Python implementation of the color map function for the PASCAL VOC data set.

color_map_info.py

""" added by CCJ """
def color_map_info(palette):
    labels = [
          'background', #0
          'aeroplane', #1
          'bicycle', #2
          'bird', #3
          'boat', #4
          'bottle', #5
          'bus', #6
          'car', #7
          'cat', #8
          'chair', #9
          'cow', #10
          'diningtable', #11
          'dog', #12
          'horse', #13
          'motorbike', #14
          'person', #15
          'pottedplant', #16
          'sheep', #17
          'sofa', #18
          'train', #19
          'tv/monitor', #20
          "void/unlabelled", #255
          ] 
    print 'class colormap and palette = {r,g,b}'
    for i in range(0,21*3,3):
        print '# {:>3d}: {:<20} (R,G,B) = {},{},{}'.format(i/3, labels[i/3], palette[i], palette[i+1],palette[i+2])
    i = 255*3
    print '# {:>3d}: {:<20} (R,G,B) = {},{},{}'.format(i/3, labels[21], palette[i], palette[i+1],palette[i+2])

color_map_viz.py

# added by CCJ:
""" arrange these 21 classes to 2D matrix with 3 rows and 7 columns"""
def color_map_viz(fname = None):
    labels = ['B-ground', 'Aero plane', 'Bicycle', 'Bird', 'Boat', 'Bottle', 
            'Bus', 'Car', 'Cat', 'Chair', 'Cow', 'Dining-Table', 'Dog', 'Horse',
            'Motorbike', 'Person', 'Potted-Plant', 'Sheep', 'Sofa', 'Train', 
            'TV/Monitor', 'Void/Unlabelled']

    nclasses = 21
    row_size = 80
    col_size = 250
    cmap = color_map()

    """ arrange these 21 classes to 2D matrix with 3 rows and 7 columns"""
    r = 3
    c = 7
    delta = 10
    array = np.empty((row_size*(r+1), col_size*c, cmap.shape[1]), dtype=cmap.dtype)
    fig=plt.figure()
    for r_idx in range(0,r):
        for c_idx in range(0,c):
            i = r_idx *c + c_idx
            array[r_idx*row_size:(r_idx+1)*row_size, c_idx*col_size: (c_idx+1)*col_size, :] = cmap[i]
            x = c_idx*col_size + delta
            y = r_idx*row_size + row_size/2
            s = labels[i]
            plt.text(x, y,s, fontsize=9, color='white')
            print "write {} at pixel (r={},c={})".format(labels[i], y,x)

    array[r*row_size:(r+1)*row_size, :] = cmap[-1]
    x = 3*col_size + delta
    y = r*row_size + row_size/2
    s = labels[-1]
    plt.text(x, y,s, fontsize=9, color='black')
    print "write {} at pixel (r={},c={})".format(labels[i], y,x)
    plt.title("PASCAL VOC Label Color Map")
    imshow(array)
    axis = plt.subplot(1, 1, 1)
    plt.axis('off')
    if fname:
        plt.savefig(fname, dpi=300,bbox_inches='tight', pad_inches=0.1)
    else:
        plt.show()

pascal-voc-label-color-map.jpg

VOClabelcolormap.m

%Official Matlab version can be found in the PASCAL VOC devkit 
%http://host.robots.ox.ac.uk/pascal/VOC/voc2012/index.html#devkit

% VOCLABELCOLORMAP Creates a label color map such that adjacent indices have different
% colors.  Useful for reading and writing index images which contain large indices,
% by encoding them as RGB images.
%
% CMAP = VOCLABELCOLORMAP(N) creates a label color map with N entries.
function cmap = labelcolormap(N)

if nargin==0
    N=256
end
cmap = zeros(N,3);
for i=1:N
    id = i-1; r=0;g=0;b=0;
    for j=0:7
        r = bitor(r, bitshift(bitget(id,1),7 - j));
        g = bitor(g, bitshift(bitget(id,2),7 - j));
        b = bitor(b, bitshift(bitget(id,3),7 - j));
        id = bitshift(id,-3);
    end
    cmap(i,1)=r; cmap(i,2)=g; cmap(i,3)=b;
end
cmap = cmap / 255;

VOClabelcolormap.py

""" 
Python implementation of the color map function for the PASCAL VOC data set. 
Official Matlab version can be found in the PASCAL VOC devkit 
http://host.robots.ox.ac.uk/pascal/VOC/voc2012/index.html#devkit
"""
import numpy as np
from skimage.io import imshow
import matplotlib.pyplot as plt

def color_map(N=256, normalized=False):
    def bitget(byteval, idx):
        return ((byteval & (1 << idx)) != 0)

    dtype = 'float32' if normalized else 'uint8'
    cmap = np.zeros((N, 3), dtype=dtype)
    for i in range(N):
        r = g = b = 0
        c = i
        for j in range(8):
            r = r | (bitget(c, 0) << 7-j)
            g = g | (bitget(c, 1) << 7-j)
            b = b | (bitget(c, 2) << 7-j)
            c = c >> 3

        cmap[i] = np.array([r, g, b])

    cmap = cmap/255 if normalized else cmap
    return cmap


def color_map_viz_1_column():
    labels = ['background', 'aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor', 'void']
    nclasses = 21
    row_size = 50
    col_size = 500
    cmap = color_map()
    array = np.empty((row_size*(nclasses+1), col_size, cmap.shape[1]), dtype=cmap.dtype)
    for i in range(nclasses):
        array[i*row_size:i*row_size+row_size, :] = cmap[i]
    array[nclasses*row_size:nclasses*row_size+row_size, :] = cmap[-1]

    imshow(array)
    plt.yticks([row_size*i+row_size/2 for i in range(nclasses+1)], labels)
    plt.xticks([])
    plt.show()

I write a jupyter note for more details. Please check this jupyter note at https://github.com/ccj5351/my_notes_jupyter/blob/master/PASCAL_VOC_2012_Label_Colormap.ipynb

@ccj5351 how do save the colormap files? This seems to be an issue; the server returns "Results are not properly generated"; any clue?