def interpolate(sxyzs,sprobs,qxyzs,ratio=1.0/(2*0.15*0.15)):
nidxs=libPointUtil.findNeighborInAnotherCPU(sxyzs,qxyzs,8)
nidxs_lens=np.asarray([len(idxs) for idxs in nidxs],dtype=np.int32)
nidxs_bgs=compute_nidxs_bgs(nidxs_lens)
nidxs=np.concatenate(nidxs,axis=0)
qprobs = libPointUtil.interpolateProbsGPU(sxyzs, qxyzs, sprobs, nidxs, nidxs_lens, nidxs_bgs, ratio)
return qprobs
def interpolate_natural_terrain(sxyzs,qxyzs,qprobs):
print sxyzs.shape,qxyzs.shape,qprobs.shape
nidxs=libPointUtil.findNeighborInAnotherCPU(sxyzs,qxyzs,0.22)
nlens=np.asarray([len(idxs) for idxs in nidxs],dtype=np.int32)
qpreds=np.argmax(qprobs,axis=1)
mask=np.logical_and(nlens>0,qpreds==0)
qprobs[mask,0]=0.0
qprobs[mask,1]=1.0
return qprobs
def interpolate(sxyzs, sprobs, qxyzs, ratio=1.0 / (2 * 0.125 * 0.125)):
bg = time.time()
nidxs = libPointUtil.findNeighborInAnotherCPU(sxyzs, qxyzs, 6)
print 'search done {} s'.format(time.time() - bg)
nidxs_lens = np.asarray([len(idxs) for idxs in nidxs], dtype=np.int32)
nidxs_bgs = compute_nidxs_bgs(nidxs_lens)
nidxs = np.concatenate(nidxs, axis=0)
qprobs = libPointUtil.interpolateProbsGPU(sxyzs, qxyzs, sprobs, nidxs,
nidxs_lens, nidxs_bgs, ratio)
return qprobs
def compute_context_idxs(context_xyzs,xyzs):
context_idxs=[]
for i in xrange(len(xyzs)):
xyz=np.ascontiguousarray(xyzs[i][:,:3],np.float32)
context_xyz=np.ascontiguousarray(context_xyzs[i][:,:3],np.float32)
leaf_size=min(context_xyz.shape[0]-1,15)
idxs=libPointUtil.findNeighborInAnotherCPU(context_xyz,xyz,1,leaf_size)
idxs=np.squeeze(np.asarray(idxs,np.int32),axis=1)
assert idxs.shape[0]==xyz.shape[0]
context_idxs.append(idxs)
return context_idxs
def global_avg_downsample():
with open('cached/semantic3d_stems.txt', 'r') as f:
stems = [line.split(' ')[0] for line in f.readlines()]
sess, pts_pl, ds_pts_op = build_avg_ds_session(ds_size=2.0, min_coor=3000.0)
train_list = read_large_block_list()
for stem in stems:
for k in xrange(6):
pts, lbls = [], []
for tfs in train_list:
if (not tfs.startswith(stem)) or (not tfs.endswith('{}.pkl'.format(k))): continue
fs = 'data/Semantic3D.Net/context/large_block/' + tfs
points, labels = read_room_pkl(fs) # [n,6],[n,1]
idxs = libPointUtil.gridDownsampleGPU(points, 0.1, False)
pts.append(points[idxs])
lbls.append(labels[idxs])
# downsample
pts = np.concatenate(pts, axis=0)
ds_pts=sess.run(ds_pts_op,feed_dict={pts_pl:pts})
# compute covar
ds_xyzs=np.ascontiguousarray(ds_pts[:,:3],np.float32)
xyzs=np.ascontiguousarray(pts[:,:3],np.float32)
nidxs=libPointUtil.findNeighborInAnotherCPU(xyzs,ds_xyzs,4.0)
nlens=np.ascontiguousarray([len(idxs) for idxs in nidxs],np.int32)
nbegs=compute_nidxs_bgs(nlens)
nidxs=np.ascontiguousarray(np.concatenate(nidxs,axis=0),dtype=np.int32)
covars=libPointUtil.computeCovarsGPU(xyzs,nidxs,nlens,nbegs)
if np.sum(np.isnan(covars))>0:
print stem,k
idxs,_=np.nonzero(np.isnan(covars))
for idx in idxs:
print '{} {}'.format(idx,nlens[idx])
exit(0)
ds_pts=np.concatenate([ds_pts,covars],axis=1)
# output_points('test_result/{}_{}_ds.txt'.format(stem,k),pts)
save_pkl('data/Semantic3D.Net/context/global_avg/{}_{}.pkl'.format(stem,k),ds_pts)
def testset_global_downsample_avg():
with open('cached/semantic3d_test_stems.txt','r') as f:
lines=f.readlines()
fss=[fn.strip('\n').split(' ')[0] for fn in lines]
fns=[int(fn.strip('\n').split(' ')[1]) for fn in lines]
sess, pts_pl, ds_pts_op = build_avg_ds_session(ds_size=2.0, min_coor=3000.0)
for fs,fn in zip(fss,fns):
pts=[]
for fni in xrange(fn):
points, labels = read_room_pkl('data/Semantic3D.Net/context/test_large_block/{}_{}.pkl'.format(fs,fni)) # [n,6],[n,1]
idxs = libPointUtil.gridDownsampleGPU(points, 0.1, False)
pts.append(points[idxs])
# downsample
pts = np.concatenate(pts, axis=0)
ds_pts=sess.run(ds_pts_op,feed_dict={pts_pl:pts})
# compute covar
ds_xyzs=np.ascontiguousarray(ds_pts[:,:3],np.float32)
xyzs=np.ascontiguousarray(pts[:,:3],np.float32)
nidxs=libPointUtil.findNeighborInAnotherCPU(xyzs,ds_xyzs,4.0)
nlens=np.ascontiguousarray([len(idxs) for idxs in nidxs],np.int32)
nbegs=compute_nidxs_bgs(nlens)
nidxs=np.ascontiguousarray(np.concatenate(nidxs,axis=0),dtype=np.int32)
covars=libPointUtil.computeCovarsGPU(xyzs,nidxs,nlens,nbegs)
if np.sum(np.isnan(covars))>0:
print fs
idxs,_=np.nonzero(np.isnan(covars))
for idx in idxs:
print '{} {}'.format(idx,nlens[idx])
exit(0)
ds_pts=np.concatenate([ds_pts,covars],axis=1)
# output_points('test_result/{}_{}_ds.txt'.format(stem,k),pts)
save_pkl('data/Semantic3D.Net/context/test_global_avg/{}.pkl'.format(fs),ds_pts)
