mindspore.dataset.vision.AdjustBrightness

class mindspore.dataset.vision.AdjustBrightness(brightness_factor)[source]

Adjust the brightness of the input image.

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

Parameters

brightness_factor (float) – How much to adjust the brightness, must be non negative. 0.0 gives a black image, 1.0 gives the original image, while 2.0 increases the brightness by a factor of 2.

Raises

  • TypeError – If brightness_factor is not of type float.

  • ValueError – If brightness_factor is less than 0.0.

  • 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.AdjustBrightness(brightness_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.random.randint(0, 256, (20, 20, 3)) / 255.0
>>> data = data.astype(np.float32)
>>> output = vision.AdjustBrightness(2.666)(data)
>>> print(output.shape, output.dtype)
(20, 20, 3) float32
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" 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"])
>>> transforms_list = [vision.AdjustBrightness(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, 256, (20, 20, 3)) / 255.0
>>> data = data.astype(np.float32)
>>> output = vision.AdjustBrightness(2.666).device("Ascend")(data)
>>> print(output.shape, output.dtype)
(20, 20, 3) float32
Tutorial Examples: