PointMLP/classification_ScanObjectNN/ScanObjectNN.py
2022-02-15 21:32:53 -05:00

81 lines
2.5 KiB
Python

"""
ScanObjectNN download: http://103.24.77.34/scanobjectnn/h5_files.zip
"""
import os
import sys
import glob
import h5py
import numpy as np
from torch.utils.data import Dataset
os.environ["HDF5_USE_FILE_LOCKING"] = "FALSE"
def download():
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
DATA_DIR = os.path.join(BASE_DIR, 'data')
if not os.path.exists(DATA_DIR):
os.mkdir(DATA_DIR)
if not os.path.exists(os.path.join(DATA_DIR, 'h5_files')):
# note that this link only contains the hardest perturbed variant (PB_T50_RS).
# for full versions, consider the following link.
www = 'https://web.northeastern.edu/smilelab/xuma/datasets/h5_files.zip'
# www = 'http://103.24.77.34/scanobjectnn/h5_files.zip'
zipfile = os.path.basename(www)
os.system('wget %s --no-check-certificate; unzip %s' % (www, zipfile))
os.system('mv %s %s' % (zipfile[:-4], DATA_DIR))
os.system('rm %s' % (zipfile))
def load_scanobjectnn_data(partition):
download()
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
all_data = []
all_label = []
h5_name = BASE_DIR + '/data/h5_files/main_split/' + partition + '_objectdataset_augmentedrot_scale75.h5'
f = h5py.File(h5_name, mode="r")
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 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
class ScanObjectNN(Dataset):
def __init__(self, num_points, partition='training'):
self.data, self.label = load_scanobjectnn_data(partition)
self.num_points = num_points
self.partition = partition
def __getitem__(self, item):
pointcloud = self.data[item][:self.num_points]
label = self.label[item]
if self.partition == 'training':
pointcloud = translate_pointcloud(pointcloud)
np.random.shuffle(pointcloud)
return pointcloud, label
def __len__(self):
return self.data.shape[0]
if __name__ == '__main__':
train = ScanObjectNN(1024)
test = ScanObjectNN(1024, 'test')
for data, label in train:
print(data.shape)
print(label)