scripts/create_weather_data.py

"""
ref: https://github.com/robustsam/RobustSAM/blob/main/data/augment.py
Source Dataset:
    BAPPS: (one to one, 150K)
    /group-volume/Human-Action-Analysis/datasets/IQA_datasets/DataDepictQA/BAPPS/images/twoafc_train/train/cnn/ref  # 38120
    /group-volume/Human-Action-Analysis/datasets/IQA_datasets/DataDepictQA/BAPPS/images/twoafc_train/train/mix/ref  # 56640
    /group-volume/Human-Action-Analysis/datasets/IQA_datasets/DataDepictQA/BAPPS/images/twoafc_train/train/traditional/ref # 56640

    PIPAL: (one to one, 50)
    /home/CORP/hsiang.chen/Project/Datasets/DataDepictQA/PIPAL/metas/train_refAB_mix_pipal_70k.json

    KADID10K: (one to one, need to find clear sample from json, estimated 70)
    /home/CORP/hsiang.chen/Project/Datasets/DataDepictQA/KADID10K/metas/train_refAB_mix_kadid_30k.json

    KADID700K: (one to one, 140k)

    DetailDescriptionLAMM/metas/detailed_description_49k.json (one to one, 57895 images, 58k)
    
Annotation Format:
    data = [
        {
            "distortion_class": "saturate_strengthen",
            "distortion_name": "saturate_strengthen_YCrCb",
            "severity": 5,
            "id": "121-cc-774921_0",
            "image_ref": "KADIS700K/ref_imgs_s224/121-cc-774921.png",
            "image_A": "KADIS700K/refA_sd_brief/dist_imgs/121-cc-774921_0.png",
            "image_B": None,
            "task_type": "quality_single_A",
            "conversations": [
                {
                    "from": "human",
                    "value": "What critical ONE quality degradation is present in the evaluated image versus the reference?",
                },
                {
                    "from": "gpt",
                    "value": "The critical ONE quality degradation presented is overly high saturation."
                }
            ]
        }
    ]
"""

import json 
import cv2
from glob import glob
import os
import numpy as np
from PIL import Image, ImageDraw
import matplotlib.pyplot as plt
import albumentations as A
import imgaug.augmenters as iaa
import random
import argparse
from tqdm import tqdm
from datetime import datetime
from pathlib import Path

import torchvision.transforms as T
import torchvision.transforms.functional as TF
import torchvision.transforms as transforms

# 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

# ----------------------------
# Level-aware parameter helpers
# ----------------------------
def lerp(a, b, t):
    return a + (b - a) * t

def choice_with_level(options_low, options_high, t):
    # 在兩組範圍之間插值得到一組範圍，再隨機抽樣
    low = (lerp(options_low[0], options_high[0], t),
           lerp(options_low[1], options_high[1], t))
    return random.uniform(low[0], low[1])

def int_range_with_level(rng_low, rng_high, t):
    low = int(round(lerp(rng_low[0], rng_high[0], t)))
    high = int(round(lerp(rng_low[1], rng_high[1], t)))
    return random.randint(low, max(low, high))

def tuple_range_with_level(rng_low, rng_high, t):
    return (lerp(rng_low[0], rng_high[0], t), lerp(rng_low[1], rng_high[1], t))

# level ∈ {1,2,3,4,5} → t ∈ [0,1],  1,2,3,4,5 -> 0, 0.25, 0.5, 0.75, 1
def level_to_t(level):
    level = max(1, min(5, int(level)))
    return (level - 1) / 4.0

# ----------------------------
# Degradations (level-aware)
# ----------------------------
def snow(image, level):
    """
    original: A.RandomSnow(brightness_coeff=1.0, snow_point_lower=0.3, snow_point_upper=0.7, p=1), 
              iaa.Snowflakes(density=0.35, flake_size=(0.6, 0.8), speed=(0.01, 0.015), angle=0)

    snow_point_lower, snow_point_upper: the cover rate of the snow
    density = (0.005, 0.075)
    density_uniformity = (0.3, 0.9)
    flake_size = (0.2, 0.7)  # ratio to image
    flake_size_unifromity = (0.4, 0.8)
    speed = (0.007, 0.03)
    
    # t = level_to_t(level)  # 1-5 to 0-1
    # 亮度係數固定 1.0，雪點密度/大小/速度隨 level 增加
    # snow_point_lower = lerp(0.1, 0.6, t)   # 0.1, 0.225, 0.35, 0.475, 0.6
    # snow_point_upper = lerp(0.3, 0.9, t)   # 0.3, 0.45, 0.6, 0.75, 0.9
    # flake_size = tuple_range_with_level((0.2, 0.4), (0.7, 1.0), t)
    # density = lerp(0.05, 0.6, t)
    # speed = tuple_range_with_level((0.005, 0.01), (0.02, 0.04), t)
    """
    brightness_coeff_level = {1: (1.1, 1.15), 2: (1.15, 1.2), 3:(1.2, 1.25), 4:(1.3, 1.35), 5:(1.35, 1.4)}
    lightness_threshold_level = {1: (60, 80), 2: (80, 100), 3:(100, 115), 4:(115, 130), 5:(130, 140)}
    brightness = brightness_coeff_level[level]
    lightness_threshold = lightness_threshold_level[level]

    aug1 = iaa.FastSnowyLandscape(
        lightness_threshold=lightness_threshold,
        lightness_multiplier=brightness
    )
    img = aug1.augment_image(image)

    density_level = {1: (0.005, 0.015), 2: (0.015, 0.030), 3: (0.030, 0.045), 4: (0.045, 0.060), 5: (0.060, 0.075)}
    density = density_level[level]
    flake_size = (0.2, 0.5)
    speed = (0.007, 0.03)
    aug = iaa.Snowflakes(density=density, density_uniformity=0.95,
                         flake_size=flake_size, speed=speed, angle=0)
    img = aug.augment_image(img) 

    params = {
        "lightness_threshold": lightness_threshold,
        "brightness": brightness,
        "flake_size": flake_size,
        "density": density,
        "speed": speed
    }
    return img, params

  
def fog(image, level):
    """
    original: fog_coef_lower = 0.1, fog_coef_upper = 0.2, alpha_coef = 0.08
    t = level_to_t(level)
    fog_coef_lower = lerp(0.05, 0.4, t)
    fog_coef_upper = lerp(0.12, 0.7, t)
    alpha_coef = lerp(0.02, 0.15, t)
    """
    ratio = {1: (0.05, 0.2), 2: (0.2, 0.4), 3:(0.4, 0.6), 4:(0.6, 0.8), 5:(0.8, 1.0)}
    aug = iaa.Fog()
    fog_map = aug.augment_image(image)   
    img = image * (1-ratio[level][1]) + fog_map * ratio[level][1]
    img = img.astype(np.uint8)

    params = {
        "alpha": ratio[level][1]
    }
    return img, params

def rain(image, level):
    """
    original: iaa.Rain(drop_size=(0.40, 0.50), speed=(0.05, 0.1))
    official: drop_size = (0.01, 0.02), speed = (0.04, 0.2)

    t = level_to_t(level)
    drop_size = tuple_range_with_level((0.20, 0.35), (0.45, 0.65), t)
    speed = tuple_range_with_level((0.02, 0.06), (0.08, 0.15), t)

    nb_iterations: 1-3 DENSITY  (1,1), (1,2), (2,2), (2,3), (3,3)
    drop_size: coarse of raindrop (corresponding to image scale), (0.01, 0.01)
    speed: length of the raindrop (more large more length but more thin)
    """
    H,W,C = image.shape

    nb_iterations_level = {1: (1,1), 2: (1,2), 3: (2,2), 4: (2, 3), 5: (3, 3)}
    drop_size_level = {1: (0.01, 0.01), 2: (0.01, 0.02), 3: (0.01, 0.02), 4: (0.01, 0.02), 5: (0.02, 0.03)}
    
    drop_rate = W / 256
    drop_size = list(np.array(drop_size_level[level]) * drop_rate)

    speed_level = (0.04, 0.2)
    speed_rate = H  / 192
    speed = list(np.array(speed_level) / speed_rate)


    aug = iaa.Rain(nb_iterations=nb_iterations_level[level], 
                   drop_size=drop_size, 
                   speed=speed)
    img = aug.augment_image(image)

    params = {"nb_iterations": nb_iterations_level[level], "drop_size": drop_size, "speed": speed}
    return img, params

DEG_FUNCS = {'Snow': snow, 'Fog': fog, 'Rain': rain}

# ----------------------------
# Main
# ----------------------------
def apply_degradation(case, image, level, seed=None):
    if seed is not None:
        random.seed(seed)
        np.random.seed(seed)
    img_out, params = DEG_FUNCS[case](image, level)
    return img_out, params

def read_rgb(path):
    bgr = cv2.imread(path, cv2.IMREAD_COLOR)
    if bgr is None:
        raise FileNotFoundError(path)
    rgb = cv2.cvtColor(bgr, cv2.COLOR_BGR2RGB)
    return rgb

def save_bgr(path, rgb):
    bgr = cv2.cvtColor(rgb, cv2.COLOR_RGB2BGR)
    cv2.imwrite(path, bgr)
    
def main():
    parser = argparse.ArgumentParser()
    parser.add_argument("--rt", type=str, default="/home/CORP/hsiang.chen/Project/Datasets/DataDepictQA")
    parser.add_argument("--save_rt", type=str, default="/home/CORP/hsiang.chen/Project/Datasets/DataDepictQA")
    parser.add_argument("--image_folder", type=str, default=None)
    parser.add_argument("--meta_folder", type=str, default=None)
    parser.add_argument("--case", type=str, default="Random", help="'Snow', 'Fog', 'Rain' or 'Random'")
    parser.add_argument("--n_per_image", type=int, default=1, help="how many degraded samples per clear image")
    parser.add_argument("--levels", type=str, default="1-5", help="level range, e.g. '3-5' or '2,3,5'")
    parser.add_argument("--seed", type=int, default=42)
    args = parser.parse_args()
    
