In a GIS I am given a single point and shall create a regular polygon out of it, basically exactly what this function does.
Unfortunately, I have no idea on how to achieve this. I do not even have an idea what to look for on Google.
Any ideas?
PS: The system is based on a SQL Server, hence I am using T-SQL (if this is helpful / harmful in any way).
This sounds a bit like homework, so forgive me if I layout an algorithm in plain language rather than T-SQL
First, we're going to create a polygon with n sides with radius 1 around the origin (0,0) and then show how to get to the general case of a polygon with radius r, rotation theta and center (x,y).
There will be n vertices in this n-sided polygon. Each vertex will have coordinates (cos(k/n), sin(k/n)), with k ranging from 0 to n-1. From there, to get your polygon, make a line from vertex k to vertex k+1.
Now, how do we make it bigger? If the polygon has radius r instead of radius 1, the coordinates will be (r*cos(k/n), r*sin(k/n)). Rotating it by an angle theta? (r*cos((k/n)+theta), r*sin((k/n)+theta). Translating it to somewhere other than the origin? (x+r*cos((k/n)+theta), y+r*sin((k/n)+theta).
Related
I want to create a set of N random convex disjoint polygons on a plane where each polygon must have at most V vertices, where N and V are parameters for my function, and I'd like to obtain a distribution as close as possible to uniform (every possible set being equally probable). Also I need to randomly select two points on the plane that either match with one of the vertices in the scene or are in empty space (not inside a polygon).
I already implemented for other reasons in the same programming language an AABB tree, Separating Axis Theorem-based collision detection between convex polygons and I can generate a random convex polygon with arbitrary amount of vertices inside a circle of given radius. My best bet thus far:
Generate a random polygon using the function I have available.
Query the AABB tree to test for interception with existing polygons.
If the AABB tree query returns empty set I push the generated polygon into it, otherwise I test with SAT against all the other polygons whose AABB overlaps with the generated one's. If SAT returns "no intersection" I push the polygon, otherwise I discard it.
Repeat from 1 until N polygons are generated.
Generate a random number in {0,1}
If the generated number is 1 I pick a random polygon and a random vertex on it as a point
If the generated number is 0 I generate a random position in (x,y) and test if it falls within some polygon (I might create a tiny AABB around it and exploit the AABB tree to reduce the required number of PiP tests). In case it's not inside any polygon I approve it as a valid point, otherwise I repeat from 5.
Repeat from 5 once more to get the second point.
I think the solution would possibly work, but unfortunately there's no way to guarantee that I can generate N such polygons for very large N, or find two good points in an acceptable time, and I'm programming in React, where long operations run on the main thread blocking the UI till they end. I could circumvent the issue by ejecting from create-react-app and learn Web Workers, which would require probably more time than it's worth for me.
This is definitely non-uniform distribution, but perhaps you could begin by generating N points in the plane and then computing the Voronoi diagram for those points. The Voronoi diagram can be computed in O(n log n) time with Fortune's algorithm. The cells of the Voronoi diagram are convex, so you can then construct a random polygon of the desired number of vertices that lies within each cell of the diagram.
By Balu Ertl - Own work, CC BY-SA 4.0, Link
Ok, here is another proposal. I have little knowledge of js, but could cook up something in Python.
Use Poisson disk sampling with distance parameter d to generate N samples of the centers
For a given center make a circle with R≤d.
Generate V angles using Dirichlet distribution such that sum of angles is equal to 2π. Sort them.
Place vertices on the circle using angles generate at step#3 and connect them. This would be be your polygon
UPDATE
Instead using Poisson disk sampling for step 1, one could use Sobol quasi-random sequences. For N points sampled in the 1x1 box (well, you have to scale it afterwards), least distance between points would be
d = 0.5 * sqrt(D) / N,
where D is dimension of the problem, 2 in your case. So radius of the circle for step 2 would be 0.25 * sqrt(2) / N. This ties nicely N and d.
https://www.sciencedirect.com/science/article/abs/pii/S0378475406002382
I'm currently working on a project where I want to draw different mathematical objects onto a 3D cube. It works as it should for Points and Lines given as a vector equation. Now I have a plane given as a parametric equation. This plane can be somewhere in the 3D space and may be visible on the screen, which is this 3D cube. The cube acts as an AABB.
