% ---------------------------------------------------------------------- % 步骤1:定义所需的属性描述算法。此%包括名称、描述和用户说明。properties(Constant) % Name算法名称%指定算法名称的字符向量。名称= '激光雷达车辆探测器';% Description算法描述%字符向量，指定算法的简短描述。Description = '使用预先训练的PointPillars对象检测器在点云中检测车辆。';%字符向量的单元格数组，指定%算法用户在使用算法时遵循的方向。UserDirections = {['ROI标签定义选择:选择其中一个'…“待标记的ROI定义”]，……['运行:按Run运行自动化算法。 '], ... ['Review and Modify: Review automated labels over the interval ', ... 'using playback controls. Modify/delete/add ROIs that were not ' ... 'satisfactorily automated at this stage. If the results are ' ... 'satisfactory, click Accept to accept the automated labels.'], ... ['Change Settings and Rerun: If automated results are not ' ... 'satisfactory, you can try to re-run the algorithm with ' ... 'different settings. To do so, click Undo Run to undo ' ... 'current automation run, click Settings, make changes to Settings,' ... 'and press Run again.'], ... ['Accept/Cancel: If the results of automation are satisfactory, ' ... 'click Accept to accept all automated labels and return to ' ... 'manual labeling. If the results of automation are not ' ... 'satisfactory, click Cancel to return to manual labeling ' ... 'without saving the automated labels.']}; end

% --------------------------------------------------------------------- % 第二步:定义您想要使用的属性在算法执行%。properties % SelectedLabelName所选标签的%名称。算法%检测到的车辆被分配这个变量名。SelectedLabelName % PretrainedDetector % PretrainedDetector保存预先训练的pointcolumns对象%检测器。PretrainedDetector % confidence threshold %指定置信度阈值，以便只使用置信度评分高于此值的检测。ConfidenceThreshold = 0.45;结束

只有点云信号数据对Lidar Vehicle %探测器算法有效。isValid = (signalType == vision.label .loading. signalType . pointcloud);结束

只有长方体ROI标签定义对Lidar %车辆探测器算法有效。isValid = labelDef。类型== labeltype . cubid;结束

是否有一个选定的ROI标签定义要自动化。isReady = ~isempty(algObj.SelectedLabelDefinitions);结束

调用带有修改置信阈值选项的对话框。lidarVehicleDetectorSettings (algObj)结束

存储所选标签定义的名称。使用此%名称标记检测到的车辆。algObj。选择edLabelName = algObj.SelectedLabelDefinitions.Name; % Load the pretrained pointPillarsObjectDetector. pretrainedDetector = load('pretrainedPointPillarsDetector.mat','detector'); algObj.PretrainedDetector = pretrainedDetector.detector; end

function autoLabels = run(algObj,ptCloud) bBoxes = [];对于i = 1:2，如果i == 2%，将点云旋转180度。θ = 180;Trans = [0,0,0];Tform = rigidtform3d([0 0 theta]， trans);ptCloud = pctransform(ptCloud,tform);使用预先训练的检测器检测边界框。[box，~，~] = detect(algObj.)PretrainedDetector，ptCloud, ... "Threshold",algObj.ConfidenceThreshold); if ~isempty(box) if i == 2 box(:,1) = -box(:,1); box(:,2) = -box(:,2); box(:,9) = -box(:,9); end bBoxes = [bBoxes;box]; end end if ~isempty(bBoxes) % Add automated labels at bounding box locations detected % by the vehicle detector, of type Cuboid and with the name % of the selected label. autoLabels.Name = algObj.SelectedLabelName; autoLabels.Type = labelType.Cuboid; autoLabels.Position = bBoxes; else autoLabels = []; end end

mkdir(“+激光雷达/ +贴标签机”);拷贝文件(fullfile (matlabroot“例子”,“激光”,“主要”、“LidarVehicleDetector.m”),…“+激光雷达/ +贴标签机”);

outputFolder = fullfile(tempdir，' pandset ');lidarDataTarFile = fullfile(outputFolder，'Pandaset_LidarData.tar.gz');if ~exist(lidarDataTarFile，'file') mkdir(outputFolder);disp('正在下载Pandaset Lidar驾驶数据(5.2BG)…');Component = 'lidar';filename = 'data/Pandaset_LidarData.tar.gz';lidarDataTarFile = matlab.internal.examples.downloadSupportFile(component,filename);解压(lidarDataTarFile outputFolder);检查是否下载了tar.gz文件，但未解压。if ~exist(fullfile(outputFolder，'Lidar')，'file') untar(lidarDataTarFile,outputFolder); end

pointCloudDir = fullfile(outputFolder，'Lidar');lidarLabeler (pointCloudDir);