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data_utils.py
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data_utils.py
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"""
@Author: Yue Wang
@Contact: yuewangx@mit.edu
@File: data.py
@Time: 2018/10/13 6:21 PM
Modified by
@Author: Manxi Lin
@Contact: manli@dtu.dk
@Time: 2022/7/7 3:00 PM
"""
import os
import glob
import h5py
import numpy as np
import torch
from torch.utils.data import Dataset
from models.utils import get_dist
def load_data_mdn(partition):
base_dir = os.path.dirname(os.path.abspath(__file__))
data_dir = os.path.join(base_dir, 'data')
all_data = []
all_label = []
for h5_name in glob.glob(os.path.join(data_dir, 'modelnet40_ply_hdf5_2048', '*%s*.h5'%partition)):
f = h5py.File(h5_name, '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 load_data_sonn(partition, bg):
base_dir = os.path.dirname(os.path.abspath(__file__))
data_dir = os.path.join(base_dir, 'data')
if bg:
head = 'main_split'
else:
head = 'main_split_nobg'
if partition == 'train':
partition = 'training'
h5_name = os.path.join(data_dir, 'h5_files', head, '%s_objectdataset.h5'%(partition))
f = h5py.File(h5_name, 'r+')
data = f['data'][:].astype('float32')
label = f['label'][:].astype('int64')
return data, label
def load_data_seg(partition):
base_dir = os.path.dirname(os.path.abspath(__file__))
data_dir = os.path.join(base_dir, 'data')
point_dir = os.path.join(data_dir, 'Toronto_3D', partition, '*_point.npy')
label_dir = os.path.join(data_dir, 'Toronto_3D', partition, '*_label.npy')
all_data = glob.glob(point_dir)
all_label = glob.glob(label_dir)
return all_data, all_label
def load_data_partseg(partition):
base_dir = os.path.dirname(os.path.abspath(__file__))
data_dir = os.path.join(base_dir, 'data')
all_data = []
all_label = []
all_seg = []
if partition == 'trainval':
file = glob.glob(os.path.join(data_dir, 'shapenet_part_seg_hdf5_data', '*train*.h5')) \
+ glob.glob(os.path.join(data_dir, 'shapenet_part_seg_hdf5_data', '*val*.h5'))
else:
file = glob.glob(os.path.join(data_dir, 'shapenet_part_seg_hdf5_data', '*%s*.h5'%partition))
for h5_name in file:
f = h5py.File(h5_name, 'r+')
data = f['data'][:].astype('float32')
label = f['label'][:].astype('int64')
seg = f['pid'][:].astype('int64')
f.close()
all_data.append(data)
all_label.append(label)
all_seg.append(seg)
all_data = np.concatenate(all_data, axis=0)
all_label = np.concatenate(all_label, axis=0)
all_seg = np.concatenate(all_seg, axis=0)
return all_data, all_label, all_seg
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
def rotate_pointcloud(pointcloud):
theta = np.pi*2 * np.random.uniform()
rotation_matrix = np.array([[np.cos(theta), -np.sin(theta)],[np.sin(theta), np.cos(theta)]])
pointcloud[:,[0,2]] = pointcloud[:,[0,2]].dot(rotation_matrix) # random rotation (x,z)
return pointcloud
class ModelNet40(Dataset):
def __init__(self, num_points, partition='train'):
self.data, self.label = load_data_mdn(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 == 'train':
pointcloud = translate_pointcloud(pointcloud)
np.random.shuffle(pointcloud)
return pointcloud, label
def __len__(self):
return self.data.shape[0]
class ModelNet40Noise(Dataset):
def __init__(self, num_points, num_noise, partition='test'):
assert partition == "test",'Noise study can only be applied during evaluation'
self.data, self.label = load_data_mdn(partition)
self.num_points = num_points
self.partition = partition
self.num_noise = num_noise
assert self.num_noise <= self.num_points,'number of noise points should be less than the point number'
def __getitem__(self, item):
pointcloud = self.data[item][:self.num_points]
label = self.label[item]
pointcloud = pointcloud[:-self.num_noise, :]
noise = np.random.rand(self.num_noise, 3)*2-1
pointcloud = np.concatenate((pointcloud, noise), axis=0).astype('float32')
np.random.shuffle(pointcloud)
return pointcloud, label
def __len__(self):
return self.data.shape[0]
class ModelNet40Resplit(Dataset):
def __init__(self, num_points, partition='train'):
train_data, train_label = load_data_mdn('train')
test_data, test_label = load_data_mdn('test')
self.num_points = num_points
self.partition = partition
all_data = np.concatenate((train_data, test_data), axis=0)
all_label = np.concatenate((train_label, test_label), axis=0)
indices = list(range(all_data.shape[0]))
np.random.shuffle(indices)
if partition == 'train':
self.data = all_data[indices[:8617], ...]
self.label = all_label[indices[:8617], ...]
elif partition == 'test':
self.data = all_data[indices[8617:8617+1847], ...]
self.label = all_label[indices[8617:8617+1847], ...]
elif partition == 'vali':
self.data = all_data[indices[8617+1847:], ...]
self.label = all_label[indices[8617+1847:], ...]
