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jacquelinelala · Oct 10, 2018 · d097bef · d097bef
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diff --git a/.DS_Store b/.DS_Store
diff --git a/datasets/dataset_hf5.py b/datasets/dataset_hf5.py
@@ -1,6 +1,5 @@
 import torch.utils.data as data
 import torch
-from PIL import Image
 from skimage.io import imread, imsave
 import numpy as np
 import random
@@ -9,10 +8,7 @@
 import h5py
 import sys
 import os
-sys.path.append('..')
 from os.path import join
-from skimage.transform import rotate
-from skimage import img_as_float
 
 #=================== Utils ===================#
 def is_image_file(filename):
@@ -24,26 +20,21 @@ def is_image_file(filename):
 
 class DataValSet(data.Dataset):
     def __init__(self, root_dir):
-        # input_dir = join(root_val_dir, 'LR_Blur')  #---------------Input path
-        #one input & two ground truths
+        #one input & ground truth
         self.input_dir = join(root_dir, 'LR_Blur')
-        self.lr_deblur_dir = join(root_dir, 'LR')
         self.sr_dir = join(root_dir, 'HR')
 
         #Online Loading
         self.input_names = [x for x in sorted(os.listdir(self.input_dir)) if is_image_file(x)]
-        self.lr_deblur_names = [x for x in sorted(os.listdir(self.lr_deblur_dir)) if is_image_file(x)]
         self.hr_names = [x for x in sorted(os.listdir(self.sr_dir)) if is_image_file(x)]
 
     def __len__(self):
         return len(self.hr_names)
 
     def __getitem__(self, index):
-        input = np.asarray(imread(join(self.input_dir, self.input_names[index])).transpose((2, 0, 1)), np.float32).copy() / 255
-        target_lr = np.asarray(imread(join(self.lr_deblur_dir, self.lr_deblur_names[index])).transpose((2, 0, 1)), np.float32).copy() /255
-        target = np.asarray(imread(join(self.sr_dir, self.hr_names[index])).transpose((2, 0, 1)), np.float32).copy() / 255
-
-        return input, target_lr, target, self.hr_names[index]
+        input     = np.asarray(imread(join(self.input_dir, self.input_names[index])).transpose((2, 0, 1)), np.float32).copy() / 255
+        target    = np.asarray(imread(join(self.sr_dir, self.hr_names[index])).transpose((2, 0, 1)), np.float32).copy() / 255
+        return input, target
 
 #=================== Training ===================#
 
@@ -52,37 +43,35 @@ def __init__(self, h5py_file_path):
         super(DataSet, self).__init__()
         self.hdf5_file  = h5py_file_path
 
-        self.file   = h5py.File(self.hdf5_file, 'r')
-        self.file.keys()
-        self.inputs = np.array(self.file.get("data"))
-        self.deblurs = np.array(self.file.get("label_db"))
-        self.hrs = np.array(self.file.get("label"))
+        self.file    = h5py.File(self.hdf5_file, 'r')
+        #self.file.keys()
+        self.inputs  = self.file.get("data")
+        self.deblurs = self.file.get("label_db")
+        self.hrs     = self.file.get("label")
 
     def __len__(self):
         return self.inputs.shape[0]
 
     def __getitem__(self, index):
-        input_patch  = self.inputs[index, :, :, :]
-        deblur_patch = self.deblurs[index, :, :, :]
-        hr_patch     = self.hrs[index, :, :, :]
+        #print(index)
+        # numpy
+        input_patch  = np.asarray(self.inputs[index, :, :, :], np.float32)
+        deblur_patch = np.asarray(self.deblurs[index, :, :, :], np.float32)
+        hr_patch     = np.asarray(self.hrs[index, :, :, :], np.float32)
         # randomly flip
         if random.randint(0, 1) == 0:
-            input_patch  = np.flip(input_patch, 1)
-            deblur_patch = np.flip(deblur_patch, 1)
-            hr_patch     = np.flip(hr_patch, 1)
-        else:
             input_patch  = np.flip(input_patch, 2)
             deblur_patch = np.flip(deblur_patch, 2)
             hr_patch     = np.flip(hr_patch, 2)
         # randomly rotation
         rotation_times = random.randint(0, 3)
-        input_patch  = np.rot90(input_patch, rotation_times, (1, 2))
-        deblur_patch = np.rot90(deblur_patch, rotation_times, (1, 2))
-        hr_patch     = np.rot90(hr_patch, rotation_times, (1, 2))
+        input_patch    = np.rot90(input_patch, rotation_times, (1, 2))
+        deblur_patch   = np.rot90(deblur_patch, rotation_times, (1, 2))
+        hr_patch       = np.rot90(hr_patch, rotation_times, (1, 2))
 
