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I'm working on a Civilization-like game and I'm looking for a good algorithm for generating Earth-like world maps. I've experimented with a few alternatives, but haven't hit on a real winner yet.
One option is to generate a heightmap using Perlin noise and add water at a level so that about 30% of the world is land. While Perlin noise (or similar fractal-based techniques) is frequently used for terrain and is reasonably realistic, it doesn't offer much in the way of control over the number, size and position of the resulting continents, which I'd like to have from a gameplay perspective.
A second option is to start with a randomly positioned one-tile seed (I'm working on a grid of tiles), determine the desired size for the continent and each turn add a tile that is horizontally or vertically adjacent to the existing continent until you've reached the desired size. Repeat for the other continents. This technique is part of the algorithm used in Civilization 4. The problem is that after placing the first few continents, it's possible to pick a starting location that's surrounded by other continents, and thus won't fit the new one. Also, it has a tendency to spawn continents too close together, resulting in something that looks more like a river than continents.
Does anyone happen to know a good algorithm for generating realistic continents on a grid-based map while keeping control over their number and relative sizes?
You could take a cue from nature and modify your second idea. Once you generate your continents (which are all about the same size), get them to randomly move and rotate and collide and deform each other and drift apart from each other. (Note: this may not be the easiest thing ever to implement.)
Edit: Here's another way of doing it, complete with an implementation — Polygonal Map Generation for Games.
I've created something similar to your first image in JavaScript. It's not super sophisticated but it works :
http://jsfiddle.net/AyexeM/zMZ9y/
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
<html xmlns="http://www.w3.org/1999/xhtml">
<head>
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
<title>Untitled Document</title>
<script src="https://ajax.googleapis.com/ajax/libs/jquery/1.7.2/jquery.min.js"></script>
<style type="text/css">
#stage{
font-family: Courier New, monospace;
}
span{
display: none;
}
.tile{
float:left;
height:10px;
width:10px;
}
.water{
background-color: #55F;
}
.earth{
background-color: #273;
}
</style>
</head>
<body>
<div id="stage">
</div>
<script type="text/javascript">
var tileArray = new Array();
var probabilityModifier = 0;
var mapWidth=135;
var mapheight=65;
var tileSize=10;
var landMassAmount=2; // scale of 1 to 5
var landMassSize=3; // scale of 1 to 5
$('#stage').css('width',(mapWidth*tileSize)+'px');
for (var i = 0; i < mapWidth*mapheight; i++) {
var probability = 0;
var probabilityModifier = 0;
if (i<(mapWidth*2)||i%mapWidth<2||i%mapWidth>(mapWidth-3)||i>(mapWidth*mapheight)-((mapWidth*2)+1)){
// make the edges of the map water
probability=0;
}
else {
probability = 15 + landMassAmount;
if (i>(mapWidth*2)+2){
// Conform the tile upwards and to the left to its surroundings
var conformity =
(tileArray[i-mapWidth-1]==(tileArray[i-(mapWidth*2)-1]))+
(tileArray[i-mapWidth-1]==(tileArray[i-mapWidth]))+
(tileArray[i-mapWidth-1]==(tileArray[i-1]))+
(tileArray[i-mapWidth-1]==(tileArray[i-mapWidth-2]));
if (conformity<2)
{
tileArray[i-mapWidth-1]=!tileArray[i-mapWidth-1];
}
}
// get the probability of what type of tile this would be based on its surroundings
probabilityModifier = (tileArray[i-1]+tileArray[i-mapWidth]+tileArray[i-mapWidth+1])*(19+(landMassSize*1.4));
}
rndm=(Math.random()*101);
tileArray[i]=(rndm<(probability+probabilityModifier));
}
for (var i = 0; i < tileArray.length; i++) {
if (tileArray[i]){
$('#stage').append('<div class="tile earth '+i+'"> </div>');
}
else{
$('#stage').append('<div class="tile water '+i+'"> </div>');
}
}
</script>
</body>
</html>
I'd suggest you back up and
Think about what makes "good" continents.
Write an algorithm that can tell a good continental layout from a bad one.
Refine the algorithm so that you can quantify how good a good layout is.
Once you have that in place, you can start to implement an algorithm which should be shaped like this:
Generate crappy continents and then improve them.
