import glob import h5py import numpy as np from torch.utils.data import Dataset import os import json os.environ["HDF5_USE_FILE_LOCKING"] = "FALSE" def load_data(partition): all_data = [] all_label = [] for h5_name in glob.glob('./data/modelnet40_ply_hdf5_2048/ply_data_%s*.h5' % partition): f = h5py.File(h5_name) data = f['data'][:].astype('float32') label = f['label'][:].astype('int64') f.close() all_data.append(data) all_label.append(label) all_data = np.concatenate(all_data, axis=0) all_label = np.concatenate(all_label, axis=0) return all_data, all_label 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 def translate_pointcloud(pointcloud): xyz1 = np.random.uniform(low=2./3., high=3./2., size=[3]) xyz2 = np.random.uniform(low=-0.2, high=0.2, size=[3]) translated_pointcloud = np.add(np.multiply(pointcloud, xyz1), xyz2).astype('float32') return translated_pointcloud def jitter_pointcloud(pointcloud, sigma=0.01, clip=0.02): N, C = pointcloud.shape pointcloud += np.clip(sigma * np.random.randn(N, C), -1*clip, clip) return pointcloud # =========== ModelNet40 ================= class ModelNet40(Dataset): def __init__(self, num_points, partition='train'): self.data, self.label = load_data(partition) self.num_points = num_points self.partition = partition # Here the new given partition will cover the 'train' def __getitem__(self, item): # indice of the pts or label pointcloud = self.data[item][:self.num_points] label = self.label[item] if self.partition == 'train': # pointcloud = pc_normalize(pointcloud) # you can try to add it or not to train our model pointcloud = translate_pointcloud(pointcloud) np.random.shuffle(pointcloud) # shuffle the order of pts return pointcloud, label def __len__(self): return self.data.shape[0] # =========== ShapeNet Part ================= class PartNormalDataset(Dataset): def __init__(self, npoints=2500, split='train', normalize=False): self.npoints = npoints self.root = './data/shapenetcore_partanno_segmentation_benchmark_v0_normal' self.catfile = os.path.join(self.root, 'synsetoffset2category.txt') self.cat = {} self.normalize = normalize 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.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: self.meta[item] = [] dir_point = os.path.join(self.root, self.cat[item]) fns = sorted(os.listdir(dir_point)) 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) 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]} 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] 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) if self.normalize: point_set = pc_normalize(point_set) choice = np.random.choice(len(seg), self.npoints, replace=True) # resample # note that the number of points in some points clouds is less than 2048, thus use random.choice # remember to use the same seed during train and test for a getting stable result point_set = point_set[choice, :] seg = seg[choice] normal = normal[choice, :] return point_set, cls, seg, normal def __len__(self): return len(self.datapath) if __name__ == '__main__': train = PartNormalDataset(npoints=2048, split='trainval', normalize=False) test = PartNormalDataset(npoints=2048, split='test', normalize=False) for data, label, _, _ in train: print(data.shape) print(label.shape)