'''
Created on 26 Mar 2015
@author: Craig
'''
import CraigIOonly as cio
import combinedLeastSqr as cbl
import numpy as np
import scipy.spatial as ss
from numpy import arccos
if __name__ == '__main__':
pointCloud = cio.readIn("c:/scripts/PCAfield.xyz", 6,
False) #can set origin within this function
cio.WritePointstoTxt("C:/scripts/PCAfieldML.xyz", pointCloud)
def main():
timer = True
areaForNormals = 1
areaIncrement = 2
testNighbourCount = True
showNeighboursCount = False
np.set_printoptions(precision=6, suppress=True, linewidth=500)
# pointCloud=getPlanePointsGrid()
pointCloud = cio.readIn("c:/scripts/urbancloud.csv", 3,
True) #can set origin within this function
size = pointCloud.shape[0]
print "Point Cloud Size: " + str(size) + " Points"
# rotation=cbl.rotationMatrix(0, pi/4., 0)
# pointCloud= pointCloud*rotation
tree, points = getKDtree(pointCloud)
# get index of each point which distance from (x=0, y=0) is under 1
# a = tree.query_ball_point([1,1,1], 2)
normals = np.zeros((len(points), 3))
theta = np.zeros((len(points), 1))
s = 0
average = 0
for point in pointCloud:
start0 = time.clock()
a = point[0, 0]
b = point[0, 1]
c = point[0, 2]
vec = [a, b, c]
#get nearest points
localPoints = tree.query_ball_point(
vec, areaForNormals) #gives index to local points close to vec
sz = len(localPoints)
i = 0
if testNighbourCount:
while sz < 20:
localPoints = tree.query_ball_point(
vec, areaForNormals +
(i *
areaIncrement)) #gives index to local points close to vec
sz = len(localPoints)
i += 1
if showNeighboursCount:
print sz
mat = []
for i in localPoints:
mat.append(points[i])
#calculate the local planes normal
# s1=time.clock()
# normal=cbl.solvePlane(mat, [0.1,0.1,1.1])
normal = cbl.solvePlanePCA(mat)
# f1=time.clock()
# print s1-f1
# s1=time.clock()
normal = getUnitVector(normal)
# f1=time.clock()
# print s1-f1
#associate the normal with that point
normals[s, 0] = normal[0]
normals[s, 1] = normal[1]
normals[s, 2] = normal[2]
a = (np.dot(normal, [0, 0, 1]))
a = float(a)
theta[s] = (np.arccos(a)) * 180 / pi
s += 1
if timer:
if (s < 100):
finish0 = time.clock()
average = (average + (finish0 - start0)) / 2
else:
print "time left:"
print int((size - s) * (average))
# finish0= time.clock()
# print finish0-start0
pointCloud = np.append(pointCloud, normals, 1)
pointCloud = np.append(pointCloud, theta, 1)
#print point cloud to file
cio.WritePointstoTxt("C:/scripts/PCAfield.xyz", pointCloud)
'''
Created on 26 Mar 2015
@author: Craig
'''
import CraigIOonly as cio
import combinedLeastSqr as cbl
import numpy as np
import scipy.spatial as ss
from numpy import arccos
if __name__ == '__main__':
pointCloud=cio.readIn("c:/scripts/PCAfield.xyz",6,False) #can set origin within this function
cio.WritePointstoTxt("C:/scripts/PCAfieldML.xyz", pointCloud)
def main():
timer=True
areaForNormals=1
areaIncrement=2
testNighbourCount=True
showNeighboursCount=False
np.set_printoptions(precision=6, suppress=True,linewidth=500)
# pointCloud=getPlanePointsGrid()
pointCloud=cio.readIn("c:/scripts/urbancloud.csv",3,True) #can set origin within this function
size=pointCloud.shape[0]
print "Point Cloud Size: "+str(size)+" Points"
# rotation=cbl.rotationMatrix(0, pi/4., 0)
# pointCloud= pointCloud*rotation
tree,points=getKDtree(pointCloud)
# get index of each point which distance from (x=0, y=0) is under 1
# a = tree.query_ball_point([1,1,1], 2)
normals=np.zeros((len(points),3))
theta=np.zeros((len(points),1))
s=0
average=0
for point in pointCloud:
start0=time.clock()
a= point[0,0]
b= point[0,1]
c= point[0,2]
vec=[a,b,c]
#get nearest points
localPoints = tree.query_ball_point(vec, areaForNormals)#gives index to local points close to vec
sz=len (localPoints)
i=0
if testNighbourCount:
while sz<20:
localPoints = tree.query_ball_point(vec, areaForNormals+(i*areaIncrement))#gives index to local points close to vec
sz=len (localPoints)
i+=1
if showNeighboursCount:
print sz
mat=[]
for i in localPoints:
mat.append(points[i])
#calculate the local planes normal
# s1=time.clock()
# normal=cbl.solvePlane(mat, [0.1,0.1,1.1])
normal=cbl.solvePlanePCA(mat)
# f1=time.clock()
# print s1-f1
# s1=time.clock()
normal=getUnitVector(normal)
# f1=time.clock()
# print s1-f1
#associate the normal with that point
normals[s,0]=normal[0]
normals[s,1]=normal[1]
normals[s,2]=normal[2]
a=(np.dot(normal,[0,0,1]))
a=float(a)
theta[s]=(np.arccos(a))*180/pi
s+=1
if timer:
if (s<100) :
finish0= time.clock()
average=(average+(finish0-start0))/2
else:
print "time left:"
print int( (size-s)*(average))
# finish0= time.clock()
# print finish0-start0
pointCloud=np.append(pointCloud, normals, 1)
pointCloud=np.append(pointCloud, theta, 1)
#print point cloud to file
cio.WritePointstoTxt("C:/scripts/PCAfield.xyz", pointCloud)
