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Compute a tight supertriangle and clean triangulation plot

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Johann Dreo 2014-04-03 22:14:00 +02:00
commit d25450444a
1 changed files with 132 additions and 109 deletions
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237
triangulation.py
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@ -1,7 +1,7 @@
import sys
import math
from utils import tour,LOG,LOGN
from utils import tour,LOG,LOGN,x,y
from itertools import ifilterfalse as filter_if_not
# Based on http://paulbourke.net/papers/triangulate/
@ -12,12 +12,6 @@ from itertools import ifilterfalse as filter_if_not
# Presented at Pan Pacific Computer Conference, Beijing, China.
# January 1989
def x( point ):
return point[0]
def y( point ):
return point[1]
def mid( xy, pa, pb ):
return ( xy(pa) + xy(pb) ) / 2.0
@ -80,7 +74,7 @@ def circumcircle( triangle, epsilon = sys.float_info.epsilon ):
def in_circle( p, center, radius, epsilon = sys.float_info.epsilon ):
"""Return True if the given point p is in the circumscribe circle of the given triangle"""
"""Return True if the given point p is in the given circle"""
assert( len(p) == 2 )
cx,cy = center
@ -104,6 +98,7 @@ def in_circumcircle( p, triangle, epsilon = sys.float_info.epsilon ):
def bounds( vertices ):
"""Return the iso-axis rectangle enclosing the given points"""
# find vertices set bounds
xmin = x(vertices[0])
ymin = y(vertices[0])
@ -120,219 +115,247 @@ def bounds( vertices ):
def edges_of( triangulation ):
"""Return a list containing the edges of the given list of 3-tuples of points"""
edges = []
for t in triangulation:
for e in utils.tour(list(t)):
for e in tour(list(t)):
edges.append( e )
return edges
def delaunay_bowyer_watson( points, epsilon = sys.float_info.epsilon, supert=20, do_plot = True ):
if do_plot and len(points) > 10:
print "WARNING it is a bad idea to plot each steps of a triangulation of many points"
return []
# sort points first on the x-axis, then on the y-axis
vertices = sorted( points )
def supertriangle( vertices, delta = 0.1 ):
"""Return a super-triangle that encloses all given points.
The super-triangle has its base at the bottom and encloses the bounding box at a distance given by:
delta*max(width,height)
"""
# Iso-rectangle bounding box.
(xmin,ymin),(xmax,ymax) = bounds( vertices )
dx = xmax - xmin
dy = ymax - ymin
dmax = max( dx, dy )
xmid = (xmax + xmin) / 2.0
ymid = (ymax + ymin ) / 2.0
supertri = ( ( xmin-dy-dmax*delta, ymin-dmax*delta ),
( xmax+dy+dmax*delta, ymin-dmax*delta ),
( xmid , ymax+(xmax-xmid)+dmax*delta ) )
return supertri
# compute the super triangle, that encompasses all the vertices
supertri = ( (xmid-supert*dmax, ymid-dmax ),
(xmid, ymid+supert*dmax),
(xmid+supert*dmax, ymid-dmax) )
def delaunay_bowyer_watson( points, supertri = None, superdelta = 0.1, epsilon = sys.float_info.epsilon,
do_plot = None, plot_filename = "Bowyer-Watson_%i.png" ):
"""Return the Delaunay triangulation of the given points
LOGN( "super-triangle",supertri )
epsilon: used for floating point comparisons, two points are considered equals if their distance is < epsilon.
do_plot: if not None, plot intermediate steps on this matplotlib object and save them as images named: plot_filename % i
"""
# it is the first triangle of the list
if do_plot and len(points) > 10:
print "WARNING it is a bad idea to plot each steps of a triangulation of many points"
# Sort points first on the x-axis, then on the y-axis.
vertices = sorted( points )
# LOGN( "super-triangle",supertri )
if not supertri:
supertri = supertriangle( vertices, superdelta )
# It is the first triangle of the list.
triangles = [ supertri ]
completed = { supertri: False }
# The predicate returns true if at least one of the vertices
# is also found in the supertriangle
# is also found in the supertriangle.
def match_supertriangle( tri ):
if tri[0] in supertri or \
tri[1] in supertri or \
tri[2] in supertri:
return True
# insert vertices one by one
it=0
# Returns the base of each plots, with points, current triangulation, super-triangle and bounding box.
def plot_base(ax,vi = len(vertices), vertex = None):
ax.set_aspect('equal')
# regular points
scatter_x = [ p[0] for p in vertices[:vi]]
scatter_y = [ p[1] for p in vertices[:vi]]
ax.scatter( scatter_x,scatter_y, s=30, marker='o', facecolor="black")
# super-triangle vertices
scatter_x = [ p[0] for p in list(supertri)]
scatter_y = [ p[1] for p in list(supertri)]
ax.scatter( scatter_x,scatter_y, s=30, marker='o', facecolor="lightgrey", edgecolor="lightgrey")
# current vertex
if vertex:
ax.scatter( vertex[0],vertex[1], s=30, marker='o', facecolor="red", edgecolor="red")
# current triangulation
uberplot.plot_segments( ax, edges_of(triangles), edgecolor = "blue", alpha=0.5, linestyle='solid' )
# bounding box
(xmin,ymin),(xmax,ymax) = bounds(vertices)
uberplot.plot_segments( ax, tour([(xmin,ymin),(xmin,ymax),(xmax,ymax),(xmax,ymin)]), edgecolor = "magenta", alpha=0.2, linestyle='dotted' )
