sfm_two_images_matlab_sample.m

%% Structure From Motion From Two Views
% Structure from motion (SfM) is the process of estimating the 3-D structure 
% of a scene from a set of 2-D images. This example shows you how to estimate
% the poses of a calibrated camera from two images, reconstruct the 3-D
% structure of the scene up to an unknown scale factor, and then recover the
% actual scale factor by detecting an object of a known size.

% Copyright 2015 The MathWorks, Inc. 

%% Overview
% This example shows how to reconstruct a 3-D scene from a pair of 2-D
% images taken with a camera calibrated using the
% <matlab:helpview(fullfile(docroot,'toolbox','vision','vision.map'),'visionCameraCalibrator'); Camera Calibrator app>.
% The algorithm consists of the following steps:
%
% # Match a sparse set of points between the two images. There are multiple
% ways of finding point correspondences between two images. This example
% detects corners in the first image using the |detectMinEigenFeatures|
% function, and tracks them into the second image using
% |vision.PointTracker|. Alternatively you can use |extractFeatures|
% followed by |matchFeatures|.
% # Estimate the fundamental matrix using |estimateFundamentalMatrix|.
% # Compute the motion of the camera using the |relativeCameraPose| function.
% # Match a dense set of points between the two images. Re-detect the point
% using |detectMinEigenFeatures| with a reduced |'MinQuality'| to get more
% points. Then track the dense points into the second image using
% |vision.PointTracker|.
% # Determine the 3-D locations of the matched points using |triangulate|.
% # Detect an object of a known size. In this scene there is a globe,
% whose radius is known to be 10cm. Use |pcfitsphere| to find the globe in
% the point cloud.
% # Recover the actual scale, resulting in a metric reconstruction.

%% Read a Pair of Images
% Load a pair of images into the workspace.
clc;
clear all;
close all;
I1 = imread('/home/nets/projects/Matlab/Round_table/im3.bmp');
I2 = imread('/home/nets/projects/Matlab/Round_table/im4.bmp');


figure
imshowpair(I1, I2, 'montage'); 
title('Original Images');

%% Load Camera Parameters 
% This example uses the camera parameters calculated by the 
% <matlab:helpview(fullfile(docroot,'toolbox','vision','vision.map'),'visionCameraCalibrator'); cameraCalibrator> 
% app. The parameters are stored in the |cameraParams| object, and include 
% the camera intrinsics and lens distortion coefficients.

% Load precomputed camera parameters
load /home/nets/projects/Matlab/Calibration_25_Nov/calibrationSession_25_Nov_2.mat
%% Remove Lens Distortion
% Lens distortion can affect the accuracy of the final reconstruction. You
% can remove the distortion from each of the images using the |undistortImage|
% function. This process straightens the lines that are bent by the radial 
% distortion of the lens.
I1 = undistortImage(I1, cameraParams);
I2 = undistortImage(I2, cameraParams);
figure 
imshowpair(I1, I2, 'montage');
title('Undistorted Images');

%% Find Point Correspondences between images (Feature based)
% Detect features. 
I1_g = rgb2gray(I1);
I2_g = rgb2gray(I2);
imagePoints1 = detectSURFFeatures(I1_g, 'MetricThreshold', 500);

% Select a subset of features, uniformly distributed throughout the image.
numPoints = 150;
imagePoints1 = selectUniform(imagePoints1, numPoints, size(I1));

% Extract features. Using 'Upright' features improves matching quality if 
% the camera motion involves little or no in-plane rotation.
prevFeatures = extractFeatures(I1_g, imagePoints1, 'Upright', true);

[imagePoints2, currFeatures, indexPairs] = helperDetectAndMatchFeatures(...
    prevFeatures, I2_g);

matchedPoints1 = imagePoints1(indexPairs(:,1));
matchedPoints2 = imagePoints2(indexPairs(:,2));

% Visualize correspondences
figure
showMatchedFeatures(I1, I2, matchedPoints1, matchedPoints2);
title('Tracked Features');

% %% Find Point Correspondences Between The Images
% % Detect good features to track. Reduce |'MinQuality'| to detect fewer
% % points, which would be more uniformly distributed throughout the image.
% % If the motion of the camera is not very large, then tracking using the 
% % KLT algorithm is a good way to establish point correspondences.
% 
% % Detect feature points
% imagePoints1 = detectMinEigenFeatures(rgb2gray(I1), 'MinQuality', 0.1);
% 
% % Visualize detected points
% figure
% imshow(I1, 'InitialMagnification', 50);
% title('150 Strongest Corners from the First Image');
% hold on
% plot(selectStrongest(imagePoints1, 150));
% 
% % Create the point tracker
% tracker = vision.PointTracker('MaxBidirectionalError', 1, 'NumPyramidLevels', 5);
% 
% % Initialize the point tracker
% imagePoints1 = imagePoints1.Location;
% initialize(tracker, imagePoints1, I1);
% 
% % Track the points
% [imagePoints2, validIdx] = step(tracker, I2);
% matchedPoints1 = imagePoints1(validIdx, :);
% matchedPoints2 = imagePoints2(validIdx, :);
% 
% % Visualize correspondences
% figure
% showMatchedFeatures(I1, I2, matchedPoints1, matchedPoints2);
% title('Tracked Features');


%% Estimate the Essential Matrix
% Use the |estimateEssenitalMatrix| function to compute the essential 
% matrix and find the inlier points that meet the epipolar constraint.

