share test

test-harris-corner-compare.m

clc; clear; close all;
%ream images
firstImg  = imread('c.png');
firstImg  = double(firstImg);
secondImg = imread('c2.png');
secondImg  = double(secondImg);

%define distance from pixel
distance2point = 5;

%first image harris
[firstImgRowNumber firstImgColNumber] = size(firstImg);
[firstCornersofImg, firstImgRows, firstImgCols] = harris(im2double(firstImg),1,0.04,15,1);
firstImgCornerCount = numel(firstImgRows);

% %mean filter
% secondImg = meanFilter(secondImg);
% secondImg  = double(secondImg);

%second image harris
[secondImgRowNumber secondImgColNumber] = size(secondImg);
[secondCornersofImg, secondImgRows, secondImgCols] = harris(im2double(secondImg),1,0.04,15,1);
secondImgCornerCount = numel(secondImgRows);

%partial initialize
firstPartial = zeros((distance2point*2+1));
firstPartial = double(firstPartial);
secondPartial = zeros((distance2point*2+1));
secondPartial = double(secondPartial);

%Image mean initialize
firstImgMeans = zeros(firstImgCornerCount,1);
firstImgMeans = double(firstImgMeans);
secondImgMeans = zeros(secondImgCornerCount,1);
secondImgMeans = double(secondImgMeans);

%{
calculate means in first image corner list
%}
for i=1:firstImgCornerCount
    % get row and col values
    row = firstImgRows(i);
    col = firstImgCols(i);  
    
    % select partial image from main image (first image)
    if(((row-distance2point)>0) && ((row + distance2point) < firstImgRowNumber) && ((col-distance2point)>0) && ((col + distance2point) < firstImgColNumber))
    firstPartial = firstImg(row - distance2point: row + distance2point, col - distance2point:col + distance2point); 
    else
    continue;  
    end
    
    % calculate mean of partial image then, add this value to first mean array
    partialmean = double(0);
    for k=1:(distance2point*2+1)
       for l=1:(distance2point*2+1)
           partialmean = double(partialmean) + double(firstPartial(k,l));
       end
    end
    partialmean = double(partialmean / (distance2point*2 + 1)^2);
    firstImgMeans(i,1) = partialmean;
end


%{
calculate means in second image corner list
%}
for i=1:secondImgCornerCount
    % get row and col values
    row = secondImgRows(i);
    col = secondImgCols(i);   

    % select partial image from main image (second image)
    if(((row-distance2point)>0) && ((row + distance2point) < secondImgRowNumber) && ((col-distance2point)>0) && ((col + distance2point) < secondImgColNumber))
    secondPartial = secondImg(row - distance2point: row + distance2point, col - distance2point:col + distance2point); 
    else
    continue;  
    end
    
    partialmean = double(0);
    for k=1:(distance2point*2+1)
       for l=1:(distance2point*2+1)
           partialmean = double(partialmean) + double(secondPartial(k,l));
       end
    end
    partialmean = double(partialmean / (distance2point*2 + 1)^2);
    secondImgMeans(i,1) = partialmean;
end



%{
normalized process in first image
%}
for i=1:firstImgCornerCount
    % get row and col values
    row = firstImgRows(i);
    col = firstImgCols(i);  
    
    % select partial image from main image (first image)
    if(((row-distance2point)>0) && ((row + distance2point) < firstImgRowNumber) && ((col-distance2point)>0) && ((col + distance2point) < firstImgColNumber))
    firstPartial = firstImg(row - distance2point: row + distance2point, col - distance2point:col + distance2point); 
    else
    continue;  
    end
    
