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dataset.py
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dataset.py
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import os
import abc
import torch
import numpy as np
import open3d as o3d
from utils.utils import (
random_rotation_matrix,
read_pickle)
def make_non_exists_dir(fn):
if not os.path.exists(fn):
os.makedirs(fn)
class EvalDataset(abc.ABC):
@abc.abstractmethod
def get_pair_ids(self):
pass
@abc.abstractmethod
def get_cloud_ids(self):
pass
@abc.abstractmethod
def get_pc_dir(self,cloud_id):
pass
@abc.abstractmethod
def get_key_dir(self,cloud_id):
pass
@abc.abstractmethod
def get_transform(self,id0,id1):
# note the order!
# target: id0, source: id1
# R @ pts1 + t = pts0
pass
@abc.abstractmethod
def get_name(self):
pass
@abc.abstractmethod
def get_kps(self,cloud_id):
pass
#The dataset class for original/ground truth datas
class SceneDataset(EvalDataset):
def __init__(self,root_dir,stationnum,gt_dir=None):
self.root=root_dir
if gt_dir==None:
self.gt_dir=f'{self.root}/PointCloud/gt.log'
else:
self.gt_dir=gt_dir
self.kps_pc_fn=[f'{self.root}/Keypoints_PC/cloud_bin_{k}Keypoints.npy' for k in range(stationnum)]
self.kps_fn=[f'{self.root}/Keypoints/cloud_bin_{k}Keypoints.txt' for k in range(stationnum)]
self.pc_ply_paths=[f'{self.root}/PointCloud/cloud_bin_{k}.ply' for k in range(stationnum)]
self.pc_txt_paths=[f'{self.root}/PointCloud/cloud_bin_{k}.txt' for k in range(stationnum)]
self.pair_id2transform=self.parse_gt_fn(self.gt_dir)
self.pair_ids=[tuple(v.split('-')) for v in self.pair_id2transform.keys()]
self.pc_ids=[str(k) for k in range(stationnum)]
self.pair_num=self.get_pair_nums()
self.name='3dmatch/kitchen'
#function for gt(input: gt.log)
@staticmethod
def parse_gt_fn(fn):
with open(fn,'r') as f:
lines=f.readlines()
pair_num=len(lines)//5
pair_id2transform={}
for k in range(pair_num):
id0,id1=np.fromstring(lines[k*5],dtype=np.float32,sep='\t')[0:2]
id0=int(id0)
id1=int(id1)
row0=np.fromstring(lines[k*5+1],dtype=np.float32,sep=' ')
row1=np.fromstring(lines[k*5+2],dtype=np.float32,sep=' ')
row2=np.fromstring(lines[k*5+3],dtype=np.float32,sep=' ')
transform=np.stack([row0,row1,row2],0)
pair_id2transform['-'.join((str(id0),str(id1)))]=transform
return pair_id2transform
def get_pair_ids(self):
return self.pair_ids
def get_pair_nums(self):
return len(self.pair_ids)
def get_cloud_ids(self):
return self.pc_ids
def get_pc_dir(self,cloud_id):
return self.pc_ply_paths[int(cloud_id)]
def get_pc(self,pc_id):
if os.path.exists(self.pc_ply_paths[int(pc_id)]):
pc=o3d.io.read_point_cloud(self.pc_ply_paths[int(pc_id)])
return np.array(pc.points)
else:
pc=np.loadtxt(self.pc_paths[int(pc_id)],delimiter=',')
return pc
