept success.\n"); 647 | //break; 648 | 649 | //生成一个子进程来完成和客户端的会话，父进程继续监听 650 | if (!fork()) 651 | { 652 | printf("create new thred success.\n"); 653 | //读取客户端发来的信息 654 | int numbytes; 655 | char buff[1024]; 656 | memset(buff, 0, 1024); 657 | if ((numbytes = recv(new_fd, buff, sizeof(buff), 0)) == -1) 658 | { 659 | perror("recv"); 660 | exit(1); 661 | } 662 | printf("%s\n", buff); 663 | printf("--------------------------------------------------------\n\n"); 664 | int i=(strcmp(buff,"0")); 665 | SerialPort_Send(i); 666 | /*if(i==0) 667 | { 668 | char success[]="success"; 669 | if (send(new_fd, success, strlen(success), 0) == -1) 670 | perror("send"); 671 | } 672 | else{ 673 | char failed[]="failed"; 674 | if (send(new_fd, failed, strlen(failed), 0) == -1) 675 | perror("send"); 676 | } 677 | 678 | close(new_fd); 679 | exit(0); 680 | }*/ 681 | close(new_fd); 682 | } 683 | } 684 | close(sockfd); 685 | } 686 | -------------------------------------------------------------------------------- /仿真程序/串口助手/UartAssist.cfg: -------------------------------------------------------------------------------- 1 | [SHORTCUT] 2 | ledon=1|0|0|DD 09 24 00 00 3 | 4 | -------------------------------------------------------------------------------- /仿真程序/串口助手/UartAssist.exe: -------------------------------------------------------------------------------- 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/客户端/FaceRecognitionAttendanceSystem/FaceDateSet.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | __author__ = '翁飞龙' 3 | import os 4 | import numpy as np 5 | import cv2 6 | # 定义图片尺寸 7 | IMAGE_SIZE = 64 8 | 9 | 10 | # 按照定义图像大小进行尺度调整 11 | def resize_image(image, height=IMAGE_SIZE, width=IMAGE_SIZE): 12 | top, bottom, left, right = 0, 0, 0, 0 13 | # 获取图像尺寸 14 | h, w, _ = image.shape 15 | # 找到图片最长的一边 16 | longest_edge = max(h, w) 17 | # 计算短边需要填充多少使其与长边等长 18 | if h < longest_edge: 19 | d = longest_edge - h 20 | top = d // 2 21 | bottom = d // 2 22 | elif w < longest_edge: 23 | d = longest_edge - w 24 | left = d // 2 25 | right = d // 2 26 | else: 27 | pass 28 | 29 | # 设置填充颜色 30 | BLACK = [0, 0, 0] 31 | # 对原始图片进行填充操作 32 | constant = cv2.copyMakeBorder(image, top, bottom, left, right, cv2.BORDER_CONSTANT, value=BLACK) 33 | # 调整图像大小并返回 34 | return cv2.resize(constant, (height, width)) 35 | 36 | images, labels = list(), list() 37 | # 读取训练数据 38 | def read_path(path): 39 | for dir_item in os.listdir(path): 40 | # 合并成可识别的操作路径 41 | full_path = os.path.abspath(os.path.join(path, dir_item)) 42 | # 如果是文件夹，则继续递归调用 43 | if os.path.isdir(full_path): 44 | read_path(full_path) 45 | else: 46 | if dir_item.endswith('.jpg'): 47 | # print(dir_item) 48 | image = cv2.imread(full_path) 49 | image = resize_image(image, IMAGE_SIZE, IMAGE_SIZE) 50 | images.append(image) 51 | labels.append(path) 52 | #print(labels) 53 | return images, labels 54 | 55 | 56 | # 从指定路径读取训练数据 57 | def load_dataset(path): 58 | images, labels = read_path(path) 59 | # 由于图片是基于矩阵计算的， 将其转为矩阵 60 | images = np.array(images) 61 | print(images.shape) 62 | labels = np.array([0 if label.endswith('wengfeilong') else 1 for label in labels]) 63 | return images, labels 64 | 65 | 66 | if __name__ == '__main__': 67 | images, labels = load_dataset(os.getcwd()+'/FaceImageDate') 68 | print('\n 读取结束，数据处理完成......') 69 | 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Model() 10 | model.load_model(file_path='./Model/me.face.model.h5') 11 | 12 | # 框住人脸的矩形边框颜色 13 | color = (0, 255, 0) 14 | 15 | # 捕获指定摄像头的实时视频流 16 | camera = cv2.VideoCapture(0, cv2.CAP_DSHOW) 17 | 18 | # 人脸识别分类器本地存储路径 19 | cascade_path = "D:\Program Files (x86)\Microsoft Visual Studio\Shared\Python37_64\Lib\site-packages\cv2\data\haarcascade_frontalface_default.xml" 20 | 21 | # 循环检测识别人脸 22 | while True: 23 | ret, img = camera.read() # 读取一帧视频 24 | 25 | # 图像灰化，降低计算复杂度 26 | gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) 27 | 28 | # 使用人脸识别分类器，读入分类器 29 | cascade = cv2.CascadeClassifier(cascade_path) 30 | 31 | # 利用分类器识别出哪个区域为人脸 32 | fac_gray = cascade.detectMultiScale(gray, 1.1, 5) 33 | if len(fac_gray) > 0: 34 | for (x, y, w, h) in fac_gray: 35 | # 截取脸部图像提交给模型识别这是谁 36 | image = img[y: y + h, x: x + w] 37 | faceID = model.face_predict(image) 38 | print(faceID) 39 | 40 | # 如果是“我” 41 | if faceID == 0: 42 | cv2.rectangle(img, (x, y), (x + w, y + h), color, thickness=2) 43 | 44 | # 文字提示是谁 45 | cv2.putText(img, 'wengfeilong', 46 | (x + 30, y + 30), # 坐标 47 | cv2.FONT_HERSHEY_SIMPLEX, # 字体 48 | 1, # 字号 49 | (255, 0, 255), # 颜色 50 | 2) # 字的线宽 51 | client = socket.socket(socket.AF_INET,socket.SOCK_STREAM) 52 | client.connect(('192.168.174.128',6666)) 53 | print('\n Client is running') 54 | client.send(str(faceID).encode('utf-8')) 55 | #data = client.recv(1024).decode('utf-8') 56 | #client.send("yes".encode("utf-8")) #响应服务器端发送请求，为防止粘包的产生 57 | #print(data) 58 | 59 | else: 60 | cv2.rectangle(img, (x, y), (x + w, y + h), color, thickness=2) 61 | 62 | # 文字提示是谁 63 | cv2.putText(img, 'others', 64 | (x + 30, y + 30), # 坐标 65 | cv2.FONT_HERSHEY_SIMPLEX, # 字体 66 | 1, # 字号 67 | (255, 0, 255), # 颜色 68 | 2) 69 | client = socket.socket(socket.AF_INET,socket.SOCK_STREAM) 70 | client.connect(('192.168.174.128',6666)) 71 | print('\nClient is running...') 72 | client.send(str(faceID).encode('utf-8')) 73 | #data = client.recv(1024).decode('utf-8') 74 | #client.send("no".encode("utf-8")) #响应服务器端发送请求，为防止粘包的产生 75 | #print(data) 76 | 77 | cv2.imshow("camera", img) 78 | 79 | # 等待200毫秒看是否有按键输入 80 | k = cv2.waitKey(200) 81 | #如果输入q则退出循环 82 | if k & 0xFF == ord('q'): 83 | break 84 | 85 | # 释放摄像头并销毁所有窗口 86 | camera.release() 87 | cv2.destroyAllWindows() -------------------------------------------------------------------------------- /客户端/FaceRecognitionAttendanceSystem/FaceRecognitionAttendanceSystem.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | 3 | # Form implementation generated from reading ui file 'FaceRecognitionAttendanceSystem.ui' 4 | # 5 | # Created by: PyQt5 UI code generator 5.15.0 6 | # 7 | # WARNING: Any manual changes made to this file will be lost when pyuic5 is 8 | # run again. Do not edit this file unless you know what you are doing. 9 | 10 | 11 | from PyQt5 import QtCore, QtGui, QtWidgets 12 | 13 | 14 | class Ui_Form(object): 15 | def setupUi(self, Form): 16 | Form.setObjectName("Form") 17 | Form.resize(1144, 845) 18 | self.centralwidget = QtWidgets.QWidget(Form) 19 | self.centralwidget.setObjectName("centralwidget") 20 | self.face = QtWidgets.QLabel(self.centralwidget) 21 | self.face.setGeometry(QtCore.QRect(400, 30, 511, 321)) 22 | self.face.setStyleSheet("\n" 23 | "background-color: rgb(6, 6, 6);") 24 | self.face.setText("") 25 | self.face.setObjectName("face") 26 | self.verticalLayoutWidget = QtWidgets.QWidget(self.centralwidget) 27 | self.verticalLayoutWidget.setGeometry(QtCore.QRect(950, 20, 160, 331)) 28 | self.verticalLayoutWidget.setObjectName("verticalLayoutWidget") 29 | self.verticalLayout = QtWidgets.QVBoxLayout(self.verticalLayoutWidget) 30 | self.verticalLayout.setContentsMargins(0, 0, 0, 0) 31 | self.verticalLayout.setObjectName("verticalLayout") 32 | self.ImageAcquisition = QtWidgets.QPushButton(self.verticalLayoutWidget) 33 | self.ImageAcquisition.setObjectName("ImageAcquisition") 34 | self.verticalLayout.addWidget(self.ImageAcquisition) 35 | self.FaceDateSet = QtWidgets.QPushButton(self.verticalLayoutWidget) 36 | self.FaceDateSet.setObjectName("FaceDateSet") 37 | self.verticalLayout.addWidget(self.FaceDateSet) 38 | self.ModelTraining = QtWidgets.QPushButton(self.verticalLayoutWidget) 39 | self.ModelTraining.setObjectName("ModelTraining") 40 | self.verticalLayout.addWidget(self.ModelTraining) 41 | self.FaceRecognition = QtWidgets.QPushButton(self.verticalLayoutWidget) 42 | self.FaceRecognition.setObjectName("FaceRecognition") 43 | self.verticalLayout.addWidget(self.FaceRecognition) 44 | self.client = QtWidgets.QListWidget(self.centralwidget) 45 | self.client.setGeometry(QtCore.QRect(400, 380, 341, 411)) 46 | self.client.setObjectName("client") 47 | item = QtWidgets.QListWidgetItem() 48 | font = QtGui.QFont() 49 | font.setPointSize(10) 50 | item.setFont(font) 51 | self.client.addItem(item) 52 | self.information = QtWidgets.QTextEdit(self.centralwidget) 53 | self.information.setGeometry(QtCore.QRect(30, 50, 311, 741)) 54 | font = QtGui.QFont() 55 | font.setPointSize(13) 56 | self.information.setFont(font) 57 | self.information.setObjectName("information") 58 | self.server = QtWidgets.QListWidget(self.centralwidget) 59 | self.server.setGeometry(QtCore.QRect(810, 380, 311, 411)) 60 | self.server.setObjectName("server") 61 | item = QtWidgets.QListWidgetItem() 62 | font = QtGui.QFont() 63 | font.setPointSize(10) 64 | item.setFont(font) 65 | self.server.addItem(item) 66 | Form.setCentralWidget(self.centralwidget) 67 | self.statusbar = QtWidgets.QStatusBar(Form) 68 | self.statusbar.setObjectName("statusbar") 69 | Form.setStatusBar(self.statusbar) 70 | 71 | self.retranslateUi(Form) 72 | QtCore.QMetaObject.connectSlotsByName(Form) 73 | 74 | def retranslateUi(self, Form): 75 | _translate = QtCore.QCoreApplication.translate 76 | Form.setWindowTitle(_translate("Form", "MainWindow")) 77 | self.ImageAcquisition.setText(_translate("Form", "成员录入")) 78 | self.FaceDateSet.setText(_translate("Form", "数据处理")) 79 | self.ModelTraining.setText(_translate("Form", "模型训练")) 80 | self.FaceRecognition.setText(_translate("Form", "人脸识别")) 81 | __sortingEnabled = self.client.isSortingEnabled() 82 | self.client.setSortingEnabled(False) 83 | item = self.client.item(0) 84 | item.setText(_translate("Form", "客户端：")) 85 | self.client.setSortingEnabled(__sortingEnabled) 86 | self.information.setHtml(_translate("Form", "\n" 87 | "\n" 90 | "

项目名称：人脸识别门禁系统

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采用技术：python+OpenCV+pyQT5

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项目组长：翁飞龙

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项目组员： 1

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2

\n" 95 | "

3

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4

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")) 98 | __sortingEnabled = self.server.isSortingEnabled() 99 | self.server.setSortingEnabled(False) 100 | item = self.server.item(0) 101 | item.setText(_translate("Form", "服务端：")) 102 | self.server.setSortingEnabled(__sortingEnabled) 103 | -------------------------------------------------------------------------------- /客户端/FaceRecognitionAttendanceSystem/FaceRecognitionAttendanceSystem.pyproj: -------------------------------------------------------------------------------- 1 | 2 | 3 | Debug 4 | 2.0 5 | d29aafc1-bf09-42b8-9d81-9a0fade1736c 6 | . 7 | FaceRecognition.py 8 | 9 | 10 | . 11 | . 12 | FaceRecognitionAttendanceSystem 13 | FaceRecognitionAttendanceSystem 14 | Global|PythonCore|3.7 15 | 16 | 17 | true 18 | false 19 | 20 | 21 | true 22 | false 23 | 24 | 25 | 26 | Code 27 | 28 | 29 | Code 30 | 31 | 32 | 33 | Code 34 | 35 | 36 | Code 37 | 38 | 39 | Code 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | 49 | 50 | 51 | 52 | 53 | 54 | 55 | 56 | 57 | 58 | 59 | 60 | 61 | 62 | 63 | 64 | 65 | 66 | 67 | 68 | 69 | 70 | 71 | 72 | 73 | 74 | 75 | 76 | 77 | 78 | 79 | 80 | 81 | 82 | 83 | 84 | 85 | 86 | 87 | 88 | 89 | 90 | 91 | 92 | 93 | 94 | 95 | 96 | 97 | 98 | 99 | 100 | 101 | 102 | 103 | 104 | 105 | 106 | 107 | 108 | 109 | 110 | 111 | 112 | 113 | 114 | 115 | 116 | 117 | 118 | 119 | 120 | 121 | 122 | 123 | 124 | 125 | 126 | 127 | 128 | 129 | 130 | 131 | 132 | 133 | 134 | 135 | 136 | 137 | 138 | 139 | 140 | 141 | 142 | 143 | 144 | 145 | 146 | 147 | 148 | 149 | 150 | 151 | 152 | 153 | 154 | 155 | 156 | 157 | 160 | 161 | 162 | 163 | 164 | 165 | -------------------------------------------------------------------------------- /客户端/FaceRecognitionAttendanceSystem/FaceRecognitionAttendanceSystem.sln: -------------------------------------------------------------------------------- 1 |  2 | Microsoft Visual Studio Solution File, Format Version 12.00 3 | # Visual Studio Version 16 4 | VisualStudioVersion = 16.0.30309.148 5 | MinimumVisualStudioVersion = 10.0.40219.1 6 | Project("{888888A0-9F3D-457C-B088-3A5042F75D52}") = "FaceRecognitionAttendanceSystem", "FaceRecognitionAttendanceSystem.pyproj", "{D29AAFC1-BF09-42B8-9D81-9A0FADE1736C}" 