90 lines
3.1 KiB
Python
Executable file
90 lines
3.1 KiB
Python
Executable file
#!/usr/bin/python
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# Copyright 2016 Google Inc. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Process the ImageNet Challenge bounding boxes for TensorFlow model training.
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Associate the ImageNet 2012 Challenge validation data set with labels.
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The raw ImageNet validation data set is expected to reside in JPEG files
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located in the following directory structure.
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data_dir/ILSVRC2012_val_00000001.JPEG
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data_dir/ILSVRC2012_val_00000002.JPEG
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...
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data_dir/ILSVRC2012_val_00050000.JPEG
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This script moves the files into a directory structure like such:
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data_dir/n01440764/ILSVRC2012_val_00000293.JPEG
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data_dir/n01440764/ILSVRC2012_val_00000543.JPEG
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...
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where 'n01440764' is the unique synset label associated with
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these images.
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This directory reorganization requires a mapping from validation image
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number (i.e. suffix of the original file) to the associated label. This
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is provided in the ImageNet development kit via a Matlab file.
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In order to make life easier and divorce ourselves from Matlab, we instead
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supply a custom text file that provides this mapping for us.
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Sample usage:
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./preprocess_imagenet_validation_data.py ILSVRC2012_img_val \
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imagenet_2012_validation_synset_labels.txt
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"""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import os
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import errno
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import os.path
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import sys
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if __name__ == '__main__':
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if len(sys.argv) < 3:
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print('Invalid usage\n'
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'usage: preprocess_imagenet_validation_data.py '
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'<validation data dir> <validation labels file>')
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sys.exit(-1)
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data_dir = sys.argv[1]
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validation_labels_file = sys.argv[2]
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# Read in the 50000 synsets associated with the validation data set.
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labels = [l.strip() for l in open(validation_labels_file).readlines()]
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unique_labels = set(labels)
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# Make all sub-directories in the validation data dir.
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for label in unique_labels:
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labeled_data_dir = os.path.join(data_dir, label)
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# Catch error if sub-directory exists
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try:
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os.makedirs(labeled_data_dir)
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except OSError as e:
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# Raise all errors but 'EEXIST'
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if e.errno != errno.EEXIST:
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raise
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# Move all of the image to the appropriate sub-directory.
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for i in range(len(labels)):
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basename = 'ILSVRC2012_val_000%.5d.JPEG' % (i + 1)
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original_filename = os.path.join(data_dir, basename)
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if not os.path.exists(original_filename):
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print('Failed to find: %s' % original_filename)
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sys.exit(-1)
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new_filename = os.path.join(data_dir, labels[i], basename)
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os.rename(original_filename, new_filename)
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