mindspore.dataset.vision.AdjustSharpness

class mindspore.dataset.vision.AdjustSharpness(sharpness_factor)[源代码]

调整输入图像的锐度。

参数：

  • sharpness_factor (float) - 锐度调节因子，需为非负数。输入 0 值将得到模糊图像， 1 值将得到原始图像， 2 值将调整图像锐度为原来的2倍。

异常：

  • TypeError - 如果 sharpness_factor 不是float类型。

  • ValueError - 如果 sharpness_factor 小于0。

  • RuntimeError - 如果输入图像的形状不是<H, W, C>或<H, W>。

支持平台：

CPU

样例：

>>> 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
教程样例：