Takes the convex hull verticies (an array of indicies) and To the below function which writes out the xy positions of hull_indices from xy_file (in the format I required): def write_convex_hull_xy ( xy_file, hull_indices, opath, file_name ): ''' The hull vertex indices (hull_indices) can then be passed in with the main xy point file (xy_file) (along with an output path (opath) and output file name (file_name)

Hull_indicies - which more precisely are the “ndices of points forming the simplical facets of the convex hull” - will look something like this: array (, dtype = int32 ) vertices print 'Found %d hull points' % len ( hull_indices ) print 'Hull indicies calculated and now being returned.' return xy_file, hull_indices ConvexHull ( xy_file, qhull_options = 'Qt' ) hull_indices = hull.

exit ( 'File for convex hull calculation doesn't exist' ) print 'Calculating hull points.' hull = spatial. loadtxt ( input_xy_file, usecols = ( 0, 1 )) else : sys. isfile ( input_xy_file ): print 'Loading file.' xy_file = np. Returning the indicies of the convex hull points This is all integrated into a single function: import os import sys import numpy as np from scipy import spatial def xy_convex_hull ( input_xy_file ): '''Ĭalculates the convex hull of a given xy data set Pulling out the “vertices” from the spatial.ConvexHull object - this gives the indicies of the convex hull points within the original xy input file: hull = spatial. This is predominantly facilitated using scipy spatial’s ConvexHull function.įor my application I required the hull points to be printed out into a txt/csv in order of position (i.e. A first approach was to calculate the convex hull of the points. Working with LiDAR point data it was necessary for me to polygonize the point cloud extent.

Point cloud convex hull scilab