scripts/1_generate_iqa_brief.py

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/work/shared-fi-datasets-01/users/hsiang.chen/Project/Datasets/IR'
dataset_dict = {
    # Image Restoration
    ## Super Resolution (3)
    "HR": {
        "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_HR.list'}, # (2650, single)
        "OST": {'train': 'SuperResolution/OST/metas/OST_HR.list'},                # (10324, single)
    },

    "Low Resolution": {
        "DIV2K": {'train1': 'SuperResolution/DIV2K/metas/DIV2K_train_pair_SR1.list',    # (800, pair)
                  'train2': 'SuperResolution/DIV2K/metas/DIV2K_train_pair_SR2.list',    # (800, pair)
                  'train3': 'SuperResolution/DIV2K/metas/DIV2K_train_pair_SR3.list',    # (800, pair)
                  'val': 'SuperResolution/DIV2K/metas/DIV2K_valid_pair_SR.list'},       # (100, pair)
        "Flickr2K": {'train1': 'SuperResolution/Flickr2K/metas/Flickr2K_train_pair_SR1.list', # (2650, pair)
                     'train2': 'SuperResolution/Flickr2K/metas/Flickr2K_train_pair_SR2.list', # (2650, pair)
                     'train3': 'SuperResolution/Flickr2K/metas/Flickr2K_train_pair_SR3.list'},# (2650, pair)
        "OST": {'train1': 'SuperResolution/OST/metas/OST_train_pair_SR1.list',        # (10324, pair)
                'train2': 'SuperResolution/OST/metas/OST_train_pair_SR2.list',        # (10324, pair)
                'train3': 'SuperResolution/OST/metas/OST_train_pair_SR3.list'},       # (10324, pair)      
    },

    ## Derain (5)
    "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)
    },

    ## Deraindrop (2)
    "RainDrop": {
        "RainDrop": {'train': 'Derain/RainDrop/metas/Raindrop_train.list',      # (861, pair)
                     'test_a': 'Derain/RainDrop/metas/Raindrop_test_a.list',    # (58, pair)
                     'test_b': 'Derain/RainDrop/metas/Raindrop_test_b.list'},   # (249, pair)
        "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',
                   'train1': 'Denoise/BSD400/metas/BSD400_Noise_L1.list',
                   'train2': 'Denoise/BSD400/metas/BSD400_Noise_L3.list',
                   'train3': 'Denoise/BSD400/metas/BSD400_Noise_L5.list',},  # (400, syn)    
        "WED": {'train': 'Denoise/WaterlooED/metas/WaterlooED.list',
                'train1': 'Denoise/WaterlooED/metas/WaterlooED_Noise_L1.list',
                'train2': 'Denoise/WaterlooED/metas/WaterlooED_Noise_L3.list',
                'train3': 'Denoise/WaterlooED/metas/WaterlooED_Noise_L5.list',},  # (4744, syn)
        "BSD68": {'test': 'Denoise/BSD68/metas/BSD68.list',
                  'test1': 'Denoise/BSD68/metas/BSD68_Noise_L1.list',
                  'test2': 'Denoise/BSD68/metas/BSD68_Noise_L3.list',
                  'test3': 'Denoise/BSD68/metas/BSD68_Noise_L5.list'},           # (68, syn)
        "Urban": {'test': 'Denoise/Urban100/metas/Urban100.list',
                  'test1': 'Denoise/Urban100/metas/Urban100_Noise_L1.list',
                  'test2': 'Denoise/Urban100/metas/Urban100_Noise_L3.list',
                  'test3': 'Denoise/Urban100/metas/Urban100_Noise_L5.list'},     # (100, syn)
        "CBSD68": {'test': 'Denoise/CBSD68/metas/CBSD68.list',
                   'test1': 'Denoise/CBSD68/metas/CBSD68_Noise_L1.list',
                   'test2': 'Denoise/CBSD68/metas/CBSD68_Noise_L3.list',
                   'test3': 'Denoise/CBSD68/metas/CBSD68_Noise_L5.list',},        # (68, syn)
        "Kodak": {'test': 'Denoise/Kodak/metas/Kodak.list',
                  'test1': 'Denoise/Kodak/metas/Kodak_Noise_L1.list',
                  'test2': 'Denoise/Kodak/metas/Kodak_Noise_L3.list',
                  'test3': 'Denoise/Kodak/metas/Kodak_Noise_L5.list'},           # (24, syn)
        "McMaster": {'test': 'Denoise/McMaster/metas/McMaster.list',
                     'test1': 'Denoise/McMaster/metas/McMaster_Noise_L1.list',
                     'test2': 'Denoise/McMaster/metas/McMaster_Noise_L3.list',
                     'test3': 'Denoise/McMaster/metas/McMaster_Noise_L5.list'},  # (18, syn)
        "Set12": {'test': 'Denoise/Set12/metas/Set12.list',
                  'test1': 'Denoise/Set12/metas/Set12_Noise_L1.list',
                  'test2': 'Denoise/Set12/metas/Set12_Noise_L3.list',
                  'test3': 'Denoise/Set12/metas/Set12_Noise_L5.list',},           # (12, syn)
        "SIDD": {'train': 'Denoise/SIDD/metas/train.list',             # (320, pair)
                'test': 'Denoise/SIDD/metas/test.list'},              # (1280, pair)

