I am looking for an algorithm for union and intersection operations on simple polygons in 2D.
Every polygon in my application is:
defined by a set of points (point is defined by x and y coordinates),
convex or concave (non-convex),
not self-intersecting,
without holes.
I met two approaches. The first is algorithm that indicates intersection points as entry or exit.
By K. Hormann and G. Greiner [ http://www.inf.usi.ch/hormann/papers/Greiner.1998.ECO.pdf ].
I very like this approache, but there is problem with degeneracies (shared edges, touching verticies).
I also read solution for degeneracies: [ http://arxiv.org/pdf/1211.3376.pdf ]. But I think that this solution returns wrong result for situations like this:
The second algorithm divides edges at intersection points and then select the "right" edges for selected operation.
By F. Martinéz et al. [ http://www.cs.ucr.edu/~vbz/cs230papers/martinez_boolean.pdf ].
But this algorithm returns one polygon instead two triangles for this situation (operation: intersection, point F lies on edge AB):
Can you please refer me to another approache? (Polygons usually have a little edges, so efficiency is not in the first place.)
I would like to use some algorithm instead existing library.
I recommend the following approach: for every vertex of a polygon, decide once for all if it is inside or outside the other polygon. The intersections points between edges tell you where the "insideness" state changes, and this allows you to delimit the fragments of both polygon outlines that you need to reassemble (for instance Out(P, Q) and Out(Q, P) are the fragments for the union, while In(P, Q) and In(Q, P) are those for the intersection).
Now to achieve good robustness at a little cost, use a coherence principle: the number of intersections along an edge must have a parity compatible with the insideness at both endoints. For instance, when joining an inside vertex to an outside vertex, you must find an odd number of intersections.
When you observe a deviation from this rule, modify the parity of the number of intersections. This can be done by discarding an intersection, or by duplicating one.
This measure will not avoid unwanted effects like very close edges not merging or microscopic intersections, but it will avoid catastrophic anomalies. It will work even with poor insideness tests or inaccurate intersection function.
Related
I am having trouble figuring out how to triangulate an x-monotone polygon. I am referencing this article. I don't understand how to check if a vertex is an ear and if there is a diagonal.
See page 13/25, "Triangulation: Theory". The diagram illustrates a test to see if p is the vertex on an ear. Its neighbors are q and r. If the line segment qr is a diagonal, then p is on an ear.
You test a line segment to see if it's a diagonal by testing if any other vertices lie on it or if any other edge line segments cross it.
You refer to the ear cutting which is a n^2 time algorithm. There are many simple algorithms to triangulate a simple polygons. One of the simplest n log n time algorithm consists of first splitting the simple polygon into monotone pieces and then triangulate these pieces. The splitting in that case takes n log n. In your case, as you already have the monotone piece(s) you can triangulate the x-monotone polygon easily in linear time.
A good explanation of this simple algorithm is given for example in the book Computational Geometry.
Roughly the idea is: You know that your polygon is x-monotone. Thus, you split it into its two monotone chains (upper and lower). Now you can walk along both chains and insert diagonals between the two chains without visibility check. You go along the upper chain as long as the next lower chain vertex is of smaller x-value. In case your vertex is reflex you put it on a stack, otherwise you insert a diagonal to the other side. When you take the next step on the other chain, you first insert diagonals to every vertex on the stack and then go on with this routine.
I'm working with a really slow renderer, and I need to approximate polygons so that they look almost the same when confined to a screen area containing very few pixels. That is, I'd need an algorithm to go through a polygon and subtract/move a bunch of vertices until the end polygon has a good combination of shape preservation and economy of vertice usage.
I don't know if there's a formal name for these kind of problems, but if anyone knows what it is it would help me get started with my research.
My untested plan is to remove the vertices that change the polygon area the least, and protect the vertices that touch the bounding box from removal, until the difference in area from the original polygon to the proposed approximate one exceeds a tolerance I specify.
This would all be done only once, not in real time.
Any other ideas?
Thanks!
You're thinking about the problem in a slightly off way. If your goal is to reduce the number of vertices with a minimum of distortion, you should be defining your distortion in terms of those same vertices, which define the shape. There's a very simple solution here, which I believe would solve your problem:
Calculate distance between adjacent vertices
Choose a tolerance between vertices, below which the vertices are resolved into a single vertex
Replace all pairs of vertices with distances lower than your cutoff with a single vertex halfway between the two.
Repeat until no vertices are removed.
Since your area is ultimately decided by the vertex placement, this method preserves shape and minimizes shape distortion. The one drawback is that distance between vertices might be slightly less intuitive than polygon area, but the two are proportional. If you really wish, you could run through the change in area that would result from vertex removal, but that's a lot more work for questionable benefit imo.
