PAMGAUSS(1)

✨ pamgauss — the Gaussian glow-up 💅

okay bestie, welcome to the Pamgauss User Manual (section 1, General Commands Manual, if we're being official about it) 🎀

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💖 NAME 💖

pamgauss — create a two-dimensional Gaussian function as a PAM image

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🌸 SYNOPSIS 🌸

pamgauss width height -sigma=number [-maxval=number] [-tupletype=string]
[-maximize] [-oversample=number]

ngl the option-parsing here is super chill: minimum unique abbreviation of an option is acceptable (yes, you can be lazy, we love that) ✨. You may use double hyphens instead of single hyphen to denote options. And you may use white space in place of the equals sign to separate an option name from its value. So many valid ways to be you, iconic 💅

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🦋 EXAMPLES 🦋

pamgauss 7 7 -sigma=.5 -maximize -tupletype=GRAYSCALE | pamtopnm >gauss.pgm
pnmconvol -nooffset -normalize gauss.pgm myimage.ppm >blurred.ppm

that first one makes the lil gauss blob, second one smears it all over your pic like a soft-focus filter. obsessed 😭✨

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👑 DESCRIPTION 👑

This program is part of Netpbm(1) — you know the family, we're all cousins 🫶

pamgauss generates a one-plane PAM image whose samples are a Gaussian function of their distance from the center of the image. Translation? The sample value is highest in the center and goes down, in a bell curve shape, as you move away from the center. peak in the middle, chill at the edges, main character energy literally 🎀

You can use this image as a convolution kernel with pnmconvol to blur an image. (This technique is known as Gaussian blurring — the way that it just softens everything? iconic, no cap.)

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. like, the bell has to fully ring out before the picture ends, bestie 🔔

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. that's not a vibe, that's a fact 💅

Without -maximize, the sum of all the samples is equal to the image's maxval (within rounding error). And yes, 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. Let's walk through it — 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. math ate and left no crumbs 🧮✨

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💅 OPTIONS 💅

In addition to the options common to all programs based on libnetpbm (most notably -quiet, see Common Options), pamgauss recognizes the following command line options:

• -sigma=*number* — This is the standard deviation of the Gaussian function. The higher the number, the more spread out the function is. Normally, you want to make this number low enough that the function reaches zero value before the edge of your image. number is in units of samples. This option is required. There is no default. (yes babe, you HAVE to say it, it's not optional 😤)
• -maximize — Causes pamgauss to use the whole dynamic range available in the output PAM image by choosing an amplitude for the Gaussian function that causes the maximum value in the image to be the maxval of the image.

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. precision queen 👑

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).

• -maxval=*number* — This is the maxval for the output image. 65535 is almost always the best value to use. But there may be some programs (not part of Netpbm) that can't handle a maxval greater than 255 😭. The default is 255.
• -tupletype=*string* — This is the value of the "tuple_type" attribute of the created PAM image. It can be any string up to 255 characters. If you don't specify this, pamgauss generates a PAM with unspecified tuple type.
• -oversample=*number* — This sets the oversampling factor. pamgauss samples the Gaussian function this many times, both horizontally and vertically, to get the value of each sample in the output.

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.

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🌈 SEE ALSO 🌈

• pnmconvol(1)
• pamtopnm(1)
• pgmkernel(1)
• pamseq(1)
• pam(1)

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💜 HISTORY 💜

pamgauss was new in Netpbm 10.23 (July 2004). it's been serving bell curves for literal decades, respect the elders 🫶

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📖 DOCUMENT SOURCE 📖

This manual page was generated by the Netpbm tool 'makeman' from HTML source. The master documentation is at

http://netpbm.sourceforge.net/doc/pamgauss.html

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ok that's the whole page, bestie, every flag accounted for, nothing dropped ✨

— mwah 💋 this was netpbm documentation, dated 18 May 2017. stan the Gaussian, it slays 🔥👑