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mindspore.ops.BoundingBoxDecode

class mindspore.ops.BoundingBoxDecode(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)

Decodes bounding boxes locations.

The function of the operator is to calculate the offset, and this operator converts the offset into a Bbox, which is used to mark the target in the subsequent images, etc.

Parameters

• max_shape (tuple) – The max size limit for decoding box calculation.

• means (tuple) – The means of deltas calculation. Default: (0.0, 0.0, 0.0, 0.0).

• stds (tuple) – The standard deviations of deltas calculation. Default: (1.0, 1.0, 1.0, 1.0).

• wh_ratio_clip (float) – The limit of width and height ratio for decoding box calculation. Default: 0.016.

Inputs:

• 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.

Outputs:

Tensor, decoded boxes. It has the same data type and shape as anchor_box.

Raises

• TypeError – If means, stds or max_shape is not a tuple.

• TypeError – If wh_ratio_clip is not a float.

• TypeError – If anchor_box or deltas is not a Tensor.

Supported Platforms:

Ascend GPU CPU

Examples

>>> 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)
>>> boundingbox_decode = ops.BoundingBoxDecode(means=(0.0, 0.0, 0.0, 0.0), stds=(1.0, 1.0, 1.0, 1.0),
...                                          max_shape=(768, 1280), wh_ratio_clip=0.016)
>>> output = boundingbox_decode(anchor_box, deltas)
>>> print(output)
[[ 4.1953125  0.         0.         5.1953125]
 [ 2.140625   0.         3.859375  60.59375  ]]