First thing I needed to know was whether the plane intersects with the cube. To do this I made lines who are identical to the edges of this cube and then doing 12 line/plane intersections, calculating whether the line is hit inside the line segment(edge) which is part of the AABB. Doing this I will get a set of Points defining the visible part of the plane in the cube which I have to draw.
I now have up to 6 points A, B, C, D, E and F defining the polygon ABCDEF I would like to draw. To do this I want to split the polygon into triangles for example: ABC, ACD, ADE, AED. I would draw this triangles like described here. The problem I am currently facing is, that I (believe I) need to order the points to get correct triangles and then a correctly drawn polygon. I found out about convex hulls and found QuickHull which works in three dimensional space. There is just one problem with this algorithm: At the beginning I need to create a three dimensional simplex to have a starting point for the algorithm. But as all my points are in the same plane they simply form a two dimensional plane. Thus I think this algorithm won't work.
My question is now: How do I order these 3D points resulting in a polygon that should be a 2D convex hull of these points? And if this is a limitation: I need to do this in C.
Thanks for your help!
One approach is to express the coordinates of the intersection points in the space of the plane, which is 2D, instead of the global 3D space. Depending on how exactly you computed these points, you may already have these (say (U, V)) coordinates. If not, compute two orthonormal vectors that belong to the plane and take the dot products with the (X, Y, Z) intersections. Then you can find the convex hull in 2D.
The 8 corners of the cube can be on either side of the plane, and have a + or - sign when the coordinates are plugged in the implicit equation of the plane (actually the W coordinate of the vertices). This forms a maximum of 2^8=256 configurations (of which not all are possible).
For efficiency, you can solve all these configurations once for all, and for every case list the intersections that form the polygon in the correct order. Then for a given case, compute the 8 sign bits, pack them in a byte and lookup the table of polygons.
Update: direct face construction.
Alternatively, you can proceed by tracking the intersection points from edge to edge.
Start from an edge of the cube known to traverse the plane. This edge belongs to two faces. Choose one arbitrarily. Then the plane cuts this face in a triangle and a pentagon, or two quadrilaterals. Go to the other the intersection with an edge of the face. Take the other face bordered by this new edge. This face is cut in a triangle and a pentagon...
Continuing this process, you will traverse a set of faces and corresponding segments that define the section polygon.
In the figure, you start from the intersection on edge HD, belonging to face DCGH. Then move to the edge GC, also in face CGFB. From there, move to edge FG, also in face EFGH. Move to edge EH, also in face ADHE. And you are back on edge HD.
Complete discussion must take into account the case of the plane through one or more vertices of the cube. (But you can cheat by slightly translating the plane, constructing the intersection polygon and removing the tiny edges that may have been artificially created this way.)
There are two polygons given. how can one determine whether one polygon is inside, outside or intersecting the other polygon?
Polygons can be Concave or convex.
You want to use the separating axis theorem for convex polygons.
Basically, for each face of each polygon you project each polygon onto the normal of that face and you see if those projections intersect.
You can perform various tricks to reduce the number of these computations that you have to perform- for example, you can draw a rectangle around the object and assume that if two objects' rectangles do not intersect, they themselves do not intersect. (This is easier because it's less computationally expensive to check the intersection of these boxes, and is generally quite intuitive.)
Concave polygons are more difficult. I think that you could decompose the polygon into a set of convex polygons and attempt to check each combination of intersection, but I wouldn't consider myself skilled enough in this area to try it.
Generally, problems like that are solved easily by a sweep-line algorithm. However, the primary purpose and benefit of using the sweep-line approach is that it can solve the problem efficiently when input consists of two relatively large sets of polygons. Once the sweep line solution is implemented, it can also be efficiently applied to a pair of polygons, if need arises. Maybe you should consider moving in that direction, in case you'll need to solve a massive problem in the future.
However, if you are sure that you need a solution for two and only two polygons , then it can be solved by a sequential point-vs-polygon and segment-vs-polygon tests.
There is an easyish method to check whether a point lies in a polygon. According to this Wikipedia article it is called the ray casting algorithm.
