mindspore.ops.check_valid

mindspore.ops.check_valid(bboxes, img_metas)[source]

Checks whether the bounding box is in the image.

bboxes contain several sets of bounding boxes, each represented by two abscissa points \((x0, x1)\) and two ordinate points \((y0, y1)\) . img_metas provides information about the original image, including three parameters \((height, width, ratio)\) , which specify the valid boundary of the image.

When the following conditions are met:

\(x0 >= 0\)

\(y0 >= 0\)

\(x1 <= width * ratio - 1\)

\(y1 <= height * ratio - 1\)

the bounding box is considered to be within the image.

Warning

The bounding box specified by bboxes and the image information specified by img_metas need to be valid, i.e.: \(x0 <= x1\) , \(y0 <= y1\) , and \((height, width, ratio)\) are all positive.
This interface is deprecated and will be removed after version 2.9.0.

Parameters:

bboxes (Tensor) – Bounding boxes tensor with shape \((N, 4)\) . \(N\) indicates the number of bounding boxes, the value 4 indicates four coordinate points \((x0, y0, x1, y1)\) . Data type must be float16 or float32.
img_metas (Tensor) – Raw image size information with the format of \((height, width, ratio)\) , specifying the valid boundary \((height * ratio - 1, width * ratio - 1)\) . Data type must be float16 or float32.

Returns:

Tensor, with shape of \((N,)\) and dtype of bool, specifying whether the bounding boxes are in the image. True indicates valid, while False indicates invalid.

Raises:

TypeError – If bboxes or img_metas is not a Tensor.
TypeError – If dtype of bboxes or img_metas is neither float16 nor float32.

Supported Platforms:: Ascend GPU CPU

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> bboxes = Tensor(np.linspace(0, 6, 12).reshape(3, 4), mindspore.float32)
>>> img_metas = Tensor(np.array([2, 1, 3]), mindspore.float32)
>>> output = ops.check_valid(bboxes, img_metas)
>>> print(output)
[ True False False]