64 lines
1.8 KiB
Python
64 lines
1.8 KiB
Python
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"""
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ScanObjectNN download: http://103.24.77.34/scanobjectnn/h5_files.zip
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"""
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import os
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import sys
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import glob
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import h5py
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import numpy as np
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from torch.utils.data import Dataset
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os.environ["HDF5_USE_FILE_LOCKING"] = "FALSE"
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def load_scanobjectnn_data(partition):
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BASE_DIR = os.path.dirname(os.path.abspath(__file__))
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all_data = []
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all_label = []
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h5_name = BASE_DIR + '/data/h5_files/main_split/' + partition + '_objectdataset_augmentedrot_scale75.h5'
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f = h5py.File(h5_name, mode="r")
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data = f['data'][:].astype('float32')
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label = f['label'][:].astype('int64')
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f.close()
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all_data.append(data)
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all_label.append(label)
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all_data = np.concatenate(all_data, axis=0)
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all_label = np.concatenate(all_label, axis=0)
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return all_data, all_label
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def translate_pointcloud(pointcloud):
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xyz1 = np.random.uniform(low=2. / 3., high=3. / 2., size=[3])
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xyz2 = np.random.uniform(low=-0.2, high=0.2, size=[3])
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translated_pointcloud = np.add(np.multiply(pointcloud, xyz1), xyz2).astype('float32')
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return translated_pointcloud
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class ScanObjectNN(Dataset):
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def __init__(self, num_points, partition='training'):
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self.data, self.label = load_scanobjectnn_data(partition)
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self.num_points = num_points
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self.partition = partition
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def __getitem__(self, item):
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pointcloud = self.data[item][:self.num_points]
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label = self.label[item]
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if self.partition == 'training':
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pointcloud = translate_pointcloud(pointcloud)
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np.random.shuffle(pointcloud)
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return pointcloud, label
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def __len__(self):
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return self.data.shape[0]
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if __name__ == '__main__':
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train = ScanObjectNN(1024)
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test = ScanObjectNN(1024, 'test')
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for data, label in train:
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print(data.shape)
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print(label)
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