# Insert vertices one by one.
LOG("Insert vertices: ")
if do_plot:
it=0
for vi,vertex in enumerate(vertices):
LOGN( "\tvertex",vertex )
# LOGN( "\tvertex",vertex )
assert( len(vertex) == 2 )
if do_plot:
fig = plot.figure()
ax = fig.add_subplot(111)
scatter_x = [ p[0] for p in vertices[:vi]+list(supertri)]
scatter_y = [ p[1] for p in vertices[:vi]+list(supertri)]
ax.scatter( scatter_x,scatter_y, s=30, marker='o', facecolor="black")
ax.scatter( vertex[0],vertex[1], s=30, marker='o', facecolor="red")
uberplot.plot_segments( ax, edges_of(triangles), edgecolor = "blue", alpha=0.3, linestyle='dashed' )
ax = do_plot.add_subplot(111)
plot_base(ax,vi,vertex)
# All the triangles whose circumcircle encloses the point to be added are identified,
# the outside edges of those triangles form an enclosing polygon.
# forget previous candidate polygon's edges
# Forget previous candidate polygon's edges.
enclosing = []
removed = []
for triangle in triangles:
LOGN( "\t\ttriangle",triangle )
# LOGN( "\t\ttriangle",triangle )
assert( len(triangle) == 3 )
# if completed has a key, test it, else return False
# Do not consider triangles already tested.
# If completed has a key, test it, else return False.
if completed.get( triangle, False ):
LOGN( "\t\t\tAlready completed" )
# LOGN( "\t\t\tAlready completed" )
# if do_plot:
# uberplot.plot_segments( ax, tour(list(triangle)), edgecolor = "magenta", alpha=1, lw=1, linestyle='dotted' )
continue
LOGN( "\t\t\tCircumcircle" )
# LOGN( "\t\t\tCircumcircle" )
assert( triangle[0] != triangle[1] and triangle[1] != triangle [2] and triangle[2] != triangle[0] )
center,radius = circumcircle( triangle, epsilon )
# if it match Delaunay's conditions
# If it match Delaunay's conditions.
if x(center) < x(vertex) and math.sqrt((x(vertex)-x(center))**2) > radius:
LOGN( "\t\t\tMatch Delaunay, mark as completed" )
# LOGN( "\t\t\tMatch Delaunay, mark as completed" )
completed[triangle] = True
# if the current vertex is inside the circumscribe circle of the current triangle
# add the current triangle's edges to the candidate polygon
# If the current vertex is inside the circumscribe circle of the current triangle,
# add the current triangle's edges to the candidate polygon.
if in_circle( vertex, center, radius, epsilon ):
LOGN( "\t\t\tIn circumcircle, add to enclosing polygon",triangle )
# LOGN( "\t\t\tIn circumcircle, add to enclosing polygon",triangle )
if do_plot:
# if not match_supertriangle( triangle ):
circ = plot.Circle(center, radius, facecolor='yellow', edgecolor="orange", alpha=0.1)
circ = plot.Circle(center, radius, facecolor='yellow', edgecolor="orange", alpha=0.2, clip_on=False)
ax.add_patch(circ)
for p0,p1 in tour(list(triangle)):
# then add this edge to the polygon enclosing the vertex
# Then add this edge to the polygon enclosing the vertex,
enclosing.append( (p0,p1) )
# and remove the corresponding triangle from the current triangulation
# and remove the corresponding triangle from the current triangulation.
removed.append( triangle )
completed.pop(triangle,None)
elif do_plot:
# if not match_supertriangle( triangle ):
circ = plot.Circle(center, radius, facecolor='lightgrey', edgecolor="grey", alpha=0.1)
circ = plot.Circle(center, radius, facecolor='lightgrey', edgecolor="grey", alpha=0.2, clip_on=False)
ax.add_patch(circ)
# end for triangle in triangles
# The triangles in the enclosing polygon are deleted and
# new triangles are formed between the point to be added and
# each outside edge of the enclosing polygon.
# actually remove triangles
# Actually remove triangles.
for triangle in removed:
# if do_plot:
# if not match_supertriangle( triangle ):
# uberplot.plot_segments( ax, tour(list(triangle)), edgecolor = "orange", alpha=0.3, lw=2 )
triangles.remove(triangle)
