View source on GitHub |

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

tf.image.non_max_suppression_with_scores( boxes, scores, max_output_size, iou_threshold=0.5, score_threshold=float('-inf'), soft_nms_sigma=0.0, name=None )

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 using the `tf.gather`

operation. For example:

selected_indices, selected_scores = tf.image.non_max_suppression_padded( boxes, scores, max_output_size, iou_threshold=1.0, score_threshold=0.1, soft_nms_sigma=0.5) selected_boxes = tf.gather(boxes, selected_indices)

This function generalizes the `tf.image.non_max_suppression`

op by also supporting a Soft-NMS (with Gaussian weighting) mode (c.f. Bodla et al, https://arxiv.org/abs/1704.04503) where boxes reduce the score of other overlapping boxes instead of directly causing them to be pruned. Consequently, in contrast to `tf.image.non_max_suppression`

, `tf.image.non_max_suppression_padded`

returns the new scores of each input box in the second output, `selected_scores`

.

To enable this Soft-NMS mode, set the `soft_nms_sigma`

parameter to be larger than 0. When `soft_nms_sigma`

equals 0, the behavior of `tf.image.non_max_suppression_padded`

is identical to that of `tf.image.non_max_suppression`

(except for the extra output) both in function and in running time.

Args | |
---|---|

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

`scores` | 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 scalar integer `Tensor` representing 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. |

`score_threshold` | A float representing the threshold for deciding when to remove boxes based on score. |

`soft_nms_sigma` | A scalar float representing the Soft NMS sigma parameter; See Bodla et al, https://arxiv.org/abs/1704.04503). When `soft_nms_sigma=0.0` (which is default), we fall back to standard (hard) NMS. |

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

Returns | |
---|---|

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

`selected_scores` | A 1-D float tensor of shape `[M]` representing the corresponding scores for each selected box, where `M <= max_output_size` . Scores only differ from corresponding input scores when using Soft NMS (i.e. when `soft_nms_sigma>0` ) |

© 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/image/non_max_suppression_with_scores