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procesar_salida.m
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procesar_salida.m
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addpath('metricas');
out='resumen2/';
pathDB='db/';
mkdir(out);
outfile=[out 'salida.xlsx'];
row = {'Imagen','cuartil3','max','min','quartil1','C','SSIM','F','CLAHE','HE','M-FIROZ'};
xlswrite(outfile,row,1,'A1:K1');
idx=2;
in='out/';
srcFiles = dir(in);
for i = 1 : length(srcFiles)
[in srcFiles(i).name]
if isdir([in,srcFiles(i).name]) & ~(strcmp(srcFiles(i).name,'.') | strcmp(srcFiles(i).name,'..'))
% Leer imagen original, mejorar y evaluar
source = strcat(pathDB,srcFiles(i).name);
S = imread(source);
if(ndims(S)==3)
S=rgb2gray(S);
end
disp(srcFiles(i).name);
CLAHE = adapthisteq(S);
HE = histeq(S);
FIROZ=firoz(S);
file=[in,srcFiles(i).name, '/bests.csv'];
% leer csv
% extraer datos de contaste y ssim
T=readtable(file,'Delimiter',';');
if size(T,2)==9
% Datos de ssim y contraste
T1=str2double(strrep(table2array(T(:, 8:9)),',','.' ) );
% datos de 1-fitness
T2=1-str2double(strrep(table2array(T(:,2)) ,',','.'));
else
aux=strrep(table2array(T(:,7)),',','.' );
r=double(zeros(numel(aux),3));
for idxr = 1:numel(aux)
gg=aux(idxr);
r(idxr,:)=str2double(strsplit(gg{1},' '));
end
T1=r(:,2:3);
T2=1-table2array(T(:,2));
end
% T=table2array(T);
% T
% T2
% T1
f=mean(T2);
mx=max(T2);
idxMAX = find(T2 == mx,1);
c=T1(idxMAX,1);
ssim=T1(idxMAX,2);
cuartil=quantile(T2,3);
cuartil3=cuartil(3);
maxi=max(T2);
mini=min(T2);
quartil1=cuartil(1);
row = {srcFiles(i).name...
,cuartil3 ,maxi ,mini ,quartil1 ,c ,ssim ,f...
,funcion_objetivo_real(S,CLAHE)...
,funcion_objetivo_real(S,HE)...
,funcion_objetivo_real(S,FIROZ)}
xlRange = strcat('A',int2str(idx),':K',int2str(idx));
xlRange
xlswrite(outfile,row,xlRange);
idx=idx+1
% almacenar en un archivo resumen
end
end