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0_generate_list.py

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+import os, sys 
+import shutil
+import numpy as np
+from pathlib import Path
+
+base_rt = f"/home/CORP/hsiang.chen/Project/Datasets/IR"
+
+"""
+Deblur: GoPro, HIDE, RealBlur
+"""
+# GoPro
+# pre-process for GoPro, dataset: https://seungjunnah.github.io/Datasets/gopro 
+# Seungjun Nah, Tae Hyun Kim, and Kyoung Mu Lee. Deep multi-scale convolutional neural network for dynamic scene deblurring. In CVPR, 2017
+rt = os.path.join(base_rt, "Deblur/GoPro")
+meta_folder = os.path.join(rt, "metas")
+os.makedirs(meta_folder, exist_ok=True)
+for dset in ['train', 'test']: # train, test
+    dset_pth = os.path.join(rt, dset)
+    list_file = []
+    total_ct = 0
+    for sample_folder in os.listdir(dset_pth): # GOPOXXX_XX_XX
+        folder_pth = os.path.join(dset_pth, sample_folder)
+        blur_folder = os.path.join(folder_pth, 'blur')
+        sharp_folder = os.path.join(folder_pth, 'sharp')
+        for ct, sample in enumerate(os.listdir(blur_folder)):
+            if ".png" in sample or ".jpg" in sample or ".jpeg" in sample or ".bmp" in sample or ".tif" in sample or ".JPG" in sample or ".PNG" in sample:
+                blur_file = os.path.join(blur_folder, sample)
+                sharp_file = os.path.join(sharp_folder, sample)
+                list_file.append((blur_file, sharp_file))
+                total_ct += 1
+
+    with open(os.path.join(meta_folder,'{}.list'.format(dset)), 'w') as fp:
+        for item in list_file:
+            fp.write('{} {} {}\n'.format(item[0], item[1], None))
+    print(dset_pth, total_ct)
+    
+# HIDE
+# pre-process for HIDE, dataset: https://github.com/joanshen0508/HA_deblur
+# Ziyi Shen, Wenguan Wang, Xiankai Lu, Jianbing Shen, Haibin Ling, Tingfa Xu, and Ling Shao. Human-aware mo- tion deblurring. In ICCV, 2019.
+# ==============================================
+# HIDE/
+# |- train/{image, gt}
+# |- test/{image, gt}
+# ==============================================
+rt = os.path.join(base_rt, "Deblur/HIDE")
+meta_folder = os.path.join(rt, "metas")
+os.makedirs(meta_folder, exist_ok=True)
+for dset in ['train', 'test']: # train, test
+    dset_pth = os.path.join(rt, dset)
+    list_file = []
+    total_ct = 0 
+    if dset == 'train':
+        blur_folder = dset_pth
+        sharp_folder = os.path.join(rt, 'GT')
+        for ct, sample in enumerate(os.listdir(blur_folder)):
+            if ".png" in sample or ".jpg" in sample or ".jpeg" in sample or ".bmp" in sample or ".tif" in sample or ".JPG" in sample or ".PNG" in sample:
+                blur_file = os.path.join(blur_folder, sample)
+                sharp_file = os.path.join(sharp_folder, sample)
+                list_file.append((blur_file, sharp_file))
+                total_ct += 1
+    else:
+        for sample_folder in ['test-close-ups', 'test-long-shot']: 
+            folder_pth = os.path.join(dset_pth, sample_folder)
+            blur_folder = folder_pth
+            sharp_folder = os.path.join(rt, 'GT')
+            for ct, sample in enumerate(os.listdir(blur_folder)):
+                if ".png" in sample or ".jpg" in sample or ".jpeg" in sample or ".bmp" in sample or ".tif" in sample or ".JPG" in sample or ".PNG" in sample:
+                    blur_file = os.path.join(blur_folder, sample)
+                    sharp_file = os.path.join(sharp_folder, sample)
+                    list_file.append((blur_file, sharp_file))
+                    total_ct += 1
+    
+    with open(os.path.join(meta_folder,'{}.list'.format(dset)), 'w') as fp:
+        for item in list_file:
+            fp.write('{} {} {}\n'.format(item[0], item[1], None))
+    print(dset_pth, total_ct)
+   
+# RealBlur
+# pre-process for RealBlur-J,R, dataset: https://github.com/rimchang/RealBlur
+# Jaesung Rim, Haeyun Lee, Jucheol Won, and Sunghyun Cho. Real-world blur dataset for learning and benchmarking de- blurring algorithms. In ECCV, 2020.
+rt = os.path.join(base_rt, "Deblur/")
+
+realblur_j_test_txt = os.path.join(rt, f"RealBlur-J_ECC_IMCORR_centroid_itensity_ref/RealBlur_J_test_list.txt")
+realblur_r_test_txt = os.path.join(rt, f"RealBlur-R_BM3D_ECC_IMCORR_centroid_itensity_ref/RealBlur_R_test_list.txt")
+
+for txt in [realblur_j_test_txt, realblur_r_test_txt]:
+    data_list = []
+    total_ct = 0
+    miss_ct = 0
+    with open(txt) as fin:
+        for ct, line in enumerate(fin): # gt, image 
+            line = line.strip().split()
+            if len(line) == 1: # no gt
+                data_list.append([None, os.path.join(rt, line[0])])    # image, None
+                miss_ct += 1
+            else:
+                data_list.append([os.path.join(rt, line[1]), os.path.join(rt, line[0])]) # image ,gt 
+            total_ct += 1
+        
+    set_dict = {realblur_j_test_txt: os.path.join(rt, f"RealBlur-J_ECC_IMCORR_centroid_itensity_ref"),
+                realblur_r_test_txt: os.path.join(rt, f"RealBlur-R_BM3D_ECC_IMCORR_centroid_itensity_ref")}
+    meta_folder = os.path.join(set_dict[txt], "metas")
+    os.makedirs(meta_folder, exist_ok=True)
+    with open(os.path.join(meta_folder,'test.list'), 'w') as fp:
+        for item in data_list:
+            fp.write('{} {} {}\n'.format(item[0], item[1], None))
+    
+    print(os.path.join(meta_folder,'test.list'), total_ct, miss_ct)
+
+"""
+Dehaze: 4kID, NH-Haze, OTS, SOTS
+"""
+# OST
+# pre-process for OTS, dataset: https://sites.google.com/view/reside-dehaze-datasets/reside-%CE%B2?authuser=0
+# Boyi Li, Wenqi Ren, Dengpan Fu, Dacheng Tao, Dan Feng, Wenjun Zeng, and Zhangyang Wang. Benchmarking single- image dehazing and beyond. TIP, 2018.
+rt = os.path.join(base_rt, "Dehaze/OTS")
+meta_folder = os.path.join(rt, "metas")
+os.makedirs(meta_folder, exist_ok=True)
+for dset in ['hazy']:
+    dset_pth = os.path.join(rt, dset)
+    list_file = []     
+    total_ct = 0
+    # 0025_0.8_0.1.jpg  
+    for sub_folder in os.listdir(dset_pth): 
+        input_folder = os.path.join(dset_pth, sub_folder)
+        gt_folder = os.path.join(rt, 'gt')
+        for ct, sample in enumerate(os.listdir(input_folder)):
+             if ".png" in sample or ".jpg" in sample or ".jpeg" in sample or ".bmp" in sample or ".tif" in sample or ".JPG" in sample or ".PNG" in sample:
+                input_file = os.path.join(input_folder, sample)
+                gt_file = os.path.join(gt_folder, "%s.jpg"%(sample.split('_')[0]))
+                list_file.append((input_file, gt_file))
+                total_ct += 1
+    
+    set_dict = {"hazy":'train'}
+    with open(os.path.join(meta_folder,'{}.list'.format(set_dict[dset])), 'w') as fp:
+        for item in list_file:
+            fp.write('{} {} {}\n'.format(item[0], item[1], None))
+    print(dset_pth, total_ct)
+
+
+# SOTS
+# pre-process for SOTS, dataset: https://sites.google.com/view/reside-dehaze-datasets/reside-standard 
+# Boyi Li, Wenqi Ren, Dengpan Fu, Dacheng Tao, Dan Feng, Wenjun Zeng, and Zhangyang Wang. Benchmarking single- image dehazing and beyond. TIP, 2018.