def get_pc_o3d(self,pc_id):
return o3d.io.read_point_cloud(self.pc_ply_paths[int(pc_id)])
def get_key_dir(self,cloud_id):
return self.kps_fn[int(cloud_id)]
def get_transform(self, id0, id1):
return self.pair_id2transform['-'.join((id0,id1))]
def get_name(self):
return self.name
def get_kps(self, cloud_id):
if not os.path.exists(self.kps_pc_fn[int(cloud_id)]):
pc=self.get_pc(cloud_id)
key_idxs=np.loadtxt(self.kps_fn[int(cloud_id)]).astype(np.int)
keys=pc[key_idxs]
make_non_exists_dir(f'{self.root}/Keypoints_PC')
np.save(self.kps_pc_fn[int(cloud_id)],keys)
return keys
return np.load(self.kps_pc_fn[int(cloud_id)])
#Get dataset items with the dataset name(output: dict)
def get_dataset_name(dataset_name,origin_data_dir):
if dataset_name=='demo':
datasets={}
datasets['wholesetname']=f'{dataset_name}'
scenes=["kitchen"]
stationnums=[60]
for i in range(len(scenes)):
root_dir=f'{origin_data_dir}/{dataset_name}/'+scenes[i]
datasets[scenes[i]]=SceneDataset(root_dir,stationnums[i])
datasets[scenes[i]].name=f'{dataset_name}/{scenes[i]}'
return datasets
if dataset_name=='3dmatch':
datasets={}
datasets['wholesetname']=f'{dataset_name}'
scenes=["kitchen","sun3d-home_at-home_at_scan1_2013_jan_1",
"sun3d-home_md-home_md_scan9_2012_sep_30","sun3d-hotel_uc-scan3",
"sun3d-hotel_umd-maryland_hotel1","sun3d-hotel_umd-maryland_hotel3",
"sun3d-mit_76_studyroom-76-1studyroom2","sun3d-mit_lab_hj-lab_hj_tea_nov_2_2012_scan1_erika"]
stationnums=[60,60,60,55,57,37,66,38]
for i in range(len(scenes)):
root_dir=f'{origin_data_dir}/{dataset_name}/'+scenes[i]
datasets[scenes[i]]=SceneDataset(root_dir,stationnums[i])
datasets[scenes[i]].name=f'{dataset_name}/{scenes[i]}'
return datasets
if dataset_name=='3dLomatch':
datasets={}
datasets['wholesetname']=f'{dataset_name}'
scenes=["kitchen","sun3d-home_at-home_at_scan1_2013_jan_1",
"sun3d-home_md-home_md_scan9_2012_sep_30","sun3d-hotel_uc-scan3",
"sun3d-hotel_umd-maryland_hotel1","sun3d-hotel_umd-maryland_hotel3",
"sun3d-mit_76_studyroom-76-1studyroom2","sun3d-mit_lab_hj-lab_hj_tea_nov_2_2012_scan1_erika"]
stationnums=[60,60,60,55,57,37,66,38]
for i in range(len(scenes)):
root_dir=f'{origin_data_dir}/3dmatch/'+scenes[i]
gt_dir=f'{root_dir}/PointCloud/gtLo.log'
datasets[scenes[i]]=SceneDataset(root_dir,stationnums[i],gt_dir)
datasets[scenes[i]].name=f'{dataset_name}/{scenes[i]}'
return datasets
if dataset_name=='ETH':
datasets={}
datasets['wholesetname']=f'{dataset_name}'
scenes=['gazebo_summer','gazebo_winter','wood_autumn','wood_summer']
stationnums=[32,31,32,37]
for i in range(len(scenes)):
root_dir=f'{origin_data_dir}/{dataset_name}/'+scenes[i]
datasets[scenes[i]]=SceneDataset(root_dir,stationnums[i])
datasets[scenes[i]].name=f'{dataset_name}/{scenes[i]}'
