target/classes/vslam/README.md

-
-## Notes
-
-Notes from Rohan:
-- could use MATLAB coder to move MATLAB code to C/C++ code
-    - not sure if this is possible for Java code as well
-    - Prof. mentioned wanting Java code s.t. other students can read and critique codebase
-- use MATLAB functions, create new MATLAB objects for data structures / database system
-- dataset used by MATLAB example: https://cvg.cit.tum.de/data/datasets/rgbd-dataset 
-
-
-#### Performing VSLAM using online dataset 
-
-- Open up MATLAB console
-- Install necessary Mathworks Library: Vision, Visual SLAM (users will be queried to download necessary packages)
-- In MATLAB console, set imagesPath to 'rgbd_dataset_freiburg3_long_office_household/rgb'
-- Run the vslam_implementation.m script with the imagesPath as input
-- Use output of worldPointSet for figuring out which key features belong to which objects

target/classes/vslam/extractPointsByViewId.m

-function extractPointsByViewId(viewId, worldPointSet)
-    % Define directories and file paths
-    % keyFramesDir = './KeyFrames';
-    keyFramePointsDir = './KeyFramePoints';
-
-    % Construct filename for the image
-    % imageFilename = sprintf('%s/KeyFrame_%04d.png', keyFramesDir, viewId);
-    
-    % Load and display the image
-    % currImage = imread(imageFilename);
-    % figure;
-    % imshow(currImage);
-    % title(['Image for View ID: ', num2str(viewId)]);
-    % hold on;
-
-    % Load corresponding CSV file for world point indices and pixel locations
-    csvFilename = sprintf('%s/KeyFramePoints_%04d.csv', keyFramePointsDir, viewId);
-    dataMatrix = readmatrix(csvFilename);
-
-    % Extract pixel locations and indices of world points
-    % pixelLocations = dataMatrix(:, 1:2); % Assuming first two columns are pixel X, Y
-    mapPointsIdx = dataMatrix(:, 3);     % Assuming third column is mapPointsIdx
-
-    % Extract world points using the indices
-    worldPoints = worldPointSet.WorldPoints(mapPointsIdx, :);
-
-    % Append world points to the CSV
-    updatedDataMatrix = [dataMatrix, worldPoints]; % Concatenate world points
-    writematrix(updatedDataMatrix, csvFilename);   % Write it back
-
-    % % Overlay features on the image
-    % plot(pixelLocations(:,1), pixelLocations(:,2), 'yo', 'MarkerSize', 5, 'LineWidth', 2);
-    % 
-    % % Optionally save or display the image
-    % figure;
-    % imshow(currImage);
-    % hold on;
-    % plot(pixelLocations(:,1), pixelLocations(:,2), 'yo', 'MarkerSize', 5, 'LineWidth', 2);
-    % title('Features Overlayed on Image');
-end
-

target/classes/vslam/imperial_london/BoundedInfo/KeyFrame_0002.csv

-Class,Confidence,X,Y,Width,Height
-sofa,0.952328,54,305,439,174

target/classes/vslam/imperial_london/BoundedInfo/KeyFrame_0003.csv

-Class,Confidence,X,Y,Width,Height
-sofa,0.965473,-12,307,437,171
-sofa,0.592514,576,322,64,156

target/classes/vslam/imperial_london/BoundedInfo/KeyFrame_0004.csv

-Class,Confidence,X,Y,Width,Height
-sofa,0.982908,-5,315,358,164
-sofa,0.981918,456,338,185,141

target/classes/vslam/imperial_london/BoundedInfo/KeyFrame_0005.csv

-Class,Confidence,X,Y,Width,Height
-sofa,0.988577,-4,315,313,163
-sofa,0.982338,403,332,238,149

target/classes/vslam/imperial_london/BoundedInfo/KeyFrame_0006.csv

-Class,Confidence,X,Y,Width,Height
-sofa,0.986512,-5,323,282,154
-sofa,0.969826,383,327,255,151

target/classes/vslam/imperial_london/BoundedInfo/KeyFrame_0007.csv

-Class,Confidence,X,Y,Width,Height
-sofa,0.975408,325,327,314,150
-sofa,0.912207,1,332,220,146

target/classes/vslam/imperial_london/BoundedInfo/KeyFrame_0008.csv

-Class,Confidence,X,Y,Width,Height
-sofa,0.886575,297,272,338,149
-sofa,0.845557,0,268,195,209

target/classes/vslam/imperial_london/BoundedInfo/KeyFrame_0009.csv

-Class,Confidence,X,Y,Width,Height
-sofa,0.965334,267,213,370,144
-sofa,0.519108,0,207,155,272

target/classes/vslam/imperial_london/BoundedInfo/KeyFrame_0010.csv

-Class,Confidence,X,Y,Width,Height
-sofa,0.972942,229,199,395,152
-diningtable,0.634724,353,337,288,143

target/classes/vslam/imperial_london/BoundedInfo/KeyFrame_0011.csv

-Class,Confidence,X,Y,Width,Height
-sofa,0.978026,232,181,393,170
-diningtable,0.623284,389,315,249,168

target/classes/vslam/imperial_london/BoundedInfo/KeyFrame_0012.csv

-Class,Confidence,X,Y,Width,Height
-sofa,0.976634,231,199,391,180

target/classes/vslam/imperial_london/BoundedInfo/KeyFrame_0013.csv

-Class,Confidence,X,Y,Width,Height
-sofa,0.962698,346,202,293,186

target/classes/vslam/imperial_london/BoundedInfo/KeyFrame_0014.csv

-Class,Confidence,X,Y,Width,Height
-sofa,0.869272,478,191,160,208

target/classes/vslam/imperial_london/BoundedInfo/KeyFrame_0015.csv

-Class,Confidence,X,Y,Width,Height
-sofa,0.786228,510,187,130,210

target/classes/vslam/imperial_london/BoundedInfo/KeyFrame_0016.csv

-Class,Confidence,X,Y,Width,Height
-sofa,0.710312,535,181,104,211
-sofa,0.671818,-11,171,481,303

target/classes/vslam/imperial_london/BoundedInfo/KeyFrame_0017.csv

-Class,Confidence,X,Y,Width,Height