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import glob import re import matplotlib.pyplot as plt import numpy as np import cv2 from sklearn.preprocessing import LabelBinarizer from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score from keras.models import Model from keras.layers import Flatten, Dense, Input, GlobalAveragePooling2D, GlobalMaxPooling2D, Activation from keras.layers import Convolution2D, MaxPooling2D from keras import optimizers from keras import backend as K from keras.callbacks import EarlyStopping SEED = 2017 Using TensorFlow backend. # Data can be downloaded at http://vis-www.cs.umass.edu/lfw/#download DATA_DIR = 'Data/lfw/' images = glob.glob(DATA_DIR + '*/*.jpg') plt.figure(figsize=(10, 10)) n_examples = 5 for i in range(5): rand = np.random.randint(len(images)) image_name = re.search(DATA_DIR+'(.+?)\/', images[rand], re.IGNORECASE).group(1).replace('_', ' ') img = cv2.imread(images[rand]) img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) plt.subplot(n_examples, n_examples, i+1) plt.title(image_name) plt.imshow(img) plt.show() |
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images_arnold = glob.glob(DATA_DIR + 'Arnold_Schwarzenegger/*.jpg') plt.figure(figsize=(10, 10)) for i in range(n_examples): image_name = re.search(DATA_DIR+'(.+?)\/', images_arnold[i], re.IGNORECASE).group(1).replace('_', ' ') img = cv2.imread(images_arnold[i]) img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) plt.subplot(n_examples, n_examples, i+1) # plt.title(image_name) plt.imshow(img) plt.show() |
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labels = np.asarray([re.search(DATA_DIR+'(.+?)\/', image, re.IGNORECASE).group(1) for image in np.asarray(images)]) print('Number of images: {}'.format(len(labels))) print('Number of unique labels: {}'.format(len(np.unique(labels)))) Number of images: 13233 Number of unique labels: 5749 encoder = LabelBinarizer() encoder.fit(labels) y = encoder.transform(labels).astype(float) X_train, X_val, y_train , y_val = train_test_split(images, y, test_size=0.2, random_state=SEED) input_shape = (250, 250, 3) img_input = Input(shape=input_shape) inputs = img_input # Block 1 x = Convolution2D(64, (3, 3), activation='relu', padding='same', name='conv1_1')(img_input) x = Convolution2D(64, (3, 3), activation='relu', padding='same', name='conv1_2')(x) x = MaxPooling2D((2, 2), strides=(2, 2), name='pool1')(x) # Block 2 x = Convolution2D(128, (3, 3), activation='relu', padding='same', name='conv2_1')(x) x = Convolution2D(128, (3, 3), activation='relu', padding='same', name='conv2_2')(x) x = MaxPooling2D((2, 2), strides=(2, 2), name='pool2')(x) # Block 3 x = Convolution2D(256, (3, 3), activation='relu', padding='same', name='conv3_1')(x) x = Convolution2D(256, (3, 3), activation='relu', padding='same', name='conv3_2')(x) x = Convolution2D(256, (3, 3), activation='relu', padding='same', name='conv3_3')(x) x = MaxPooling2D((2, 2), strides=(2, 2), name='pool3')(x) # Block 4 x = Convolution2D(512, (3, 3), activation='relu', padding='same', name='conv4_1')(x) x = Convolution2D(512, (3, 3), activation='relu', padding='same', name='conv4_2')(x) x = Convolution2D(512, (3, 3), activation='relu', padding='same', name='conv4_3')(x) x = MaxPooling2D((2, 2), strides=(2, 2), name='pool4')(x) # Block 5 x = Convolution2D(512, (3, 3), activation='relu', padding='same', name='conv5_1')(x) x = Convolution2D(512, (3, 3), activation='relu', padding='same', name='conv5_2')(x) x = Convolution2D(512, (3, 3), activation='relu', padding='same', name='conv5_3')(x) x = MaxPooling2D((2, 2), strides=(2, 2), name='pool5')(x) x = Flatten(name='flatten')(x) x = Dense(4096, name='fc6')(x) x = Activation('relu', name='fc6/relu')(x) x = Dense(4096, name='fc7')(x) x = Activation('relu', name='fc7/relu')(x) x = Dense(len(y[0]), name='fc8')(x) x = Activation('relu', name='fc8/softmax')(x) model = Model(inputs, x) opt = optimizers.Adam() model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy']) def random_shifts(image, shift_max_x=100, shift_max_y=100): width, height, _ = image.shape shift_x = np.random.randint(shift_max_x) shift_y = np.random.randint(shift_max_y) M = np.float32([[1, 0, shift_x],[0, 1, shift_y]]) return (cv2.warpAffine(image, M, (height, width))) def random_flip(image, p_flip=0.5): rand = np.random.random() if rand < p_flip: image = cv2.flip(image, 1) return image def scale_image(image, scale_range=[0.6, 1.4]): width, height, _ = image.shape scale_x = np.random.uniform(low=scale_range[0], high=scale_range[1]) scale_y = np.random.uniform(low=scale_range[0], high=scale_range[1]) scale_matrix = np.array([[scale_x, 0., (1. - scale_x) * width / 2.], [0., scale_y, (1. - scale_y) * height / 2.]], dtype=np.float32) return(cv2.warpAffine(image, scale_matrix, (width, height), flags=cv2.INTER_LINEAR, borderMode=cv2.BORDER_REFLECT_101)) img_rows = img_cols = 250 img_channels = 3 def batchgen(x, y, batch_size, transform=False): # Create empty numpy arrays images = np.zeros((batch_size, img_rows, img_cols, img_channels)) class_id = np.zeros((batch_size, len(y[0]))) while 1: for n in range(batch_size): i = np.random.randint(len(x)) x_ = cv2.imread(x[i]) x_ = cv2.cvtColor(x_, cv2.COLOR_BGR2RGB) if transform: x_ = random_shifts(x_, 10, 10) x_ = random_flip(x_) x_ = scale_image(x_, [0.8, 1,2]) images[n] = x_ class_id[n] = y[i] yield images, class_id batch_size = 32 n_epochs = 1000 s_epoch = len(X_train) // batch_size val_steps = len(X_val) // batch_size callbacks = [EarlyStopping(monitor='val_acc', patience=5)] train_generator = batchgen(X_train, y_train, batch_size, True) val_generator = batchgen(X_val, y_val, batch_size, False) history = model.fit_generator(train_generator, steps_per_epoch=s_epoch, epochs=n_epochs, validation_data=val_generator, validation_steps = val_steps, callbacks=callbacks ) Epoch 1/1000 330/330 [==============================] - 881s - loss: 15.0265 - acc: 0.0342 - val_loss: 14.8600 - val_acc: 0.0446 Epoch 2/1000 330/330 [==============================] - 868s - loss: 15.0487 - acc: 0.0402 - val_loss: 14.8502 - val_acc: 0.0507 Epoch 3/1000 301/330 [==========================>...] - ETA: 70s - loss: 15.0633 - acc: 0.0384 test_generator = batchgen(X_val, y_val, batch_size, False) preds = model.predict_generator(test_generator, steps=1) y_val_ = [np.argmax(x) for x in y_val] y_preds = [np.argmax(x) for x in preds] accuracy_score(y_val_, y_preds) |
