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

tf.raw_ops.NonMaxSuppression( boxes, scores, max_output_size, iou_threshold=0.5, name=None )

pruning 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 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)

Args | |
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`boxes` | A `Tensor` of type `float32` . A 2-D float tensor of shape `[num_boxes, 4]` . |

`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. |

`iou_threshold` | An optional `float` . Defaults to `0.5` . A float representing the threshold for deciding whether boxes overlap too much with respect to IOU. |

`name` | A name for the operation (optional). |

Returns | |
---|---|

A `Tensor` of type `int32` . |

© 2020 The TensorFlow Authors. All rights reserved.

Licensed under the Creative Commons Attribution License 3.0.

Code samples licensed under the Apache 2.0 License.

https://www.tensorflow.org/versions/r2.4/api_docs/python/tf/raw_ops/NonMaxSuppression