For improvement you can try all sorts of standard optimization tricks, whether it's simulated annealing, genetic programming, or something completely ad hoc, like moving a randomly chosen edge square from whereever it is on the continent to the edge opposite the continent's center of mass. But the key is to be able to write a program that can tell good continents from bad ones. Start out with hand-drawn continents as well as your test continents, until you get something you like.
I wrote something similar to what you're after for an automated screensaver-style clone of Civilization 1. For the record I wrote this in VB.net but since you don't mention anything about language or platform in your question I'll keep it abstract.
The "map" specifies the number of continents, continent size variance (eg 1.0 would keep all continents with the same approximate land area, down to 0.1 would allow continents to exist with 1/10th the mass of the largest continent), maximum land area (as a percentage) to generate, and the central land bias. A "seed" is distributed randomly around the map for each continent, weighted towards the centre of the map as per the central bias (eg a low bias produces distributed continents more similar to Earth, where as a high central bias will resemble more of a Pangaea). Then for each iteration of growth, the "seeds" assign land tiles according to a distribution algorithm (more on that later) until a maximum land area has been reached.
The land distribution algorithm can be as precise as you want but I found more interesting results applying various genetic algorithms and rolling the dice. Conway's "Game of Life" is a really easy one to start out with. You'll need to add SOME globally aware logic to avoid things like continents growing into each other but for the most part things take care of themselves. The problem I found with more fractal-based approaches (which was my first inclination) was the results either looked too patterned, or lead to too many scenarios requiring hacky-feeling workaround rules to get a result which still didn't feel dynamic enough. Depending on the algorithm you use, you may want to apply a "blurring" pass over the result to eliminate things like abundant single-square ocean tiles and checkered coastlines. In the event something like a continent being spawned surrounded by several others and having nowhere left to grow, relocate the seed to a new point on the map and continue the growth passes. Yes, it can mean you sometimes end up with more continents than planned, but if it's really something you firmly don't want then another way to help avoid it is bias the growth algorithms so they favour growth in the direction with least proximity to other seeds. At worst (in my opinion anyway), you can flag a series as invalid when a seed has nowhere left to grow and generate a new map. Just make sure you set a maximum number of attempts so if anything unrealistic is specified (like fitting 50 even-weighted continents on a 10x10 board) it doesn't spend forever trying to find a valid solution.
I can't vouch for how Civ etc do it, and of course doesn't cover things like climate, land age etc but by playing around with the seed growth algorithm you can get pretty interesting results that resemble continents, archipelagos etc. You can use the same approach to produce 'organic' looking rivers, mountain ranges etc too.
Just thinking off the cuff here:
Pick some starting points, and assign each a randomly drawn (hoped for) size. You can can maintain a separate size draw for planned continents and planned islands if you want.
Loop over the land elements, and where they are not yet at the planned size add one square. But the fun part is weighing the chance that each neighboring element will be the one. Some suggested thing that might factor in:
Distance to the nearest "other" land. Further is better generates wide oceanic spaces. Nearer is better makes narrow channels. You have to decide if you're going to let bits merge as well.
Distance from the seed. Nearer is better means compact land masses, farther is better means long strung out bits
Number of existing land squares adjacent. Weighting in favor of many adjacent squares gives you smooth coast, preferring few gives you lots of inlets and peninsulas.
Presence of "resources" squares nearby? Depends on the game rules, when you generate resource square, and if you want to make it easy.
Will you allow bits to approach or join with the poles?
??? don't know what else
Continue until all land masses have reached the planned size or can't grow anymore for some reason.
Notice that diddling the parameter to these weighting factors allows you to tune the kind of world generated , which is a feature I liked about some of the Civs.
This way you'll need to do terrain generation on each bit separately.
You could try a diamond square algorithm or perlin noise to generate something like a height map. Then, assign ranges values to what shows up on the map. If your "height" goes from 0 to 100, then make 0 - 20 water, 20 - 30 beach, 30 - 80 grass, 80 - 100 mountains. I think notch did something similar to this in minicraft, but I'm not an expert, I'm just in a diamond square mindset after finally getting it working.
I think you can use "dynamic programming" style approach here.
Solve small problems first and combine
solutions smartly to solve bigger
problem.
A1= [elliptical rectangular random ... ]// list of continents with area A1 approx.
A2= [elliptical rectangular random ... ]// list of continents with area A2 approx.
A3= [elliptical rectangular random ... ]// list of continents with area A3 approx.
...
An= [elliptical rectangular random ... ]// list of continents with area An approx.
// note that elliptical is approximately elliptical in shape and same for the other shapes.
Choose one/more randomly from each of the lists (An).
Now you have control over number and area of continents.
You can use genetic algorithm for positioning them
as you see "fit" ;)
It will be very good to take a look at some "Graph Layout Algorithms"
Force Based Algorithms
Genetic Algorithm for Graph Layout
You can modify these to suit your purpose.
I had an idea for map creation similar to the tectonic plates answer. It went something like this:
sweep through the grid squares giving each square a "land" square if rnd <= 0.292 (the actual percentage of dry land on planet earth).
Migrate each land chunk one square toward its nearest larger neighbour. If neighbours are equidistant, go toward the larger chunk. If chunks are equal size, choose one randomly.
if two land squares touch, group them into a chunk, moving all squares as one from now on.
repeat from step 2. Stop when all chunks are connected.
This is similar to how gravity works in a 3D space. It's pretty complicated. A simpler algorithm for your needs would work as follows:
Drop in n starter land squares at random x,y positions and acceptable distances from each other. These are seeds for your continents. (Use the Pythagorean theorem to ensure the seeds have a minimum distance between themselves and all others.)
spawn a land square from an existing land square in a random direction, if that direction is an ocean square.
repeat step 2. Stop when land squares fill 30% of total map size.
if continents are close enough to each other, drop in land bridges as desired to simulate a Panama type effect.
Drop in smaller, random islands as desired for a more natural look.
for each extra "island" square you add, cut out inland seas and lake squares from the continents using the same algorithm in reverse. This will maintain the land percentage at the desired amount.
Let me know how this works out. I've never tried it myself.
PS. I see this is similar to what you tried. Except it sets up all the seeds at once, before beginning, so the continents will be far enough apart and will stop when the map is sufficiently filled.
I haven't actually tried this but it was inspired by David Johnstone's answer regarding tectonic plates. I tried implementing it myself in my old Civ project and when it came to handling collisions I had another idea. Instead of generating tiles directly, each continent consists of nodes. Distribute mass to each node then generate a series of "blob" continents using a 2D metaball approach. Tectonics and continental drift would be ridiculously easy to "fake" simply by moving the nodes around. Depending on how complex you want to go, you could even apply things like currents to handle the node movement and generate mountain ranges that correspond to plate boundaries overlapping. Probably wouldn't add that much to the gameplay side of things, but it could make for an interesting map generation from a purely academic perspective :)
A good explanation of metaballs if you haven't worked with them before:
http://www.gamedev.net/page/resources/_//feature/fprogramming/exploring-metaballs-and-isosurfaces-in-2d-r2556
Here's what I'm thinking, since I'm about to implement something like this that I have for a game in development. :
The world divided into regions. depending on the size of the world, it will determine how many regions. For this example, we'll assume a medium sized world, with 6 regions. Each grid zone breaks into 9 grid zones. those grid zones break into 9 grids each. (this is not for character movement, but merely for map creation) The Grids are for biomes, grid zones are for over arching land features, (continent vs ocean) and the regions are for overall climate. The grids break down into tiles.
Randomly generated, the regions get assigned logical climate sets. Grid zones get randomly assigned to, for instance; ocean or land. Grids get assigned biomes randomly with modifiers based on their grid zones and climate, these being forest, desert, plains, glacial, swamp or volcanic. Once all those basics are assigned, it's time to blend them together, using a random percentage based function that fills in tile sets. For example; if you have a forest biome, next to a desert biome, you have an algorithm that decreases the likely hood that a tile will be "foresty" and increases that it will be "deserty." So, about half way between them, you'll see a sort of blended affect combining the two biomes to off a somewhat smooth transition between them. Transition from one grid zone to the next would probably take a little more work to insure logic landmass formations.Like, for example, a biome from one grid zone that touches the biome from another, instead of having a simple switching percentage based on proximity. For example, there are 50 tiles from the center of the biome to the edge of the biome, meaning, there are 50 from the edge it touches to the center of the next biome. That would logically leave a 100% change from one biome to the next. So as the tiles get nearer to the border of the two biomes, the percentage narrows out to around 60% or so. It'd, I think, be unwise to give too much probability of crossing biomes far from the border, but you'll want the border to be somewhat blended. For the grid zones, the percentage change will be much more pronounced. Instead of the % going down to around 60%, it'd only drop down to around 80%. And a secondary check would then have to be performed to ensure that there's not a random water tile in the middle of a land biome next to the ocean without some logic to it. So, either, connect that water tile to the ocean mass to make a channel to explain the water tile, or remove it altogether. Land in a water based biome is easier to explain using rock outcrops and such.
Oh, kinda dumb, sorry.
I'd place fractal terrain according to some layout that you know "works" (e.g. 2x2 grid, diamond, etc, with some jitter) but with a Gaussian distribution damping peaks down towards the edges of the continent centers. Place the water level lower so that is mostly land until you get near the edges.
Related
I want to identify lego bricks for building a lego sorting machine (I use c++ with opencv).
That means I have to distinguish between objects which look very similar.
The bricks are coming to my camera individually on a flat conveyer. But they might lay in any possible way: upside down, on the side or "normal".
My approach is to teach the sorting machine the bricks by taping them with the camera in lots of different positions and rotations. Features of each and every view are calculated by surf-algorythm.
void calculateFeatures(const cv::Mat& image,
std::vector<cv::KeyPoint>& keypoints,
cv::Mat& descriptors)
{
// detector == cv::SurfFeatureDetector(10)
detector->detect(image,keypoints);
// extractor == cv::SurfDescriptorExtractor()
extractor->compute(image,keypoints,descriptors);
}
If there is an unknown brick (the brick that i want to sort) its features also get calculated and matched with known ones.
To find wrongly matched features I proceed as described in the book OpenCV 2 Cookbook:
with the matcher (=cv::BFMatcher(cv::NORM_L2)) the two nearest neighbours in both directions are searched
matcher.knnMatch(descriptorsImage1, descriptorsImage2,
matches1,
2);
matcher.knnMatch(descriptorsImage2, descriptorsImage1,
matches2,
2);
I check the ratio between the distances of the found nearest neighbours. If the two distances are very similar it's likely that a false value is used.
// loop for matches1 and matches2
for(iterator matchIterator over all matches)
if( ((*matchIterator)[0].distance / (*matchIterator)[1].distance) > 0.65 )
throw away
Finally only symmatrical match-pairs are accepted. These are matches in which not only n1 is the nearest neighbour to feature f1, but also f1 is the nearest neighbour to n1.
for(iterator matchIterator1 over all matches)
for(iterator matchIterator2 over all matches)
if ((*matchIterator1)[0].queryIdx == (*matchIterator2)[0].trainIdx &&
(*matchIterator2)[0].queryIdx == (*matchIterator1)[0].trainIdx)
// good Match
Now only pretty good matches remain. To filter out some more bad matches I check which matches fit the projection of img1 on img2 using the fundamental matrix.
std::vector<uchar> inliers(points1.size(),0);
cv::findFundamentalMat(
cv::Mat(points1),cv::Mat(points2), // matching points
inliers,
CV_FM_RANSAC,
3,
0.99);
std::vector<cv::DMatch> goodMatches
// extract the surviving (inliers) matches
std::vector<uchar>::const_iterator itIn= inliers.begin();
std::vector<cv::DMatch>::const_iterator itM= allMatches.begin();
// for all matches
for ( ;itIn!= inliers.end(); ++itIn, ++itM)
if (*itIn)
// it is a valid match
The result is pretty good. But in cases of extreme alikeness faults still occur.
In the picture above you can see that a similar brick is recognized well.
However in the second picture a wrong brick is recognized just as well.
Now the question is how I could improve the matching.
I had two different ideas:
The matches in the second picture trace back to the features really fitting, but only if the visual field is intensely changed. To recognize a brick I have to compare it in many different positions anyway (at least as shown in figure three). This means I know that I am only allowed to minimally change the visual field. The information how intensely the visual field is changed should be hidden in the fundamental matrix. How can I read out of this matrix how far the position in the room has changed? Especially the rotation and strong scaling should be of interest; if the brick once is taped farer on the left side this shouldn't matter.
Second idea:
I calculated the fundamental matrix out of 2 pictures and filtered out features that don't fit the projections - shouldn't there be a way to do the same using three or more pictures? (keyword Trifocal tensor). This way the matching should become more stable. But I neither know how to do this using OpenCV nor could I find any information on this on google.
I don't have a complete answer, but I have a few suggestions.
On the image analysis side:
It looks like your camera setup is pretty constant. Easy to just separate the brick from the background. I also see your system finding features in the background. This is unnecessary. Set all non-brick pixels to black to remove them from the analysis.
When you have located just the brick, your first step should be to just filter likely candidates based on the size (i.e. number of pixels) in the brick. That way the example faulty match you show is already less likely.
You can take other features into account such as the aspect ratio of the bounding box of the brick, the major and minor axes (eigevectors of the covariance matrix of the central moments) of the brick etc.
These simpler features will give you a reasonable first filter to limit your search space.
On the mechanical side:
If bricks are actually coming down a conveyor you should be able to "straighten" the bricks along a straight edge using something like a rod that lies at an angle to the direction of the conveyor across the belt so that the bricks arrive more uniformly at your camera like so.
Similar to the previous point, you could use something like a very loose brush suspended across the belt to topple bricks standing up as they pass.
Again both these points will limit your search space.
I'm trying to find an algorithm (or algorithm ideas) for following a ridge on a 3D image, derived from a digital elevation model (DEM). I've managed to get very basic program working which just iterates across each row of the image marking a ridge line wherever it finds a large change in aspect (ie. from < 180 degrees to > 180 degrees).
However, the lines this produces aren't brilliant, there are often gaps and various strange artefacts. I'm hoping to try and extend this by using some sort of algorithm to follow the ridge lines, thus producing lines that are complete (that is, no gaps) and more accurate.
A number of people have mentioned snake algorithms to me, but they don't seem to be quite what I'm looking for. I've also done a lot of searching about path-finding algorithms, but again, they don't seem to be quite the right thing.
Does anyone have any suggestions for types or algorithms or specific algorithms I should look at?
Update: I've been asked to add some more detail on the exact area I'll be applying this to. It's working with gridded elevation data of sand dunes. I'm trying to extract the crests if these sand dunes, which look similar to the boundaries between drainage basins, but can be far more complex (for example, there can be multiple sand dunes very close to each other with gradually merging crests)
You can get a good estimate of the ridges using sign changes of the curvature. Note that the curvature will be near infinity at flat regions. Hence possible psuedo-code for a ridge detection algorithm could be:
for each face in the mesh
compute 1/curvature
if abs(1/curvature) != zeroTolerance
flag face as ridge
else
continue
(zeroTolerance is a number near but not equal to zero e.g. 0.003 etc)
Also Meshlab provides a module for normal & curvature estimation on most formats. You can test the idea using it, before you code it up.
I don't know how what your data is like or how much automation you need. This won't work if if consists of peaks without clear ridges (but then you probably wouldn't be asking the question.)
startPoint = highest point in DEM (or on ridge)
curPoint = startPoint;
line += curPoint;
Loop
curPoint = highest point adjacent to curPoint not in line; // (Don't backtrack)
line += point;
Repeat
Curious what the real solution turns out to be.
Edited to add: depending on the coarseness of your data set, 'point' can be a single point or a smoothed average of a local region of points.
http://en.wikipedia.org/wiki/Ridge_detection
You can treat the elevation as you would a grayscale color, then use a 2D edge recognition filter. There are lots of edge recognition methods available. The best would depend on your specific needs.
I'm working on a game, and I've come up with a rather interesting problem: clever ways to draw starfields.
It's a 2D game, so the action can scroll in the X and Y directions. In addition, we can adjust the scale to show more or less of the play area. I'd also like the starfield to have fake parallax to give an impression of depth.
Right now I'm doing this in the traditional way, by having a big array of stars, each of which is tagged by a 'depth' factor. To draw, I translate each star according to the camera position multiplied by the 'depth', so some stars move a lot, and some move a little. This all works fine, but of course since I have a finite number of stars in my array I have issues when the camera moves too far or we zoom out too much. This is will all work, but is involving lots of code and special cases.
This offends my sense of elegance. There has got be a better way of achieving this.
