from __future__ import absolute_import, division, print_function ''' From https://github.com/fchollet/deep-learning-models ''' import json from keras.utils.data_utils import get_file from keras import backend as K CLASS_INDEX = None CLASS_INDEX_PATH = 'https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json' def preprocess_input(x, dim_ordering='default'): if dim_ordering == 'default': dim_ordering = K.image_dim_ordering() assert dim_ordering in {'tf', 'th'} if dim_ordering == 'th': x[:, 0, :, :] -= 103.939 x[:, 1, :, :] -= 116.779 x[:, 2, :, :] -= 123.68 # 'RGB'->'BGR' x = x[:, ::-1, :, :] else: x[:, :, :, 0] -= 103.939 x[:, :, :, 1] -= 116.779 x[:, :, :, 2] -= 123.68 # 'RGB'->'BGR' x = x[:, :, :, ::-1] return x def decode_imagenet_predictions(preds, top=5): global CLASS_INDEX if len(preds.shape) != 2 or preds.shape[1] != 1000: raise ValueError('`decode_predictions` expects ' 'a batch of predictions ' '(i.e. a 2D array of shape (samples, 1000)). ' 'Found array with shape: ' + str(preds.shape)) if CLASS_INDEX is None: fpath = get_file('imagenet_class_index.json', CLASS_INDEX_PATH, cache_subdir='models') CLASS_INDEX = json.load(open(fpath)) results = [] for pred in preds: top_indices = pred.argsort()[-top:][::-1] result = [tuple(CLASS_INDEX[str(i)]) + (pred[i],) for i in top_indices] results.append(result) return results