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| # Copyright 2016 The TensorFlow Authors 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. | |
| # ============================================================================== | |
| """Generate the sprites tfrecords from raw_images.""" | |
| import os | |
| import random | |
| import re | |
| import sys | |
| import numpy as np | |
| import scipy.misc | |
| from six.moves import xrange | |
| import tensorflow as tf | |
| tf.flags.DEFINE_string('data_filepattern', '', 'The raw images.') | |
| tf.flags.DEFINE_string('out_file', '', | |
| 'File name for the tfrecord output.') | |
| def _read_images(): | |
| """Read images from image files into data structure.""" | |
| sprites = dict() | |
| files = tf.gfile.Glob(tf.flags.FLAGS.data_filepattern) | |
| for f in files: | |
| image = scipy.misc.imread(f) | |
| m = re.search('image_([0-9]+)_([0-9]+)_([0-9]+).jpg', os.path.basename(f)) | |
| if m.group(1) not in sprites: | |
| sprites[m.group(1)] = dict() | |
| character = sprites[m.group(1)] | |
| if m.group(2) not in character: | |
| character[m.group(2)] = dict() | |
| pose = character[m.group(2)] | |
| pose[int(m.group(3))] = image | |
| return sprites | |
| def _images_to_example(image, image2): | |
| """Convert 2 consecutive image to a SequenceExample.""" | |
| example = tf.SequenceExample() | |
| feature_list = example.feature_lists.feature_list['moving_objs'] | |
| feature = feature_list.feature.add() | |
| feature.float_list.value.extend(np.reshape(image, [-1]).tolist()) | |
| feature = feature_list.feature.add() | |
| feature.float_list.value.extend(np.reshape(image2, [-1]).tolist()) | |
| return example | |
| def generate_input(): | |
| """Generate tfrecords.""" | |
| sprites = _read_images() | |
| sys.stderr.write('Finish reading images.\n') | |
| train_writer = tf.python_io.TFRecordWriter( | |
| tf.flags.FLAGS.out_file.replace('sprites', 'sprites_train')) | |
| test_writer = tf.python_io.TFRecordWriter( | |
| tf.flags.FLAGS.out_file.replace('sprites', 'sprites_test')) | |
| train_examples = [] | |
| test_examples = [] | |
| for i in sprites: | |
| if int(i) < 24: | |
| examples = test_examples | |
| else: | |
| examples = train_examples | |
| character = sprites[i] | |
| for j in character.keys(): | |
| pose = character[j] | |
| for k in xrange(1, len(pose), 1): | |
| image = pose[k] | |
| image2 = pose[k+1] | |
| examples.append(_images_to_example(image, image2)) | |
| sys.stderr.write('Finish generating examples: %d, %d.\n' % | |
| (len(train_examples), len(test_examples))) | |
| random.shuffle(train_examples) | |
| _ = [train_writer.write(ex.SerializeToString()) for ex in train_examples] | |
| _ = [test_writer.write(ex.SerializeToString()) for ex in test_examples] | |
| def main(_): | |
| generate_input() | |
| if __name__ == '__main__': | |
| tf.app.run() | |