NonMaxSuppression

public final class NonMaxSuppression

Greedily selects a subset of bounding boxes in descending order of score,

pruning away boxes that have high intersection-over-union (IOU) overlap with previously selected boxes. Bounding boxes with score less than `score_threshold` are removed. Bounding boxes are supplied as [y1, x1, y2, x2], where (y1, x1) and (y2, x2) are the coordinates of any diagonal pair of box corners and the coordinates can be provided as normalized (i.e., lying in the interval [0, 1]) or absolute. Note that this algorithm is agnostic to where the origin is in the coordinate system and more generally is invariant to orthogonal transformations and translations of the coordinate system; thus translating or reflections of the coordinate system result in the same boxes being selected by the algorithm. The output of this operation is a set of integers indexing into the input collection of bounding boxes representing the selected boxes. The bounding box coordinates corresponding to the selected indices can then be obtained using the `tf.gather operation`. For example: selected_indices = tf.image.non_max_suppression_v2( boxes, scores, max_output_size, iou_threshold, score_threshold) selected_boxes = tf.gather(boxes, selected_indices) This op also supports a Soft-NMS (with Gaussian weighting) mode (c.f. Bodla et al, https://arxiv.org/abs/1704.04503) where boxes reduce the score of other overlapping boxes instead of directly causing them to be pruned. To enable this Soft-NMS mode, set the `soft_nms_sigma` parameter to be larger than 0.

Nested Classes

class

NonMaxSuppression.Options

Optional attributes for NonMaxSuppression

Constants

String

OP_NAME

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

Public Methods

static <T extends TNumber> NonMaxSuppression<T>	create(Scope scope, Operand<T> boxes, Operand<T> scores, Operand<TInt32> maxOutputSize, Operand<T> iouThreshold, Operand<T> scoreThreshold, Operand<T> softNmsSigma, Options... options) Factory method to create a class wrapping a new NonMaxSuppression operation.
static NonMaxSuppression.Options	padToMaxOutputSize(Boolean padToMaxOutputSize)
Output<TInt32>	selectedIndices() A 1-D integer tensor of shape `[M]` representing the selected indices from the boxes tensor, where `M <= max_output_size`.
Output<T>	selectedScores() A 1-D float tensor of shape `[M]` representing the corresponding scores for each selected box, where `M <= max_output_size`.
Output<TInt32>	validOutputs() A 0-D integer tensor representing the number of valid elements in `selected_indices`, with the valid elements appearing first.

Inherited Methods

From class org.tensorflow.op.RawOp

final boolean	equals(Object obj)
final int	hashCode()
Operation	op() Return this unit of computation as a single Operation.
final String	toString()

From class java.lang.Object

boolean	equals(Object arg0)
final Class<?>	getClass()
int	hashCode()
final void	notify()
final void	notifyAll()
String	toString()
final void	wait(long arg0, int arg1)
final void	wait(long arg0)
final void	wait()

From interface org.tensorflow.op.Op

abstract ExecutionEnvironment	env() Return the execution environment this op was created in.
abstract Operation	op() Return this unit of computation as a single Operation.

Constants

public static final String OP_NAME

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

Constant Value: "NonMaxSuppressionV5"

Public Methods

public static NonMaxSuppression<T> create (Scope scope, Operand<T> boxes, Operand<T> scores, Operand<TInt32> maxOutputSize, Operand<T> iouThreshold, Operand<T> scoreThreshold, Operand<T> softNmsSigma, Options... options)

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

Parameters

scope	current scope
boxes	A 2-D float tensor of shape `[num_boxes, 4]`.
scores	A 1-D float tensor of shape `[num_boxes]` representing a single score corresponding to each box (each row of boxes).
maxOutputSize	A scalar integer tensor representing the maximum number of boxes to be selected by non max suppression.
iouThreshold	A 0-D float tensor representing the threshold for deciding whether boxes overlap too much with respect to IOU.
scoreThreshold	A 0-D float tensor representing the threshold for deciding when to remove boxes based on score.
softNmsSigma	A 0-D float tensor representing the sigma parameter for Soft NMS; see Bodla et al (c.f. https://arxiv.org/abs/1704.04503). When `soft_nms_sigma=0.0` (which is default), we fall back to standard (hard) NMS.
options	carries optional attributes values

Returns

• a new instance of NonMaxSuppression

public static NonMaxSuppression.Options padToMaxOutputSize (Boolean padToMaxOutputSize)

Parameters

padToMaxOutputSize	If true, the output `selected_indices` is padded to be of length `max_output_size`. Defaults to false.

public Output<TInt32> selectedIndices ()

A 1-D integer tensor of shape `[M]` representing the selected indices from the boxes tensor, where `M <= max_output_size`.

public Output<T> selectedScores ()

A 1-D float tensor of shape `[M]` representing the corresponding scores for each selected box, where `M <= max_output_size`. Scores only differ from corresponding input scores when using Soft NMS (i.e. when `soft_nms_sigma>0`)

public Output<TInt32> validOutputs ()

A 0-D integer tensor representing the number of valid elements in `selected_indices`, with the valid elements appearing first.