I've considered procedurally generating my stars, which allows me to have an unlimited number: e.g. by using a fixed seed and PRNG to determine the coordinates. I would need to divide the sky up into tiles, generate the seed by hashing the tile coordinates, and then draw, say, 100 stars per tile. This allows me to extend my starfield indefinitely in all directions while still only needing to consider the tiles that are visible --- but this doesn't work with the 'depth' factor, as this allows stars to stray outside their tile. I could simply use multiple layered non-parallax starfields using this algorithm but this strikes me as cheating.
And, of course, I need to do all this every frame, so it's got to be fast.
What do you all reckon?
Have a few layers of stars.
For each layer, use a seeded random number generator (or just an array) to generate the amount of blank space between a star and the next one (a poisson distibution, if you want to be picky about it). You want the stars pretty sparse, so the blank space will often be more than whole row. The back layers will be more dense than the front ones, obviously.
Use this to give yourself several tiles each (say) two screens wide. Scroll the starfield by keeping track of where that "first" star is for each layer.
The player won't notice the tiling, because you scroll the tiles at different rates for each layer, especially if you use a few layers that are each fairly sparse.
As stars in the background don't move as fast as those in the foreground, you could maybe make multi-layer tiles for the background and replace them with one-layer-ones when you've got time to do that. Oh, and how about repeating patterns in the background layers? This would maybe allow you to pregenerate all background tiles - you could still shift them in height and overlay multiple ones with random offsets or so to make it look random.
Is there anything wrong with wrapping the star field around in X and Y? Because of your depth, the wraparound distance should depend on the depth, but you can do that. Each recorded star at (x,y,depth) should appear at all points
[x + j * S * depth, y + k * S * depth]
for all integers j and k. S is a wraparound parameter. If S is 1 then wraparound happens immediately and all stars are always shown somewhere. If S is higher wraparound doesn't happen immediately and some stars are shown off screen. You'll probably want S big enough to ensure no repeats at maximum zoom out.
Each frame, render the stars on one single bitmap/layer. They are only dots, and so it will be faster than using any algorithm with multiple layers.
Now you need an infinite 2D-grid of 3D-boxes filled with a finite number of stars. For each box, you can define an individual RANDOM_SEED value, using its grid-coordinates. The stars in each box can be generated on-the-fly.
Remember to correct the perspective when you zoom: Each 3D-box has a near-rectangle (front-face) and a far-rectangle. You will see more stars of neighbouring boxes, whenever the far-rectangle or near-rectangle shrinks in your view.
Your far-rectangles should never be smaller than half the width of the near-rectangles, otherwise it might be troublesome: You might have to scan huge lists of stars where most of them are out of bounds. You can realize stars behind the far-rectangles via additional 2D-grids of 3D-boxes with other sizes and depths.
Why not combine the coordinates of the starfield 3D boxes to form the random number seed? Use a global "adjustment" if you want to produce different universes. That way you don't need to track the boxes you can't see because the contents are fixed by their location.
I'm toying around with writing a random map generator, and am not quite sure how to randomly generate realistic landscapes. I'm working with these sorts of local-scale maps, which presents some interesting problems.
One of the simplest cases is the forest:
Sparse Medium Dense
Typical trees 50% 70% 80%
Massive trees — 10% 20%
Light undergrowth 50% 70% 50%
Heavy undergrowth — 20% 50%
Trees and undergrowth can exist in the same space, so an average sparse forest has 25% typical trees and light undergrowth, 25% typical trees, 25% light undergrowth, and 25% open space. Medium and dense forests will take a bit more thinking, but it's not where my problem lies either, as it's all evenly dispersed.
My problem lies in generating clusters and paths, while keeping the percentage constraints. Marshes are a good example of this:
Moor Swamp
Shallow bog 20% 40%
Deep bog 5% 20%
Light undergrowth 30% 20%
Heavy undergrowth 10% 20%
Deep bog squares are usually clustered together and surrounded by an irregular ring of shallow bog squares.
An additional map element, a hedgerow, may also be present, as well as a path of open ground, snaking through the bog. Both of these types of map elements (clusters and paths) present problems, as the total composition of the map should contain X% of the element, but it's not evenly distributed. Other elements, such as streams, ponds, and quicksand need either a cluster or path-type generation as well.
