imglist_generator.py

import os
import ipdb
import torch
import ftfy
import html
import re
# from src.clip import clip as clip
# from openood.networks.clip import clip
# import openood.networks.clip as clip
# from PIL import Image
from PIL import Image, ImageFile

ImageFile.LOAD_TRUNCATED_IMAGES = True

def basic_clean(text):
    text = ftfy.fix_text(text)
    text = html.unescape(html.unescape(text))
    return text.strip()

def whitespace_clean(text):
    text = re.sub(r'\s+', ' ', text)
    text = text.strip()
    return text

'''
path="./data/images_classic/cinic/valid"
save_path="./data/benchmark_imglist/cifar10/val_cinic10.txt"
prefix="cinic/valid/"
category=["airplane","automobile","bird","cat","deer","dog","frog","horse","ship","truck"]
with open(save_path,'a') as f:
    for name in category:
        label=category.index(name)
        sub_path=path+'/'+name
        files=os.listdir(sub_path)
        for file in files:
            line=prefix+name+'/'+file+' '+str(label)+'\n'
            f.write(line)
    f.close()       
'''
############################################################ prepare ood dataset for SUN, Place, and dtd (full textures)s
path="./data/images_largescale/dtd/images/"
save_path="./data/benchmark_imglist/imagenet/test_dtd.txt"
prefix="dtd/images/"
classes=os.listdir(path)
valid_extensions = {'.jpg', '.jpeg', '.png'}
with open(save_path,'w') as f:
    for cla in classes:
        used_prefix = prefix + cla 
        cla_path = path + cla
        files = os.listdir(cla_path)
        for file in files:
            ext = os.path.splitext(file)[1].lower()
            if ext in valid_extensions:
                line=used_prefix+ '/' +file+" -1\n"
                f.write(line)
            else:
                line=used_prefix+ '/' +file+" -1\n" 
                print(line)
    f.close()  

# path="./data/images_largescale/Places/images/"
# save_path="./data/benchmark_imglist/imagenet/test_places.txt"
# prefix="Places/images/"
# files=os.listdir(path)
# with open(save_path,'w') as f:
#     for file in files:
#         line=prefix+file+" -1\n"
#         f.write(line)
#     f.close()  

# path="./data/images_largescale/SUN/images/"
# save_path="./data/benchmark_imglist/imagenet/test_sun.txt"
# prefix="SUN/images/"
# files=os.listdir(path)
# with open(save_path,'w') as f:
#     for file in files:
#         line=prefix+file+" -1\n"
#         f.write(line)
#     f.close()  


# path="./data/images_classic/cifar100c"
# save_path="./data/benchmark_imglist/cifar100/test_cifar100c.txt"
# prefix="cifar100c/"
# files=os.listdir(path)
# with open(save_path,'a') as f:
#     for file in files:
#         splits=file.split("_")
#         label=(splits[1].split("."))[0]
#         line=prefix+file+" "+label+'\n'
#         f.write(line)
#     f.close()  

'''
path="./data/images_largescale/imagenet_v2"
save_path="./data/benchmark_imglist/imagenet/test_imagenetv2.txt"
prefix="imagenet_v2/"
with open(save_path,'a') as f:
    for i in range(0,1000):
        label=str(i)
        sub_path=path+'/'+label
        files=os.listdir(sub_path)
        for file in files:
            line=prefix+label+'/'+file+' '+label+'\n'
            f.write(line)
    f.close() 
'''

# classnames = [
#     'tench', 'goldfish', 'great white shark', 'tiger shark',
#     'hammerhead shark', 'electric ray', 'stingray', 'rooster', 'hen',
#     'ostrich', 'brambling', 'goldfinch', 'house finch', 'junco',
#     'indigo bunting', 'American robin', 'bulbul', 'jay', 'magpie', 'chickadee',
#     'American dipper', 'kite (bird of prey)', 'bald eagle', 'vulture',
#     'great grey owl', 'fire salamander', 'smooth newt', 'newt',
#     'spotted salamander', 'axolotl', 'American bullfrog', 'tree frog',
#     'tailed frog', 'loggerhead sea turtle', 'leatherback sea turtle',
#     'mud turtle', 'terrapin', 'box turtle', 'banded gecko', 'green iguana',
#     'Carolina anole', 'desert grassland whiptail lizard', 'agama',
#     'frilled-necked lizard', 'alligator lizard', 'Gila monster',
#     'European green lizard', 'chameleon', 'Komodo dragon', 'Nile crocodile',
#     'American alligator', 'triceratops', 'worm snake', 'ring-necked snake',
#     'eastern hog-nosed snake', 'smooth green snake', 'kingsnake',
#     'garter snake', 'water snake', 'vine snake', 'night snake',
#     'boa constrictor', 'African rock python', 'Indian cobra', 'green mamba',
#     'sea snake', 'Saharan horned viper', 'eastern diamondback rattlesnake',
#     'sidewinder rattlesnake', 'trilobite', 'harvestman', 'scorpion',
#     'yellow garden spider', 'barn spider', 'European garden spider',
#     'southern black widow', 'tarantula', 'wolf spider', 'tick', 'centipede',
#     'black grouse', 'ptarmigan', 'ruffed grouse', 'prairie grouse', 'peafowl',
#     'quail', 'partridge', 'african grey parrot', 'macaw',
#     'sulphur-crested cockatoo', 'lorikeet', 'coucal', 'bee eater', 'hornbill',
#     'hummingbird', 'jacamar', 'toucan', 'duck', 'red-breasted merganser',
#     'goose', 'black swan', 'tusker', 'echidna', 'platypus', 'wallaby', 'koala',
#     'wombat', 'jellyfish', 'sea anemone', 'brain coral', 'flatworm',
#     'nematode', 'conch', 'snail', 'slug', 'sea slug', 'chiton',
#     'chambered nautilus', 'Dungeness crab', 'rock crab', 'fiddler crab',
#     'red king crab', 'American lobster', 'spiny lobster', 'crayfish',
#     'hermit crab', 'isopod', 'white stork', 'black stork', 'spoonbill',
#     'flamingo', 'little blue heron', 'great egret', 'bittern bird',
#     'crane bird', 'limpkin', 'common gallinule', 'American coot', 'bustard',
#     'ruddy turnstone', 'dunlin', 'common redshank', 'dowitcher',
#     'oystercatcher', 'pelican', 'king penguin', 'albatross', 'grey whale',
#     'killer whale', 'dugong', 'sea lion', 'Chihuahua', 'Japanese Chin',
#     'Maltese', 'Pekingese', 'Shih Tzu', 'King Charles Spaniel', 'Papillon',
#     'toy terrier', 'Rhodesian Ridgeback', 'Afghan Hound', 'Basset Hound',
#     'Beagle', 'Bloodhound', 'Bluetick Coonhound', 'Black and Tan Coonhound',
#     'Treeing Walker Coonhound', 'English foxhound', 'Redbone Coonhound',
#     'borzoi', 'Irish Wolfhound', 'Italian Greyhound', 'Whippet',
#     'Ibizan Hound', 'Norwegian Elkhound', 'Otterhound', 'Saluki',
#     'Scottish Deerhound', 'Weimaraner', 'Staffordshire Bull Terrier',
#     'American Staffordshire Terrier', 'Bedlington Terrier', 'Border Terrier',
#     'Kerry Blue Terrier', 'Irish Terrier', 'Norfolk Terrier',
#     'Norwich Terrier', 'Yorkshire Terrier', 'Wire Fox Terrier',
#     'Lakeland Terrier', 'Sealyham Terrier', 'Airedale Terrier',
#     'Cairn Terrier', 'Australian Terrier', 'Dandie Dinmont Terrier',
#     'Boston Terrier', 'Miniature Schnauzer', 'Giant Schnauzer',
#     'Standard Schnauzer', 'Scottish Terrier', 'Tibetan Terrier',
#     'Australian Silky Terrier', 'Soft-coated Wheaten Terrier',
#     'West Highland White Terrier', 'Lhasa Apso', 'Flat-Coated Retriever',
#     'Curly-coated Retriever', 'Golden Retriever', 'Labrador Retriever',
#     'Chesapeake Bay Retriever', 'German Shorthaired Pointer', 'Vizsla',
#     'English Setter', 'Irish Setter', 'Gordon Setter', 'Brittany dog',
#     'Clumber Spaniel', 'English Springer Spaniel', 'Welsh Springer Spaniel',
#     'Cocker Spaniel', 'Sussex Spaniel', 'Irish Water Spaniel', 'Kuvasz',
#     'Schipperke', 'Groenendael dog', 'Malinois', 'Briard', 'Australian Kelpie',
#     'Komondor', 'Old English Sheepdog', 'Shetland Sheepdog', 'collie',
#     'Border Collie', 'Bouvier des Flandres dog', 'Rottweiler',
#     'German Shepherd Dog', 'Dobermann', 'Miniature Pinscher',
#     'Greater Swiss Mountain Dog', 'Bernese Mountain Dog',
#     'Appenzeller Sennenhund', 'Entlebucher Sennenhund', 'Boxer', 'Bullmastiff',
#     'Tibetan Mastiff', 'French Bulldog', 'Great Dane', 'St. Bernard', 'husky',
#     'Alaskan Malamute', 'Siberian Husky', 'Dalmatian', 'Affenpinscher',
#     'Basenji', 'pug', 'Leonberger', 'Newfoundland dog', 'Great Pyrenees dog',
#     'Samoyed', 'Pomeranian', 'Chow Chow', 'Keeshond', 'brussels griffon',
#     'Pembroke Welsh Corgi', 'Cardigan Welsh Corgi', 'Toy Poodle',
#     'Miniature Poodle', 'Standard Poodle',
#     'Mexican hairless dog (xoloitzcuintli)', 'grey wolf',
#     'Alaskan tundra wolf', 'red wolf or maned wolf', 'coyote', 'dingo',
#     'dhole', 'African wild dog', 'hyena', 'red fox', 'kit fox', 'Arctic fox',
#     'grey fox', 'tabby cat', 'tiger cat', 'Persian cat', 'Siamese cat',
#     'Egyptian Mau', 'cougar', 'lynx', 'leopard', 'snow leopard', 'jaguar',
#     'lion', 'tiger', 'cheetah', 'brown bear', 'American black bear',
#     'polar bear', 'sloth bear', 'mongoose', 'meerkat', 'tiger beetle',
#     'ladybug', 'ground beetle', 'longhorn beetle', 'leaf beetle',
#     'dung beetle', 'rhinoceros beetle', 'weevil', 'fly', 'bee', 'ant',
#     'grasshopper', 'cricket insect', 'stick insect', 'cockroach',
#     'praying mantis', 'cicada', 'leafhopper', 'lacewing', 'dragonfly',
#     'damselfly', 'red admiral butterfly', 'ringlet butterfly',
#     'monarch butterfly', 'small white butterfly', 'sulphur butterfly',
#     'gossamer-winged butterfly', 'starfish', 'sea urchin', 'sea cucumber',
#     'cottontail rabbit', 'hare', 'Angora rabbit', 'hamster', 'porcupine',
#     'fox squirrel', 'marmot', 'beaver', 'guinea pig', 'common sorrel horse',
#     'zebra', 'pig', 'wild boar', 'warthog', 'hippopotamus', 'ox',
#     'water buffalo', 'bison', 'ram (adult male sheep)', 'bighorn sheep',
#     'Alpine ibex', 'hartebeest', 'impala (antelope)', 'gazelle',
#     'arabian camel', 'llama', 'weasel', 'mink', 'European polecat',
#     'black-footed ferret', 'otter', 'skunk', 'badger', 'armadillo',
#     'three-toed sloth', 'orangutan', 'gorilla', 'chimpanzee', 'gibbon',
#     'siamang', 'guenon', 'patas monkey', 'baboon', 'macaque', 'langur',
#     'black-and-white colobus', 'proboscis monkey', 'marmoset',
#     'white-headed capuchin', 'howler monkey', 'titi monkey',
#     "Geoffroy's spider monkey", 'common squirrel monkey', 'ring-tailed lemur',
#     'indri', 'Asian elephant', 'African bush elephant', 'red panda',
#     'giant panda', 'snoek fish', 'eel', 'silver salmon', 'rock beauty fish',
#     'clownfish', 'sturgeon', 'gar fish', 'lionfish', 'pufferfish', 'abacus',
#     'abaya', 'academic gown', 'accordion', 'acoustic guitar',
#     'aircraft carrier', 'airliner', 'airship', 'altar', 'ambulance',
#     'amphibious vehicle', 'analog clock', 'apiary', 'apron', 'trash can',
#     'assault rifle', 'backpack', 'bakery', 'balance beam', 'balloon',
#     'ballpoint pen', 'Band-Aid', 'banjo', 'baluster / handrail', 'barbell',
#     'barber chair', 'barbershop', 'barn', 'barometer', 'barrel', 'wheelbarrow',
#     'baseball', 'basketball', 'bassinet', 'bassoon', 'swimming cap',
#     'bath towel', 'bathtub', 'station wagon', 'lighthouse', 'beaker',
#     'military hat (bearskin or shako)', 'beer bottle', 'beer glass',
#     'bell tower', 'baby bib', 'tandem bicycle', 'bikini', 'ring binder',
#     'binoculars', 'birdhouse', 'boathouse', 'bobsleigh', 'bolo tie',
#     'poke bonnet', 'bookcase', 'bookstore', 'bottle cap', 'hunting bow',
#     'bow tie', 'brass memorial plaque', 'bra', 'breakwater', 'breastplate',
#     'broom', 'bucket', 'buckle', 'bulletproof vest', 'high-speed train',
#     'butcher shop', 'taxicab', 'cauldron', 'candle', 'cannon', 'canoe',
#     'can opener', 'cardigan', 'car mirror', 'carousel', 'tool kit',
#     'cardboard box / carton', 'car wheel', 'automated teller machine',
#     'cassette', 'cassette player', 'castle', 'catamaran', 'CD player', 'cello',
#     'mobile phone', 'chain', 'chain-link fence', 'chain mail', 'chainsaw',
#     'storage chest', 'chiffonier', 'bell or wind chime', 'china cabinet',
#     'Christmas stocking', 'church', 'movie theater', 'cleaver',
#     'cliff dwelling', 'cloak', 'clogs', 'cocktail shaker', 'coffee mug',
#     'coffeemaker', 'spiral or coil', 'combination lock', 'computer keyboard',
#     'candy store', 'container ship', 'convertible', 'corkscrew', 'cornet',
#     'cowboy boot', 'cowboy hat', 'cradle', 'construction crane',
#     'crash helmet', 'crate', 'infant bed', 'Crock Pot', 'croquet ball',
#     'crutch', 'cuirass', 'dam', 'desk', 'desktop computer',
#     'rotary dial telephone', 'diaper', 'digital clock', 'digital watch',
#     'dining table', 'dishcloth', 'dishwasher', 'disc brake', 'dock',
#     'dog sled', 'dome', 'doormat', 'drilling rig', 'drum', 'drumstick',
#     'dumbbell', 'Dutch oven', 'electric fan', 'electric guitar',
#     'electric locomotive', 'entertainment center', 'envelope',
#     'espresso machine', 'face powder', 'feather boa', 'filing cabinet',
#     'fireboat', 'fire truck', 'fire screen', 'flagpole', 'flute',
#     'folding chair', 'football helmet', 'forklift', 'fountain', 'fountain pen',
#     'four-poster bed', 'freight car', 'French horn', 'frying pan', 'fur coat',
#     'garbage truck', 'gas mask or respirator', 'gas pump', 'goblet', 'go-kart',
#     'golf ball', 'golf cart', 'gondola', 'gong', 'gown', 'grand piano',
#     'greenhouse', 'radiator grille', 'grocery store', 'guillotine',
#     'hair clip', 'hair spray', 'half-track', 'hammer', 'hamper', 'hair dryer',
#     'hand-held computer', 'handkerchief', 'hard disk drive', 'harmonica',
#     'harp', 'combine harvester', 'hatchet', 'holster', 'home theater',
#     'honeycomb', 'hook', 'hoop skirt', 'gymnastic horizontal bar',
#     'horse-drawn vehicle', 'hourglass', 'iPod', 'clothes iron',
#     'carved pumpkin', 'jeans', 'jeep', 'T-shirt', 'jigsaw puzzle', 'rickshaw',
#     'joystick', 'kimono', 'knee pad', 'knot', 'lab coat', 'ladle', 'lampshade',
#     'laptop computer', 'lawn mower', 'lens cap', 'letter opener', 'library',
#     'lifeboat', 'lighter', 'limousine', 'ocean liner', 'lipstick',
#     'slip-on shoe', 'lotion', 'music speaker', 'loupe magnifying glass',
#     'sawmill', 'magnetic compass', 'messenger bag', 'mailbox', 'tights',
#     'one-piece bathing suit', 'manhole cover', 'maraca', 'marimba', 'mask',
#     'matchstick', 'maypole', 'maze', 'measuring cup', 'medicine cabinet',
#     'megalith', 'microphone', 'microwave oven', 'military uniform', 'milk can',
#     'minibus', 'miniskirt', 'minivan', 'missile', 'mitten', 'mixing bowl',
#     'mobile home', 'ford model t', 'modem', 'monastery', 'monitor', 'moped',
#     'mortar and pestle', 'graduation cap', 'mosque', 'mosquito net', 'vespa',
#     'mountain bike', 'tent', 'computer mouse', 'mousetrap', 'moving van',
#     'muzzle', 'metal nail', 'neck brace', 'necklace', 'baby pacifier',
#     'notebook computer', 'obelisk', 'oboe', 'ocarina', 'odometer',
#     'oil filter', 'pipe organ', 'oscilloscope', 'overskirt', 'bullock cart',
#     'oxygen mask', 'product packet / packaging', 'paddle', 'paddle wheel',
#     'padlock', 'paintbrush', 'pajamas', 'palace', 'pan flute', 'paper towel',
#     'parachute', 'parallel bars', 'park bench', 'parking meter',
#     'railroad car', 'patio', 'payphone', 'pedestal', 'pencil case',
#     'pencil sharpener', 'perfume', 'Petri dish', 'photocopier', 'plectrum',
#     'Pickelhaube', 'picket fence', 'pickup truck', 'pier', 'piggy bank',
#     'pill bottle', 'pillow', 'ping-pong ball', 'pinwheel', 'pirate ship',
#     'drink pitcher', 'block plane', 'planetarium', 'plastic bag', 'plate rack',
#     'farm plow', 'plunger', 'Polaroid camera', 'pole', 'police van', 'poncho',
#     'pool table', 'soda bottle', 'plant pot', "potter's wheel", 'power drill',
#     'prayer rug', 'printer', 'prison', 'missile', 'projector', 'hockey puck',
#     'punching bag', 'purse', 'quill', 'quilt', 'race car', 'racket',
#     'radiator', 'radio', 'radio telescope', 'rain barrel',
#     'recreational vehicle', 'fishing casting reel', 'reflex camera',
#     'refrigerator', 'remote control', 'restaurant', 'revolver', 'rifle',
#     'rocking chair', 'rotisserie', 'eraser', 'rugby ball',
#     'ruler measuring stick', 'sneaker', 'safe', 'safety pin', 'salt shaker',
#     'sandal', 'sarong', 'saxophone', 'scabbard', 'weighing scale',
#     'school bus', 'schooner', 'scoreboard', 'CRT monitor', 'screw',
#     'screwdriver', 'seat belt', 'sewing machine', 'shield', 'shoe store',
#     'shoji screen / room divider', 'shopping basket', 'shopping cart',
#     'shovel', 'shower cap', 'shower curtain', 'ski', 'balaclava ski mask',
#     'sleeping bag', 'slide rule', 'sliding door', 'slot machine', 'snorkel',
#     'snowmobile', 'snowplow', 'soap dispenser', 'soccer ball', 'sock',
#     'solar thermal collector', 'sombrero', 'soup bowl', 'keyboard space bar',
#     'space heater', 'space shuttle', 'spatula', 'motorboat', 'spider web',
#     'spindle', 'sports car', 'spotlight', 'stage', 'steam locomotive',
#     'through arch bridge', 'steel drum', 'stethoscope', 'scarf', 'stone wall',
#     'stopwatch', 'stove', 'strainer', 'tram', 'stretcher', 'couch', 'stupa',
#     'submarine', 'suit', 'sundial', 'sunglasses', 'sunglasses', 'sunscreen',
#     'suspension bridge', 'mop', 'sweatshirt', 'swim trunks / shorts', 'swing',
#     'electrical switch', 'syringe', 'table lamp', 'tank', 'tape player',
#     'teapot', 'teddy bear', 'television', 'tennis ball', 'thatched roof',
#     'front curtain', 'thimble', 'threshing machine', 'throne', 'tile roof',
#     'toaster', 'tobacco shop', 'toilet seat', 'torch', 'totem pole',
#     'tow truck', 'toy store', 'tractor', 'semi-trailer truck', 'tray',
#     'trench coat', 'tricycle', 'trimaran', 'tripod', 'triumphal arch',
#     'trolleybus', 'trombone', 'hot tub', 'turnstile', 'typewriter keyboard',
#     'umbrella', 'unicycle', 'upright piano', 'vacuum cleaner', 'vase',
#     'vaulted or arched ceiling', 'velvet fabric', 'vending machine',
#     'vestment', 'viaduct', 'violin', 'volleyball', 'waffle iron', 'wall clock',
#     'wallet', 'wardrobe', 'military aircraft', 'sink', 'washing machine',
#     'water bottle', 'water jug', 'water tower', 'whiskey jug', 'whistle',
#     'hair wig', 'window screen', 'window shade', 'Windsor tie', 'wine bottle',
#     'airplane wing', 'wok', 'wooden spoon', 'wool', 'split-rail fence',
#     'shipwreck', 'sailboat', 'yurt', 'website', 'comic book', 'crossword',
#     'traffic or street sign', 'traffic light', 'dust jacket', 'menu', 'plate',
#     'guacamole', 'consomme', 'hot pot', 'trifle', 'ice cream', 'popsicle',
#     'baguette', 'bagel', 'pretzel', 'cheeseburger', 'hot dog',
#     'mashed potatoes', 'cabbage', 'broccoli', 'cauliflower', 'zucchini',
#     'spaghetti squash', 'acorn squash', 'butternut squash', 'cucumber',
#     'artichoke', 'bell pepper', 'cardoon', 'mushroom', 'Granny Smith apple',
#     'strawberry', 'orange', 'lemon', 'fig', 'pineapple', 'banana', 'jackfruit',
#     'cherimoya (custard apple)', 'pomegranate', 'hay', 'carbonara',
#     'chocolate syrup', 'dough', 'meatloaf', 'pizza', 'pot pie', 'burrito',
#     'red wine', 'espresso', 'tea cup', 'eggnog', 'mountain', 'bubble', 'cliff',
#     'coral reef', 'geyser', 'lakeshore', 'promontory', 'sandbar', 'beach',
#     'valley', 'volcano', 'baseball player', 'bridegroom', 'scuba diver',
#     'rapeseed', 'daisy', "yellow lady's slipper", 'corn', 'acorn', 'rose hip',
#     'horse chestnut seed', 'coral fungus', 'agaric', 'gyromitra',
#     'stinkhorn mushroom', 'earth star fungus', 'hen of the woods mushroom',
#     'bolete', 'corn cob', 'toilet paper'
# ]

# category = ['n01440764', 'n01443537', 'n01484850', 'n01491361', 'n01494475', 'n01496331', 'n01498041', 'n01514668', 'n01514859', 