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Processcropped.m
function [ x1,y1 ] = processCropped( category,fileName,saveName)% automate the process of feature extraction
% Detailed explanation goes heredir = strcat(category,'/',fileName);
I = getnewvector(dir,0, 0, 1601, 1601);[x1,y1,I] = getcenter(I);dir2 = strcat(category,'/',saveName); % save directoryimwrite(I,dir2,'bmp');end
Processcategory.m
function processCategory( category, numOfPics, saveAbrev )
% process the entire category% Detailed explanation goes here
center = zeros(numOfPics,2);for n = 1:numOfPics,
fileName = strcat(category,num2str(n),'.jpg');saveName = strcat(saveAbrev,num2str(n),'.bmp');
center(n,:) = processCropped( category,fileName,saveName);end
csvwrite(strcat(category,'/','centers.dat'),center);end
Polarize.m
function [theta, r] = polarize(binaryMatrix, center)%POLARIZE Summary of this function goes here
% Center: 1x2[m,n] = size(binaryMatrix);theta = zeros(500,1);
r = zeros(500,1);numOfDots = 0;
for i = 1:m, % for each rowfor j = 1:n, % for each column
if (binaryMatrix(i,j) == 1),numOfDots = numOfDots + 1;
dot = [i,j]- center; % find the relative coordinate with centroid
[theta(numOfDots), r(numOfDots)]= cart2pol(dot(1),dot(2)); % convert into polarend
endend
end
Divideintobins.m
Testdriver.m
clear all
close alldir =char('down','up','okay','metal','point','stop','vulcan');
right=0;wrong=0;
for n=1:7,traning1=csvread(strcat(dir(n,:),'/','features.dat'));[x y]=size(traning1);
for k=1:x,training=traning1(k,:);type=classify(training,2,7,8);type1=mode(type);if type1==nright=right+1;else
wrong=wrong+1;end
endendsize(type)
rightwrong
% I1 = getnewvector('metal/metal3.JPG',800, 700, 1400, 800);% I2 = getnewvector('metal/metal1.JPG',800, 700, 1400, 800);
% I3 = getnewvector('metal/metal2.JPG',800, 700, 1400, 800);% I4=getnewvector('down1.JPG',800, 700, 1400, 800);
% I5=getnewvector('down2.JPG',800, 700, 1400, 800);% I6=getnewvector('down3.JPG',800, 700, 1400, 800);
% % need to rescale all images here%
% [x, y,I1]=getcenter(I1);
% [x1, y1,I2]=getcenter(I2);
% [x2, y2,I3]=getcenter(I3);
% [x3, y3,I4]=getcenter(I4);
% [x4,y4,I5]=getcenter(I5);
% [x5,y5,I6]=getcenter(I6);
% [theta1, r1]= polarize(I1, [x,y]);% [theta2, r2] = polarize(I2, [x1,y1]);
% [theta3, r3]= polarize(I3, [x2,y2]);% [theta4, r4] = polarize(I4, [x3,y3]);
% [theta5, r5]= polarize(I5, [x4,y4]);% [theta6, r6] = polarize(I6, [x5,y5]);
% interval=pi/18;% [ counter1, dividedData1 ] = divideIntoBins(theta1, r1, interval);% [ counter2, dividedData2 ] = divideIntoBins(theta2, r2, interval);% [ counter3, dividedData3 ] = divideIntoBins(theta3, r3, interval);% [ counter4, dividedData4 ] = divideIntoBins(theta4, r4, interval);% [ counter5, dividedData5 ] = divideIntoBins(theta5, r5, interval);% [ counter6, dividedData6 ] = divideIntoBins(theta6, r6, interval);% counter2 = counter2/(length(theta2));
% counter3 = counter3/(length(theta3));% counter1 = counter1/(length(theta1));
% counter4 = counter4/(length(theta4));% counter5 = counter5/(length(theta5));
% % figure;% % subplot(3,1,1);
% % plot(counter1);% % subplot(3,1,2);
% % plot(counter2);% % subplot(3,1,3);
% % plot(counter3);% disp(norm(counter1-counter2,2));
% disp(norm(counter1-counter3,2));% disp(norm(counter1-counter4,2));
% disp(norm(counter1-counter5,2));%
% %n1=(norm(counter2,1)+norm(counter3,1))/2% %n2=(norm(counter4,1)+norm(counter5,1)+norm(counter6,1))/3
%%
%% %
% % % comparing within the same category% % v1 = getnewvector('vulcan1.JPG');
% % v2 = getnewvector('vulcan2.JPG');% % v3 = getnewvector('vulcan3.JPG');
% % d = zeros(3,1);% % inner = zeros(3,1);
% % [d(1),inner(1)]= getdistance(v1,v2);
% % [d(2),inner(2)]= getdistance(v2,v3);
% % [d(3),inner(3)]= getdistance(v1,v3);
% % %
Classify.m(part1)
Classify.m(part2)
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