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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. | |
| # ============================================================================== | |
| """Read image sequence.""" | |
| from six.moves import xrange | |
| import tensorflow as tf | |
| def SequenceToImageAndDiff(images): | |
| """Convert image sequence batch into image and diff batch. | |
| Each image pair is converted to the first image and their diff. | |
| Batch size will increase if sequence length is larger than 2. | |
| Args: | |
| images: Image sequence with shape | |
| [batch_size, seq_len, image_size, image_size, channel] | |
| Returns: | |
| the list of (image, diff) tuples with shape | |
| [batch_size2, image_size, image_size, channel]. image_sizes are | |
| [32, 64, 128, 256]. | |
| """ | |
| image_diff_list = [] | |
| image_seq = tf.unstack(images, axis=1) | |
| for size in [32, 64, 128, 256]: | |
| resized_images = [ | |
| tf.image.resize_images(i, [size, size]) for i in image_seq] | |
| diffs = [] | |
| for i in xrange(0, len(resized_images)-1): | |
| diffs.append(resized_images[i+1] - resized_images[i]) | |
| image_diff_list.append( | |
| (tf.concat(axis=0, values=resized_images[:-1]), tf.concat(axis=0, values=diffs))) | |
| return image_diff_list | |
| def ReadInput(data_filepattern, shuffle, params): | |
| """Read the tf.SequenceExample tfrecord files. | |
| Args: | |
| data_filepattern: tf.SequenceExample tfrecord filepattern. | |
| shuffle: Whether to shuffle the examples. | |
| params: parameter dict. | |
| Returns: | |
| image sequence batch [batch_size, seq_len, image_size, image_size, channel]. | |
| """ | |
| image_size = params['image_size'] | |
| filenames = tf.gfile.Glob(data_filepattern) | |
| filename_queue = tf.train.string_input_producer(filenames, shuffle=shuffle) | |
| reader = tf.TFRecordReader() | |
| _, example = reader.read(filename_queue) | |
| feature_sepc = { | |
| 'moving_objs': tf.FixedLenSequenceFeature( | |
| shape=[image_size * image_size * 3], dtype=tf.float32)} | |
| _, features = tf.parse_single_sequence_example( | |
| example, sequence_features=feature_sepc) | |
| moving_objs = tf.reshape( | |
| features['moving_objs'], [params['seq_len'], image_size, image_size, 3]) | |
| if shuffle: | |
| examples = tf.train.shuffle_batch( | |
| [moving_objs], | |
| batch_size=params['batch_size'], | |
| num_threads=64, | |
| capacity=params['batch_size'] * 100, | |
| min_after_dequeue=params['batch_size'] * 4) | |
| else: | |
| examples = tf.train.batch([moving_objs], | |
| batch_size=params['batch_size'], | |
| num_threads=16, | |
| capacity=params['batch_size']) | |
| examples /= params['norm_scale'] | |
| return examples | |