Merge pull request #1 from TJCoding/TJCoding-patch-2

Add files via upload

m/MatchColumns.m

@@ -0,0 +1,71 @@
+function [U_t,A_t] = MatchColumns(us,ut,at)
+% Ensures consistency between the target & source image rotation matrices.
+
+% This routine matches columns in the target image rotation matrix to those
+% in the source image rotation matrix.
+
+% Each rotation matrix is derived by undertaking a singular value
+% decomposition of the respective cross covariance matrices. The
+% outcome of the decomposition is ordered within the rotation matrix in 
+% the order of the descending singular values.  This often leads to 
+% compatible rotation matrices but that is not guaranteed.  Sometimes the 
+% colour ordering of one rotation matrix may be different from the other.
+%
+% Additionally even when the matrices do correspond in orientation, they
+% need not correspond in direction. The direction axis rotation in one
+% matrix may be the negative of the rotation in the other.
+
+% Rotations of the individual colour axes are given by the columns of the
+% rotation matrices.  In the following processing, all rearrangements are 
+% considered of the columns in the target rotation matrix 'ut', to find 
+% the arrangement that best matches the source rotation matrix 'us'. 
+% Matching is measured by taking the vector dot products of the 
+% corresponding matrix columns and finding the arrangement with the 
+% largest sum of absolute dot product values.  Absolute values are used 
+% to accommodate axis pairs that have similar orientations but different 
+% directions.
+%
+% Once the best match has been found this is taken as the correct target
+% image rotation matrix.  Columns are negated where the vector cross
+% product has a negative value, to ensure direction compatibility.  The 
+% singular value matrix for the target image is reordered to match the 
+% reordering of the rotation matrix.
+%
+% This processing method and routine copyright Dr T E Johnson 2020.
+% [email protected]
+
+% All 6 permutations of the three columns need to be addressed.
+perm = perms([1 2 3]);
+
+max=0.0;
+
+for i=1:6
+    % Compute the sum of the absolute dot products for the matrix columns. 
+    sum=abs(ut(:,perm(i,1)).'*us(:,1)) + ...
+        abs(ut(:,perm(i,2)).'*us(:,2)) + ...
+        abs(ut(:,perm(i,3)).'*us(:,3)) ;
+
+    if(sum>max)
+        % Best aligned axes give the biggest sum.
+        max=sum;
+        bestperm=i;
+    end
+end
+    % Now recover the best permutation and implement it.
+    b1=perm(bestperm,1);
+    b2=perm(bestperm,2);
+    b3=perm(bestperm,3);
+
+    % Set the rotation matrix columns to the new order, changing the 
+    % direction of rotations if necessary by negating column values.
+    U_t(:,1)=ut(:,b1)*sign(ut(:,b1).'*us(:,1));
+    U_t(:,2)=ut(:,b2)*sign(ut(:,b2).'*us(:,2));
+    U_t(:,3)=ut(:,b3)*sign(ut(:,b3).'*us(:,3));    
+
+    % Similarly reorder the singular values.
+    A_t(1,1)=at(b1,b1);
+    A_t(2,2)=at(b2,b2);
+    A_t(3,3)=at(b3,b3);     
+end
+
+

m/cf_Xiao06.m

@@ -32,6 +32,17 @@
 [U_s,A_s,~] = svd(cov_s);
 [U_t,A_t,~] = svd(cov_t);
 
+%**********************************************************
+% Processing modification by T E Johnson.
+% Set true (default) to implement ruggedisation processing.
+% Set false for standard (original) processing.
+ruggedisation=true;
+
+if (ruggedisation)
+    [U_t,A_t]=MatchColumns(U_s,U_t,A_t);
+end 
+%**********************************************************
+
 rgbh_s = [rgb_s;ones(1,size(rgb_s,2))];
 
 % compute transforms

m/demo.m

@@ -1,8 +1,11 @@
-% Demostration of Xiao's Image Colour Transfer
+% Demonstration of Xiao's Image Colour Transfer
 
 % Copyright 2015 Han Gong <[email protected]>, University of East
 % Anglia.
 
+% Now ruggedised by T E Johnson (April 2020) to ensure consistent 
+% processing.
+
 % References:
 % Xiao, Xuezhong, and Lizhuang Ma. "Color transfer in correlated color
 % space." % In Proceedings of the 2006 ACM international conference on