%% Finding the Rotation and Scale of a Distorted Image
% If you know that one image is distorted relative to another only by a
% rotation and scale change, you can use |cp2tform| to find the rotation
% angle and scale factor. You can then transform the distorted image to
% recover the original image.

% Copyright 1993-2003 The MathWorks, Inc. 

%% Step 1: Read image
% Bring your favorite image into the workspace.

I = imread('cameraman.tif');
imshow(I);
text(size(I,2),size(I,1)+15, ...
    'Image courtesy of Massachusetts Institute of Technology', ...
    'FontSize',7,'HorizontalAlignment','right');

%% Step 2: Resize the image

scale = 0.6;
J = imresize(I,scale); % Try varying the scale factor.

%% Step 3: Rotate the image

theta = 30;
K = imrotate(J,theta); % Try varying the angle, theta.
figure, imshow(K)

%% Step 4: Select control points
% Use the Control Point Selection Tool to pick at least two pairs of
% control points. You can run the rest of the demo with these pre-picked
% points, but try picking your own points to see how the results vary.

input_points = [129.87  141.25; 112.63 67.75];
base_points = [135.26  200.15; 170.30 79.30];
cpselect(K,I,input_points,base_points);

%%
% Save control points by choosing the *File* menu, then the *Save Points to
% Workspace* option. Save the points, overwriting variables |input_points|
% and |base_points|.

%% Step 5: Infer transform
% Find a |TFORM| structure that is consistent with your control points.

t = cp2tform(input_points,base_points,'linear conformal');

%%
% After you have done Steps 6 and 7, repeat Steps 5 through 7 but try using
% 'affine' instead of 'linear conformal'. What happens? Are the results as
% good as they were with `linear conformal'?

%% Step 6: Solve for scale and angle
% The |TFORM| structure, |t|, contains a transformation matrix in
% |t.tdata.Tinv|. Since you know that the transformation includes only
% rotation and scaling, the math is relatively simple to recover the scale
% and angle.
%
%  Let sc = s*cos(theta)
%  Let ss = s*sin(theta)
%
%  Then, Tinv = t.tdata.Tinv = [sc -ss  0;
%                               ss  sc  0;
%                               tx  ty  1]
%
%  where tx and ty are x and y translations, respectively.

ss = t.tdata.Tinv(2,1);
sc = t.tdata.Tinv(1,1);
scale_recovered = sqrt(ss*ss + sc*sc)
theta_recovered = atan2(ss,sc)*180/pi

%%
% The value of |scale_recovered| should be |0.6| or whatever |scale| you used
% in *Step 2: Resize the Image*. The value of |theta_recovered| should be
% |30| or whatever |theta| you used in *Step 3: Rotate the Image*.

%% Step 7: Recover original image
% Recover the original image by transforming |K|, the rotated-and-scaled
% image, using |TFORM| structure |t| and what you know about the size of
% |I|. 
%
% In the |recovered| image, notice that the resolution is not as good as in
% the original image |I|. This is due to the sequence which included
% shrinking-and-rotating then growing-and-rotating. Shrinking reduces the
% number of pixels in the image |K| so it effectively has less information
% than the original image |I|.
%
% The artifacts around the edges are due to the limited accuracy of the
% transformation. If you were to pick more points in *Step 4: Select
% control points*, the transformation would be more accurate.

D = size(I);
recovered = imtransform(K,t,'XData',[1 D(2)],'YData',[1 D(1)]);

% Compare recovered to I.
figure, imshow(I)
title('I')
figure, imshow(recovered) 
title('recovered')