initial sd-vbs

initial sd-vbs

add sd-vbs

sd-vbs

21 files changed, 775 insertions, 0 deletions

diff --git a/SD-VBS/common/toolbox/toolbox_basic/affine/README b/SD-VBS/common/toolbox/toolbox_basic/affine/README
new file mode 100755
index 0000000..e578a74
--- /dev/null
+++ b/SD-VBS/common/toolbox/toolbox_basic/affine/README
@@ -0,0 +1,5 @@
+Top level program is "compute_AD.m".  Use "compute_AD_disp.m" if one
+wants to display results as program runs.
+
+The testing programs are called "simulation.m" for synthetic images,
+and "test_affine.m" for real images.
diff --git a/SD-VBS/common/toolbox/toolbox_basic/affine/carve_it.m b/SD-VBS/common/toolbox/toolbox_basic/affine/carve_it.m
new file mode 100755
index 0000000..1a44f89
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+++ b/SD-VBS/common/toolbox/toolbox_basic/affine/carve_it.m
@@ -0,0 +1,25 @@
+function img = carve_it(I,center,window_size_h)
+
+[size_y,size_x]= size(I);
+min_x = round(center(1)-window_size_h(1));
+max_x = round(center(1)+window_size_h(1));
+min_y = round(center(2)-window_size_h(2));
+max_y = round(center(2)+window_size_h(2));
+window_size = window_size_h*2 +1;
+
+if (min_x <1)|(max_x > size_x)|(min_y<1)|(max_y>size_y),
+  disp('window too big');
+  center
+  window_size_h
+  img = zeros(window_size(2),window_size(1));
+  n_min_x = max(1,round(min_x));
+  n_min_y = max(1,round(min_y));
+  n_max_x = min(size_x,round(max_x));
+  n_max_y = min(size_y,round(max_y));
+  img(1+(n_min_y-min_y):window_size(2)-(max_y-n_max_y),1+(n_min_x-min_x):window_size(1)-(max_x-n_max_x))=I(n_min_y:n_max_y,n_min_x:n_max_x);
+else
+  img = I(center(2)-window_size_h(2):center(2)+window_size_h(2),...
+        center(1)-window_size_h(1):center(1)+window_size_h(1));
+end
+
+
diff --git a/SD-VBS/common/toolbox/toolbox_basic/affine/compute_AD.m b/SD-VBS/common/toolbox/toolbox_basic/affine/compute_AD.m
new file mode 100755
index 0000000..a39acd6
--- /dev/null
+++ b/SD-VBS/common/toolbox/toolbox_basic/affine/compute_AD.m
@@ -0,0 +1,90 @@
+function [A,D,mask] =...
+compute_AD(img_i,img_j,center_i,center_j,window_size_h,num_iter,w,num_trans,Dest,mask)
+%
+% function [A,D,mask] = ...
+%     compute_AD(img_i,img_j,center_i,center_j,window_size_h,num_iter,w,
+%                mask,num_trans)
+%
+%  A:    Affine motion;
+%  D:    Displacement;
+%  
+%  img_i, img_j: the two image(in full size);
+%  center_i, center_j:  the centers of the feature in two images;
+%  window_size_h: half size of the feature window;
+%  num_iter:    number of iterations;
+%  w:           parameter used in "grad.m" for computing gaussians used for
+%               gradient estimation;
+%
+%  num_trans:   OPTIONAL, number of translation iteration; default = 3;
+%  mask:        OPTIONAL, if some area of the square shaped feature window should
+%                         be weighted less;
+%
+
+%
+%    Jianbo Shi
+%
+
+if ~exist('Dest'),
+  Dest = [0,0];
+end
+
+if ~exist('mask'),
+   mask = ones(2*window_size_h+1)';
+end
+
+% set the default num_trans
+if ~exist('num_trans'),
+   num_trans= 3;
+end
+
+% normalize image intensity to the range of 0.0-1.0
+img_i = norm_inten(img_i);
+img_j = norm_inten(img_j);
+
+window_size = 2*window_size_h + 1;
+I = carve_it(img_i,center_i,window_size_h);
+J = carve_it(img_j,center_j,window_size_h);
+
+% init. step
+J_computed = I;
+D_computed = Dest;
+A_computed = eye(2);
+J_computed = compute_J(A_computed,D_computed,img_i,center_i,window_size_h);
+
+%% level of noise
+sig = 0.1;
+
+records = zeros(num_iter,6);
+errs = zeros(1,num_iter);
+
+k = 1;
+% iteration
+while k <= num_iter,
+  [A,D] = iter_AD(J_computed,J,mask,w,k,num_trans);
+
+  A_computed = A*A_computed;
+  D_computed = (A*D_computed')' + D;
+
+  % compute the warped image
+  J_computed = compute_J(A_computed,D_computed,img_i,center_i,window_size_h);
+
+  % compute the SSD error
+  errs(k) = sqrt(sum(sum((mask.*(J_computed-J)).^2)))/prod(size(J));
+
+  % update the mask, discounting possible occlusion region
+  if (k>num_trans),
+    mask = exp(-abs(J_computed-J)/sig);
+  end
+
+  % record the A and D
+  records(k,:) = [reshape(A_computed,1,4),reshape(D_computed,1,2)];
+
+  k = k+1;
+end  
+
+[tmp,id] = min(errs);
+A = reshape(records(id,1:4),2,2);
+D = reshape(records(id,5:6),1,2);
+
+J_computed = compute_J(A,D,img_i,center_i,window_size_h);
+mask = exp(-abs(J_computed-J)/sig);
diff --git a/SD-VBS/common/toolbox/toolbox_basic/affine/compute_AD_disp.m b/SD-VBS/common/toolbox/toolbox_basic/affine/compute_AD_disp.m
new file mode 100755
index 0000000..f2e6c62
--- /dev/null
+++ b/SD-VBS/common/toolbox/toolbox_basic/affine/compute_AD_disp.m
@@ -0,0 +1,103 @@
+function [A,D,mask] =...
+compute_AD_disp(img_i,img_j,center_i,center_j,window_size_h,num_iter,w,fig_disp,num_trans,Dest,mask)
+%
+% function [A,D,mask] = ...
+%     compute_AD_disp(img_i,img_j,center_i,center_j,window_size_h,num_iter,w,
+%                fig_disp,mask,num_trans)
+%  
+%  Computing affine transform for matching to image patches.  Display results
+%  as program runs.
+%
+%  A: Affine motion;
+%  D: Displacement;
+%  
+%
+%  img_i, img_j: the two image(in full size);
+%  center_i, center_j:  the centers of the feature in two images;
+%  window_size_h: half size of the feature window;
+%  num_iter:    number of iterations;
+%  w:           parameter used in "grad.m" for computing gaussians used for
+%               gradient estimation;
+%  fig_disp:    figure for display;
+%
+%  num_trans:   OPTIONAL, number of translation iteration;
+%  mask:        OPTIONAL, if some area of the square shaped feature window should
+%                         be weighted less;