extract_Feature.py

# -*- coding: utf-8 -*-
"""
Created on Thu Mar 22 17:03:44 2018

@author: Saurabh
"""

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

# extract features from each photo in the directory
def extract_features(directory):
        
    	# load the model
    	model = VGG16()
    	# re-structure the model
    	model.layers.pop()
    	model = Model(inputs=model.inputs, outputs=model.layers[-1].output)
    	# summarize
    	print(model.summary())
    	number = 1# extract features from each photo
    	features = dict() 
    	for name in listdir(directory):
            
            filename = directory + '/' + name
            image = load_img(filename, target_size=(224, 224))
            # convert the image pixels to a numpy array
            image = img_to_array(image)
            # reshape data for the model
            image = image.reshape((1, image.shape[0], image.shape[1], image.shape[2]))
            # prepare the image for the VGG model
            image = preprocess_input(image)
            # get features
            feature = model.predict(image, verbose=0)
            # get image id
            image_id = name.split('.')[0]
            # store feature
            features[image_id] = feature
            print(number,' >%s' % name)
            number+=1
            # load an image from file
    		
    	return features

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