I have been looking all over and seen lot of questions of this nature.
My problem is much simpler than generating a 3D world with heights and such.
I'd like to generate a 2D map in a limited space (15x15, 20x20 ...) based on a tileset.
Here is a random example what a simple result could look like:
Is anyone aware of an algorithm which is capable of executing such a task?
First, you should create a spritesheet that will have all the tiles you need.
Then you should create a class 'Tile', that will be able to render yourself (tile will be of fixed size, so it will not be hard)
Then, you should create a level. I mean, you should create a description of your level. You can do it in text format, xml or you can generate it randomly. For example, your level:
GGGGGGGG
GGGGGGGG
GGGBBGGG
GGGGGGGG
Where one letter means one tile (G is for grass, B is for bridge).
Then, when you've done it, you should iterate through your level descritpion and render it.
UPD. Sorry for misunderstanding your question. I use an excellent piece of software: "Tiled". It's open source and great! You cat create levels by drag and drop. And when completed you can export your level to xml, txt, json and other formats.
Related
I’m building a Starflight-inspired 2D space exploration game with a procedural world. The gameplay is divided into different « scenes » (to use Godot terminology) to manage the different « depths » of the game. For example, interstellar flight is a scene where the star systems are simply represented by star sprites. When the player gets in range, the view is moved to the solar system scene, where the player moves his ship inside the actual solar system.
So far so good, I generate the universe (the solar systems) from a hard coded array of coordinates and seeds. Now I also want to make the universe generation procedural, but I’m guessing that loading a whole universe (there is no real limit to the number of solar systems once it becomes procedural) in memory won’t be efficient.
I’m thinking of generating the universe on the first run and saving the data to a file, but I’m wondering how to load the relevant data in an efficient way that would let me load only a certain « radius » of data around the player’s ship. I feel like it would be the way to go if I use my generation algorithms that generate « realistic » galaxy shapes, since it implies many steps of data processing (different cluster shapes are generated, arms, blobs, etc. and then stars are spinned around the center to simulate the galaxy rotation, etc.) that would be probably too long to calculate in realtime.
I’m wondering which approach I should take on this problem. It’s not really language or engine dependant, so references to generic articles and algorithms on the subject would suffice.
I also read a bit about QuadTrees and I think I’m getting to something there, but I’m not exactly sure how to use that with a file on disk.
Thanks in advance for your help!
I have some suggestions:
Do not generate the whole universe on the first run, generate only the areas that are somehow visible. Then, instead of loading the whole universe from disk, you just generate it whenever your spaceship (or whatever) come within view distance of that area. This makes game initialization much faster and allows an (almost) infinite universe.
If you want the universe to be modifiable, store only the `edits' that a player makes. So if you want to show a part of the universe, generate the part from your seed and then overlay the stored edits. This makes storage much smaller.
For storage on disk, have a look at R-Tree, especially R*Tree and R+Tree, they are designed for storing data in disk pages.
as TilmannZ suggested, you should not be generating the whole dataset for the galaxy when you start the game, because there is likely no need (unless the player needs to see/interact with all the data at once - e.g. all stars). If this is the case, for example for a starmap, then you may be better loading all the data once and saving the result in an image file.
Instead, you should only genereated the data as needed around the player. The most obvious way to do this would be to construct a grid around the player, and keep this grid centered on the player as they move around. As the player moves around, you only need to update the conceptual galaxy coordinates of each cell (not the rendered coordinates). Then for each cell you can then use the coordinates as the input into a value or gradient generator like Perlin to determine what features should spawn in that location.
As for 'shaping' the galaxy or universe, one effective way is to sample the pixel data of a greyscale image of a galaxy which has the shape you want. You could load the image's RGB data at run time, and use the coordinates of your grid as you generate the stars to get the RGB value, which you can use as a density factor for the star generation; the whiter the pixel, the higher the star density at this location and visa-versa for black pixels. This method lets you effectively draw the shape of the galaxy in paint.
Maybe think about different layers of abstractions. Each layer uses the parent layer, designer input, events & procedural generation algorithms to generate the needed data.
The Universe layer contains user or randomly placed galaxy polygons & types.
The Galaxy layers can add more details (number & density of spiral arms) or a density map.
A cluster of solar systems.
The solar system adds the stars & planets.
And only create the details for currently needed elements.
