mindspore.dataset.vision.AdjustContrast
- class mindspore.dataset.vision.AdjustContrast(contrast_factor)[source]
Adjust the contrast of the input image.
Supports Ascend hardware acceleration and can be enabled through the .device("Ascend") method.
- Parameters
contrast_factor (float) – How much to adjust the contrast, must be non negative.
0
gives a solid gray image,1
gives the original image, while2
increases the contrast by a factor of 2.- Raises
TypeError – If contrast_factor is not of type float.
ValueError – If contrast_factor is less than 0.
RuntimeError – If shape of the input image is not <H, W, C>.
- Supported Platforms:
CPU
Ascend
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.AdjustContrast(contrast_factor=2.0)] >>> 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.array([[0, 1, 2, 3, 4, 5], [0, 1, 2, 3, 4, 5]], dtype=np.uint8).reshape((2, 2, 3)) >>> output = vision.AdjustContrast(2.0)(data) >>> print(output.shape, output.dtype) (2, 2, 3) uint8
- Tutorial Examples:
- device(device_target='CPU')[source]
Set the device for the current operator execution.
When the device is Ascend, input shape should be limited from [4, 6] to [8192, 4096].
- Parameters
device_target (str, optional) – The operator will be executed on this device. Currently supports
CPU
andAscend
. Default:CPU
.- Raises
TypeError – If device_target is not of type str.
ValueError – If device_target is not within the valid set of ['CPU', 'Ascend'].
- Supported Platforms:
CPU
Ascend
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.AdjustContrast(0).device("Ascend")] >>> 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.AdjustContrast(2.0).device("Ascend")(data) >>> print(output.shape, output.dtype) (100, 100, 3) uint8
- Tutorial Examples: