When polygons are simple or relatively simple, like convex polygons, it is relatively easy to compute their intersection part and area. I refered to this post: Compute intersection area of two concave polygons with cgal.
But when polygons are concave or self-intersection, is there any way of computing the intersection part?
For example, the points sequences of complex polygons what I want to use are shown below(It doesn't work with the above post):
Point points[] = { Point(0,0), Point(0,1), Point(2,0), Point(0,-2), Point(0,-1), Point(2,-1)};
Point points2[] = { Point(1,1), Point(3,1), Point(3,-2), Point(1,-2) };
Maybe I should decompose self-intersection polygons into simple polygons before I do the intersection. I'm new to CGAL, and I tried myself to browse the help document of CGAL, but maybe I still miss some points.
Thanks a lot.
Related
I am currently creating a feature and patterning it across a flat plane to get the maximum number of features to fit on the plane. I do this frequently enough to warrant building some sort of marcro for this if possible. The issue that I run into is I still have to manually set the spacing between the parts. I want to be able to create a feature and have it determine "best" fit spacing given an area while avoiding overlaps. I have had very little luck finding any resources describing this. Any information or links to potentially helpful resources on this would be much appreciated!
Thank you.
Before, you start the linear pattern bit:
Select the face2 of that feature2, get the outer most loop2 of edges. You can test for that using loop2.IsOuter.
Now:
if the loop has one edge: that means it's a circle and the spacing must superior to the circle's radius
if the loop has more that one edge, that you need to calculate all the distances between the vertices and assume that the largest distance is the safest spacing.
NOTA: If one of the edges is a spline, then you need a different strategy:
You would need to convert the face into a sketch and finds the coordinates of that spline to calculate the highest distances.
Example: The distance between the edges is lower than the distance between summit of the splines. If the linear pattern has the a vertical direction, then spacing has to be superior to the distance between the summit.
When I say distance, I mean the distance projected on the linear pattern direction.
I have a polygon as such:
{0}, {1}, {2}, {3},... denote the sequence of the points on the polygon.
I wonder whether CGAL polygon boolean set-operations work with polygon as such?
From the user manual, the input polygon must be simple or relatively simple in order for the CGAL polygon boolean set-operations to work:
A relatively simple polygon allows vertices with a degree >2, but all
of its edges are disjoint in their interior. Furthermore, it must be
an orientable polygon. Namely when it is inserted into an arrangement
and its outer boundary is traversed, the same face is adjacent to all
of the halfedges (no crossing over any curve during the traversal).
Note that while polygon C has the same curves as polygon B, traversal
of the curves leads to crossing over a previously traversed curve, and
is therefore neither simple nor relatively simple.
Not sure what it really means, but my guess is that the polygon is still ( at least) relatively simple, and hence the CGAL polygon boolean operation still works... am I right?
If you look at the condition for a valid polygon in the user manual, you'll see that the input polygon must be simple. Yours is not since you have a duplicated edge.
I'm needing to implement a Minkowski sum function that can return the Minkowski sum of either 2 circles, 2 convex polygons or a circle and a convex polygon. I found this thread that explained how to do this for convex polygons, but I'm not sure how to do this for a circle and polygon. Also, how would I even represent the answer?! I'd like the algorithm to run in O(n) time but beggars can't be choosers.
Circle is trivial -- just add the center points, and add the radii. Circle + ConvexPoly is nearly as simple: move each segment perpendicularly outward by the circle radius, and connect adjacent segments with circular arcs centered at the original poly vertices. Translate the whole by the circle center point.
As for how you represent the answer: Well, it depends on what you want to do with it. You could convert it to a NURBS if you just want to draw it with a vector drawing library. You could approximate the circular arcs with polylines if you just want a polygonal approximation. Or you might store it as is -- "this polygon, expanded by such-and-such a radius". That would be the best choice for things like raycasting, for instance. Or as a compromise, you could connect adjacent segments linearly instead of with circular arcs, and store it as the union of the (new) convex polygon and a list of circles at the vertices.
Oh, about ConvexPoly + ConvexPoly. That's the trickiest one, but still straightforward. The basic idea is that you take the list of segment vectors for each polygon (starting from some particular extremal point, like the point on each poly with the lowest X coordinate), then merge the two lists together, keeping it sorted by angle. Sum the two points you started with, then apply each vector from the merged vector list to produce the other points.
I have a number of 2D (possibly intersecting) polygons which I rendered using OpenGL ES on the screen. All the polygons are completely contained within the screen. What is the most timely way to find the percentage area of the union of these polygons to the total screen area? Timeliness is required as I have a requirement for the coverage area to be immediately updated whenever a polygon is shifted.
