I would like to deconstruct the following polygon shown in blue removing all of the points from the polygon that cause concavity.
Currently, what I have been attempting to do is:
Take each point out of the polygon
Test the point to see if it falls within the polygon created by the rest of the set
If true remove the point
If false keep the point
This works in most cases, but in the previous case the points at (2,3) and (2,4) will not both be removed. In both cases either one of the points will be removed, but the other will not depending on the order which the array is passed in.
What I am wondering is this:
Is there some way to test to see if the polygon I am dealing with happens to have one of these cases (IE: 3 points of failure in a row?)
or
Is there simply a more effective way of creating convex polygons?
Thank you.
I think perhaps you're looking for the convex hull?
The first algorithm that springs to mind is QuickHull. Initially, take the leftmost and the rightmost points, l and r. They must be on the hull.
Construct a first guess at the hull that's two outward faces, one from l to r and one from r to l. So you have a polygon with zero volume.
Divide all remaining points into those in front of lr and those in front of rl.
From then on, while any face has any points in front of it:
find the furthest point from the face
delete this edge and replace it with two edges, one from the original start point to the furthest point and one from the furthest point to the original end point
of all the points that were in front of the old face, put those in front of the first of the new faces you've added into its in front set, put those in front of the second into its in front set and don't retain any reference to those now inside
At the end you'll have the convex hull.
Why not simply compute the convex hull of the points?
This is a well studied problem with a number of algorithms in books and online. A method of "sweeping angles" is particularly common, eg.
http://courses.csail.mit.edu/6.854/06/scribe/s25-rasmu-sweepline.pdf
What you are looking for is known as "convex hull" finding. Look here at wikipedia for algorithms for this problem. The "gift wrapping" algorithm is easy to implement. When yout found the hull, just remove all points that are not part of the hull.
Be aware that the Convex Hull is already implemented in some languages/environments.
Example in Mathematica:
<< ComputationalGeometry`;
data2D = {{4.4, 14}, {6.7, 15.25}, {6.9,12.8}, {2.1, 11.1}, {9.5, 14.9},
{13.2, 11.9}, {10.3, 12.3}, {6.8, 9.5}, {3.3, 7.7}, {0.6, 5.1},
{5.3, 2.4}, {8.45, 4.7}, {11.5,9.6}, {13.8, 7.3}, {12.9, 3.1},
{11, 1.1}};
PlanarGraphPlot[data2D, ConvexHull[data2D]]
Output:
Related
Given two 2D polygons, how do I calculate the shortest translation that brings the first inside the second?
Assume there is a solution (i.e. the first does in fact fit inside the second)
Prefer a simple algorithm over completeness of solution. For example if the algorithm is simplified by making assumptions about the shapes having a certain number of sides, being concave, etc. then make those assumptions.
I can imagine a brute force solution, where I first calculate which are the offending vertices that lie outside the initial polygon. I'd then iterate through these external vertices and find the closest edge to each. Then I'm stuck. Each distance from an external vertex to an edge creates a constraint (a "need to move"). I then need to solve this system of constraints to find the movement that fulfills them all without creating any new violations.
I'm not sure if this can be a general solution, but here is at least a point to start with:
We want to move the green polygon into the red polygon. We use several translations. Each translation is defined by a start point and an end point.
Step 1: Start point is the mid-point between the left-most vertex and the right-most vertex in green polygon. End point, same criterion with the red polygon:
Step 2: Start point is the mid-point between the top-most vertex and the low-most vertex. End point, same criterion with the red polygon:
Notice that setps 1 & 2 are kind of centering. This method with mid points is similar to use the bounding boxes. Other way would be using circumcircles, but they are hard to get.
Step 3: Find the vertex in red polygon closest to an edge in the green polygon. You will need to iterate over all of them. Find the line perpendicular to that edge:
Well, this is not perfect. Depending on the given polygons it's better to proceed the other way: closest vertex in green to edges in red. Choose the smallest distance.
Finally, move the green polygon along that line:
If this method doesn't work (I'm sure there are cases where it fails), then you can also move the inner polygon along a line (a red edge or a perpendicular) that solves the issue. And continue moving until no issues are found.
We have sets of labeled points whose convex hulls do not overlap. There is some empty space between the convex hulls.
Given an unlabeled point that is not in our data we want to approximately determine which convex hull it lies within.
To make the computation faster, we want to reduce the number of sides on the convex hull (thus expanding the convex hull a bit, but not too much).
What algorithms could I use?
