% ---------------------------------------------------------------------- % 步骤1:定义所需的属性描述算法,%,包括名称、描述和UserDirections。properties(Constant) % Name算法名称%指定算法名称的字符向量。Name = '多信号车辆检测器';% Description算法描述%字符向量，指定算法的简短描述。Description =['使用ACF车辆检测器检测车辆'…'图像和估计他们在点云。'];% UserDirections算法使用方向%字符向量的单元格数组，指定算法用户要遵循的方向。UserDirections ={['选择一个矩形ROI标签'…'标签对象为Vehicle。'],……['点击设置并打开激光雷达摄像机校准'… 'Parameters tab, load the cameraIntrinsics and rigid3d ' ... 'objects from the workspace.'], ... ['Specify additional parameters under Settings.'], ... ['Click Run to detect vehicles in each image and point cloud.'], ... ['Review automated labels manually. You can modify, delete ', ... 'and add new labels.'], ... ['If you are not satisfied with the results, click Undo ' ... 'Run. Click Settings to modify algorithm settings and click ', ... 'Run again.'] ... ['When you are satisfied with the results, click Accept and ', ... 'return to manual labeling.']}; end

％----------------------------------------------------------------％步骤2：定义用于管理算法执行的属性。属性％selectentlabelname选择的标签名称％所选标签的名称。通过算法检测的车辆将分配此变量名称。SelectentLabelname％探测器检测器％普里托运车检测器，类别％ACFObjectDetector的对象。探测器％车辆销售器型号的售货员型号％净化车辆探测器模型的名称。vehiclemodelname ='全视图';％重叠阈值重叠阈值％阈值用于消除参考边界框周围的重叠边界框，在0和1之间。％边界框重叠比率分母'ratiotype'被设置为％'min'。重叠= 0.45;％ScoreThreshold分量得分阈值％阈值用于拒绝具有低检测％分数的检测。 ScoreThreshold = 20; % ConfigureDetector Detection configuration flag % Boolean value that determines whether the detector is % configured using monoCamera sensor. ConfigureDetector = false; % SensorObj monoCamera sensor % Monocular camera sensor object, monoCamera, used to configure % the detector. A configured detector runs faster and can % potentially result in better detections. SensorObj = []; % SensorStr monoCamera sensor variable name % Character vector specifying the monoCamera object variable name % used to configure the detector. SensorStr = ''; % VehicleWidth Vehicle width % Vehicle width used to configure the detector, specified as % [minWidth, maxWidth], which describes the approximate width of the % object in world units. VehicleWidth = [1.5 2.5]; % VehicleLength Vehicle length % Vehicle length used to configure the detector, specified as % [minLength, maxLength] vector, which describes the approximate % length of the object in world units. VehicleLength = []; % IntrinsicsObj Camera intrinsics % cameraIntrinsics object, which represents a projective % transformation from camera to image coordinates. IntrinsicsObj = []; % IntrinsicsStr cameraIntrinsics variable name % cameraIntrinsics object variable name. IntrinsicsStr = ''; % ExtrinsicsObj Camera-to-lidar rigid transformation % rigid3d object representing the 3-D rigid geometric transformation % from the camera to the lidar. ExtrinsicsObj = []; % ExtrinsicsStr rigid3d variable name % Camera-to-lidar rigid3d object variable name. ExtrinsicsStr = ''; % ClusterThreshold Clustering threshold for two adjacent points % Threshold specifying the maximum distance between two adjacent points % for those points to belong to the same cluster. ClusterThreshold = 1; end

function success = 金宝appsupportsMultisignalAutomation(~) %支持MultiSignal。成功= true;结尾

函数ISValid = CheckSignalType（SignalType）％仅视频/图像序列和点云信号数据％是有效的。IsValid = Any（signaltype == vision.labeler.loading.signaltype.image）&& ...任何（signaltype == vision.labeler.loading.signaltype.pointCloud）;结尾

只有矩形/长方体的ROI标签定义对%车辆检测器有效。isValid = (labelDef。= = labelType类型。长方体||labelDef.Type == labelType.Rectangle); end

函数ISREADY = checkstrup（Algobj，〜）％是有一个选择的ROI标签定义来自动化吗？Isready =〜isempty（allobj.selectedlabeldefinitions）;结尾

函数SettingsDialog（Algobj）％调用对话框输入摄像机内在函数和％相机到LIDAR刚性转换和选择％预级模型的选项，重叠阈值，检测得分％阈值和聚类阈值。可选地，输入％校准的单纸传感器以配置检测器。MultiSignalveHicledEtectorsettings（Algobj）;结尾

