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Demo_Analysis_P_DCS.m
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Demo_Analysis_P_DCS.m
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% @article{Canh2018_MSCSNet,
% title={Multi-Scale Deep Compressive Sensing Network},
% author={Thuong, Nguyen Canh and Byeungwoo, Jeon},
% conference={IEEE International Conference on Visual Comunication and Image Processing},
% year={2018}
% }
% by Thuong Nguyen Canh (9/2018)
% ngcthuong@gmail.com
% https://github.com/AtenaKid
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% You need to install Matconvnet in order to run this code
warning('off','all')
addpath('D:\matconvnet-1.0-beta25\matlab\mex'); % Link to your Matconvnet Mex file
% addpath('matconvnet-1.0-beta25\matlab');
addpath('.\utilities');
folderTest = 'Classic13_512';
% networkTest = {'MS-CSNet1' 'MS-CSNet2' 'MS-CSNet3'}; % 10
networkTest = { 'P-DCS1' 'P-DCS2' 'P-DCS3'};
showResult = 0;
writeRecon = 1;
featureSize = 64;
blkSize = 32;
isLearnMtx = [1, 0];
for netId = [1]
network = networkTest{netId};
for subRate = [0.1:0.1:0.3]
modelName = [network '_r' num2str(subRate)]; %%% model name
data = load(fullfile('models', network ,[modelName,'.mat']));
net = dagnn.DagNN.loadobj(data.net);
if strcmp(network,'CSNet')
net.renameVar('x0', 'input');
net.renameVar('x12', 'prediction');
else
net.removeLayer(net.layers(end).name) ;
end
net.mode = 'train';
net.move('gpu');
%%% read images
ext = {'*.jpg','*.png','*.bmp', '*.pgm', '*.tif'};
filePaths = [];
for i = 1 : length(ext)
filePaths = cat(1,filePaths, dir(fullfile('testsets',folderTest,ext{i})) );
end
PSNRs_CSNet = zeros(1,length(filePaths));
SSIMs_CSNet = zeros(1,length(filePaths));
count = 1;
allName = cell(1);
for i = 1:1:length(filePaths)
%%% read images
image = imread(fullfile('testsets', folderTest, filePaths(i).name));
[~,nameCur,extCur] = fileparts(filePaths(i).name);
allName{count} = nameCur;
if size(image,3) == 3
image = modcrop(image,32);
image = rgb2ycbcr(image);
image = image(:,:,1);
end
label = im2single(image);
if mod(size(label, 1), blkSize) ~= 0 || mod(size(label, 2), blkSize) ~= 0
continue
end
input = label;
input = gpuArray(input);
net.conserveMemory = false;
tic
net.eval({'input', input}) ;
time(i) = toc;
%% =========== Compressed Measurement ==========================
Y = squeeze(gather(net.vars(10).value));
edges = [-8:0.1:8];
his = histogram(Y(:), edges);
value = his.Values./sum(his.Values);
range = [min(Y(:)), max(Y(:))];
if ~exist(['histResults\' network '\']), mkdir(['histResults\' network '\']); end
save(['histResults\' network '\' nameCur '_r' num2str(subRate) '.mat'], 'his', 'value', 'range', 'Y');
% %% =========== Decomposition Measurement ==========================
%
% im1 = squeeze(gather(net.vars(2).value)); %imagesc(im1); axis square;axis off; colormap(gray); colorbar;
% %im = export_fig; imwrite(im, ['Decomp\1' modelName '_rate' num2str(subRate) '_grey.tif']);
%
% im2 = squeeze(gather(net.vars(4).value)); %imagesc(im2); axis square;axis off; colormap(gray); colorbar;
