def minor_road(vertex, b):
#Sammelt Numerische Werte aus Variables-Objekt
pForward=singleton.minor_roadpForward
pTurn=singleton.minor_roadpTurn
lMin=singleton.minor_roadlMin
lMax=singleton.minor_roadlMax
suggested_vertices=[]
#Berechnet den Vektor des letzten Weges zu diesem Punkt
previous_vector=np.array(vertex.coords-vertex.neighbours[len(vertex.neighbours)-1].coords)
previous_vector=previous_vector/np.linalg.norm(previous_vector)
n=np.array([-previous_vector[1], previous_vector[0]])
#Geradeaus
v=random.uniform(lMin, lMax)*previous_vector
random_number=random.randint(0, 100)
if random_number<pForward*b:
k=Vertex(vertex.coords+v)
#k.neighbours.append(vertex)
k.minor_road=True
suggested_vertices.append(k)
#Rechts
v=random.uniform(lMin, lMax)*previous_vector
random_number=random.randint(0, 100)
if random_number<pTurn*b:
k=Vertex(vertex.coords+n)
#k.neighbours.append(vertex)
k.minor_road=True
suggested_vertices.append(k)
#Links
v=random.uniform(lMin, lMax)*previous_vector
random_number=random.randint(0, 100)
if random_number<pTurn*b:
k=Vertex(vertex.coords-n)
#k.neighbours.append(vertex)
k.minor_road=True
suggested_vertices.append(k)
return suggested_vertices
def minor_road(vertex, b):
# Sammelt Numerische Werte aus Variables-Objekt
pForward = singleton.minor_roadpForward
pTurn = singleton.minor_roadpTurn
lMin = singleton.minor_roadlMin
lMax = singleton.minor_roadlMax
suggested_vertices = []
# Berechnet den Vektor des letzten Weges zu diesem Punkt
previous_vector = np.array(vertex.coords -
vertex.neighbours[len(vertex.neighbours) -
1].coords)
previous_vector = previous_vector / np.linalg.norm(previous_vector)
n = np.array([-previous_vector[1], previous_vector[0]])
# Geradeaus
v = random.uniform(lMin, lMax) * previous_vector
random_number = random.randint(0, 100)
if random_number < pForward * b:
k = Vertex(vertex.coords + v)
# k.neighbours.append(vertex)
k.minor_road = True
suggested_vertices.append(k)
# Rechts
v = random.uniform(lMin, lMax) * previous_vector
random_number = random.randint(0, 100)
if random_number < pTurn * b:
k = Vertex(vertex.coords + n)
# k.neighbours.append(vertex)
k.minor_road = True
suggested_vertices.append(k)
# Links
v = random.uniform(lMin, lMax) * previous_vector
random_number = random.randint(0, 100)
if random_number < pTurn * b:
k = Vertex(vertex.coords - n)
# k.neighbours.append(vertex)
k.minor_road = True
suggested_vertices.append(k)
return suggested_vertices
def seed(vertex,b): pSeed=singleton.pSeed lMin=singleton.seedlMin lMax=singleton.seedlMax suggested_vertices=[] l=len(vertex.neighbours) v1=rotate(90,vertex.neighbours[0].coords-vertex.coords) v2=None if l==1: v2=v1 elif l==2: v2=rotate(90,vertex.neighbours[1].coords-vertex.coords)*-1 else: return [] v1=v1/np.linalg.norm(v1) v2=v2/np.linalg.norm(v2) #Rechts if b*b*pSeed>np.random.randint(0,100): l=np.random.uniform(lMin,lMax) k=np.random.uniform(0,1) coords=((1-k)*v1+k*v2)*l k=Vertex(vertex.coords+coords) k.minor_road=True suggested_vertices.append(k) v1=v1*-1 v2=v2*-1 #Links if b*b*pSeed>np.random.randint(0,100): l=np.random.uniform(lMin,lMax) k=np.random.uniform(0,1) coords=((1-k)*v1+k*v2)*l k=Vertex(vertex.coords+coords) k.minor_road=True suggested_vertices.append(k) return suggested_vertices
def seed(vertex, b):
pSeed = singleton.pSeed
lMin = singleton.seedlMin
lMax = singleton.seedlMax
suggested_vertices = []
l = len(vertex.neighbours)
v1 = rotate(90, vertex.neighbours[0].coords - vertex.coords)
v2 = None
if l == 1:
v2 = v1
elif l == 2:
v2 = rotate(90, vertex.neighbours[1].coords - vertex.coords) * -1
else:
return []
v1 = v1 / np.linalg.norm(v1)
v2 = v2 / np.linalg.norm(v2)
#Rechts
if b * b * pSeed > np.random.randint(0, 100):
l = np.random.uniform(lMin, lMax)
k = np.random.uniform(0, 1)
coords = ((1 - k) * v1 + k * v2) * l
k = Vertex(vertex.coords + coords)
k.minor_road = True
suggested_vertices.append(k)
v1 = v1 * -1
v2 = v2 * -1
#Links
if b * b * pSeed > np.random.randint(0, 100):
l = np.random.uniform(lMin, lMax)
k = np.random.uniform(0, 1)
coords = ((1 - k) * v1 + k * v2) * l
k = Vertex(vertex.coords + coords)
k.minor_road = True
suggested_vertices.append(k)
return suggested_vertices
def reconstruct(path=None): if path is None: import os import procedural_city_generation path=os.path.dirname(procedural_city_generation.__file__)+"/outputs/output.json" import json try: with open(path,'r') as d: data=d.read() except IOError: print "Input could not be located. Try to run the previous program in the chain first." return 0 data=json.loads(data) from procedural_city_generation.roadmap.Vertex import Vertex import numpy as np vertex_list=[] vertex_list=[0]*len(data) for x in data: y=data[x] k=Vertex(np.array(y[0])) k.minor_road,k.seed,k.neighboursindizes=y[1],y[2],y[3] vertex_list[int(x)]=k index=0 for k in vertex_list: for x in k.neighboursindizes: k.neighbours.append(vertex_list[x]) k.selfindex=index index+=1 setliste=[] return vertex_list
def reconstruct(path=None): if path is None: import os import procedural_city_generation path=os.path.dirname(procedural_city_generation.__file__)+"/outputs/output.json" import json try: with open(path,'r') as d: data=d.read() except IOError: print "Input could not be located. Try to run the previous program in the chain first." return 0 data=json.loads(data) from procedural_city_generation.roadmap.Vertex import Vertex import numpy as np vertex_list=[] vertex_list=[0]*len(data) for x in data: y=data[x] k=Vertex(np.array(y[0])) k.minor_road,k.seed,k.neighboursindizes=y[1],y[2],y[3] vertex_list[int(x)]=k index=0 for k in vertex_list: for x in k.neighboursindizes: k.neighbours.append(vertex_list[x]) k.selfindex=index index+=1 setliste=[] return vertex_list
