mindspore.dataset.vision.c_transforms.RandomColorAdjust

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

Randomly adjust the brightness, contrast, saturation, and hue of the input image.

Parameters
  • brightness (Union[float, list, 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, list, 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, list, 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, list, 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.

Raises
  • TypeError – If brightness is not of type float or sequence of float.

  • TypeError – If contrast is not of type float or sequence of float.

  • TypeError – If saturation is not of type float or sequence of float.

  • TypeError – If hue is not of type float or sequence of float.

  • ValueError – If brightness is negative.

  • ValueError – If contrast is negative.

  • ValueError – If saturation is negative.

  • ValueError – If hue is not in range [-0.5, 0.5].

  • RuntimeError – If given tensor shape is not <H, W, C>.

Supported Platforms:

CPU

Examples

>>> decode_op = c_vision.Decode()
>>> transform_op = c_vision.RandomColorAdjust(brightness=(0.5, 1),
...                                           contrast=(0.4, 1),
...                                           saturation=(0.3, 1))
>>> transforms_list = [decode_op, transform_op]
>>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list,
...                                                 input_columns=["image"])