tf.image.combined_non_max_suppression

View source on GitHub

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

tf.image.combined_non_max_suppression(
    boxes, scores, max_output_size_per_class, max_total_size, iou_threshold=0.5,
    score_threshold=float('-inf'), pad_per_class=False, clip_boxes=True, name=None
)

This operation performs non_max_suppression on the inputs per batch, across all classes. 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. Also 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 the final boxes, scores and classes tensor returned after performing non_max_suppression.

Args:

• boxes: A 4-D float Tensor of shape [batch_size, num_boxes, q, 4]. If q is 1 then same boxes are used for all classes otherwise, if q is equal to number of classes, class-specific boxes are used.
• scores: A 3-D float Tensor of shape [batch_size, num_boxes, num_classes] representing a single score corresponding to each box (each row of boxes).
• max_output_size_per_class: A scalar integer Tensor representing the maximum number of boxes to be selected by non max suppression per class
• max_total_size: A scalar representing maximum number of boxes retained over all classes.
• 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.
• pad_per_class: If false, the output nmsed boxes, scores and classes are padded/clipped to max_total_size. If true, the output nmsed boxes, scores and classes are padded to be of length max_size_per_class*num_classes, unless it exceeds max_total_size in which case it is clipped to max_total_size. Defaults to false.
• clip_boxes: If true, the coordinates of output nmsed boxes will be clipped to [0, 1]. If false, output the box coordinates as it is. Defaults to true.
• name: A name for the operation (optional).

Returns:

'nmsed_boxes': A [batch_size, max_detections, 4] float32 tensor containing the non-max suppressed boxes. 'nmsed_scores': A [batch_size, max_detections] float32 tensor containing the scores for the boxes. 'nmsed_classes': A [batch_size, max_detections] float32 tensor containing the class for boxes. 'valid_detections': A [batch_size] int32 tensor indicating the number of valid detections per batch item. Only the top valid_detections[i] entries in nms_boxes[i], nms_scores[i] and nms_class[i] are valid. The rest of the entries are zero paddings.