| """ |
| 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_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 |
|
|
| |
| |
| |
| def lerp(a, b, t): |
| return a + (b - a) * t |
|
|
| def choice_with_level(options_low, options_high, t): |
| |
| 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)) |
|
|
| |
| def level_to_t(level): |
| level = max(1, min(5, int(level))) |
| return (level - 1) / 4.0 |
|
|
| |
| |
| |
| 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} |
|
|
| |
| |
| |
| 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() |
| |
| |
| 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])) |
| |
| |
| 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()) |
|
|
| |
| 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") |
|
|
| |
| 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): |
| |
| 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) |
|
|
| |
| |
| local_seed = (hash((base, k, case, level)) ^ g_seed) & 0xFFFFFFFF |
| out_img, params = apply_degradation(case, rgb, level, seed=local_seed) |
| |
| |
| 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) |
|
|
| |
| 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() |
|
|
