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# Copyright 2017 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.
# ==============================================================================
"""Image summaries and TensorFlow operations to create them.
An image summary stores the width, height, and PNG-encoded data for zero
or more images in a rank-1 string array: `[w, h, png0, png1, ...]`.
NOTE: This module is in beta, and its API is subject to change, but the
data that it stores to disk will be supported forever.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow as tf
from tensorboard import util
from tensorboard.plugins.image import metadata
def op(name,
images,
max_outputs=3,
display_name=None,
description=None,
collections=None):
"""Create an image summary op for use in a TensorFlow graph.
Arguments:
name: A unique name for the generated summary node.
images: A `Tensor` representing pixel data with shape `[k, h, w, c]`,
where `k` is the number of images, `h` and `w` are the height and
width of the images, and `c` is the number of channels, which
should be 1, 3, or 4. Any of the dimensions may be statically
unknown (i.e., `None`).
max_outputs: Optional `int` or rank-0 integer `Tensor`. At most this
many images will be emitted at each step. When more than
`max_outputs` many images are provided, the first `max_outputs` many
images will be used and the rest silently discarded.
display_name: Optional name for this summary in TensorBoard, as a
constant `str`. Defaults to `name`.
description: Optional long-form description for this summary, as a
constant `str`. Markdown is supported. Defaults to empty.
collections: Optional list of graph collections keys. The new
summary op is added to these collections. Defaults to
`[Graph Keys.SUMMARIES]`.
Returns:
A TensorFlow summary op.
"""
if display_name is None:
display_name = name
summary_metadata = metadata.create_summary_metadata(
display_name=display_name, description=description)
with tf.name_scope(name), \
tf.control_dependencies([tf.assert_rank(images, 4),
tf.assert_type(images, tf.uint8),
tf.assert_non_negative(max_outputs)]):
limited_images = images[:max_outputs]
encoded_images = tf.map_fn(tf.image.encode_png, limited_images,
dtype=tf.string,
name='encode_each_image')
image_shape = tf.shape(images)
dimensions = tf.stack([tf.as_string(image_shape[2], name='width'),
tf.as_string(image_shape[1], name='height')],
name='dimensions')
tensor = tf.concat([dimensions, encoded_images], axis=0)
return tf.summary.tensor_summary(name='image_summary',
tensor=tensor,
collections=collections,
summary_metadata=summary_metadata)
def pb(name, images, max_outputs=3, display_name=None, description=None):
"""Create an image summary protobuf.
This behaves as if you were to create an `op` with the same arguments
(wrapped with constant tensors where appropriate) and then execute
that summary op in a TensorFlow session.
Arguments:
name: A unique name for the generated summary, including any desired
name scopes.
images: An `np.array` representing pixel data with shape
`[k, h, w, c]`, where `k` is the number of images, `w` and `h` are
the width and height of the images, and `c` is the number of
channels, which should be 1, 3, or 4.
max_outputs: Optional `int`. At most this many images will be
emitted. If more than this many images are provided, the first
`max_outputs` many images will be used and the rest silently
discarded.
display_name: Optional name for this summary in TensorBoard, as a
`str`. Defaults to `name`.
description: Optional long-form description for this summary, as a
`str`. Markdown is supported. Defaults to empty.
Returns:
A `tf.Summary` protobuf object.
"""
images = np.array(images).astype(np.uint8)
if images.ndim != 4:
raise ValueError('Shape %r must have rank 4' % (images.shape, ))
limited_images = images[:max_outputs]
encoded_images = [util.encode_png(image) for image in limited_images]
(width, height) = (images.shape[2], images.shape[1])
content = [str(width), str(height)] + encoded_images
tensor = tf.make_tensor_proto(content, dtype=tf.string)
if display_name is None:
display_name = name
summary_metadata = metadata.create_summary_metadata(
display_name=display_name, description=description)
summary = tf.Summary()
summary.value.add(tag='%s/image_summary' % name,
metadata=summary_metadata,
tensor=tensor)
return summary
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