+rt = os.path.join(base_rt, "Dehaze/SOTS")
+meta_folder = os.path.join(rt, "metas")
+os.makedirs(meta_folder, exist_ok=True)
+for dset in ['outdoor']:
+    dset_pth = os.path.join(rt, dset)
+    list_file = []     
+    total_ct = 0
+    input_folder = os.path.join(dset_pth, 'hazy')
+    gt_folder = os.path.join(dset_pth, 'gt')
+    for ct, sample in enumerate(os.listdir(input_folder)):
+        if ".png" in sample or ".jpg" in sample or ".jpeg" in sample or ".bmp" in sample or ".tif" in sample or ".JPG" in sample or ".PNG" in sample:
+            input_file = os.path.join(input_folder, sample)
+            gt_file = os.path.join(gt_folder, "%s.png"%(sample.split("_")[0]))
+            list_file.append((input_file, gt_file))
+            total_ct += 1
+
+    set_dict = {"outdoor":'test'}
+    with open(os.path.join(meta_folder,'{}.list'.format(set_dict[dset])), 'w') as fp:
+        for item in list_file:
+            fp.write('{} {} {}\n'.format(item[0], item[1], None))
+    print(input_folder, total_ct)
+
+# 4kID
+# pre-process for 4kID, dataset: https://github.com/zzr-idam/4KDehazing
+# Ultra-High-Definition Image Dehazing via Multi-Guided Bilateral Learning, CVPR21.
+rt = os.path.join(base_rt, "Dehaze/4kID")
+meta_folder = os.path.join(rt, "metas")
+os.makedirs(meta_folder, exist_ok=True)
+for dset in ['4KDehazing', '4KDehazing_test']: # train, test
+    dset_pth = os.path.join(rt, dset)
+    list_file = [] 
+    total_ct = 0
+    input_folder = os.path.join(dset_pth, 'inputs')
+    gt_folder = os.path.join(dset_pth, 'groundtrues')
+    for ct, sample in enumerate(os.listdir(input_folder)):
+        if ".png" in sample or ".jpg" in sample or ".jpeg" in sample or ".bmp" in sample or ".tif" in sample or ".JPG" in sample or ".PNG" in sample:
+            input_file = os.path.join(input_folder, sample)
+            gt_file = os.path.join(gt_folder, sample)
+            list_file.append((input_file, gt_file))
+            total_ct += 1
+    
+    set_dict = {"4KDehazing":'train', "4KDehazing_test":'test'}
+    with open(os.path.join(meta_folder,'{}.list'.format(set_dict[dset])), 'w') as fp:
+        for item in list_file:
+            fp.write('{} {} {}\n'.format(item[0], item[1], None))
+    print(dset_pth, total_ct)
+
+
+# Unann
+# pre-process for unann, dataset: https://sites.google.com/view/reside-dehaze-datasets/reside-%CE%B2?authuser=0
+rt = os.path.join(base_rt, "Dehaze/UnannotatedHazyImages")
+meta_folder = os.path.join(rt, "metas")
+os.makedirs(meta_folder, exist_ok=True)
+list_file = [] 
+total_ct = 0
+input_folder = os.path.join(rt, "Image")
+for ct, sample in enumerate(os.listdir(input_folder)):
+    if ".png" in sample or ".jpg" in sample or ".jpeg" in sample or ".bmp" in sample or ".tif" in sample or ".JPG" in sample or ".PNG" in sample:
+        input_file = os.path.join(input_folder, sample)
+        list_file.append((input_file, ""))
+        total_ct += 1
+
+with open(os.path.join(meta_folder,'test.list'), 'w') as fp:
+    for item in list_file:
+        fp.write('{} {} {}\n'.format(item[0], None, None))
+print(rt, total_ct)
+
+# NH-Haze
+rt = os.path.join(base_rt, "Dehaze/NH-Haze")
+meta_folder = os.path.join(rt, "metas")
+os.makedirs(meta_folder, exist_ok=True)
+list_file = [] 
+total_ct = 0
+input_folder = os.path.join(rt, "images")
+for ct, sample in enumerate(os.listdir(input_folder)):
+    if ".png" in sample or ".jpg" in sample or ".jpeg" in sample or ".bmp" in sample or ".tif" in sample or ".JPG" in sample or ".PNG" in sample:
+        input_file = os.path.join(input_folder, sample)
+        list_file.append((input_file, ""))
+        total_ct += 1
+
+with open(os.path.join(meta_folder,'test.list'), 'w') as fp:
+    for item in list_file:
+        fp.write('{} {} {}\n'.format(item[0], None, None))
+print(rt, total_ct)
+
+"""
+Denoise: BSD68, BSD400, CBSD68, KodaK, McMaster, Set12, SIDD, Urban100, WaterlooED
+"""
+"""
+pre-process for denoise dataset
+1. BSD400: 
+	* David Martin, Charless Fowlkes, Doron Tal, and Jitendra Malik. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001, volume 2, pages 416–423. IEEE, 2001.
+	* https://github.com/smartboy110/denoising-datasets/tree/main
+2. WED: 
+	* Kede Ma, Zhengfang Duanmu, Qingbo Wu, Zhou Wang, Hongwei Yong, Hongliang Li, and Lei Zhang. Waterloo exploration database: New challenges for image quality as- sessment models. IEEE Transactions on Image Processing, 26(2):1004–1016, 2016.
+	* https://kedema.org/project/exploration/index.html
+3. BSD68:
+    * David Martin, Charless Fowlkes, Doron Tal, and Jitendra Malik. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001, volume 2, pages 416–423. IEEE, 2001.
+	* https://github.com/smartboy110/denoising-datasets/tree/main
+4. CBSD68: 
+	* David Martin, Charless Fowlkes, Doron Tal, and Jitendra Malik. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001, volume 2, pages 416–423. IEEE, 2001.
+	* https://github.com/smartboy110/denoising-datasets/tree/main
+5. Urban100: 
+	* Jia-BinHuang,AbhishekSingh,andNarendraAhuja.Single image super-resolution from transformed self-exemplars. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 5197–5206, 2015.
+	* https://github.com/jbhuang0604/SelfExSR
+6. Kodak: 
+	* Rich Franzen. Kodak lossless true color image suite. source: http://r0k. us/graphics/kodak, 4(2), 1999.
