mindspore.dataset.vision.AdjustSharpness
- class mindspore.dataset.vision.AdjustSharpness(sharpness_factor)[source]
Adjust the sharpness of the input image.
Supports Ascend hardware acceleration and can be enabled through the .device("Ascend") method.
- Parameters
sharpness_factor (float) – How much to adjust the sharpness, must be non negative.
0
gives a blurred image,1
gives the original image while2
increases the sharpness by a factor of 2.- Raises
TypeError – If sharpness_factor is not of type float.
ValueError – If sharpness_factor is less than 0.
RuntimeError – If shape of the input image is not <H, W> or <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 >>> # create a dataset that reads all files in dataset_dir with 8 threads >>> data = np.random.randint(0, 255, size=(1, 100, 100, 3)).astype(np.uint8) >>> numpy_slices_dataset = ds.NumpySlicesDataset(data, ["image"]) >>> transforms_list = [vision.AdjustSharpness(sharpness_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((3, 4)) >>> output = vision.AdjustSharpness(sharpness_factor=0)(data) >>> print(output.shape, output.dtype) (3, 4) uint8
- Tutorial Examples:
- device(device_target='CPU')[source]
Set the device for the current operator execution.
When the device is Ascend, input type supports uint8 or float32 , input channel supports 1 and 3. The input data has a height limit of [4, 8192] and a width limit of [6, 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 >>> # create a dataset that reads all files in dataset_dir with 8 threads >>> data = np.random.randint(0, 255, size=(1, 100, 100, 3)).astype(np.uint8) >>> numpy_slices_dataset = ds.NumpySlicesDataset(data, ["image"]) >>> transforms_list = [vision.AdjustSharpness(sharpness_factor=2.0).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.AdjustSharpness(sharpness_factor=0).device("Ascend")(data) >>> print(output.shape, output.dtype) (100, 100, 3) uint8
- Tutorial Examples: