% ----------------------------------------------------------------------
    % Step 1: Define the properties required for describing the algorithm,
    % which include Name, Description, and UserDirections.
    properties(Constant)
        
        % Name Algorithm name
        %   Character vector specifying the name of the algorithm.
        Name = 'Multisignal Vehicle Detector';
        
        % Description Algorithm description
        %   Character vector specifying the short description of the algorithm.
        Description = ['Detect vehicles using ACF Vehicle Detector in ' ...
            'image and estimate them in point cloud.'];
        
        % UserDirections Algorithm usage directions
        %   Cell array of character vectors specifying directions for
        %   algorithm users to follow.
        UserDirections = {['Select one of the rectangle ROI labels to ' ...
            'label objects as Vehicle.'], ...
            ['Click Settings and on the Lidar Camera Calibration ' ...
            '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

    % ---------------------------------------------------------------------
    % Step 2: Define properties to be used to manage algorithm execution.
    properties
        
        % SelectedLabelName Selected label name
        %   Name of the selected label. Vehicles detected by the algorithm will
        %   be assigned this variable name.
        SelectedLabelName
        
        % Detector Detector
        %   Pretrained vehicle detector, an object of class
        %   acfObjectDetector.
        Detector
        
        % VehicleModelName Vehicle detector model name
        %   Name of pretrained vehicle detector model.
        VehicleModelName = 'full-view';
        
        % OverlapThreshold Overlap threshold
        %   Threshold value used to eliminate overlapping bounding boxes
        %   around the reference bounding box, between 0 and 1. The
        %   bounding box overlap ratio denominator, 'RatioType', is set to
        %   'Min'.
        OverlapThreshold = 0.45;
        
        % ScoreThreshold Classification score threshold
        %   Threshold value used to reject detections with low detection
        %   scores.
        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 = supportsMultisignalAutomation(~)
            % Supports MultiSignal.
            success = true;
        end

        
        function isValid = checkSignalType(signalType)
            % Only video/image sequence and point cloud signal data 
            % is valid. 
            isValid = any(signalType == vision.labeler.loading.SignalType.Image) && ...
               any(signalType == vision.labeler.loading.SignalType.PointCloud);  
        end

        function isValid = checkLabelDefinition(~, labelDef)            
            % Only Rectangular/Cuboid ROI Label definitions are valid for the
            % Vehicle Detector.
            isValid = (labelDef.Type == labelType.Cuboid || labelDef.Type == labelType.Rectangle);
        end

        function isReady = checkSetup(algObj, ~)
            % Is there one selected ROI Label definition to automate?
            isReady = ~isempty(algObj.SelectedLabelDefinitions);
        end

        function settingsDialog(algObj)
            % Invoke dialog box to input camera intrinsics and
            % camera-to-lidar rigid transformation and options for choosing
            % a pretrained model, overlap threshold, detection score
            % threshold, and clustering threshold. Optionally, input a
            % calibrated monoCamera sensor to configure the detector.
            multiSignalVehicleDetectorSettings(algObj);
        end

       function initialize(algObj, ~)
            
            % Store the name of the selected label definition. Use this
            % name to label the detected vehicles.
            algObj.SelectedLabelName = algObj.SelectedLabelDefinitions.Name;
            
            % Initialize the vehicle detector with a pretrained model.
            algObj.Detector = vehicleDetectorACF(algObj.VehicleModelName);
        end

       function autoLabels = run(algObj, I)
            % autoLabels a cell array of length the same as the number of 
            %  signals.
            autoLabels = cell(size(I,1),1);
            
            % Get the index of Image and PointCloud frames.
            if isa(I{1,1},"pointCloud")
                pcIdx = 1;
                imIdx = 2;
            else
                imIdx = 1;
                pcIdx = 2;
            end
            
            % Detect bounding boxes on image frame.
            selectedBboxes = detectVehicle(algObj, I{imIdx,1});
            
            % Estimate bounding boxes on point cloud frame.
            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

       function terminate(~)
       end

    mkdir('+vision/+labeler');
    copyfile(fullfile(matlabroot,'examples','driving','main','MultiSignalVehicleDetector.m'), ...
        '+vision/+labeler');

    imageURL = 'https://www.mathworks.com/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('Downloading WPI Image driving data (225 MB)...');
        websave(imageDataTarFile, imageURL);
        untar(imageDataTarFile, imageDataFolder);
    end
    
    % Check if image tar.gz file is downloaded, but not uncompressed.
    if ~exist(fullfile(imageDataFolder,'imageData'),'dir')
        untar(imageDataTarFile, imageDataFolder)
    end

    % Create new folder and copy the images.
    imDataFolder = imageDataFolder + "imageDataSequence";
    if ~exist(imDataFolder,'dir')
        mkdir(imDataFolder);
    end

    for i = 920 : 940
        filename = strcat(num2str(i,'%06.0f'),'.jpg');
        source = fullfile(imageDataFolder,'imageData',filename);
        destination = fullfile(imageDataFolder,'imageDataSequence',filename);
        copyfile(source,destination)
    end

    lidarURL = 'https://www.mathworks.com/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('Downloading WPI Lidar driving data (760 MB)...');
        websave(lidarDataTarFile,lidarURL);
        untar(lidarDataTarFile,lidarDataFolder);
    end
    
    % Check if lidar tar.gz file is downloaded, but not uncompressed.
    if ~exist(fullfile(lidarDataFolder,'WPI_LidarData.mat'),'file')
        untar(lidarDataTarFile,lidarDataFolder);
    end

    % Load downloaded lidar data into the workspace.
    load(fullfile(lidarDataFolder,'WPI_LidarData.mat'),'lidarData');
    lidarData = reshape(lidarData,size(lidarData,2),1);
    
    % Create new folder and write lidar data to PCD files.
    pcdDataFolder = lidarDataFolder + "lidarDataSequence";
    if ~exist(pcdDataFolder, 'dir')
        mkdir(fullfile(lidarDataFolder,'lidarDataSequence'));
    end

    disp('Saving WPI Lidar driving data to PCD files ...');
    for i = 920:940
        filename = strcat(fullfile(lidarDataFolder,'lidarDataSequence',filesep), ...
            num2str(i,'%06.0f'),'.pcd');
        pcwrite(lidarData{i},filename);
    end

    data = load(fullfile(toolboxdir('lidar'),'lidardata','lcc','bboxGT.mat'));
    cameraParams = data.cameraParams;
    camToLidar = data.camToLidar;

    imageDir = fullfile(tempdir, 'WPI_ImageData', 'imageDataSequence');
    pointCloudDir = fullfile(tempdir, 'WPI_LidarData', 'lidarDataSequence');

    groundTruthLabeler