I am writing a fairly simple top down, 2D game. It uses an evenly spaced 2D grid of tiles for all collision data. Each tile in the grid is either solid or empty.
For path finding I am using A* (A Star), and have tried both Manhattan and Diagonal (aka Chebyshev distance) heuristics.
It works well in most cases, but becomes quite expensive when trying to path find to a target sitting in the recess of a concave shape (eg. a U shape).
For example, in the picture below, the guy on the right will path find to the guy on the left. All the grass (green, dark green and yellow) is empty space. The only solid tiles are the brown "wood" tiles, and grey "Stone" tiles, making the shape of a sideways "U".
Now here is the results of the path search (in this case A* with Manhattan Heuristics):
The red and green debug draw squares show which tiles were visited during the A* search. Red are in the "Closed" list and green are in the "Open" list (as per A* specifications). The blue line in the final path chosen (which is correct).
As you can see, the search has been fairly exhaustive, visiting lots of tiles, creating an almost perfect circle.
I understand why this is happening based on the A* algorithm, and my chosen heuristics (as you move passed the target along the wall, the tiles further away begin to have better F values, causing them to be explored before coming back to the correct path). What I don't know is how to make this better :)
Any suggestions on possible improvements? Possibly a different heuristic? Maybe a different path searching algorithm all together?
Thanks!
Based on some suggestions, I am leaning towards updating my A* implementation to include the improvements found in HPA*. Based on some initial reading, it seems that it will address cases like the one above quite well, given the right amount of granularity in the hierarchy. I'll update once I finish looking into this.
You need to break ties towards the endpoint.
(Without breaking ties towards endpoint)
(With breaking ties towards endpoint)
(Example with an obstacle)
I ended up using Dynamic HPA*. I have written details on the solution here:
http://www.matthughson.com/2013/03/05/dynamic-hpa-part-1/
Related
I am working on a program that can essentially determine the electrostatic field of some arbitrarily shaped mesh with some surface charge. To test my program I make use of a cube whose left and right faces are oppositely charged.
I use a finite element method (FEM) that discretizes the object's surface into triangles and gives to each triangle 3 integration points (see below figure, bottom-left and -right). To obtain the field I then simply sum over all these points, whilst taking into account some weight factor (because not all triangles have the same size).
In principle this works all fine, until I get too close to a triangle. Since three individual points are not the same as a triangular surface, the program breaks and gives these weird dots. (block spots precisely between two integration points).
Below you see a figure showing the simulation of the field (top left), the discretized surface mesh (bottom left). The picture in the middle depicts what you see when you zoom in on the surface of the cube. The right-most picture shows qualitatively how the integration points are distributed on a triangle.
Because the electric field of one integration point always points away from that point, two neighbouring points will cancel each other out since their vectors aim in the exact opposite direction. Of course what I need instead is that both vectors point away from the surface instead.
I have tried many solutions, mostly around the following points:
Patching the regions near an integration point with a theoretically correct uniform field pointing away from the surface.
Reorienting the vectors only nearby the integration point to manually put them in the right direction.
Apply a sigmoid or other decay function to make the above look more smooth.
Though, none of the methods above allow me to properly connect the nearby and faraway regions.
I guess what might work is some method to extrapolate the correct value from the surroundings. Though, because of the large number of computations, I moved the simulation the my GPU, which means that I have to be careful allowing two pixels to write to each other.
Either way, my question here is as follows:
What would be a good way to smooth out my results? That is, I need a more accurate description of my model when I get closer to a triangle.
As a final note I want to add that it is not my goal to simply obtain a smooth image. Later in the program I need this data to determine the response of a conducting material, which is where these black dots internally become a real pain...
Thank you for your help !!!
My last question on image recognition seemed to be too broad, so I would like to ask a more concrete question.
First the background. I have already developed a (round) pill counter. It uses something similar to this tutorial. After I made it I also found something similar with this other tutorial.
However my method fails for something like this image
Although the segmentation process is a bit complicated (because of the semi-transparency of the tablets) I have managed to get it
My problem is here. How can I count the elongated tablets, separating each one from the image, similar to the final results in the linked tutorials?
So far I have applied distance transform and then my own version of watershed and I got
As you can see it fails in the adjacent tablets (distance transform usually does).
Take into account that the solution does have to work for this image and also for other arrangements of the tablets, the most difficult being for example
I am open to use OpenCV or if necessary implement on my own algorithms. So far I have tried both (used OpenCV functions and also programmed my own libraries) I am also open to use C++, or python or other. (I programmed them in C++ and I have done it on C# too).
I am also working on this pill counting problem (I'm much earlier in this process than you are), and to solve the piece you are working on - of touching pills, my general idea how to solve this is to capture contours of the pills once you have a good mask of the pills, and then calculate the area of a single pill.
For this approach I'm assuming that I have enough pills in the image such that the amount of them that are untouching is greater than those which are touching, and no pills overlap one another. For my application, placing this restriction I think is reasonable (humans can do a quick look at the pills they've dumped out, and at least roughly make them not touching without too much work. It's also possible that I could design a tray with some sort of dimples in it such that it would coerce the pills to not be touching)
I do this by sorting the contour areas (which, with the right thresholding should lead to only pills and pill-groups being in the identified contours), and taking the median value.
