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Copy pathgreedy_nbr.m
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greedy_nbr.m
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%Clear all variables from memory
clear
%local variables: edit as needed
numIts = 4;
h=Daub(4); %coefficients for wavelet transform
%Array holding pic names
picNames={'A1.png','A2.png','A3.png','B1.png','B2.png','B3.png','G1.png','G2.png','G3.png','C1.png','C2.png','C3.png','M1.png','M2.png','M3.png','T1.png','T2.png','T3.png'};
[rows,numPics]=size(picNames);
for picNum = 1 : numPics
disp(picNames{picNum})
%Read in an image
A=ImageRead(picNames{picNum});
%Perform numIts iterations of wavelet transform
WA = WT2D(A, h, numIts);
disp('Completed wavelet transform')
%Get dimensions of transformed pic (should be same as original)
[m,n]=size(WA);
%Get subbands. Store in cell arrays.containers
for k = 1 : numIts
V{k} = WA([1:m/2^k], [n/2^k + 1:n/2^(k-1)]);
D{k} = WA([m/2^k + 1:m/2^(k-1)], [n/2^k + 1:n/2^(k-1)]);
H{k} = WA([m/2^k + 1:m/2^(k-1)], [1:n/2^k]);
end
%ImagePlot(H{3})
%Get q for V, D and H bands. Send correct subbands, correct order!
for k = 1 : numIts - 1
qV{k} = vertPredictorNhbrs(V{k}, V{k+1}, D{k}, D{k+1})
qD{k} = diagPredictorNhbrs(D{k}, D{k+1}, H{k}, V{k})
qH{k} = horzPredictorNhbrs(H{k}, H{k+1}, D{k}, D{k+1})
end
disp('q vectors calculated')
%size(qD{3})
%Trim edges from subbands and flatten them
for k = 1 : numIts - 1
[m,n] = size(V{k}); %same as D{k}, H{k}
V{k} = V{k}([3:m-2], [3:n-2]); %remove edges
V{k} = V{k}'; %take transpose so entries will line up w/ q
V{k} = V{k}(:); %and flatten
D{k} = D{k}([3:m-2], [3:n-2]);
D{k} = D{k}';
D{k} = D{k}(:);
H{k} = H{k}([3:m-2], [3:n-2]);
H{k} = H{k}';
H{k} = H{k}(:);
end
disp('Subbands trimmed and flattened')
%Calculate weight vectors for each band
for k = 1 : numIts - 1
wV{k} = (inv(qV{k}' * qV{k}))*(qV{k}' * V{k});
wD{k} = (inv(qD{k}' * qD{k}))*(qD{k}' * D{k});
wH{k} = (inv(qH{k}' * qH{k}))*(qH{k}' * H{k});
end
disp('Weight vectors calculated')
%wV{3}
%wH{3}
%Find error between linear approx and actual pixel value
for k = 1 : numIts - 1
%Construct lin. approx.
LV{k} = qV{k}*wV{k};
LD{k} = qD{k}*wD{k};
LH{k} = qH{k}*wH{k};
%calculate errors
errV{k} = LV{k} - V{k};
errD{k} = LD{k} - D{k};
errH{k} = LH{k} - H{k};
end
%Build signature vector
tempSigVec = [];
for k = 1 : numIts - 1
tempSigVec = [tempSigVec mean(wV{k})]; %appends to sigVec
tempSigVec = [tempSigVec var(wV{k})];
tempSigVec = [tempSigVec skewness(wV{k})];
tempSigVec = [tempSigVec kurtosis(wV{k})];
tempSigVec = [tempSigVec mean(errV{k})];
tempSigVec = [tempSigVec var(errV{k})];
tempSigVec = [tempSigVec skewness(errV{k})];
tempSigVec = [tempSigVec kurtosis(errV{k})];
tempSigVec = [tempSigVec mean(wD{k})];
tempSigVec = [tempSigVec var(wD{k})];
tempSigVec = [tempSigVec skewness(wD{k})];
tempSigVec = [tempSigVec kurtosis(wD{k})];
tempSigVec = [tempSigVec mean(errD{k})];
tempSigVec = [tempSigVec var(errD{k})];
tempSigVec = [tempSigVec skewness(errD{k})];
tempSigVec = [tempSigVec kurtosis(errD{k})];
tempSigVec = [tempSigVec mean(wH{k})];
tempSigVec = [tempSigVec var(wH{k})];
tempSigVec = [tempSigVec skewness(wH{k})];
tempSigVec = [tempSigVec kurtosis(wH{k})];
tempSigVec = [tempSigVec mean(errH{k})];
tempSigVec = [tempSigVec var(errH{k})];
tempSigVec = [tempSigVec skewness(errH{k})];
tempSigVec = [tempSigVec kurtosis(errH{k})];
end
sigVec{picNum} = tempSigVec;
end
%Each sigVec lives in R^72. Get stats for 1st, 2nd,... entry for each sigVec
numCoords = 3*(numIts-1)*8 %3 bands, numIts-1 levels, 8 stats each
for i = 1 : numCoords
for j = 1 : numPics
z(j) = sigVec{j}(i); %holds ith coord for each sigVec
end
coordMean(i) = mean(z);
coordSTD(i) = std(z);
end
%Standardize each coord of the sigVec's to have mean = 0, std = 1
for i = 1 : numCoords
for j = 1 : numPics
sigVec{j}(i) = (sigVec{j}(i) - coordMean(i)) / coordSTD(i); %holds ith coord for each sigVec
end
end
%Test for mean = 0, STD = 1
% for i = 1 : numCoords
% for j = 1 : numPics
% z(j) = sigVec{j}(i); %holds ith coord for each sigVec
% end
% coordMean(i) = mean(z);
% coordSTD(i) = std(z);
% end
% coordMean
% coordSTD
%Create mutual-distance matrix
for i = 1 : numPics
for j = 1 : numPics
mutualDist(i,j)= norm(sigVec{i}-sigVec{j});
end
end
mutualDist
%Now process out of sample pics
outOfSampPics={'A4.png','B4.png','G4.png','C4.png','M4.png','T4.png'};
[rows,numOutOfSampPics]=size(outOfSampPics);
for picNum = 1 : numOutOfSampPics
%Read in an image
A=ImageRead(outOfSampPics{picNum});
%Perform numIts iterations of wavelet transform
WA = WT2D(A, h, numIts);
disp('Completed wavelet transform')
%Get dimensions of transformed pic (should be same as original)
[m,n]=size(WA);
%Get subbands. Store in cell arrays.containers
for k = 1 : numIts
V{k} = WA([1:m/2^k], [n/2^k + 1:n/2^(k-1)]);
D{k} = WA([m/2^k + 1:m/2^(k-1)], [n/2^k + 1:n/2^(k-1)]);
H{k} = WA([m/2^k + 1:m/2^(k-1)], [1:n/2^k]);
end
%ImagePlot(H{3})
%Get q for V, D and H bands. Send correct subbands, correct order!
