k2tf_eval.py

'''
This script evaluates a simple Convolutional Nerual Network (CNN)
against a supplied data set. It is used in a tutorial demonstrating
how to build Keras models and run them in native C++ Tensorflow applications.

MIT License

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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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SOFTWARE.
'''
import argparse

import numpy as np

from keras.preprocessing import image
from keras.models import load_model

def invertKVPairs( someDictionary ):
    '''
    Inverts the key/value pairs of the supplied dictionary.
    
    Args:
        someDictionary (dict): The dictionary for which you would like the inversion
    Returns:
        Dictionary - the inverse key-value pairing of someDictionary
    '''
    ret = {}
    for k, v in someDictionary.items():
        ret[v] = k

    return ret

if __name__ == '__main__':

    parser = argparse.ArgumentParser()
    parser.add_argument('--model','-m', dest='model', required=True, help='The HDF5 Keras model you wish to run')
    parser.add_argument('--image','-i', dest='image', required=True, help='The image you wish to test')
    parser.add_argument('--shape','-s', type=int, dest='shape', required=True, help='The shape to resize the image for the model')
    parser.add_argument('--labels','-l', dest='labels', required=False, help='The indices.txt file containing the class_indices of the Keras training set')
    args = parser.parse_args()

    model = load_model(args.model)
    
    # These indices are saved on the output of our trainer
    class_indices = { 0:'daisy',  1:'dandelion',  2:'roses', 3:'sunflowers', 4:'tulips' }
    if args.labels:
        with open( args.labels, 'rt' ) as infile:
            label_str = infile.read()
            str2dict = eval(label_str)
            class_indices = invertKVPairs( str2dict )

    test_image = image.load_img(args.image, target_size=(args.shape,args.shape))
    test_image = np.expand_dims( test_image, axis=0 )
    test_image = test_image/255.0
    result = model.predict(test_image)

    for idx,val in enumerate( result[0] ):
        print( '%s : %4.2f percent' % (class_indices[idx], val*100. ) )