What technique can I use to generate realistic maps given these constraints?
I'm using C#, FYI (but this isn't a C#-specific question.)
Realistic "random" distribution is often done using Perlin Noise, which can be used to give a distribution with "clumps" like you mention. It works by summing/combining multiple layers of linearly interpolated values from random data points. Each layer (or "octave") has twice as many data points as the last, and confined to a narrower range of values. The result is "realistic" looking random texture.
Here is a beautiful demonstration of the theory behind Perlin Noise by Hugo Elias.
Here is the first thing I found on Perlin Noise in C#.
What you can do is generate a Perlin Noise image and set a "threshold", where anything above a value is "on" and everything below it is "off". What you will end up with is clumps where things are above the threshold, which look irregular and awesome. Simply assign the ones above the threshold to where you want your terrain feature to be.
Here is a demonstration if a program generating a Perlin Noise bitmap and then adjusting the cut-off threshold over time. A clear "clumping" is visible. It could be just what you wanted.
Notice that, with a high threshold, very few points are above it, and it's sparse. But as the threshold lowers, those points "grow" into clumps (by the nature of perlin noise), and some of these clumps will join eachother, and basically create something very natural and terrain-like.
Note that you could also set the "clump factor", or the tendency of features to clump, by setting the "turbulence" of your Perlin Noise function, which basically causes peaks and valleys of your PN function to be accentuated and closer together.
Now, where to set the threshold? The higher the threshold, the lower the percentage of the feature on the final map. The lower the threshold, the higher the percentage. You can mess around with them. You could probably get exact percentages by fiddling around with a little math (it seems that the distribution of values follows a Normal Distribution; I could be wrong). Tweak it until it's just right :)
EDIT As pointed out in the comments, you can find the exact percentage by creating a cumulative histogram (index of what % of the map is under a threshold) and pick the threshold that gives you the percent you need.
The coolest thing here is that you can create features that clump around certain other features (like your marsh features) trivially here -- just use the same Perlin Noise map twice -- the second time, lowering the threshold. The first one will be clumpy, and the second one will be clumpy around the same areas, but with the clumps enlarged (refer to the flash animation posted earlier).
As for other features like hedgerows, you could try modeling simple random walk lines that have a higher tendency to go straight than turn, and place them anywhere randomly on your perlin-based map.
samples
Here is a sample 50x50 tile Sparse Forest Map. The undergrowth is colored brown and the trees are colored blue (sorry) to make it clear which is which.
For this map I didn't make exact thresholds to match 50%; I only set the threshold at 50% of the maximum. Statistically, this will average out to exactly 50% every time. But it might not be exact enough for your purposes; see the earlier note for how to do this.
Here is a demo of your Marsh features (not including undergrowth, for clarity), with shallow marsh in grey and deep marsh in back:
This is just 50x50, so there are some artifacts from that, but you can see how easily you can make the shallow marsh "grow" from the deep marsh -- simply by adjusting the threshold on the same Perlin map. For this one, I eyeballed the threshold level to give the most eye-pleasing results, but for your own purposes, you could do what was mentioned before.
Here is a marsh map generated from the same Perlin Noise map, but on stretched out over 250x250 tiled map instead:
I've never done this sort of thing, but here are some thoughts.
You can obtain clusters by biasing random selection to locations on the grid that are close to existing elements of that type. Assign a default value of 1 to all squares. For squares with existing clustered elements, add clustering value to to adjacent squares (the higher the clustering value, the stronger the clustering will be). Then do random selection for the next element of that type on the probability distribution function of all the squares.
For paths, you could have a similar procedure, except that paths would be extended step-wise (probability of path is finite at squares next to the end of the path and zero everywhere else). Directional paths could be done by increasing the probability of selection in the direction of the path. Meandering paths could have a direction that changes over the course of random extension (new_direction = mf * old_direction + (1-mf) * rand_direction, where mf is a momentum factor between 0 and 1).
To expand on academicRobot's comments, you could start with a default marsh or forest seed in some of the grid cells and let them grow from the source using a correlated random number. For instance a bog might have eight adjacent grid cells each of which has a 90% probability of also being a bog, but a 10% probability of being something else. You can let the ecosytem form from the seed and adjust the correlation until you get something that looks right. Probably pretty easy to implement even in a spreadsheet.