return datasets
if dataset_name=='scannet':
datasets={}
datasets['wholesetname']=f'{dataset_name}'
scenes=["scene0197_01","scene0030_02","scene0406_02","scene0694_00",
"scene0701_01","scene0457_01","scene0208_00","scene0578_01",
"scene0286_02","scene0569_00","scene0309_00","scene0265_02",
"scene0588_02","scene0474_01","scene0477_01","scene0334_02",
"scene0353_00","scene0043_00","scene0224_00","scene0661_00",
"scene0335_02","scene0231_01","scene0025_01","scene0642_02",
"scene0493_01","scene0057_01","scene0575_02","scene0146_02",
"scene0223_00","scene0262_01","scene0229_01","scene0676_01"]
stationnums=[30]*len(scenes)
for i in range(len(scenes)):
root_dir=f'{origin_data_dir}/scannet/'+scenes[i]
gt_dir=f'{root_dir}/PointCloud/gt.log'
datasets[scenes[i]]=SceneDataset(root_dir,stationnums[i],gt_dir)
datasets[scenes[i]].name=f'{dataset_name}/{scenes[i]}'
return datasets
if dataset_name=='3dmatch_train':
datasets={}
datasets['wholesetname']=f'{dataset_name}'
datasets['valscenes']=['sun3d-brown_bm_4-brown_bm_4','sun3d-harvard_c11-hv_c11_2','7-scenes-heads','rgbd-scenes-v2-scene_10','bundlefusion-office0','analysis-by-synthesis-apt2-kitchen']
scenes=['bundlefusion-apt0', 'rgbd-scenes-v2-scene_02', 'bundlefusion-office1', 'sun3d-brown_cogsci_1-brown_cogsci_1', 'rgbd-scenes-v2-scene_06', 'analysis-by-synthesis-apt2-kitchen', 'rgbd-scenes-v2-scene_03', 'bundlefusion-apt1', 'sun3d-harvard_c8-hv_c8_3', 'bundlefusion-copyroom', 'sun3d-home_bksh-home_bksh_oct_30_2012_scan2_erika', 'rgbd-scenes-v2-scene_04', '7-scenes-pumpkin', 'rgbd-scenes-v2-scene_01', 'analysis-by-synthesis-office2-5a', 'sun3d-brown_bm_1-brown_bm_1', 'bundlefusion-apt2', 'sun3d-brown_cs_2-brown_cs2', 'bundlefusion-office2', 'sun3d-hotel_sf-scan1', 'sun3d-hotel_nips2012-nips_4', 'bundlefusion-office3', 'rgbd-scenes-v2-scene_09', 'rgbd-scenes-v2-scene_05', 'rgbd-scenes-v2-scene_07', '7-scenes-heads', 'sun3d-harvard_c3-hv_c3_1', 'rgbd-scenes-v2-scene_08', 'sun3d-mit_76_417-76-417b', 'sun3d-mit_32_d507-d507_2', 'sun3d-mit_46_ted_lab1-ted_lab_2', '7-scenes-chess', 'rgbd-scenes-v2-scene_10', 'sun3d-harvard_c11-hv_c11_2', 'analysis-by-synthesis-apt2-living', 'sun3d-mit_w20_athena-sc_athena_oct_29_2012_scan1_erika', 'analysis-by-synthesis-apt1-living', 'analysis-by-synthesis-apt1-kitchen', 'sun3d-mit_dorm_next_sj-dorm_next_sj_oct_30_2012_scan1_erika', '7-scenes-stairs', 'sun3d-brown_bm_4-brown_bm_4', 'bundlefusion-office0', 'sun3d-harvard_c6-hv_c6_1', 'rgbd-scenes-v2-scene_14', 'rgbd-scenes-v2-scene_12', 'analysis-by-synthesis-office2-5b', 'analysis-by-synthesis-apt2-luke', '7-scenes-office', 'sun3d-harvard_c5-hv_c5_1', 'sun3d-brown_cs_3-brown_cs3', '7-scenes-fire', 'rgbd-scenes-v2-scene_11', 'analysis-by-synthesis-apt2-bed', 'rgbd-scenes-v2-scene_13']