    # 構建 level 候選
    level_tokens = [s.strip() for s in args.levels.split(",")]
    levels = []
    for tok in level_tokens:
        if "-" in tok:
            a, b = tok.split("-")
            a, b = int(a), int(b)
            levels.extend(list(range(min(a, b), max(a, b) + 1)))
        else:
            levels.append(int(tok))
    levels = sorted(set([L for L in levels if 1 <= L <= 5]))
    
    # 濾清晰圖
    clear_dir = args.image_folder
    jpg_list = glob("%s/*.jpg"%clear_dir)
    png_list = glob("%s/*.png"%clear_dir)
    bmp_list = glob("%s/*.bmp"%clear_dir)
    print(len(jpg_list), len(png_list), len(bmp_list))
    image_list = sorted(jpg_list + png_list + bmp_list)

    if len(image_list) == 0:
        print(f"No images found under {clear_dir}")
        return

    all_cases = list(DEG_FUNCS.keys())

    # save folder
    clear_folder = Path(clear_dir)
    distortion_dir = clear_folder.with_name(clear_folder.name + "_weather")
    meta_refA_path = os.path.join(args.meta_folder, "train_refA_weather_brief.json")
    meta_A_path = os.path.join(args.meta_folder, "train_A_weather_brief.json")

    # create to other dst folder (since the auth issues)
    distortion_dir = os.path.join(args.save_rt, os.path.relpath(distortion_dir, args.rt))
    os.makedirs(distortion_dir, exist_ok=True)
    meta_refA_path = os.path.join(args.save_rt, os.path.relpath(meta_refA_path, args.rt))
    os.makedirs(os.path.dirname(meta_refA_path), exist_ok=True)
    meta_A_path = os.path.join(args.save_rt, os.path.relpath(meta_A_path, args.rt))
    os.makedirs(os.path.dirname(meta_A_path), exist_ok=True)

    print("="*100)
    print(f"Found {len(image_list)} clear images from {args.image_folder}")
    print(f"Save images in {distortion_dir}, and save annotation in {meta_A_path}, {meta_refA_path}")
    print(f"Cases: {'ALL (random)' if args.case=='random' else args.case}, Levels: {levels}, n_per_image={args.n_per_image}, n_per_image={args.n_per_image}, seed={args.seed}")
    print("="*100)

    g_seed = args.seed
    random.seed(g_seed)
    np.random.seed(g_seed)

    meta_refA = []
    meta_A = []

    for path in tqdm(image_list):
        base = os.path.basename(path)
        rgb = read_rgb(path)
        
        save_folder = os.path.join(distortion_dir, os.path.relpath(os.path.dirname(path), clear_dir))
        os.makedirs(save_folder, exist_ok=True)
        
        for k in range(args.n_per_image):
            # 隨機選 type & level
            if args.case == "Random":
                case = random.choice(all_cases)
            else:
                if args.case not in DEG_FUNCS:
                    raise ValueError(f"Unknown case: {args.case}")
                case = args.case
            level = random.choice(levels)

            # 為了可重現，每張圖/樣本組合一個 seed
            # 這邊用全域 seed + hash(base,k,case,level)
            local_seed = (hash((base, k, case, level)) ^ g_seed) & 0xFFFFFFFF
            out_img, params = apply_degradation(case, rgb, level, seed=local_seed)
                        
            # save images, 檔名：原檔名 + _case_L{level}_{k}.jpg
            stem, ext = os.path.splitext(base)
            out_name = f"{stem}_{case}_L{level}_{k}{ext}"
            out_path = os.path.join(save_folder, out_name)
            save_bgr(out_path, out_img)

            # 記錄 metadata
            meta_refA.append({
                "distortion_class": case,
                "distortion_name": case,
                "severity": level,
                "id": f"{stem}_{k}",
                "image_ref": os.path.relpath(path, args.rt).replace("\\", "/"), 
                "image_A": os.path.relpath(out_path, args.save_rt).replace("\\", "/"), 
                "image_B": None, 
                "task_type": "quality_single_A",
                "conversations": [
                    {
                        "from": "human",
                        "value": question_generate(ref="Full-Reference", degra="ONE"),
                    },
                    {
                        "from": "gpt",
                        "value": case
                    }
                ],
                "params": params
            })

            meta_A.append({
                "distortion_class": case,
                "distortion_name": case,
                "severity": level,
                "id": f"{stem}_{k}",
                "image_ref": None, 
                "image_A": os.path.relpath(out_path, args.save_rt).replace("\\", "/"), 
                "image_B": None, 
                "task_type": "quality_single_A_noref",
                "conversations": [
                    {
                        "from": "human",
                        "value": question_generate(ref="Non-Reference", degra="ONE"),
                    },
                    {
                        "from": "gpt",
                        "value": case
                    }
                ],
                "params": params
            })
        
    with open(meta_refA_path, "w") as f:
        json.dump(meta_refA, f, indent=4)

    with open(meta_A_path, "w") as f:
        json.dump(meta_A, f, indent=4)

    print(f"Done. Metadata saved to: {meta_refA_path}, {meta_A_path}")

if __name__ == "__main__":
    main()