CoimbatoreArchive for March 2013
Resizing multiple images using MATLAB
Get all the image files that are need to be re sized in matlab current directory and run this code
dirData = dir('*.jpg');
fileNames = {dirData.name};
for iFile = 1:numel(fileNames)
newName = sprintf('%1d.jpg',iFile);
movefile(fileNames{iFile},newName);
end
After renaming the files (by running above program ) run this code
q=1;
p=1;
for i=1:10
oq=imread(strcat(num2str(q),'.jpg'));
odprz=imresize(oq,[150 150]); %# specify your size here
imwrite(odprz,strcat(num2str(p),'.png'));
q=q+1;
p=p+1;
end
collect only .png format files.That files are re-sized files.
Automated image comparison using MATLAB
Automated image
comparison using MATLAB
MAIN MOTIVE
This project presents the new machine learning process on images. In competitive intelligence information comparison the text analytic decides the product match type to enhance that automation process image comparison of the products is automated and presented here. Here the corner detection and comparison on images is used to compare the images.
USE
CASES
Mainly developed
for competitive intelligence (i.e.) to compare online marketing client and
competitor images.
Considerations
To compare images in large amount if we download images and compare the
space needed will be more to overcome that scenario the image URLs are used
from both client and competitor side. Images can be of any format in the case
of comparison of images from URLs.
If input is image URLs mean that should be in excel file and output
status of images equal or not equal will be stored in the specified column of
excel sheet.
If images are stored in drives mean other process like renaming of images
will be followed to get the images into the code execution. (Pre-processing is needed)
PROCESS
FLOW
Example images processed
COMPARISON DURING EXECUTION
OUTPUT
output will be stored in excel sheet.
status 1: equal
status 2: not equal
*********************************************************************************
q=2; %% source image name ('2.jpg')
w=2; %% another image ('2.png')
sd=1;
for i=1:1
hIdtc = video.ImageDataTypeConverter();
hCsc = video.ColorSpaceConverter('Conversion','RGB to intensity');
oq=imread(strcat(num2str(q),'.jpg'));
odprz=imresize(oq,[150 150]);
imwrite(odprz,'oq1.jpg');
leftI3chan = step(hIdtc,imread('oq1.jpg'));
leftI = step(hCsc,leftI3chan);
disp(leftI)
sw=imread(strcat(num2str(w),'.png'));
stprz=imresize(sw,[150 150]);
imwrite(stprz,'sw1.png');
rightI3chan = step(hIdtc,imread('sw1.png'));
rightI = step(hCsc,rightI3chan);
harris = video.CornerDetector( 'MaximumCornerCount', 1000, ...
'CornerThreshold', 1e-4, ...
'NeighborhoodSize', [9 9] );
pointsLeft = flipud(step(harris, leftI))+1;
pointsRight = flipud(step(harris, rightI))+1;
% Trim point lists to minimum size.
pointsLeft = pointsLeft(:, 1:find(all(pointsLeft==1,1),1) - 1);
pointsRight = pointsRight(:, 1:find(all(pointsRight==1,1),1) - 1);
halfBlockSize = 4; % Block half-size.
blockSize = 2*halfBlockSize+1; % Full block size.
[r,c] = size(leftI);
matchThreshold = 0.7;
% Extract features for pointsRight
features = zeros(blockSize^2,size(pointsRight,2), 'single');
for i=1:size(pointsRight,2)
T = zeros(blockSize, 'single');
minr = max(1,pointsRight(2,i)-halfBlockSize);
maxr = min(r,pointsRight(2,i)+halfBlockSize);
minc = max(1,pointsRight(1,i)-halfBlockSize);
maxc = min(c,pointsRight(1,i)+halfBlockSize);
T( halfBlockSize+1-(pointsRight(2,i)-minr):halfBlockSize+1+(maxr-pointsRight(2,i)), ...
halfBlockSize+1-(pointsRight(1,i)-minc):halfBlockSize+1+(maxc-pointsRight(1,i)) ) = ...
rightI( minr:maxr, minc:maxc );
features(:,i) = T(:);
end
% Loop over pointsLeft, looking for best matches in pointsRight via features.
ix1 = zeros(1,size(pointsLeft,2), 'single');
d = zeros(size(ix1), 'single');
for i=1:size(pointsLeft,2)
T = zeros(blockSize, 'single');
minr = max(1,pointsLeft(2,i)-halfBlockSize);
maxr = min(r,pointsLeft(2,i)+halfBlockSize);
minc = max(1,pointsLeft(1,i)-halfBlockSize);
maxc = min(c,pointsLeft(1,i)+halfBlockSize);
T( halfBlockSize+1-(pointsLeft(2,i)-minr):halfBlockSize+1+(maxr-pointsLeft(2,i)), ...
halfBlockSize+1-(pointsLeft(1,i)-minc):halfBlockSize+1+(maxc-pointsLeft(1,i)) ) = ...
leftI( minr:maxr, minc:maxc );
% Find matching point in pointsRight with features features.
[v,ix] = min( sum(bsxfun(@minus,features,T(:)).^2,1) );
if v < matchThreshold
ix1(i) = ix;
end
end
% Extract indices of matched points on each side.
ix2 = nonzeros(ix1);
ix1 = find(ix1);
% Create subselected, homogenized point lists.
pointsLeftH = [double(pointsLeft(:,ix1)); ones(1,length(ix1), 'single')];
pointsRightH = [double(pointsRight(:,ix2)); ones(1,length(ix2), 'single')];
figure(1), clf;
imshow(cat(3,rightI,leftI,leftI)), hold on;
plot(pointsLeftH(1,:),pointsLeftH(2,:),'x','Color',[0 0 1],'MarkerSize',8);
plot(pointsRightH(1,:),pointsRightH(2,:),'x','Color',[1 1 0],'MarkerSize',8);
plot([pointsLeftH(1,:);pointsRightH(1,:)],...