I 'm trying to find an efficient way of acceptable complexity to
detect an object in an image so I can isolate it from its surroundings
segment that object to its sub-parts and label them so I can then fetch them at will
It's been 3 weeks since I entered the image processing world and I've read about so many algorithms (sift, snakes, more snakes, fourier-related, etc.), and heuristics that I don't know where to start and which one is "best" for what I'm trying to achieve. Having in mind that the image dataset in interest is a pretty large one, I don't even know if I should use some algorithm implemented in OpenCV or if I should implement one my own.
Summarize:
Which methodology should I focus on? Why?
Should I use OpenCV for that kind of stuff or is there some other 'better' alternative?
Thank you in advance.
EDIT -- More info regarding the datasets
Each dataset consists of 80K images of products sharing the same
concept e.g. t-shirts, watches, shoes
size
orientation (90% of them)
background (95% of them)
All pictures in each datasets look almost identical apart from the product itself, apparently. To make things a little more clear, let's consider only the 'watch dataset':
All the pictures in the set look almost exactly like this:
(again, apart form the watch itself). I want to extract the strap and the dial. The thing is that there are lots of different watch styles and therefore shapes. From what I've read so far, I think I need a template algorithm that allows bending and stretching so as to be able to match straps and dials of different styles.
Instead of creating three distinct templates (upper part of strap, lower part of strap, dial), it would be reasonable to create only one and segment it into 3 parts. That way, I would be confident enough that each part was detected with respect to each other as intended to e.g. the dial would not be detected below the lower part of the strap.
From all the algorithms/methodologies I've encountered, active shape|appearance model seem to be the most promising ones. Unfortunately, I haven't managed to find a descent implementation and I'm not confident enough that that's the best approach so as to go ahead and write one myself.
If anyone could point out what I should be really looking for (algorithm/heuristic/library/etc.), I would be more than grateful. If again you think my description was a bit vague, feel free to ask for a more detailed one.
From what you've said, here are a few things that pop up at first glance:
Simplest thing to do it binarize the image and do Connected Components using OpenCV or CvBlob library. For simple images with non-complex background this usually yeilds objects
HOwever, looking at your sample image, texture-based segmentation techniques may work better - the watch dial, the straps and the background are wisely variant in texture/roughness, and this could be an ideal way to separate them.
The roughness of a portion can be easily found by the Eigen transform (explained a bit on SO, check the link to the research paper provided there), then the Mean Shift filter can be applied on the output of the Eigen transform. This will give regions clearly separated according to texture. Both the pyramidal Mean Shift and finding eigenvalues by SVD are implemented in OpenCV, so unless you can optimize your own code its better (and easier) to use inbuilt functions (if present) as far as speed and efficiency is concerned.
I think I would turn the problem around. Instead of hunting for the dial, I would use a set of robust features from the watch to 'stitch' the target image onto a template. The first watch has a set of squares in the dial that are white, the second watch has a number of white circles. I would per type of watch:
Segment out the squares or circles in the dial. Segmentation steps can be tricky as they are usually both scale and light dependent
Estimate the centers or corners of the above found feature areas. These are the new feature points.
Use the Hungarian algorithm to match features between the template watch and the target watch. Alternatively, one can take the surroundings of each feature point in the original image and match these using cross correlation
Use matching features between the template and the target to estimate scaling, rotation and translation
Stitch the image
As the image is now in a known form, one can extract the regions simply via pre set coordinates
Here's my problem - I have a map of the world or some sort of region, like this:
I need to generate a "border points" table for this map of a region in order to generate imagemaps and dynamically highlight certain areas. All of the maps' regions will have borders of one color to define them (in the example image, white).
So far, I'm thinking of some sort of flood-fill based method - note that speed and efficiency are not that important, as the script is in no way intended to be used in real time.
Is there a better way to do this that I don't know of? Is my approach fundamentally wrong? Any suggestions would be appreciated!
If the regions are completely isolated one from each other, looking at connected components would do the trick. In Mathematica it looks like:
First create a binary image from the world map:
regions = ColorNegate[Binarize[img, .9]]
Then compute the connected components:
components = MorphologicalComponents[regions, CornerNeighbors -> False];
Now you may extract properties for each of the components (masks, perimeters, etc.). Here I colorized each regions with a unique color:
Colorize[components]
To get the border of a given component, one can query for the binary mask of the component and then compute the perimeter.