函数初始化（algobj，〜）％存储所选标签定义的名称。使用此％名称来标记检测到的车辆。algobj.selectedlabelname = algobj.selectedLabeldefinitions.name;％用预磨料模型初始化车辆检测器。Algobj.detector =车辆levledetectoracf（allobj.vehiclemodelname）;结尾

autoLabels = run(algObj, I) % autoLabels一个单元格数组的长度与%信号的数量相同。autoLabels =细胞(大小(我,1),1);%获取Image帧和PointCloud帧的索引。if isa(I{1,1}，"pointCloud") pcIdx = 1;imIdx = 2;else imIdx = 1;pcIdx = 2;end %检测图像帧上的边框。select dbboxes = detectVehicle(algObj, I{imIdx,1});在点云框架上估计边界框。 if ~isempty(selectedBboxes) % Store labels from the image. imageLabels = struct('Type', labelType.Rectangle, ... 'Name', algObj.SelectedLabelDefinitions.Name, ... 'Position', selectedBboxes); autoLabels{imIdx, 1} = imageLabels; % Remove the ground plane for the point cloud. groundPtsIndex = segmentGroundFromLidarData(I{pcIdx,1}, ... "ElevationAngleDelta", 15, "InitialElevationAngle", 10); nonGroundPts = select(I{pcIdx,1}, ~groundPtsIndex); % Predict 3-D bounding boxes. pcBboxes = bboxCameraToLidar(selectedBboxes, nonGroundPts, algObj.IntrinsicsObj, ... algObj.ExtrinsicsObj, "ClusterThreshold", algObj.ClusterThreshold); % Store labels from the point cloud. if(~isempty(pcBboxes)) pcLabels = struct('Type', labelType.Cuboid,... 'Name', algObj.SelectedLabelDefinitions.Name,... 'Position', pcBboxes); autoLabels{pcIdx, 1} = pcLabels; else autoLabels{pcIdx, 1} = {}; end else autoLabels{imIdx, 1} = {}; autoLabels{pcIdx, 1} = {}; end end

函数终止（〜）结束

mkdir(“+视觉/ +贴标签机”);拷贝文件(fullfile (matlabroot“例子”,“驾驶”,“主要”,“MultiSignalVehicleDetector.m”),…“+视觉/ +贴标签机”);

imageURL = ' //www.tatmou.com金宝app/supportfiles/lidar/data/WPI_ImageData.tar.gz ';imageDataFolder = fullfile(tempdir， 'WPI_ImageData'，filesep);imageDataTarFile = imageDataFolder + "WPI_ImageData.tar.gz";if ~exist(imageDataFolder，'dir') mkdir(imageDataFolder) end if ~exist(imageDataTarFile， 'file') disp('正在下载WPI镜像驱动数据(225mb)…');websave (imageDataTarFile imageURL);解压(imageDataTarFile imageDataFolder);end %检查是否下载了image tar.gz文件，但没有解压。如果存在(fullfile(imageDataFolder，'imageData')，'dir') untar(imageDataTarFile, imageDataFolder)结束

%创建新文件夹并复制图像。imDataFolder = imageDataFolder + "imageDataSequence";如果~存在(imDataFolder“dir”)mkdir (imDataFolder);End for I = 920: 940 filename = strcat(num2str(I，'%06.0f')，'.jpg');源= fullfile (imageDataFolder imageData,文件名);目的地= fullfile (imageDataFolder, imageDataSequence,文件名);拷贝文件(来源、目的地)

lidarURL = ' //www.tatmou.com金宝app/supportfiles/lidar/data/WPI_LidarData.tar.gz ';lidarDataFolder = fullfile (tempdir WPI_LidarData, filesep);lidarDataTarFile = lidarDataFolder + "WPI_LidarData.tar.gz";if ~exist(lidarDataFolder) mkdir(lidarDataFolder) end if ~exist(lidarDataTarFile， 'file') disp('正在下载WPI Lidar驱动数据(760 MB)…');websave (lidarDataTarFile lidarURL);解压(lidarDataTarFile lidarDataFolder);end %检查lidar tar.gz文件是否已下载，但未解压。如果存在~ (fullfile (lidarDataFolder WPI_LidarData.mat),“文件”)解压(lidarDataTarFile lidarDataFolder);结尾

%加载下载的激光雷达数据到工作区。负载(fullfile (lidarDataFolder WPI_LidarData.mat),“lidarData”);lidarData =重塑(lidarData、大小(lidarData, 2), 1);%创建新文件夹并将激光雷达数据写入PCD文件。pcdDataFolder = lidarDataFolder + "lidarDataSequence";如果存在(pcdDataFolder， 'dir') mkdir(fullfile(lidarDataFolder，'lidarDataSequence'));end disp('保存WPI激光雷达驱动数据到PCD文件…');for i = 920:940 filename = strcat(fullfile(lidarDataFolder，'lidarDataSequence'，filesep)，…num2str(我,“% 06.0 f”)、“.pcd”);我pcwrite (lidarData{},文件名); end

数据=加载（Fullfile（ToolboxDir（'lidar'），'lidardata'，'lcc'，'bboxgt.mat'））;cameraparams = data.cameraparams;Camtolidar = data.camtolidar;

imageageR = fullfile（tempdir，'wpi_imagedata'，'imagedatasquence'）;pointclouddir = fullfile（tempdir，'wpi_lidardata'，'lidardatasequence'）;地下标签