mindspore.dataset.vision.py_transforms.RandomColorAdjust

class mindspore.dataset.vision.py_transforms.RandomColorAdjust(brightness=(1, 1), contrast=(1, 1), saturation=(1, 1), hue=(0, 0))[source]

Perform a random brightness, contrast, saturation, and hue adjustment on the input PIL image.

Parameters

brightness (Union[float, tuple], optional) – Brightness adjustment factor (default=(1, 1)). Cannot be negative. If it is a float, the factor is uniformly chosen from the range [max(0, 1-brightness), 1+brightness]. If it is a sequence, it should be [min, max] for the range.
contrast (Union[float, tuple], optional) – Contrast adjustment factor (default=(1, 1)). Cannot be negative. If it is a float, the factor is uniformly chosen from the range [max(0, 1-contrast), 1+contrast]. If it is a sequence, it should be [min, max] for the range.
saturation (Union[float, tuple], optional) – Saturation adjustment factor (default=(1, 1)). Cannot be negative. If it is a float, the factor is uniformly chosen from the range [max(0, 1-saturation), 1+saturation]. If it is a sequence, it should be [min, max] for the range.
hue (Union[float, tuple], optional) – Hue adjustment factor (default=(0, 0)). If it is a float, the range will be [-hue, hue]. Value should be 0 <= hue <= 0.5. If it is a sequence, it should be [min, max] where -0.5 <= min <= max <= 0.5.

Examples

>>> from mindspore.dataset.transforms.py_transforms import Compose
>>> transforms_list = Compose([py_vision.Decode(),
...                            py_vision.RandomColorAdjust(0.4, 0.4, 0.4, 0.1),
...                            py_vision.ToTensor()])
>>> # apply the transform to dataset through map function
>>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list,
...                                                 input_columns="image")