def process(self, path, display=True):
datadict = npy.read(path)
# mah = MAHelper(datadict)
# data_arrays = {}
# for key in mah.arrays: data_arrays[key] = mah.D[key]
clusters = None
segment_graph = None
if 'ma_clusters' in datadict:
clusters = datadict['ma_clusters']
if 'ma_segment_graph' in datadict:
segment_graph = datadict['ma_segment_graph']
coords = normals = colors = seg_link_flip = ma_segment_lidx = None
if 'coords' in datadict:
coords = datadict['coords']
if 'normals' in datadict:
normals = datadict['normals']
if 'colors' in datadict:
colors = datadict['colors']
if 'seg_link_flip' in datadict:
seg_link_flip = datadict['seg_link_flip'].astype(np.int32)
if 'ma_segment_lidx' in datadict:
ma_segment_lidx = datadict['ma_segment_lidx']
return {
'datadict': datadict,
'coords': coords,
'normals': normals,
'colors': colors,
'clusters': clusters,
'segment_graph': segment_graph,
'seg_link_flip': seg_link_flip,
'ma_segment_lidx': ma_segment_lidx
}
def main(args):
keys = []
if args.key:
keys.append(args.key)
datadict = npy.read(args.infile, keys)
pcd.write(args.outfile, datadict, keys)
'-m',
'--mincount',
help='Minimum edge count used during connected compenent analysis',
default=20,
type=int)
parser.add_argument(
'-d',
'--highdegree',
help='Remove the specified number of highest degree vertices',
default=0,
type=int)
# parser.add_argument('-c', '--contract', help='contract edges', default=None, type=float)
# parser.add_argument('-a', '--analyse', help='Also compute statistics for each sheet', dest='analyse', action='store_true')
args = parser.parse_args()
D = npy.read(args.infile)
mah = MAHelper(D)
# if not args.contract is None:
# contract_edges(mah.D['ma_segment_graph'], args.contract)
clustering.get_clusters(mah, args.mincount, args.highdegree)
# clustering.classify_clusters(mah)
print("I found {} clusters".format(len(mah.D['ma_clusters'])))
# for g in mah.D['ma_clusters']:
# if g['classification'] == 4: # building class
# clustering.analyse_cluster(mah, g)
npy.write(args.infile, mah.D, ['ma_clusters'])
# get ma_coords per cluster like:
for cluster in mah.D['ma_clusters']:
def main(args): datadict = npy.read(args.infile) las.write(args.outfile,datadict)