mindspore.dataset.vision.Invert
- class mindspore.dataset.vision.Invert[source]
Invert the colors of the input RGB image.
For each pixel in the image, if the original pixel value is pixel, the inverted pixel value will be 255 - pixel.
Supports Ascend hardware acceleration and can be enabled through the .device("Ascend") method.
- Raises
RuntimeError – If the input image is not in shape of <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.Invert()] >>> 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.Invert()(data) >>> print(output.shape, output.dtype) (2, 2, 3) uint8
- Tutorial Examples:
- device(device_target='CPU')[source]
Set the device for the current operator execution.
When the device is CPU, input type only support uint8 , input channel support 1/2/3.
When the device is Ascend, input type supports uint8/float32, input channel supports 1/3. 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
andAscend
. 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 >>> from mindspore.dataset.vision import Inter >>> >>> # 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"]) >>> invert_op = vision.Invert() >>> transforms_list = [invert_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.Invert().device("Ascend")(data) >>> print(output.shape, output.dtype) (100, 100, 3) uint8
- Tutorial Examples: