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# Copyright 2018 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, V2 versions.
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, ...]`.
"""
from tensorboard.compat import tf2 as tf
from tensorboard.plugins.image import metadata
from tensorboard.util import lazy_tensor_creator
def image(name, data, step=None, max_outputs=3, description=None):
"""Write an image summary.
See also `tf.summary.scalar`, `tf.summary.SummaryWriter`.
Writes a collection of images to the current default summary writer. Data
appears in TensorBoard's 'Images' dashboard. Like `tf.summary.scalar` points,
each collection of images is associated with a `step` and a `name`. All the
image collections with the same `name` constitute a time series of image
collections.
This example writes 2 random grayscale images:
```python
w = tf.summary.create_file_writer('test/logs')
with w.as_default():
image1 = tf.random.uniform(shape=[8, 8, 1])
image2 = tf.random.uniform(shape=[8, 8, 1])
tf.summary.image("grayscale_noise", [image1, image2], step=0)
```
To avoid clipping, data should be converted to one of the following:
- floating point values in the range [0,1], or
- uint8 values in the range [0,255]
```python
# Convert the original dtype=int32 `Tensor` into `dtype=float64`.
rgb_image_float = tf.constant([
[[1000, 0, 0], [0, 500, 1000]],
]) / 1000
tf.summary.image("picture", [rgb_image_float], step=0)
# Convert original dtype=uint8 `Tensor` into proper range.
rgb_image_uint8 = tf.constant([
[[1, 1, 0], [0, 0, 1]],
], dtype=tf.uint8) * 255
tf.summary.image("picture", [rgb_image_uint8], step=1)
```
Arguments:
name: A name for this summary. The summary tag used for TensorBoard will
be this name prefixed by any active name scopes.
data: 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, 2, 3, or 4 (grayscale, grayscale with alpha, RGB, RGBA).
Any of the dimensions may be statically unknown (i.e., `None`).
Floating point data will be clipped to the range [0,1]. Other data types
will be clipped into an allowed range for safe casting to uint8, using
`tf.image.convert_image_dtype`.
step: Explicit `int64`-castable monotonic step value for this summary. If
omitted, this defaults to `tf.summary.experimental.get_step()`, which must
not be 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.
description: Optional long-form description for this summary, as a
constant `str`. Markdown is supported. Defaults to empty.
Returns:
True on success, or false if no summary was emitted because no default
summary writer was available.
Raises:
ValueError: if a default writer exists, but no step was provided and
`tf.summary.experimental.get_step()` is None.
"""
summary_metadata = metadata.create_summary_metadata(
display_name=None, description=description
)
# TODO(https://github.com/tensorflow/tensorboard/issues/2109): remove fallback
summary_scope = (
getattr(tf.summary.experimental, "summary_scope", None)
or tf.summary.summary_scope
)
with summary_scope(
name, "image_summary", values=[data, max_outputs, step]
) as (tag, _):
# Defer image encoding preprocessing by passing it as a callable to write(),
# wrapped in a LazyTensorCreator for backwards compatibility, so that we
# only do this work when summaries are actually written.
@lazy_tensor_creator.LazyTensorCreator
def lazy_tensor():
tf.debugging.assert_rank(data, 4)
tf.debugging.assert_non_negative(max_outputs)
images = tf.image.convert_image_dtype(data, tf.uint8, saturate=True)
limited_images = images[:max_outputs]
encoded_images = tf.image.encode_png(limited_images)
image_shape = tf.shape(input=images)
dimensions = tf.stack(
[
tf.as_string(image_shape[2], name="width"),
tf.as_string(image_shape[1], name="height"),
],
name="dimensions",
)
return tf.concat([dimensions, encoded_images], axis=0)
# To ensure that image encoding logic is only executed when summaries
# are written, we pass callable to `tensor` parameter.
return tf.summary.write(
tag=tag, tensor=lazy_tensor, step=step, metadata=summary_metadata
)
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