stationnums=[85, 8, 57, 28, 10, 9, 8, 84, 10, 44, 96, 8, 54, 8, 14, 65, 38, 52, 34, 92, 62, 37, 7, 11, 9, 18, 19, 9, 77, 54, 75, 54, 7, 8, 10, 70, 15, 11, 26, 24, 32, 60, 15, 6, 7, 17, 19, 90, 20, 34, 36, 6, 10, 4]
for i in range(len(scenes)):
root_dir=f'{origin_data_dir}/{dataset_name}/'+scenes[i]
datasets[scenes[i]]=SceneDataset(root_dir,stationnums[i])
datasets[scenes[i]].name=f'{dataset_name}/{scenes[i]}'
return datasets
else:
raise NotImplementedError
##########################################---For training---#####################################################
class scenewisedataset(torch.utils.data.Dataset):
def __init__(
self,
cfg,
stage = 'train',
scan_limit = [8,60],
point_limit = 5000
):
#### the basic information
self.cfg = cfg
# the origin dir
self.d_xyz = self.cfg.origin_data_dir
# the input feature dir
self.d_feat = self.cfg.input_feat_dir
#### the usage related doors
self.scan_limit = scan_limit
self.point_limit = point_limit
if stage == 'train':
self.augmentation = True
self.scan_sampling = True
self.rot_range = self.cfg.aug_r_range
self.tran_range = self.cfg.aug_t_range # random(-0.5,0.5)*4
self.noise_range = self.cfg.aug_n_range # random(-0.5,0.5)*random noise range added to keypts
self.datasets = get_dataset_name(self.cfg.trainset, self.d_xyz)
self.metadata = read_pickle(self.cfg.trainlist) # train-list pkl file
elif stage == 'val':
self.augmentation = False
self.scan_sampling = False
self.datasets = get_dataset_name(self.cfg.valset, self.d_xyz)
self.metadata = read_pickle(self.cfg.vallist) # val-list pkl file
elif stage == 'test':
self.augmentation = False
self.scan_sampling = False
self.datasets = get_dataset_name(self.cfg.testset, self.d_xyz)
self.metadata = read_pickle(self.cfg.testlist) # val-list pkl file
else:
print('wrong sign for dataset')
def _resample_scans(self, n_scan, gt_overlap):
# randomly sample a set of point clouds as well as their ground truth overlap ratios.
# if not self.augmentation: return np.arange(n_scan), gt_overlap
if not self.scan_sampling: return np.arange(n_scan), gt_overlap
if n_scan <= self.scan_limit[0]: return np.arange(n_scan), gt_overlap
n_scan_ds = np.random.choice(np.arange(self.scan_limit[0],self.scan_limit[1]), 1)[0]
ds_index = np.random.permutation(np.arange(n_scan))[0:n_scan_ds]
# gt_overlap
gt_overlap = gt_overlap[ds_index,:]
gt_overlap = gt_overlap[:,ds_index]
return ds_index, gt_overlap
def _load_pt_feat_yoho(self, sn, pid):
# all base informations
dataset = self.datasets[sn]
# load point cloud
pt = dataset.get_kps(pid)
# load pre-calculated YOHO features
feat = np.load(f'{self.d_feat}/{dataset.name}/yoho_desc/{pid}.npy')
return pt, feat
def _resample_point_cloud(self, points, feats):
