data/lib/vis/palette.py

##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## Created by: RainbowSecret
## Microsoft Research
## yuyua@microsoft.com
## Copyright (c) 2019
##
## This source code is licensed under the MIT-style license found in the
## LICENSE file in the root directory of this source tree 
##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

import os
import sys
import cv2
import pdb
import numpy as np
import scipy.io as sio


def get_autonue21_colors():
    """
    https://github.com/AutoNUE/public-code/blob/master/helpers/anue_labels.py
    """
    num_cls = 26
    colors = [0] * (num_cls * 3)
    colors[0:3] = (128, 64, 128)
    colors[3:6] = (250, 170, 160)
    colors[6:9] = (244, 35, 232)
    colors[9:12] = (230, 150, 140)
    colors[12:15] = (220, 20, 60)
    colors[15:18] = (255, 0, 0)
    colors[18:21] = (0, 0, 230)
    colors[21:24] = (119, 11, 32)
    colors[24:27] = (255, 204, 54)
    colors[27:30] = (0, 0, 142)
    colors[30:33] = (0, 0, 70)
    colors[33:36] = (0, 60, 100)
    colors[36:39] = (0, 0, 90)
    colors[39:42] = (220, 190, 40)
    colors[42:45] = (102, 102, 156)
    colors[45:48] = (190, 153, 153)
    colors[48:51] = (190, 153, 153)
    colors[51:54] = (180, 165, 180)
    colors[54:57] = (174, 64, 67)
    colors[57:60] = (220, 220, 0)
    colors[60:63] = (250, 170, 30)
    colors[63:66] = (153, 153, 153)
    colors[66:69] = (169, 187, 214)
    colors[69:72] = (70, 70, 70)
    colors[72:75] = (150, 100, 100)
    colors[75:78] = (107, 142, 35)
    colors[78:81] = (70, 130, 180)
    return colors


# Sky = [128,128,128]
# 	Building = [128,0,0]
# 	Pole = [192,192,128]
# 	Road = [128,64,128]
# 	Pavement = [60,40,222]
# 	Tree = [128,128,0]
# 	SignSymbol = [192,128,128]
# 	Fence = [64,64,128]
# 	Car = [64,0,128]
# 	Pedestrian = [64,64,0]
# 	Bicyclist = [0,128,192]
# 	Unlabelled = [0,0,0]
def get_camvid_colors():
    """ Returns the color map for visualizing the segmentation mask.
    Args:
        num_cls: Number of classes
    Returns:
        The color map
    """
    num_cls = 12
    colors = [0] * (num_cls * 3)
    colors[0:3] = (128, 128, 128)
    colors[3:6] = (128, 0, 0)
    colors[6:9] = (192, 192, 128)
    colors[9:12] = (128, 64, 128)
    colors[12:15] = (60, 40, 222)
    colors[15:18] = (128, 128, 0)
    colors[18:21] = (192, 128, 128)
    colors[21:24] = (64, 64, 128)
    colors[24:27] = (64, 0, 128)
    colors[27:30] = (64, 64, 0)
    colors[30:33] = (0, 128, 192)
    colors[33:36] = (0, 0, 0)
    return colors


def get_cityscapes_colors():
    """ Returns the color map for visualizing the segmentation mask.
    Args:
        num_cls: Number of classes
    Returns:
        The color map
    """
    num_cls = 20
    colors = [0] * (num_cls * 3)
    colors[0:3] = (128, 64, 128)  # 0: 'road'
    colors[3:6] = (244, 35, 232)  # 1 'sidewalk'
    colors[6:9] = (70, 70, 70)  # 2''building'
    colors[9:12] = (102, 102, 156)  # 3 wall
    colors[12:15] = (190, 153, 153)  # 4 fence
    colors[15:18] = (153, 153, 153)  # 5 pole
    colors[18:21] = (250, 170, 30)  # 6 'traffic light'
    colors[21:24] = (220, 220, 0)  # 7 'traffic sign'
    colors[24:27] = (107, 142, 35)  # 8 'vegetation'
    colors[27:30] = (152, 251, 152)  # 9 'terrain'
    colors[30:33] = (70, 130, 180)  # 10 sky
    colors[33:36] = (220, 20, 60)  # 11 person
    colors[36:39] = (255, 0, 0)  # 12 rider
    colors[39:42] = (0, 0, 142)  # 13 car
    colors[42:45] = (0, 0, 70)  # 14 truck
    colors[45:48] = (0, 60, 100)  # 15 bus
    colors[48:51] = (0, 80, 100)  # 16 train
    colors[51:54] = (0, 0, 230)  # 17 'motorcycle'
    colors[54:57] = (119, 11, 32)  # 18 'bicycle'
    colors[57:60] = (105, 105, 105)
    return colors


def get_ade_colors():
    colors = sio.loadmat(os.path.dirname(os.path.abspath(__file__)) + '/color150.mat')['colors']
    colors = colors[:, ::-1, ]
    colors = np.array(colors).astype(int).tolist()
    colors.insert(0, [0, 0, 0])
    colors = sum(colors, [])
    return colors


def get_pascal_context_colors():
    colors = sio.loadmat(os.path.dirname(os.path.abspath(__file__)) + '/color60.mat')['color60']
    colors = colors[:, ::-1, ]
    colors = np.array(colors).astype(int).tolist()
    colors = sum(colors, [])
    return colors


def get_lip_colors():
    """ Returns the color map for visualizing the segmentation mask.
    Args:
        num_cls: Number of classes
    Returns:
        The color map
    """
    n = 20
    colors = [0] * (n * 3)
    for j in range(0, n):
        lab = j
        colors[j * 3 + 0] = 0
        colors[j * 3 + 1] = 0
        colors[j * 3 + 2] = 0
        i = 0
        while lab:
            colors[j * 3 + 0] |= (((lab >> 0) & 1) << (7 - i))
            colors[j * 3 + 1] |= (((lab >> 1) & 1) << (7 - i))
            colors[j * 3 + 2] |= (((lab >> 2) & 1) << (7 - i))
            i += 1
            lab >>= 3
    return colors


def get_cocostuff_colors():
    """ Returns the color map for visualizing the segmentation mask.
    Args:
        num_cls: Number of classes
    Returns:
        The color map
    """
    n = 171
    colors = [0] * (n * 3)
    for j in range(0, n):
        lab = j
        colors[j * 3 + 0] = 0
        colors[j * 3 + 1] = 0
        colors[j * 3 + 2] = 0
        i = 0
        while lab:
            colors[j * 3 + 0] |= (((lab >> 0) & 1) << (7 - i))
            colors[j * 3 + 1] |= (((lab >> 1) & 1) << (7 - i))
            colors[j * 3 + 2] |= (((lab >> 2) & 1) << (7 - i))
            i += 1
            lab >>= 3
    return colors


def get_pascal_voc_colors():
    """Load the mapping that associates pascal classes with label colors
        Returns:
            np.ndarray with dimensions (21, 3)
        """
    return np.asarray(
        [
            [0, 0, 0],
            [128, 0, 0],
            [0, 128, 0],
            [128, 128, 0],
            [0, 0, 128],
            [128, 0, 128],
            [0, 128, 128],
            [128, 128, 128],
            [64, 0, 0],
            [192, 0, 0],
            [64, 128, 0],
            [192, 128, 0],
            [64, 0, 128],
            [192, 0, 128],
            [64, 128, 128],
            [192, 128, 128],
            [0, 64, 0],
            [128, 64, 0],
            [0, 192, 0],
            [128, 192, 0],
            [0, 64, 128],
        ]
    )