Greedily selects a subset of bounding boxes in descending order of score.
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 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.
|
pad_to_max_output_size
|
bool. If True, size of selected_indices output is
padded to max_output_size .
|
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 .
|
valid_outputs
|
A scalar integer Tensor denoting how many elements in
selected_indices are valid. Valid elements occur first, then padding.
|