def main(border,path): from scipy.spatial import Delaunay as delaunay rows,cols=border[0],border[1] array=randommap(rows*10,cols*10) array+=array.min() array/=array.max() array-=array.max() indices = np.vstack(np.unravel_index(np.arange(array.shape[0]*array.shape[1]),array.shape)).T points= np.column_stack((indices,array[indices[:,0],indices[:,1]])) triangles=np.sort(delaunay(indices).simplices) points*=np.array([0.1,0.1,10]) points-=np.array([-rows/2,-cols/2,0]) import pickle with open(path+"/temp/randommap.txt","w") as f: f.write(pickle.dumps([points.tolist(),triangles.tolist()])) print "New random heightmap generated. If you wish to use an existing one, change the corresponding variable heightmap_name in roadmap.conf" return 0
def main(border, path):
from scipy.spatial import Delaunay as delaunay
rows, cols=border[0], border[1]
array=randommap(rows*10, cols*10)
array+=array.min()
array/=array.max()
array-=array.max()
indices = np.vstack(np.unravel_index(np.arange(array.shape[0]*array.shape[1]), array.shape)).T
points= np.column_stack((indices, array[indices[:, 0], indices[:, 1]]))
triangles=np.sort(delaunay(indices).simplices)
points*=np.array([0.1, 0.1, 10])
points-=np.array([-rows/2, -cols/2, 0])
import pickle
with open(os.path.join(path, "temp", "randommap_"+str(border[0])+"_"+str(border[1])), "w") as f:
f.write(pickle.dumps([points.tolist(), triangles.tolist()]))
print("New random heightmap generated. If you wish to use an existing one, change the corresponding variable heightmap_name in roadmap.conf")
return 0
def return_meshed_point_cloud(self, sourcepoint, points, simplify=0.1):
#from sklearn.cluster import KMeans as kmeans
from sklearn.cluster import MiniBatchKMeans as mbkmeans
from scipy.spatial import Delaunay as delaunay
nclusters = int(len(points) * simplify)
#km = kmeans(n_clusters=nclusters, init='k-means++', n_init=10, max_iter=300, tol=0.0001, precompute_distances='auto', verbose=0, random_state=None, copy_x=True, n_jobs=1, algorithm='auto').fit(points)
km = mbkmeans(n_clusters=nclusters,
init='k-means++',
n_init=10,
max_iter=300,
tol=0.001,
verbose=0,
random_state=None,
init_size=3 * nclusters).fit(points)
self.clusters = km.cluster_centers_
clusters = [list(item) for item in list(km.cluster_centers_)]
if list(sourcepoint) in clusters:
print('sourcepoint already in clusters')
else:
clusters = [list(sourcepoint)] + clusters
print('got ' + str(len(clusters)) + ' clusters')
tri_volume = delaunay(np.array(clusters),
furthest_site=False,
incremental=False,
qhull_options=None)
return (tri_volume.points, tri_volume.simplices)
def get_triangulation(self, v):
from scipy.spatial import Delaunay as delaunay
flags = 'QJ Qc Pp'
tri = delaunay(v, incremental=False, qhull_options=flags)
return tri
def get_triangulation(self,v):
from scipy.spatial import Delaunay as delaunay
flags = 'QJ Qc Pp'
tri = delaunay(v,incremental=False,qhull_options=flags)
return tri
def __triangulate(self,v):
'''
Performs triangulation on vertices in v ((n,3) array)
'''
from scipy.spatial import Delaunay as delaunay
flags = 'QJ Qc Pp'
tri = delaunay(v,
incremental=False,
qhull_options=flags)
return tri
def delauPoints(self):
# print 'len self.points', len(self.points[:self.npoints]['pos'])
tri = delaunay(self.points['pos'])
self.ntets = tri.nsimplex
self.tets = zeros(self.ntets, dtype = [('tet', '4int'),('vol', 'float'),('anyInCell', 'bool')])
self.tets['tet'] = tri.simplices #vertex labels
self.tets['anyInCell'] = False
for itet, tet in enumerate(self.tets[:]['tet']):
for ivert in range(4): #find those with at least one vertex in the cell
if self.points[tet[ivert]]['inCell']:
self.tets[itet]['anyInCell'] = True
break
if self.tets[itet]['anyInCell']:
