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//*********************************************************************//
// Author : Asmita Bhar, Kevin George
//*********************************************************************//
function [val] = cvVariance(image, varargin)
// Finds variance values in an input
//
// Calling Sequence
// val = cvVariance(image);
// val = cvVariance(image, name, value,...);
// val = cvVariance(image, name, value,c,r); (only when ROI Processing is true)
//
// Parameters
// image : Input image matrix
// Dimension (Output) : Dimension along which the function operates - Row, Column, All or Custom. Default : All
// CustomDimension (Optional) : The integer dimension over which the function calculates the minimum. This value cannot exceed the number of dimensions in input. It applies only when 'Dimension' property is set to 'Custom'. Default : 1
// ROIProcessing (Optional) : It applies only when 'Dimension' property is set to 'All'. It calculates the variance within a particular region of the image. Default : false
// c (Optional): vector of y-coordinates of vectices of a rectangle(ROI). Applicable only when 'ROIProcssing' is set to 'true'.
// r (Optional): vector of x-coordinates of vectices of a rectangle(ROI). Applicable only when 'ROIProcssing' is set to 'true'.
// val : Stores the variance calculated
//
// Description
// The function calculates the variance value in a given image matrix.
//
// Examples
// //Load an image
// I = imread('peppers.png');
// //Calculate the variance (default dimension is 'All')
// val = cvVariance(I);
// //Calculate the variance when dimension is 'Row'
// val = cvVariance(I,'Dimension','Row');
// //Calculate the variance within a given ROI
// a = [0 100 100 0];
// b = [0 0 100 100];
// val = cvVariance(I,'ROIProcessing','true',a,b);
//
// Authors
// Asmita Bhar
// Kevin George
//
[lhs,rhs] = argn(0);
if rhs<1 then
error(msprintf("Not enough input arguments"));
end
if rhs>9 then
error(msprintf("Too many input arguments"));
end
[iRows iCols]=size(image(1))
iChannels = size(image)
dimension = 'All';
customDimension = 1;
roiProcessing = 'false';
flag1=0;
i=1;
while(i<rhs-1)
if strcmpi(varargin(i),'Dimension')==0 then
dimension = varargin(i+1)
if strcmpi(dimension,"Column") & strcmpi(dimension,"Row") &strcmpi(dimension,"All") & strcmpi(dimension,"Custom") then
error(msprintf(" wrong input argument #%d, Dimension not matched",i))
end
i=i+2;
elseif strcmpi(varargin(i),'CustomDimension')==0 then
customDimension = varargin(i+1)
flag1=1;
i=i+2;
elseif strcmpi(varargin(i), 'ROIProcessing')==0 then
roiProcessing = varargin(i+1)
if(roiProcessing=='true') then
c = varargin(i+2);
r = varargin(i+3);
i=i+4;
else
i=i+2;
end
end
end
if (strcmpi(dimension,'Custom') & (flag1==1))
error(msprintf("The CustomDimension property is not relevant in this configuration"));
end
if (strcmpi(dimension,"All") & strcmpi(roiProcessing,'true'))
error(msprintf("ROI Property is not relevant in this configuration"));
end
if(customDimension<1)
error(msprintf("CustomDimension must be greater than or equal to 1"));
end
if(iChannels==1) then
if(customDimension>2)
error(msprintf("CustomDimension cannot be greater than the dimension of the input."));
end
elseif(iChannels==3) then
if(customDimension>3)
error(msprintf("CustomDimension cannot be greater than the dimension of the input."));
end
end
if(iChannels==1)
I = double(image(1));
elseif(iChannels==3)
I1 = double(image(1));
I2 = double(image(2));
I3 = double(image(3));
end
if(roiProcessing=='false') then
if (dimension=='All') then
if(iChannels==1) then
val = variance(I)
elseif (iChannels==3) then
val1 = variance(I1)
val2 = variance(I2)
val3 = variance(I3)
val = variance([val1 val2 val3])
end
end
if (dimension=='Row') then
if(iChannels==1) then
val = variance(I,'c');
elseif(iChannels==3) then
val1 = variance(I1,'c');
val2 = variance(I2,'c');
val3 = variance(I3,'c');
val(:,:,1) = val1;
val(:,:,2) = val2;
val(:,:,3) = val3;
end
end
if (dimension=='Column') then
if(iChannels==1) then
val = variance(I,'r');
elseif(iChannels==3) then
val1 = variance(I1,'r');
val2 = variance(I2,'r');
val3 = variance(I3,'r');
val(:,:,1) = val1;
val(:,:,2) = val2;
val(:,:,3) = val3;
end
end
if (dimension=='Custom') then
if(iChannels==1) then
if(customDimension==1) then
val = variance(I,'r');
elseif(customDimension==2) then
val = variance(I,'c');
end
elseif(iChannels==3) then
if(customDimension==1) then
val1 = variance(I1,'r');
val2 = variance(I2,'r');
val3 = variance(I3,'r');
val(:,:,1) = val1;
val(:,:,2) = val2;
val(:,:,3) = val3;
elseif(customDimension==2) then
val1 = variance(I1,'c');
val2 = variance(I2,'c');
val3 = variance(I3,'c');
val(:,:,1) = val1;
val(:,:,2) = val2;
val(:,:,3) = val3;
elseif(customDimension==3) then
for i=1:iRows
for j=1:iCols
val(i,j)= variance([I1(i,j) I2(i,j) I3(i,j)]);
end
end
end
end
end
end
if(roiProcessing=='true') then
I4 = roipoly(image,c,r);
out = I4;
output = find(out>0);
[rows cols] = size(out);
if(iChannels==1)
ROI = zeros(iRows,iCols);
for i=1:cols
ROI(output(i)) = image(1)(output(i));
end
elseif(iChannels==3)
ROI1 = zeros(iRows,iCols);
ROI2 = zeros(iRows,iCols);
ROI3 = zeros(iRows,iCols);
for i=1:cols
ROI1(output(i)) = image(1)(output(i));
ROI2(output(i)) = image(2)(output(i));
ROI3(output(i)) = image(3)(output(i));
end
ROI = list(ROI1,ROI2,ROI3);
end
if (dimension=='All') then
if(iChannels==1) then
a=ROI;
val = variance(a(find(a>0)));
elseif (iChannels==3) then
a=ROI(1);
b=ROI(2);
c=ROI(3);
val1 = variance(a(find(a>0)));
val2 = variance(b(find(b>0)));
val3 = variance(c(find(c>0)));
val = variance([val1 val2 val3]);
end
end
end
endfunction
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