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nyuv2.py
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#coding:utf-8
import os
import torch
import torch.utils.data as data
from PIL import Image
from scipy.io import loadmat
import numpy as np
import glob
from torchvision import transforms
from torchvision.datasets import VisionDataset
import random
def colormap(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
class NYUv2(VisionDataset):
"""NYUv2 dataset
Args:
root (string): Root directory path.
split (string, optional): 'train' for training set, and 'test' for test set. Default: 'train'.
target_type (string, optional): Type of target to use, ``semantic``, ``depth`` or ``normal``.
num_classes (int, optional): The number of classes, must be 40 or 13. Default:13.
transform (callable, optional): A function/transform that takes in an PIL image and returns a transformed version.
target_transform (callable, optional): A function/transform that takes in the target and transforms it.
transforms (callable, optional): A function/transform that takes input sample and its target as entry and returns a transformed version.
"""
cmap = colormap()
def __init__(self,
root,
split='train',
target_type='semantic',
num_classes=13,
transforms=None,
transform=None,
target_transform=None):
super( NYUv2, self ).__init__(root, transforms=transforms, transform=transform, target_transform=target_transform)
assert(split in ('train', 'test'))
self.root = root
self.split = split
self.target_type = target_type
self.num_classes = num_classes
self.train_idx = np.array([255, ] + list(range(num_classes)))
split_mat = loadmat(os.path.join(self.root, 'splits.mat'))
idxs = split_mat[self.split+'Ndxs'].reshape(-1) - 1
img_names = os.listdir( os.path.join(self.root, 'image', self.split) )
img_names.sort()
images_dir = os.path.join(self.root, 'image', self.split)
self.images = [os.path.join(images_dir, name) for name in img_names]
if self.target_type=='semantic':
semantic_dir = os.path.join(self.root, 'seg%d'%self.num_classes, self.split)
self.labels = [os.path.join(semantic_dir, name) for name in img_names]
self.targets = self.labels
if self.target_type=='depth':
depth_dir = os.path.join(self.root, 'depth', self.split)
self.depths = [os.path.join(depth_dir, name) for name in img_names]
self.targets = self.depths
if self.target_type=='normal':
normal_dir = os.path.join(self.root, 'normal', self.split)
self.normals = [os.path.join(normal_dir, name) for name in img_names]
self.targets = self.normals
def __getitem__(self, idx):
image = Image.open(self.images[idx])
target = Image.open(self.targets[idx])
if self.transforms is not None:
image, target = self.transforms( image, target )
return image, target
def __len__(self):
return len(self.images)
if __name__=='__main__':
from torchvision import transforms
import matplotlib.pyplot as plt
nyu_semantic13 = NYUv2( root='NYUv2', split='train', target_type='semantic', num_classes=13,
transform=transforms.Compose([
transforms.Resize(512),
transforms.ToTensor()
]),
target_transform=transforms.Compose([
transforms.Resize(512, interpolation=Image.NEAREST),
transforms.Lambda(lambda lbl: torch.from_numpy( np.array(lbl, dtype='uint8')-1 ) ) # 0->255, 1->0, 2->1
]),
)
nyu_semantic40 = NYUv2( root='NYUv2', split='train', target_type='semantic', num_classes=40,
transform=transforms.Compose([
transforms.Resize(512),
transforms.ToTensor()
]),
target_transform=transforms.Compose([
transforms.Resize(512, interpolation=Image.NEAREST),
transforms.Lambda(lambda lbl: torch.from_numpy( np.array(lbl, dtype='uint8')-1 ) ) # 0->255, 1->0, 2->1
]),
)
nyu_depth = NYUv2( root='NYUv2', split='train', target_type='depth',
transform=transforms.Compose([
transforms.Resize(512),
transforms.ToTensor()
]),
target_transform=transforms.Compose([
transforms.Resize(512),
transforms.Lambda(lambda lbl: torch.from_numpy( np.array(lbl, dtype='float') )/1e3 ) # uint16 to depth
]),
)
nyu_normal = NYUv2( root='NYUv2', split='train', target_type='normal',
transform=transforms.Compose([
transforms.Resize(512),
transforms.ToTensor()
]),
target_transform=transforms.Compose([
transforms.ToTensor(),
transforms.Lambda(lambda normal: normal * 2 - 1)
]),
)
os.makedirs('test', exist_ok=True)
# Semantic
img_id = 0
img, lbl13 = nyu_semantic13[img_id]
Image.fromarray((img*255).numpy().transpose( 1,2,0 ).astype('uint8')).save('test/image.png')
Image.fromarray( nyu_semantic13.cmap[ (lbl13.numpy().astype('uint8')+1) ] ).save('test/semantic13.png')
img, lbl40 = nyu_semantic40[img_id]
Image.fromarray( nyu_semantic40.cmap[ (lbl40.numpy().astype('uint8')+1) ] ).save('test/semantic40.png')
# Depth
img, depth = nyu_depth[img_id]
norm = plt.Normalize()
depth = plt.cm.jet(norm(depth))
plt.imsave('test/depth.png', depth)
# Normal
img, normal = nyu_normal[img_id]
normal = (normal+1)/2
Image.fromarray((normal*255).numpy().transpose( 1,2,0 ).astype('uint8')).save('test/normal.png')