        "DIV2K": {'train1': 'SuperResolution/DIV2K/metas/DIV2K_train_pair_Noise_L1.list',    # (800, pair)
                  'train2': 'SuperResolution/DIV2K/metas/DIV2K_train_pair_Noise_L3.list',    # (800, pair)
                  'train3': 'SuperResolution/DIV2K/metas/DIV2K_train_pair_Noise_L5.list',    # (800, pair)
                  'val': 'SuperResolution/DIV2K/metas/DIV2K_valid_pair_Noise.list'},         # (100, pair)
        "Flickr2K": {'train1': 'SuperResolution/Flickr2K/metas/Flickr2K_train_pair_Noise_L1.list', # (2650, pair)
                     'train2': 'SuperResolution/Flickr2K/metas/Flickr2K_train_pair_Noise_L3.list', # (2650, pair)
                     'train3': 'SuperResolution/Flickr2K/metas/Flickr2K_train_pair_Noise_L5.list'},# (2650, 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)
    },

    ## Composite (1)
    "Composite": {
        "CDD": {
            'train': 'Composite/CDD11/metas/train.list',                        # (13013, pair)
            'test_haze': 'Composite/CDD11/metas/test_haze.list',                # (200, pair)
            'test_haze_rain': 'Composite/CDD11/metas/test_haze_rain.list',      # (200, pair)
            'test_haze_snow': 'Composite/CDD11/metas/test_haze_snow.list',      # (200, pair)
            'test_low': 'Composite/CDD11/metas/test_low.list',                  # (200, pair)
            'test_low_haze': 'Composite/CDD11/metas/test_low_haze.list',        # (200, pair)
            'test_low_haze_rain': 'Composite/CDD11/metas/test_low_haze_rain.list',    # (200, pair)
            'test_low_haze_snow': 'Composite/CDD11/metas/test_low_haze_snow.list',    # (200, pair)
            'test_low_rain': 'Composite/CDD11/metas/test_low_rain.list',        # (200, pair)
            'test_low_snow': 'Composite/CDD11/metas/test_low_snow.list',        # (200, pair)
            'test_rain': 'Composite/CDD11/metas/test_rain.list',                # (200, pair)
            'test_snow': 'Composite/CDD11/metas/test_snow.list',                # (200, 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 = []
    labels = []
    for data in paths:
        lq_pth, hq_pth, label = data
        if os.path.isfile(lq_pth):
            LQ_list.append(lq_pth)
        else:
            LQ_list.append(None)

        if os.path.isfile(hq_pth):
            HQ_list.append(hq_pth)
        else:
            HQ_list.append(None)

        
    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(): # Low-light, [LOL, DICM ...]
        for dname, ddict in degra_dict.items():          # LOL, [train, test, ...]
            for dset, list_path in ddict.items():        # train, path.
                meta_refA = []
                meta_A = []
                meta_syn = []

                # read data list
                paths = []
                list_path = os.path.join(base_rt, list_path)
                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

                    if degradation == "Composite":
                        key_dict = {"haze": "Fog",
                                    "low": "Low-light",
                                    "rain": "Rain",
                                    "snow": "Snow"}
                        new_label = []
                        for one_label in label.split("_"):
                            new_label.append(key_dict[one_label])
                        annotation = ", ".join(new_label)
                    else:
                        annotation = degradation

                    if degradation == "Composite":
                        ref_question = question_generate(ref="Full-Reference", degra="Common")
                        nref_question = question_generate(ref="Non-Reference", degra="Common")
                    else:
                        ref_question = question_generate(ref="Full-Reference", degra="ONE")
                        nref_question = question_generate(ref="Non-Reference", degra="ONE")

                    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": ref_question,
                            },
                            {
                                "from": "gpt",
                                "value": annotation
                            }
                        ],
                    })

                    meta_A.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_noref",
                        "conversations": [
                            {
                                "from": "human",
                                "value": nref_question,
                            },
                            {
                                "from": "gpt",
                                "value": annotation
                            }
                        ],
                    })

                    meta_syn.append({
                        "distortion_class": degradation,
                        "distortion_name": degradation,
                        "severity": 3,
                        "id": id,
                        "image_ref": image_ref, 
                        "image_A": None, 
                        "image_B": None, 
                        "task_type": "quality_single_A_noref",
                        "conversations": [
                            {
                                "from": "human",
                                "value": nref_question,
                            },
                            {
                                "from": "gpt",
                                "value": annotation
                            }
                        ],
                    })

                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"[{os.path.relpath(meta_refA_pth, base_rt)}, ],     # LQ[{len(LQ_list)}], HQ[{len(HQ_list)}], quality_single_A,       {degradation}, {dname}-{dset}")
                    print(f"[{os.path.relpath(meta_A_pth, base_rt)}, ],     # LQ[{len(LQ_list)}], HQ[{len(HQ_list)}], quality_single_A_noref, {degradation}, {dname}-{dset}")
                
                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"[{os.path.relpath(meta_A_pth, base_rt)}, ],     # LQ[{len(LQ_list)}], HQ[{len(HQ_list)}], quality_single_A_noref, {degradation}, {dname}-{dset}")
                
                elif len(LQ_list) == 0 and len(HQ_list) > 0:    # syn image
                    meta_refA_pth = list_path.replace(".list", "_iqa_syn_refA_brief.json")
                    meta_syn_pth = list_path.replace(".list", "_iqa_syn_A_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"[{os.path.relpath(meta_refA_pth, base_rt)}, ],     # LQ[{len(LQ_list)}], HQ[{len(HQ_list)}], quality_single_A,       {degradation}, {dname}-{dset}")
                    print(f"[{os.path.relpath(meta_syn_pth, base_rt)}, ],     # LQ[{len(LQ_list)}], HQ[{len(HQ_list)}], quality_single_A_noref, {degradation}, {dname}-{dset}")
                
                else:
                    raise KeyError(f"the task is not matched, please check the dataset {list_path}")