(scores, bboxes, nms_threshold=0.5, keep_top_k=200, scope=None)
Apply non-maximum selection to bounding boxes. In comparison to TF
    implementation, use classes information for matching.
    Should only be used on single-entries. Use batch version otherwise.

    Args:
      scores: N Tensor containing float scores.
      bboxes: N x 4 Tensor containing boxes coordinates.
      nms_threshold: Matching threshold in NMS algorithm;
      keep_top_k: Number of total object to keep after NMS.
    Return:
      classes, scores, bboxes Tensors, sorted by score.
        Padded with zero if necessary.
Apply non-maximum selection to bounding boxes. In comparison to TF
    implementation, use classes information for matching.
    Should only be used on single-entries. Use batch version otherwise.
Apply non - maximum selection to bounding boxes . In comparison to TF implementation use classes information for matching . Should only be used on single - entries . Use batch version otherwise .
def bboxes_nms(scores, bboxes, nms_threshold=0.5, keep_top_k=200, scope=None):
    """Apply non-maximum selection to bounding boxes. In comparison to TF
    implementation, use classes information for matching.
    Should only be used on single-entries. Use batch version otherwise.

    Args:
      scores: N Tensor containing float scores.
      bboxes: N x 4 Tensor containing boxes coordinates.
      nms_threshold: Matching threshold in NMS algorithm;
      keep_top_k: Number of total object to keep after NMS.
    Return:
      classes, scores, bboxes Tensors, sorted by score.
        Padded with zero if necessary.
    """
    with tf.name_scope(scope, 'bboxes_nms_single', [scores, bboxes]):
        # Apply NMS algorithm.
        idxes = tf.image.non_max_suppression(bboxes, scores,
                                             keep_top_k, nms_threshold)
        scores = tf.gather(scores, idxes)
        bboxes = tf.gather(bboxes, idxes)
        # Pad results.
        scores = pad_axis(scores, 0, keep_top_k, axis=0)
        bboxes = pad_axis(bboxes, 0, keep_top_k, axis=0)
        return scores, bboxes
def bboxes_nms ( scores , bboxes , nms_threshold = 0.5 , keep_top_k = 200 , scope = None ) : with tf . name_scope ( scope , 'bboxes_nms_single' , [ scores , bboxes ] ) : # Apply NMS algorithm. idxes = tf . image . non_max_suppression ( bboxes , scores , keep_top_k , nms_threshold ) scores = tf . gather ( scores , idxes ) bboxes = tf . gather ( bboxes , idxes ) # Pad results. scores = pad_axis ( scores , 0 , keep_top_k , axis = 0 ) bboxes = pad_axis ( bboxes , 0 , keep_top_k , axis = 0 ) return scores , bboxes