|View source on GitHub|
Greedily selects a subset of bounding boxes in descending order of score.
Compat aliases for migration
See Migration guide for more details.
tf.image.non_max_suppression_overlaps( overlaps, scores, max_output_size, overlap_threshold=0.5, score_threshold=float('-inf'), name=None )
Prunes away boxes that have high overlap with previously selected boxes. N-by-n overlap values are supplied as square matrix. 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_overlaps( overlaps, scores, max_output_size, iou_threshold) selected_boxes = tf.gather(boxes, selected_indices)
| || A 2-D float |
| || A 1-D float |
| || A scalar integer |
| ||A float representing the threshold for deciding whether boxes overlap too much with respect to the provided overlap values.|
| ||A float representing the threshold for deciding when to remove boxes based on score.|
| ||A name for the operation (optional).|
| || A 1-D integer |
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Code samples licensed under the Apache 2.0 License.