As mentioned by Angus, if you want a direct solution for the change in area, it's not actually super difficult. Was originally going to leave this as an exercise to the reader, but it's totally possible to solve this exactly, though you need to include vertices on either side.
Assume you're looking at a window of vertices [A, B, C, D] that are connected in that order. In this example we're determining the "cost" of combining B and C.
Calculate the angle offset from collinearity from A toward C. Basically you just want to see how far from collinear the two points are. This is |sin(|arctan(B - A)| - |arctan(C - A)|)| Where pipes are absolute value, and differences are the sensical notion of difference.
Calculate the total distance over which the angle change will effectively be applied, this is just the euclidean distance from A to B times the euclidean distance from B to C.
Multiply the terms from 2 and 3 to get your first term
To get your second term, repeat steps 2 - 4 replacing A with D, B with C, and C with B (just going in the opposite direction)
Calculate the geometric mean of the two terms obtained.
The number that results in step 6 presents the full-picture minus a couple constants.
I tried my own plan first: Protect the vertices touching the bounding box, then remove the rest in the order that changes the resultant area the least, until you can't find a vertice to remove that keeps the new polygon area within X% of the original one. This is the result with X = 5%:
When the user zooms out really far these shapes fit the bill well enough for me. I haven't tried any of the other suggestions. The savings are quite astonishing, sometimes from 80-100 vertices down to 4 or 5.
Im looking for some fairly easy (I know polygon union is NOT an easy operation but maybe someone could point me in the right direction with a relativly easy one) algorithm on merging two intersecting polygons. Polygons could be concave without holes and also output polygon should not have holes in it. Polygons are represented in counter-clockwise manner. What I mean is presented on a picture. As you can see even if there is a hole in union of polygons I dont need it in the output. Input polygons are for sure without holes. I think without holes it should be easier to do but still I dont have an idea.
Remove all the vertices of the polygons which lie inside the other polygon: http://paulbourke.net/geometry/insidepoly/
Pick a starting point that is guaranteed to be in the union polygon (one of the extremes would work)
Trace through the polygon's edges in counter-clockwise fashion. These are points in your union. Trace until you hit an intersection (note that an edge may intersect with more than one edge of the other polygon).
Find the first intersection (if there are more than one). This is a point in your Union.
Go back to step 3 with the other polygon. The next point should be the point that makes the greatest angle with the previous edge.
You can proceed as below:
First, add to your set of points all the points of intersection of your polygons.
Then I would proceed like graham scan algorithm but with one more constraint.
Instead of selecting the point that makes the highest angle with the previous line (have a look at graham scan to see what I mean (*), chose the one with the highest angle that was part of one of the previous polygon.
You will get an envellope (not convex) that will describe your shape.
Note:
It's similar to finding the convex hull of your points.
For example graham scan algorithm will help you find the convex hull of the set of points in O (N*ln (N) where N is the number of points.
Look up for convex hull algorithms, and you can find some ideas.
Remarques:
(*)From wikipedia:
The first step in this algorithm is to find the point with the lowest
y-coordinate. If the lowest y-coordinate exists in more than one point
in the set, the point with the lowest x-coordinate out of the
candidates should be chosen. Call this point P. This step takes O(n),
where n is the number of points in question.
Next, the set of points must be sorted in increasing order of the
angle they and the point P make with the x-axis. Any general-purpose
sorting algorithm is appropriate for this, for example heapsort (which
is O(n log n)). In order to speed up the calculations, it is not
necessary to calculate the actual angle these points make with the
x-axis; instead, it suffices to calculate the cosine of this angle: it
is a monotonically decreasing function in the domain in question
(which is 0 to 180 degrees, due to the first step) and may be
calculated with simple arithmetic.
In the convex hull algorithm you chose the point of the angle that makes the largest angle with the previous side.
To "stick" with your previous polygon, just add the constraint that you must select a side that previously existed.
And you take off the constraint of having angle less than 180°
I don't have a full answer but I'm about to embark on a similar problem. I think there are two step which are fairly important. First would be to find a point on some polygon which lies on the outside edge. Second would be to make a list of bounding boxes for all the vertices and see which of these overlap. This means when you iterate through vertices, you don't have to do tests for all of them, only those which you know have a chance of intersecting (bounding box problems are lightweight).
Since you now have an outside point, you can now iterate through connected points until you detect an intersection. If you know which side is inside and which outside (you may need to do some work on the first vertex to know this), you know which way to go on the intersection. Then it's merely a matter of switching polygons.
This gets a little more interesting if you want to maintain that hole (which I do) in which case, I would probably make sure I had used up all my intersecting bounding boxes. You also didn't specify what should happen if your polygons don't intersect at all. But that's either going to be leave them alone (which could potentially be a problem if you're expecting one polygon out) or return an error.
