mindspore.dataset.vision.HWC2CHW

class mindspore.dataset.vision.HWC2CHW

Transpose the input image from shape <H, W, C> to <C, H, W>. If the input image is of shape <H, W>, it will remain unchanged.

Note

This operation is executed on the CPU by default, but it is also supported to be executed on the GPU or Ascend via heterogeneous acceleration.

Raises

RuntimeError – If shape of the input image is not <H, W> or <H, W, C>.

Supported Platforms:

CPU GPU Ascend

Examples

>>> import numpy as np
>>> import mindspore.dataset as ds
>>> import mindspore.dataset.vision as vision
>>>
>>> # Use the transform in dataset pipeline mode
>>> data = np.random.randint(0, 255, size=(1, 100, 100, 3)).astype(np.uint8)
>>> numpy_slices_dataset = ds.NumpySlicesDataset(data, ["image"])
>>> transforms_list = [vision.RandomHorizontalFlip(0.75),
...                    vision.RandomCrop(64),
...                    vision.HWC2CHW()]
>>> numpy_slices_dataset = numpy_slices_dataset.map(operations=transforms_list, input_columns=["image"])
>>> for item in numpy_slices_dataset.create_dict_iterator(num_epochs=1, output_numpy=True):
...     print(item["image"].shape, item["image"].dtype)
...     break
(3, 64, 64) uint8
>>>
>>> # Use the transform in eager mode
>>> data = np.array([[0, 1, 2, 3, 4, 5], [0, 1, 2, 3, 4, 5]], dtype=np.uint8).reshape((2, 2, 3))
>>> output = vision.HWC2CHW()(data)
>>> print(output.shape, output.dtype)
(3, 2, 2) uint8

Tutorial Examples:

• Illustration of vision transforms