else:
raise NameError
def __getitem__(self, item):
pointcloud = self.data[item][:self.num_points]
label = self.label[item]
if self.partition == 'train':
pointcloud = translate_pointcloud(pointcloud)
np.random.shuffle(pointcloud)
return pointcloud, label
def __len__(self):
return self.data.shape[0]
class ScanObjectNN(Dataset):
def __init__(self, num_points, partition='train', bg=False):
self.data, self.label = load_data_sonn(partition, bg)
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 == 'train':
pointcloud = translate_pointcloud(pointcloud)
pointcloud = rotate_pointcloud(pointcloud)
pointcloud = jitter_pointcloud(pointcloud)
np.random.shuffle(pointcloud)
return pointcloud, label
def __len__(self):
return self.data.shape[0]
class ModelNet40C(Dataset):
def __init__(self, corruption, severity):
base_dir = os.path.dirname(os.path.abspath(__file__))
data_dir = os.path.join(base_dir, 'data', 'modelnet40_c')
DATA_DIR = os.path.join(data_dir, 'data_' + corruption + '_' +str(severity) + '.npy')
LABEL_DIR = os.path.join(data_dir, 'label.npy')
self.data = np.load(DATA_DIR)
self.label = np.load(LABEL_DIR)
def __getitem__(self, item):
pointcloud = self.data[item]
label = self.label[item]
label = label
return torch.from_numpy(pointcloud), torch.from_numpy(label).long()
def __len__(self):
return self.data.shape[0]
class Toronto3D(Dataset):
def __init__(self, num_points=2048, partition='train'):
self.data, self.seg = load_data_seg(partition)
self.num_points = num_points
self.partition = partition
def __getitem__(self, item):
pointcloud = np.load(self.data[item])
seg = np.load(self.seg[item])
pointcloud = pointcloud[:self.num_points]
seg = seg[:self.num_points]
# normalize point cloud
pointcloud = pointcloud - np.min(pointcloud, axis=0)
pointcloud /= 5 # normalize point cloud in 5x5 blocks
if self.partition == 'train':
indices = list(range(pointcloud.shape[0]))
pointcloud = translate_pointcloud(pointcloud)
pointcloud = jitter_pointcloud(pointcloud)
pointcloud[:,:3] = rotate_pointcloud(pointcloud[:,:3])
np.random.shuffle(indices)
pointcloud = pointcloud[indices]
seg = seg[indices]
seg = torch.LongTensor(seg)
pointcloud = torch.from_numpy(pointcloud)
return pointcloud, seg
def __len__(self):
return len(self.data)
class ShapeNetPart(Dataset):
def __init__(self, num_points, partition='train'):
self.data, self.label, self.seg = load_data_partseg(partition)
self.seg_num = [4, 2, 2, 4, 4, 3, 3, 2, 4, 2, 6, 2, 3, 3, 3, 3]
self.index_start = [0, 4, 6, 8, 12, 16, 19, 22, 24, 28, 30, 36, 38, 41, 44, 47]
self.num_points = num_points
self.partition = partition
self.seg_num_all = 50
self.seg_start_index = 0
def __getitem__(self, item):
pointcloud = self.data[item][:self.num_points]
label = self.label[item]
seg = self.seg[item][:self.num_points]
if self.partition == 'trainval':
pointcloud = translate_pointcloud(pointcloud)
indices = list(range(pointcloud.shape[0]))
np.random.shuffle(indices)
pointcloud = pointcloud[indices]
seg = seg[indices]
return pointcloud, label, seg
def __len__(self):
return self.data.shape[0]
class ShapeNetPartNoise(Dataset):
def __init__(self, num_points, num_noise, partition='test'):
assert partition == "test",'Noise study can only be applied during evaluation'
self.data, self.label, self.seg = load_data_partseg(partition)
self.seg_num = [4, 2, 2, 4, 4, 3, 3, 2, 4, 2, 6, 2, 3, 3, 3, 3]
self.index_start = [0, 4, 6, 8, 12, 16, 19, 22, 24, 28, 30, 36, 38, 41, 44, 47]
self.seg_num_all = 50
self.seg_start_index = 0
self.num_points = num_points
self.num_noise = num_noise
assert self.num_noise <= self.num_points,'number of noise points should be less than the point number'
def __getitem__(self, item):
pointcloud = self.data[item][:self.num_points]
label = self.label[item]
seg = self.seg[item][:self.num_points]
noise = np.random.rand(self.num_noise, 3)*2-1
dist = get_dist(torch.from_numpy(noise.astype('float32')).cuda().unsqueeze(0),
torch.from_numpy(pointcloud.astype('float32')).cuda().unsqueeze(0))
noise_idx = torch.min(dist, dim=-1)[1].squeeze(0).cpu().numpy()
noise_seg = seg[noise_idx]
pointcloud = pointcloud[:-self.num_noise, :]
seg = seg[:-self.num_noise]
pointcloud = np.concatenate((pointcloud, noise), axis=0).astype('float32')
seg = np.concatenate((seg, noise_seg), axis=0)
indices = list(range(pointcloud.shape[0]))
np.random.shuffle(indices)
pointcloud = pointcloud[indices]
seg = seg[indices]
return pointcloud, label, seg
def __len__(self):
return self.data.shape[0]