+	* https://www.kaggle.com/datasets/sherylmehta/kodak-dataset
+7. McMaster, Set12
+"""
+
+rt = os.path.join(base_rt, "Denoise")
+dataset = ['BSD68', 'BSD400', 'CBSD68', 'Kodak', 
+           'McMaster', 'Set12', 'Urban100', 'WaterlooED']
+
+for dset in dataset: 
+    # list sample
+    list_file = []   
+    total_ct = 0
+    dset_pth = os.path.join(rt, dset)
+    meta_folder = os.path.join(dset_pth, "metas")
+    os.makedirs(meta_folder, exist_ok=True)
+    input_folder = os.path.join(rt, '%s/image'%(dset))
+    for ct, sample in enumerate(os.listdir(input_folder)):
+        if ".png" in sample or ".jpg" in sample or ".jpeg" in sample or ".bmp" in sample or ".tif" in sample or ".JPG" in sample or ".PNG" in sample:
+            input_file = os.path.join(input_folder, sample)
+            list_file.append([input_file])
+            total_ct += 1
+
+    with open(os.path.join(meta_folder,'{}.list'.format(dset)), 'w') as fp:
+        for item in list_file:
+            fp.write('{} {} {}\n'.format(None, item[0], None))
+    print(input_folder, total_ct)
+   
+
+# SIDD
+rt = os.path.join(base_rt, "Denoise/SIDD")
+meta_folder = os.path.join(rt, "metas")
+os.makedirs(meta_folder, exist_ok=True)
+for dset in ['train', 'test']: 
+    # list sample
+    list_file = []   
+    total_ct = 0
+    dset_folder = os.path.join(rt, dset)
+    if dset == 'train':
+        for folder in os.listdir(dset_folder):
+            file_folder = os.path.join(dset_folder, folder)
+            for ct, sample in enumerate(os.listdir(file_folder)):
+                if "NOISY_SRGB" in sample:
+                    input_file = os.path.join(file_folder, sample)
+                    gt_file = os.path.join(file_folder, sample.replace("NOISY", "GT"))
+                    list_file.append([input_file, gt_file])
+                    total_ct += 1  
+    elif dset == 'test':
+        image_folder = os.path.join(dset_folder, 'NOISY')
+        gt_folder = os.path.join(dset_folder, "GT")
+        for ct, sample in enumerate(os.listdir(image_folder)):
+                if ".png" in sample or ".jpg" in sample or ".jpeg" in sample or ".bmp" in sample or ".tif" in sample or ".JPG" in sample or ".PNG" in sample:
+                    input_file = os.path.join(image_folder, sample)
+                    gt_file = os.path.join(gt_folder, sample.replace("NOISY", "GT"))
+                    list_file.append([input_file, gt_file])
+                    total_ct += 1  
+
+    with open(os.path.join(meta_folder,'{}.list'.format(dset)), 'w') as fp:
+        for item in list_file:
+            fp.write('{} {} {}\n'.format(item[0], item[1], None))
+    print(dset_folder, total_ct)
+
+
+"""
+Derain: LHPRain, Practical, Rain100L, RainDS, RainTrianL, UHD-Rain
+"""
+# RainDS
+rt = os.path.join(base_rt, "Derain/RainDS")
+meta_folder = os.path.join(rt, "metas")
+os.makedirs(meta_folder, exist_ok=True)
+for dset in ['RainDS_syn', 'RainDS_real']: # dataset
+    dataset_folder = os.path.join(rt, dset)
+    if dset == 'RainDS_syn':
+        set_list = ['train', 'test']
+    elif dset == 'RainDS_real':
+        set_list = ['train_set', 'test_set']
+    for subset in set_list:
+        input_folder = os.path.join(dataset_folder, subset)
+        for raintype in ['rainstreak', 'raindrop', 'rainstreak_raindrop']:
+            rain_folder = os.path.join(input_folder, raintype)
+            clear_folder = os.path.join(input_folder, 'gt')
+            list_file = []        
+            total_ct = 0
+            rain_dict = {'rainstreak_raindrop': 'rd-rain', 'rainstreak': 'rain', 'raindrop': 'rd'}
+            # rd-rain-97, rain-97, rd-97 -> norain-97
+            # pie-rd-rain-97, pie-rain-97, pie-rd-97 -> pie-norain-97
+            for ct, sample in enumerate(os.listdir(rain_folder)):
+                if ".png" in sample or ".jpg" in sample or ".jpeg" in sample or ".bmp" in sample or ".tif" in sample or ".JPG" in sample or ".PNG" in sample:
+                    input_file = os.path.join(rain_folder, sample)
+                    gt_file = os.path.join(clear_folder, sample.replace(rain_dict[raintype], "norain"))
+                    list_file.append((input_file, gt_file))
+                    total_ct += 1
+
+            with open(os.path.join(meta_folder,'{}_{}_{}.list'.format(dset, subset, raintype)), 'w') as fp:
+                for item in list_file:
+                    fp.write('{} {} {}\n'.format(item[0], item[1], None))
+            print(rain_folder, total_ct)
+
+# LHPRain
+# pre-process for LHPRain: https://github.com/yunguo224/LHP-Rain
+# From Sky to the Ground: A Large-scale Benchmark and Simple Baseline Towards Real Rain Removal (ICCV 2023)
+rt = os.path.join(base_rt, "Derain/LHPRain")
+meta_folder = os.path.join(rt, "metas")
+os.makedirs(meta_folder, exist_ok=True)
+for dset in ['train', 'val', 'test']: # train, test
+    input_folder = os.path.join(rt, "input/%s"%(dset))
+    gt_folder = os.path.join(rt, "gt/%s"%(dset))
+    list_file = []   
+    total_ct = 0
+    for ct, sample in enumerate(os.listdir(input_folder)):
+        if ".png" in sample or ".jpg" in sample or ".jpeg" in sample or ".bmp" in sample or ".tif" in sample or ".JPG" in sample or ".PNG" in sample:
+            input_file = os.path.join(input_folder, sample)
+            gt_file = os.path.join(gt_folder, sample)
+            list_file.append((input_file, gt_file))
+            total_ct += 1
+
+    with open(os.path.join(meta_folder,'{}.list'.format(dset)), 'w') as fp:
+        for item in list_file:
+            fp.write('{} {} {}\n'.format(item[0], item[1], None))
+    print(input_folder, total_ct)
+
+# Practical
+# pre-process for LHPRain: https://github.com/ZhangXinNan/RainDetectionAndRemoval
+# WenhanYang,RobbyTTan,JiashiFeng,JiayingLiu,Zong- ming Guo, and Shuicheng Yan. Deep joint rain detection and removal from a single image. In CVPR, 2017
+rt = os.path.join(base_rt, "Derain/Practical")
+meta_folder = os.path.join(rt, "metas")
+os.makedirs(meta_folder, exist_ok=True)
+# create image folder
+input_folder = os.path.join(rt, "image")
+list_file = []    
+total_ct = 0
+for ct, sample in enumerate(os.listdir(input_folder)):
+    if ".png" in sample or ".jpg" in sample or ".jpeg" in sample or ".bmp" in sample or ".tif" in sample or ".JPG" in sample or ".PNG" in sample:
+        input_file = os.path.join(input_folder, sample)
+        list_file.append((input_file))
+        total_ct += 1
+    
+with open(os.path.join(meta_folder,'test.list'), 'w') as fp:
+    for item in list_file:
+        fp.write('{} {} {}\n'.format(item, None, None))
+print(rt, total_ct)
+
+
+# Rain100L
+# pre-process for Rain100L: https://github.com/shangwei5/BRN
+# WenhanYang,RobbyTTan,JiashiFeng,JiayingLiu,Zong- ming Guo, and Shuicheng Yan. Deep joint rain detection and removal from a single image. In Proceedings of the IEEE conference on computer vision and pattern recogni- tion, pages 1357–1366, 2017.
+# norain-xxx.png 
+# rain-xxx.png 
+# rainregion-xxx.png 
+# rainstreak-xxx.png
+rt = os.path.join(base_rt, "Derain/Rain100L")
+meta_folder = os.path.join(rt, "metas")
+os.makedirs(meta_folder, exist_ok=True)
+
+image_folder = os.path.join(rt, 'image')
+gt_folder = os.path.join(rt, 'gt')
+list_file = []  
+total_ct = 0
+for ct, sample in enumerate(os.listdir(image_folder)):
+    if ".png" in sample or ".jpg" in sample or ".jpeg" in sample or ".bmp" in sample or ".tif" in sample or ".JPG" in sample or ".PNG" in sample:
+        input_file = os.path.join(image_folder, sample)
+        gt_file = os.path.join(gt_folder, sample.replace('rain', 'norain'))
+        list_file.append((input_file, gt_file))
+        total_ct += 1
+
+with open(os.path.join(meta_folder,'test.list'), 'w') as fp:
+    for item in list_file:
+        fp.write('{} {} {}\n'.format(item[0], item[1], None))
+print(rt, total_ct)
+
+# Rain200L
+# pre-process for RainTrainL(Rain200L): https://github.com/shangwei5/BRN
+# Yang W, Tan RT, Feng J, Liu J, Guo Z, Yan S. Deep joint rain detection and removal from a single image. In IEEE CVPR 2017.
