visual-recognition-using-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
# load the model

model = VGG16()
# load an image from file
image = load_img('2.jpg', 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
When the VGG model was trained in 2014,
the creators subtracted the average of each
of the three (R,G,B) channels first, so that
the data for each channel had a mean of zero.
Furthermore, their software that expected
the channels to be in B,G,R order,
whereas Python by default uses R,G,B.
We need to preprocess our data to make these two changes
'''
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))


Sample Output
Staffordshire_bullterrier (48.21%)