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
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)