tf.raw_ops.NonMaxSuppressionWithOverlaps

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

tf.raw_ops.NonMaxSuppressionWithOverlaps(
    overlaps,
    scores,
    max_output_size,
    overlap_threshold,
    score_threshold,
    name=None
)

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)

overlaps A Tensor of type float32. A 2-D float tensor of shape [num_boxes, num_boxes] representing the n-by-n box overlap values.
scores A Tensor of type float32. 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 Tensor of type int32. A scalar integer tensor representing the maximum number of boxes to be selected by non max suppression.
overlap_threshold A Tensor of type float32. A 0-D float tensor representing the threshold for deciding whether boxes overlap too.
score_threshold A Tensor of type float32. A 0-D float tensor representing the threshold for deciding when to remove boxes based on score.
name A name for the operation (optional).

A Tensor of type int32.