import numpy as np

########################################################################
#                         Download information                         #
########################################################################

'''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"'''



########################################################################
#                              Data splits                             #
########################################################################

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"
    ]
}


########################################################################
#                                Labels                                #
########################################################################

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]])

# For instance segmentation
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]