Update: Ideally I want to do the expansion under the constraint that it not intersect a given nearby polygon. (The motivation for this constraint is that I have several disjoint hulls and want to reduce the number of sides of all of them while still keeping them disjoint. But treat this as a parenthetical because I do not want to do a joint modification. I am happy to modify one hull while keeping the others constant. I am happy to hack this simple case to do a joint modification iteratively.)
Perhaps this is worth trying.
Find the convex hull A' of A union x, and the convex hull B' of B union x.
Select whichever increases the hull area the least.
In the example below, A' is the winner.
Added in response to comment:
One route is via "minimal enclosing k-gons":
Mictchell et al.: "Minimum-Perimeter Enclosing k-gon" 2006 (CiteSeer link)
Aggarwal et al.: "Minimum Area Circumscribing Polygons" 1985 (CiteSeer link)
O'Rourke et al.: "An optimal algorithm for fnding minimal enclosing triangles" 1986, Algorithmica (ACM link)
These algorithms are, however, quite intricate and unlikely to help much.
The "point in convex polygon" test is not so expensive, as it can be performed in Lg(N) comparisons by dichotomy (split the polygon in two with a straight line, recursively until you have a single triangle left). N is the number of sides. Actually, a polygon of 27 (resp. 130) sides will cost you the double (triple) of a triangle.
If you have many hulls, exhaustive comparisons of the point against every hull is a waste. There are better approaches such as using monotone subdivisions, which could lower the search time to O(Log(M)) query time for a total of M sides, after preprocessing.
I wouldn't be surprised if the saving of processing one less edge in your rough-phase contains check is outweighed by the increased false-positive rate of the inflated hull. Indeed, you might even be making more work for yourself - every point that passes the rough-phase check will have to also be checked against the true hull anyway.
Instead of trying to reduce the n in your O(n) contains check, I'd be tempted to go straight to something amortised O(1) for the rough passes:
1st pass - check against the axis-aligned bounding box (AABB). This gives quick rejection for the vast majority of points outside the polygon.
2nd pass - divide the AABB into a grid, where each grid quad is in one of three states: fully outside the hull, intersecting the hull edge or fully inside the hull. If your point lies in an "in" or "out" quad, you can stop here.
3rd pass - any point that lies in a grid quad that intersects the polygon is checked against the hull as normal.
The state of the grid can be computed ahead of time:
Initialise each grid quad to be outside of the hull
Use the algorithm linked in this answer to trace the edges of the hull over the grid and set all intersecting quads.
The grid now contains the outline of the hull, so use a simple floodfill or scanline approach to find and set all quads on the interior of the hull.
The resolution of the grid can be varied to give a tradeoff between memory cost (2 bits per quad) and false-positive rate (low-resolution grids will lead to more O(n) conventional hull checks).
It looks like your ultimate goal is not really about convex hulls, it is about solving the point location problem (https://en.wikipedia.org/wiki/Point_location). And you seem to be determined to solve it by simply iteratively checking your point against a number of convex hulls. While I understand where convex hulls come from (they actually represent sets of points), it is still not a reason to directly use them in the algorithm. Point location problem can be solved by a number of more efficent algorithms (like search tree based on trapezoidal decomposition), which are much less sensitive to the number of edges in your hulls.
Let's assume I have a polygon and I have computed all of its self-intersections. How do I determine whether a specific edge is inside or outside according to the nonzero fill rule? By "outside edge" I mean an edge which lies between a filled region and a non-filled region.
Example:
On the left is an example polygon, filled according to the nonzero fill rule. On the right is the same polygon with its outside edges highlighted in red. I'm looking for an algorithm that, given the edges of the polygon and their intersections with each other, can mark each of the edges as either outside or inside.
Preferably, the solution should generalize to paths that are composed of e.g. Bezier curves.
[EDIT] two more examples to consider:
I've noticed that the "outside edge" that is enclosed within the shape must cross an even number of intersections before they get to the outside. The "non-outside edges" that are enclosed must cross an odd number of intersections.
You might try an algorithm like this
isOutside = true
edge = find first outside edge*
edge.IsOutside = isOutside
while (not got back to start) {
edge = next
if (gone over intersection)
isOutside = !isOutside
edge.IsOutside = isOutside
}
For example:
*I think that you can always find an outside edge by trying each line in turn: try extending it infinitely - if it does not cross another line then it should be on the outside. This seems intuitively true but I wonder if there are some pathological cases where you cannot find a start line using this rule. Using this method of finding the first line will not work with curves.
I think, you problem can be solved in two steps.