Then, with a good value for the area of a pill, you can look for contours with areas that are a multiple of that median area (+/- some % error value).
I also use that median value to filter out contours that are clearly not big enough to be pills, and ones that are far too large to be a pill (the latter though could be more troublesome, since it could still be a grouping of touching pills).
Given that the pills are all identical and don’t overlap, simply divide the total pill area by the area of a single pill.
The area is estimated simply counting the number of “pill” pixels.
You do need to calibrate the method by giving it the area of a single pill. This can be trivially obtained by giving the correct solution to one of the images (manual counting), then all the other images can be counted automatically.
I am currently facing a, in my opinion, rather common problem which should be quite easy to solve but so far all my approached have failed so I am turning to you for help.
I think the problem is explained best with some illustrations. I have some Patterns like these two:
I also have an Image like (probably better, because the photo this one originated from was quite poorly lit) this:
(Note how the Template was scaled to kinda fit the size of the image)
The ultimate goal is a tool which determines whether the user shows a thumb up/thumbs down gesture and also some angles in between. So I want to match the patterns against the image and see which one resembles the picture the most (or to be more precise, the angle the hand is showing). I know the direction in which the thumb is showing in the pattern, so if i find the pattern which looks identical I also have the angle.
I am working with OpenCV (with Python Bindings) and already tried cvMatchTemplate and MatchShapes but so far its not really working reliably.
I can only guess why MatchTemplate failed but I think that a smaller pattern with a smaller white are fits fully into the white area of a picture thus creating the best matching factor although its obvious that they dont really look the same.
Are there some Methods hidden in OpenCV I havent found yet or is there a known algorithm for those kinds of problem I should reimplement?
Happy New Year.
A few simple techniques could work:
After binarization and segmentation, find Feret's diameter of the blob (a.k.a. the farthest distance between points, or the major axis).
Find the convex hull of the point set, flood fill it, and treat it as a connected region. Subtract the original image with the thumb. The difference will be the area between the thumb and fist, and the position of that area relative to the center of mass should give you an indication of rotation.
Use a watershed algorithm on the distances of each point to the blob edge. This can help identify the connected thin region (the thumb).
Fit the largest circle (or largest inscribed polygon) within the blob. Dilate this circle or polygon until some fraction of its edge overlaps the background. Subtract this dilated figure from the original image; only the thumb will remain.
If the size of the hand is consistent (or relatively consistent), then you could also perform N morphological erode operations until the thumb disappears, then N dilate operations to grow the fist back to its original approximate size. Subtract this fist-only blob from the original blob to get the thumb blob. Then uses the thumb blob direction (Feret's diameter) and/or center of mass relative to the fist blob center of mass to determine direction.
Techniques to find critical points (regions of strong direction change) are trickier. At the simplest, you might also use corner detectors and then check the distance from one corner to another to identify the place when the inner edge of the thumb meets the fist.
For more complex methods, look into papers about shape decomposition by authors such as Kimia, Siddiqi, and Xiaofing Mi.
MatchTemplate seems like a good fit for the problem you describe. In what way is it failing for you? If you are actually masking the thumbs-up/thumbs-down/thumbs-in-between signs as nicely as you show in your sample image then you have already done the most difficult part.
MatchTemplate does not include rotation and scaling in the search space, so you should generate more templates from your reference image at all rotations you'd like to detect, and you should scale your templates to match the general size of the found thumbs up/thumbs down signs.
[edit]
The result array for MatchTemplate contains an integer value that specifies how well the fit of template in image is at that location. If you use CV_TM_SQDIFF then the lowest value in the result array is the location of best fit, if you use CV_TM_CCORR or CV_TM_CCOEFF then it is the highest value. If your scaled and rotated template images all have the same number of white pixels then you can compare the value of best fit you find for all different template images, and the template image that has the best fit overall is the one you want to select.
There are tons of rotation/scaling independent detection functions that could conceivably help you, but normalizing your problem to work with MatchTemplate is by far the easiest.
For the more advanced stuff, check out SIFT, Haar feature based classifiers, or one of the others available in OpenCV
I think you can get excellent results if you just compute the two points that have the furthest shortest path going through white. The direction in which the thumb is pointing is just the direction of the line that joins the two points.
You can do this easily by sampling points on the white area and using Floyd-Warshall.
How can I implement the A* algorithm on a gridless 2D plane with no nodes or cells? I need the object to maneuver around a relatively high number of static and moving obstacles in the way of the goal.
My current implementation is to create eight points around the object and treat them as the centers of imaginary adjacent squares that might be a potential position for the object. Then I calculate the heuristic function for each and select the best. The distances between the starting point and the movement point, and between the movement point and the goal I calculate the normal way with the Pythagorean theorem. The problem is that this way the object often ignores all obstacle and even more often gets stuck moving back and forth between two positions.
I realize how silly mu question might seem, but any help is appreciated.
