keras_vgg_face.py

# tensorflow compiling
# https://stackoverflow.com/a/54048937
# 
# https://github.com/fo40225/tensorflow-windows-wheel
# https://github.com/fo40225/tensorflow-windows-wheel/blob/master/1.12.0/py36/CPU/avx2/tensorflow-1.12.0-cp36-cp36m-win_amd64.whl

from keras.applications.vgg16 import VGG16
from keras_vggface.vggface import VGGFace

image_size = 224

face_model = VGGFace(model='vgg16', 
                weights='vggface',
                input_shape=(224,224,3)) 
face_model.summary()


for layer in face_model.layers:
    layer.trainable = False

from keras.models import Model, Sequential
from keras.layers import Input, Convolution2D, ZeroPadding2D, MaxPooling2D, Flatten, Dense, Dropout, Activation

person_count = 5

last_layer = face_model.get_layer('pool5').output

x = Flatten(name='flatten')(last_layer)
x = Dense(1024, activation='relu', name='fc6')(x)
x = Dense(1024, activation='relu', name='fc7')(x)
out = Dense(person_count, activation='softmax', name='fc8')(x)

custom_face = Model(face_model.input, out)


from keras.preprocessing.image import ImageDataGenerator
batch_size = 5
train_path = 'data/'
eval_path = 'eval/'

train_datagen = ImageDataGenerator(rescale=1./255,
                                   shear_range=0.2,
                                   zoom_range=0.2,
                                   horizontal_flip=True)

valid_datagen = ImageDataGenerator(rescale=1./255,
                                   shear_range=0.2,
                                   zoom_range=0.2,
                                   horizontal_flip=True)

train_generator = train_datagen.flow_from_directory(
                        train_path,
                        target_size=(image_size,image_size),
                        batch_size=batch_size,
                        class_mode='sparse',
                        color_mode='rgb')

valid_generator = valid_datagen.flow_from_directory(
    directory=eval_path,
    target_size=(224, 224),
    color_mode='rgb',
    batch_size=batch_size,
    class_mode='sparse',
    shuffle=True,
)


"""
from keras.optimizers import SGD

custom_face.compile(loss='sparse_categorical_crossentropy',
                         optimizer=SGD(lr=1e-4, momentum=0.9),
                         metrics=['accuracy'])

history = custom_face.fit_generator(
        train_generator,
        validation_data=valid_generator,
        steps_per_epoch=49/batch_size,
        validation_steps=valid_generator.n,
        epochs=50)

custom_face.evaluate_generator(valid_generator,  (10/batch_size))

custom_face.save('vgg_face.h5')

"""


# test 
from keras.models import load_model
from keras.preprocessing.image import load_img, save_img, img_to_array
from keras_vggface.utils import preprocess_input
import numpy as np

custom_face = load_model('vgg_face.h5')

test_img = load_img('eval/json_statham/8.jpg', target_size=(224, 224))
img_test = img_to_array(test_img)
img_test = np.expand_dims(img_test, axis=0)
img_test = preprocess_input(img_test)
predictions = custom_face.predict(img_test)
print(person_count)
predicted_class=np.argmax(predictions,axis=1)

labels = (train_generator.class_indices)
labels = dict((v,k) for k,v in labels.items())
predictions = [labels[k] for k in predicted_class]
print(predictions)