+# norain-xxx.png 
+# rain-xxx.png 
+# rainregion-xxx.png 
+# rainstreak-xxx.png
+rt = os.path.join(base_rt, "Derain/RainTrainL")
+meta_folder = os.path.join(rt, "metas")
+os.makedirs(meta_folder, exist_ok=True)
+image_folder = os.path.join(rt, 'image')
+gt_folder = os.path.join(rt, 'gt')
+list_file = []    
+total_ct = 0
+for ct, sample in enumerate(os.listdir(image_folder)):
+    if ".png" in sample or ".jpg" in sample or ".jpeg" in sample or ".bmp" in sample or ".tif" in sample or ".JPG" in sample or ".PNG" in sample:
+        input_file = os.path.join(image_folder, sample)
+        gt_file = os.path.join(gt_folder, sample.replace('rain', 'norain'))
+        list_file.append((input_file, gt_file))
+        total_ct += 1
+    
+with open(os.path.join(meta_folder,'train.list'), 'w') as fp:
+    for item in list_file:
+        fp.write('{} {} {}\n'.format(item[0], item[1], None))
+print(rt, total_ct)
+
+# UHD-Rain
+# pre-process for UHD-Rain: https://github.com/wlydlut/uhddip
+# UHDDIP: Ultra-High-Definition Restoration: New Benchmarks and A Dual Interaction Prior-Driven Solution
+rt = os.path.join(base_rt, "Derain/UHD-Rain")
+meta_folder = os.path.join(rt, "metas")
+os.makedirs(meta_folder, exist_ok=True)
+
+for dset in ['training_set', 'testing_set']: # train, test
+    dset_path = os.path.join(rt, dset)
+    input_folder = os.path.join(dset_path, 'input')
+    gt_folder = os.path.join(dset_path, 'gt')
+    list_file = []        
+    for ct, sample in enumerate(os.listdir(input_folder)):
+        input_file = os.path.join(input_folder, sample)
+        gt_file = os.path.join(gt_folder, sample)
+        list_file.append((input_file, gt_file))
+    
+    set_dict = {'training_set': 'train', 'testing_set':'test'}
+    with open(os.path.join(meta_folder,'{}.list'.format(set_dict[dset])), 'w') as fp:
+        for item in list_file:
+            fp.write('{} {} {}\n'.format(item[0], item[1], None))
+    print(rt, ct+1)
+
+
+"""
+Desnow: Snow100k, UHD-Snow
+"""
+# Snow100k: download: https://pan.baidu.com/s/1Y8fq8qQjC0YK5DTktYPfbQ?pwd=nyop#list/path=/sharelink688030094-540249285017805/snow100k&parentPath=/sharelink688030094-540249285017805
+# Training set (50,000 images, 7.8GB), Test set (50,000 images, 7.8GB), Realistic snowy images (1,329 images, 67MB).  
+rt = os.path.join(base_rt, "Desnow/Snow100k")
+meta_folder = os.path.join(rt, "metas")
+os.makedirs(meta_folder, exist_ok=True)
+
+folder = Path(rt)
+img_list = list(folder.rglob("*.[jp][pn]g"))
+for data in img_list:
+    if " " in str(data):
+        old = str(data)
+        new = old.replace(" ", "_")
+        os.rename(old, new)
+
+# # training
+train_rt = os.path.join(rt, 'all')
+train_gt_rt = os.path.join(train_rt, 'gt')
+train_img_rt = os.path.join(train_rt, 'synthetic')
+list_file = []
+total_ct = 0
+for ct, sample in enumerate(os.listdir(train_img_rt)):
+    if ".png" in sample or ".jpg" in sample or ".jpeg" in sample or ".bmp" in sample or ".tif" in sample or ".JPG" in sample or ".PNG" in sample:
+        img_ = os.path.join(train_img_rt, sample)
+        gt_ = os.path.join(train_gt_rt, sample)
+        list_file.append((img_, gt_)) 
+        total_ct += 1
+
+with open(os.path.join(meta_folder,'train.list'), 'w') as fp:
+    for item in list_file:
+        fp.write('{} {} {}\n'.format(item[0], item[1], None))
+print(train_rt, total_ct)
+
+# test_syn
+test_rt = os.path.join(rt, 'media/jdway/GameSSD/overlapping/test')
+for dset in os.listdir(test_rt):
+    test_folder = os.path.join(test_rt, dset)
+    test_gt_rt = os.path.join(test_folder, 'gt')
+    test_img_rt = os.path.join(test_folder, 'synthetic')
+    list_file = []
+    total_ct = 0
+    for ct, sample in enumerate(os.listdir(test_img_rt)):
+        if ".png" in sample or ".jpg" in sample or ".jpeg" in sample or ".bmp" in sample or ".tif" in sample or ".JPG" in sample or ".PNG" in sample:
+            img_ = os.path.join(test_img_rt, sample)
+            gt_ = os.path.join(test_gt_rt, sample)
+            list_file.append((img_, gt_)) 
+            total_ct += 1
+
+    with open(os.path.join(meta_folder,'test_%s.list'%(dset[-1])), 'w') as fp:
+        for item in list_file:
+            fp.write('{} {} {}\n'.format(item[0], item[1], None))
+    print(test_folder, total_ct)
+
+# real-world testing
+test_rt = os.path.join(rt, 'realistic')
+list_file = []
+total_ct = 0
+for ct, sample in enumerate(os.listdir(test_rt)):
+    if ".png" in sample or ".jpg" in sample or ".jpeg" in sample or ".bmp" in sample or ".tif" in sample or ".JPG" in sample or ".PNG" in sample:
+        img_ = os.path.join(test_rt, sample)
+        list_file.append(img_) 
+        total_ct += 1
+
+with open(os.path.join(meta_folder,'test_realistic.list'), 'w') as fp:
+    for item in list_file:
+        fp.write('{} {} {}\n'.format(item, None, None))
+print(test_rt, total_ct)
+
+
+# UHD-Snow
+# pre-process for UHD-Snow: https://github.com/wlydlut/uhddip
+# UHDDIP: Ultra-High-Definition Restoration: New Benchmarks and A Dual Interaction Prior-Driven Solution
+rt = os.path.join(base_rt, "Desnow/UHD-Snow")
+meta_folder = os.path.join(rt, "metas")
+os.makedirs(meta_folder, exist_ok=True)
+for dset in ['training_set', 'testing_set']: # train, test
+    dset_path = os.path.join(rt, dset)
+    input_folder = os.path.join(dset_path, 'input')
+    gt_folder = os.path.join(dset_path, 'gt')
+    list_file = []        
+    total_ct = 0
+    for ct, sample in enumerate(os.listdir(input_folder)):
+        if ".png" in sample or ".jpg" in sample or ".jpeg" in sample or ".bmp" in sample or ".tif" in sample or ".JPG" in sample or ".PNG" in sample:
+            input_file = os.path.join(input_folder, sample)
+            gt_file = os.path.join(gt_folder, sample)
+            list_file.append((input_file, gt_file))
+            total_ct += 1
+    
+    set_dict = {'training_set': 'train', 'testing_set':'test'}
+    with open(os.path.join(meta_folder,'{}.list'.format(set_dict[dset])), 'w') as fp:
+        for item in list_file:
+            fp.write('{} {} {}\n'.format(item[0], item[1], None))
+    print(dset_path, total_ct)
+
+"""
+Lowlight: DICM, LIME, LOL, MEF, NPE, VV
+"""
+# LOL
+# pre-process for LOL: https://github.com/fediory/hvi-cidnet
+# Chen Wei, Wenjing Wang, Wenhan Yang, and Jiaying Liu. Deep retinex decomposition for low-light enhancement. In BMVC,2018
+rt = os.path.join(base_rt, "LowLight/LOL")
+meta_folder = os.path.join(rt, "metas")
+os.makedirs(meta_folder, exist_ok=True)
+for dset in ['our485', 'eval15']: # train, test
+    dset_path = os.path.join(rt, dset)
+    input_folder = os.path.join(dset_path, "low")
+    gt_folder = os.path.join(dset_path, "high")
+    list_file = [] 
+    total_ct = 0
+    for ct, sample in enumerate(os.listdir(input_folder)):
+        if ".png" in sample or ".jpg" in sample or ".jpeg" in sample or ".bmp" in sample or ".tif" in sample or ".JPG" in sample or ".PNG" in sample:
+            input_file = os.path.join(input_folder, sample)
+            gt_file = os.path.join(gt_folder, sample)
+            list_file.append((input_file, gt_file))
+            total_ct += 1
+    
+    set_dict = {'our485':'train', 'eval15':'test'}
+    with open(os.path.join(meta_folder,'{}.list'.format(set_dict[dset])), 'w') as fp:
+        for item in list_file:
+            fp.write('{} {} {}\n'.format(item[0], item[1], None))
+    print(dset_path, total_ct)
+
+    
+# pre-process for low-light dataset
+# 2. DICM: 
+# 	* Chulwoo Lee, Chul Lee, and Chang-Su Kim. Contrast en- hancement based on layered difference representation. In ICIP, 2012.
