VOClabelcolormap.py

"""
Adapted from: https://gist.github.com/wllhf/a4533e0adebe57e3ed06d4b50c8419ae

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
import matplotlib.pyplot as plt
from skimage.io import imshow
from matplotlib.colors import LinearSegmentedColormap


def color_map(N=256, normalized=True, matplotlib=True):
    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
    if matplotlib:
        assert(normalized is True)
        return LinearSegmentedColormap.from_list('VOClabel_cmap', cmap)
    else:
        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()