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Greedily selects a subset of bounding boxes in descending order of score.
tf.image.non_max_suppression(
boxes,
scores,
max_output_size,
iou_threshold=0.5,
score_threshold=float('inf'),
name=None
)
Prunes away boxes that have high intersectionoverunion (IOU) overlap
with previously selected boxes. 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. Note that this
algorithm 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(
boxes, scores, max_output_size, iou_threshold)
selected_boxes = tf.gather(boxes, selected_indices)
Returns  

selected_indices

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