mindspore.ops.bounding_box_encode
- mindspore.ops.bounding_box_encode(anchor_box, groundtruth_box, means=(0.0, 0.0, 0.0, 0.0), stds=(1.0, 1.0, 1.0, 1.0))[source]
Encode the bounding box locations, calculate the offset between the predicted bounding boxes and the real bounding boxes, and the offset will be used as a variable for the loss.
- Parameters
anchor_box (Tensor) – Anchor boxes. The shape of anchor_box must be \((n, 4)\).
groundtruth_box (Tensor) – Ground truth boxes. Which has the same shape with anchor_box.
means (tuple, optional) – Means for encoding bounding boxes 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)
.
- Returns
Tensor, encoded bounding boxes. It has the same data type and shape as input anchor_box.
- Raises
- Supported Platforms:
Ascend
GPU
CPU
Examples
>>> import mindspore >>> from mindspore import Tensor, ops >>> anchor_box = Tensor([[2, 2, 2, 3], [2, 2, 2, 3]], mindspore.float32) >>> groundtruth_box = Tensor([[1, 2, 1, 4], [1, 2, 1, 4]], mindspore.float32) >>> output = ops.bounding_box_encode(anchor_box, groundtruth_box, means=(0.0, 0.0, 0.0, 0.0), ... stds=(1.0, 1.0, 1.0, 1.0)) >>> print(output) [[ -1. 0.25 0. 0.40551758] [ -1. 0.25 0. 0.40551758]]