Upload create_weather_data.py with huggingface_hub

create_weather_data.py

@@ -0,0 +1,495 @@
+"""
+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()