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)