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Demo_Analysis_MS_CSNet_v1.m
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Demo_Analysis_MS_CSNet_v1.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)
% 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');
addpath('D:\matconvnet-1.0-beta25\matlab\simplenn');
% addpath('D:\matconvnet-1.0-beta25\matlab');
addpath('.\utilities');
folderTest = 'Classic13_512';
networkTest = {'W-DCS1' 'W-DCS2' 'W-DCS3'}; % 102
showResult = 0;
writeRecon = 1;
featureSize = 64;
blkSize = 32;
isLearnMtx = [1, 0];
network = networkTest{3};
padSize = [1, 1, 2];
subrate_all = [0.1:0.1:0.3];
rangeSize = [0.15 0.3 0.25];
for subRate_id = 2:1:2
subRate = subrate_all(subRate_id);
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 = 'test';
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
%%% 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);
tic
net.eval({'input', input}) ;
time(i) = toc;
out1 = net.getVarIndex('prediction') ;
output = gather(squeeze(gather(net.vars(out1).value)));
%% Check the sampling matrix
Phi = squeeze(gather(net.params(1).value));
tLegend = {'1Low-Low', '2Low-High', '3High-Low', '4High-High'};
[w, h, c, k] = size(Phi);
k0 = floor(sqrt(k));
% k0 = 5;
bigMtx = [];
count = 1;
if subRate == 0.1, color_range = [0.23, 0.1; 0.1, 0.02]; end
if subRate == 0.2, color_range = [0.3, 0.3; 0.3, 0.06]; end
for idx1 = 1:1:2
tmpBig = [];
for idx2 = 1:1:2
idx = (idx1-1) * 2 + idx2;
m1 = 0; m2 = 0;
pr = figure(idx);
mtx = [];
for i0 = 1:1:k0
im = [];
%count = 1;
for i1 = 1:1:k0
i2 = (i0 - 1)*k0 + i1;
Phi_i = Phi(:, :, idx, i2);
figure('Position', [10 10 900 600]);
f1 = imshow(Phi_i,[-color_range(idx1, idx2), color_range(idx1, idx2)]); axis square;axis off;
%colormap(parula);
m1(count) = min(Phi_i(:));
m2(count) = max(Phi_i(:));
count = count + 1;
if i1 == 1
im = padarray(export_fig, [0, padSize(subRate_id)], 255, 'post');
else
im = cat(2, im, padarray(export_fig, [0, padSize(subRate_id)], 255, 'post'));
end
close all;
end
im = padarray(im, [padSize(subRate_id), 0], 255, 'post');
mtx = cat(1, mtx, im);
end % End a subband
imshow(mtx, []); %title([tLegend{idx}]);
im = export_fig; %color bar;
imwrite(im, ['mtxResults_grey\' tLegend{idx} '_meas_' modelName ...
'_rate' num2str(subRate) '_' num2str(color_range(idx1, idx2)) '.png']);
tmpBig = cat(2, tmpBig, padarray(im, [0, padSize(subRate_id) + 2], 255, 'post'));
end
tmpBig = padarray(tmpBig, [padSize(subRate_id) + 2, 0], 255, 'post');
bigMtx = cat(1, bigMtx, tmpBig);
end
pr = figure(6); imshow(bigMtx);
im = export_fig;
imwrite(im, ['mtxResults_grey\0meas_' modelName '_rate' num2str(subRate) '_' num2str(rangeSize(subRate_id)) '.png']);
saveas(pr, ['mtxResults_grey\0meas_' modelName '_rate' num2str(subRate) '_' num2str(rangeSize(subRate_id)) '.fig']);
end
end