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Image source or Camera identification using MATLAB for Cannon and Nikkon DSLR Camera
02-07-2012, 10:42 PM
Post: #11
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RE: Image source or Camera identification using MATLAB for Cannon and Nikkon DSLR Camera
r u serious..?

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03-20-2012, 02:05 AM
Post: #12
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RE: Image source or Camera identification using MATLAB for Cannon and Nikkon DSLR Camera
hey.....pls explain me whats happening in these matlab code




function [output, output2, sigma, ncc2, ncc, shift] = f_dmap_est5(img0, th, SHOW, smooth, dustvector, SW, CorrW, sigma)


% GIVEN A CAMERA, GET THE DUST MAP

% Consider areas for dust selection. Big areas -> most prob points
% Use multiple psf > check maximum correlation and use appropiate ones.
% Write code for smooth region selection
% Add template more smooth regions for against collusion

% Get dust map



%clc;clear all; close all;

%path_1='C:\\Documents and Settings\\Emir\\My Documents\\Folder_share\\Projects\\_Sensor_Dust\\Test_1\\CM6\\';
%str=[path_1 '070.jpg'];
%img0=double(imread(str));

%SHOW=0;


% read dust map image

% find a proper sigma

% find dust points

% save dust positions into CM1_dust.mat file

% read image file


[r c d]=size(img0);
if d==3
%img=img0(:,:,2);
img=double(rgb2gray(uint8(img0)));
%filter
%img=wiener2(img);
else
img=img0;
end
if SHOW==1
s=sprintf('Image = %d x %d \n',r,c); disp(s);
end
%-------------------------------------------------
%
% Dust model
%
%-------------------------------------------------
%th=0.5; % threshold for correlation
width=20;
Amp=5;
%-------------------------------------------------
%W=2; % smoothness Width
W=SW;
tr=smooth; % threshold for smoothness

imap=0; % apply intensity map
ith=60; % intensity threshold >ith
%-------------------------------------------------

%Intensity map
if imap==1
imgb=double((img>ith));
else
imgb=double(img>-1);
end

% Smoothness map
% map=dustmap_func1(str,W);
% map=dustmap_func2(img,10);
map=smoothness_map3(img,W);
map2=map %save smap map map2

% Find autococrrelation output


%disp(sigma);
width=CorrW+3*sigma;
template2=f_dustmodel(width,sigma,150,Amp);
shift=width;

ncc = normxcorr2_mex(template2, img, 'same');
ncc2 = (ncc.*map2).*imgb;
%korr{sigma}=ncc2;
%MX(sigma)=max(max(ncc2));

% dust vectore gore hesap yap

%width=6*sigma;
width=10+3*sigma;
if SHOW==1
s=sprintf('std dev = %d',sigma);disp(s);
end
% Now we can find dust positions accordungto std dev value
%[r2, c2]=finddust_func2_line(ncc2, th);
[r2, c2]=finddust_func2(ncc2, th); % Eliminate line typed dusts

output=img.*0;
output2=img.*0;

if length(r2)==0
if SHOW==1
disp('No dust');
disp(' ');
end

return

end

%Show Outputs

if SHOW==1
figure; imagesc(imgb);
title('intensity');
hold on

figure; imagesc(ncc2);
hold on
plot(c2,r2,'ws');

figure; imagesc(map);
figure; image(ncc2.*((ncc2)>th).*map2);colormap(gray);
hold on
plot(c2,r2,'ws');
%plot(C,R,'ws');

figure;image(uint8(img0));
hold on
if d==1 colormap(gray); end
axis('image'); title('Detection results');
hold on
plot(c2,r2,'r.');
end

%Nikon Dust template
%load dust_n50
%load canon
%plot(C,R,'ms');
if SHOW==1
s=sprintf('Std deviation = %f',sigma); disp(s);
s=sprintf('Threshold = %f',th); disp(s);
s=sprintf('Gain = %f',Amp); disp(s);
s=sprintf('\n# of Dust = %d',length(r2)); disp(s);
maks=max(max(ncc2));
s=sprintf('Max Correlation %f',maks); disp(s);
end

temp_map=ncc2.*((ncc2)>(th));
output=temp_map;

if SHOW==1
figure; imagesc(temp_map);
title('Most Likely positions');hold on
end

R=r2;
C=c2;
L=length®;

for k=1:L
output2(R(k),C(k))=1;
end

if SHOW==1
figure; imagesc(output2); hold on; colormap(gray);
plot(C,R,'o')
end

a=cd;

return

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11-19-2014, 01:24 PM
Post: #13
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RE: Modification: Image source identification
(01-06-2012 05:23 PM)Rupam Wrote:  replace f_dmap_est5.m with this new f_dmap_est5.m file. First run Create_dust_map once by commenting the nicon image part, then after execution run canonimage part.

Same while testing. Run nikonimage first than cannon image. You can find the execution instruction in the readme file inside.

