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- # *_*coding:utf-8 *_*
- import os
- import json
- import warnings
- import numpy as np
- from torch.utils.data import Dataset
- warnings.filterwarnings('ignore')
-
- 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 PartNormalDataset(Dataset):
- def __init__(self,root = './data/shapenetcore_partanno_segmentation_benchmark_v0_normal', 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
- self.seg_num_all = 50
- self.seg_start_index = 0
-
-
-
- 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 == '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 = {}
- 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])
-
- self.cache = {} # from index to (point_set, cls, seg) tuple
- self.cache_size = 20000
-
-
- def __getitem__(self, index):
- if index in self.cache:
- ppoint_set, cls, seg = 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)
- if not self.normal_channel:
- point_set = data[:, 0:3]
- else:
- point_set = data[:, 0:6]
- seg = data[:, -1].astype(np.int32)
- if len(self.cache) < self.cache_size:
- self.cache[index] = (point_set, cls, seg)
- 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]
-
- return point_set, cls, seg
-
- def __len__(self):
- return len(self.datapath)
-
- import utils.data_util as utils
-
- class PartNormalDatasetSPU(Dataset):
- def __init__(self,root = './data/shapenetcore_partanno_segmentation_benchmark_v0_normal', npoints=2500, split='train', class_choice=None, normal_channel=False,scale_factor=4):
- self.npoints = npoints
- self.root = root
- self.scale_factor = scale_factor
-
- self.catfile = os.path.join(self.root, 'synsetoffset2category.txt')
- self.cat = {}
- self.normal_channel = normal_channel
- self.seg_num_all = 50
- self.seg_start_index = 0
-
- 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 == '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 = {}
- 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])
-
- self.cache = {} # from index to (point_set, cls, seg) tuple
- self.cache_size = 20000
-
-
- def __getitem__(self, index):
- if index in self.cache:
- ppoint_set, cls, seg = 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)
- if not self.normal_channel:
- point_set = data[:, 0:3]
- else:
- point_set = data[:, 0:6]
- seg = data[:, -1].astype(np.int32)
- if len(self.cache) < self.cache_size:
- self.cache[index] = (point_set, cls, seg)
- point_set[:, 0:3] = pc_normalize(point_set[:, 0:3])
-
-
- point_set = point_set[0:self.npoints, :]
- seg = seg[0:self.npoints]
-
- #choice = utils.nonuniform_sampling(self.npoints, self.npoints//self.scale_factor)
- choice = np.random.choice(len(seg), self.npoints//self.scale_factor, replace=True)
- # resample
- lr_point_set = point_set[choice, :]
- lr_seg = seg[choice]
-
- return lr_point_set,point_set, lr_seg, seg, cls
-
- def __len__(self):
- return len(self.datapath)
-
-
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