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scannet_dataset.py
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scannet_dataset.py
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import pickle
import os
import sys
import numpy as np
import pc_util
import scene_util
class ScannetDataset():
def __init__(self, root, npoints=8192, split='train'):
self.npoints = npoints
self.root = root
self.split = split
self.data_filename = os.path.join(self.root, 'scannet_%s.pickle'%(split))
with open(self.data_filename,'rb') as fp:
self.scene_points_list = pickle.load(fp)
self.semantic_labels_list = pickle.load(fp)
if split=='train':
labelweights = np.zeros(21)
for seg in self.semantic_labels_list:
tmp,_ = np.histogram(seg,range(22))
labelweights += tmp
labelweights = labelweights.astype(np.float32)
labelweights = labelweights/np.sum(labelweights)
self.labelweights = 1/np.log(1.2+labelweights)
elif split=='test':
self.labelweights = np.ones(21)
def __getitem__(self, index):
point_set = self.scene_points_list[index]
semantic_seg = self.semantic_labels_list[index].astype(np.int32)
coordmax = np.max(point_set,axis=0)
coordmin = np.min(point_set,axis=0)
smpmin = np.maximum(coordmax-[1.5,1.5,3.0], coordmin)
smpmin[2] = coordmin[2]
smpsz = np.minimum(coordmax-smpmin,[1.5,1.5,3.0])
smpsz[2] = coordmax[2]-coordmin[2]
isvalid = False
for i in range(10):
curcenter = point_set[np.random.choice(len(semantic_seg),1)[0],:]
curmin = curcenter-[0.75,0.75,1.5]
curmax = curcenter+[0.75,0.75,1.5]
curmin[2] = coordmin[2]
curmax[2] = coordmax[2]
curchoice = np.sum((point_set>=(curmin-0.2))*(point_set<=(curmax+0.2)),axis=1)==3
cur_point_set = point_set[curchoice,:]
cur_semantic_seg = semantic_seg[curchoice]
if len(cur_semantic_seg)==0:
continue
mask = np.sum((cur_point_set>=(curmin-0.01))*(cur_point_set<=(curmax+0.01)),axis=1)==3
vidx = np.ceil((cur_point_set[mask,:]-curmin)/(curmax-curmin)*[31.0,31.0,62.0])
vidx = np.unique(vidx[:,0]*31.0*62.0+vidx[:,1]*62.0+vidx[:,2])
isvalid = np.sum(cur_semantic_seg>0)/len(cur_semantic_seg)>=0.7 and len(vidx)/31.0/31.0/62.0>=0.02
if isvalid:
break
choice = np.random.choice(len(cur_semantic_seg), self.npoints, replace=True)
point_set = cur_point_set[choice,:]
semantic_seg = cur_semantic_seg[choice]
mask = mask[choice]
sample_weight = self.labelweights[semantic_seg]
sample_weight *= mask
return point_set, semantic_seg, sample_weight
def __len__(self):
return len(self.scene_points_list)
class ScannetDatasetWholeScene():
def __init__(self, root, npoints=8192, split='train'):
self.npoints = npoints
self.root = root
self.split = split
self.data_filename = os.path.join(self.root, 'scannet_%s.pickle'%(split))
with open(self.data_filename,'rb') as fp:
self.scene_points_list = pickle.load(fp)
self.semantic_labels_list = pickle.load(fp)
if split=='train':
labelweights = np.zeros(21)
for seg in self.semantic_labels_list:
tmp,_ = np.histogram(seg,range(22))
labelweights += tmp
labelweights = labelweights.astype(np.float32)
labelweights = labelweights/np.sum(labelweights)
self.labelweights = 1/np.log(1.2+labelweights)
elif split=='test':
self.labelweights = np.ones(21)
def __getitem__(self, index):
point_set_ini = self.scene_points_list[index]
semantic_seg_ini = self.semantic_labels_list[index].astype(np.int32)
coordmax = np.max(point_set_ini,axis=0)
coordmin = np.min(point_set_ini,axis=0)
nsubvolume_x = np.ceil((coordmax[0]-coordmin[0])/1.5).astype(np.int32)
nsubvolume_y = np.ceil((coordmax[1]-coordmin[1])/1.5).astype(np.int32)
point_sets = list()
semantic_segs = list()
sample_weights = list()
