@Operator(group="image") public final class NonMaxSuppression extends PrimitiveOp
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)
Modifier and Type | Class and Description |
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static class |
NonMaxSuppression.Options
Optional attributes for
NonMaxSuppression |
operation
Modifier and Type | Method and Description |
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static <T extends Number,U extends Number> |
create(Scope scope,
Operand<T> boxes,
Operand<T> scores,
Operand<Integer> maxOutputSize,
Operand<U> iouThreshold,
Operand<U> scoreThreshold,
NonMaxSuppression.Options... options)
Factory method to create a class wrapping a new NonMaxSuppression operation.
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static NonMaxSuppression.Options |
padToMaxOutputSize(Boolean padToMaxOutputSize) |
Output<Integer> |
selectedIndices()
A 1-D integer tensor of shape `[M]` representing the selected
indices from the boxes tensor, where `M <= max_output_size`.
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Output<Integer> |
validOutputs()
A 0-D integer tensor representing the number of valid elements in
`selected_indices`, with the valid elements appearing first.
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equals, hashCode, op, toString
public static <T extends Number,U extends Number> NonMaxSuppression create(Scope scope, Operand<T> boxes, Operand<T> scores, Operand<Integer> maxOutputSize, Operand<U> iouThreshold, Operand<U> scoreThreshold, NonMaxSuppression.Options... options)
scope
- current scopeboxes
- 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.options
- carries optional attributes valuespublic static NonMaxSuppression.Options padToMaxOutputSize(Boolean padToMaxOutputSize)
padToMaxOutputSize
- If true, the output `selected_indices` is padded to be of length
`max_output_size`. Defaults to false.public Output<Integer> selectedIndices()
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