The basic idea of the algorithm is that you cast a ray in some arbitrary direction from the point you are testing and count with how many of the edges of the polygon it intersects. If this number is even then the point lies outside the polygon, otherwise if it is odd the point lies inside the polygon.
There are a number of issues with this algorithm that I won't delve into (they're discussed in the Wikipedia article I linked earlier), but they are the reason I call this algorithm easyish. But to give you an idea you have to handle corner cases involving the ray intersecting vertices, the ray running parallel and intersecting an edge and numeric stability issues with the point lying close to an edge.
You can then use this method in the way Thomas described in his answer to test whether two polygons intersect. This should give you an O(NM) algorithm where the two polygons have N and M vertices respectively.
Here is a simple algorithm to know if a given point is inside or outside a given polygon:
bool isInside(point a, polygon B)
{
double angle = 0;
for(int i = 0; i < B.nbVertices(); i++)
{
angle += angle(B[i],a,B[i+1]);
}
return (abs(angle) > pi);
}
If a line segment of A intersects a line segment of B then the two polygons intersect each other.
Else if all points of polygon A are inside polygon B, then A is inside B.
Else if all points of polygon B are inside polygon A, then B is inside A.
Else if all points of polygon A are outside polygon B, then A is outside B.
Else the two polygons intersect each other.
I have the following geometry problem:
You are given a circle with the center in origin - C(0, 0), and radius 1. Inside the circle are given N points which represent the centers of N different circles. You are asked to find the minimum radius of the small circles (the radius of all the circles are equal) in order to cover all the boundary of the large circle.
The number of circles is: 3 ≤ N ≤ 10000 and the problem has to be solved with a precision of P decimals where 1 ≤ P ≤ 6.
For example:
N = 3 and P = 4
and the coordinates:
(0.193, 0.722)
(-0.158, -0.438)
(-0.068, 0.00)
The radius of the small circles is: 1.0686.
I have the following idea but my problem is implementing it. The idea consists of a binary search to find the radius and for each value given by the binary search to try and find all the intersection point between the small circles and the large one. Each intersection will have as result an arc. The next step is to 'project' the coordinates of the arcs on to the X axis and Y axis, the result being a number of intervals. If the reunions of the intervals from the X and the Y axis have as result the interval [-1, 1] on each axis, it means that all the circle is covered.
In order to avoid precision problems I thought of searching between 0 and 2×10P, and also taking the radius as 10P, thus eliminating the figures after the comma, but my problem is figuring out how to simulate the intersection of the circles and afterwards how to see if the reunion of the resulting intervals form the interval [-1, 1].
Any suggestions are welcomed!
Each point in your set has to cover the the intersection of its cell in the point-set's voronoi diagram and the test-circle around the origin.
To find the radius, start by computing the voronoi diagram of your point set. Now "close" this voronoi diagram by intersecting all infinite edges with your target-circle. Then for each point in your set, check the distance to all the points of its "closed" voronoi cell. The maximum should be your solution.
It shouldn't matter that the cells get closed by an arc instead of a straight line by the test-circle until your solution radius gets greater than 1 (because then the "small" circles will arc stronger). In that case, you also have to check the furthest point from the cell center to that arc.
I might be missing something, but it seems that you only need to find the maximal minimal distance between a point in the circle and the given points.
That is, if you consider the set of all points on the circle, and take the minimal distance between each point to one of the given points, and then take the maximal values of all these - you have found your radius.
This is, of course, not an algorithm, as there are uncountably many points.
I think what I'll do would be along the line of:
Find the minimal distance between the circumference and the set of points, this is your initial radius R.
Check if the entire circle was covered, like so:
For any two points whose distance from each other is more than 2R, check if the entire segment was covered (for each point, check if the circle around it intersects, and if so, remove that segment and keep going). That should take about o(N^3) (you iterate over all of the points for each pair of points). If I'm correct (though I didn't formally prove it) the circle is covered iff all of the segments are covered.
Of all the segment which weren't covered, take the long one, and add half it's length to R.
Repeat.
This algorithm will never cover the circle per se, but it's easy to prove that it exponentially converges to a full cover, so it should be able to find the needed radius with arbitrary accuracy within a reasonable amount of iterations.
Hope that helps.
What is the most user friendly way to store only the rotation part of an OGL modelview (4x4) matrix?
For example; in a level editor to set the rotation for an object it would be easy to use the XYZ Euler angles. However this seems a very tricky system to use with matrices.
I need to be able to get AND set the rotation from this new representation.
(The alternative is to store the rotation part (4*3 numbers) but it is hard for a user to manipulate these)
I found some code here http://www.google.com/codesearch/p?hl=en#HQY9Wd_snmY/mesh/matrix3.h&q=matrix3&sa=N&cd=1&ct=rc that allows me to set and get rotation from angles (3 floats). This is ideal.
Although they're used regularily, I disregard the use of Euler angles. They're problematic as they only preserve the pointing direction of the object, but not the bitangent to that direction. More important: They're prone to gibal lock http://en.wikipedia.org/wiki/Gimbal_lock
A far superior method for storing rotations are Quarternions. In layman terms a quaternion consists of the rotational axis and the angle of rotation around this axis. It is thus a tuple of 4 scalars a,b,c,d. The quaternion is then Q = a + i*b + j*c + k*d, |Q| = 1, with the special properties of i,j,k that i² = j² = k² = i·j·k = -1 and i·j = k, j·k = i, k·i = j, which implies j·i = -k, k·j = -i, i·k = -j
Quaterions are thus extensions of complex numbers. If you recall complex number theory, you'll remember that the product of two complex numbers a =/= b with |a| = |b| = 1 is a rotation in the complex plane. It is thus easy to assume that rotations in 3D can be described by an extension of complex numbers into a complex hyperplane. This is what quaternions are.
See this article on the details.
http://en.wikipedia.org/wiki/Quaternions_and_spatial_rotation
In a standard 3D matrix you only need the top left 3x3 values to give the rotation. To apply the matrix as a 4x4 later on, you need to make the other values 0 apart from on the diagonal.
Here's a rotation only matrix where the values vXY give the rotations.
[v00 v01 v02 0]
[v10 v11 v12 0]
[v20 v21 v22 0]
[ 0 0 0 1]
Interestingly, the values form the bases of the coordinate system you have rotated the object into, so in the new system, the x-axis is along [v00 v01 v02], the y-axis is along [v10 v11 v12] and the z-axis obviously [v20 v21 v22].
You could show these axes beside the object and let the other drag them around to change the rotation, perhaps.
I would say this depends on the user, but to me the most "user friendly" way is to store "roll", "pitch" and "yaw". These are very non-technical terms that an average user can understand and adjust, and it should be easy for you to take these values and compute the matrix.
IMO, the most 'user friendly format' for rotation is storing Euler XYZ angles, this is generally how rotations are exposed in any 3d content creation software.
Euler angles are easy to transform to matrices, see here for the involved matrix product.
But you should not confuse the format given to the GUI/user and the storage format of the data: Euler XYZ angles have problems of their own when doing animation, gimbal lock can introduce unwanted behaviour.
Another candidate for storing/computing rotations is quaternions. They offer mathematical advantages over XYZ angles, essentially when interpolating between two rotations. Of course, you don't want to expose the quaternion values directly to any human user, you'll need to convert them to XYZ angles. You'll find plenty of code to that on the Web.
I would not recommend storing the rotation directly in matrix format. Extracting user friendly values from it is difficult, it does not offer any interesting behaviour for animation/interpolation, it takes for storage. IMO, matrices are to be created when needed to transform the geometry.
To conclude, there are a few options, you should select what suits you most. Do you plan to having animation or not ? etc.
EDIT
Also, you should not make an amalgam with model and view matrices. They are semantically very different, and are combined in OpenGL only for performance reasons. What I had in mind above in the 'model matrix'. The view matrix is generally given by your view system/camera manager, and is combined with you model matrix.
A quaternion is, although the math is "obscure and unintellegible" surprisingly user friendly, as it represents rotation around an axis by a given angle.
The axis of rotation is simply a unit vector pointing in that direction, multiplied by the sine of 1/2 the rotation angle, and the "obscure" 4th component equals the cosine of 1/2 the rotation angle.
It feels kind of "unnatural" at first sight, but once you grasp it... can it be any easier?