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    <p>So, this is what I ended up doing: I figured that unless you are actually dealing with 3D images, rectifying the perspective of a photo is a 2D operation. With this in mind, I replaced the z-axis values of the transformation matrix with zeros and ones, and applied a 2D Affine transformation to the image.</p> <p>Rotation of the initial image (see initial post) with measured Roll = -10 and Pitch = -30 was done in the following manner:</p> <pre><code>R_rotation = R_y(-60)*R_x(10); R_2d = [ R_rot(1,1) R_rot(1,2) 0; R_rot(2,1) R_rot(2,2) 0; 0 0 1 ] </code></pre> <p>This implies a rotation of the camera platform to a virtual camera orientation where the camera is placed above the scene, pointing straight downwards. Note the values used for roll and pitch in the matrix above.</p> <p>Additionally, if rotating the image so that is aligned with the platform heading, a rotation about the z-axis might be added, giving:</p> <pre><code>R_rotation = R_y(-60)*R_x(10)*R_z(some_heading); R_2d = [ R_rot(1,1) R_rot(1,2) 0; R_rot(2,1) R_rot(2,2) 0; 0 0 1 ] </code></pre> <p>Note that this does not change the actual image - it only rotates it.</p> <p>As a result, the initial image rotated about the Y- and X-axes looks like:</p> <p><img src="https://i.stack.imgur.com/FBJA7.jpg" alt="enter image description here"></p> <p>The full code for doing this transformation, as displayed above, was:</p> <pre><code>% Load image img = imread('initial_image.jpg'); % Full rotation matrix. Z-axis included, but not used. R_rot = R_y(-60)*R_x(10)*R_z(0); % Strip the values related to the Z-axis from R_rot R_2d = [ R_rot(1,1) R_rot(1,2) 0; R_rot(2,1) R_rot(2,2) 0; 0 0 1 ]; % Generate transformation matrix, and warp (matlab syntax) tform = affine2d(R_2d); outputImage = imwarp(img,tform); % Display image figure(1), imshow(outputImage); %*** Rotation Matrix Functions ***% %% Matrix for Yaw-rotation about the Z-axis function [R] = R_z(psi) R = [cosd(psi) -sind(psi) 0; sind(psi) cosd(psi) 0; 0 0 1]; end %% Matrix for Pitch-rotation about the Y-axis function [R] = R_y(theta) R = [cosd(theta) 0 sind(theta); 0 1 0 ; -sind(theta) 0 cosd(theta) ]; end %% Matrix for Roll-rotation about the X-axis function [R] = R_x(phi) R = [1 0 0; 0 cosd(phi) -sind(phi); 0 sind(phi) cosd(phi)]; end </code></pre> <p>Thank you for the support, I hope this helps someone! </p>
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