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June 1, 2018 06:22
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#!/usr/bin/python | |
# Copyright 2016 Google Inc. All Rights Reserved. | |
# | |
# Licensed under the Apache License, Version 2.0 (the "License"); | |
# you may not use this file except in compliance with the License. | |
# You may obtain a copy of the License at | |
# | |
# http://www.apache.org/licenses/LICENSE-2.0 | |
# | |
# Unless required by applicable law or agreed to in writing, software | |
# distributed under the License is distributed on an "AS IS" BASIS, | |
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
# See the License for the specific language governing permissions and | |
# limitations under the License. | |
# ============================================================================== | |
"""Process the ImageNet Challenge bounding boxes for TensorFlow model training. | |
Associate the ImageNet 2012 Challenge validation data set with labels. | |
The raw ImageNet validation data set is expected to reside in JPEG files | |
located in the following directory structure. | |
data_dir/ILSVRC2012_val_00000001.JPEG | |
data_dir/ILSVRC2012_val_00000002.JPEG | |
... | |
data_dir/ILSVRC2012_val_00050000.JPEG | |
This script moves the files into a directory structure like such: | |
data_dir/n01440764/ILSVRC2012_val_00000293.JPEG | |
data_dir/n01440764/ILSVRC2012_val_00000543.JPEG | |
... | |
where 'n01440764' is the unique synset label associated with | |
these images. | |
This directory reorganization requires a mapping from validation image | |
number (i.e. suffix of the original file) to the associated label. This | |
is provided in the ImageNet development kit via a Matlab file. | |
In order to make life easier and divorce ourselves from Matlab, we instead | |
supply a custom text file that provides this mapping for us. | |
Sample usage: | |
./preprocess_imagenet_validation_data.py ILSVRC2012_img_val \ | |
imagenet_2012_validation_synset_labels.txt | |
""" | |
from __future__ import absolute_import | |
from __future__ import division | |
from __future__ import print_function | |
import os | |
import errno | |
import os.path | |
import sys | |
if __name__ == '__main__': | |
if len(sys.argv) < 3: | |
print('Invalid usage\n' | |
'usage: preprocess_imagenet_validation_data.py ' | |
'<validation data dir> <validation labels file>') | |
sys.exit(-1) | |
data_dir = sys.argv[1] | |
validation_labels_file = sys.argv[2] | |
# Read in the 50000 synsets associated with the validation data set. | |
labels = [l.strip() for l in open(validation_labels_file).readlines()] | |
unique_labels = set(labels) | |
# Make all sub-directories in the validation data dir. | |
for label in unique_labels: | |
labeled_data_dir = os.path.join(data_dir, label) | |
# Catch error if sub-directory exists | |
try: | |
os.makedirs(labeled_data_dir) | |
except OSError as e: | |
# Raise all errors but 'EEXIST' | |
if e.errno != errno.EEXIST: | |
raise | |
# Move all of the image to the appropriate sub-directory. | |
for i in range(len(labels)): | |
basename = 'ILSVRC2012_val_000%.5d.JPEG' % (i + 1) | |
original_filename = os.path.join(data_dir, basename) | |
if not os.path.exists(original_filename): | |
print('Failed to find: %s' % original_filename) | |
sys.exit(-1) | |
new_filename = os.path.join(data_dir, labels[i], basename) | |
os.rename(original_filename, new_filename) |
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