7 | EndProject 8 | Global 9 | GlobalSection(SolutionConfigurationPlatforms) = preSolution 10 | Debug|Any CPU = Debug|Any CPU 11 | Release|Any CPU = Release|Any CPU 12 | EndGlobalSection 13 | GlobalSection(ProjectConfigurationPlatforms) = postSolution 14 | {D29AAFC1-BF09-42B8-9D81-9A0FADE1736C}.Debug|Any CPU.ActiveCfg = Debug|Any CPU 15 | {D29AAFC1-BF09-42B8-9D81-9A0FADE1736C}.Release|Any CPU.ActiveCfg = Release|Any CPU 16 | EndGlobalSection 17 | GlobalSection(SolutionProperties) = preSolution 18 | HideSolutionNode = FALSE 19 | EndGlobalSection 20 | GlobalSection(ExtensibilityGlobals) = postSolution 21 | SolutionGuid = {1BB3F36A-D741-4A53-A3C9-DB58DF1DC4A1} 22 | EndGlobalSection 23 | EndGlobal 24 | -------------------------------------------------------------------------------- /客户端/FaceRecognitionAttendanceSystem/FaceRecognitionAttendanceSystem.ui: -------------------------------------------------------------------------------- 1 | 2 | 3 | Form 4 | 5 | 6 | 7 | 0 8 | 0 9 | 1144 10 | 845 11 | 12 | 13 | 14 | MainWindow 15 | 16 | 17 | 18 | 19 | 20 | 400 21 | 30 22 | 511 23 | 321 24 | 25 | 26 | 27 | 28 | background-color: rgb(6, 6, 6); 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 950 38 | 20 39 | 160 40 | 331 41 | 42 | 43 | 44 | 45 | 46 | 47 | 成员录入 48 | 49 | 50 | 51 | 52 | 53 | 54 | 数据处理 55 | 56 | 57 | 58 | 59 | 60 | 61 | 模型训练 62 | 63 | 64 | 65 | 66 | 67 | 68 | 人脸识别 69 | 70 | 71 | 72 | 73 | 74 | 75 | 76 | 77 | 400 78 | 380 79 | 341 80 | 411 81 | 82 | 83 | 84 | 85 | 客户端： 86 | 87 | 88 | 89 | 10 90 | 91 | 92 | 93 | 94 | 95 | 96 | 97 | 30 98 | 50 99 | 311 100 | 741 101 | 102 | 103 | 104 | 105 | 13 106 | 107 | 108 | 109 | <!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.0//EN" "http://www.w3.org/TR/REC-html40/strict.dtd"> 110 | <html><head><meta name="qrichtext" content="1" /><style type="text/css"> 111 | p, li { white-space: pre-wrap; } 112 | </style></head><body style=" font-family:'SimSun'; font-size:13pt; font-weight:400; font-style:normal;"> 113 | <p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;"><span style=" font-size:12pt;">项目名称：人脸识别门禁系统</span></p> 114 | <p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;"><span style=" font-size:12pt;">采用技术：python+OpenCV+pyQT5</span></p> 115 | <p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;"><span style=" font-size:12pt;">项目组长：翁飞龙</span></p> 116 | <p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;"><span style=" font-size:12pt;">项目组员： 1</span></p> 117 | <p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;"><span style=" font-size:12pt;"> 2</span></p> 118 | <p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;"><span style=" font-size:12pt;"> 3</span></p> 119 | <p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;"><span style=" font-size:12pt;"> 4</span></p> 120 | <p style="-qt-paragraph-type:empty; margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px; font-size:12pt;"><br /></p></body></html> 121 | 122 | 123 | 124 | 125 | 126 | 810 127 | 380 128 | 311 129 | 411 130 | 131 | 132 | 133 | 134 | 服务端： 135 | 136 | 137 | 138 | 10 139 | 140 | 141 | 142 | 143 | 144 | 145 | 146 | 147 | 148 | 149 | -------------------------------------------------------------------------------- /客户端/FaceRecognitionAttendanceSystem/ImageAcquisition.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | __author__ = '翁飞龙' 3 | import cv2 4 | import numpy as np 5 | import os 6 | 7 | def path(): 8 | name=input('\n enter user name:') 9 | path="./FaceImageDate/" + str(name) 10 | path=path.strip() 11 | path=path.rstrip("\\") 12 | isExists=os.path.exists(path) 13 | if not isExists: 14 | os.makedirs(path) 15 | print ( '\n ' + path + ' 创建成功') 16 | else: 17 | print ( '\n ' + path+ ' 名称已存在') 18 | 19 | return path 20 | def CatchPICFromVideo(path,catch_num): 21 | faceCascade = cv2.CascadeClassifier(r'D:\Program Files (x86)\Microsoft Visual Studio\Shared\Python37_64\Lib\site-packages\cv2\data\haarcascade_frontalface_default.xml') 22 | eyeCascade = cv2.CascadeClassifier(r'D:\Program Files (x86)\Microsoft Visual Studio\Shared\Python37_64\Lib\site-packages\cv2\data\haarcascade_eye.xml') 23 | 24 | # 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 25 | camera = cv2.VideoCapture(0, cv2.CAP_DSHOW) 26 | face_id = input('\n enter user id:') 27 | print('\n 采集数据前请摘下您的眼镜、口罩等遮蔽物，请保持光线良好 ... ') 28 | print('\n 正在采集人脸数据，请稍后 ...') 