tensorflow:: ops:: StatelessSampleDistortedBoundingBox
#include <image_ops.h>
Generate a randomly distorted bounding box for an image deterministically.
Summary
Bounding box annotations are often supplied in addition to ground-truth labels in image recognition or object localization tasks. A common technique for training such a system is to randomly distort an image while preserving its content, i.e. data augmentation. This Op, given the same seed
, deterministically outputs a randomly distorted localization of an object, i.e. bounding box, given an image_size
, bounding_boxes
and a series of constraints.
The output of this Op is a single bounding box that may be used to crop the original image. The output is returned as 3 tensors: begin
, size
and bboxes
. The first 2 tensors can be fed directly into tf.slice
to crop the image. The latter may be supplied to tf.image.draw_bounding_boxes
to visualize what the bounding box looks like.
Bounding boxes are supplied and returned as [y_min, x_min, y_max, x_max]
. The bounding box coordinates are floats in [0.0, 1.0]
relative to the width and the height of the underlying image.
The output of this Op is guaranteed to be the same given the same seed
and is independent of how many times the function is called, and independent of global seed settings (e.g. tf.random.set_seed
).
Example usage:
image = np.array([[[1], [2], [3]], [[4], [5], [6]], [[7], [8], [9]]]) bbox = tf.constant( ... [0.0, 0.0, 1.0, 1.0], dtype=tf.float32, shape=[1, 1, 4]) seed = (1, 2)
Generate a single distorted bounding box.
bbox_begin, bbox_size, bbox_draw = ( ... tf.image.stateless_sample_distorted_bounding_box( ... tf.shape(image), bounding_boxes=bbox, seed=seed))Employ the bounding box to distort the image.
tf.slice(image, bbox_begin, bbox_size)Draw the bounding box in an image summary.
colors = np.array([[1.0, 0.0, 0.0], [0.0, 0.0, 1.0]]) tf.image.draw_bounding_boxes( ... tf.expand_dims(tf.cast(image, tf.float32),0), bbox_draw, colors)Note that if no bounding box information is available, setting use_image_if_no_bounding_boxes = true
will assume there is a single implicit bounding box covering the whole image. If use_image_if_no_bounding_boxes
is false and no bounding boxes are supplied, an error is raised.
Args:
- scope: A Scope object
- image_size: 1-D, containing
[height, width, channels]
. - bounding_boxes: 3-D with shape
[batch, N, 4]
describing the N bounding boxes associated with the image. - min_object_covered: The cropped area of the image must contain at least this fraction of any bounding box supplied. The value of this parameter should be non-negative. In the case of 0, the cropped area does not need to overlap any of the bounding boxes supplied.
- seed: 1-D with shape
[2]
. The seed to the random number generator. Must have dtypeint32
orint64
. (When using XLA, onlyint32
is allowed.)
Optional attributes (see Attrs
):
- aspect_ratio_range: The cropped area of the image must have an aspect ratio = width / height within this range.
- area_range: The cropped area of the image must contain a fraction of the supplied image within this range.
- max_attempts: Number of attempts at generating a cropped region of the image of the specified constraints. After
max_attempts
failures, return the entire image. - use_image_if_no_bounding_boxes: Controls behavior if no bounding boxes supplied. If true, assume an implicit bounding box covering the whole input. If false, raise an error.
Returns:
Output
begin: 1-D, containing[offset_height, offset_width, 0]
. Provide as input totf.slice
.Output
size: 1-D, containing[target_height, target_width, -1]
. Provide as input totf.slice
.Output
bboxes: 3-D with shape[1, 1, 4]
containing the distorted bounding box. Provide as input totf.image.draw_bounding_boxes
.
Constructors and Destructors |
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StatelessSampleDistortedBoundingBox(const ::tensorflow::Scope & scope, ::tensorflow::Input image_size, ::tensorflow::Input bounding_boxes, ::tensorflow::Input min_object_covered, ::tensorflow::Input seed)
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StatelessSampleDistortedBoundingBox(const ::tensorflow::Scope & scope, ::tensorflow::Input image_size, ::tensorflow::Input bounding_boxes, ::tensorflow::Input min_object_covered, ::tensorflow::Input seed, const StatelessSampleDistortedBoundingBox::Attrs & attrs)
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Public attributes |
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bboxes
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begin
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operation
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size
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Public static functions |
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AreaRange(const gtl::ArraySlice< float > & x)
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AspectRatioRange(const gtl::ArraySlice< float > & x)
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MaxAttempts(int64 x)
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UseImageIfNoBoundingBoxes(bool x)
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Structs |
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tensorflow:: |
Optional attribute setters for StatelessSampleDistortedBoundingBox. |
Public attributes
bboxes
::tensorflow::Output bboxes
begin
::tensorflow::Output begin
operation
Operation operation
size
::tensorflow::Output size
Public functions
StatelessSampleDistortedBoundingBox
StatelessSampleDistortedBoundingBox( const ::tensorflow::Scope & scope, ::tensorflow::Input image_size, ::tensorflow::Input bounding_boxes, ::tensorflow::Input min_object_covered, ::tensorflow::Input seed )
StatelessSampleDistortedBoundingBox
StatelessSampleDistortedBoundingBox( const ::tensorflow::Scope & scope, ::tensorflow::Input image_size, ::tensorflow::Input bounding_boxes, ::tensorflow::Input min_object_covered, ::tensorflow::Input seed, const StatelessSampleDistortedBoundingBox::Attrs & attrs )
Public static functions
AreaRange
Attrs AreaRange( const gtl::ArraySlice< float > & x )
AspectRatioRange
Attrs AspectRatioRange( const gtl::ArraySlice< float > & x )
MaxAttempts
Attrs MaxAttempts( int64 x )
UseImageIfNoBoundingBoxes
Attrs UseImageIfNoBoundingBoxes( bool x )