def downsample_random_gpu(points, labels, sample_stride=0.01):
points[:, :3] -= np.min(points[:, :3], axis=0, keepdims=True)
points = np.ascontiguousarray(points, dtype=np.float32)
labels = np.ascontiguousarray(labels, dtype=np.int32)
ds_points, ds_labels = PointsUtil.GridDownSample(points, labels,
sample_stride)
return ds_points, ds_labels
def points2voxel_gpu_s3dis(points, split_num, gpu_index=0):
'''
:param points: n,k,3
:param split_num:
:return:
'''
pts = np.ascontiguousarray(np.copy(points[:, :, :3]))
pts[:, :, :2] += 0.5
voxels = PointsUtil.Points2VoxelBatchGPU(pts, split_num, gpu_index)
voxels[voxels > 1.0] = 1.0
return voxels
def points2covars_gpu(points,nidxs,nn_size,gpu_index=0):
'''
:param points: n,k,3
:param ndixs: n,k,nn_size
:param nn_size:
:param gpu_index:
:return:
covars: n,k,9
'''
nidxs=np.ascontiguousarray(nidxs,dtype=np.int32)
points=np.ascontiguousarray(points,dtype=np.float32)
covars=PointsUtil.ComputeCovars(points,nidxs,nn_size,gpu_index)
return covars
def test_random_rotate_sample_block():
train_list,test_list=get_block_train_test_split()
import random
random.shuffle(train_list)
for fn in train_list[:1]:
points,labels=read_room_pkl('../data/S3DIS/room_block_10_10/{}'.format(fn))
labels=np.asarray(labels,dtype=np.int32)
points=np.ascontiguousarray(points,dtype=np.float32)
points[:,:2]-=np.min(points[:,:2],axis=0,keepdims=True)
max_coor=np.max(points[:,:2],axis=0)
maxx,maxy=max_coor[0],max_coor[1]
begin = time.time()
block_points_list,block_labels_list=PointsUtil.UniformSampleBlock(points,labels,1.0,5.0,0.8,10,maxx,maxy)
print 'cost {} s'.format(time.time()-begin)
for i,pts in enumerate(block_points_list):
output_points('test/{}.txt'.format(i),pts)
def points2voxel_color_gpu(points,split_num,gpu_index=0):
'''
:param points: n,k,6 xyz:(-0.5,0.5)(-0.5,0.5)(0.0,1.0) rgb:(-1.0,1.0)*3
:param split_num:
:param gpu_index:
:return:
'''
pts = np.ascontiguousarray(np.copy(points[:, :, :6]))
pts[:, :, :2] += 0.5
pts[:, :, 3:] += 1.0
pts[:, :, 3:] /= 2.0
voxels = PointsUtil.Points2VoxeColorlBatchGPU(pts, split_num, gpu_index)
voxel_state,voxel_color=voxels[:,:,0],voxels[:,:,1:]
voxel_color/=voxel_state[:,:,None]+1e-6
voxel_state[voxel_state>1.0]=1.0
voxel_color[voxel_state>1.0]=1.0
return voxel_state,voxel_color
maxx,maxy=max_coor[0],max_coor[1]
begin = time.time()
block_points_list,block_labels_list=PointsUtil.UniformSampleBlock(points,labels,1.0,5.0,0.8,10,maxx,maxy)
print 'cost {} s'.format(time.time()-begin)
for i,pts in enumerate(block_points_list):
output_points('test/{}.txt'.format(i),pts)
if __name__=="__main__":
train_list,test_list=get_block_train_test_split()
import random
random.shuffle(train_list)
for fn in train_list[:1]:
points,labels=read_room_pkl('../data/S3DIS/room_block_10_10/'+fn)
points=np.ascontiguousarray(points,dtype=np.float32)
labels=np.ascontiguousarray(labels,dtype=np.int32)
points[:,:3]-=np.min(points[:,:3],axis=0,keepdims=True)
output_points('original.txt', points)
begin = time.time()
points,labels=PointsUtil.GridDownSample(points,labels,0.05)
print 'cost {} s'.format(time.time()-begin)
colors=get_class_colors()
output_points('downsample.txt', points)
:return:
covars: n,k,9
'''
nidxs=np.ascontiguousarray(nidxs,dtype=np.int32)
points=np.ascontiguousarray(points,dtype=np.float32)
covars=PointsUtil.ComputeCovars(points,nidxs,nn_size,gpu_index)
return covars
if __name__=="__main__":
from draw_util import output_points
import time
points=np.loadtxt('/home/liuyuan/tmp/0_8_true.txt',dtype=np.float32)
points[:,:2]+=0.5
print np.min(points,axis=0)
print np.max(points,axis=0)
output_points('points.txt',points)
bg=time.time()
points=np.repeat(points[None,:,:],2,axis=0)
# print points.shape
voxels=PointsUtil.Points2VoxelBatchGPU(points,30)
print 'cost {} s'.format(time.time()-bg)
voxels=voxels[0]
voxel_points= voxel2points(voxels)
voxel_points=voxel_points.astype(np.float32)
voxel_points[:,:3]/=np.max(voxel_points[:,:3],axis=0,keepdims=True)
output_points('voxels.txt',voxel_points)