A triangulation of a source polygon with algorithm that supports self-intersecting polygons. Good start is Seidel algorithm. The section 5.2 of the linked PDF document describes self-intersecting polygons.
A merge triangles into the single polygon with algorithm that supports holes, i.e. Weiler-Atherton algorithm. This algorithm can be used for both the clipping and the merging, so you need it's "merging" case. Maybe you can simplify the algorithm, cause triangles form first step are not intersecting.
I realized this can be determined in a fairly simple way, using a slight modification of the standard routine that computes the winding number. It is conceptually similar to evaluating the winding both immediately to the left and immediately to the right of the target edge. Here is the algorithm for arbitrary curves, not just line segments:
Pick a point on the target segment. Ensure the Y derivative at that point is nonzero.
Subdivide the target segment at the Y roots of its derivative. In the next point, ignore the portion of the segment that contains the point you picked in step 1.
Determine the winding number at the point picked in 1. This can be done by casting a ray in the +X direction and seeing what intersects it, and in what direction. Intersections at points where Y component of derivative is positive are counted as +1. While doing this, ignore the Y-monotonic portion that contains the point you picked in step 1.
If the winding number is 0, we are done - this is definitely an outside edge. If it is nonzero and different than -1, 0 or 1, we are done - this is definitely an inside edge.
Inspect the derivative at the point picked in step 1. If intersection of the ray with that point would be counted as -1 and the winding number obtained in step 3 is +1, this is an outside edge; similarly for +1/-1 case. Otherwise this is an inside edge.
In essence, we are checking whether intersection of the ray with the target segment changes the winding number between zero and non-zero.
I'd suggest what I feel is a simpler implementation of your solution that has worked for me:
1. Pick ANY point on the target segment. (I arbitrarily pick the midpoint.)
2. Construct a ray from that point normal to the segment. (I use a left normal ray for a CW polygon and a right normal ray for a CCW polygon.)
3. Count the intersections of the ray with the polygon, ignoring the target segment itself. Here you can chose a NonZero winding rule [decrement for polygon segments crossing to the left (CCW) and increment for a crossing to the right (CW); where an inside edge yields a zero count] or an EvenOdd rule [count all crossings where an inside edge yields an odd count]. For line segments, crossing direction is determined with a simple left-or-right test for its start and end points. For arcs and curves it can be done with tangents at the intersection, an exercise for the reader.
My purpose for this analysis is to divide a self-intersecting polygon into an equivalent set of not self-intersecting polygons. To that end, it's useful to likewise analyze the ray in the opposite direction and sense if the original polygon would be filled there or not. This results in an inside/outside determination for BOTH sides of the segment, yielding four possible states. I suspect an OUTSIDE-OUTSIDE state might be valid only for a non-closed polygon, but for this analysis it might be desirable to temporarily close it. Segments with the same state can be collected into non-intersecting polygons by tracing their shared intersections. In some cases, such as with a pure fill, you might even decide to eliminate INSIDE-INSIDE polygons as redundant since they fill an already-filled space.
And thanks for your original solution!!
I have given the coordinates of 1000 triangles on a plane (triangle number (T0001-T1000) and its coordinates (x1,y1) (x2,y2),(x3,y3)). Now, for a given point P(x,y), I need to find a triangle which contains the point P.
One option might be to check all the triangles and find the triangle that contain P. But, I am looking for efficient solution for this problem.
You are going to have to check every triangle at some point during the execution of your program. That's obvious right? If you want to maximize the efficiency of this calculation then you are going to create some kind of cache data structure. The details of the data structure depend on your application. For example: How often do the triangles change? How often do you need to calculate where a point is?
One way to make the cache would be this: Divide your plane in to a finite grid of boxes. For each box in the grid, store a list of the triangles that might intersect with the box.
Then when you need to find out which triangles your point is inside of, you would first figure out which box it is in (this would be O(1) time because you just look at the coordinates) and then look at the triangles in the triangle list for that box.
Several different ways you could search through your triangles. I would start by eliminating impossibilities.
Find a lowest left corner for each triangle and eliminate any that lie above and/or to the right of your point. continue search with the other triangles and you should eliminate the vast majority of the original triangles.
Take what you have left and use the polar coordinate system to gather the rest of the needed information based on angles between the corners and the point (java does have some tools for this, I do not know about other languages).
Some things to look at would be convex hull (different but somewhat helpful), Bernoullies triangles, and some methods for sorting would probably be helpful.
Suppose there are a number of convex polygons on a plane, perhaps a map. These polygons can bump up against each other and share an edge, but cannot overlap.