isvalid = False
for i in range(nsubvolume_x):
for j in range(nsubvolume_y):
curmin = coordmin+[i*1.5,j*1.5,0]
curmax = coordmin+[(i+1)*1.5,(j+1)*1.5,coordmax[2]-coordmin[2]]
curchoice = np.sum((point_set_ini>=(curmin-0.2))*(point_set_ini<=(curmax+0.2)),axis=1)==3
cur_point_set = point_set_ini[curchoice,:]
cur_semantic_seg = semantic_seg_ini[curchoice]
if len(cur_semantic_seg)==0:
continue
mask = np.sum((cur_point_set>=(curmin-0.001))*(cur_point_set<=(curmax+0.001)),axis=1)==3
choice = np.random.choice(len(cur_semantic_seg), self.npoints, replace=True)
point_set = cur_point_set[choice,:] # Nx3
semantic_seg = cur_semantic_seg[choice] # N
mask = mask[choice]
if sum(mask)/float(len(mask))<0.01:
continue
sample_weight = self.labelweights[semantic_seg]
sample_weight *= mask # N
point_sets.append(np.expand_dims(point_set,0)) # 1xNx3
semantic_segs.append(np.expand_dims(semantic_seg,0)) # 1xN
sample_weights.append(np.expand_dims(sample_weight,0)) # 1xN
point_sets = np.concatenate(tuple(point_sets),axis=0)
semantic_segs = np.concatenate(tuple(semantic_segs),axis=0)
sample_weights = np.concatenate(tuple(sample_weights),axis=0)
return point_sets, semantic_segs, sample_weights
def __len__(self):
return len(self.scene_points_list)
class ScannetDatasetVirtualScan():
def __init__(self, root, npoints=8192, split='train'):
self.npoints = npoints
self.root = root
self.split = split
self.data_filename = os.path.join(self.root, 'scannet_%s.pickle'%(split))
with open(self.data_filename,'rb') as fp:
self.scene_points_list = pickle.load(fp)
self.semantic_labels_list = pickle.load(fp)
if split=='train':
labelweights = np.zeros(21)
for seg in self.semantic_labels_list:
tmp,_ = np.histogram(seg,range(22))
labelweights += tmp
labelweights = labelweights.astype(np.float32)
labelweights = labelweights/np.sum(labelweights)
self.labelweights = 1/np.log(1.2+labelweights)
elif split=='test':
self.labelweights = np.ones(21)
def __getitem__(self, index):
point_set_ini = self.scene_points_list[index]
semantic_seg_ini = self.semantic_labels_list[index].astype(np.int32)
sample_weight_ini = self.labelweights[semantic_seg_ini]
point_sets = list()
semantic_segs = list()
sample_weights = list()
for i in xrange(8):
smpidx = scene_util.virtual_scan(point_set_ini,mode=i)
if len(smpidx)<300:
continue
point_set = point_set_ini[smpidx,:]
semantic_seg = semantic_seg_ini[smpidx]
sample_weight = sample_weight_ini[smpidx]
choice = np.random.choice(len(semantic_seg), self.npoints, replace=True)
point_set = point_set[choice,:] # Nx3
semantic_seg = semantic_seg[choice] # N
sample_weight = sample_weight[choice] # N
point_sets.append(np.expand_dims(point_set,0)) # 1xNx3
semantic_segs.append(np.expand_dims(semantic_seg,0)) # 1xN
sample_weights.append(np.expand_dims(sample_weight,0)) # 1xN
point_sets = np.concatenate(tuple(point_sets),axis=0)
semantic_segs = np.concatenate(tuple(semantic_segs),axis=0)
sample_weights = np.concatenate(tuple(sample_weights),axis=0)
return point_sets, semantic_segs, sample_weights
def __len__(self):
return len(self.scene_points_list)
if __name__=='__main__':
d = ScannetDatasetWholeScene(root = './data', split='test', npoints=8192)
labelweights_vox = np.zeros(21)
for ii in xrange(len(d)):
print ii
ps,seg,smpw = d[ii]
for b in xrange(ps.shape[0]):
_, uvlabel, _ = pc_util.point_cloud_label_to_surface_voxel_label_fast(ps[b,smpw[b,:]>0,:], seg[b,smpw[b,:]>0], res=0.02)
tmp,_ = np.histogram(uvlabel,range(22))
labelweights_vox += tmp
print labelweights_vox[1:].astype(np.float32)/np.sum(labelweights_vox[1:].astype(np.float32))
exit()