https://github.com/liuxinhai/Point2Sequence
Tip revision: 3ee6a8c42b210a1175180dfb29ce7f2dc82ba2d7 authored by Xinhai Liu on 25 November 2020, 05:51:20 UTC
Update README.md
Update README.md
Tip revision: 3ee6a8c
part_dataset.py
'''
Dataset for shapenet part segmentaion.
'''
import os
import os.path
import json
import numpy as np
import sys
def pc_normalize(pc):
l = pc.shape[0]
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 PartDataset():
def __init__(self, root, npoints = 2500, classification = False, class_choice = None, split='train', normalize=True):
self.npoints = npoints
self.root = root
self.catfile = os.path.join(self.root, 'synsetoffset2category.txt')
self.cat = {}
self.classification = classification
self.normalize = normalize
with open(self.catfile, 'r') as f:
for line in f:
ls = line.strip().split()
self.cat[ls[0]] = ls[1]
#print(self.cat)
if not class_choice is None:
self.cat = {k:v for k,v in self.cat.items() if k in class_choice}
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], 'points')
dir_seg = os.path.join(self.root, self.cat[item], 'points_label')
#print(dir_point, dir_seg)
fns = sorted(os.listdir(dir_point))
#print(fns[0][0:-4])
if split=='trainval':
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 + '.pts'), os.path.join(dir_seg, token + '.seg')))
self.datapath = []
for item in self.cat:
for fn in self.meta[item]:
self.datapath.append((item, fn[0], fn[1]))
self.classes = dict(zip(self.cat, range(len(self.cat))))
self.num_seg_classes = 0
if not self.classification:
for i in range(len(self.datapath)/50):
l = len(np.unique(np.loadtxt(self.datapath[i][-1]).astype(np.uint8)))
if l > self.num_seg_classes:
self.num_seg_classes = l
#print(self.num_seg_classes)
self.cache = {} # from index to (point_set, cls, seg) tuple
self.cache_size = 10000
def __getitem__(self, index):
if index in self.cache:
point_set, seg, cls = self.cache[index]
else:
fn = self.datapath[index]
cls = self.classes[self.datapath[index][0]]
cls = np.array([cls]).astype(np.int32)
point_set = np.loadtxt(fn[1]).astype(np.float32)
if self.normalize:
point_set = pc_normalize(point_set)
seg = np.loadtxt(fn[2]).astype(np.int64) - 1
#print(point_set.shape, seg.shape)
if len(self.cache) < self.cache_size:
self.cache[index] = (point_set, seg, cls)
choice = np.random.choice(len(seg), self.npoints, replace=True)
#resample
point_set = point_set[choice, :]
seg = seg[choice]
if self.classification:
return point_set, cls
else:
return point_set, seg
def __len__(self):
return len(self.datapath)
if __name__ == '__main__':
d = PartDataset(root = '../data/shapenetcore_partanno_segmentation_benchmark_v0', class_choice = ['Airplane'], split='test')
print(len(d))
import time
tic = time.time()
for i in range(100):
ps, seg = d[i]
print np.max(seg), np.min(seg)
print(time.time() - tic)
print(ps.shape, type(ps), seg.shape,type(seg))
d = PartDataset(root = '../data/shapenetcore_partanno_segmentation_benchmark_v0', classification = True)
print(len(d))
ps, cls = d[0]
print(ps.shape, type(ps), cls.shape,type(cls))