Update generated Python Op docs.

Change: 127597496

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.