    % normalize partial image
    totalvalueminusmeansquare  = double(0);
    for k=1:(distance2point*2+1)
       for l=1:(distance2point*2+1)
           totalvalueminusmeansquare = totalvalueminusmeansquare + ((double(firstPartial(k,l)) - firstImgMeans(i))^2);
       end
    end
    totalvalueminusmeansquare = sqrt(totalvalueminusmeansquare);
    for k=1:(distance2point*2+1)
        for l=1:(distance2point*2+1)
        firstPartial(k,l) = (double(firstPartial(k,l)) - firstImgMeans(i))/totalvalueminusmeansquare;
        end
    end
    firstImg(row - distance2point: row + distance2point, col - distance2point:col + distance2point) = firstPartial;
end


%{
normalized process in second image
%}
for i=1:secondImgCornerCount
    % get row and col values
    row = secondImgRows(i);
    col = secondImgCols(i);  
    
    % select partial image from main image (second image)
    if(((row-distance2point)>0) && ((row + distance2point) < secondImgRowNumber) && ((col-distance2point)>0) && ((col + distance2point) < secondImgColNumber))
    secondPartial = secondImg(row - distance2point: row + distance2point, col - distance2point:col + distance2point); 
    else
    continue;  
    end
    
    % normalize partial image
    totalvalueminusmeansquare  = double(0);
    for k=1:(distance2point*2+1)
       for l=1:(distance2point*2+1)
           totalvalueminusmeansquare = totalvalueminusmeansquare + ((double(secondPartial(k,l)) - secondImgMeans(i))^2);
       end
    end
    totalvalueminusmeansquare = sqrt(totalvalueminusmeansquare);
    for k=1:(distance2point*2+1)
        for l=1:(distance2point*2+1)
        secondPartial(k,l) = (double(secondPartial(k,l)) - secondImgMeans(i))/totalvalueminusmeansquare;
        end
    end
    secondImg(row - distance2point: row + distance2point, col - distance2point:col + distance2point) = secondPartial;
end


    I = zeros([size(firstImg,1) size(firstImg,2)*2 size(firstImg,3)]);
    I(:,1:size(firstImg,2),:)=firstImg; I(:,size(firstImg,2)+1:size(firstImg,2)+size(secondImg,2),:)=secondImg;
    figure, imshow(I/255); hold on;

%{
cross correlation process in first image
%}    
for i=1:firstImgCornerCount
    % get row and col values
    row = firstImgRows(i);
    col = firstImgCols(i); 
    minimum = 255;
    index = 0;
    
    if(((row-distance2point)>0) && ((row + distance2point) < firstImgRowNumber) && ((col-distance2point)>0) && ((col + distance2point) < firstImgColNumber))
    firstPartial = firstImg(row - distance2point: row + distance2point, col - distance2point:col + distance2point); 
    else
    continue;  
    end
    
    for j=1:secondImgCornerCount
        % get row and col values
        row = secondImgRows(j);
        col = secondImgCols(j);
        
        if(((row-distance2point)>0) && ((row + distance2point) < secondImgRowNumber) && ((col-distance2point)>0) && ((col + distance2point) < secondImgColNumber))
        secondPartial = secondImg(row - distance2point: row + distance2point, col - distance2point:col + distance2point); 
        else
        continue;  
        end
        
        sumofsquareddifferences = 0;
        for m=1:(distance2point*2+1)
           for n=1:(distance2point*2+1)
               sumofsquareddifferences = sumofsquareddifferences + (firstPartial(m,n) - secondPartial(m,n))^2;
           end
        end
        if(sumofsquareddifferences < minimum)
           minimum =  sumofsquareddifferences;
           index = j;
        end
    end
    c=rand(1,3);
    plot([firstImgCols(i) secondImgCols(index)+size(firstImg,2)],[firstImgRows(i) secondImgRows(index)],'-','Color',c)
    plot([firstImgCols(i) secondImgCols(index)+size(firstImg,2)],[firstImgRows(i) secondImgRows(index)],'o','Color',c)
    
    fprintf('Matched coordinate (%d,%d) to (%d,%d)  \n',firstImgRows(i),firstImgCols(i),secondImgRows(index),secondImgCols(index));
  
end