You could start reading links here. I remember looking at much better document. Will post it if I find it (it was also based on L-systems).
But that's on the general side; on the particular problem you face I guess you should model it in terms of
percentages
other rules (clusters and paths)
The point is that even though you don't know how to construct the map with given properties, if you are able to evaluate the properties (clustering ratio; path niceness) and score on them you can then brute force or do some other problem space transversal.
If you still want to do generative approach then you will have to examine generative rules a bit closer; here's an idea that I would pursue
create patterns of different terrains and terrain covers that have required properties of 'clusterness', 'pathness' or uniformity
create the patterns in such a way that the values for deep bog are not discreet, but assign probability value; after the pattern had been created you can normalize this probability in such a way that it will produce required percentage of cover
mix different patterns together
You might have some success for certain types of area with a Voronoi pattern. I've never seen it used to create maps but I have seen it used in a number of similar fields.
I'm trying to write a simple tracking routine to track some points on a movie.
Essentially I have a series of 100-frames-long movies, showing some bright spots on dark background.
I have ~100-150 spots per frame, and they move over the course of the movie. I would like to track them, so I'm looking for some efficient (but possibly not overkilling to implement) routine to do that.
A few more infos:
the spots are a few (es. 5x5) pixels in size
the movement are not big. A spot generally does not move more than 5-10 pixels from its original position. The movements are generally smooth.
the "shape" of these spots is generally fixed, they don't grow or shrink BUT they become less bright as the movie progresses.
the spots don't move in a particular direction. They can move right and then left and then right again
the user will select a region around each spot and then this region will be tracked, so I do not need to automatically find the points.
As the videos are b/w, I though I should rely on brigthness. For instance I thought I could move around the region and calculate the correlation of the region's area in the previous frame with that in the various positions in the next frame. I understand that this is a quite naïve solution, but do you think it may work? Does anyone know specific algorithms that do this? It doesn't need to be superfast, as long as it is accurate I'm happy.
Thank you
nico
Sounds like a job for Blob detection to me.
I would suggest the Pearson's product. Having a model (which could be any template image), you can measure the correlation of the template with any section of the frame.
The result is a probability factor which determine the correlation of the samples with the template one. It is especially applicable to 2D cases.
It has the advantage to be independent from the sample absolute value, since the result is dependent on the covariance related with the mean of the samples.
Once you detect an high probability, you can track the successive frames in the neightboor of the original position, and select the best correlation factor.
However, the size and the rotation of the template matter, but this is not the case as I can understand. You can customize the detection with any shape since the template image could represent any configuration.
Here is a single pass algorithm implementation , that I've used and works correctly.
This has got to be a well reasearched topic and I suspect there won't be any 100% accurate solution.
Some links which might be of use:
Learning patterns of activity using real-time tracking. A paper by two guys from MIT.
Kalman Filter. Especially the Computer Vision part.
Motion Tracker. A student project, which also has code and sample videos I believe.
Of course, this might be overkill for you, but hope it helps giving you other leads.
Simple is good. I'd start doing something like:
1) over a small rectangle, that surrounds a spot:
2) apply a weighted average of all the pixel coordinates in the area
3) call the averaged X and Y values the objects position
4) while scanning these pixels, do something to approximate the bounding box size
5) repeat next frame with a slightly enlarged bounding box so you don't clip spot that moves
The weight for the average should go to zero for pixels below some threshold. Number 4 can be as simple as tracking the min/max position of anything brighter than the same threshold.
This will of course have issues with spots that overlap or cross paths. But for some reason I keep thinking you're tracking stars with some unknown camera motion, in which case this should be fine.
I'm afraid that blob tracking is not simple, not if you want to do it well.
Start with blob detection as genpfault says.
Now you have spots on every frame and you need to link them up. If the blobs are moving independently, you can use some sort of correspondence algorithm to link them up. See for instance http://server.cs.ucf.edu/~vision/papers/01359751.pdf.
Now you may have collisions. You can use mixture of gaussians to try to separate them, give up and let the tracks cross, use any other before-and-after information to resolve the collisions (e.g. if A and B collide and A is brighter before and will be brighter after, you can keep track of A; if A and B move along predictable trajectories, you can use that also).
Or you can collaborate with a lab that does this sort of stuff all the time.