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32f7033c
编写于
2月 25, 2019
作者:
A
A. Unique TensorFlower
提交者:
TensorFlower Gardener
2月 25, 2019
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Fix tf.image.non_max_suppression docstrings.
PiperOrigin-RevId: 235541426
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e866995a
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tensorflow/python/ops/image_ops_impl.py
tensorflow/python/ops/image_ops_impl.py
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tensorflow/python/ops/image_ops_impl.py
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@@ -2244,9 +2244,9 @@ def non_max_suppression(boxes,
Prunes 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
`[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
(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
...
...
@@ -2254,10 +2254,12 @@ def non_max_suppression(boxes,
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:
using the `tf.gather` operation. For example:
```python
selected_indices = tf.image.non_max_suppression(
boxes, scores, max_output_size, iou_threshold)
selected_boxes = tf.gather(boxes, selected_indices)
```
Args:
boxes: A 2-D float `Tensor` of shape `[num_boxes, 4]`.
...
...
@@ -2301,12 +2303,14 @@ def non_max_suppression_padded(boxes,
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:
```python
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]`.
...
...
@@ -2355,10 +2359,12 @@ def non_max_suppression_with_overlaps(overlaps,
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:
using the `tf.gather` operation. For example:
```python
selected_indices = tf.image.non_max_suppression_overlaps(
overlaps, scores, max_output_size, iou_threshold)
selected_boxes = tf.gather(boxes, selected_indices)
```
Args:
overlaps: A 2-D float `Tensor` of shape `[num_boxes, num_boxes]`.
...
...
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