'n01518878', 'n01530575', 'n01531178', 'n01532829', 'n01534433', 'n01537544', 'n01558993', 'n01560419', 'n01580077', 'n01582220', 'n01592084', 'n01601694', 'n01608432', 'n01614925', 'n01616318', 'n01622779', 'n01629819', 'n01630670', 'n01631663', 'n01632458', 'n01632777', 'n01641577', 'n01644373', 'n01644900', 'n01664065', 'n01665541', 'n01667114', 'n01667778', 'n01669191', 'n01675722', 'n01677366', 'n01682714', 'n01685808', 'n01687978', 'n01688243', 'n01689811', 'n01692333', 'n01693334', 'n01694178', 'n01695060', 'n01697457', 'n01698640', 'n01704323', 'n01728572', 'n01728920', 'n01729322', 'n01729977', 'n01734418', 'n01735189', 'n01737021', 'n01739381', 'n01740131', 'n01742172', 'n01744401', 'n01748264', 'n01749939', 'n01751748', 'n01753488', 'n01755581', 'n01756291', 'n01768244', 'n01770081', 'n01770393', 'n01773157', 'n01773549', 'n01773797', 'n01774384', 'n01774750', 'n01775062', 'n01776313', 'n01784675', 'n01795545', 'n01796340', 'n01797886', 'n01798484', 'n01806143', 'n01806567', 'n01807496', 'n01817953', 'n01818515', 'n01819313', 'n01820546', 'n01824575', 'n01828970', 'n01829413', 'n01833805', 'n01843065', 'n01843383', 'n01847000', 'n01855032', 'n01855672', 'n01860187', 'n01871265', 'n01872401', 'n01873310', 'n01877812', 'n01882714', 'n01883070', 'n01910747', 'n01914609', 'n01917289', 'n01924916', 'n01930112', 'n01943899', 'n01944390', 'n01945685', 'n01950731', 'n01955084', 'n01968897', 'n01978287', 'n01978455', 'n01980166', 'n01981276', 'n01983481', 'n01984695', 'n01985128', 'n01986214', 'n01990800', 'n02002556', 'n02002724', 'n02006656', 'n02007558', 'n02009229', 'n02009912', 'n02011460', 'n02012849', 'n02013706', 'n02017213', 'n02018207', 'n02018795', 'n02025239', 'n02027492', 'n02028035', 'n02033041', 'n02037110', 'n02051845', 'n02056570', 'n02058221', 'n02066245', 'n02071294', 'n02074367', 'n02077923', 'n02085620', 'n02085782', 'n02085936', 'n02086079', 'n02086240', 'n02086646', 'n02086910', 'n02087046', 'n02087394', 'n02088094', 'n02088238', 'n02088364', 'n02088466', 'n02088632', 'n02089078', 'n02089867', 'n02089973', 'n02090379', 'n02090622', 'n02090721', 'n02091032', 'n02091134', 'n02091244', 'n02091467', 'n02091635', 'n02091831', 'n02092002', 'n02092339', 'n02093256', 'n02093428', 'n02093647', 'n02093754', 'n02093859', 'n02093991', 'n02094114', 'n02094258', 'n02094433', 'n02095314', 'n02095570', 'n02095889', 'n02096051', 'n02096177', 'n02096294', 'n02096437', 'n02096585', 'n02097047', 'n02097130', 'n02097209', 'n02097298', 'n02097474', 'n02097658', 'n02098105', 'n02098286', 'n02098413', 'n02099267', 'n02099429', 'n02099601', 'n02099712', 'n02099849', 'n02100236', 'n02100583', 'n02100735', 'n02100877', 'n02101006', 'n02101388', 'n02101556', 'n02102040', 'n02102177', 'n02102318', 'n02102480', 'n02102973', 'n02104029', 'n02104365', 'n02105056', 'n02105162', 'n02105251', 'n02105412', 'n02105505', 'n02105641', 'n02105855', 'n02106030', 'n02106166', 'n02106382', 'n02106550', 'n02106662', 'n02107142', 'n02107312', 'n02107574', 'n02107683', 'n02107908', 'n02108000', 'n02108089', 'n02108422', 'n02108551', 'n02108915', 'n02109047', 'n02109525', 'n02109961', 'n02110063', 'n02110185', 'n02110341', 'n02110627', 'n02110806', 'n02110958', 'n02111129', 'n02111277', 'n02111500', 'n02111889', 'n02112018', 'n02112137', 'n02112350', 'n02112706', 'n02113023', 'n02113186', 'n02113624', 'n02113712', 'n02113799', 'n02113978', 'n02114367', 'n02114548', 'n02114712', 'n02114855', 'n02115641', 'n02115913', 'n02116738', 'n02117135', 'n02119022', 'n02119789', 'n02120079', 'n02120505', 'n02123045', 'n02123159', 'n02123394', 'n02123597', 'n02124075', 'n02125311', 'n02127052', 'n02128385', 'n02128757', 'n02128925', 'n02129165', 'n02129604', 'n02130308', 'n02132136', 'n02133161', 'n02134084', 'n02134418', 'n02137549', 'n02138441', 'n02165105', 'n02165456', 'n02167151', 'n02168699', 'n02169497', 'n02172182', 'n02174001', 'n02177972', 'n02190166', 