tf.image.non_max_suppression( boxes, scores, max_output_size, iou_threshold=0.5, name=None )
See the guide: Images > Working with Bounding Boxes
Greedily selects a subset of bounding boxes in descending order of score.
Prunes away boxes that have high intersection-over-union (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
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)
boxes: A 2-D float
scores: A 1-D float
[num_boxes]representing a single score corresponding to each box (each row of boxes).
max_output_size: A scalar integer
Tensorrepresenting the maximum number of boxes to be selected by non max suppression.
iou_threshold: A float representing the threshold for deciding whether boxes overlap too much with respect to IOU.
name: A name for the operation (optional).
selected_indices: A 1-D integer
[M]representing the selected indices from the boxes tensor, where
M <= max_output_size.