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| import glob | |
| import h5py | |
| import numpy as np | |
| from torch.utils.data import Dataset | |
| import os | |
| import json | |
| from PointWOLF import PointWOLF | |
| 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', args=None): | |
| self.data, self.label = load_data(partition) | |
| self.num_points = num_points | |
| self.partition = partition | |
| self.PointWOLF = PointWOLF(args) if args is not None else None | |
| def __getitem__(self, item): | |
| pointcloud = self.data[item][:self.num_points] | |
| label = self.label[item] | |
| if self.partition == 'train': | |
| np.random.shuffle(pointcloud) | |
| if self.PointWOLF is not None: | |
| _, pointcloud = self.PointWOLF(pointcloud) | |
| 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 = ModelNet40(1024) | |
| test = ModelNet40(1024, 'test') | |
| for data, label in train: | |
| print(data.shape) | |
| print(label.shape) | |