'n02206856', 'n02219486', 'n02226429', 'n02229544', 'n02231487', 'n02233338', 'n02236044', 'n02256656', 'n02259212', 'n02264363', 'n02268443', 'n02268853', 'n02276258', 'n02277742', 'n02279972', 'n02280649', 'n02281406', 'n02281787', 'n02317335', 'n02319095', 'n02321529', 'n02325366', 'n02326432', 'n02328150', 'n02342885', 'n02346627', 'n02356798', 'n02361337', 'n02363005', 'n02364673', 'n02389026', 'n02391049', 'n02395406', 'n02396427', 'n02397096', 'n02398521', 'n02403003', 'n02408429', 'n02410509', 'n02412080', 'n02415577', 'n02417914', 'n02422106', 'n02422699', 'n02423022', 'n02437312', 'n02437616', 'n02441942', 'n02442845', 'n02443114', 'n02443484', 'n02444819', 'n02445715', 'n02447366', 'n02454379', 'n02457408', 'n02480495', 'n02480855', 'n02481823', 'n02483362', 'n02483708', 'n02484975', 'n02486261', 'n02486410', 'n02487347', 'n02488291', 'n02488702', 'n02489166', 'n02490219', 'n02492035', 'n02492660', 'n02493509', 'n02493793', 'n02494079', 'n02497673', 'n02500267', 'n02504013', 'n02504458', 'n02509815', 'n02510455', 'n02514041', 'n02526121', 'n02536864', 'n02606052', 'n02607072', 'n02640242', 'n02641379', 'n02643566', 'n02655020', 'n02666196', 'n02667093', 'n02669723', 'n02672831', 'n02676566', 'n02687172', 'n02690373', 'n02692877', 'n02699494', 'n02701002', 'n02704792', 'n02708093', 'n02727426', 'n02730930', 'n02747177', 'n02749479', 'n02769748', 'n02776631', 'n02777292', 'n02782093', 'n02783161', 'n02786058', 'n02787622', 'n02788148', 'n02790996', 'n02791124', 'n02791270', 'n02793495', 'n02794156', 'n02795169', 'n02797295', 'n02799071', 'n02802426', 'n02804414', 'n02804610', 'n02807133', 'n02808304', 'n02808440', 'n02814533', 'n02814860', 'n02815834', 'n02817516', 'n02823428', 'n02823750', 'n02825657', 'n02834397', 'n02835271', 'n02837789', 'n02840245', 'n02841315', 'n02843684', 'n02859443', 'n02860847', 'n02865351', 'n02869837', 'n02870880', 'n02871525', 'n02877765', 'n02879718', 'n02883205', 'n02892201', 'n02892767', 'n02894605', 'n02895154', 'n02906734', 'n02909870', 'n02910353', 'n02916936', 'n02917067', 'n02927161', 'n02930766', 'n02939185', 'n02948072', 'n02950826', 'n02951358', 'n02951585', 'n02963159', 'n02965783', 'n02966193', 'n02966687', 'n02971356', 'n02974003', 'n02977058', 'n02978881', 'n02979186', 'n02980441', 'n02981792', 'n02988304', 'n02992211', 'n02992529', 'n02999410', 'n03000134', 'n03000247', 'n03000684', 'n03014705', 'n03016953', 'n03017168', 'n03018349', 'n03026506', 'n03028079', 'n03032252', 'n03041632', 'n03042490', 'n03045698', 'n03047690', 'n03062245', 'n03063599', 'n03063689', 'n03065424', 'n03075370', 'n03085013', 'n03089624', 'n03095699', 'n03100240', 'n03109150', 'n03110669', 'n03124043', 'n03124170', 'n03125729', 'n03126707', 'n03127747', 'n03127925', 'n03131574', 'n03133878', 'n03134739', 'n03141823', 'n03146219', 'n03160309', 'n03179701', 'n03180011', 'n03187595', 'n03188531', 'n03196217', 'n03197337', 'n03201208', 'n03207743', 'n03207941', 'n03208938', 'n03216828', 'n03218198', 'n03220513', 'n03223299', 'n03240683', 'n03249569', 'n03250847', 'n03255030', 'n03259280', 'n03271574', 'n03272010', 'n03272562', 'n03290653', 'n03291819', 'n03297495', 'n03314780', 'n03325584', 'n03337140', 'n03344393', 'n03345487', 'n03347037', 'n03355925', 'n03372029', 'n03376595', 'n03379051', 'n03384352', 'n03388043', 'n03388183', 'n03388549', 'n03393912', 'n03394916', 'n03400231', 'n03404251', 'n03417042', 'n03424325', 'n03425413', 'n03443371', 'n03444034', 'n03445777', 'n03445924', 'n03447447', 'n03447721', 'n03450230', 'n03452741', 'n03457902', 'n03459775', 'n03461385', 'n03467068', 'n03476684', 'n03476991', 'n03478589', 'n03481172', 'n03482405', 'n03483316', 'n03485407', 'n03485794', 'n03492542', 'n03494278', 'n03495258', 'n03496892', 'n03498962', 'n03527444', 'n03529860', 'n03530642', 'n03532672', 'n03534580', 'n03535780', 'n03538406', 'n03544143', 'n03584254', 'n03584829', 'n03590841', 'n03594734', 