mindspore.dataset.vision.RandomSolarize

class mindspore.dataset.vision.RandomSolarize(threshold=(0, 255))[source]

Randomly selects a subrange within the specified threshold range and sets the pixel value within the subrange to (255 - pixel).

Parameters

threshold (tuple, optional) – Range of random solarize threshold. Default: (0, 255). Threshold values should always be in (min, max) format, where min and max are integers in the range [0, 255], and min <= max. The pixel values belonging to the [min, max] range will be inverted. If min=max, then invert all pixel values greater than or equal min(max).

Raises

TypeError – If threshold is not of type tuple.
ValueError – If threshold is not in range of [0, 255].

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.RandomSolarize(threshold=(10,100))]
>>> 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.RandomSolarize(threshold=(1, 10))(data)
>>> print(output.shape, output.dtype)
(100, 100, 3) uint8

Tutorial Examples:

Illustration of vision transforms