-        return torch.from_numpy(np.asarray(input_patch, np.float32).copy()),\
-               torch.from_numpy(np.asarray(deblur_patch, np.float32).copy()),\
-               torch.from_numpy(np.asarray(hr_patch, np.float32).copy())
+        return input_patch.copy(),\
+               deblur_patch.copy(),\
+               hr_patch.copy()
 
 
 
diff --git a/evaluation/.DS_Store b/evaluation/.DS_Store
diff --git a/evaluation/delete_image.m b/evaluation/delete_image.m
@@ -16,7 +16,9 @@
 %       http://xinyizhang.tech/bmvc2018
 %       https://github.com/jacquelinelala/GFN
 %% deleting
-path = '/4TB/datasets/LR-GOPRO/Validation_4x/Results';
+function delete_image(folder)
+path = fullfile(folder, 'Validation_4x/Results');
+%path = fullfile(folder, 'Validation_4x/HR');
 result_dir = dir(fullfile(path, '*GFN_4x.png'));
 len = length(result_dir);
 

diff --git a/evaluation/test_RGB.m b/evaluation/test_RGB.m
@@ -1,17 +1,17 @@
 %   Description:
-%   -testting RGB channels PSNR & SSIM
+%   -testting RGB channels PSNR.
 %
 %   Input:    
 %   - Validation/Results: some specific group images with limited endings.
 %   - Validation/HR
 %
 %   Output:
 %       - PSNR  Average PSNR
-%       - SSIM  Average SSIM
+%
 %   Citation: 
 %       Gated Fusion Network for Joint Image Deblurring and Super-Resolution
 %       The British Machine Vision Conference(BMVC2018 oral)
-%       Xinyi Zhang, Hang Dong, Zhe Hu, Wei-Sheng Lai,?Fei Wang and?Ming-Hsuan Yang
+%       Xinyi Zhang, Hang Dong, Zhe Hu, Wei-Sheng Lai, Fei Wang and Ming-Hsuan Yang
 %   Thanks:
 %       Many thanks to Wei-Sheng Lai al. for LapSRN. In this project we use
 %       some codes from their project.
@@ -22,34 +22,28 @@
 %       https://github.com/jacquelinelala/GFN
 
 %% testing
+function test_RGB(folder)
 addpath(genpath('utils'));
-results_path = '/4TB/datasets/LR-GOPRO/Validation_4x/Results';
-hr_path = '/4TB/datasets/LR-GOPRO/Validation_4x/HR';
+results_path = fullfile(folder, 'Validation_4x/Results');
+hr_path = fullfile(folder, 'Validation_4x/HR');
 
 result_dir = dir(fullfile(results_path, '*GFN_4x.png'));
 hr_dir = dir(fullfile(hr_path, '*.png'));
 count = length(hr_dir);
 PSNR = zeros(count, 1);
-SSIM = zeros(count, 1);
 IFC = zeros(count, 1);
 ifc = 0;
 scale = 4;
 
-
 for i = 1 : count
+    fprintf("Calculating %s\n", hr_dir(i).name);
 	HR = imread(fullfile(hr_dir(i).folder, hr_dir(i).name));
 	Results = imread(fullfile(result_dir(i).folder, result_dir(i).name));
-	[PSNR(i), SSIM(i), IFC(i)] = evaluate_SR(HR, Results, scale, ifc);
+	[PSNR(i), IFC(i)] = evaluate_SR(HR, Results, scale, ifc);
 end
 
