mindspore.dataset.vision.AdjustGamma
- class mindspore.dataset.vision.AdjustGamma(gamma, gain=1)[source]
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
- 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: