基于opencv ，Keras, Tensorflow的人脸识别.zip

# encoding: utf-8 ''' @author: weiyang_tang @contact: weiyang_tang@126.com @file: model_train.py @time: 2019-02-22 10:18 @desc: 训练模型 ''' from __future__ import print_function import random import numpy as np from keras import backend as K from keras.layers import Convolution2D, MaxPooling2D from keras.layers import Dense, Dropout, Activation, Flatten from keras.models import Sequential from keras.models import load_model from keras.optimizers import SGD from keras.preprocessing.image import ImageDataGenerator from keras.utils import np_utils # from sklearn.cross_validation import train_test_split from sklearn.model_selection import train_test_split from data_input import extract_data, resize_with_pad, IMAGE_SIZE, read_file faceData_file_path = './faceData/' class Dataset(object): def __init__(self): self.X_train = None self.X_valid = None self.X_test = None self.Y_train = None self.Y_valid = None self.Y_test = None def read(self, img_rows=IMAGE_SIZE, img_cols=IMAGE_SIZE, img_channels=3, nb_classes=3): # images, labels = extract_data('./faceData/') images, labels, counter = read_file(faceData_file_path) nb_classes = counter print(nb_classes) print(images) print(labels) labels = np.reshape(labels, [-1]) # numpy.reshape X_train, X_test, y_train, y_test = train_test_split(images, labels, test_size=0.3, random_state=random.randint(0, 100)) X_valid, X_test, y_valid, y_test = train_test_split(images, labels, test_size=0.5, random_state=random.randint(0, 100)) if K.image_dim_ordering() == 'th': X_train = X_train.reshape(X_train.shape[0], 3, img_rows, img_cols) X_valid = X_valid.reshape(X_valid.shape[0], 3, img_rows, img_cols) X_test = X_test.reshape(X_test.shape[0], 3, img_rows, img_cols) input_shape = (3, img_rows, img_cols) else: X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, 3) X_valid = X_valid.reshape(X_valid.shape[0], img_rows, img_cols, 3) X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, 3) input_shape = (img_rows, img_cols, 3) # the data, shuffled and split between train and test sets print('X_train shape:', X_train.shape) print(X_train.shape[0], 'train samples') print(X_valid.shape[0], 'valid samples') print(X_test.shape[0], 'test samples') # convert class vectors to binary class matrices Y_train = np_utils.to_categorical(y_train, nb_classes) Y_valid = np_utils.to_categorical(y_valid, nb_classes) Y_test = np_utils.to_categorical(y_test, nb_classes) X_train = X_train.astype('float32') X_valid = X_valid.astype('float32') X_test = X_test.astype('float32') X_train /= 255 X_valid /= 255 X_test /= 255 self.X_train = X_train self.X_valid = X_valid self.X_test = X_test self.Y_train = Y_train self.Y_valid = Y_valid self.Y_test = Y_test class Model(object): FILE_PATH = './model/model.h5' def __init__(self): self.model = None def build_model(self, dataset, nb_classes=3): images, labels, counter = read_file(faceData_file_path) nb_classes = counter self.model = Sequential() self.model.add(Convolution2D(32, 3, 3, border_mode='same', input_shape=dataset.X_train.shape[1:])) self.model.add(Activation('relu')) self.model.add(Convolution2D(32, 3, 3)) self.model.add(Activation('relu')) self.model.add(MaxPooling2D(pool_size=(2, 2))) self.model.add(Dropout(0.25)) self.model.add(Convolution2D(64, 3, 3, border_mode='same')) self.model.add(Activation('relu')) self.model.add(Convolution2D(64, 3, 3)) self.model.add(Activation('relu')) self.model.add(MaxPooling2D(pool_size=(2, 2))) self.model.add(Dropout(0.25)) self.model.add(Flatten()) self.model.add(Dense(512)) self.model.add(Activation('relu')) self.model.add(Dropout(0.5)) self.model.add(Dense(nb_classes)) self.model.add(Activation('softmax')) self.model.summary() def train(self, dataset, batch_size=32, nb_epoch=40, data_augmentation=True): # let's train the model using SGD + momentum (how original). sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True) self.model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy']) if not data_augmentation: print('Not using data augmentation.') self.model.fit(dataset.X_train, dataset.Y_train, batch_size=batch_size, nb_epoch=nb_epoch, validation_data=(dataset.X_valid, dataset.Y_valid), shuffle=True) else: print('Using real-time data augmentation.') # this will do preprocessing and realtime data augmentation datagen = ImageDataGenerator( featurewise_center=False, # set input mean to 0 over the dataset samplewise_center=False, # set each sample mean to 0 featurewise_std_normalization=False, # divide inputs by std of the dataset samplewise_std_normalization=False, # divide each input by its std zca_whitening=False, # apply ZCA whitening rotation_range=20, # randomly rotate images in the range (degrees, 0 to 180) width_shift_range=0.2, # randomly shift images horizontally (fraction of total width) height_shift_range=0.2, # randomly shift images vertically (fraction of total height) horizontal_flip=True, # randomly flip images vertical_flip=False) # randomly flip images # compute quantities required for featurewise normalization # (std, mean, and principal components if ZCA whitening is applied) datagen.fit(dataset.X_train) # fit the model on the batches generated by datagen.flow() self.model.fit_generator(datagen.flow(dataset.X_train, dataset.Y_train, batch_size=batch_size), samples_per_epoch=dataset.X_train.shape[0], nb_epoch=nb_epoch, validation_data=(dataset.X_valid, dataset.Y_valid)) def save(self, file_path=FILE_PATH): print('Model Saved.') self.model.save(file_path) def load(self, file_path=FILE_PATH): print('Model Loaded.') self.model = load_model(file_path) def predict(self, image): if K.image_dim_ordering() == 'th' and image.shape != (1, 3, IMAGE_SIZE, IMAGE_SIZE): image = resize_with_pad(image) image = image.reshape((1, 3, IMAGE_SIZE, IMAGE_SIZE)) elif K.image_dim_ordering() == 'tf' and image.shape != (1, IMAGE_SIZE, IMAGE_SIZE, 3): image = resize_with_pad(image) image = image.reshape((1, IMAGE_SIZE, IMAGE_SIZE, 3)) image = image.astype('float32') image /= 255 # 将图像矩阵转化到0-1之间 result = self.model.predict_proba(image) max_index = np.argmax(result) # 找出概率最高的 print('准确度为', result[0][max_index]) # if (result < 0.8): # 获取识别的可靠性 # return -1 result = self.model.predict_classes(image) return result[0] def evaluate(self, dataset): score = self.model.evaluate(dataset.X_test, dataset