mindspore.dataset.vision.RandomSharpness

class mindspore.dataset.vision.RandomSharpness(degrees=(0.1, 1.9))[source]

Adjust the sharpness of the input image by a fixed or random degree. Degree of 0.0 gives a blurred image, degree of 1.0 gives the original image, and degree of 2.0 gives a sharpened image.

Parameters

degrees (Union[list, tuple], optional) – Range of random sharpness adjustment degrees, which must be non-negative. It should be in (min, max) format. If min=max, then it is a single fixed magnitude operation. Default: (0.1, 1.9).

Raises

  • TypeError – If degrees is not a list or a tuple.

  • ValueError – If degrees is negative.

  • ValueError – If degrees is in (max, min) format instead of (min, max).

Supported Platforms:

CPU

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.RandomSharpness(degrees=(0.2, 1.9))]
>>> 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
(100, 100, 3) uint8
>>>
>>> # Use the transform in eager mode
>>> data = np.random.randint(0, 255, size=(100, 100, 3)).astype(np.uint8)
>>> output = vision.RandomSharpness(degrees=(0, 0.6))(data)
>>> print(output.shape, output.dtype)
(100, 100, 3) uint8
Tutorial Examples: