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Greedily selects a subset of bounding boxes in descending order of score.
Aliases:
tf.image.non_max_suppression_padded(
boxes,
scores,
max_output_size,
iou_threshold=0.5,
score_threshold=float('-inf'),
pad_to_max_output_size=False,
name=None
)
Performs algorithmically equivalent operation to tf.image.non_max_suppression,
with the addition of an optional parameter which zero-pads the output to
be of size max_output_size
.
The output of this operation is a tuple containing the set of integers
indexing into the input collection of bounding boxes representing the selected
boxes and the number of valid indices in the index set. The bounding box
coordinates corresponding to the selected indices can then be obtained using
the tf.slice
and tf.gather
operations. For example:
selected_indices_padded, num_valid = tf.image.non_max_suppression_padded( boxes, scores, max_output_size, iou_threshold, score_threshold, pad_to_max_output_size=True) selected_indices = tf.slice( selected_indices_padded, tf.constant([0]), num_valid) selected_boxes = tf.gather(boxes, selected_indices)
Args:
boxes
: A 2-D floatTensor
of shape[num_boxes, 4]
.scores
: A 1-D floatTensor
of shape[num_boxes]
representing a single score corresponding to each box (each row of boxes).max_output_size
: A scalar integerTensor
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.pad_to_max_output_size
: bool. If True, size ofselected_indices
output is padded tomax_output_size
.name
: A name for the operation (optional).
Returns:
selected_indices
: A 1-D integerTensor
of shape[M]
representing the selected indices from the boxes tensor, whereM <= max_output_size
.valid_outputs
: A scalar integerTensor
denoting how many elements inselected_indices
are valid. Valid elements occur first, then padding.