preprocess.py

from os import listdir
from pickle import dump
from keras.applications.vgg16 import VGG16
from keras.preprocessing.image import load_img
from keras.preprocessing.image import img_to_array
from keras.applications.vgg16 import preprocess_input
from keras.models import Model
from utils import load_doc, load_descriptions, clean_descriptions, to_vocabulary, save_descriptions
# extract features from each photo in the directory
def extract_features(directory):
	model = VGG16()
	model.layers.pop()
	model = Model(inputs=model.inputs, outputs=model.layers[-1].output)
	print(model.summary())
	features = dict()
	for name in listdir(directory):
		filename = directory + '/' + name
		image = load_img(filename, target_size=(224, 224))
		image = img_to_array(image)
		image = image.reshape((1, image.shape[0], image.shape[1], image.shape[2]))
		image = preprocess_input(image)
		feature = model.predict(image, verbose=0)
		image_id = name.split('.')[0]
		features[image_id] = feature
		print('>%s' % name)
	return features

# extract features from all images
directory = 'dataset/Flicker8k_Dataset'
features = extract_features(directory)
print('Extracted Features: %d' % len(features))
# save to file
dump(features, open('features.pkl', 'wb'))


filename = 'dataset/Flickr8k.token.txt'
doc = load_doc(filename)
# parse descriptions
descriptions = load_descriptions(doc)
print('Loaded: %d ' % len(descriptions))
clean_descriptions(descriptions)
vocabulary = to_vocabulary(descriptions)
print('Vocabulary Size: %d' % len(vocabulary))
# save to file
save_descriptions(descriptions, 'descriptions.txt')