+# 	* Link: Fediory/HVI-CIDNet: "You Only Need One Color Space: An Efficient Network for Low-light Image Enhancement" (github.com)
+# 3. MEF: 
+# 	* Kede Ma, Kai Zeng, and Zhou Wang. Perceptual quality assessment for multi-exposure image fusion. TIP, 2015.
+# 	* Link: Fediory/HVI-CIDNet: "You Only Need One Color Space: An Efficient Network for Low-light Image Enhancement" (github.com)
+# 4. NPE: 
+# 	* ShuhangWang,JinZheng,Hai-MiaoHu,andBoLi.Naturalness preserved enhancement algorithm for non-uniform illumination images. TIP, 2013.
+# 	* Link: Fediory/HVI-CIDNet: "You Only Need One Color Space: An Efficient Network for Low-light Image Enhancement" (github.com)
+# 5. LIME: 
+# 	* Link: Fediory/HVI-CIDNet: "You Only Need One Color Space: An Efficient Network for Low-light Image Enhancement" (github.com)
+# 6. VV: 
+# 	* Link: Fediory/HVI-CIDNet: "You Only Need One Color Space: An Efficient Network for Low-light Image Enhancement" (github.com)
+rt = os.path.join(base_rt, "LowLight")
+
+dataset = ['DICM', 'LIME', 'MEF', 'NPE', 'VV']
+for dset in dataset: 
+    # create image folder
+    # dset_pth = "./%s"%(dset)
+    # os.rename(dset_pth, 'image')
+    # os.makedirs(dset_pth)
+    # shutil.move('./image', dset_pth)
+
+    # list sample
+    dset_pth = os.path.join(rt, dset)
+    list_file = []    
+    input_folder = os.path.join(dset_pth, 'image')
+    meta_folder = os.path.join(dset_pth, "metas")
+    os.makedirs(meta_folder, exist_ok=True)
+    folder = Path(input_folder)
+    img_list = list(folder.rglob("*.[jp][pn]g"))
+    for data in img_list:
+        if " " in str(data):
+            old = str(data)
+            new = old.replace(" ", "_")
+            os.rename(old, new)
+
+    total_ct = 0
+    for ct, sample in enumerate(os.listdir(input_folder)):
+        if ".png" in sample or ".jpg" in sample or ".jpeg" in sample or ".bmp" in sample or ".tif" in sample or ".JPG" in sample or ".PNG" in sample:
+            input_file = os.path.join(input_folder, sample)
+            list_file.append((input_file))
+            total_ct += 1
+
+    with open(os.path.join(meta_folder, 'test.list'), 'w') as fp:
+        for item in list_file:
+            fp.write('{} {} {}\n'.format(item, None, None))
+    print(input_folder, total_ct)
+
+"""
+Other: UDC
+"""
+# pre-process for UDC(TOLED+POLED): https://yzhouas.github.io/projects/UDC/udc.html 
+# Yuqian Zhou, David Ren, Neil Emerton, Sehoon Lim, and Timothy Large. Image restoration for under-display camera. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9179–9188, 2021.
+def mat2png(dset_folder, name, rt):
+    # dset_folder: '~/UDC/{poled,toled}', name: 'poled_test_display.mat', save_folder: '~/UDC/image/'
+    udc_key = name.split('.')[0][6:]
+    udc_file = os.path.join(dset_folder, name)
+    udc_mat = loadmat(udc_file)[udc_key]
+    dset, imggt = udc_key.split('_')[0], udc_key.split('_')[1]
+    dset_folder = os.path.join(rt, dset)
+    os.makedirs(dset_folder, exist_ok=True)
+    if imggt == 'display':
+        imggt = 'image'
+    else: 
+        imggt = 'gt'
+    imggt_folder = os.path.join(dset_folder, imggt)
+    os.makedirs(imggt_folder, exist_ok=True)
+
+    # restoration
+    n_im, h, w, c = udc_mat.shape
+    results = udc_mat.copy()
+    for i in range(n_im):
+        print(i, end='\r')
+        udc = np.reshape(udc_mat[i, :, :, :], (h, w, c))
+        sample = Image.fromarray(np.uint8(udc)).convert('RGB')
+        sample = sample.save(os.path.join(imggt_folder, '%s_%d.png'%(name.split('_')[0], i)))
+
+rt = os.path.join(base_rt, "Other/UDC")
+meta_folder = os.path.join(rt, "metas")
+os.makedirs(meta_folder, exist_ok=True)
+# pre-processing
+poled_folder = os.path.join(rt, 'poled')
+poled_sample = ['poled_test_display.mat', 'poled_test_gt.mat',
+                'poled_val_display.mat', 'poled_val_gt.mat']
+toled_folder = os.path.join(rt, 'toled')
+toled_sample = ['toled_test_display.mat', 'toled_test_gt.mat',
+                'toled_val_display.mat', 'toled_val_gt.mat']
+
+# for sample_set in poled_sample:
+#     print(sample_set)
+#     mat2png(poled_folder, sample_set, rt)
+#     print()
+
+# for sample_set in toled_sample:
+#     print(sample_set)
+#     mat2png(toled_folder, sample_set, rt)
+#     print()
+
+for dset in ['val', 'test']: # train, test
+    dset_folder = os.path.join(rt, dset)
+    input_folder = os.path.join(dset_folder, 'image')
+    gt_folder = os.path.join(dset_folder, 'gt')
+    list_file = []   
+    total_gt = 0
+    for ct, sample in enumerate(os.listdir(input_folder)):
+        if ".png" in sample or ".jpg" in sample or ".jpeg" in sample or ".bmp" in sample or ".tif" in sample or ".JPG" in sample or ".PNG" in sample:
+            input_file = os.path.join(input_folder, sample)
+            gt_file = os.path.join(gt_folder, sample)
+            list_file.append((input_file, gt_file))
+            total_gt += 1
+    
+    with open(os.path.join(meta_folder,'{}.list'.format(dset)), 'w') as fp:
+        for item in list_file:
+            fp.write('{} {} {}\n'.format(item[0], item[1], None))
+    print(dset_folder, total_gt)
+
+
+
+"""
+SR: DIV2K, Flickr2K, OST
+"""
+# OST
+rt = os.path.join(base_rt, "SuperResolution/OST")
+meta_folder = os.path.join(rt, "metas")
+os.makedirs(meta_folder, exist_ok=True)
+list_file = [] 
+total_ct = 0
+image_folder = os.path.join(rt, "images")
+for dset in os.listdir(image_folder): # train, test
+    input_folder = os.path.join(image_folder, dset)
+    for ct, sample in enumerate(os.listdir(input_folder)):
+        if ".png" in sample or ".jpg" in sample or ".jpeg" in sample or ".bmp" in sample or ".tif" in sample or ".JPG" in sample or ".PNG" in sample:
+            input_file = os.path.join(input_folder, sample)
+            list_file.append((input_file, None))
+            total_ct += 1
+    
+with open(os.path.join(meta_folder,'OST.list'), 'w') as fp:
+    for item in list_file:
+        fp.write('{} {} {}\n'.format(None, item[0], None))
+print(rt, total_ct)
+
+
+# Flickr2K
+rt = os.path.join(base_rt, "SuperResolution/Flickr2K")
+meta_folder = os.path.join(rt, "metas")
+os.makedirs(meta_folder, exist_ok=True)
+
+list_file = [] 
+total_ct = 0
+for dset in ['images']: # train, test
+    input_folder = os.path.join(rt, dset)
+    for ct, sample in enumerate(os.listdir(input_folder)):
+        if ".png" in sample or ".jpg" in sample or ".jpeg" in sample or ".bmp" in sample or ".tif" in sample or ".JPG" in sample or ".PNG" in sample:
+            input_file = os.path.join(input_folder, sample)
+            list_file.append((input_file, None))
+            total_ct += 1
+
+    with open(os.path.join(meta_folder,'Flickr2K.list'), 'w') as fp:
+        for item in list_file:
+            fp.write('{} {} {}\n'.format(None, item[0], None))
+    print(rt, total_ct)
+
+    
+# DIV2K
+rt = os.path.join(base_rt, "SuperResolution/DIV2K")
+meta_folder = os.path.join(rt, "metas")
+os.makedirs(meta_folder, exist_ok=True)
+for dset in ['DIV2K_train_HR', 'DIV2K_valid_HR']: # train, test
+    input_folder = os.path.join(rt, dset)
+    list_file = []  
+    total_ct = 0
+    for ct, sample in enumerate(os.listdir(input_folder)):
+        print(ct, end='\r')
+        if ".png" in sample or ".jpg" in sample or ".jpeg" in sample or ".bmp" in sample or ".tif" in sample or ".JPG" in sample or ".PNG" in sample:
+            input_file = os.path.join(input_folder, sample)
+            list_file.append((input_file, None))
+            total_ct += 1
+    
+    with open(os.path.join(meta_folder,'{}.list'.format(dset)), 'w') as fp:
+        for item in list_file:
+            fp.write('{} {} {}\n'.format(None, item[0], None))
+    print(input_folder, total_ct)

1_generate_iqa.py

@@ -0,0 +1,393 @@
+import os
+from PIL import Image
+from pathlib import Path
+import numpy as np
+import random
+import torchvision.transforms.functional as TF
+import torchvision.transforms as transforms
+import cv2
+import re
+import json
+
+# ir dataset dictionary:
+base_rt = f'/home/CORP/hsiang.chen/Project/Datasets/IR'
+dataset_dict = {
+    # Image Restoration
+    ## Super Resolution (3)
+    "SR": {
+        "DIV2K": {'train': 'SuperResolution/DIV2K/metas/DIV2K_train_HR.list',  # (800, single)
+                'val': 'SuperResolution/DIV2K/metas/DIV2K_valid_HR.list'},   # (100, single)
+        "Flickr2K": {'train': 'SuperResolution/Flickr2K/metas/Flickr2K.list'}, # (2650, single)
+        "OST": {'train': 'SuperResolution/OST/metas/OST.list'},                # (10324, single)
+    },
+
+    ## Derain (6)
+    "Rain": {
+        "RainTrainL": {'train': 'Derain/RainTrainL/metas/train.list'}, # (200, pair)
+        "Rain100L": {'test': 'Derain/Rain100L/metas/test.list'},       # (100, pair)
+        "LHPRain": {'train': 'Derain/LHPRain/metas/train.list',        # (2100, pair)    
+                    'val': 'Derain/LHPRain/metas/val.list',            # (600, pair)
+                    'test': 'Derain/LHPRain/metas/test.list'},         # (300, pair)
+        "UHDRain": {'train': 'Derain/UHD-Rain/metas/train.list',       # (3000, pair)
+                    'test': 'Derain/UHD-Rain/metas/test.list'},        # (200, pair)
+        "Practical": {'test': 'Derain/Practical/metas/test.list'},     # (15, real)
+        "RainDS_syn_rainstreak": {'train': 'Derain/RainDS/metas/RainDS_syn_train_rainstreak.list',  # (1000, pair)
+                                'test': 'Derain/RainDS/metas/RainDS_syn_test_rainstreak.list'},   # (200, pair)
+        "RainDS_syn_raindrop": {'train': 'Derain/RainDS/metas/RainDS_syn_train_raindrop.list',      # (1000, pair)
+                                'test': 'Derain/RainDS/metas/RainDS_syn_test_raindrop.list'},       # (200, pair)
+        "RainDS_syn_rainstreak_raindrop": {'train': 'Derain/RainDS/metas/RainDS_syn_train_rainstreak_raindrop.list', # (1000, pair)
+                                        'test': 'Derain/RainDS/metas/RainDS_syn_test_rainstreak_raindrop.list'},  # (200, pair)
+        "RainDS_real_rainstreak": {'train': 'Derain/RainDS/metas/RainDS_real_train_set_rainstreak.list', # (150, pair)
+                                'test': 'Derain/RainDS/metas/RainDS_real_test_set_rainstreak.list'},  # (98, pair)
+        "RainDS_real_raindrop": {'train': 'Derain/RainDS/metas/RainDS_real_train_set_raindrop.list',     # (150, pair)
+                                'test': 'Derain/RainDS/metas/RainDS_real_test_set_raindrop.list'},      # (98, pair)
+        "RainDS_real_rainstreak_raindrop": {'train': 'Derain/RainDS/metas/RainDS_real_train_set_rainstreak_raindrop.list', # (150, pair)
+                                            'test': 'Derain/RainDS/metas/RainDS_real_test_set_rainstreak.list'},           # (98, pair)
+    },
+
+    ## Dehaze (5)
+    "Fog":{
+        "SOTS": {'test': 'Dehaze/SOTS/metas/test.list'},           # (500, pair)
+        "OTS": {'train': 'Dehaze/OTS/metas/train.list'},           # (72135, pair)
+        "4kID": {'train': 'Dehaze/4kID/metas/train.list',          # (15606, pair)
+                'test': 'Dehaze/4kID/metas/test.list'},           # (97, pair)
+        "Unann": {'test': 'Dehaze/UnannotatedHazyImages/metas/test.list'},   # (4809, real)
+        "NH-Haze": {'test': 'Dehaze/NH-Haze/metas/test.list'},     # (5, real)
+    },
+
+    ## Denoise  (9)
+    "Noise": {
+        "BSD400": {'train': 'Denoise/BSD400/metas/BSD400.list'},       # (400, syn)
+        "WED": {'train': 'Denoise/WaterlooED/metas/WaterlooED.list'},  # (4744, syn)
+        "BSD68": {'test': 'Denoise/BSD68/metas/BSD68.list'},           # (68, syn)
+        "Urban": {'test': 'Denoise/Urban100/metas/Urban100.list'},     # (100, syn)
+        "CBSD68": {'test': 'Denoise/CBSD68/metas/CBSD68.list'},        # (68, syn)
+        "Kodak": {'test': 'Denoise/Kodak/metas/Kodak.list'},           # (24, syn)
+        "McMaster": {'test': 'Denoise/McMaster/metas/McMaster.list'},  # (18, syn)
+        "Set12": {'test': 'Denoise/Set12/metas/Set12.list'},           # (12, syn)
+        "SIDD": {'train': 'Denoise/SIDD/metas/train.list',             # (320, pair)
+                'test': 'Denoise/SIDD/metas/test.list'},              # (1280, pair)
+    },
+
+    ## Desnow (2)
+    "Snow": {
+        "Snow100k": {'train': 'Desnow/Snow100k/metas/train.list'},     # (50000, syn)
+        "Snow100k-S": {'test': 'Desnow/Snow100k/metas/test_S.list'},   # (16611, syn)
+        "Snow100k-M": {'test': 'Desnow/Snow100k/metas/test_M.list'},   # (16588, syn)
+        "Snow100k-L": {'test': 'Desnow/Snow100k/metas/test_L.list'},   # (16801, syn)
+        "Snow100k-R": {'test': 'Desnow/Snow100k/metas/test_realistic.list'},   # (1329, real)
+        "UHDSnow": {'train': 'Desnow/UHD-Snow/metas/train.list',   # (3000, pair)
+                    'test': 'Desnow/UHD-Snow/metas/test.list'},    # (200, pair)
+    },
+
+    ## Deblur (3)
+    "Blur": {
+        "GoPro": {'train': 'Deblur/GoPro/metas/train.list',        # (2103, pair)
+                'test': 'Deblur/GoPro/metas/test.list'},         # (1111, pair)
+        "HIDE": {'train': 'Deblur/HIDE/metas/train.list',  # (6397, pair)
+                'test': 'Deblur/HIDE/metas/test.list'},   # (2025, pair)
+        "RealBlur-J": {'test': 'Deblur/RealBlur-J_ECC_IMCORR_centroid_itensity_ref/metas/test.list'},     # (980, pair)
+        "RealBlur-R": {'test': 'Deblur/RealBlur-R_BM3D_ECC_IMCORR_centroid_itensity_ref/metas/test.list'},# (980, pair)
+    },
+
+    ## Lowlight (6)
+    "Low-light": {
+        "LOL": {'train': 'LowLight/LOL/metas/train.list',      # (485, pair)
+                'test': 'LowLight/LOL/metas/test.list'},       # (15, pair)
+        "DICM": {'test': 'LowLight/DICM/metas/test.list'},     # (69, real)
+        "MEF": {'test': 'LowLight/MEF/metas/test.list'},       # (17, real)