self.tets[itet]['vol'] = abs(self.volTet([self.points[iv]['pos'] for iv in tet]))
self.tets.sort(order='vol');self.tets = reverseStructured(self.tets)
def delauPoints(self):
# print 'len self.points', len(self.points[:self.npoints]['pos'])
tri = delaunay(self.points['pos'])
self.ntets = tri.nsimplex
self.tets = zeros(self.ntets,
dtype=[('tet', '4int'), ('vol', 'float'),
('anyInCell', 'bool')])
self.tets['tet'] = tri.simplices #vertex labels
self.tets['anyInCell'] = False
for itet, tet in enumerate(self.tets[:]['tet']):
for ivert in range(
4): #find those with at least one vertex in the cell
if self.points[tet[ivert]]['inCell']:
self.tets[itet]['anyInCell'] = True
break
if self.tets[itet]['anyInCell']:
self.tets[itet]['vol'] = abs(
self.volTet([self.points[iv]['pos'] for iv in tet]))
self.tets.sort(order='vol')
self.tets = reverseStructured(self.tets)
def plot_triangulation(x_obs, y_obs, xlabel = "", ylabel = "", title = "", fileo = ""):
# Tracé de la triangulation sur des sites d'observations
# x_obs, y_obs : observations
# xlabel, ylabel : étiquettes des axes (facultatif)
# title : titre (facultatif)
# fileo : nom du fichier d'enregistrement de la figure (facultatif)
from scipy.spatial import Delaunay as delaunay
tri = delaunay(np.hstack((x_obs,y_obs)))
plt.figure()
plt.triplot(x_obs[:,0], y_obs[:,0], tri.simplices)
plt.plot(x_obs, y_obs, 'or', ms=4)
plt.xlim(0.95*min(x_obs),max(x_obs)+0.05*min(x_obs))
plt.ylim(0.95*min(y_obs),max(y_obs)+0.05*min(y_obs))
plt.gca().set_aspect('equal', adjustable='box')
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.title(title)
plt.grid()
if not fileo == "": plt.savefig(fileo,bbox_inches='tight')
def setup_heightmap(singleton, path):
#TODO: Document
'''Sets up the heightmap image from roadmap.conf entry heightmap_name, writes ./Heightmaps/inuse.txt so other functions know which heightmap to load
possible inputs:
random: generates a new random map with randommap.py
insert_name
insert_name.png
insert_name.txt
'''
#TODO make inputs more flexible
name=singleton.heightmap_name
if name == "random":
print("New random heightmap is being created with randommap.py")
#Writes correct inuse.txt
from procedural_city_generation.additional_stuff import randommap
randommap.main(singleton.border, path)
with open(path+"/temp/"+singleton.output_name+"_heightmap.txt", 'w') as f:
f.write("randommap_"+str(singleton.border[0])+"_"+str(singleton.border[1]))
return 0
#Writes correct inuse.txt
with open(path+"/temp/"+singleton.output_name+"_heightmap.txt", 'w') as f:
f.write(name[0:-4]+"_"+str(singleton.border[0])+"_"+str(singleton.border[1]))
#If a txt has already been written for the input in the image, OR if the input was a .txt to begin with, simply load that txt
if (name[0:-4]+"_"+str(singleton.border[0])+"_"+str(singleton.border[1]) in os.listdir(path+"/temp/")):
return 0
#If the given image has no .txt yet, write the corresponding.txt
#Load image and resize
from PIL import Image
img = Image.open(path+'/inputs/heightmaps/'+name)
#TODO: set these numbers to some file where they can be edited easier
rsize = img.resize(((singleton.border[1]+20)*10, (singleton.border[0]+20)*10))
array = np.asarray(rsize)
from copy import copy
array= np.rot90(copy(array), k=3)
#If image is a jpeg, all values have to be divided by 255
array=array[::, :, 0]/255.
print("You have selected a heightmap which has no .txt file yet, OR the given .txt file has the wrong dimensions. The parameter heightDif will be used to describe the height difference between the lowest and the highest points on the map.")
h=singleton.heightDif
print("Processing image")
#TODO: Find and Fix this Bug
array*=abs(h)
#Caused weird bugs when -=h was used.. I still can't explain them...
