tensorflow::ops::NonMaxSuppressionWithOverlaps

#include <image_ops.h>

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

Summary

pruning away boxes that have high overlaps with previously selected boxes. Bounding boxes with score less than score_threshold are removed. N-by-n overlap values are supplied as square matrix, which allows for defining a custom overlap criterium (eg. intersection over union, intersection over area, etc.).

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_with_overlaps( overlaps, scores, max_output_size, overlap_threshold, score_threshold) selected_boxes = tf.gather(boxes, selected_indices)

Arguments:

• scope: A Scope object
• overlaps: A 2-D float tensor of shape [num_boxes, num_boxes] representing the n-by-n box overlap values.
• 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.
• overlap_threshold: A 0-D float tensor representing the threshold for deciding whether boxes overlap too.
• score_threshold: A 0-D float tensor representing the threshold for deciding when to remove boxes based on score.

Returns:

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

Public attributes

Operation operation

::tensorflow::Output selected_indices

Public functions

 NonMaxSuppressionWithOverlaps(
  const ::tensorflow::Scope & scope,
  ::tensorflow::Input overlaps,
  ::tensorflow::Input scores,
  ::tensorflow::Input max_output_size,
  ::tensorflow::Input overlap_threshold,
  ::tensorflow::Input score_threshold
)

::tensorflow::Node * node() const 

operator::tensorflow::Input() const 

operator::tensorflow::Output() const