www.gusucode.com > images 案例代码 matlab源码程序 > images/PadShearImageExample.m
%% Padding and Shearing an Image Simultaneously
% This example shows how to construct a |tform| struct that represents a
% simple shear transformation and then applies it to an image. We explore how
% the transformation affects straight lines and circles, and then use it as
% a vehicle to explore the various options for image padding that can be
% used with |imtransform| and |tformarray|.
%
% Copyright 1993-2013 The MathWorks, Inc.
%% Step 1: Transform an Image Using Simple Shear
% In two dimensions, a simple shear transformation that maps a pair of
% input coordinates |[u v]| to a pair of output coordinates |[x y]| has the form
%
% $$x = u + a * v$$
%
%
% $$y = v$$
%
%
% where |a| is a constant.
%
% Any simple shear is a special case of an affine transformation. You can
% easily verify that
%
% $$[\begin{array}{c c}x\ y\ 1\end{array}] = [\begin{array}{c c}u\ v\ 1\end{array}] * \left[\begin{array}{c c c}1\;0\;0\\a\;1\;0\\0\;0\;1\end{array}\right]$$
%
% yields the values for |x| and |y| that you received from the first two equations.
%%
% Setting |a| = 0.45, we construct an affine |tform| struct using
% |maketform|.
a = 0.45;
T = maketform('affine', [1 0 0; a 1 0; 0 0 1] );
%%
% We select, read, and view and image to transform.
A = imread('football.jpg');
h1 = figure; imshow(A); title('Original Image');
%%
% We choose a shade of orange as our fill value.
orange = [255 127 0]';
%%
% We are ready to use |T| to transform |A|. We could call |imtransform| as follows:
%
% |B = imtransform(A,T,'cubic','FillValues',orange);|
%
% but this is wasteful since we would apply cubic interpolation along both
% columns and rows. (With our pure shear transform, we really only need to
% interpolate along each row.) Instead, we create and use a resampler that
% applies cubic interpolation along the rows but simply uses nearest
% neighbor interpolation along the columns, then call |imtransform| and display the result.
R = makeresampler({'cubic','nearest'},'fill');
B = imtransform(A,T,R,'FillValues',orange);
h2 = figure; imshow(B);
title('Sheared Image');
%% Step 2: Explore the Transformation
% Transforming a grid of straight lines or an array of circles with
% |tformfwd| is a good way to understand a transformation (as long as it has
% both forward and inverse functions).
%
% Define a grid of lines covering the original image, and
% display it over the image Then use |tformfwd| to apply the pure shear to
% each line in the grid, and display the result over the sheared image.
[U,V] = meshgrid(0:64:320,0:64:256);
[X,Y] = tformfwd(T,U,V);
gray = 0.65 * [1 1 1];
figure(h1);
hold on;
line(U, V, 'Color',gray);
line(U',V','Color',gray);
figure(h2);
hold on;
line(X, Y, 'Color',gray);
line(X',Y','Color',gray);
%%
% You can do the same thing with an array of circles.
gray = 0.65 * [1 1 1];
for u = 0:64:320
for v = 0:64:256
theta = (0 : 32)' * (2 * pi / 32);
uc = u + 20*cos(theta);
vc = v + 20*sin(theta);
[xc,yc] = tformfwd(T,uc,vc);
figure(h1); line(uc,vc,'Color',gray);
figure(h2); line(xc,yc,'Color',gray);
end
end
%% Step 3: Compare the 'fill', 'replicate', and 'bound' Pad Methods
% When we applied the shear transformation, |imtransform| filled in the
% orange triangles to the left and right, where there was no data. That's
% because we specified a pad method of |'fill'| when calling |makeresampler|.
% There are a total of five different pad method choices (|'fill'|,
% |'replicate'|, |'bound'|, |'circular'|, and |'symmetric'|). Here we compare the first three.
%
% First, to get a better look at how the |'fill'| option worked, use the
% |'XData'| and |'YData'| options in |imtransform| to force some additional space
% around the output image.
R = makeresampler({'cubic','nearest'},'fill');
Bf = imtransform(A,T,R,'XData',[-49 500],'YData',[-49 400],...
'FillValues',orange);
figure, imshow(Bf);
title('Pad Method = ''fill''');
%%
% Now, try the |'replicate'| method (no need to specify fill values in this
% case).
R = makeresampler({'cubic','nearest'},'replicate');
Br = imtransform(A,T,R,'XData',[-49 500],'YData', [-49 400]);
figure, imshow(Br);
title('Pad Method = ''replicate''');
%%
% And try the |'bound'| method.
R = makeresampler({'cubic','nearest'}, 'bound');
Bb = imtransform(A,T,R,'XData',[-49 500],'YData',[-49 400],...
'FillValues',orange);
figure, imshow(Bb);
title('Pad Method = ''bound''');
%%
% Results with |'fill'| and |'bound'| look very similar, but look closely and
% you'll see that the edges are smoother with |'fill'|. That's because the
% input image is padded with the fill values, then the cubic interpolation
% is applied across the edge, mixing fill and image values. In contrast,
% |'bound'| recognizes a strict boundary between the inside and outside of
% the input image. Points falling outside are filled. Points falling inside
% are interpolated, using replication when they're near the edge. A close
% up look helps show this more clearly. We choose |XData| and |YData| to
% bracket a point near the lower right corner of the image, in the output
% image space, the resize with |'nearest'| to preserve the appearance of
% the individual pixels.
R = makeresampler({'cubic','nearest'},'fill');
Cf = imtransform(A,T,R,'XData',[423 439],'YData',[245 260],...
'FillValues',orange);
R = makeresampler({'cubic','nearest'},'bound');
Cb = imtransform(A,T,R,'XData',[423 439],'YData',[245 260],...
'FillValues',orange);
Cf = imresize(Cf,12,'nearest');
Cb = imresize(Cb,12,'nearest');
figure;
subplot(1,2,1); imshow(Cf); title('Pad Method = ''fill''');
subplot(1,2,2); imshow(Cb); title('Pad Method = ''bound''');
%% Step 4: Exercise the 'circular' and 'symmetric' Pad Methods
% The remaining two pad methods are |'circular'| (circular repetition in each
% dimension) and |'symmetric'| (circular repetition of the image with an
% appended mirror image). To show more of the pattern that emerges, we
% redefine the transformation to cut the scale in half.
Thalf = maketform('affine',[1 0; a 1; 0 0]/2);
R = makeresampler({'cubic','nearest'},'circular');
Bc = imtransform(A,Thalf,R,'XData',[-49 500],'YData',[-49 400],...
'FillValues',orange);
figure, imshow(Bc);
title('Pad Method = ''circular''');
%%
R = makeresampler({'cubic','nearest'},'symmetric');
Bs = imtransform(A,Thalf,R,'XData',[-49 500],'YData',[-49 400],...
'FillValues',orange);
figure, imshow(Bs);
title('Pad Method = ''symmetric''');
%%