Create an imaginary grid at whatever resolution is suitable for your problem: As coarse grained as possible for good performance but fine-grained enough to find (desirable) gaps between obstacles. Your grid might relate to a quadtree with your obstacle objects as well.
Execute A* over the grid. The grid may even be pre-populated with useful information like proximity to static obstacles. Once you have a path along the grid squares, post-process that path into a sequence of waypoints wherever there's an inflection in the path. Then travel along the lines between the waypoints.
By the way, you do not need the actual distance (c.f. your mention of Pythagorean theorem): A* works fine with an estimate of the distance. Manhattan distance is a popular choice: |dx| + |dy|. If your grid game allows diagonal movement (or the grid is "fake"), simply max(|dx|, |dy|) is probably sufficient.
Uh. The first thing that come to my mind is, that at each point you need to calculate the gradient or vector to find out the direction to go in the next step. Then you move by a small epsilon and redo.
This basically creates a grid for you, you could vary the cell size by choosing a small epsilon. By doing this instead of using a fixed grid you should be able to calculate even with small degrees in each step -- smaller then 45° from your 8-point example.
Theoretically you might be able to solve the formulas symbolically (eps against 0), which could lead to on optimal solution... just a thought.
How are the obstacles represented? Are they polygons? You can then use the polygon vertices as nodes. If the obstacles are not represented as polygons, you could generate some sort of convex hull around them, and use its vertices for navigation. EDIT: I just realized, you mentioned that you have to navigate around a relatively high number of obstacles. Using the obstacle vertices might be infeasible with to many obstacles.
I do not know about moving obstacles, I believe A* doesn't find an optimal path with moving obstacles.
You mention that your object moves back and fourth - A* should not do this. A* visits each movement point only once. This could be an artifact of generating movement points on the fly, or from the moving obstacles.
I remember encountering this problem in college, but we didn't use an A* search. I can't remember the exact details of the math but I can give you the basic idea. Maybe someone else can be more detailed.
We're going to create a potential field out of your playing area that an object can follow.
Take your playing field and tilt or warp it so that the start point is at the highest point, and the goal is at the lowest point.
Poke a potential well down into the goal, to reinforce that it's a destination.
For every obstacle, create a potential hill. For non-point obstacles, which yours are, the potential field can increase asymptotically at the edges of the obstacle.
Now imagine your object as a marble. If you placed it at the starting point, it should roll down the playing field, around obstacles, and fall into the goal.
The hard part, the math I don't remember, is the equations that represent each of these bumps and wells. If you figure that out, add them together to get your final field, then do some vector calculus to find the gradient (just like towi said) and that's the direction you want to go at any step. Hopefully this method is fast enough that you can recalculate it at every step, since your obstacles move.
Sounds like you're implementing The Wumpus game based on Norvig and Russel's discussion of A* in Artifical Intelligence: A Modern Approach, or something very similar.
If so, you'll probably need to incorporate obstacle detection as part of your heuristic function (hence you'll need to have sensors that alert your agent to the signs of obstacles, as seen here).
To solve the back and forth issue, you may need to store the traveled path so you can tell if you've already been to a location and have the heurisitic function examine the past N number of moves (say 4) and use that as a tie-breaker (i.e. if I can go north and east from here, and my last 4 moves have been east, west, east, west, go north this time)
I'm trying to render 6 spot lights to create a point light for a shadow mapping algorithm.
I'm not sure if I'm doing this right, I've more or less followed the instructions here when setting up my view and projection matrices but the end result looks like this:
White areas are parts which are covered by one of the 6 shadow maps, the darker areas are ones which aren't covered by the shadowmaps. Obviously I don't have a problem with the teapots and boxes having their shadows projected onto the scene, however as you can see the 6 shadow maps have blindspots. Is this how a cubed shadow map is supposed to look? It doesn't look like a shadowmap of a point light source...
Actually you can adjust your six spots to have cones that perfectly fill each face of your cubemap. You can achieve this by setting each cone's aperture to create a circumscribed circle around each cubemap face. In this case you don't have to worry about overlapping, since the would be overlapping parts are out of the faces' area.
In other terms: adjust the lights' projection matrix' FOV, so it won't the view frustum that includes the light cone, but the cone will include the view frustum.
The a whole implementation see this paper.
What you're seeing here are a circle and two hyperbolas -- conic sections -- exactly the result you might expect if you took a double ended cone and intersected it with a plane.
This math may seem removed from the situation but it explains your problem. A spotlight creates a cone of light, and you can't entirely fill a solid space with a bunch of cones coming from the same point. (I'd suggest rolling up a bunch of pieces of paper and taping them together at the points to try it out.)
However, as you get far from the origin of your simulated-point-source, the cones converge to their assymptotes, and there is an infinitesimally-narrow gap in the light.
One option to solve this is to change the focus of the cones so that they overlap slightly -- this will create areas that are overexposed, but the overexposure will only become obvious as you get farther away. So long as all of your objects are near the point light source, this might not be much of an issue.
Another option is to move the focus of all of the lights much closer to their sources. This way, they'd converge to their assymptotes more quickly.