pamgauss - create a two-dimensional Gaussian function as a PAM image
pamgauss width height -sigma=number [-maxval=number] [-tupletype=string] [-maximize] [-oversample=number]
Minimum unique abbreviation of option is acceptable. You may use double hyphens instead of single hyphen to denote options. You may use white space in place of the equals sign to separate an option name from its value.
pamgauss 7 7 -sigma=.5 -maximize -tupletype=GRAYSCALE | pamtopnm >gauss.pgm pnmconvol -nooffset -normalize gauss.pgm myimage.ppm >blurred.ppm
This program is part of Netpbm(1).
pamgauss generates a one-plane PAM image whose samples are a Gaussian function of their distance from the center of the image. I.e. the sample value is highest in the center and goes down, in a bell curve shape, as you move away from the center.
You can use this image as a convolution kernel with pnmconvol to blur an image. (This technique is known as Gaussian blurring).
width and height are the dimensions of the image that pamgauss generates. Mathematically speaking, they are the domain of the two-dimensional Gaussian function. If you want to be sure you get a whole Gaussian function, make sure that you choose a standard deviation and image dimensions so that if you made it any larger, the sample values at the edges would be zero.
The output image is PAM. To make it usable with pnmconvol, specify -tupletype=GRAYSCALE so pnmconvol can use it as if it were PGM. You must use the -nooffset option on pnmconvol because zero means zero in the PAM that pamgauss generates.
Without -maximize, the sum of all the samples is equal to the image's maxval (within rounding error). This is true even if you clip the Gaussian function by making the image too small. This is what is normally required of a convolution kernel.
pamgauss oversamples and averages to represent the continuous Gaussian function in discrete samples in the PAM output. Consider an image 11 samples wide and an oversampling factor of 10. The samples can be thought of as contiguous squares one unit wide. The center of the image is thus the center of the 6th sample from the left. The 3rd sample from the left covers a range of distances from 3 to 4 units from the center of the image. Because the oversampling factor is 10, pamgauss computes the value of the Gaussian function at 10 points evenly spaced between 3 and 4 units from the center of the image and assigns the 3rd sample from the left the mean of those 10 values.
number is in units of samples.
This option is required. There is no default.
If you select this, you probably want to normalize the output (scale the samples down so the volume under the surface of the two-dimensional Gaussian function is the maxval) before you use it, for example with pnmconvol's -normalize option. The reason this is different from just not using -maximize is that this subsequent normalization can be done with much more precision than can be represented in a PAM image.
Without this option, pamgauss uses an amplitude that makes the volume under the surface of the two-dimensional Gaussian function the maxval of the image. This means all the samples in the image are normally considerably less than the maxval.
This option was new in Netpbm 10.79 (June 2017).
The default is 255.
If you don't specify this, pamgauss generates a PAM with unspecified tuple type.
An oversampling factor of 1 means no oversampling, which means each sample is based only on the value of the Gaussian function at the center of the sample.
The default is 5 divided by the standard deviation, rounded up to a whole number.
This option was new in Netpbm 10.79 (June 2017). Before that, it is essentially 1 - there is no oversampling.