(Original image by GoAwayStupidAI).
Below are four C++ implementations of the region quadtree (the kind used for image compression, for example). The different implementations were made in an attempt to optimise construction of quadtrees. (For a tutorial on implementing region quadtrees, see Issue 26 [6.39 MB zip] of Dev.Mag).
- NaiveQuadtree is the straightforward implementation.
- AreaSumTableQuadtree uses a summed area table to perform fast calculations of the mean and variance of regions in the data grid.
- AugmentedAreaSumTableQuadtree is the same, except that the area sum table has an extra row and column of zeros to prevents if-then logic that slows it down and makes it tricky to understand.
- SimpleQuadtree is the same as AugmentedAreaSumTableQuadtree , except that no distinction is made (at a class level) between different node types.
Continue reading “Region Quadtrees in C++”
When implementing image algorithms, I am prone to make these mistakes:
- swapping x and y;
- working on the wrong channel;
- making off-by-one errors, especially in window algorithms;
- making division-by-zero errors;
- handling borders incorrectly; and
- handling non-power-of-two sized images incorrectly.
Since these types of errors are prevalent in many image-processing algorithms, it would be useful to develop, once and for all, general tests that will catch these errors quickly for any algorithm.
This post is about such tests.
Continue reading “Catching Common Image Processing Programming Errors with Generic Unit Tests”
A while back I wrote about a simple texture algorithm that I have been exploring. The Python implementation was very slow – so much, that I decided to implement it in C++ to see what performance gain I would get. Surprisingly, the C++ version is about 100 faster, if not more. I expected a decent increase, but what once took several hours can now be done in a minute!
Continue reading “A simple texture algorithm – faster code and more results”
Random steering is often a useful for simulating interesting steering motion. In this post we look at components that make up a random steering toolkit. These can be combined in various ways to get agents to move in interesting ways.
You might want to have a look at Craig Reynolds’ Steering Behaviour for Autonomous Characters — the wander behaviour is what is essentially covered in this tutorial. The main difference is that we control the angle of movement directly, while Reynolds produce a steering force. This post only look at steering — we assume the forward speed is constant. All references to velocity or acceleration refers to angular velocity and angular acceleration.
Whenever I say “a random number”, I mean a uniformly distributed random floating point value between 0 and 1.
Continue reading “Random Steering – 7 Components for a Toolkit”
(Photo by Darren Hester)
Some algorithms take a long time to return their results. Whether it is because the algorithm has to operate on a huge data set, or because it has combinatorial complexity; every time you run it you have to wait minutes or even hours for the thing to finish, making errors very expensive.
This post gives some advice on how to prototype slow algorithms with as little frustration as possible. We assume that this algorithm is being implemented experimentally – that is, you will tweak it and change it often before it is finished (it is not the kind of algorithm you type in straight from a text book). For example, I used the ideas outlined here while playing with the texture generating algorithm of the previous post.
Continue reading “5 Tips for Prototyping Slow Algorithms”