Table Of Contents
Table Of Contents

Source code for gluoncv.utils.viz.segmentation

"""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 in ('pascal_voc', '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 elif dataset == 'citys': out_img = Image.fromarray(npimg.astype('uint8')) out_img.putpalette(cityspallete) return out_img out_img = Image.fromarray(npimg.astype('uint8')) out_img.putpalette(vocpallete) return out_img
[docs]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))
[docs] 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] cityspallete = [ 128, 64, 128, 244, 35, 232, 70, 70, 70, 102, 102, 156, 190, 153, 153, 153, 153, 153, 250, 170, 30, 220, 220, 0, 107, 142, 35, 152, 251, 152, 0, 130, 180, 220, 20, 60, 255, 0, 0, 0, 0, 142, 0, 0, 70, 0, 60, 100, 0, 80, 100, 0, 0, 230, 119, 11, 32, ]