mindspore.ops.bounding_box_decode
- mindspore.ops.bounding_box_decode(anchor_box, deltas, max_shape, means=(0.0, 0.0, 0.0, 0.0), stds=(1.0, 1.0, 1.0, 1.0), wh_ratio_clip=0.016)[source]
Decode the bounding box locations, calculate the offset, and convert the offset into a Bbox, which is used to mark the target in the subsequent images, etc.
- Parameters
anchor_box (Tensor) – Anchor boxes. The shape of anchor_box must be \((n, 4)\).
deltas (Tensor) – Delta of boxes. Which has the same shape with anchor_box.
max_shape (tuple) – The max size limit for decoding box calculation.
means (tuple, optional) – The means of deltas calculation. Default:
(0.0, 0.0, 0.0, 0.0)
.stds (tuple, optional) – The standard deviations of deltas calculation. Default:
(1.0, 1.0, 1.0, 1.0)
.wh_ratio_clip (float, optional) – The limit of width and height ratio for decoding box calculation. Default:
0.016
.
- Returns
Tensor, decoded boxes. It has the same data type and shape as anchor_box.
- Raises
- Supported Platforms:
Ascend
GPU
CPU
Examples
>>> import mindspore >>> from mindspore import Tensor, ops >>> anchor_box = Tensor([[4, 1, 2, 1], [2, 2, 2, 3]], mindspore.float32) >>> deltas = Tensor([[3, 1, 2, 2], [1, 2, 1, 4]], mindspore.float32) >>> output = ops.bounding_box_decode(anchor_box, deltas, max_shape=(768, 1280), means=(0.0, 0.0, 0.0, 0.0), ... stds=(1.0, 1.0, 1.0, 1.0), wh_ratio_clip=0.016) >>> print(output) [[ 4.1953125 0. 0. 5.1953125] [ 2.140625 0. 3.859375 60.59375 ]]