import argparse
import numpy as np
from keras.applications.xception import preprocess_input
from keras.preprocessing import image
from keras.models import load_model
parser = argparse.ArgumentParser()
parser.add_argument('model')
parser.add_argument('classes')
parser.add_argument('image')
parser.add_argument('--top_n', type=int, default=10)
def main(args):
# create model
model = load_model(args.model)
# load class names
classes = []
with open(args.classes, 'r') as f:
classes = list(map(lambda x: x.strip(), f.readlines()))
# load an input image
img = image.load_img(args.image, target_size=(299, 299))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
# predict
pred = model.predict(x)[0]
result = [(classes[i], float(pred[i]) * 100.0) for i in range(len(pred))]
result.sort(reverse=True, key=lambda x: x[1])
for i in range(args.top_n):
(class_name, prob) = result[i]
print("Top %d ====================" % (i + 1))
print("Class name: %s" % (class_name))
print("Probability: %.2f%%" % (prob))
if __name__ == '__main__':
args = parser.parse_args()
main(args)
