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part_dataset_all_normal.py
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'''
Dataset for ShapeNetPart segmentation
'''
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 PartNormalDataset():
def __init__(self, root, npoints = 2500, classification = False, split='train', normalize=True, return_cls_label = False):
self.npoints = npoints
self.root = root
self.catfile = os.path.join(self.root, 'synsetoffset2category.txt')
self.cat = {}
self.classification = classification
self.normalize = normalize
self.return_cls_label = return_cls_label
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()}
#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=='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 + '.txt'))
self.datapath = []
for item in self.cat:
for fn in self.meta[item]:
self.datapath.append((item, fn))
self.classes = dict(zip(self.cat, range(len(self.cat))))
# 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])
self.cache = {} # from index to (point_set, cls, seg) tuple
self.cache_size = 20000
def __getitem__(self, index):
if index in self.cache:
point_set, normal, seg, cls = self.cache[index]
else:
fn = self.datapath[index]
cat = self.datapath[index][0]
cls = self.classes[cat]
cls = np.array([cls]).astype(np.int32)
data = np.loadtxt(fn[1]).astype(np.float32)
point_set = data[:,0:3]
if self.normalize:
point_set = pc_normalize(point_set)
normal = data[:,3:6]
seg = data[:,-1].astype(np.int32)
if len(self.cache) < self.cache_size:
self.cache[index] = (point_set, normal, seg, cls)
choice = np.random.choice(len(seg), self.npoints, replace=True)
#resample
point_set = point_set[choice, :]
seg = seg[choice]
normal = normal[choice,:]
if self.classification:
return point_set, normal, cls
else:
if self.return_cls_label:
return point_set, normal, seg, cls
else:
return point_set, normal, seg
def __len__(self):
return len(self.datapath)
if __name__ == '__main__':
d = PartNormalDataset(root = '../data/shapenetcore_partanno_segmentation_benchmark_v0_normal', split='trainval', npoints=3000)
print(len(d))
i = 500
ps, normal, seg = d[i]
print d.datapath[i]
print np.max(seg), np.min(seg)
print(ps.shape, seg.shape, normal.shape)
print ps
print normal
sys.path.append('../utils')
import show3d_balls
show3d_balls.showpoints(ps, normal+1, ballradius=8)
d = PartNormalDataset(root = '../data/shapenetcore_partanno_segmentation_benchmark_v0_normal', classification = True)
print(len(d))
ps, normal, cls = d[0]
print(ps.shape, type(ps), cls.shape,type(cls))