 PSNR(count + 1) = mean(PSNR(:));
-SSIM(count + 1) = mean(SSIM(:));
-
 fprintf("Average PSNR is %f\n", PSNR(count + 1));
-fprintf("Average SSIM is %f\n", SSIM(count + 1));
-
-PSNR_path = fullfile('/4TB/datasets/LR-GOPRO/Validation_4x', 'PSNR-GFN_4x_HR.txt');
-SSIM_path = fullfile('/4TB/datasets/LR-GOPRO/Validation_4x', 'SSIM-GFN_4x_HR.txt');
+PSNR_path = fullfile(folder, 'Validation_4x', 'PSNR-GFN_4x_HR.txt');
 save_matrix(PSNR, PSNR_path);
-save_matrix(SSIM, SSIM_path);
 
diff --git a/evaluation/test_bicubic.m b/evaluation/test_bicubic.m
@@ -1,7 +1,7 @@
 % -------------------------------------------------------------------------
 %   Description:
 %       Use bicubic to upscale the input, and evaluate the results in terms
-%       of PSNR and SSIM (RGB channel)
+%       of PSNR.
 %
 %   Input:
 %       - Validation/LR_Blur : Blurry low-resolution input
@@ -11,7 +11,7 @@
 %       - Validation/Results: images using bicubic upscaling and formatting
 %       with 'Bic****.png' in Results directory
 %       - PSNR: Average PSNR
-%       - SSIM: Average SSIM
+%       
 %   Citation: 
 %       Gated Fusion Network for Joint Image Deblurring and Super-Resolution
 %       The British Machine Vision Conference(BMVC2018 oral)
@@ -26,10 +26,11 @@
 %       https://github.com/jacquelinelala/GFN
 
 %% testing
+function test_bicubic(folder)
 addpath(genpath('utils'));
-lr_blur_path = '/4TB/datasets/LR-GOPRO/Validation_4x/LR_Blur';
-hr_path = '/4TB/datasets/LR-GOPRO/Validation_4x/HR';
-results_path = '/4TB/datasets/LR-GOPRO/Validation_4x/Results';
+lr_blur_path = fullfile(folder, 'Validation_4x/LR_Blur');
+hr_path = fullfile(folder, 'Validation_4x/HR');
+results_path = fullfile(folder, 'Validation_4x/Results');
 lr_blur_dir = dir(fullfile(lr_blur_path, '*.png'));
 hr_dir = dir(fullfile(hr_path, '*.png'));
 count = length(lr_blur_dir);
@@ -45,17 +46,13 @@
 	LR_Blur = im2double(imread(fullfile(lr_blur_dir(i).folder, lr_blur_dir(i).name)));
 	LR_Blur_Bic = imresize(LR_Blur, 4, 'bicubic');
 	imwrite(LR_Blur_Bic, fullfile(results_path, strcat(hr_dir(i).name(1:4), 'Bic.png')),'png');
-	[PSNR(i), SSIM(i), IFC(i)] = evaluate_SR(HR, LR_Blur_Bic, scale, ifc);
+	[PSNR(i), IFC(i)] = evaluate_SR(HR, LR_Blur_Bic, scale, ifc);
 end
 
 PSNR(count + 1) = mean(PSNR(:));
-SSIM(count + 1) = mean(SSIM(:));
 
 fprintf("Average PSNR is %f\n", PSNR(count + 1));
-fprintf("Average SSIM is %f\n", SSIM(count + 1));
 
-PSNR_path = fullfile('/4TB/datasets/LR-GOPRO/Validation_4x', 'PSNR-LRBlurBic_HR.txt');
-SSIM_path = fullfile('/4TB/datasets/LR-GOPRO/Validation_4x', 'SSIM-LRBlurBic_HR.txt');
+PSNR_path = fullfile(folder, 'Validation_4x', 'PSNR-LRBlurBic_HR.txt');
 save_matrix(PSNR, PSNR_path);
-save_matrix(SSIM, SSIM_path);
 
diff --git a/evaluation/utils/.DS_Store b/evaluation/utils/.DS_Store
diff --git a/evaluation/utils/IFC/distsub_est_M.m b/evaluation/utils/IFC/distsub_est_M.m