[pointsLeftH(2,:);pointsRightH(2,:)],'-','Color',[0 1 0]);
set(gca,'XTick',[],'YTick',[]);
title('Initial corresponded points between the two images');
d1=mean(pointsRightH);
d2=mean(pointsRightH);
t1=mode(d2);
t2=mode(d1);
if t1 == t2 %% modify here put one extra loop if t1<5 means result will be not a match(for better comparison)
result={'exact'};
disp(result)
else
result={'not found'};
disp(result)
end
xlswrite('myfile.xls',result,'sheet1',strcat('A',num2str(sd))) %% excel file to store result
q=q+1;
w=w+1;
sd=sd+1;
end
*********************************************************************************
q=2; %% source image name ('2.jpg')
w=2; %% another image ('2.png')
sd=1;
for i=1:1
hIdtc = video.ImageDataTypeConverter();
hCsc = video.ColorSpaceConverter('Conversion','RGB to intensity');
oq=imread(strcat(num2str(q),'.jpg'));
odprz=imresize(oq,[150 150]);
imwrite(odprz,'oq1.jpg');
leftI3chan = step(hIdtc,imread('oq1.jpg'));
leftI = step(hCsc,leftI3chan);
disp(leftI)
sw=imread(strcat(num2str(w),'.png'));
stprz=imresize(sw,[150 150]);
imwrite(stprz,'sw1.png');
rightI3chan = step(hIdtc,imread('sw1.png'));
rightI = step(hCsc,rightI3chan);
harris = video.CornerDetector( 'MaximumCornerCount', 1000, ...
'CornerThreshold', 1e-4, ...
'NeighborhoodSize', [9 9] );
pointsLeft = flipud(step(harris, leftI))+1;
pointsRight = flipud(step(harris, rightI))+1;
% Trim point lists to minimum size.
pointsLeft = pointsLeft(:, 1:find(all(pointsLeft==1,1),1) - 1);
pointsRight = pointsRight(:, 1:find(all(pointsRight==1,1),1) - 1);
halfBlockSize = 4; % Block half-size.
blockSize = 2*halfBlockSize+1; % Full block size.
[r,c] = size(leftI);
matchThreshold = 0.7;
% Extract features for pointsRight
features = zeros(blockSize^2,size(pointsRight,2), 'single');
for i=1:size(pointsRight,2)
T = zeros(blockSize, 'single');
minr = max(1,pointsRight(2,i)-halfBlockSize);
maxr = min(r,pointsRight(2,i)+halfBlockSize);
minc = max(1,pointsRight(1,i)-halfBlockSize);
maxc = min(c,pointsRight(1,i)+halfBlockSize);
T( halfBlockSize+1-(pointsRight(2,i)-minr):halfBlockSize+1+(maxr-pointsRight(2,i)), ...
halfBlockSize+1-(pointsRight(1,i)-minc):halfBlockSize+1+(maxc-pointsRight(1,i)) ) = ...
rightI( minr:maxr, minc:maxc );
features(:,i) = T(:);
end
% Loop over pointsLeft, looking for best matches in pointsRight via features.
ix1 = zeros(1,size(pointsLeft,2), 'single');
d = zeros(size(ix1), 'single');
for i=1:size(pointsLeft,2)
T = zeros(blockSize, 'single');
minr = max(1,pointsLeft(2,i)-halfBlockSize);
maxr = min(r,pointsLeft(2,i)+halfBlockSize);
minc = max(1,pointsLeft(1,i)-halfBlockSize);
maxc = min(c,pointsLeft(1,i)+halfBlockSize);
T( halfBlockSize+1-(pointsLeft(2,i)-minr):halfBlockSize+1+(maxr-pointsLeft(2,i)), ...
halfBlockSize+1-(pointsLeft(1,i)-minc):halfBlockSize+1+(maxc-pointsLeft(1,i)) ) = ...
leftI( minr:maxr, minc:maxc );
% Find matching point in pointsRight with features features.
[v,ix] = min( sum(bsxfun(@minus,features,T(:)).^2,1) );
if v < matchThreshold
ix1(i) = ix;
end
end
% Extract indices of matched points on each side.
ix2 = nonzeros(ix1);
ix1 = find(ix1);
% Create subselected, homogenized point lists.
pointsLeftH = [double(pointsLeft(:,ix1)); ones(1,length(ix1), 'single')];
pointsRightH = [double(pointsRight(:,ix2)); ones(1,length(ix2), 'single')];
figure(1), clf;
imshow(cat(3,rightI,leftI,leftI)), hold on;
plot(pointsLeftH(1,:),pointsLeftH(2,:),'x','Color',[0 0 1],'MarkerSize',8);
plot(pointsRightH(1,:),pointsRightH(2,:),'x','Color',[1 1 0],'MarkerSize',8);
plot([pointsLeftH(1,:);pointsRightH(1,:)],...
[pointsLeftH(2,:);pointsRightH(2,:)],'-','Color',[0 1 0]);
set(gca,'XTick',[],'YTick',[]);
title('Initial corresponded points between the two images');
d1=mean(pointsRightH);
d2=mean(pointsRightH);
t1=mode(d2);
t2=mode(d1);
if t1 == t2 %% modify here put one extra loop if t1<5 means result will be not a match(for better comparison)
result={'exact'};
disp(result)
else
result={'not found'};
disp(result)
end
xlswrite('myfile.xls',result,'sheet1',strcat('A',num2str(sd))) %% excel file to store result
q=q+1;
w=w+1;
sd=sd+1;
end
********************************** THANK YOU***********************************
Your valid suggestions are anticipated to enhance the program.
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