The following functions construct Tensors like Gaussian or Laplacian kernels, or images like Lenna and Fabio.
Returns the classic Lenna.jpg image as a 3 x 512 x 512 Tensor.
Returns the fabio.jpg image as a 257 x 271 Tensor.
Returns a 2D Gaussian
kernel of size height x width. When used as a Gaussian smoothing operator in a 2D
convolution, this kernel is used to blur images and remove detail and noise
(ref.: Gaussian Smoothing).
Optional arguments [...] expand to
width, height, sigma_horz, sigma_vert, mean_horz, mean_vert and tensor.
The default value of height and width is size, where the latter
has a default value of 3. The amplitude of the Gaussian (its maximum value)
is amplitude. The default is 1.
When normalize=true, the kernel is normalized to have a sum of 1.
This overrides the amplitude argument. The default is false.
The default value of the horizontal and vertical standard deviation
sigma_horz and sigma_vert of the Gaussian kernel is sigma, where
the latter has a default value of 0.25. The default values for the
corresponding means mean_horz and mean_vert are 0.5. Both the
standard deviations and means are relative to kernels of unit width and height
where the top-left corner is the origin. In other works, a mean of 0.5 is
the center of the kernel size, while a standard deviation of 0.25 is a quarter
of it. When tensor is provided (a 2D Tensor), the height, width and size are ignored.
It is used to store the returned gaussian kernel.
Note that arguments can also be specified as key-value arguments (in a table).
Returns a 1D Gaussian kernel of size size, mean mean and standard
deviation sigma.
Respectively, these arguments have default values of 3, 0.25 and 0.5.
The amplitude of the Gaussian (its maximum value)
is amplitude. The default is 1.
When normalize=true, the kernel is normalized to have a sum of 1.
This overrides the amplitude argument. The default is false. Both the
standard deviation and mean are relative to a kernel of unit size.
In other works, a mean of 0.5 is the center of the kernel size,
while a standard deviation of 0.25 is a quarter of it.
When tensor is provided (a 1D Tensor), the size is ignored.
It is used to store the returned gaussian kernel.
Note that arguments can also be specified as key-value arguments (in a table).
Returns a 2D Laplacian
kernel of size height x width.
When used in a 2D convolution, the Laplacian of an image highlights
regions of rapid intensity change and is therefore often used for edge detection
(ref.: Laplacian/Laplacian of Gaussian).
Optional arguments [...] expand to
width, height, sigma_horz, sigma_vert, mean_horz, mean_vert.
The default value of height and width is size, where the latter
has a default value of 3. The amplitude of the Laplacian (its maximum value)
is amplitude. The default is 1.
When normalize=true, the kernel is normalized to have a sum of 1.
This overrides the amplitude argument. The default is false.
The default value of the horizontal and vertical standard deviation
sigma_horz and sigma_vert of the Laplacian kernel is sigma, where
the latter has a default value of 0.25. The default values for the
corresponding means mean_horz and mean_vert are 0.5. Both the
standard deviations and means are relative to kernels of unit width and height
where the top-left corner is the origin. In other works, a mean of 0.5 is
the center of the kernel size, while a standard deviation of 0.25 is a quarter
of it.
Creates an optimally-spaced RGB color mapping of nColor colors.
Note that the mapping is obtained by generating the colors around
the HSV wheel, varying the Hue component.
The returned res Tensor has size nColor x 3.
Creates a jet (blue to red) RGB color mapping of nColor colors.
The returned res Tensor has size nColor x 3.