# remove duplicated edges
# this leaves the edges of the enclosing polygon only,
# Remove duplicated edges.
# This leaves the edges of the enclosing polygon only,
# because enclosing edges are only in a single triangle,
# but edges inside the polygon are at least in two triangles.
# duplicated = []
# for i,ei in enumerate(enclosing):
# for j,ej in enumerate(enclosing,i+1):
# if (ei[0] == ej[1] and ei[1] == ej[0]) or (ei[0] == ej[0] and ei[1] == ej[1]):
# duplicated.append( ei )
# for e in duplicated:
# enclosing.remove(e)
hull = []
for i,(p0,p1) in enumerate(enclosing):
# Clockwise edges can only be in the remaining part of the list.
# Search for counter-clockwise edges as well.
if (p0,p1) not in enclosing[i+1:] and (p1,p0) not in enclosing:
hull.append((p0,p1))
elif do_plot:
uberplot.plot_segments( ax, [(p0,p1)], edgecolor = "white", alpha=1, lw=1, linestyle='dotted' )
if do_plot:
uberplot.plot_segments( ax, hull, edgecolor = "red", alpha=1, lw=1, linestyle='solid' )
# create new triangles using the current vertex and the enclosing hull
# All candidates should be arranged in clockwise order!
LOGN( "\t\tCreate new triangles" )
# Create new triangles using the current vertex and the enclosing hull.
# LOGN( "\t\tCreate new triangles" )
for p0,p1 in hull:
assert( p0 != p1 )
# if p0 != vertex and p1 != vertex:
# triangle = tuple(sorted([p0,p1,vertex]))
triangle = tuple([p0,p1,vertex])
LOGN("\t\t\tNew triangle",triangle)
# LOGN("\t\t\tNew triangle",triangle)
triangles.append( triangle )
completed[triangle] = False
if do_plot: # linestyle = ['solid' | 'dashed' | 'dashdot' | 'dotted']
uberplot.plot_segments( ax, tour(list(triangle)), edgecolor = "green", alpha=0.3, linestyle='solid' )
with open("triangulation_%i.dat" % it, 'w') as fd:
for triangle in triangles:
for edge in tour(list(triangle)):
coords = tuple([coord for point in edge for coord in point])
fd.write( "%f %f %f %f\n" % coords )
if do_plot:
uberplot.plot_segments( ax, [(p0,vertex),(p1,vertex)], edgecolor = "green", alpha=1, linestyle='solid' )
if do_plot:
# ax.set_ylim([-100,200])
# ax.set_xlim([-100,200])
plot.savefig("triangulation_%i.png" % it, dpi=300)
plot.close()
plot.savefig( plot_filename % it, dpi=150)
plot.clf()
it+=1
it+=1
LOG(".")
# end for vertex in vertices
LOGN(" done")
# Remove triangles that have at least one of the supertriangle vertices
LOGN( "\tRemove super-triangles" )
# Remove triangles that have at least one of the supertriangle vertices.
# LOGN( "\tRemove super-triangles" )
# filter out elements for which the predicate is False
# here: *keep* elements that *do not* have a common vertex
triangulation = filter_if_not( match_supertriangle, triangles )
# Filter out elements for which the predicate is False,
# here: *keep* elements that *do not* have a common vertex.
# The filter is a generator, so we must make a list with it to actually get the data.
triangulation = list(filter_if_not( match_supertriangle, triangles ))
if do_plot:
ax = do_plot.add_subplot(111)
plot_base(ax)
uberplot.plot_segments( ax, edges_of(triangles), edgecolor = "red", alpha=0.5, linestyle='solid' )
uberplot.plot_segments( ax, edges_of(triangulation), edgecolor = "blue", alpha=1, linestyle='solid' )
plot.savefig( plot_filename % it, dpi=150)
plot.clf()
return triangulation
if __name__ == "__main__":
import random
import utils
import uberplot
import matplotlib.pyplot as plot
from matplotlib.path import Path
import matplotlib.patches as patches
scale = 100
nb = 10
points = [ (scale*random.random(),scale*random.random()) for i in range(nb)]
# points = [
# (0,40),
# (100,60),
# (40,0),
# (50,100),
# (90,10),
# ]
if len(sys.argv) > 1:
scale = 100
nb = int(sys.argv[1])
points = [ (scale*random.random(),scale*random.random()) for i in range(nb)]
else:
points = [
(0,40),
(100,60),
(40,0),
(50,100),
(90,10),
# (50,50),
]
triangles = delaunay_bowyer_watson( points, epsilon=10e-4, supert=3 )
fig = plot.figure()
triangles = delaunay_bowyer_watson( points, do_plot = fig )
edges = edges_of( triangles )
fig = plot.figure()
ax = fig.add_subplot(111)
ax.set_aspect('equal')
uberplot.scatter_segments( ax, edges, facecolor = "red" )
uberplot.plot_segments( ax, edges, edgecolor = "blue", alpha=0.2 )
uberplot.plot_segments( ax, edges, edgecolor = "blue" )
plot.show()