"""Segmentation Utils"""
from PIL import Image
import mxnet as mx
from mxnet.gluon import HybridBlock
__all__ = ['get_color_pallete', 'DeNormalize']
[docs]def get_color_pallete(npimg, dataset='pascal_voc'):
"""Visualize image.
Parameters
----------
npimg : numpy.ndarray
Single channel image with shape `H, W, 1`.
dataset : str, default: 'pascal_voc'
The dataset that model pretrained on. ('pascal_voc', 'ade20k')
Returns
-------
out_img : PIL.Image
Image with color pallete
"""
# recovery boundary
if dataset == 'pascal_voc' or dataset == 'pascal_aug':
npimg[npimg == -1] = 255
# put colormap
if dataset == 'ade20k':
npimg = npimg + 1
out_img = Image.fromarray(npimg.astype('uint8'))
out_img.putpalette(adepallete)
return out_img
out_img = Image.fromarray(npimg.astype('uint8'))
out_img.putpalette(vocpallete)
return out_img
class DeNormalize(HybridBlock):
"""Denormalize the image"""
# pylint: disable=arguments-differ,unused-argument
def __init__(self, mean, std):
super(DeNormalize, self).__init__()
self.mean = mx.nd.array(mean, ctx=mx.cpu(0))
self.std = mx.nd.array(std, ctx=mx.cpu(0))
def hybrid_forward(self, F, x):
return x * self.std .reshape(shape=(3, 1, 1)) - self.mean.reshape(shape=(3, 1, 1))
def _getvocpallete(num_cls):
n = num_cls
pallete = [0]*(n*3)
for j in range(0, n):
lab = j
pallete[j*3+0] = 0
pallete[j*3+1] = 0
pallete[j*3+2] = 0
i = 0
while (lab > 0):
pallete[j*3+0] |= (((lab >> 0) & 1) << (7-i))
pallete[j*3+1] |= (((lab >> 1) & 1) << (7-i))
pallete[j*3+2] |= (((lab >> 2) & 1) << (7-i))
i = i + 1
lab >>= 3
return pallete
vocpallete = _getvocpallete(256)
# pylint: disable=bad-whitespace
adepallete = [
0,0,0,120,120,120,180,120,120,6,230,230,80,50,50,4,200,3,120,120,80,140,140,140,204,
5,255,230,230,230,4,250,7,224,5,255,235,255,7,150,5,61,120,120,70,8,255,51,255,6,82,
143,255,140,204,255,4,255,51,7,204,70,3,0,102,200,61,230,250,255,6,51,11,102,255,255,
7,71,255,9,224,9,7,230,220,220,220,255,9,92,112,9,255,8,255,214,7,255,224,255,184,6,
10,255,71,255,41,10,7,255,255,224,255,8,102,8,255,255,61,6,255,194,7,255,122,8,0,255,
20,255,8,41,255,5,153,6,51,255,235,12,255,160,150,20,0,163,255,140,140,140,250,10,15,
20,255,0,31,255,0,255,31,0,255,224,0,153,255,0,0,0,255,255,71,0,0,235,255,0,173,255,
31,0,255,11,200,200,255,82,0,0,255,245,0,61,255,0,255,112,0,255,133,255,0,0,255,163,
0,255,102,0,194,255,0,0,143,255,51,255,0,0,82,255,0,255,41,0,255,173,10,0,255,173,255,
0,0,255,153,255,92,0,255,0,255,255,0,245,255,0,102,255,173,0,255,0,20,255,184,184,0,
31,255,0,255,61,0,71,255,255,0,204,0,255,194,0,255,82,0,10,255,0,112,255,51,0,255,0,
194,255,0,122,255,0,255,163,255,153,0,0,255,10,255,112,0,143,255,0,82,0,255,163,255,
0,255,235,0,8,184,170,133,0,255,0,255,92,184,0,255,255,0,31,0,184,255,0,214,255,255,
0,112,92,255,0,0,224,255,112,224,255,70,184,160,163,0,255,153,0,255,71,255,0,255,0,
163,255,204,0,255,0,143,0,255,235,133,255,0,255,0,235,245,0,255,255,0,122,255,245,0,
10,190,212,214,255,0,0,204,255,20,0,255,255,255,0,0,153,255,0,41,255,0,255,204,41,0,
255,41,255,0,173,0,255,0,245,255,71,0,255,122,0,255,0,255,184,0,92,255,184,255,0,0,
133,255,255,214,0,25,194,194,102,255,0,92,0,255]