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- '''
- 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))
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