array-= h+0.01
#Create all necessary stuff for the heightmap
from scipy.spatial import Delaunay as delaunay
indices = np.vstack(np.unravel_index(np.arange(array.shape[0]*array.shape[1]), array.shape)).T
points= np.column_stack((indices, array[indices[:, 0], indices[:, 1]]))
triangles=np.sort(delaunay(indices).simplices)
print("Processed image being saved as ", name)
#TODO: set thse numbers to some file where they can be edited easier
points*=[0.1, 0.1, 1]
points-=np.array([ (singleton.border[1]+20)/2, (singleton.border[0]+20)/2, 0])
points=points.tolist()
import pickle
with open(path+"/temp/"+name[0:-4]+"_"+str(singleton.border[0])+"_"+str(singleton.border[1]), "wb") as f:
f.write(pickle.dumps([points, triangles.tolist()]))
return 0
def setup_heightmap(singleton, path):
#TODO: Document
'''Sets up the heightmap image from roadmap.conf entry heightmap_name, writes ./Heightmaps/inuse.txt so other functions know which heightmap to load
possible inputs:
random: generates a new random map with randommap.py
insert_name
insert_name.png
insert_name.txt
'''
#TODO make inputs more flexible
name = singleton.heightmap_name
if name == "random":
print("New random heightmap is being created with randommap.py")
#Writes correct inuse.txt
from procedural_city_generation.additional_stuff import randommap
randommap.main(singleton.border, path)
with open(path + "/temp/" + singleton.output_name + "_heightmap.txt",
'w') as f:
f.write("randommap_" + str(singleton.border[0]) + "_" +
str(singleton.border[1]))
return 0
#Writes correct inuse.txt
with open(path + "/temp/" + singleton.output_name + "_heightmap.txt",
'w') as f:
f.write(name[0:-4] + "_" + str(singleton.border[0]) + "_" +
str(singleton.border[1]))
#If a txt has already been written for the input in the image, OR if the input was a .txt to begin with, simply load that txt
if (name[0:-4] + "_" + str(singleton.border[0]) + "_" +
str(singleton.border[1]) in os.listdir(path + "/temp/")):
return 0
#If the given image has no .txt yet, write the corresponding.txt
#Load image and resize
from PIL import Image
img = Image.open(path + '/inputs/heightmaps/' + name)
#TODO: set these numbers to some file where they can be edited easier
rsize = img.resize(
((singleton.border[1] + 20) * 10, (singleton.border[0] + 20) * 10))
array = np.asarray(rsize)
from copy import copy
array = np.rot90(copy(array), k=3)
#If image is a jpeg, all values have to be divided by 255
array = array[::, :, 0] / 255.
print(
"You have selected a heightmap which has no .txt file yet, OR the given .txt file has the wrong dimensions. The parameter heightDif will be used to describe the height difference between the lowest and the highest points on the map."
)
h = singleton.heightDif
print("Processing image")
#TODO: Find and Fix this Bug
array *= abs(h)
#Caused weird bugs when -=h was used.. I still can't explain them...
array -= h + 0.01
#Create all necessary stuff for the heightmap
from scipy.spatial import Delaunay as delaunay
indices = np.vstack(
np.unravel_index(np.arange(array.shape[0] * array.shape[1]),
array.shape)).T
points = np.column_stack((indices, array[indices[:, 0], indices[:, 1]]))
triangles = np.sort(delaunay(indices).simplices)
print("Processed image being saved as ", name)
#TODO: set thse numbers to some file where they can be edited easier
points *= [0.1, 0.1, 1]
points -= np.array([(singleton.border[1] + 20) / 2,
(singleton.border[0] + 20) / 2, 0])
points = points.tolist()
import pickle
with open(
path + "/temp/" + name[0:-4] + "_" + str(singleton.border[0]) +
"_" + str(singleton.border[1]), "wb") as f:
f.write(pickle.dumps([points, triangles.tolist()]))
return 0
import numpy as np
import sys
import matplotlib.pyplot as plt
def plot_triangle(a,b,c):
x = [a[0],b[0],c[0]]
y = [a[1],b[1],c[1]]
z = [a[2],b[2],c[2]]
verts = [zip(x, y,z)]
ax.add_collection3d(Poly3DCollection(verts))
n_samples=10**5
data = np.random.uniform(size=3*n_samples).reshape((-1,3))
data=np.loadtxt('cup.csv',dtype='float',delimiter=',')
triangulation = delaunay(data)
n_exterior_faces=(triangulation.neighbors==-1).sum(axis=None)
print n_exterior_faces
exterior_faces=np.zeros(shape=(n_exterior_faces,3),dtype='int')
face_counter=0
for i,tetrahedron in enumerate(triangulation.neighbors):
for j,face in enumerate(tetrahedron):
if face ==-1:
exterior_faces[face_counter,:j]=triangulation.simplices[i,:j]
exterior_faces[face_counter,j:]=triangulation.simplices[i,j+1:]
face_counter+=1
from mpl_toolkits.mplot3d import Axes3D
from mpl_toolkits.mplot3d.art3d import Poly3DCollection
fig = plt.figure()
