|
|
import numpy as np |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
'''FORM_URL = 'https://docs.google.com/forms/d/e/1FAIpQLSefhHMMvN0Uwjnj_vWQgYSvtFOtaoGFWsTIcRuBTnP09NHR7A/viewform?fbzx=5530674395784263977' |
|
|
|
|
|
# DALES in LAS format |
|
|
LAS_TAR_NAME = 'dales_semantic_segmentation_las.tar.gz' |
|
|
LAS_UNTAR_NAME = "dales_las" |
|
|
|
|
|
# DALES in PLY format |
|
|
PLY_TAR_NAME = 'dales_semantic_segmentation_ply.tar.gz' |
|
|
PLY_UNTAR_NAME = "dales_ply" |
|
|
|
|
|
# DALES in PLY, only version with intensity and instance labels |
|
|
OBJECTS_TAR_NAME = 'DALESObjects.tar.gz' |
|
|
OBJECTS_UNTAR_NAME = "DALESObjects"''' |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
TILES = { |
|
|
"train": [ |
|
|
"t1z6a", |
|
|
"t1z6b", |
|
|
"t2z5", |
|
|
"t3z3", |
|
|
"t3z6", |
|
|
"t3z7", |
|
|
"t5a1", |
|
|
"t5a3", |
|
|
"t5a4", |
|
|
"t5a5", |
|
|
"t5b2", |
|
|
"t5b3", |
|
|
"t5b4", |
|
|
"t5b6", |
|
|
"t5c1", |
|
|
"t5c2", |
|
|
"t5c3", |
|
|
"t6z2", |
|
|
"t6z3", |
|
|
"t6z4", |
|
|
"t6z6" |
|
|
], |
|
|
"val": [ |
|
|
"t1z5b", |
|
|
"t1z8", |
|
|
"t3z4", |
|
|
"t4z1", |
|
|
"t5b1", |
|
|
"t5b5" |
|
|
], |
|
|
"test": [ |
|
|
"t1z4", |
|
|
"t1z5a", |
|
|
"t1z7", |
|
|
"t3z1", |
|
|
"t3z2", |
|
|
"t3z5", |
|
|
"t5a2", |
|
|
"t6z1", |
|
|
"t6z5" |
|
|
] |
|
|
} |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
GRIDNET_NUM_CLASSES = 11 |
|
|
|
|
|
ID2TRAINID = np.asarray([0,0,0,0,0,1,2,2,3,3,3, |
|
|
3,11,11,4,5,6,7,7,8,9, |
|
|
10,11,11,11,11,11,11,11,11,11, |
|
|
11,11,11,11,11,11,11,11,11,11, |
|
|
11,11,11,11,11,11,11,11,11,11, |
|
|
11,11,11,11,11,11,11,11,11,11, |
|
|
11,11,11,11,11,11,11,11,11,11, |
|
|
11,11,11,11,11,11,11,11,11,11, |
|
|
11,11,11,11,11,11,11,11,11,11, |
|
|
11,11,11,11,11,11,11,11,11,11, |
|
|
11,11,11,11,11,11,11,11,11,11, |
|
|
11,11,11,11,11,11,11,11,11,11, |
|
|
11,11,11,11,11,11,11,11,11,11, |
|
|
11,11,11,11,11,11,11,11,11,11, |
|
|
11,11,11,11,11,11,11,11,11,11, |
|
|
11,11,11,11,11,11,11,11,11,11, |
|
|
11,11,11,11,11,11,11,11,11,11, |
|
|
11,11,11,11,11,11,11,11,11,11, |
|
|
11,11,11,11,11,11,11,11,11,11, |
|
|
11,11,11,11,11,11,11,11,11,11, |
|
|
11,11,11,11,11,11,11,11,11,11, |
|
|
11,11,11,11,11,11,11,11,11,11, |
|
|
11,11,11,11,11,11,11,11,11,11, |
|
|
11,11,11,11,11,11,11,11,11,11, |
|
|
11,11,11,11,11,11,11,11,11,11, |
|
|
11,11,11,11,11]) |
|
|
|
|
|
CLASS_NAMES = [ |
|
|
'Pylone', |
|
|
'Conductor cable', |
|
|
'Structural cable', |
|
|
'Insulator', |
|
|
'High vegetation', |
|
|
'Low vegetation', |
|
|
'Herbaceous vegetation', |
|
|
'Rock, gravel, soil', |
|
|
'Impervious soil (Road)', |
|
|
'Water', |
|
|
'Building', |
|
|
'Unassigned-Unlabeled'] |
|
|
|
|
|
CLASS_COLORS = np.asarray([ |
|
|
[243, 214, 171], |
|
|
[ 70, 115, 66], |
|
|
[233, 50, 239], |
|
|
[243, 238, 0], |
|
|
[190, 153, 153], |
|
|
[214, 0, 54], |
|
|
[243, 90, 171], |
|
|
[ 70, 45, 66], |
|
|
[233, 60, 239], |
|
|
[243, 140, 0], |
|
|
[190, 100, 153], |
|
|
[ 0, 0, 0]]) |
|
|
|
|
|
|
|
|
MIN_OBJECT_SIZE = 100 |
|
|
THING_CLASSES = [0,1,2,3,4,10] |
|
|
|
|
|
STUFF_CLASSES = [i for i in range(GRIDNET_NUM_CLASSES) if not i in THING_CLASSES] |
|
|
|
