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shapenet_dataset.py
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import os
import numpy as np
import torch
import json
import torch.utils as utils
from scipy import spatial
def mkdir_ifnotexists(directory):
if not os.path.exists(directory):
os.mkdir(directory)
def pc_normalize(pc):
# centroid=np.mean(pc,axis=0)
# pc=pc-centroid
# m=np.max(np.sqrt(np.sum(pc**2,axis=1)))
# pc=pc/m
return (pc)
class shapenet_v0(utils.data.Dataset):
def __init__(self,root,npoints=2500,split='train',class_choice=None,normal_channel=False):
self.npoints=npoints
self.root=root
self.catfile=os.path.join(self.root,'synsetoffset2category.txt')
self.cat={}
self.normal_channel=normal_channel
with open(self.catfile, 'r') as f:
for line in f:
ls=line.strip().split()
self.cat[ls[0]]=ls[1]
self.cat={k:v for k,v in self.cat.items()}
self.classes_original=dict(zip(self.cat,range(len(self.cat))))
if not class_choice is None:
self.cat={k:v for k,v in self.cat.items() if k in class_choice}
# print(self.cat)
self.meta = {}
with open(os.path.join(self.root, 'train_test_split', 'shuffled_train_file_list.json'),'r') as f:
train_ids=set([str(d.split('/')[2]) for d in json.load(f)])
with open(os.path.join(self.root, 'train_test_split', 'shuffled_val_file_list.json'),'r') as f:
val_ids=set([str(d.split('/')[2]) for d in json.load(f)])
with open(os.path.join(self.root, 'train_test_split', 'shuffled_test_file_list.json'),'r') as f:
test_ids=set([str(d.split('/')[2]) for d in json.load(f)])
for item in self.cat:
# print('category', item)
self.meta[item]=[]
dir_point=os.path.join(self.root,self.cat[item])
fns=sorted(os.listdir(dir_point))
# print(fns[0][0:-4])
if split=='all':
fns=[fn for fn in fns if ((fn[0:-4] in train_ids) or (fn[0:-4] in val_ids))]
elif split=='train':
fns=[fn for fn in fns if fn[0:-4] in train_ids]
elif split=='val':
fns=[fn for fn in fns if fn[0:-4] in val_ids]
elif split=='test':
fns=[fn for fn in fns if fn[0:-4] in test_ids]
else:
print('Unknown split: %s. Exiting..' % (split))
exit(-1)
# print(os.path.basename(fns))
for fn in fns:
token=(os.path.splitext(os.path.basename(fn))[0])
self.meta[item].append(os.path.join(dir_point, token + '.txt'))
self.datapath=[]
for item in self.cat:
for fn in self.meta[item]:
self.datapath.append((item,fn))
self.classes={}
for i in self.cat.keys():
self.classes[i]=self.classes_original[i]
# Mapping from category ('Chair') to a list of int [10,11,12,13] as segmentation labels
self.seg_classes={'Earphone':[16,17,18],'Motorbike':[30,31,32,33,34,35],'Rocket':[41,42,43],'Car':[8,9,10,11],
'Laptop':[28,29],'Cap':[6,7],'Skateboard':[44,45,46],'Mug':[36,37],'Guitar':[19,20,21],'Bag':[4,5],'Lamp':[24,25,26,27],
'Table':[47,48,49],'Airplane':[0,1,2,3],'Pistol':[38,39,40],'Chair':[12,13,14,15],'Knife':[22,23]}
# for cat in sorted(self.seg_classes.keys()):
# print(cat, self.seg_classes[cat])
def __getitem__(self,index):
fn=self.datapath[index]
# print(fn)
cat=self.datapath[index][0]
cls=self.classes[cat]
cls=np.array([cls]).astype(np.int64)
data=np.loadtxt(fn[1]).astype(np.float32)
if not self.normal_channel:
point_set=data[:,0:3]
else:
point_set=data[:,0:6]
seg=data[:,-1].astype(np.int32)
point_set[:,0:3]=pc_normalize(point_set[:,0:3])
choice=np.random.choice(len(seg),self.npoints,replace=True)
# resample
point_set=point_set[choice,:]
seg=seg[choice]
jittered_pc=point_set+np.random.normal(0,0.1,size=point_set.shape) # random jitter
return (point_set,cls,seg,index)
def __len__(self):
return len(self.datapath)
# class_name="Airplane"
# bs=1
# mkdir_ifnotexists("/vinai/sskar/unsup_implicit/shapenet_v0/"+str(class_name))
# root="/vinai/sskar/TTA/shapenetcore_partanno_segmentation_benchmark_v0_normal"
# ds=shapenet_v0(root,npoints=2500,split='train',class_choice=class_name,normal_channel=False)
# train_loader=utils.data.DataLoader(ds,batch_size=bs,shuffle=True,num_workers=8,drop_last=False)
# for batch_idx,(point_cloud,cat_label,seg_label,idx) in enumerate(train_loader):
# print(point_cloud.shape)
# if batch_idx == 0:
# np.save("v0_plane1.npy",point_cloud.squeeze().numpy())
# elif batch_idx == 1:
# np.save("v0_plane2.npy",point_cloud.squeeze().numpy())
# else:
# break
# print(cat_label)
# print(seg_label)
class single_shape_dataset(utils.data.Dataset):
def __init__(self,point_cloud):
tree=spatial.KDTree(point_cloud)
dists,indices=tree.query(point_cloud,k=50+1)
radius=dists[:,-1]
self.point_cloud=point_cloud.astype(np.float32)
self.radius=radius
def __len__(self):
length=len(self.point_cloud)
return (length)
def __getitem__(self,idx):
pts=self.point_cloud[idx]
radius=self.radius[idx]
return (pts,radius)