# NOTE: setting "point_limit" with "num_workers" > 1 will cause nondeterminism.
# We randomly sample a local N point patch of a random sampled center point.
if not self.scan_sampling:
# index = np.random.permutation(np.arange(points.shape[0]))[0:5000]
# points, feats = points[index], feats[index]
return points, feats
# Determine how many points to be sampled
k = np.random.choice(np.arange(1024,self.point_limit), 1)[0]
# random center
center = np.random.choice(points.shape[0], 1)[0]
center = points[center]
# calculate Knn of the selected center
cpdist = np.sum(np.square(points - center[None,:]),axis=-1)
argp = np.argsort(cpdist)[0:int(1.2*k)]
# resample n_sample points
index = np.random.permutation(argp)[0:k]
# the down sampled keypoints
points_ds = points[index]
feats_ds = feats[index]
return points_ds, feats_ds
def _resample_point_cloud_global(self, points, feats):
if not self.scan_sampling: k = 2500
# Determine how many points to be sampled
else: k = np.random.choice(np.arange(1024,self.point_limit), 1)[0]
index = np.random.permutation(np.arange(points.shape[0]))[0:2500]
points, feats = points[index], feats[index]
return points, feats
def _l2dist(self, pci, pcj):
pci = torch.from_numpy(pci.astype(np.float32)).cuda()
pcj = torch.from_numpy(pcj.astype(np.float32)).cuda()
dist = -2*pci@pcj.T
dist += torch.sum(pci ** 2, dim=1, keepdim=True)
dist += torch.sum(pcj ** 2, dim=1, keepdim=True).T
return dist.cpu().numpy()
def _determine_overlaps(self, points_list, gt_overlap, ird = 0.08):
# Calculate the overlap ratio between any two downsampled and *pre-aligned* point clouds.
if not self.scan_sampling: return gt_overlap
n_scans = len(points_list)
overlap = np.zeros([n_scans,n_scans])
for i in range(n_scans):
for j in range(i+1,n_scans):
if gt_overlap[i,j] == 0:
overlap[i,j], overlap[j,i] = 0, 0
continue
pci, pcj = points_list[i], points_list[j]
dist = self._l2dist(pci, pcj)
# determine the minimum distance
mi = np.min(dist, axis=1)
mj = np.min(dist, axis=0)
overlap_ij = np.sum(mi<ird*ird) + np.sum(mj<ird*ird)
overlap_ij /= (pci.shape[0]+pcj.shape[0])
overlap[i,j], overlap[j,i] = overlap_ij, overlap_ij
return overlap
def _aug_point_clouds(self, points_list):
# if self.augmentation is true, we randomly rot the pc
aug_Ts = np.eye(4)[None].repeat(len(points_list),axis=0)
if not self.augmentation:
return points_list, aug_Ts
# random rotation
else:
aug_points_list = []
for i, points in enumerate(points_list):
aug_r = random_rotation_matrix(self.rot_range)
aug_t = (np.random.rand(1,3) - 0.5) * self.tran_range
aug_n = (np.random.rand(points.shape[0],3) - 0.5) * self.noise_range
# apply to the point cloud
points = points @ aug_r.T + aug_t + aug_n
aug_points_list.append(points)
# save the augmentation transformation
aug_Ts[i,0:3,0:3], aug_Ts[i,0:3,3] = aug_r, aug_t
return aug_points_list, aug_Ts
def _to_float32(self, data_list):
if type(data_list) is list:
for i, item in enumerate(data_list):
data_list[i] = torch.from_numpy(item.astype(np.float32))
else:
data_list = torch.from_numpy(data_list.astype(np.float32))
return data_list
def __getitem__(self, index):
name2feat = {
'yoho': self._load_pt_feat_yoho,
}
# get the next item
item_info = self.metadata[index]
# name, ground truth overlap matrix
sn, gt_overlap = item_info
# resample scans
ds_index, gt_overlap = self._resample_scans(gt_overlap.shape[0], gt_overlap)
# load point clouds and conduct inner-scan sampling
points_list, feats_list = [], []
for pid in ds_index:
# points, feats = self._load_pt_feat(sn, pid)
points, feats = name2feat[self.cfg.backbone](sn, pid)
points, feats = self._resample_point_cloud(points, feats)
points_list.append(points)
feats_list.append(feats)
# calculate the gt_overlap now
gt_overlap = self._determine_overlaps(points_list, gt_overlap)
# point cloud augmentation
points_list, augTs = self._aug_point_clouds(points_list)
# final type change
points_list = self._to_float32(points_list)
feats_list = self._to_float32(feats_list)
# prepare an item
item = {
'points': points_list,
'feats': feats_list,
'gt_overlap': self._to_float32(gt_overlap),
'transformation': self._to_float32(augTs)
}
return item
def __len__(self):
return len(self.metadata)