% testing various ways to find the centeres of tiles. 
function test

addpath /research/src/DNAdesign
addpath /research/src/AFMutils

    %%
    %% rizal's beautiful image
    %%
    [im,in] = showafm('~winfree/Research/DNA/AFM_old/data.rizal/02-07-20/QBOX.097',0); 
    im = flipud(im);
    figure(1); 
        imagesc(im);
        axis square;
        title 'raw image'
    %%
    
    h = fspecial( 'gaussian', [3,3]);
    imc = imfilter(im, h); 
     imc = imfilter(imc, h);
      imc = imfilter(imc, h);
    imc = imc .^2;
     imagesc(imc);
    
    pause;
% from: notes-rizal
    %%
    %% find local maxima by making sure a pixel is greater than left, right, up, down
    %% neighbors
    imcD = imc([2:end 1],:);                                
    imcU = imc([end 1:end-1],:);                            
    imcR = imc(:,[2:end 1]);                     
    imcL = imc(:,[end 1:end-1]);  
%     figure(2)
%         imagesc(imc>=max(imcL,imcR));
%         % these show excellent detection
%         title 'left right local maxima'
%     figure(3)
%         imagesc(imc>=max(imcU,imcD));
%         % these show excellent detection
%         title 'up down local maxima'
    figure(4)
        imagesc(imc>=max(.1,max(max(imcL,imcR),max(imcU,imcD))));       % arbitrary threshold here
        colormap gray
        title 'detected maxima'
    %% unfortunately, a bunch of spurious points show up
    %%
    
pause;

    newimage = colfilt(imc , [3,3], 'sliding', @centermax);
    figure(5)
        imagesc(newimage);
        colormap gray
        title 'local max'
 
pause;
close all;

% can change function and use to find local max/min?
% @centermax -- returns 1 if the center value is the greatest, zero if
% otherwise.
% output =  input_mtx(ceil(length/2))) == max(input_mtx);
% should have odd length of matrix.
function output = centermax ( input_mtx )
% 
% el = numel(input_mtx);
% middle = ceil(el/2);
% output =  (input_mtx(middle) >= max(input_mtx));
output =  (input_mtx(5) == max(input_mtx));

%%-------------------------------------------------------------------------

% %% testing speed of varous implementaitons of median filters
% 

% addpath /home/wrightc/program
% 
% filepath = '/research/windows/backups/afm/d/Rizal/03-06-23/mov0623a.043'
% rawim = showafm(filepath,1);

% pause;
% image = medfilt2 (rawim, [5,5]);
% disp('done')
% figure(1)
% imagesc (image)
% colormap gray
% truesize
% title 'median filtered image'
% 
% pause;
% 
% image2 = ordfilt2(rawim, 5, ones(5,5));
% disp('done')
% figure(2)
% imagesc (image2)
% colormap gray
% truesize
% title 'median filtered image 2'
% 
% pause;
% 
% image3 = colfilt(rawim,[5,5],'sliding',@median);
% disp('done')
% figure(3)
% imagesc (image3)
% colormap gray
% truesize
% title 'median filtered image 3'
% 
% pause;
% close all
% 
% %% results: medfilt2 is best as far as i can tell.

% x = rand([10,1])
% d = fdesign.lowpass('n,fc')
% hd = butter(d)
% filter(hd,x)

% [r,c] = size(rawim);
% [x,y] = meshgrid (r/(-2):r/2-1,c/(-2):c/2-1);
% z = sqrt(x.^2+y.^2);
% threshold = 2;
% c=(z< threshold);
% figure(3)
% imagesc(c)
% truesize
% % this part is fine.
% 
% 
% fimage = fftshift(fft2(rawim));
% fimage = fimage .* c;
% image = ifft2(fimage);
% 
% figure(2)
% imagesc(flipud(image))
% colormap gray;
% 
% pause;
% close all;
% 
% 
% % i cant figure out why there is a grid superimposed on the image.....
% % that would be  a high frequency