tensorflow::ops::NonMaxSuppressionV5

#include <image_ops.h>

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

Summary

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.

Arguments:

• scope: A Scope object
• 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).
• max_output_size: A scalar integer tensor representing the maximum number of boxes to be selected by non max suppression.
• iou_threshold: A 0-D float tensor representing the threshold for deciding whether boxes overlap too much with respect to IOU.
• score_threshold: A 0-D float tensor representing the threshold for deciding when to remove boxes based on score.
• soft_nms_sigma: 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.

Optional attributes (see Attrs):

• pad_to_max_output_size: If true, the output selected_indices is padded to be of length max_output_size. Defaults to false.

Returns:

• Output selected_indices: A 1-D integer tensor of shape [M] representing the selected indices from the boxes tensor, where M <= max_output_size.
• Output selected_scores: 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)
• Output valid_outputs: A 0-D integer tensor representing the number of valid elements in selected_indices, with the valid elements appearing first.

Public attributes

Operation operation

::tensorflow::Output selected_indices

::tensorflow::Output selected_scores

::tensorflow::Output valid_outputs

Public functions

 NonMaxSuppressionV5(
  const ::tensorflow::Scope & scope,
  ::tensorflow::Input boxes,
  ::tensorflow::Input scores,
  ::tensorflow::Input max_output_size,
  ::tensorflow::Input iou_threshold,
  ::tensorflow::Input score_threshold,
  ::tensorflow::Input soft_nms_sigma
)

 NonMaxSuppressionV5(
  const ::tensorflow::Scope & scope,
  ::tensorflow::Input boxes,
  ::tensorflow::Input scores,
  ::tensorflow::Input max_output_size,
  ::tensorflow::Input iou_threshold,
  ::tensorflow::Input score_threshold,
  ::tensorflow::Input soft_nms_sigma,
  const NonMaxSuppressionV5::Attrs & attrs
)

Public static functions

Attrs PadToMaxOutputSize(
  bool x
)