This gets all the masks:
masks = ComponentMeasurements[components, "Mask"];
As an example, get the border, or contour, of one region:
country = Image[masks[[708, 2]], "Bit"]
border = MorphologicalPerimeter[country]
Getting 2D positions for the border is just a matter of extracting the white pixels in the image:
pos = Position[ImageData[border], 1]
If possible; try to get the vector data behind your map from another source. I understand this doesn't answer your question, but for world borders (and many others) you can find them publicly on the internet (google for "world borders shapefile"). This will give you more precise data, allow you to zoom at any level, reproject your map, use google maps or other layers, etc. You can display the vector data nicely with libraries like openlayers etc, but then you're slowly moving towards more complex GIS stuff.
If all you really need is based on an image, your flood fill approach might work (if the borders are indeed completely closed).
i want to identify a ball in the picture. I am thiking of using sobel edge detection algorithm,with this i can detect the round objects in the image.
But how do i differentiate between different objects. For example, a foot ball is there in one picture and in another picture i have a picture of moon.. how to differentiate what object has been detected.
When i use my algorithm i get ball in both the cases. Any ideas?
Well if all the objects you would like to differentiate are round, you could even use a hough transformation for round objects. This is a very good way of distinguishing round objects.
But your basic problem seems to be classification - sorting the objects on your image into different classes.
For this you don't really need a Neural Network, you could simply try with a Nearest Neighbor match. It's functionalities are a bit like neural networks since you can give it several reference pictures where you tell the system what can be seen there and it will optimize itself to the best average values for each attribute you detected. By this you get a dictionary of clusters for the different types of objects.
But for this you'll of course first need something that distinguishes a ball from a moon.
Since they are all real round objects (which appear as circles) it will be useless to compare for circularity, circumference, diameter or area (only if your camera is steady and if you know a moon will always have the same size on your images, other than a ball).
So basically you need to look inside the objects itself and you can try to compare their mean color value or grayscale value or the contrast inside the object (the moon will mostly have mid-gray values whereas a soccer ball consists of black and white parts)
You could also run edge filters on the segmented objects just to determine which is more "edgy" in its texture. But for this there are better methods I guess...
So basically what you need to do first:
Find several attributes that help you distinguish the different round objects (assuming they are already separated)
Implement something to get these values out of a picture of a round object (which is already segmented of course, so it has a background of 0)
Build a system that you feed several images and their class to have a supervised learning system and feed it several images of each type (there are many implementations of that online)
Now you have your system running and can give other objects to it to classify.
For this you need to segment the objects in the image, by i.e Edge filters or a Hough Transformation
For each of the segmented objects in an image, let it run through your classification system and it should tell you which class (type of object) it belongs to...
Hope that helps... if not, please keep asking...
When you apply an edge detection algorithm you lose information.
Thus the moon and the ball are the same.
The moon has a diiferent color, a different texture, ... you can use these informations to differnentiate what object has been detected.
That's a question in AI.
If you think about it, the reason you know it's a ball and not a moon, is because you've seen a lot of balls and moons in your life.
So, you need to teach the program what a ball is, and what a moon is. Give it some kind of dictionary or something.
The problem with a dictionary of course would be that to match the object with all the objects in the dictionary would take time.
So the best solution would probably using Neural networks. I don't know what programming language you're using, but there are Neural network implementations to most languages i've encountered.
You'll have to read a bit about it, decide what kind of neural network, and its architecture.
After you have it implemented it gets easy. You just give it a lot of pictures to learn (neural networks get a vector as input, so you can give it the whole picture).
For each picture you give it, you tell it what it is. So you give it like 20 different moon pictures, 20 different ball pictures. After that you tell it to learn (built in function usually).
The neural network will go over the data you gave it, and learn how to differentiate the 2 objects.
Later you can use that network you taught, give it a picture, and it a mark of what it thinks it is, like 30% ball, 85% moon.
This has been discussed before. Have a look at this question. More info here and here.
I'd like to implement a system that generates unique NxN blocks when given a MD5 hash as an input, currently I'm splitting the MD5 into sub-strings and just using them as Hex colour-codes, does anyone have any good ideas on systems I could use to generate these images?
I've considered using the values as the constants in a Lorenz attractor but the output just isn't that visually distinguishable for large numbers of generated outputs.
You could try generating a face-like image. People are fairly good at distinguishing different face-like images. For example, see Wavatars, which are goofy face-like images in which the shape is varied, the color, the background the eyes, the mouth, and so on, leading to a very large number of different, fairly recognizable images.
If not faces, then pick some other class of fairly recognizable objects that can vary on several axes, like cars or animals or something.
See, for example, this thread on Meta about automatically generating unicorn avatars.
The auto generated avatars on SO are called Identicons, you could use a variant of that.
See this question for more information:
How is the user id image generated on SO?