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44 lines
1.2 KiB
Python
44 lines
1.2 KiB
Python
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import numpy as np
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import json
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from keras.utils.data_utils import get_file
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from keras import backend as K
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CLASS_INDEX = None
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CLASS_INDEX_PATH = 'https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json'
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def preprocess_input(x, dim_ordering='default'):
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if dim_ordering == 'default':
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dim_ordering = K.image_dim_ordering()
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assert dim_ordering in {'tf', 'th'}
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if dim_ordering == 'th':
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x[:, 0, :, :] -= 103.939
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x[:, 1, :, :] -= 116.779
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x[:, 2, :, :] -= 123.68
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# 'RGB'->'BGR'
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x = x[:, ::-1, :, :]
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else:
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x[:, :, :, 0] -= 103.939
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x[:, :, :, 1] -= 116.779
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x[:, :, :, 2] -= 123.68
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# 'RGB'->'BGR'
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x = x[:, :, :, ::-1]
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return x
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def decode_predictions(preds):
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global CLASS_INDEX
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assert len(preds.shape) == 2 and preds.shape[1] == 1000
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if CLASS_INDEX is None:
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fpath = get_file('imagenet_class_index.json',
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CLASS_INDEX_PATH,
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cache_subdir='models')
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CLASS_INDEX = json.load(open(fpath))
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indices = np.argmax(preds, axis=-1)
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results = []
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for i in indices:
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results.append(CLASS_INDEX[str(i)])
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return results
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