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mindspore.dataset.vision.AdjustGamma

class mindspore.dataset.vision.AdjustGamma(gamma, gain=1)

Apply gamma correction on input image. Input image is expected to be in <…, H, W, C> or <H, W> format.

$$I_{\text{out}}=255\times \text{gain}\times {\left(\frac{I_{\text{in}}}{255}\right)}^{\gamma }$$

See Gamma Correction for more details.

Parameters

• gamma (float) – Non negative real number. The output image pixel value is exponentially related to the input image pixel value. gamma larger than 1 make the shadows darker, while gamma smaller than 1 make dark regions lighter.

• gain (float, optional) – The constant multiplier. Default: 1.0.

Raises

• TypeError – If gain is not of type float.

• TypeError – If gamma is not of type float.

• ValueError – If gamma is less than 0.

• 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.AdjustGamma(gamma=10.0, gain=1.0)]
>>> 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.AdjustGamma(gamma=0.1, gain=1.0)(data)
>>> print(output.shape, output.dtype)
(2, 2, 3) uint8

Tutorial Examples:

• Illustration of vision transforms