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HyperSIGMA_super-resolution/crop_image.m

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+function [out] = crop_image(img, patch_size, stride, factor, file_name)
+
+% Normalization for CAVE dataset
+%img = double(img)./65535;
+% Normalization for Pavia Center dataset
+%img = double(img)./8000;
+% Normalization for Chikusei dataset
+img = double(img);
+
+[H, W, C] = size(img);
+p = patch_size;
+pat_col_num = 1:stride:(H - p + 1);
+pat_row_num = 1:stride:(W - p + 1);
+total_num = length(pat_col_num) * length(pat_row_num);
+index = 1;
+
+% crop a single patch from whole image
+for i=1:length(pat_col_num)
+    for j = 1:length(pat_row_num)
+        up = pat_col_num(i);
+        down = up + p - 1;
+        left = pat_row_num(j);
+        right = left + p - 1;
+        gt = img(up:down, left:right, :);
+        ms = single(imresize(gt, factor));
+        ms_bicubic = single(imresize(ms, 1/factor));
+        gt = single(gt);
+        file_path = strcat('/Users/miaoyuchun/Desktop/superx2/dataset/Houston_x2/trains_solely/block_', file_name, '_', num2str(index), '.mat');
+%         file_path = strcat('./dataset/Pavia_x2/trains/block_', file_name, '_', num2str(index), '.mat');
+%       file_path = strcat('./dataset/Chikusei_x2/trains/block_', file_name, '_', num2str(index), '.mat');
+%       file_path = strcat('./dataset/Chikusei_x8/trains/block_', file_name, '_', num2str(index), '.mat');
+        save(file_path,'gt','ms','ms_bicubic','-v6');
+        index = index + 1;
+    end
+end
+out = total_num;
+end
+
+        

HyperSIGMA_super-resolution/generate_Houston.m

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+%% This is a demo code to show how to generate training and testing samples from the HSI %%
+clc
+clear
+close all
+
+addpath('include');
+
+%% Step 1: generate the training and testing images from the original HSI
+load('Houston2018.mat');%% Please down the Chikusei dataset (mat format) from https://www.sal.t.u-tokyo.ac.jp/hyperdata/
+%% center crop this image to size 4172 x 1202
+img = Houston2018;
+clear Houston2018;
+% normalization
+img = single(img);
+img = img ./ max(max(max(img)));
+
+%% select first column as test images
+[H, W, C] = size(img);
+test_img_size = 256;
+test_pic_num = floor(W / test_img_size);
+mkdir test_Houston;
+for i = 1:test_pic_num
+    left = (i - 1) * test_img_size + 1;
+    right = left + test_img_size - 1;
+    test = img(1:test_img_size,left:right,:);
+    save(strcat('./test_Houston/Houston_test_', int2str(i), '.mat'),'test');
+end
+
+%% the rest bottom for training
+mkdir ('train_Houston');
+img = img((test_img_size+1):end,:,:);
+save('./train_Houston/Houston_train.mat', 'img');
+
+%% Step 2: generate the testing images used in mains.py
+generate_test_data;
+
+%% Step 3: generate the training samples (patches) cropped from the training images
+generate_train_data;
+
+%% Step 4: Please manually remove 10% of the samples to the folder of evals

HyperSIGMA_super-resolution/generate_test_data.m

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+fileFolder=fullfile('/Users/miaoyuchun/Desktop/superx2/test_Houston/');
+dirOutput=dir(fullfile(fileFolder,'*.mat'));
+fileNames={dirOutput.name};
+factor = 0.5;
+img_size = 256;
+bands = 48;
+gt = zeros(numel(fileNames),img_size,img_size,bands);
+ms = zeros(numel(fileNames),img_size*factor,img_size*factor,bands);
+ms_bicubic = zeros(numel(fileNames),img_size,img_size,bands);
+% testFolder=fullfile('.\Cave\test\');
+%testOutput=dir(fullfile(testFolder,'*.mat'));
+%testNames={testOutput.name};
+
+% cd test_Chikusei;
+% cd test_Houston;
+cd test_Houston;
+for i = 1:numel(fileNames)
+    load(fileNames{i},'test');
+    img_ms = single(imresize(test, factor));
+    gt(i,:,:,:) = test;
+    ms(i,:,:,:) = img_ms;
+    ms_bicubic(i,:,:,:) = single(imresize(img_ms, 1/factor));
+end
+
+cd ..;
+gt = single(gt);
+ms = single(ms);
+ms_bicubic = single(ms_bicubic);
+save('/Users/miaoyuchun/Desktop/superx2/dataset/Houston_x2/Houston_x2.mat','gt','ms','ms_bicubic');
+

HyperSIGMA_super-resolution/generate_train_data.m

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+% Convert HS dataset to patches
+
+% List all '.mat' file in folder
+% file_folder=fullfile('.\train');
+file_folder=fullfile('/Users/miaoyuchun/Desktop/superx2/train_Houston');
+file_list=dir(fullfile(file_folder,'*.mat'));
+file_names={file_list.name};
+
+% store cropped images in folders
+for i = 1:1:numel(file_names)
+    name = file_names{i};
+    name = name(1:end-4); 
+    load(strcat('/Users/miaoyuchun/Desktop/superx2/train_Houston/',file_names{i}));
+    %crop_image(img, 256, 128, 0.125, name);
+    crop_image(img, 128, 32, 0.5, name);
+    %crop_image(img, 128, 32, 0.125, name);
+%     crop(img, 512, 512, 0.5, name);
+    %crop_image(img, 128, 64, 0.25, name);
+end