Rinat M rmukh

A handcrafted collection of 3D convex hull implementations.

Implementations

QuickHull3D: A Robust 3D Convex Hull Algorithm in Java. O(n log(n)) complexity, works with double precision
C++ implementation of the 3D QuickHull algorithm. O(n log(n)) complexity. Can be included as header.
CGAL library convex hull based on quickhull. O(n log(n)) complexity. Require building/installation of CGAL. Static version works faster than dynamic.
Incremental convex hull in C++. O(n^2) complexity.
S-hull: a fast sweep-hull routine for Delaunay triangulation in C++. O(n log(n)) complexity. Require library building/installation.
Qhull. Performance is balanced in the sense that the average case happens on average. For more details re

	import numpy as np
	import re

	"""
	The link to the table: https://surfer.nmr.mgh.harvard.edu/fswiki/FsTutorial/AnatomicalROI/FreeSurferColorLUT
	It is also available locally if FreeSurfer is installed: $FREESURFER_HOME/FreeSurferColorLUT.txt

	Read and store FreeSufer LUT color table
	The result is two variables:

	template<typename T>
	T UtilMath::convhulln(T& u, double tol = 0.001) {
	/*
	Convex hull (might be a conflict between VTK 7 and 8 versions)
	*/

	// Convert from Eigen to vtkPoints (probably make as an function)
	vtkSmartPointer<vtkPoints> points = vtkSmartPointer<vtkPoints>::New();
	for (unsigned i = 0; i < u.rows(); ++i)
	points->InsertNextPoint(u(i, 0), u(i, 1), u(i, 2));

	from os import remove, path, makedirs
	from random import choices, randint
	from sys import argv, exit
	from urllib.request import urlretrieve
	from string import digits, ascii_uppercase, ascii_lowercase
	from threading import Thread

	#global variables
	incrt_sizes = [0, 503, 4939, 4940, 4941, 12003, 5556]
	temp = 1