for k = 1 : numIts - 1
qV{k} = vertPredictorNhbrs(V{k}, V{k+1}, D{k}, D{k+1});
qD{k} = diagPredictorNhbrs(D{k}, D{k+1}, H{k}, V{k});
qH{k} = horzPredictorNhbrs(H{k}, H{k+1}, D{k}, D{k+1});
end
disp('q vectors calculated')
%size(qD{3})
%Trim edges from subbands and flatten them
for k = 1 : numIts - 1
[m,n] = size(V{k}); %same as D{k}, H{k}
V{k} = V{k}([3:m-2], [3:n-2]); %remove edges
V{k} = V{k}'; %take transpose so entries will line up w/ q
V{k} = V{k}(:); %and flatten
D{k} = D{k}([3:m-2], [3:n-2]);
D{k} = D{k}';
D{k} = D{k}(:);
H{k} = H{k}([3:m-2], [3:n-2]);
H{k} = H{k}';
H{k} = H{k}(:);
end
disp('Subbands trimmed and flattened')
%Calculate weight vectors for each band
for k = 1 : numIts - 1
wV{k} = (inv(qV{k}' * qV{k}))*(qV{k}' * V{k});
wD{k} = (inv(qD{k}' * qD{k}))*(qD{k}' * D{k});
wH{k} = (inv(qH{k}' * qH{k}))*(qH{k}' * H{k});
end
disp('Weight vectors calculated')
%wV{3}
%wH{3}
%Find error between linear approx and actual pixel value
for k = 1 : numIts - 1
%Construct lin. approx.
LV{k} = qV{k}*wV{k};
LD{k} = qD{k}*wD{k};
LH{k} = qH{k}*wH{k};
%calculate errors
errV{k} = LV{k} - V{k};
errD{k} = LD{k} - D{k};
errH{k} = LH{k} - H{k};
end
%Build signature vector
tempSigVec = [];
for k = 1 : numIts - 1
tempSigVec = [tempSigVec mean(wV{k})]; %appends to sigVec
tempSigVec = [tempSigVec var(wV{k})];
tempSigVec = [tempSigVec skewness(wV{k})];
tempSigVec = [tempSigVec kurtosis(wV{k})];
tempSigVec = [tempSigVec mean(errV{k})];
tempSigVec = [tempSigVec var(errV{k})];
tempSigVec = [tempSigVec skewness(errV{k})];
tempSigVec = [tempSigVec kurtosis(errV{k})];
tempSigVec = [tempSigVec mean(wD{k})];
tempSigVec = [tempSigVec var(wD{k})];
tempSigVec = [tempSigVec skewness(wD{k})];
tempSigVec = [tempSigVec kurtosis(wD{k})];
tempSigVec = [tempSigVec mean(errD{k})];
tempSigVec = [tempSigVec var(errD{k})];
tempSigVec = [tempSigVec skewness(errD{k})];
tempSigVec = [tempSigVec kurtosis(errD{k})];
tempSigVec = [tempSigVec mean(wH{k})];
tempSigVec = [tempSigVec var(wH{k})];
tempSigVec = [tempSigVec skewness(wH{k})];
tempSigVec = [tempSigVec kurtosis(wH{k})];
tempSigVec = [tempSigVec mean(errH{k})];
tempSigVec = [tempSigVec var(errH{k})];
tempSigVec = [tempSigVec skewness(errH{k})];
tempSigVec = [tempSigVec kurtosis(errH{k})];
end
outOfSampSigVec{picNum} = tempSigVec;
end
%Standardize each coord of the sigVec's to have mean = 0, std = 1
for i = 1 : numCoords
for j = 1 : numOutOfSampPics
outOfSampSigVec{j}(i) = (outOfSampSigVec{j}(i) - coordMean(i)) / coordSTD(i); %holds ith coord for each sigVec
end
end
%Find distances to in-sample pics
mutualDist = [];
for i = 1 : numPics
for j = 1 : numOutOfSampPics
mutualDist(i,j)= norm(sigVec{i}-outOfSampSigVec{j});
end
end
mutualDist