I am looking for / trying to develop an optimal algorithm for rectilinear polygon intersection with rectangles. The polygons I am testing do not have holes.
Answers like those given here and here are for very general polygons, and the solutions are understandably quite complex.
Hoping that the S.O. community can help me document algorithms for the special cases with just rectilinear polygons.
I am looking for the polygon filled in green in the image below:
The book Computational Geometry: an Introduction by Preparata and Shamos has a chapter on rectilinear polygons.
Use a sweep line algorithm, making use of the fact that a rectilinear polygon is defined by its vertices.
Represent the vertices along with the rectangle that they belong to, i.e. something like (x, y, #rect). To this set of points, add those points that result from the intersections of all edges. These new points are of the form (x, y, final), since we already know that they belong to the resulting set of points.
Now:
sort all points by their x-value
use a sweep line, starting at the first x-coordinate; for each new point:
if it's a "start point", add it to a temporary set T. Mark it "final" if it's a point from rectangle A and between y-coordinates from points from rectangle B in T (or vice versa).
if it's an "end point", remove it and its corresponding start point from T.
After that, all points that are marked "final" denote the vertices of the resulting polygon.
Let N be the total number of points. Further assuming that testing whether we should mark a point as being "final" takes time O(log(n)) by looking up T, this whole algorithm is in O(N*log(N)).
Note that the task of finding all intersections can be incorporated into the above algorithm, since finding all intersections efficiently is itself a sweep line algorithm usually. Also note that the resulting set of points may contain more than one polygon, which makes it slightly harder to reconstruct the solution polygons out of the "final" vertices.
I need to compute the area of the region of overlap between two triangles in the 2D plane. Oddly, I have written up code for the triangle-circle problem, and that works quite well and robustly, but I have trouble with the triangle-triangle problem.
I already first check to see if one entirely contains the other, or if the other contains the first, as well as obtain all the edge-wise intersection points. These intersection points (up to 6, as in the star of David), combined with the triangle vertices that are contained within the other triangle, are the vertices of the intersection region. These points must form a convex polygon.
The solution I seek is the answer to either of these questions:
Given a set of points known to all lie on the convex hull of the point set, compute the area of the convex hull. Note that they are in random order.
Given a set of half-planes, determine the intersecting area. This is equivalent to describing both triangles as the intersection of three half-planes, and computing the solution as the direct intersection of this description.
I have considered for question 1 simply adding up all areas of all possible triangles, and then dividing by the multiplicity in counting, but that seems dumb, and I'm not sure if it is correct. I feel like there is some kind of sweep-line algorithm that would do the trick. However, the solution must also be relatively numerically robust.
I simply have no idea how to solve question 2, but a general answer would be very useful, and providing code would make my day. This would allow for direct computation of intersection areas of convex polygons instead of having to perform a triangle decomposition on them.
Edit: I am aware of this article which describes the general case for finding the intersection polygon of two convex polygons. It seems rather involved for just triangles, and furthermore, I don't really need the resulting polygon itself. So maybe this question is just asked in laziness at this point.
Question 1: why are the points in a random order? If they are, you have to order them so that connecting consecutive points with straight lines yields a convex polygon. How to order them -- for example, by running a convex hull algorithm (though there are probably also simpler methods). Once you have ordered them, compute the area as described here.
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Question 2 is simpler. Half-plane is defined by a single line having an implicit equation a*x+b*y+c=0 ; all points (x, y) for which a*x+b*y+c <= 0 (note the inequality) are "behind" the half-plane. Now, you need at least three planes so that the intersection of their negative half-spaces is closed (this is necessary, but not sufficient condition). If the intersection is closed, it will be a convex polygon.
I suggest that you maintain a linked list of vertices. The algorithm is initialized with THREE lines. Compute the three points (in general case) where the lines intersect; these are the starting vertices of your region (triangle). You must also check that each vertex is "behind" the half-plane defined by the line going through the other two vertices; this guarantees that the intersection actually IS a closed region.
These three vertices define also the the three edges of a triangle. When you intersect by a new half-plane, simply check for the intersection between the line defining the half-plane and each of the edges of the current region; in general you will get two intersection points, but you must watch out for degenerate cases where the line goes through a vertex of the region. (You can also end up with an empty set!)
The new intersection vertices define a line that splits the current region in TWO regions. Again, use orientation of the new half-plane to decide which of the two new regions to assign to the new "current region", and which one to discard.
The points in the list defining the edges of the current region will be correctly ordered so you can apply the formula in the above link to compute its area.
If this description is not detailed/understandable, the next-best advice I can give you is that you invest in a book on computational geometry and linear algebra.