【Python深度学习】Tensorflow+CNN进行人脸识别实战（附源码和数据集）

需要源码和数据集请点赞关注收藏后评论区留言私信~~~

下面利用Tensorflow平台进行人脸识别实战，使用的是Olivetti Faces人脸图像 部分数据集展示如下

 程序训练过程如下

 接下来训练CNN模型 可以看到训练进度和损失值变化

接下来展示人脸识别结果

程序会根据一张图片自动去图片集中寻找相似的人脸 如上图所示

部分代码如下 需要全部源码和数据集请点赞关注收藏后评论区留言私信~~~

from os import listdirimport numpy as npfrom PIL import Imageimport cv2from tensorflow.keras.models import Sequential, load_modelfrom tensorflow.keras.layers import Dense, Activation, Convolution2D, MaxPooling2D, Flattenfrom sklearn.model_selection import train_test_splitfrom tensorflow.python.keras.utils import np_utils# 读取人脸图片数据def img2vector(fileNamestr):    # 创建向量    returnVect = np.zeros((57,47))        image = Image.open(fileNamestr).convert('L')        img = np.asarray(image).reshape(57,47)        return img# 制作人脸数据集def GetDataset(imgDataDir):       print('| Step1 |: Get dataset...')    imgDataDir='faces_4/'    FileDir = listdir(imgDataDir)    m = len(FileDir)    imgarray=[]    hwLabels=[]    hwdata=[]    # 逐个读取图片文件    for i in range(m):        # 提取子目录        className=i        subdirName='faces_4/'+str(FileDir[i])+'/'        fileNames = listdir(subdirName)                        lenFiles=len(fileNames)        # 提取文件名        for j in range(lenFiles):             fileNamestr = subdirName+fileNames[j]            hwLabels.append(className)                imgarray=img2vector(fileNamestr)            hwdata.append(imgarray)    hwdata = np.array(hwdata)    return hwdata,hwLabels,6# CNN模型类class MyCNN(object):    FILE_PATH = "face_recognition.h5"  # 模型存储/读取目录    picHeight = 57  # 模型的人脸图片长47，宽57    picWidth = 47      def __init__(self):        self.model = None    # 获取训练数据集    def read_trainData(self, dataset):                self.dataset = dataset    # 建立Sequential模型，并赋予参数    def build_model(self):        print('| Step2 |: Init CNN model...')        self.model = Sequential()        print('self.dataset.X_train.shape[1:]',self.dataset.X_train.shape[1:])        self.model.add( Convolution2D( filters=32,          kernel_size=(5, 5),          padding='same',          #dim_ordering='th',          input_shape=self.dataset.X_train.shape[1:]))        self.model.add(Activation('relu'))        self.model.add( MaxPooling2D(pool_size=(2, 2),         strides=(2, 2),         padding='same' ) )        self.model.add(Convolution2D(filters=64,          kernel_size=(5, 5),          padding='same') )        self.model.add(Activation('relu'))        self.model.add(MaxPooling2D(pool_size=(2, 2),         strides=(2, 2),         padding='same') )        self.model.add(Flatten())        self.model.add(Dense(512))        self.model.add(Activation('relu'))        self.model.add(Dense(self.dataset.num_classes))        self.model.add(Activation('softmax'))        self.model.summary()    # 模型训练    def train_model(self):        print('| Step3 |: Train CNN model...')        self.model.compile( optimizer='adam', loss='cateGorical_crossentropy', metrics=['accuracy'])        # epochs：训练代次、batch_size：每次训练样本数        self.model.fit(self.dataset.X_train, self.dataset.Y_train, epochs=10, batch_size=20)    def evaluate_model(self):        loss, accuracy = self.model.evaluate(self.dataset.X_test, self.dataset.Y_test)        print('| Step4 |: Evaluate perfORMance...')        print('===================================')                print('Loss   Value   is :', loss)        print('Accuracy Value is :', accuracy)    def save(self, file_path=FILE_PATH):                print('| Step5 |: Save model...')        self.model.save(file_path)        print('Model ',file_path,'is succeesfuly saved.')# 建立一个用于存储和格式化读取训练数据的类class DataSet(object):    def __init__(self, path):        self.num_classes = None        self.X_train = None        self.X_test = None        self.Y_train = None        self.Y_test = None        self.picWidth = 47        self.picHeight = 57                self.makeDataSet(path)  # 在这个类初始化的过程中读取path下的训练数据    def makeDataSet(self, path):        # 根据指定路径读取出图片、标签和类别数        imgs, labels, clasNum = GetDataset(path)        # 将数据集打乱随机分组        X_train, X_test, y_train, y_test = train_test_split(imgs, labels, test_size=0.2,random_state=1)        # 重新格式化和标准化        X_train = X_train.reshape(X_train.shape[0], 1, self.picHeight, self.picWidth) / 255.0        X_test = X_test.reshape(X_test.shape[0], 1, self.picHeight, self.picWidth) / 255.0        X_train = X_train.astype('float32')        X_test = X_test.astype('float32')        # 将labels转成 binary class matrices        Y_train = np_utils.to_categorical(y_train, num_classes=clasNum)        Y_test = np_utils.to_categorical(y_test, num_classes=clasNum)        # 将格式化后的数据赋值给类的属性上        self.X_train = X_train        self.X_test = X_test        self.Y_train = Y_train        self.Y_test = Y_test        self.num_classes = clasNum# 人脸图片目录dataset = DataSet('faces_4/')model = MyCNN()model.read_trainData(dataset)model.build_model()model.train_model()model.evaluate_model()model.save()

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来源地址：https://blog.csdn.net/jiebaoshayebuhui/article/details/128550477