StatelessSampleDistortedBoundingBox

public final class StatelessSampleDistortedBoundingBox

Generate a randomly distorted bounding box for an image deterministically.

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.

Nested Classes

class StatelessSampleDistortedBoundingBox.Options Optional attributes for StatelessSampleDistortedBoundingBox  

Constants

String OP_NAME The name of this op, as known by TensorFlow core engine

Public Methods

static StatelessSampleDistortedBoundingBox.Options
areaRange(List<Float> areaRange)
static StatelessSampleDistortedBoundingBox.Options
aspectRatioRange(List<Float> aspectRatioRange)
Output<TFloat32>
bboxes()
3-D with shape `[1, 1, 4]` containing the distorted bounding box.
Output<T>
begin()
1-D, containing `[offset_height, offset_width, 0]`.
static <T extends TNumber> StatelessSampleDistortedBoundingBox<T>
create(Scope scope, Operand<T> imageSize, Operand<TFloat32> boundingBoxes, Operand<TFloat32> minObjectCovered, Operand<? extends TNumber> seed, Options... options)
Factory method to create a class wrapping a new StatelessSampleDistortedBoundingBox operation.
static StatelessSampleDistortedBoundingBox.Options
maxAttempts(Long maxAttempts)
Output<T>
size()
1-D, containing `[target_height, target_width, -1]`.
static StatelessSampleDistortedBoundingBox.Options
useImageIfNoBoundingBoxes(Boolean useImageIfNoBoundingBoxes)

Inherited Methods

Constants

public static final String OP_NAME

The name of this op, as known by TensorFlow core engine

Constant Value: "StatelessSampleDistortedBoundingBox"

Public Methods

public static StatelessSampleDistortedBoundingBox.Options areaRange (List<Float> areaRange)

Parameters
areaRange The cropped area of the image must contain a fraction of the supplied image within this range.

public static StatelessSampleDistortedBoundingBox.Options aspectRatioRange (List<Float> aspectRatioRange)

Parameters
aspectRatioRange The cropped area of the image must have an aspect ratio = width / height within this range.

public Output<TFloat32> bboxes ()

3-D with shape `[1, 1, 4]` containing the distorted bounding box. Provide as input to tf.image.draw_bounding_boxes.

public Output<T> begin ()

1-D, containing `[offset_height, offset_width, 0]`. Provide as input to tf.slice.

public static StatelessSampleDistortedBoundingBox<T> create (Scope scope, Operand<T> imageSize, Operand<TFloat32> boundingBoxes, Operand<TFloat32> minObjectCovered, Operand<? extends TNumber> seed, Options... options)

Factory method to create a class wrapping a new StatelessSampleDistortedBoundingBox operation.

Parameters
scope current scope
imageSize 1-D, containing `[height, width, channels]`.
boundingBoxes 3-D with shape `[batch, N, 4]` describing the N bounding boxes associated with the image.
minObjectCovered 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 dtype `int32` or `int64`. (When using XLA, only `int32` is allowed.)
options carries optional attributes values
Returns
  • a new instance of StatelessSampleDistortedBoundingBox

public static StatelessSampleDistortedBoundingBox.Options maxAttempts (Long maxAttempts)

Parameters
maxAttempts Number of attempts at generating a cropped region of the image of the specified constraints. After `max_attempts` failures, return the entire image.

public Output<T> size ()

1-D, containing `[target_height, target_width, -1]`. Provide as input to tf.slice.

public static StatelessSampleDistortedBoundingBox.Options useImageIfNoBoundingBoxes (Boolean useImageIfNoBoundingBoxes)

Parameters
useImageIfNoBoundingBoxes Controls behavior if no bounding boxes supplied. If true, assume an implicit bounding box covering the whole input. If false, raise an error.