- nvidia.dali.fn.gaussian_blur(*inputs, **kwargs)¶
Applies a Gaussian Blur to the input.
Gaussian blur is calculated by applying a convolution with a Gaussian kernel, which can be parameterized with
sigma. If only the sigma is specified, the radius of the Gaussian kernel defaults to
ceil(3 * sigma), so the kernel window size is
2 * ceil(3 * sigma) + 1.
If only the window size is provided, the sigma is calculated by using the following formula:
radius = (window_size - 1) / 2 sigma = (radius - 1) * 0.3 + 0.8
The sigma and kernel window size can be specified as one value for all data axes or a value per data axis.
When specifying the sigma or window size per axis, the axes are provided same as layouts, from outermost to innermost.
Fdimensions are not considered data axes. If channels are present, only channel-first or channel-last inputs are supported.
For example, with
HWCinput, you can provide
sigma=(1.0, 2.0)because there are two data axes, H and W.
The same input can be provided as per-sample tensors.
This operator allows sequence inputs and supports volumetric data.
- Supported backends
input (TensorList) – Input to the operator.
- Keyword Arguments:
bytes_per_sample_hint (int or list of int, optional, default = ) –
Output size hint, in bytes per sample.
If specified, the operator’s outputs residing in GPU or page-locked host memory will be preallocated to accommodate a batch of samples of this size.
nvidia.dali.types.DALIDataType, optional) –
Output data type.
Supported type: FLOAT. If not set, the input type is used.
preserve (bool, optional, default = False) – Prevents the operator from being removed from the graph even if its outputs are not used.
seed (int, optional, default = -1) –
If not provided, it will be populated based on the global seed of the pipeline.
sigma (float or list of float or TensorList of float, optional, default = [0.0]) –
Sigma value for the Gaussian Kernel.
window_size (int or list of int or TensorList of int, optional, default = ) –
The diameter of the kernel.