mindspore.dataset.vision.ResizeWithBBox

class mindspore.dataset.vision.ResizeWithBBox(size, interpolation=Inter.LINEAR)[source]

Resize the input image to the given size and adjust bounding boxes accordingly.

Parameters

size (Union[int, Sequence[int]]) – The output size of the resized image. If size is an integer, smaller edge of the image will be resized to this value with the same image aspect ratio. If size is a sequence of length 2, it should be (height, width).
interpolation (Inter, optional) – Image interpolation method defined by Inter . Default: Inter.LINEAR.

Raises

TypeError – If size is not of type int or Sequence[int].
TypeError – If interpolation is not of type Inter .
ValueError – If size is not positive.
RuntimeError – If given tensor shape is not <H, W> or <H, W, C>.

Supported Platforms:: CPU

Examples

>>> import numpy as np
>>> import mindspore.dataset as ds
>>> import mindspore.dataset.vision as vision
>>> from mindspore.dataset.vision import Inter
>>>
>>> # Use the transform in dataset pipeline mode
>>> data = np.random.randint(0, 255, size=(100, 100, 3)).astype(np.float32)
>>> numpy_slices_dataset = ds.NumpySlicesDataset(data, ["image"])
>>> func = lambda img: (data, np.array([[0, 0, data.shape[1], data.shape[0]]]).astype(np.float32))
>>> numpy_slices_dataset = numpy_slices_dataset.map(operations=[func],
...                                                 input_columns=["image"],
...                                                 output_columns=["image", "bbox"])
>>> bbox_op = vision.ResizeWithBBox(50, Inter.NEAREST)
>>> transforms_list = [bbox_op]
>>> numpy_slices_dataset = numpy_slices_dataset.map(operations=transforms_list, input_columns=["image", "bbox"])
>>> for item in numpy_slices_dataset.create_dict_iterator(num_epochs=1, output_numpy=True):
...     print(item["image"].shape, item["image"].dtype)
...     print(item["bbox"].shape, item["bbox"].dtype)
...     break
(50, 50, 3) float32
(1, 4) float32
>>>
>>> # Use the transform in eager mode
>>> data = np.random.randint(0, 255, size=(100, 100, 3)).astype(np.float32)
>>> func = lambda img: (data, np.array([[0, 0, data.shape[1], data.shape[0]]]).astype(data.dtype))
>>> func_data, func_bboxes = func(data)
>>> output = vision.ResizeWithBBox(100)(func_data, func_bboxes)
>>> print(output[0].shape, output[0].dtype)
(100, 100, 3) float32
>>> print(output[1].shape, output[1].dtype)
(1, 4) float32

Tutorial Examples:

Illustration of vision transforms