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 listdir import numpy as np from PIL import Image import cv2 from tensorflow.keras.models import Sequential, load_model from tensorflow.keras.layers import Dense, Activation, Convolution2D, MaxPooling2D, Flatten from sklearn.model_selection import train_test_split from 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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