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深層学習用の点群データのインポート
深層学習ワークフローで点群データを使用する場合、そのデータはデータセットから未処理の形式で MATLAB に読み取らなければなりません。この例では、Sydney Urban Objects Dataset [1] を使用します。この例では、MATLAB のデータストアを使用して、深層学習用データの読み取りと表示を行う方法を説明します。
Sydney Urban Objects Dataset のダウンロード
Sydney Urban Objects のデータは非圧縮形式で 122 MB あり、お使いのネットワークの接続速度によってはダウンロードにしばらく時間がかかることがあります。
sydneyUrbanObjectsPath = downloadSydneyUrbanObjects(tempdir());
点群データ用データストアの定義
データストアを作成して、点群データを Sydney Urban Objects から、関連付けられたオブジェクト ラベルとともに読み込みます。
ds = loadSydneyUrbanObjectsData(sydneyUrbanObjectsPath);
データストアから 1 番目の観測値を読み取り、表示します。
data = preview(ds)
data=1×2 cell array
{1×1 pointCloud} {[4wd]}
disp(data)
{1×1 pointCloud} {[4wd]}
データストアの読み取りとプレビューのメソッドの出力は cell 配列であり、配列の 1 列目は pointCloud オブジェクト、2 列目は関連付けられたクラス ラベルです。pointCloud オブジェクトは関数 pcshow を使用して視覚化できます。
figure pcshow(data{1}) title(string(data{2}))
参考文献
[1] Alastair Quadros, James Underwood, Bertrand Douillard; 2013. Sydney Urban Objects Dataset.
サポート関数
function datasetPath = downloadSydneyUrbanObjects(dataLoc) % This function downloads the Sydney Urban Objects tar archive to tempdir % provides as output the location of where the data was saved. if nargin == 0 dataLoc = pwd(); end dataLoc = string(dataLoc); url = "http://www.acfr.usyd.edu.au/papers/data/"; name = "sydney-urban-objects-dataset.tar.gz"; if ~exist(fullfile(dataLoc,'sydney-urban-objects-dataset'),'dir') disp('Downloading Sydney Urban Objects Dataset...'); untar(url+name,dataLoc); end datasetPath = dataLoc.append('sydney-urban-objects-dataset'); end function ds = loadSydneyUrbanObjectsData(datapath,folds) % loadSydneyUrbanObjectsData Create datastore with point clouds and % associated categorical labels for Sydney Urban Objects dataset. % % ds = loadSydneyUrbanObjectsData(datapath) returns a datastore that % represents point clouds and associated categories for the Sydney Urban % Objects dataset. The input, datapath, is a string or char array which % represents the path to the root directory of the Sydney Urban Objects % Dataset. % % ds = loadSydneyUrbanObjectsData(___,folds) optionally allows % specification of desired folds that you wish to be included in the % output ds. For example, [1 2 4] specifies that you want the first, % second, and fourth folds of the Dataset. Default: [1 2 3 4]. if nargin < 2 folds = 1:4; end datapath = string(datapath); path = fullfile(datapath,'objects',filesep); % For now, include all folds in Datastore foldNames{1} = importdata(fullfile(datapath,'folds','fold0.txt')); foldNames{2} = importdata(fullfile(datapath,'folds','fold1.txt')); foldNames{3} = importdata(fullfile(datapath,'folds','fold2.txt')); foldNames{4} = importdata(fullfile(datapath,'folds','fold3.txt')); names = foldNames(folds); names = vertcat(names{:}); fullFilenames = append(path,names); ds = fileDatastore(fullFilenames,'ReadFcn',@extractTrainingData,'FileExtensions','.bin'); end function dataOut = extractTrainingData(fname) [pointData,intensity] = readbin(fname); [~,name] = fileparts(fname); name = string(name); name = extractBefore(name,'.'); labelNames = ["4wd","bench","bicycle","biker",... "building","bus","car","cyclist","excavator","pedestrian","pillar",... "pole","post","scooter","ticket_machine","traffic_lights","traffic_sign",... "trailer","trash","tree","truck","trunk","umbrella","ute","van","vegetation"]; label = categorical(name,labelNames); dataOut = {pointCloud(pointData,'Intensity',intensity),label}; end function [pointData,intensity] = readbin(fname) % readbin Read point and intensity data from Sydney Urban Object binary % files. % names = ['t','intensity','id',... % 'x','y','z',... % 'azimuth','range','pid'] % % formats = ['int64', 'uint8', 'uint8',... % 'float32', 'float32', 'float32',... % 'float32', 'float32', 'int32'] fid = fopen(fname, 'r'); c = onCleanup(@() fclose(fid)); fseek(fid,10,-1); % Move to the first X point location 10 bytes from beginning X = fread(fid,inf,'single',30); fseek(fid,14,-1); Y = fread(fid,inf,'single',30); fseek(fid,18,-1); Z = fread(fid,inf,'single',30); fseek(fid,8,-1); intensity = fread(fid,inf,'uint8',33); pointData = [X,Y,Z]; end