diff options
Diffstat (limited to 'tensorflow/g3doc/api_docs/python/image.md')
-rw-r--r-- | tensorflow/g3doc/api_docs/python/image.md | 48 |
1 files changed, 48 insertions, 0 deletions
diff --git a/tensorflow/g3doc/api_docs/python/image.md b/tensorflow/g3doc/api_docs/python/image.md index 8aa6ff9f5a..5237b50ec6 100644 --- a/tensorflow/g3doc/api_docs/python/image.md +++ b/tensorflow/g3doc/api_docs/python/image.md @@ -1249,6 +1249,54 @@ Parts of the bounding box may fall outside the image. - - - +### `tf.image.non_max_suppression(boxes, scores, max_output_size, iou_threshold=None, name=None)` {#non_max_suppression} + +Greedily selects a subset of bounding boxes in descending order of score, + +pruning 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 = tf.image.non_max_suppression( + boxes, scores, max_output_size, iou_threshold) + selected_boxes = tf.gather(boxes, selected_indices) + +##### Args: + + +* <b>`boxes`</b>: A `Tensor` of type `float32`. + A 2-D float tensor of shape `[num_boxes, 4]`. +* <b>`scores`</b>: A `Tensor` of type `float32`. + A 1-D float tensor of shape `[num_boxes]` representing a single + score corresponding to each box (each row of boxes). +* <b>`max_output_size`</b>: A `Tensor` of type `int32`. + A scalar integer tensor representing the maximum number of + boxes to be selected by non max suppression. +* <b>`iou_threshold`</b>: An optional `float`. Defaults to `0.5`. + A float representing the threshold for deciding whether boxes + overlap too much with respect to IOU. +* <b>`name`</b>: A name for the operation (optional). + +##### Returns: + + A `Tensor` of type `int32`. + A 1-D integer tensor of shape `[M]` representing the selected + indices from the boxes tensor, where `M <= max_output_size`. + + +- - - + ### `tf.image.sample_distorted_bounding_box(image_size, bounding_boxes, seed=None, seed2=None, min_object_covered=None, aspect_ratio_range=None, area_range=None, max_attempts=None, use_image_if_no_bounding_boxes=None, name=None)` {#sample_distorted_bounding_box} Generate a single randomly distorted bounding box for an image. |