+        "NPE": {'test': 'LowLight/NPE/metas/test.list'},       # (8, real)
+        "LIME": {'test': 'LowLight/LIME/metas/test.list'},     # (10, real)
+        "VV": {'test': 'LowLight/VV/metas/test.list'},         # (24, real)
+    },
+
+    ## Others (1)
+    "Unknown": {
+        "UDC": {'val': 'Other/UDC/metas/val.list',            # (60, pair)
+                'test': 'Other/UDC/metas/test.list'},         # (60, pair)
+    },
+}
+
+def IRImageData(listfile):
+    # [degradation, clean, label]
+    paths = []
+    with open(listfile) as fin:
+        for line in fin:
+            line = line.strip().split()
+            if len(line) == 3:
+                paths.append(line)
+    paths = sorted(paths)
+    # Dataset
+    LQ_list = []
+    HQ_list = []
+    for data in paths:
+        lq_pth, hq_pth, label = data
+        if os.path.isfile(lq_pth):
+            LQ_list.append(lq_pth)
+        if os.path.isfile(hq_pth):
+            HQ_list.append(hq_pth)  
+    return LQ_list, HQ_list
+
+# question dictionary:
+question_dict = {
+    "Full-Reference": {
+        "ONE": [
+            "Compared to the reference, what ONE distortion stands out most in the evaluated image?",
+            "Determine the leading ONE degradation when comparing the evaluated image to the reference.",
+            "Determine the most impactful ONE distortion in the evaluated image compared to the reference.",
+            "Highlight the most significant ONE distortion in the evaluated image in comparison to the reference.",
+            "Identify the chief ONE degradation in the evaluated image when compared to the reference.",
+            "Identify the most notable ONE distortion in the evaluated image's quality when compared to the reference.",
+            "In comparison to the reference, what ONE distortion is most prominent in the evaluated image?",
+            "What ONE distortion is most apparent in the evaluated image relative to the reference?",
+            "What ONE distortion most significantly affects the evaluated image compared to the reference?",
+            "What ONE distortion stands out in the evaluated image against the reference?",
+            "What critical ONE quality degradation is present in the evaluated image versus the reference?",
+        ], 
+        "TWO": [
+            "Compared to the reference, what TWO distortions stand out most in the evaluated image?",
+            "Determine the leading TWO degradations when comparing the evaluated image to the reference.",
+            "Determine the most impactful TWO distortions in the evaluated image compared to the reference.",
+            "Highlight the most significant TWO distortions in the evaluated image in comparison to the reference.",
+            "Identify the chief TWO degradations in the evaluated image when compared to the reference.",
+            "Identify the most notable TWO distortions in the evaluated image's quality when compared to the reference.",
+            "In comparison to the reference, what TWO distortions are most prominent in the evaluated image?",
+            "What TWO distortions are most apparent in the evaluated image relative to the reference?",
+            "What TWO distortions most significantly affect the evaluated image compared to the reference?",
+            "What TWO distortions stand out in the evaluated image against the reference?",
+            "What critical TWO quality degradations are present in the evaluated image versus the reference?",
+        ],
+        "Common": [
+            "Compared to the reference, what distortion(s) stand out most in the evaluated image?",
+            "Determine the leading degradation(s) when comparing the evaluated image to the reference.",
+            "Determine the most impactful distortion(s) in the evaluated image compared to the reference.",
+            "Highlight the most significant distortion(s) in the evaluated image in comparison to the reference.",
+            "Identify the chief degradation(s) in the evaluated image when compared to the reference.",
+            "Identify the most notable distortion(s) in the evaluated image's quality when compared to the reference.",
+            "In comparison to the reference, what distortion(s) are most prominent in the evaluated image?",
+            "What critical quality degradation(s) are present in the evaluated image versus the reference?",
+            "What distortion(s) are most apparent in the evaluated image relative to the reference?",
+            "What distortion(s) most significantly affect the evaluated image compared to the reference?",
+            "What distortion(s) stand out in the evaluated image against the reference?"
+        ]
+    },
+    "Non-Reference": {
+        "ONE": [
+            "Determine the leading ONE degradation in the evaluated image.",
+            "Determine the most impactful ONE distortion in the evaluated image.",
+            "Highlight the most significant ONE distortion in the evaluated image.",
+            "Identify the chief ONE degradation in the evaluated image.",
+            "Identify the most critical ONE distortion in the evaluated image.",
+            "Identify the most notable ONE distortion in the evaluated image's quality.",
+            "In terms of image quality, what is the most glaring ONE issue with the evaluated image?",
+            "In the evaluated image, what ONE distortion is most detrimental to image quality?",
+            "Pinpoint the foremost ONE image quality issue in the evaluated image.",
+            "What ONE distortion is most apparent in the evaluated image?",
+            "What ONE distortion is most evident in the evaluated image?",
+            "What ONE distortion is most prominent in the evaluated image?",
+            "What ONE distortion is most prominent when examining the evaluated image?",
+            "What ONE distortion most detrimentally affects the overall quality of the evaluated image?",
+            "What ONE distortion most notably affects the clarity of the evaluated image?",
+            "What ONE distortion most significantly affects the evaluated image?",
+            "What ONE distortion stands out in the evaluated image?",
+            "What ONE quality degradation is most apparent in the evaluated image?",
+            "What critical ONE quality degradation is present in the evaluated image?",
+            "What is the foremost ONE distortion affecting the evaluated image's quality?",
+            "What is the leading ONE distortion in the evaluated image?",
+            "What is the most critical ONE image quality issue in the evaluated image?",
+            "What is the most severe ONE degradation observed in the evaluated image?",
+            "What is the primary ONE degradation observed in the evaluated image?"
