def main():
# read points and normals from a ply file
datadict = io_ply.read_ply('example-data/house_dyke_tree.ply')
# compute interior and exterior MAT
ma = MASB(datadict, 10)
ma.compute_balls()
# write MA points to file
io_npy.write_npy('house_dyke_tree_npy', datadict)
def main():
# read points and normals from a ply file
datadict = io_ply.read_ply('example-data/house_dyke_tree.ply')
# compute interior and exterior MAT
ma = MASB(datadict, 10)
ma.compute_balls()
# write MA points to file
io_npy.write_npy('house_dyke_tree_npy', datadict)
def main(args):
if args.infile.endswith('ply'):
datadict = io_ply.read_ply(args.infile)
elif args.infile.endswith('npy'):
if args.ma:
datadict = io_npy.read_npy(args.infile, ['coords', 'normals'])
else:
datadict = io_npy.read_npy(args.infile, ['coords', 'ma_coords_in', 'ma_coords_out'])
# compute interior and exterior MAT
if args.ma:
ma = MASB(datadict, args.radius, denoise=args.denoise, detect_planar=args.planar)
ma.compute_balls()
if args.lfs or not args.ma:
metacompute.compute_lfs(datadict)
io_npy.write_npy(args.outfile, datadict)
def main(args):
if args.infile.endswith('ply'):
datadict = io_ply.read_ply(args.infile)
elif args.infile.endswith('npy'):
if args.ma:
datadict = io_npy.read_npy(args.infile, ['coords', 'normals'])
else:
datadict = io_npy.read_npy(
args.infile, ['coords', 'ma_coords_in', 'ma_coords_out'])
# compute interior and exterior MAT
if args.ma:
ma = MASB(datadict,
args.radius,
denoise=args.denoise,
detect_planar=args.planar)
ma.compute_balls()
if args.lfs or not args.ma:
metacompute.compute_lfs(datadict)
io_npy.write_npy(args.outfile, datadict)
def main(args):
if args.infile.endswith('ply'):
from masbpy import io_ply
datadict = io_ply.read_ply(args.infile)
elif args.infile.endswith('las'):
from masbpy import io_las
datadict = io_las.read_las(args.infile)
elif args.infile.endswith('npy'):
datadict = io_npy.read_npy(args.infile, ['coords'])
kd_tree = KDTree( datadict['coords'] )
neighbours = kd_tree.query( datadict['coords'], args.k+1 )[1]
neighbours = datadict['coords'][neighbours]
p = Pool()
t1 = time()
normals = p.map(compute_normal, neighbours)
t2 = time()
print "finished normal computation in {} s".format(t2-t1)
datadict['normals'] = np.array(normals, dtype=np.float32)
io_npy.write_npy(args.outfile, datadict)
def main(args):
if args.infile.endswith('ply'):
from masbpy import io_ply
datadict = io_ply.read_ply(args.infile)
elif args.infile.endswith('las'):
from masbpy import io_las
datadict = io_las.read_las(args.infile)
elif args.infile.endswith('npy'):
datadict = io_npy.read_npy(args.infile, ['coords'])
kd_tree = KDTree(datadict['coords'])
neighbours = kd_tree.query(datadict['coords'], args.k + 1)[1]
neighbours = datadict['coords'][neighbours]
p = Pool()
t1 = time()
normals = p.map(compute_normal, neighbours)
t2 = time()
print "finished normal computation in {} s".format(t2 - t1)
datadict['normals'] = np.array(normals, dtype=np.float32)
io_npy.write_npy(args.outfile, datadict)
def calcEt(original_data):
point_kd_tree = KDTree(original_data['coords'])
vertices = pd.read_pickle('./tmp/burned-vertices.pickle')
def getRadii(row):
index = row['index']
if index % 500 == 0:
print 'et:', index
here = row.values[:3].reshape([1, 3]).astype(np.float32)
neighbor, idx = point_kd_tree.query(here, k=3)
radii = np.linalg.norm(here - neighbor)
return radii
def getEt(row):
return row['time'] - row['radii']
vertices.loc[:, 'radii'] = vertices.apply(getRadii, axis=1)
vertices.loc[:, 'et'] = vertices.apply(getEt, axis=1)
vertices.to_pickle('./tmp/et-vertices.pickle')
if __name__ == '__main__':
original_data = io_ply.read_ply(sys.argv[1])
calcEt(original_data)
def main():
datadict = io_ply.read_ply(sys.argv[1])
original_data = io_ply.read_ply(sys.argv[2])
generateGraph(datadict, original_data)