triangulation

we're trying to use GLU's tesselation functions on a headless, GNU/linux server. We'd like to use PyOpenGL for that, but the problem is that it crashes on a call to gluNewTess (Segmentation fault) gdb backtrace says it's in glGetError, that makes me think that GLU tesselation needs a GL context? Or is it just some intricacy in PyOpenGL? I tried to find some information on how to initialize GL context on a headless (and virtualized) machine, no luck. Any information on these topics is appreciated. easiest: Xvfb :5 -screen 0 800x600x24 & export DISPLAY=:5 glxgears instead of glxgears, replace

I'm trying to construct a Delaunay triangulation for the very specific case where the input x and y coordinates are orthogonal and relatively equidistant. Given the data size is relatively large (1000x1200 triangulation points) and that the Qhull algorithm doesn't know about my extra orthogonal condition, the triangulation is relatively slow (25 seconds on my machine). As such, I'd like to manually construct a Delaunay triangulation with each of my known quads subdivided into two triangles. I appreciate that this won't always result in a valid Delaunay triangulation (e.g. when the x and y step

I currently have a stereo camera setup. I have calibrated both cameras and have the intrinsic matrix for both cameras K1 and K2 . K1 = [2297.311, 0, 319.498; 0, 2297.313, 239.499; 0, 0, 1]; K2 = [2297.304, 0, 319.508; 0, 2297.301, 239.514; 0, 0, 1]; I have also determined the Fundamental matrix F between the two cameras using findFundamentalMat() from OpenCV. I have tested the Epipolar constraint using a pair of corresponding points x1 and x2 (in pixel coordinates) and it is very close to 0 . F = [5.672563368940768e-10, 6.265600996978877e-06, -0.00150188302445251; 6.766518121363063e-06, 4

I am trying to get a 3x4 camera matrix for triangulation process but calibrateCamera() returns only 3x3 and 4x1 matrices. How can i get the 3x4 out of those matrices? Thanks in advance!! calibrateCamera() returns you a 3x3 matrix as cameraMatrix, a 4x1 matrix as distCoeffs, and rvecs and tvecs that are vectors of 3x1 rotation(R) and 3x1 transformation(t) matrices. What you want is ProjectionMatrix, which is multiply [cameraMatrix] by [R|t]. Therefore, it returs you a 3x4 ProjectionMatrix. You can read OpenCV documentation for more info. If you are using cameraCalibrate(), you must be getting

I have a pointlist=[p1,p2,p3...] where p1 = [x1,y1],p2=[x2,y2] ... I want to use scipy.spatial.Delaunay to do trianglation on these point clouds and then plot it How can i do this ? The documentation for the Delaunay is really scarce so far i have this code from subprocess import Popen, PIPE import os os.environ['point_num'] = "2000" cmd = 'rbox $point_num D2 | tail -n $point_num' sub_process = Popen(cmd, shell=True,stdout=PIPE,stderr=PIPE) output = sub_process.communicate() points = [line.split() for line in output[0].split('\n') if line] x = [p[0] for p in points if p] y = [p[1] for p in

What is the best way to triangulate a polygon with Boost? I use Boost.polygon . My current algorithm: Compute a voronoï diagram from my polygon vertices. Create one directed polygon-edge for each cell-edge (this will create two directed polygon edge per cell-edge) Iterate over all created edges to create triangles (not trivial) Any better solution? Edit: I just realized that it is probably possible to walk through the cells in a special way to create the triangles directly (3 neighbor cells create a triangle). Giovanni Funchal The main idea is to iterate through the Voronoi vertices, and

I succesfully implemented a Delaunay triangulation of a contour in OpenCV 2.3.1. With cvPointPolygonTest I can get all the triangles in the convex hull, then I tried to perform another cvPointPolygonTest on triangles centroid to know if they are in the main contour or not , so I can have the constrained triangulation of the contour. But, it doesn't work well, as some triangles are (eg. with a walking man who has his two legs distant) 'over' a hole . Does anyone know a way to perform a constrained triangulation. I thought about convexityDefects, but can't manage to understand how to begin with