To test if two polygons P and Q overlap, first I can test each edge in P to see if it intersects with any of the edges in Q. If an intersection is found, I declare that P and Q intersect. If none intersect, I then have to test for the case that P is completely contained by Q, and vice versa. Next, there's the case that P==Q. Finally, there's the case that share a few edges, but not all of them. (These last two cases can probably be thought of as the same general case, but that might not be important.)
I have an algorithm that detects where two line segments intersect. If the two segments are co-linear, they are not considered to intersect for my purposes.
Have I properly enumerated the cases? Any suggestions for testing for these cases?
Note that I'm not looking to find the new convex polygon that is the intersection, I just want to know if an intersection exists. There are many well documented algorithms for finding the intersection, but I don't need to go through all the effort.
You could use this collision algorithm:
To be able to decide whether two convex polygons are intersecting (touching each other) we can use the Separating Axis Theorem. Essentially:
If two convex polygons are not intersecting, there exists a line that passes between them.
Such a line only exists if one of the sides of one of the polygons forms such a line.
The first statement is easy. Since the polygons are both convex, you'll be able to draw a line with one polygon on one side and the other polygon on the other side unless they are intersecting. The second is slightly less intuitive. Look at figure 1. Unless the closest sided of the polygons are parallel to each other, the point where they get closest to each other is the point where a corner of one polygon gets closest to a side of the other polygon. This side will then form a separating axis between the polygons. If the sides are parallel, they both are separating axes.
So how does this concretely help us decide whether polygon A and B intersect? Well, we just go over each side of each polygon and check whether it forms a separating axis. To do this we'll be using some basic vector math to squash all the points of both polygons onto a line that is perpendicular to the potential separating line (see figure 2). Now the whole problem is conveniently 1-dimensional. We can determine a region in which the points for each polygon lie, and this line is a separating axis if these regions do not overlap.
If, after checking each line from both polygons, no separating axis was found, it has been proven that the polygons intersect and something has to be done about it.
There are several ways to detect intersection and / or containment between convex polygons. It all depends on how efficient you want the algorithm to be. Consider two convex polygons R and B with r and b vertices, respectively:
Sweep line based algorithm. As you mentioned you can perform a sweep line procedure and keep the intervals resulting from the intersection of the polygons with the sweeping line. If at any time the intervals overlap, then the polygons intersect. The complexity is O((r + b) log (r + b)) time.
Rotating Callipers based algorithm. See here and here for more details. The complexity is O(r + b) time.
The most efficient methods can be found here and here. These algorithms take O(log r + log b) time.
if the polygons are always convex, first calculate the angle of a line drawn from center to center of the polygons. you can then eliminate needing to test edge segments in the half of the polygon(s) 180 degrees away from the other polygon(s).
to eliminate the edges, Start with the polygon on the left. take the line segment from the center of the polygon that is perpendicular to the line segment from the previous bullet, and touches both sides of the polygon. call this line segment p, with vertexes p1 and p2. Then, for all vertexes if the x coordinate is less than p1.x and p2.x That vertex can go in the "safe bucket".
if it doesn't, you have to check to make sure it is on the "safe" side of the line (just check the y coordinates too)
-if a line segment in the polygon has all vertexes in the "safe bucket" you can ignore it.
-reverse the polarity so you are "right oriented" for the second polygon.
GJK collision detection should work.
Since altCognito already gave you a solution, I'll only point out an excellent book on computational geometry that might interest you.
Your test cases should work, since you're checking the case where the polygons don't intersect at all (completely outside or completely inside), as well as where there is any form of partial intersection (edges intersect always if there is overlap).
For testing, I would just make sure to test every potential combination. The one missing above from what I see is a single edge shared, but one poly contained in the other. I would also add tests for some more complex poly shapes, from tri -> many sided, just as a precaution.
Also, if you had a U shaped poly that completely surrounded the poly, but didn't overlap, I believe your case would handle that, but I would add that as a check, as well.
Here's an idea:
Find the center point of each polygon
Find the two points of each polygon closest to the center point of the other. They will be adjacent points in convex polygons. These define the nearest edge of each polygon, let's call the points {A, B} and {Y, Z}
Find the intersection of lines AB and YZ. If the line segments cross (the intersection on AB lies between A and B), your polygons intersect. If AB and XY are parallel ignore this condition, the next step will trap the problem.
There is one more case you need to check for, which is when the polygons intersect heavily enough that AB and XY are completely past each other and don't actually intersect.
To trap this case, calculate the perpendicular distances of AB and XY to each polygons center points. If either center point is closer to the opposite polygon's line segment your polygon overlap heavily.