Code MATLAB :
 
function [output, output2, sigma, ncc2, ncc, shift] = f_dmap_est5(img0, th, SHOW, smooth, dustvector, SW, CorrW, sigma)
 
 
% GIVEN A CAMERA, GET THE DUST MAP
 
% Consider areas for dust selection. Big areas -> most prob points
% Use multiple psf > check maximum correlation and use appropiate ones.
% Write code for smooth region selection
% Add template more smooth regions for against collusion
 
% Get dust map
 
 
 
%clc;clear all; close all;
 
%path_1='C:\\Documents and Settings\\Emir\\My Documents\\Folder_share\\Projects\\_Sensor_Dust\\Test_1\\CM6\\';
%str=[path_1 '070.jpg'];
%img0=double(imread(str));
 
%SHOW=0;
 
 
% read dust map image
 
% find a proper sigma
 
% find dust points
 
% save dust positions into CM1_dust.mat file
 
% read image file
 
 
[r c d]={FNAMEL}.html">size(img0);
if d==3
%img=img0(:,:,2);
img={FNAMEL}.html">double(rgb2gray({FNAMEL}.html">uint8(img0)));
%filter
%img=wiener2(img);
else
img=img0;
end
if SHOW==1
s={FNAMEL}.html">sprintf('{FNAMEL}.html">Image = %d x %d \n',r,c); disp(s);
end
%-------------------------------------------------
%
% Dust model
%
%-------------------------------------------------
%th=0.5; % threshold for correlation
width=20;
Amp=5;
%-------------------------------------------------
%W=2; % smoothness Width
W=SW;
tr=smooth; % threshold for smoothness
 
imap=0; % apply intensity map
ith=60; % intensity threshold >ith
%-------------------------------------------------
 
%Intensity map
if imap==1
imgb={FNAMEL}.html">double((img>ith));
else
imgb={FNAMEL}.html">double(img>-1);
end
 
% Smoothness map
% map=dustmap_func1(str,W);
% map=dustmap_func2(img,10);
map=smoothness_map3(img,W);
map2=map<tr;
%save smap map map2
 
% Find autococrrelation output
 

%disp(sigma);
width=CorrW+3*sigma;
template2=f_dustmodel(width,sigma,150,Amp);
shift=width;

%ncc = normxcorr2_mex(template2, img, 'same');
ncc = normxcorr2(template2, img);
[x y]={FNAMEL}.html">size(imgb);
ncc=imresize(ncc,[x y]);
{FNAMEL}.html">save {FNAMEL}.html">trace ncc img map2 imgb
ncc2 = (ncc.*map2).*imgb;
%korr{sigma}=ncc2;
%MX(sigma)=max(max(ncc2));

% dust vectore gore hesap yap

%width=6*sigma;
width=10+3*sigma;
if SHOW==1
s={FNAMEL}.html">sprintf('{FNAMEL}.html">std dev = %d',sigma);disp(s);
end
% Now we can find dust positions accordungto std dev value
%[r2, c2]=finddust_func2_line(ncc2, th);
[r2, c2]=finddust_func2(ncc2, th); % Eliminate line typed dusts
 
output=img.*0;
output2=img.*0;
 
if {FNAMEL}.html">length(r2)==0
if SHOW==1
{FNAMEL}.html">disp('No dust');
{FNAMEL}.html">disp(' ');
end

return

end
 
%Show Outputs
 
if SHOW==1
{FNAMEL}.html">figure; imagesc(imgb);
{FNAMEL}.html">title('intensity');
{FNAMEL}.html">hold on

{FNAMEL}.html">figure; imagesc(ncc2);
{FNAMEL}.html">hold on
{FNAMEL}.html">plot(c2,r2,'ws');

{FNAMEL}.html">figure; imagesc(map);
{FNAMEL}.html">figure; {FNAMEL}.html">image(ncc2.*((ncc2)>th).*map2);colormap({FNAMEL}.html">gray);
{FNAMEL}.html">hold on
{FNAMEL}.html">plot(c2,r2,'ws');
%plot(C,R,'ws');

{FNAMEL}.html">figure;image({FNAMEL}.html">uint8(img0));
{FNAMEL}.html">hold on
if d==1 {FNAMEL}.html">colormap({FNAMEL}.html">gray); end
{FNAMEL}.html">axis('{FNAMEL}.html">image'); {FNAMEL}.html">title('Detection results');
{FNAMEL}.html">hold on
{FNAMEL}.html">plot(c2,r2,'r.');
end
 
%Nikon Dust template
%load dust_n50
%load canon
%plot(C,R,'ms');
if SHOW==1
s={FNAMEL}.html">sprintf('{FNAMEL}.html">Std deviation = %f',sigma); disp(s);
s={FNAMEL}.html">sprintf('Threshold = %f',th); disp(s);
s={FNAMEL}.html">sprintf('Gain = %f',Amp); disp(s);
s={FNAMEL}.html">sprintf('\n# of Dust = %d',length(r2)); disp(s);
maks={FNAMEL}.html">max({FNAMEL}.html">max(ncc2));
s={FNAMEL}.html">sprintf('{FNAMEL}.html">Max Correlation %f',maks); disp(s);
end
 
temp_map=ncc2.*((ncc2)>(th));
output=temp_map;
 
if SHOW==1
{FNAMEL}.html">figure; imagesc(temp_map);
{FNAMEL}.html">title('Most Likely positions');hold on
end

R=r2;
C=c2;
L=length®;
 
for k=1:L
output2(R(k),C(k))=1;
end
 
if SHOW==1
{FNAMEL}.html">figure; imagesc(output2); {FNAMEL}.html">hold on; {FNAMEL}.html">colormap({FNAMEL}.html">gray);
{FNAMEL}.html">plot(C,R,'o')
end
 
a={FNAMEL}.html">cd;
 
return
 
 
 
Hi hello..
i have try to run this coding but it gave me this error..
??? Undefined function or variable "l".

Error in ==> f_dustmap at 30
if l>3

Error in ==> RUN_CREATE_DUSTMAP at 52
f_dustmap(path_1);

since i am new in matlab could u help me to trouble shot this problem..
really appreciate ur help

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05-10-2018, 01:42 PM
Post: #14
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RE: Image source or Camera identification using MATLAB for Cannon and Nikkon DSLR Camera
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05-17-2018, 03:45 PM
Post: #15
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RE: Image source or Camera identification using MATLAB for Cannon and Nikkon DSLR Camera
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