% %im = export_fig; imwrite(im, ['Decomp\2' modelName '_rate' num2str(subRate) '_grey.tif']);
%
% im3 = squeeze(gather(net.vars(6).value)); %imagesc(im3); axis square;axis off; colormap(gray); colorbar;
% %im = export_fig; imwrite(im, ['Decomp\3' modelName '_rate' num2str(subRate) '_grey.tif']);
%
% im4 = squeeze(gather(net.vars(8).value)); %imagesc(im4); axis square;axis off; colormap(gray); colorbar;
% %im = export_fig; imwrite(im, ['Decomp\4' modelName '_rate' num2str(subRate) '_grey.tif']);
%
% im = cat(1, cat(2, im1, im2), cat(2, im3, im4)); imagesc(im);
% axis square;axis off; colormap(gray); colorbar; set(gca, 'fontsize', 20);
% im = export_fig; imwrite(im, ['Decomp\' modelName '_rate' num2str(subRate) '_grey.tif']);
%
%
%
% % out1 = net.getVarIndex('prediction') ;
% % output = gather(squeeze(gather(net.vars(out1).value)));
%
%
% %% Check the sampling matrix
%
% % Phit = squeeze(gather(net.params(3).value));
%
% % Phi = zeros(size(Phit, 1), size(Phit, 2),4, size(Phit, 3));
% % Phi(:, :, 1, :) = Phit;
% % Phi(:, :, 2, :) = (gather(net.params(3).value));
% % Phi(:, :, 3, :) = (gather(net.params(7).value));
% % Phi(:, :, 4, :) = (gather(net.params(11).value));
%
% tLegend = {'1', '2', '3', '4'};
%
% index_Phi = [3, 7, 11, 15];
%
% bigMtx = [];
% for idx1 = 1:1:2
% tmpBig = [];
% for idx2 = 1:1:2
% idx = (idx1-1) * 2 + idx2;
%
% %pr = figure(idx);
% Phi = squeeze(gather(net.params(index_Phi(idx)).value));
%
% mtx = [];
% [w, h, c, k] = size(Phi);
% k0 = floor(sqrt(c));
%
% for i0 = 1:1:k0
% tmp = [];
% for i1 = 1:1:k0
% i2 = (i0 - 1)*k0 + i1;
% %pr = figure(1);
% Phi_i = Phi(:, :, i2);
%
%
% tmp = [tmp, Phi_i];
%
% end
% mtx = [mtx; tmp];
% end
%
% % tmpBig = [tmpBig, mtx] ;
% imagesc(mtx); axis square;axis off;
% colormap(gray ); colorbar; set(gcf,'color','w');set(gca, 'fontsize', 26);
% im = export_fig;
% imwrite(im, ['mtxResults_grey\' num2str(size(Phi,1)) '_meas_' modelName '_rate' num2str(subRate) '_grey.tif']);
% end
% % bigMtx = [bigMtx; tmpBig];
% end
%
% % pr = figure(1); imshow(bigMtx, []); set(gcf,'color','w'); set(gca, 'fontsize', 18);
% %
% % im = export_fig;
% % imwrite(im, ['mtxResults_grey\meas_' modelName '_rate' num2str(subRate) '.tif']);
% % saveas(pr, ['mtxResults_grey\meas_' modelName '_rate' num2str(subRate) '.png']);
% % saveas(pr, ['mtxResults_grey\meas_' modelName '_rate' num2str(subRate) '.fig']);
% % save(['mtxResults_grey\meas_' modelName '_rate' num2str(subRate) '.mat'], 'Phi');
%
% if subRate == 0.1
% imagesc(bigMtx,[-0.5 0.5]); axis square;axis off;
% colormap(gray ); colorbar; set(gcf,'color','w');set(gca, 'fontsize', 26);
% elseif subRate == 0.2
% imagesc(bigMtx, [-0.5 0.5]); axis square;axis off;
% colormap(gray); colorbar; set(gcf,'color','w');set(gca, 'fontsize', 26);
% end
% im = export_fig;
% imwrite(im, ['mtxResults_grey\meas_' modelName '_rate' num2str(subRate) '_grey.tif']);
% %% Check CS measurement
% %meas = net.getVarIndex('conv2') ;
% output = gather(squeeze(gather(net.vars(1).value))) ;
%
end
end
end