29 | 30 | count = 0 31 | 32 | while True: 33 | 34 | # 从摄像头读取图片 35 | 36 | sucess, img = camera.read() 37 | 38 | # 转为灰度图片 39 | 40 | gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) 41 | 42 | # 人脸检测 43 | faces = faceCascade.detectMultiScale( 44 | gray, 45 | scaleFactor=1.1, 46 | minNeighbors=5, 47 | minSize=(64, 64) 48 | ) 49 | # 在检测人脸的基础上检测眼睛 50 | result = [] 51 | for (x, y, w, h) in faces: 52 | fac_gray = gray[y: (y+h), x: (x+w)] 53 | eyes = eyeCascade.detectMultiScale(fac_gray, 1.3, 2) 54 | 55 | # 眼睛坐标的换算，将相对位置换成绝对位置 56 | for (ex, ey, ew, eh) in eyes: 57 | result.append((x+ex, y+ey, ew, eh)) 58 | 59 | for (x, y, w, h) in faces: 60 | cv2.rectangle(img, (x, y), (x+w, y+w), (255, 0, 0),2) 61 | for (ex, ey, ew, eh) in result: 62 | cv2.rectangle(img, (ex, ey), (ex+ew, ey+eh), (0, 255, 0), 2) 63 | # 显示捕捉了多少张人脸 64 | font = cv2.FONT_HERSHEY_SIMPLEX 65 | cv2.putText(img, f'count:{str(count)}', (x + 30, y + 30), font, 1, (255, 0, 255), 4) 66 | 67 | count += 1 68 | 69 | img_name = path + '/' + str(face_id) + '_' + str(count) + '.jpg' 70 | # 保存图像（路径不能包含中文） 71 | #cv2.imwrite(img_name, gray[y: y + h, x: x + w]) 72 | 73 | #保存图像 74 | cv2.imencode('.jpg', gray[y: y + h, x: x + w])[1].tofile(img_name) 75 | 76 | cv2.imshow('image', img) 77 | 78 | # 保持画面的持续。1ms 79 | 80 | k = cv2.waitKey(1) 81 | 82 | if k == 27: # 通过esc键退出摄像 83 | break 84 | elif count >= catch_num: 85 | break; 86 | 87 | print("\n 人脸信息采集完成") 88 | # 关闭摄像头 89 | camera.release() 90 | cv2.destroyAllWindows() 91 | 92 | if __name__ == '__main__': 93 | path=path() 94 | CatchPICFromVideo(path,100) -------------------------------------------------------------------------------- /客户端/FaceRecognitionAttendanceSystem/Model/me.face.model.h5: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Color-l/FaceRecognitionAttendanceSystem/55b4ba4ef7280a888616f898287ccf7e4caa3807/客户端/FaceRecognitionAttendanceSystem/Model/me.face.model.h5 -------------------------------------------------------------------------------- /客户端/FaceRecognitionAttendanceSystem/ModelTraining.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | __author__ = '翁飞龙' 3 | import random 4 | import numpy as np 5 | from sklearn.model_selection import train_test_split 6 | from keras.preprocessing.image import ImageDataGenerator 7 | from keras.models import Sequential 8 | from keras.layers import Dense, Activation, Flatten, Dropout 9 | from keras.layers import Conv2D, MaxPool2D 10 | from keras.optimizers import SGD 11 | from keras.utils import np_utils 12 | from keras.models import load_model 13 | from keras import backend as K 14 | from FaceDateSet import load_dataset, resize_image, IMAGE_SIZE 15 | import warnings 16 | warnings.filterwarnings('ignore') 17 | 18 | 19 | class Dataset: 20 | def __init__(self, path_name): 21 | # 训练集 22 | self.train_images = None 23 | self.train_labels = None 24 | # 验证集 25 | # self.valid_images = None 26 | # self.valid_labels = None 27 | # 测试集 28 | self.test_images = None 29 | self.test_labels = None 30 | # 数据加载路径 31 | self.path_name = path_name 32 | # 当前库采用的维度顺序 33 | self.input_shape = None 34 | 35 | def load(self, img_rows=IMAGE_SIZE, img_cols=IMAGE_SIZE, img_channels=3, nb_classes=2): 36 | # 加载数据集至内存 37 | images, labels = load_dataset(self.path_name) 38 | train_images, test_images, train_labels, test_labels = train_test_split(images, labels, test_size=0.3, 39 | random_state=random.randint(0, 10)) 40 | #if K.image_dim_ordering() == 'th': 41 | if K.image_data_format() == 'channels_first': 42 | train_images = train_images.reshape(train_images.shape[0], img_channels, img_rows, img_cols) 43 | test_images = test_images.reshape(test_images.shape[0], img_channels, img_rows, img_cols) 44 | self.input_shape = (img_channels, img_rows, img_cols) 45 | else: 46 | train_images = train_images.reshape(train_images.shape[0], img_rows, img_cols, img_channels) 47 | test_images = test_images.reshape(test_images.shape[0], img_rows, img_cols, img_channels) 48 | self.input_shape = (img_rows, img_cols, img_channels) 49 | 50 | # 输出训练集、测试集的数量 51 | print(train_images.shape[0], 'train samples') 52 | print(test_images.shape[0], 'test samples') 53 | # 我们的模型使用categorical_crossentropy作为损失函数，因此需要根据类别数量nb_classes将 54 | # 类别标签进行one-hot编码使其向量化，在这里我们的类别只有两种，经过转化后标签数据变为二维 55 | train_labels = np_utils.to_categorical(train_labels, nb_classes) 56 | test_labels = np_utils.to_categorical(test_labels, nb_classes) 57 | # 像素数据浮点化以便归一化 58 | train_images = train_images.astype('float32') 59 | test_images = test_images.astype('float32') 60 | # 将其归一化,图像的各像素值归一化到0~1区间 61 | train_images /= 255.0 62 | test_images /= 255.0 63 | self.train_images = train_images 64 | self.test_images = test_images 65 | self.train_labels = train_labels 66 | self.test_labels = test_labels 67 | 68 | 69 | # CNN网络模型类 70 | class Model: 71 | def __init__(self): 72 | self.model = None 73 | 74 | # 建立模型 75 | def build_model(self, dataset, nb_classes=2): 76 | # 构建一个空的网络模型，它是一个线性堆叠模型，各神经网络层会被顺序添加，专业名称为序贯模型或线性堆叠模型 77 | self.model = Sequential() 78 | 79 | # 以下代码将顺序添加CNN网络需要的各层，一个add就是一个网络层 80 | self.model.add(Conv2D(32, 3, 3, border_mode='same', 81 | input_shape=dataset.input_shape)) # 1 2维卷积层 82 | self.model.add(Activation('relu')) # 2 激活函数层 83 | 84 | self.model.add(Conv2D(32, 3, 3)) # 3 2维卷积层 85 | self.model.add(Activation('relu')) # 4 激活函数层 86 | 87 | self.model.add(MaxPool2D(pool_size=(2, 2))) # 5 池化层 88 | self.model.add(Dropout(0.25)) # 6 Dropout层 89 | 90 | self.model.add(Conv2D(64, 3, 3, border_mode='same')) # 7 2维卷积层 91 | self.model.add(Activation('relu')) # 8 激活函数层 92 | 93 | self.model.add(Conv2D(64, 3, 3)) # 9 2维卷积层 94 | self.model.add(Activation('relu')) # 10 激活函数层 95 | 96 | self.model.add(MaxPool2D(pool_size=(2, 2))) # 11 池化层 97 | self.model.add(Dropout(0.25)) # 12 Dropout层 98 | 99 | self.model.add(Flatten()) # 13 Flatten层 100 | self.model.add(Dense(512)) # 14 Dense层,又被称作全连接层 101 | self.model.add(Activation('relu')) # 15 激活函数层 102 | self.model.add(Dropout(0.5)) # 16 Dropout层 103 | self.model.add(Dense(nb_classes)) # 17 Dense层 104 | self.model.add(Activation('softmax')) # 18 分类层，输出最终结果 105 | 106 | # 输出模型概况 107 | self.model.summary() 108 | 109 | # 训练模型 110 | def train(self, dataset, batch_size=20, nb_epoch=100, data_augmentation=True): 111 | sgd = SGD(lr=0.01, decay=1e-6, 112 | momentum=0.9, nesterov=True) # 采用SGD+momentum的优化器进行训练，首先生成一个优化器对象 113 | self.model.compile(loss='categorical_crossentropy', 114 | optimizer=sgd, 115 | metrics=['accuracy']) # 完成实际的模型配置工作 116 | 117 | # 不使用数据提升，所谓的提升就是从我们提供的训练数据中利用旋转、翻转、加噪声等方法创造新的 118 | # 训练数据，有意识的提升训练数据规模，增加模型训练量 119 | if not data_augmentation: 120 | self.model.fit(dataset.train_images, 121 | dataset.train_labels, 122 | batch_size=batch_size, 123 | nb_epoch=nb_epoch, 124 | validation_data=(dataset.test_images, dataset.test_labels), 125 | shuffle=True) 126 | # 使用实时数据提升 127 | else: 128 | # 定义数据生成器用于数据提升，其返回一个生成器对象datagen，datagen每被调用一 129 | # 次其生成一组数据（顺序生成），节省内存，其实就是python的数据生成器 130 | datagen = ImageDataGenerator( 131 | featurewise_center=False, # 是否使输入数据去中心化（均值为0）， 132 | samplewise_center=False, # 是否使输入数据的每个样本均值为0 133 | featurewise_std_normalization=False, # 是否数据标准化（输入数据除以数据集的标准差） 134 | samplewise_std_normalization=False, # 是否将每个样本数据除以自身的标准差 135 | zca_whitening=False, # 是否对输入数据施以ZCA白化 136 | rotation_range=20, # 数据提升时图片随机转动的角度(范围为0～180) 137 | width_shift_range=0.2, # 数据提升时图片水平偏移的幅度（单位为图片宽度的占比，0~1之间的浮点数） 138 | height_shift_range=0.2, # 同上，只不过这里是垂直 139 | horizontal_flip=True, # 是否进行随机水平翻转 140 | vertical_flip=False) # 是否进行随机垂直翻转 141 | 142 | # 计算整个训练样本集的数量以用于特征值归一化、ZCA白化等处理 143 | datagen.fit(dataset.train_images) 144 | 145 | # 利用生成器开始训练模型 146 | self.model.fit_generator(datagen.flow(dataset.train_images, dataset.train_labels, 147 | batch_size=batch_size), 148 | samples_per_epoch=dataset.train_images.shape[0], 149 | nb_epoch=nb_epoch, 150 | validation_data=(dataset.test_images, dataset.test_labels)) 151 | 152 | MODEL_PATH = './Model/face.model.h5' 153 | 154 | def save_model(self, file_path=MODEL_PATH): 155 | self.model.save(file_path) 156 | 157 | def load_model(self, file_path=MODEL_PATH): 158 | self.model = load_model(file_path) 159 | 160 | def evaluate(self, dataset): 161 | score = self.model.evaluate(dataset.test_images, dataset.test_labels, verbose=1) 162 | # print("%s: %.2f%%" % (self.model.metrics_names[1], score[1] * 100)) 163 | print(f'{self.model.metrics_names[1]}:{score[1] * 100}%') 164 | 165 | # 识别人脸 166 | def face_predict(self, image): 167 | # 依然是根据后端系统确定维度顺序 168 | #if K.image_dim_ordering() == 'th' 169 | if K.image_data_format() == 'channels_first'and image.shape != (1, 3, IMAGE_SIZE, IMAGE_SIZE): 170 | image = resize_image(image) # 尺寸必须与训练集一致都应该是IMAGE_SIZE x IMAGE_SIZE 171 | image = image.reshape((1, 3, IMAGE_SIZE, IMAGE_SIZE)) # 与模型训练不同，这次只是针对1张图片进行预测 172 | #elif K.image_dim_ordering() == 'tf' 173 | elif K.image_data_format() == 'channels_last'and image.shape != (1, IMAGE_SIZE, IMAGE_SIZE, 3): 174 | image = resize_image(image) 175 | image = image.reshape((1, IMAGE_SIZE, IMAGE_SIZE, 3)) 176 | 177 | # 浮点并归一化 178 | image = image.astype('float32') 179 | image /= 255.0 180 | 181 | # 给出输入属于各个类别的概率，我们是二值类别，则该函数会给出输入图像属于0和1的概率各为多少 182 | result = self.model.predict_proba(image) 183 | print('result:', result) 184 | 185 | # 给出类别预测：0或者1 186 | result = self.model.predict_classes(image) 187 | 188 | # 返回类别预测结果 189 | return result[0] 190 | 191 | 192 | if __name__ == '__main__': 193 | dataset = Dataset('./FaceImageDate/') 194 | dataset.load() 195 | 196 | # 训练模型 197 | model = Model() 198 | model.build_model(dataset) 199 | # 测试训练函数的代码 200 | model.train(dataset) 201 | model.save_model(file_path='./Model/me.face.model.h5') 202 | # 评估模型 203 | model = Model() 204 | model.load_model(file_path='./Model/me.face.model.h5') 205 | model.evaluate(dataset) -------------------------------------------------------------------------------- /客户端/FaceRecognitionAttendanceSystem/__pycache__/FaceDateSet.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Color-l/FaceRecognitionAttendanceSystem/55b4ba4ef7280a888616f898287ccf7e4caa3807/客户端/FaceRecognitionAttendanceSystem/__pycache__/FaceDateSet.cpython-37.pyc -------------------------------------------------------------------------------- /客户端/FaceRecognitionAttendanceSystem/__pycache__/FaceRecognitionAttendanceSystem.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Color-l/FaceRecognitionAttendanceSystem/55b4ba4ef7280a888616f898287ccf7e4caa3807/客户端/FaceRecognitionAttendanceSystem/__pycache__/FaceRecognitionAttendanceSystem.cpython-37.pyc -------------------------------------------------------------------------------- /客户端/FaceRecognitionAttendanceSystem/__pycache__/ImageAcquisition.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Color-l/FaceRecognitionAttendanceSystem/55b4ba4ef7280a888616f898287ccf7e4caa3807/客户端/FaceRecognitionAttendanceSystem/__pycache__/ImageAcquisition.cpython-37.pyc -------------------------------------------------------------------------------- /客户端/FaceRecognitionAttendanceSystem/__pycache__/ModelTraining.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Color-l/FaceRecognitionAttendanceSystem/55b4ba4ef7280a888616f898287ccf7e4caa3807/客户端/FaceRecognitionAttendanceSystem/__pycache__/ModelTraining.cpython-37.pyc -------------------------------------------------------------------------------- /客户端/FaceRecognitionAttendanceSystem/main.py: -------------------------------------------------------------------------------- 1 | import sys 2 | import os 3 | from PyQt5.QtGui import * 4 | from PyQt5.QtWidgets import * 5 | from PyQt5.QtCore import * 6 | import FaceRecognitionAttendanceSystem 7 | # coding:utf-8 8 | def click_ImageAcquisition(): 9 | pass 10 | def click_FaceDateSet(): 11 | pass 12 | def click_ModelTraining(): 13 | pass 14 | def click_FaceRecognition(): 15 | pass 16 | if __name__ == '__main__': 17 | app = QApplication(sys.argv) 18 | MainWindow = QMainWindow() 19 | ui = FaceRecognitionAttendanceSystem.Ui_Form() 20 | ui.setupUi(MainWindow) 21 | MainWindow.show() 22 | ui.ImageAcquisition.clicked.connect(click_ImageAcquisition)#绑定成员录入按钮 23 | ui.FaceDateSet.clicked.connect(click_FaceDateSet)#绑定数据处理按钮 24 | ui.ModelTraining.clicked.connect(click_ModelTraining)#绑定模型训练按钮 25 | ui.FaceRecognition.clicked.connect(click_FaceRecognition)#绑定人脸识别按钮 26 | 27 | sys.exit(app.exec_()) -------------------------------------------------------------------------------- /服务端/a.out: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Color-l/FaceRecognitionAttendanceSystem/55b4ba4ef7280a888616f898287ccf7e4caa3807/服务端/a.out -------------------------------------------------------------------------------- /服务端/server.c: -------------------------------------------------------------------------------- 1 | #include 2 | #include 3 | #include 4 | #include 5 | #include 6 | #include 7 | #include 8 | #include 9 | #include 10 | #include 11 | #include 12 | #include 13 | 14 | #define BAUDRATE B115200 ///Baud rate : 115200 15 | #define DEVICE "/dev/ttyS1"//设置端口号 16 | #define FALSE 0 17 | #define TRUE 1 18 | #define _POSIX_SOURCE 1 //POSIX系统兼容 19 | int SerialPort_Send(int i){ 20 | 21 | int fd,res; 22 | struct termios oldtio,newtio; 23 | 24 | fd=open(DEVICE,O_RDWR | O_NOCTTY); 25 | if(fd<0){ 26 | perror(DEVICE); 27 | exit(-1); 28 | } 29 | tcgetattr(fd,&oldtio);//保存原来的参数 30 | bzero(&newtio,sizeof(newtio)); 31 | newtio.c_cflag=BAUDRATE | CS8 | CLOCAL | CREAD | HUPCL; 32 | newtio.c_iflag=IGNBRK; 33 | newtio.c_oflag=0; 34 | newtio.c_lflag=ICANON; 35 | tcflush(fd,TCIFLUSH); 36 | tcsetattr(fd,TCSANOW,&newtio);//设置串口参数 37 | printf("%d\n",i); 38 | if(i==0){ 39 | char openbuf[255]={0xdd,0x05,0x24,0x00,0x09}; 40 | char closebj[255]={0xdd,0x05,0x24,0x00,0x03}; 41 | write(fd,openbuf,5); 42 | write(fd,closebj,5); 43 | close(fd); 44 | } 45 | else{ 46 | char closebuf[255]={0xdd,0x05,0x24,0x00,0x0a}; 47 | char baojing[255]={0xdd,0x05,0x24,0x00,0x02}; 48 | write(fd,closebuf,5); 49 | write(fd,baojing,5); 50 | close(fd); 51 | } 52 | 53 | } 54 | 55 | int main() 56 | { 57 | int sockfd, new_fd; 58 | struct sockaddr_in my_addr; 59 | struct sockaddr_in their_addr; 60 | int sin_size; 61 | //建立TCP套接口 62 | if ((sockfd = socket(AF_INET, SOCK_STREAM, 0)) == -1) 63 | { 64 | printf("create socket error"); 65 | perror("socket"); 66 | exit(1); 67 | } 68 | //初始化结构体，并绑定6666端口 69 | my_addr.sin_family = AF_INET; 70 | my_addr.sin_port = htons(6666); 71 | my_addr.sin_addr.s_addr = INADDR_ANY; 72 | bzero(&(my_addr.sin_zero), 8); 73 | int on; 74 | on = 1; 75 | setsockopt( sockfd, SOL_SOCKET, SO_REUSEADDR, &on, sizeof(on) ); 76 | //绑定套接口 77 | if (bind(sockfd, (struct sockaddr*)&my_addr, sizeof(struct sockaddr)) == -1) 78 | { 79 | perror("bind socket error"); 80 | exit(1); 81 | } 82 | //创建监听套接口 83 | if (listen(sockfd, 10) == -1) 84 | { 85 | perror("listen"); 86 | exit(1); 87 | } 88 | //等待连接 89 | while (1) 90 | { 91 | sin_size = sizeof(struct sockaddr_in); 92 | printf("server is run......\n"); 93 | //如果建立连接，将产生一个全新的套接字 94 | if ((new_fd = accept(sockfd, (struct sockaddr*)&their_addr, &sin_size)) == -1) 95 | { 96 | perror("accept"); 97 | exit(1); 98 | } 99 | printf("accept success.\n"); 100 | //break; 101 | 102 | //生成一个子进程来完成和客户端的会话，父进程继续监听 103 | if (!fork()) 104 | { 105 | printf("create new thred success.\n"); 106 | //读取客户端发来的信息 107 | int numbytes; 108 | char buff[1024]; 109 | memset(buff, 0, 1024); 110 | if ((numbytes = recv(new_fd, buff, sizeof(buff), 0)) == -1) 111 | { 112 | perror("recv"); 113 | exit(1); 114 | } 115 | printf("%s\n", buff); 116 | printf("--------------------------------------------------------\n\n"); 117 | int i=(strcmp(buff,"0")); 118 | SerialPort_Send(i); 119 | /*if(i==0) 120 | { 121 | char success[]="success"; 122 | if (send(new_fd, success, strlen(success), 0) == -1) 123 | perror("send"); 124 | } 125 | else{ 126 | char failed[]="failed"; 127 | if (send(new_fd, failed, strlen(failed), 0) == -1) 128 | perror("send"); 129 | } 130 | 131 | close(new_fd); 132 | exit(0); 133 | }*/ 134 | close(new_fd); 135 | } 136 | } 137 | close(sockfd); 138 | } 139 | --------------------------------------------------------------------------------