mindspore.dataset.vision.GaussianBlur

class mindspore.dataset.vision.GaussianBlur(kernel_size, sigma=None)

Blur input image with the specified Gaussian kernel.

Supports Ascend hardware acceleration and can be enabled through the .device(“Ascend”) method.

Parameters

• kernel_size (Union[int, Sequence[int, int]]) – The size of the Gaussian kernel. Must be positive and odd. If the input type is int, the value will be used as both the width and height of the Gaussian kernel. If the input type is Sequence[int, int], the two elements will be used as the width and height of the Gaussian kernel respectively.

• sigma (Union[float, Sequence[float, float]], optional) – The standard deviation of the Gaussian kernel. Must be positive. If the input type is float, the value will be used as the standard deviation of both the width and height of the Gaussian kernel. If the input type is Sequence[float, float], the two elements will be used as the standard deviation of the width and height of the Gaussian kernel respectively. Default: None , the standard deviation of the Gaussian kernel will be obtained by the formula \(((kernel\_size - 1) * 0.5 - 1) * 0.3 + 0.8\) .

Raises

• TypeError – If kernel_size is not of type int or Sequence[int].

• TypeError – If sigma is not of type float or Sequence[float].

• ValueError – If kernel_size is not positive and odd.

• ValueError – If sigma is not positive.

• 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.GaussianBlur(3, 3)]
>>> 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.GaussianBlur(3, 3)(data)
>>> print(output.shape, output.dtype)
(2, 2, 3) uint8

Tutorial Examples:

• Illustration of vision transforms

device(device_target='CPU')

Set the device for the current operator execution.

• When the device is Ascend, the parameter kernel_size only supports values 1, 3, and 5. 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 and Ascend . 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"])
>>> blur_op = vision.GaussianBlur(3, 3).device("Ascend")
>>> transforms_list = [blur_op]
>>> 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.GaussianBlur(3, 3).device("Ascend")(data)
>>> print(output.shape, output.dtype)
(100, 100, 3) uint8

Tutorial Examples:

• Illustration of vision transforms