% Estimate the fundamental matrix
[E, epipolarInliers] = estimateEssentialMatrix(...
    matchedPoints1, matchedPoints2, cameraParams, 'Confidence', 99.99);

% Find epipolar inliers
inlierPoints1 = matchedPoints1(epipolarInliers, :);
inlierPoints2 = matchedPoints2(epipolarInliers, :);

% Display inlier matches
figure
showMatchedFeatures(I1, I2, inlierPoints1, inlierPoints2);
title('Epipolar Inliers');

%% Compute the Camera Pose
% Compute the location and orientation of the second camera relative to the
% first one. Note that |t| is a unit vector, because translation can only 
% be computed up to scale.

[orient, loc] = relativeCameraPose(E, cameraParams, inlierPoints1, inlierPoints2);

%% Reconstruct the 3-D Locations of Matched Points
% Re-detect points in the first image using lower |'MinQuality'| to get
% more points. Track the new points into the second image. Estimate the 
% 3-D locations corresponding to the matched points using the |triangulate|
% function, which implements the Direct Linear Transformation
% (DLT) algorithm [1]. Place the origin at the optical center of the camera
% corresponding to the first image.

% Detect dense feature points. Use an ROI to exclude points close to the
% image edges.
roi = [30, 30, size(I1, 2) - 30, size(I1, 1) - 30];


imagePoints1 = detectMinEigenFeatures(rgb2gray(I1), 'ROI', roi, ...
    'MinQuality', 0.001);

% Create the point tracker
tracker = vision.PointTracker('MaxBidirectionalError', 1, 'NumPyramidLevels', 5);

% Initialize the point tracker
imagePoints1 = imagePoints1.Location;
initialize(tracker, imagePoints1, I1);

% Track the points
[imagePoints2, validIdx] = step(tracker, I2);
matchedPoints1 = imagePoints1(validIdx, :);
matchedPoints2 = imagePoints2(validIdx, :);

% Compute the camera matrices for each position of the camera
% The first camera is at the origin looking along the Z-axis. Thus, its
% rotation matrix is identity, and its translation vector is 0.
camMatrix1 = cameraMatrix(cameraParams, eye(3), [0 0 0]);

% Compute extrinsics of the second camera
[R, t] = cameraPoseToExtrinsics(orient, loc);
camMatrix2 = cameraMatrix(cameraParams, R, t);

% Compute the 3-D points
points3D = triangulate(matchedPoints1, matchedPoints2, camMatrix1, camMatrix2);

% Get the color of each reconstructed point
numPixels = size(I1, 1) * size(I1, 2);
allColors = reshape(I1, [numPixels, 3]);
colorIdx = sub2ind([size(I1, 1), size(I1, 2)], round(matchedPoints1(:,2)), ...
    round(matchedPoints1(:, 1)));
color = allColors(colorIdx, :);

% Create the point cloud
ptCloud = pointCloud(points3D, 'Color', color);

%% Display the 3-D Point Cloud
% Use the |plotCamera| function to visualize the locations and orientations
% of the camera, and the |pcshow| function to visualize the point cloud.

% Visualize the camera locations and orientations
cameraSize = 0.3;
figure
plotCamera('Size', cameraSize, 'Color', 'r', 'Label', '1', 'Opacity', 0);
hold on
grid on
plotCamera('Location', loc, 'Orientation', orient, 'Size', cameraSize, ...
    'Color', 'b', 'Label', '2', 'Opacity', 0);

% Visualize the point cloud
pcshow(ptCloud, 'VerticalAxis', 'y', 'VerticalAxisDir', 'down', ...
    'MarkerSize', 45);

% Rotate and zoom the plot
%camorbit(0, -30);
%camzoom(1.5);

% Label the axes
xlabel('x-axis');
ylabel('y-axis');
zlabel('z-axis')

title('Up to Scale Reconstruction of the Scene');


%% Summary
% This example showed how to recover camera motion and reconstruct the 3-D 
% structure of a scene from two images taken with a calibrated camera.

%% References
%
% [1] Hartley, Richard, and Andrew Zisserman. Multiple View Geometry in
% Computer Vision. Second Edition. Cambridge, 2000.

%displayEndOfDemoMessage(mfilename)