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generate_train.m
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generate_train.m
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clear;close all;
folder = 'training_hr';
savepath = 'train.h5';
size_input = 41;
size_label = 41;
stride = 41;
%% scale factors
scale = [2,3,4];
%% downsizing
downsizes = [1,0.7,0.5];
%% initialization
data = zeros(size_input, size_input, 1, 1);
label = zeros(size_label, size_label, 1, 1);
count = 0;
margain = 0;
%% generate data
filepaths = [];
filepaths = [filepaths; dir(fullfile(folder, '*.jpg'))];
filepaths = [filepaths; dir(fullfile(folder, '*.bmp'))];
for i = 1 : length(filepaths)
for flip = 1: 3
for degree = 1 : 4
for s = 1 : length(scale)
for downsize = 1 : length(downsizes)
image = imread(fullfile(folder,filepaths(i).name));
if flip == 1
image = flipdim(image ,1);
end
if flip == 2
image = flipdim(image ,2);
end
image = imrotate(image, 90 * (degree - 1));
image = imresize(image,downsizes(downsize),'bicubic');
if size(image,3)==3
image = rgb2ycbcr(image);
image = im2double(image(:, :, 1));
im_label = modcrop(image, scale(s));
[hei,wid] = size(im_label);
im_input = imresize(imresize(im_label,1/scale(s),'bicubic'),[hei,wid],'bicubic');
filepaths(i).name
for x = 1 : stride : hei-size_input+1
for y = 1 :stride : wid-size_input+1
subim_input = im_input(x : x+size_input-1, y : y+size_input-1);
subim_label = im_label(x : x+size_label-1, y : y+size_label-1);
count=count+1;
data(:, :, 1, count) = subim_input;
label(:, :, 1, count) = subim_label;
end
end
end
end
end
end
end
end
order = randperm(count);
data = data(:, :, 1, order);
label = label(:, :, 1, order);
%% writing to HDF5
chunksz = 64;
created_flag = false;
totalct = 0;
for batchno = 1:floor(count/chunksz)
batchno
last_read=(batchno-1)*chunksz;
batchdata = data(:,:,1,last_read+1:last_read+chunksz);
batchlabs = label(:,:,1,last_read+1:last_read+chunksz);
startloc = struct('dat',[1,1,1,totalct+1], 'lab', [1,1,1,totalct+1]);
curr_dat_sz = store2hdf5(savepath, batchdata, batchlabs, ~created_flag, startloc, chunksz);
created_flag = true;
totalct = curr_dat_sz(end);
end
h5disp(savepath);