Using CGAL, I have a 3D Delaunay Triangulation of a set of random points on the unit sphere, that I obtained via:
Delaunay T(points.begin(), points.end());
Now, what I would like to be able to do is query T (using locate() or something like that) to find the three vertices of the surface facet that an arbitrary point (also on the unit sphere) is contained inside.
When I use locate(), I get interior cells as results sometimes, which include the infinite vertex. I don't want any of these. I just want the surface facets and to be able to do this for any arbitrary point I try to find that is also on the unit sphere. Trying to figure this out has taken a lot longer than I thought it would.
Any help would be much obliged. Thanks.
So I would use find_conflit(), with CGAL::Emptyset_iterator for cit because you don't need these.
In bfit, you will get the facets of the boundary of the "hole", and the hole is all tetrahedra in conflict with your point (whose circumscribing sphere contains the point, with a natural extension to the infinite vertex).
So, for bfit, put them in a standard container using std::back_inserter for example. Then, iterate over these, tests if they are finite facets. The finite facets you get are those that separate your point from the rest of the triangulation, so, you can then do orientation() tests with the center of the sphere to get the one you are interested in.
Related
So I have a list of points (2d) that should be inside a polygon, and a second list of points that should be outside the polygon. I want to find the polygon that encloses the "inside" points while excluding the "outside" points.
The resulting polygon will almost certainly be concave. I would like to minimise the number of edges it has, but any other sensible criteria would probably do. It seems likely that some approach that started with the convex hull, and then took chunks out of it would work.
I am wondering if this could be achieved using alpha shapes, while varying alpha. There is already a nice python implementation for that; https://pypi.org/project/alphashape/
but I haven't worked out exactly how I'd need to vary alpha.
i am trying to use an octree to find fast intersection between a volume of interest and several curves in 3d space, similar to what you see here at 0:25
https://www.youtube.com/watch?v=tL2AjQ4XEn4&t=119s
basically, i want to be able to quickly detect which curves intersect my volume of interest, which in my case is a sphere. so i want to avoid looping over all the points in each curve, to check the distance from the sphere which takes too long for a real time application.
can anyone give me the basic call structure to an octree that would be used to accomplish this? i know an octree is the correct data structure, i'm just not sure how to use it to accomplish this task.
thanks
I have an .obj file representing a 3D object.
I need to extract from this 3D object the contour that is obtained by intersection with a plane. So for example, I have an object representing a cylinder oriented with vertical axis, then I want to extract a circle contour when the intersecting plane is horizontal or a rectangular contour when the intersection plane is vertical. Any suggestion about how to do it?
Since I didn't know how to visualise this obj file, I have converted to a patch with the following code (some function taken from loadawobj from Matlab file exchange).
modelname='file.obj';
S=loadawobj(modelname);
mtl=loadawmtl(['obj/' S.mtllib]);
p3=patch('Vertices',S.v','Faces',S.f3');
for ii=1:length(S.umat3)
mtlnum=S.umat3(ii);
fvcd3(ii,:)=mtl(1).Kd';
end
p3.FaceVertexCData=fvcd3;
p3.FaceColor='flat';
But I don't necessarily need to extract the contour from the resulting patch if this is too complex to accomplish. If there is an easier procedure starting from the obj file, it's also fine and acceptable. Thank you!
That's the way I solved the problem, after collecting information all around the web. I couldn't find anything ready on line so I had to implement an algorithm on my own. The basic idea is very simple but there are many steps required. I start from two info: one array containing the coordinates of the cloud point and another array containing a bunch of tuples about how the 3 vertex are connected to form a triangle.
First of all you need to find a representation of the plane you want to use for your cutting. That means you just use one point and the normal to the plane to represent it. That plane is required in order to identify the cutting point on the structure.
Second step is to identify the triangles on the plane. In few words you just need to scroll over all the triangles of the structure and find those having one corner above the cutting plane and another corner below the cutting plane. Also don't forget to account for the condition where one corner is on the plane or two are on the plane. All the other triangles are not needed, since they are totally above or below the cutting plane.
Now you have a subset of all your triangles. You need to extract points of the contour. So for each triangle you have 3 vertex: in general case you can imagine that one vertex is above the plane and the other two are below. Then you have two lines cutting the plane. You can extract two point by simply intersecting these lines with the cutting plane.
By repeating this operation you get a series of points on 2D space. But they have no order and if you plot them as a continuous plot, you get lines jumping up and down since the points you have extracted are randomly located in the array. So, it's required to order them in a proper way. The method I used is the very simple: start from one point and connect to the closest one. There are some bad situations where that doesn't work but you can probably avoid it by adding some more rules on the algorithm.
I want to generate a heat map image of a floor. I have the following things:
A black & white .png image of the floor
A three column array stored in Matlab.
-- The first two columns indicate the X & Y coordinates of the floorpan image
-- The third coordinate denotes the "temperature" of that particular coordinate
I want to generate a heat map of the floor that will show the "temperature" strength in those coordinates. However, I want to display the heat map on top of the floor plan so that the viewers can see which rooms lead to which "temperatures".
Is there any software that does this job? Can I use Matlab or Python to do this?
Thanks,
Nazmul
One way to do this would be:
1) Load in the floor plan image with Matlab or NumPy/matplotlib.
2) Use some built-in edge detection to locate the edge pixels in the floor plan.
3) Form a big list of (x,y) locations where an edge is found in the floor plan.
4) Plot your heat map
5) Scatterplot the points of the floor plan as an overlay.
It sounds like you know how to do each of these steps individually, so all you'll need to do is look up some stuff on how to overlay plots onto the same axis, which is pretty easy in both Matlab and matplotlib.
If you're unfamiliar, the right commands look at are things like meshgrid and surf, possibly contour and their Python equivalents. I think Matlab has a built-in for Canny edge detection. I believe this was more difficult in Python, but if you use the PIL library, the Mahotas library, the scikits.image library, and a few others tailored for image manipulation, it's not too bad. SciPy may actually have an edge filter by now though, so check there first.
The only sticking point will be if your (x,y) data for the temperature are not going to line up with the (x,y) pixel locations in the image. In that case, you'll have to play around with some x-scale factor and y-scale factor to transform your heat map's coordinates into pixel coordinates first, and then plot the heat map, and then the overlay should work.
This is a fairly low-tech way to do it; I assume you just need a quick and dirty plot to illustrate how something's working. This method does have the advantage that you can change the style of the floorplan points easily, making them larger, thicker, thinner, different colors, or transparent, depending on how you want it to interact with the heat map. However, to do this for real, use GIMP, Inkscape, or Photoshop and overlay the heatmap onto the image after the fact.
I would take a look at using Python with a module called Polygon
Polygon will allow you to draw up the room using geometric shapes and I believe you can just do the borders of a room as an overlay on your black and white image. While I haven't used to a whole lot at this point, I do know that you only need a single (x,y) coordinate pair to be able to "hit test" against the given shape and then use that "hit test" to know the shape who's color you'd want to change.
Ultimately I think polygon would make your like a heck of a lot easier when it comes to creating the room shapes, especially when they aren't nice rectangles =)
A final little note though. Make sure to read through all of the documentation that Jorg has with his project. I haven't used it in the Python 3.x environment yet, but it was a little painstaking to get it up an running in 2.7.
Just my two cents, enjoy!
I have 3D space. And I know for example N points in this space (x1,y1,z1), (x2,y2,z2)..., (xn,yn,zn). I want to interplolate points, that is different from this. How can I do this in Matlab?
interp3 may help you. Here is the documentation.
As always, there are questions left unanswered by your one line query.
If the data is of the form where there is a functional relationship z(x,y), (or y(x,z) or x(y,z)) then you might potentially be able to use one of the interpolation tools. Thus, suppose you have data that lies on a lattice in the (x,y) plane, thus some value of z at each point in that lattice. In this case, you can use interp2.
Alternatively, if the data is scattered, but there is some single valued functional relationship z(x,y) that you don't have, but it is some continuous function. Infinite first derivatives are a problem too here. In this case, assuming that you have data that at least fills some convex domain in the (x,y) plane, you can still interpolate a value of z. For this, use griddata, or TriScatteredInterp. Or you might use my own gridfit tool, as found on the file exchange.
Next, the way you describe the data, I'm not at all positive that you have something in one of the above forms. For example, if your data lies along some curved path in this 3D domain, and you wish to interpolate points along that curved arc can be done using my interparc tool, also found on the file exchange.
One last case that people often seem to have when they talk about interpolation of a spatial set like this, is just a general surface, that they wish to build a neatly interpolated, smooth surface. It might be something as simple as the surface of a sphere, or something wildly more complex. (These things are never simple.) For this, you might be able to use a convex hull to approximate something, if it is a closed convex surface. More complex surfaces might require a tool like CRUST, although I have no implementation of it I can offer to you. Google will help you there, if that is what you need.
The point of all this is, how you interpolate your data depends on what form the data is in, what it represents, and the shape of the relationship you will be interpolating.