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geraInfoQuantity.m
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function infoMat = geraInfoQuantity(N,delta,i_0,i_f)
% Lets see if it works to load all the vectors first
% Harris
% Hessian
% Laplacian
% Hessian-Laplace
% Harris-Laplace
% Sift
% Surf
% Kaze g1
% Kaze g2
% Kaze g3
infoQuantityP1 = zeros(10,1);
infoQuantityP2 = zeros(10,1);
infoQuantityP3 = zeros(10,1);
infoMat = zeros(10,20);
for i=i_0:i_f
calcturbcurve;
y1 = polyval(p,x);
turb = exp(y1/11);
load('vecphoto1bil');
im = imvec{i};
Vnorm = computeVnorm(im);
VnormCon = uint32(zeros(size(Vnorm,1),size(Vnorm,2)));
Vnorm =int32(Vnorm);
VnormUint = uint32(Vnorm + 128);
%for k=1:size(im,1)
% for j=1:size(im,2)
VnormCon=VnormUint(:,:,4).*(2^24)+VnormUint(:,:,3).*(2^16)+VnormUint(:,:,2).*(2^8) + VnormUint(:,:,1);
%VnormCon(k,j)
% end
% end
VnormConDouble = double(VnormCon);
imen = entropyfilt(VnormConDouble./max(max(VnormConDouble)));
figure; imshow(mat2gray(imen));
clear imvec;
%im = imread(sprintf('Images/Photo1/Cortadas/%d.jpg',i));
features= geraFeatures(im,'harris',N,delta); % the subsequent image features
infoQuantityP1(1) = countEntropy(features,imen);
features= geraFeatures(im,'hessian',N,delta); % the subsequent image features
infoQuantityP1(2) = countEntropy(features,imen);
features= geraFeatures(im,'laplacian',N,delta); % the subsequent image features
infoQuantityP1(3) = countEntropy(features,imen);
features= geraFeatures(im,'hessianlap',N,delta); % the subsequent image features
infoQuantityP1(4) = countEntropy(features,imen);
features= geraFeatures(im,'harrislap',N,delta); % the subsequent image features
infoQuantityP1(5) = countEntropy(features,imen);
features= geraFeatures(im,'sift',N,delta); % the subsequent image features
infoQuantityP1(6) = countEntropy(features,imen);
features= geraFeatures(im,'surf',N,delta); % the subsequent image features
infoQuantityP1(7) = countEntropy(features,imen);
features= geraFeatures(im,'kazeg1',N,delta,turb(i)); % the subsequent image features
infoQuantityP1(8) = countEntropy(features,imen);
features= geraFeatures(im,'kazeg2',N,delta,turb(i)); % the subsequent image features
infoQuantityP1(9) = countEntropy(features,imen);
features= geraFeatures(im,'kazeg3',N,delta,turb(i)); % the subsequent image features
infoQuantityP1(10) = countEntropy(features,imen);
load('vecphoto2bil');
im = imvec{i};
Vnorm = computeVnorm(im);
VnormCon = uint32(zeros(size(Vnorm,1),size(Vnorm,2)));
Vnorm =int32(Vnorm);
VnormUint = uint32(Vnorm + 128);
%for k=1:size(im,1)
% for j=1:size(im,2)
VnormCon=VnormUint(:,:,4).*(2^24)+VnormUint(:,:,3).*(2^16)+VnormUint(:,:,2).*(2^8) + VnormUint(:,:,1);
%VnormCon(k,j)
% end
% end
VnormConDouble = double(VnormCon);
imen = entropyfilt(VnormConDouble./max(max(VnormConDouble)));
figure; imshow(mat2gray(imen));
clear imvec;
features= geraFeatures(im,'harris',N,delta); % the subsequent image features
infoQuantityP2(1) = countEntropy(features,imen);
features= geraFeatures(im,'hessian',N,delta); % the subsequent image features
infoQuantityP2(2) = countEntropy(features,imen);
features= geraFeatures(im,'laplacian',N,delta); % the subsequent image features
infoQuantityP2(3) = countEntropy(features,imen);
features= geraFeatures(im,'hessianlap',N,delta); % the subsequent image features
infoQuantityP2(4) = countEntropy(features,imen);
features= geraFeatures(im,'harrislap',N,delta); % the subsequent image features
infoQuantityP2(5) = countEntropy(features,imen);
features= geraFeatures(im,'sift',N,delta); % the subsequent image features
infoQuantityP2(6) = countEntropy(features,imen);
infoQuantityP2(7) = countEntropy(features,imen);
features= geraFeatures(im,'kazeg1',N,delta,turb(i)); % the subsequent image features
infoQuantityP2(8) = countEntropy(features,imen);
features= geraFeatures(im,'kazeg2',N,delta,turb(i)); % the subsequent image features
infoQuantityP2(9) = countEntropy(features,imen);
features= geraFeatures(im,'kazeg3',N,delta,turb(i)); % the subsequent image features
infoQuantityP2(10) = countEntropy(features,imen);
load('vecphoto3bil');
im = imvec{i};
Vnorm = computeVnorm(im);
VnormCon = uint32(zeros(size(Vnorm,1),size(Vnorm,2)));
Vnorm =int32(Vnorm);
VnormUint = uint32(Vnorm + 128);
%for k=1:size(im,1)
% for j=1:size(im,2)
VnormCon=VnormUint(:,:,4).*(2^24)+VnormUint(:,:,3).*(2^16)+VnormUint(:,:,2).*(2^8) + VnormUint(:,:,1);
%VnormCon(k,j)
% end
% end
VnormConDouble = double(VnormCon);
imen = entropyfilt(VnormConDouble./max(max(VnormConDouble)));
figure; imshow(mat2gray(imen));
features= geraFeatures(im,'harris',N,delta); % the subsequent image features
infoQuantityP3(1) = countEntropy(features,imen);
features= geraFeatures(im,'hessian',N,delta); % the subsequent image features
infoQuantityP3(2) = countEntropy(features,imen);
features= geraFeatures(im,'laplacian',N,delta); % the subsequent image features
infoQuantityP3(3) = countEntropy(features,imen);
features= geraFeatures(im,'hessianlap',N,delta); % the subsequent image features
infoQuantityP3(4) = countEntropy(features,imen);
features= geraFeatures(im,'harrislap',N,delta); % the subsequent image features
infoQuantityP3(5) = countEntropy(features,imen);
features= geraFeatures(im,'sift',N,delta); % the subsequent image features
infoQuantityP3(6) = countEntropy(features,imen);
features= geraFeatures(im,'surf',N,delta); % the subsequent image features
infoQuantityP3(7) = countEntropy(features,imen);
features= geraFeatures(im,'kazeg1',N,delta,turb(i)); % the subsequent image features
infoQuantityP3(8) = countEntropy(features,imen);
features= geraFeatures(im,'kazeg2',N,delta,turb(i)); % the subsequent image features
infoQuantityP3(9) = countEntropy(features,imen);
features= geraFeatures(im,'kazeg3',N,delta,turb(i)); % the subsequent image features
infoQuantityP3(10) = countEntropy(features,imen);
infoMat(:,i) = (infoQuantityP1 + infoQuantityP2 + infoQuantityP3)/3;
infoQuantity = (infoQuantityP1 + infoQuantityP2 + infoQuantityP3)/3;
save(sprintf('infoQuantity%d',i),'infoQuantity');
% validFeatures = intersect(features,region,'rows');
% repVec(i) = countFeatures(validFeatures0,validFeatures,epsilon,size(rgb2gray(imvec{i})));
end
end