+        ],
+        "TWO": [
+            "Determine the leading TWO degradations in the evaluated image.",
+            "Determine the most impactful TWO distortions in the evaluated image.",
+            "Highlight the most significant TWO distortions in the evaluated image.",
+            "Identify the chief TWO degradations in the evaluated image.",
+            "Identify the most critical TWO distortions in the evaluated image.",
+            "Identify the most notable TWO distortions in the evaluated image's quality.",
+            "In terms of image quality, what are the most glaring TWO issues with the evaluated image?",
+            "In the evaluated image, what TWO distortions are most detrimental to image quality?",
+            "Pinpoint the foremost TWO image quality issues in the evaluated image.",
+            "What TWO distortions are most apparent in the evaluated image?",
+            "What TWO distortions are most evident in the evaluated image?",
+            "What TWO distortions are most prominent in the evaluated image?",
+            "What TWO distortions are most prominent when examining the evaluated image?",
+            "What TWO distortions most detrimentally affect the overall quality of the evaluated image?",
+            "What TWO distortions most notably affect the clarity of the evaluated image?",
+            "What TWO distortions most significantly affect the evaluated image?",
+            "What TWO distortions stand out in the evaluated image?",
+            "What TWO quality degradations are most apparent in the evaluated image?",
+            "What are the foremost TWO distortions affecting the evaluated image's quality?",
+            "What are the leading TWO distortions in the evaluated image?",
+            "What are the most critical TWO image quality issues in the evaluated image?",
+            "What are the most severe TWO degradations observed in the evaluated image?",
+            "What are the primary TWO degradations observed in the evaluated image?",
+            "What critical TWO quality degradations are present in the evaluated image?",
+        ],
+        "Common": [
+            "Determine the leading degradation(s) in the evaluated image.",
+            "Determine the most impactful distortion(s) in the evaluated image.",
+            "Highlight the most significant distortion(s) in the evaluated image.",
+            "Identify the chief degradation(s) in the evaluated image.",
+            "Identify the most critical distortion(s) in the evaluated image.",
+            "Identify the most notable distortion(s) in the evaluated image's quality.",
+            "In terms of image quality, what are the most glaring issue(s) with the evaluated image?",
+            "In the evaluated image, what distortion(s) are most detrimental to image quality?",
+            "Pinpoint the foremost image quality issue(s) in the evaluated image.",
+            "What are the foremost distortion(s) affecting the evaluated image's quality?",
+            "What are the leading distortion(s) in the evaluated image?",
+            "What are the most critical image quality issue(s) in the evaluated image?",
+            "What are the most severe degradation(s) observed in the evaluated image?",
+            "What are the primary degradation(s) observed in the evaluated image?",
+            "What critical quality degradation(s) are present in the evaluated image?",
+            "What distortion(s) are most apparent in the evaluated image?",
+            "What distortion(s) are most evident in the evaluated image?",
+            "What distortion(s) are most prominent in the evaluated image?",
+            "What distortion(s) are most prominent when examining the evaluated image?",
+            "What distortion(s) most detrimentally affect the overall quality of the evaluated image?",
+            "What distortion(s) most notably affect the clarity of the evaluated image?",
+            "What distortion(s) most significantly affect the evaluated image?",
+            "What distortion(s) stand out in the evaluated image?",
+            "What quality degradation(s) are most apparent in the evaluated image?"
+        ]
+    }
+}
+
+def question_generate(ref="Full-Reference", degra="Common"):
+    option = f" Answer the question using a single word or phrase."
+    template = random.choice(question_dict[ref]["Common"] + question_dict[ref][degra])
+    if random.random() >= 0.4:
+        template += option
+    return template
+
+if __name__ == "__main__":
+    for degradation, degra_dict in dataset_dict.items():
+        for dname, ddict in degra_dict.items():
+            for dset, list_path in ddict.items():
+
+                meta_refA = []
+                meta_A = []
+                meta_syn = []
+
+                # read data list
+                paths = []
+                with open(list_path) as fin:
+                    for line in fin:
+                        line = line.strip().split()
+                        if len(line) == 3:
+                            paths.append(line)
+                paths = sorted(paths)
+
+                # Dataset
+                LQ_list = []
+                HQ_list = []
+                for data in paths:
+                    lq_pth, hq_pth, label = data
+
+                    if os.path.isfile(hq_pth):
+                        HQ_list.append(hq_pth)  
+                        image_ref = os.path.relpath(hq_pth, base_rt).replace("\\", "/")
+                        id = os.path.basename(image_ref)
+                    else:
+                        image_ref = None
+
+                    if os.path.isfile(lq_pth):
+                        LQ_list.append(lq_pth)
+                        image_A = os.path.relpath(lq_pth, base_rt).replace("\\", "/")
+                        id = os.path.basename(image_A)
+                    else:
+                        image_A = None
+
+                    meta_refA.append({
+                        "distortion_class": degradation,
+                        "distortion_name": degradation,
+                        "severity": 3,
+                        "id": id,
+                        "image_ref": image_ref, 
+                        "image_A": image_A, 
+                        "image_B": None, 
+                        "task_type": "quality_single_A",
+                        "conversations": [
+                            {
+                                "from": "human",
+                                "value": question_generate(ref="Full-Reference", degra="ONE"),
+                            },
+                            {
+                                "from": "gpt",
+                                "value": degradation
+                            }
+                        ],
+                    })
+
+                    meta_A.append({
+                        "distortion_class": degradation,
+                        "distortion_name": degradation,
+                        "severity": 3,
+                        "id": id,
+                        "image_ref": None, 
+                        "image_A": image_A, 
+                        "image_B": None, 
+                        "task_type": "quality_single_A_noref",
+                        "conversations": [
+                            {
+                                "from": "human",
+                                "value": question_generate(ref="Non-Reference", degra="ONE"),
+                            },
+                            {
+                                "from": "gpt",
+                                "value": degradation
+                            }
+                        ],
+                    })
+
+                    meta_syn.append({
+                        "distortion_class": degradation,
+                        "distortion_name": degradation,
+                        "severity": 3,
+                        "id": id,
+                        "image_ref": None, 
+                        "image_A": image_ref, 
+                        "image_B": None, 
+                        "task_type": "quality_single_A_noref",
+                        "conversations": [
+                            {
+                                "from": "human",
+                                "value": question_generate(ref="Non-Reference", degra="ONE"),
+                            },
+                            {
+                                "from": "gpt",
+                                "value": degradation
+                            }
+                        ],
+                    })
+
+                print(f"[{degradation}]: {dname}-{dset} - LQ[{len(LQ_list)}], HQ[{len(HQ_list)}]")
+
+                if len(LQ_list) > 0 and len(HQ_list) > 0 and len(LQ_list) == len(HQ_list):   # pair
+                    meta_refA_pth = list_path.replace(".list", "_iqa_refA_brief.json")
+                    meta_A_pth = list_path.replace(".list", "_iqa_A_brief.json")
+                    with open(meta_refA_pth, "w") as f:
+                        json.dump(meta_refA, f, indent=4)
+                    with open(meta_A_pth, "w") as f:
+                        json.dump(meta_A, f, indent=4)
+                    print(f"Done. Metadata saved to: {meta_refA_pth} and {meta_A_pth}")
+                
+                elif len(LQ_list) > 0 and len(HQ_list) == 0:    # real image
+                    meta_A_pth = list_path.replace(".list", "_iqa_A_brief.json")
+                    with open(meta_A_pth, "w") as f:
+                        json.dump(meta_A, f, indent=4)
+                    print(f"Done. Metadata saved to: {meta_A_pth}")
+                
+                elif len(LQ_list) == 0 and len(HQ_list) > 0:    # syn image
+                    meta_refA_pth = list_path.replace(".list", "_iqa_refA_brief.json")
+                    meta_syn_pth = list_path.replace(".list", "_iqa_syn_brief.json")
+                    with open(meta_refA_pth, "w") as f:
+                        json.dump(meta_refA, f, indent=4)
+                    with open(meta_syn_pth, "w") as f:
+                        json.dump(meta_syn, f, indent=4)
+                    print(f"Done. Metadata saved to: {meta_refA_pth} and {meta_syn_pth}")
+
+                else:
+                    raise KeyError(f"the task is not matched, please check the dataset {list_path}")
+            
+

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