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

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():
    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()

Thanks, here is some code to visualize blended image and corresponding segmentation mask:

from PIL import Image
import numpy as np
image = Image.open('VOCdevkit/VOC2012/JPEGImages/2007_000129.jpg')
target = np.array(Image.open('VOCdevkit/VOC2012/SegmentationClass/2007_000129.png'))[:, :, np.newaxis]
cmap = color_map()[:, np.newaxis, :]
new_im = np.dot(target == 0, cmap[0])
for i in range(1, cmap.shape[0]):
    new_im += np.dot(target == i, cmap[i])
new_im = Image.fromarray(new_im.astype(np.uint8))
blend_image = Image.blend(image, new_im, alpha=0.8)
blend_image.save('tmp.jpg')

output: