StructureFromMotion.m

%% directory.
imageDir = fullfile('C:\Users\Administrator.USER-20190423VA\Desktop\DATA\test');
imds = imageDatastore(imageDir);

% Convert the images to grayscale.
images = cell(1, numel(imds.Files));
for i = 1:numel(imds.Files)
    I = readimage(imds, i);
    images{i} = rgb2gray(I);
end
%% Load Camera Parameters
load(fullfile('C:\Users\Administrator.USER-20190423VA\Desktop\DATA\camparament\Ecameradata.mat'));
%% Create a View Set Containing the First View
I = undistortImage(images{1}, cameraParams); 

border = 50;
roi = [border, border, size(I, 2)- 2*border, size(I, 1)- 2*border];
prevPoints  = detectSURFFeatures(I, 'NumOctaves', 8, 'ROI', roi);

% Extract features. Using 'Upright' features improves matching, as long as
% the camera motion involves little or no in-plane rotation.
prevFeatures = extractFeatures(I, prevPoints, 'Upright', true);

vSet = viewSet;

% Add the first view. Place the camera associated with the first view
% and the origin, oriented along the Z-axis.
viewId = 1;
vSet = addView(vSet, viewId, 'Points', prevPoints, 'Orientation', ...
    eye(3, 'like', prevPoints.Location), 'Location', ...
    zeros(1, 3, 'like', prevPoints.Location));

%% Add the Rest of the Views
for i = 2:numel(images)
    % Undistort the current image.
    I = undistortImage(images{i}, cameraParams);
    
    % Detect, extract and match features.
    currPoints   = detectSURFFeatures(I, 'NumOctaves', 8, 'ROI', roi);
    currFeatures = extractFeatures(I, currPoints, 'Upright', true);    
    indexPairs = matchFeatures(prevFeatures, currFeatures, ...
        'MaxRatio', .7, 'Unique',  true);
    
    % Select matched points.
    matchedPoints1 = prevPoints(indexPairs(:, 1));
    matchedPoints2 = currPoints(indexPairs(:, 2));
    

    [relativeOrient, relativeLoc, inlierIdx] = helperEstimateRelativePose(...
        matchedPoints1, matchedPoints2, cameraParams);
    
    % Add the current view to the view set.
    vSet = addView(vSet, i, 'Points', currPoints);
    
    % Store the point matches between the previous and the current views.
    vSet = addConnection(vSet, i-1, i, 'Matches', indexPairs(inlierIdx,:));
    
    % Get the table containing the previous camera pose.
    prevPose = poses(vSet, i-1);
    prevOrientation = prevPose.Orientation{1};
    prevLocation    = prevPose.Location{1};
        
    % Compute the current camera pose in the global coordinate system 
    % relative to the first view.
    orientation = relativeOrient * prevOrientation;
    location    = prevLocation + relativeLoc * prevOrientation;
    vSet = updateView(vSet, i, 'Orientation', orientation, ...
        'Location', location);
    
    % Find point tracks across all views.
    tracks = findTracks(vSet);

    % Get the table containing camera poses for all views.
    camPoses = poses(vSet);

    % Triangulate initial locations for the 3-D world points.
    xyzPoints = triangulateMultiview(tracks, camPoses, cameraParams);
    
    % Refine the 3-D world points and camera poses.
    [xyzPoints, camPoses, reprojectionErrors] = bundleAdjustment(xyzPoints, ...
        tracks, camPoses, cameraParams, 'FixedViewId', 1, ...
        'PointsUndistorted', true);

    % Store the refined camera poses.
    vSet = updateView(vSet, camPoses);

    prevFeatures = currFeatures;
    prevPoints   = currPoints;  
end

%% Compute Dense Reconstruction
I = undistortImage(images{1}, cameraParams); 

% Detect corners in the first image.
prevPoints = detectMinEigenFeatures(I, 'MinQuality', 0.001);

% Create the point tracker object to track the points across views.
tracker = vision.PointTracker('MaxBidirectionalError', 1, 'NumPyramidLevels', 6);

% Initialize the point tracker.
prevPoints = prevPoints.Location;
initialize(tracker, prevPoints, I);

% Store the dense points in the view set.
vSet = updateConnection(vSet, 1, 2, 'Matches', zeros(0, 2));
vSet = updateView(vSet, 1, 'Points', prevPoints);

% Track the points across all views.
for i = 2:numel(images)
    % Read and undistort the current image.
    I = undistortImage(images{i}, cameraParams); 
    
    % Track the points.
    [currPoints, validIdx] = step(tracker, I);
    
    % Clear the old matches between the points.
    if i < numel(images)
        vSet = updateConnection(vSet, i, i+1, 'Matches', zeros(0, 2));
    end
    vSet = updateView(vSet, i, 'Points', currPoints);
    
    % Store the point matches in the view set.
    matches = repmat((1:size(prevPoints, 1))', [1, 2]);
    matches = matches(validIdx, :);        
    vSet = updateConnection(vSet, i-1, i, 'Matches', matches);
end

% Find point tracks across all views.
tracks = findTracks(vSet);

% Find point tracks across all views.
camPoses = poses(vSet);

% Triangulate initial locations for the 3-D world points.
xyzPoints = triangulateMultiview(tracks, camPoses,...
    cameraParams);

% Refine the 3-D world points and camera poses.
[xyzPoints, camPoses, reprojectionErrors] = bundleAdjustment(...
    xyzPoints, tracks, camPoses, cameraParams, 'FixedViewId', 1, ...
    'PointsUndistorted', true);

%% Display Dense Reconstruction
figure;
plotCamera(camPoses, 'Size', 0.2);
hold on

% Exclude noisy 3-D world points.
goodIdx = (reprojectionErrors < 5);

% Display the dense 3-D world points.
pcshow(xyzPoints(goodIdx, :), 'VerticalAxis', 'y', 'VerticalAxisDir', 'down', ...
    'MarkerSize', 45);
grid on
hold off

% Specify the viewing volume.
loc1 = camPoses.Location{1};
xlim([loc1(1)-5, loc1(1)+4]);
ylim([loc1(2)-5, loc1(2)+4]);
zlim([loc1(3)-1, loc1(3)+20]);
camorbit(0, -30);

title('Dense Reconstruction');