Currently, I am representing each polygon as a 2D array of booleans. Using a point-in-polygon function (from a geometry package), I sample each point (x,y) on the screen to check if it belongs to the polygon, and set polygon[x][y] = true if so, false otherwise.
After doing that to all the polygons in the screen, I loop through all the screen pixels again, and check through each polygon array, counting that pixel as "covered" if any polygon has its polygon[x][y] value set to true.
This works, but the performance is not ideal as the number of polygons increases. Are there any better ways to do this, using open-source libraries if possible? I thought of:
(1) Unioning the polygons to get one or more non-overlapping polygons. Then compute the area of each polygon using the standard area-of-polygon formula. Then sum them up. Not sure how to get this to work?
(2) Using OpenGL somehow. Imagine that I am rendering all these polygons with a single color. Is it possible to count the number of pixels on the screen buffer with that certain color? This would really sound like a nice solution.
Any efficient means for doing this?
If you know background color and all polygons have other colors, you can read all pixels from framebuffer glReadPixels() and simply count all pixels that have color different than background.
If first condition is not met you may consider creating custom framebuffer and render all polygons with the same color (For example (0.0, 0.0, 0.0) for backgruond and (1.0, 0.0, 0.0) for polygons). Next, read resulting framebuffer and calculate mean of red color across the whole screen.
If you want to get non-overlapping polygons, you can run a line intersection algorithm. A simple variant is the Bentley–Ottmann algorithm, but even faster algorithms of O(n log n + k) (with n vertices and k crossings) are possible.
Given a line intersection, you can unify two polygons by constructing a vertex connecting both polygons on the intersection point. Then you follow the vertices of one of the polygons inside of the other polygon (you can determine the direction you have to go in using your point-in-polygon function), and remove all vertices and edges until you reach the outside of the polygon. There you repair the polygon by creating a new vertex on the second intersection of the two polygons.
Unless I'm mistaken, this can run in O(n log n + k * p) time where p is the maximum overlap of the polygons.
After unification of the polygons you can use an ordinary area function to calculate the exact area of the polygons.
I think that attempt to calculate area of polygons with number of pixels is too complicated and sometimes inaccurate. You can see something similar in stackoverflow answer about calculation the area covered by a polygon and if you construct regular polygons see area of a regular polygon ,
I'm trying to implement a geometry templating engine. One of the parts is taking a prototypical polygonal mesh and aligning an instantiation with some points in the larger object.
So, the problem is this: given 3d point positions for some (perhaps all) of the verts in a polygonal mesh, find a scaled rotation that minimizes the difference between the transformed verts and the given point positions. I also have a centerpoint that can remain fixed, if that helps. The correspondence between the verts and the 3d locations is fixed.
I'm thinking this could be done by solving for the coefficients of a transformation matrix, but I'm a little unsure how to build the system to solve.
An example of this is a cube. The prototype would be the unit cube, centered at the origin, with vert indices:
4----5
|\ \
| 6----7
| | |
0 | 1 |
\| |
2----3
An example of the vert locations to fit:
v0: 1.243,2.163,-3.426
v1: 4.190,-0.408,-0.485
v2: -1.974,-1.525,-3.426
v3: 0.974,-4.096,-0.485
v5: 1.974,1.525,3.426
v7: -1.243,-2.163,3.426
So, given that prototype and those points, how do I find the single scale factor, and the rotation about x, y, and z that will minimize the distance between the verts and those positions? It would be best for the method to be generalizable to an arbitrary mesh, not just a cube.
Assuming you have all points and their correspondences, you can fine-tune your match by solving the least squares problem:
minimize Norm(T*V-M)
where T is the transformation matrix you are looking for, V are the vertices to fit, and M are the vertices of the prototype. Norm refers to the Frobenius norm. M and V are 3xN matrices where each column is a 3-vector of a vertex of the prototype and corresponding vertex in the fitting vertex set. T is a 3x3 transformation matrix. Then the transformation matrix that minimizes the mean squared error is inverse(V*transpose(V))*V*transpose(M). The resulting matrix will in general not be orthogonal (you wanted one which has no shear), so you can solve a matrix Procrustes problem to find the nearest orthogonal matrix with the SVD.
Now, if you don't know which given points will correspond to which prototype points, the problem you want to solve is called surface registration. This is an active field of research. See for example this paper, which also covers rigid registration, which is what you're after.
If you want to create a mesh on an arbitrary 3D geometry, this is not the way it's typically done.
You should look at octree mesh generation techniques. You'll have better success if you work with a true 3D primitive, which means tetrahedra instead of cubes.
If your geometry is a 3D body, all you'll have is a surface description to start with. Determining "optimal" interior points isn't meaningful, because you don't have any. You'll want them to be arranged in such a way that the tetrahedra inside aren't too distorted, but that's the best you'll be able to do.