mindspore.dataset.vision.RandomAutoContrast
- class mindspore.dataset.vision.RandomAutoContrast(cutoff=0.0, ignore=None, prob=0.5)[source]
Automatically adjust the contrast of the image with a given probability.
- Parameters
cutoff (float, optional) – Percent of the lightest and darkest pixels to be cut off from the histogram of the input image. The value must be in range of [0.0, 50.0]. Default:
0.0
.ignore (Union[int, sequence], optional) – The background pixel values to be ignored, each of which must be in range of [0, 255]. Default:
None
.prob (float, optional) – Probability of the image being automatically contrasted, which must be in range of [0.0, 1.0]. Default:
0.5
.
- Raises
TypeError – If cutoff is not of type float.
TypeError – If ignore is not of type integer or sequence of integer.
TypeError – If prob is not of type float.
ValueError – If cutoff is not in range [0.0, 50.0).
ValueError – If ignore is not in range [0, 255].
ValueError – If prob is not in range [0.0, 1.0].
RuntimeError – If given tensor shape is not <H, W> or <H, W, C>.
- 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.RandomAutoContrast(cutoff=0.0, ignore=None, prob=0.5)] >>> 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.RandomAutoContrast(cutoff=0.0, ignore=None, prob=1.0)(data) >>> print(output.shape, output.dtype) (100, 100, 3) uint8
- Tutorial Examples: