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Correctly filter the input to the convex hull function for duplicates.

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Contributes to CURA-1512
This commit is contained in:
Simon Edwards 2016-05-12 16:54:34 +02:00
parent 34b257d0f4
commit 5f0c96d4a5
1 changed files with 12 additions and 3 deletions
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15
cura/ConvexHullJob.py
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@ -45,10 +45,19 @@ class ConvexHullJob(Job):
# This is done to greatly speed up further convex hull calculations as the convex hull
# becomes much less complex when dealing with highly detailed models.
vertex_data = numpy.round(vertex_data, 1)
duplicates = (vertex_data[:,0] == vertex_data[:,1]) | (vertex_data[:,1] == vertex_data[:,2]) | (vertex_data[:,0] == vertex_data[:,2])
vertex_data = numpy.delete(vertex_data, numpy.where(duplicates), axis = 0)
hull = Polygon(vertex_data[:, [0, 2]])
vertex_data = vertex_data[:, [0, 2]] # Drop the Y components to project to 2D.
# Grab the set of unique points.
#
# This basically finds the unique rows in the array by treating them as opaque groups of bytes
# which are as long as the 2 float64s in each row, and giving this view to numpy.unique() to munch.
# See http://stackoverflow.com/questions/16970982/find-unique-rows-in-numpy-array
tmp = numpy.ascontiguousarray(vertex_data).view(numpy.dtype((numpy.void, vertex_data.dtype.itemsize * vertex_data.shape[1])))
_, idx = numpy.unique(tmp, return_index=True)
vertex_data = vertex_data[idx] # Select the unique rows by index.
hull = Polygon(vertex_data)
# First, calculate the normal convex hull around the points
hull = hull.getConvexHull()