vgg16.py

from keras.preprocessing.image import load_img
from keras.preprocessing.image import img_to_array
from keras.applications.vgg16 import preprocess_input
from keras.applications.vgg16 import decode_predictions
from keras.applications.vgg16 import VGG16
from shutil import copyfile
# load the model
model = VGG16()
import os
for file in os.listdir():
    if file.endswith('.jpg'):
        # load an image from file
        image = load_img(file, 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)
        # predict the probability across all output classes
        yhat = model.predict(image)
        # convert the probabilities to class labels
        label = decode_predictions(yhat)
        # retrieve the most likely result, e.g. highest probability
        label = label[0][0]
        # print the classification
        print('%s (%.2f%%)' % (label[1], label[2]*100))
        copyfile(file, label[1] + '.jpg')