'n03594945', 'n03595614', 'n03598930', 'n03599486', 'n03602883', 'n03617480', 'n03623198', 'n03627232', 'n03630383', 'n03633091', 'n03637318', 'n03642806', 'n03649909', 'n03657121', 'n03658185', 'n03661043', 'n03662601', 'n03666591', 'n03670208', 'n03673027', 'n03676483', 'n03680355', 'n03690938', 'n03691459', 'n03692522', 'n03697007', 'n03706229', 'n03709823', 'n03710193', 'n03710637', 'n03710721', 'n03717622', 'n03720891', 'n03721384', 'n03724870', 'n03729826', 'n03733131', 'n03733281', 'n03733805', 'n03742115', 'n03743016', 'n03759954', 'n03761084', 'n03763968', 'n03764736', 'n03769881', 'n03770439', 'n03770679', 'n03773504', 'n03775071', 'n03775546', 'n03776460', 'n03777568', 'n03777754', 'n03781244', 'n03782006', 'n03785016', 'n03786901', 'n03787032', 'n03788195', 'n03788365', 'n03791053', 'n03792782', 'n03792972', 'n03793489', 'n03794056', 'n03796401', 'n03803284', 'n03804744', 'n03814639', 'n03814906', 'n03825788', 'n03832673', 'n03837869', 'n03838899', 'n03840681', 'n03841143', 'n03843555', 'n03854065', 'n03857828', 'n03866082', 'n03868242', 'n03868863', 'n03871628', 'n03873416', 'n03874293', 'n03874599', 'n03876231', 'n03877472', 'n03877845', 'n03884397', 'n03887697', 'n03888257', 'n03888605', 'n03891251', 'n03891332', 'n03895866', 'n03899768', 'n03902125', 'n03903868', 'n03908618', 'n03908714', 'n03916031', 'n03920288', 'n03924679', 'n03929660', 'n03929855', 'n03930313', 'n03930630', 'n03933933', 'n03935335', 'n03937543', 'n03938244', 'n03942813', 'n03944341', 'n03947888', 'n03950228', 'n03954731', 'n03956157', 'n03958227', 'n03961711', 'n03967562', 'n03970156', 'n03976467', 'n03976657', 'n03977966', 'n03980874', 'n03982430', 'n03983396', 'n03991062', 'n03992509', 'n03995372', 'n03998194', 'n04004767', 'n04005630', 'n04008634', 'n04009552', 'n04019541', 'n04023962', 'n04026417', 'n04033901', 'n04033995', 'n04037443', 'n04039381', 'n04040759', 'n04041544', 'n04044716', 'n04049303', 'n04065272', 'n04067472', 'n04069434', 'n04070727', 'n04074963', 'n04081281', 'n04086273', 'n04090263', 'n04099969', 'n04111531', 'n04116512', 'n04118538', 'n04118776', 'n04120489', 'n04125021', 'n04127249', 'n04131690', 'n04133789', 'n04136333', 'n04141076', 'n04141327', 'n04141975', 'n04146614', 'n04147183', 'n04149813', 'n04152593', 'n04153751', 'n04154565', 'n04162706', 'n04179913', 'n04192698', 'n04200800', 'n04201297', 'n04204238', 'n04204347', 'n04208210', 'n04209133', 'n04209239', 'n04228054', 'n04229816', 'n04235860', 'n04238763', 'n04239074', 'n04243546', 'n04251144', 'n04252077', 'n04252225', 'n04254120', 'n04254680', 'n04254777', 'n04258138', 'n04259630', 'n04263257', 'n04264628', 'n04265275', 'n04266014', 'n04270147', 'n04273569', 'n04275548', 'n04277352', 'n04285008', 'n04286575', 'n04296562', 'n04310018', 'n04311004', 'n04311174', 'n04317175', 'n04325704', 'n04326547', 'n04328186', 'n04330267', 'n04332243', 'n04335435', 'n04336792', 'n04344873', 'n04346328', 'n04347754', 'n04350905', 'n04355338', 'n04355933', 'n04356056', 'n04357314', 'n04366367', 'n04367480', 'n04370456', 'n04371430', 'n04371774', 'n04372370', 'n04376876', 'n04380533', 'n04389033', 'n04392985', 'n04398044', 'n04399382', 'n04404412', 'n04409515', 'n04417672', 'n04418357', 'n04423845', 'n04428191', 'n04429376', 'n04435653', 'n04442312', 'n04443257', 'n04447861', 'n04456115', 'n04458633', 'n04461696', 'n04462240', 'n04465501', 'n04467665', 'n04476259', 'n04479046', 'n04482393', 'n04483307', 'n04485082', 'n04486054', 'n04487081', 'n04487394', 'n04493381', 'n04501370', 'n04505470', 'n04507155', 'n04509417', 'n04515003', 'n04517823', 'n04522168', 'n04523525', 'n04525038', 'n04525305', 'n04532106', 'n04532670', 'n04536866', 'n04540053', 'n04542943', 'n04548280', 'n04548362', 'n04550184', 'n04552348', 'n04553703', 'n04554684', 'n04557648', 'n04560804', 'n04562935', 'n04579145', 'n04579432', 'n04584207', 'n04589890', 'n04590129', 'n04591157', 'n04591713', 'n04592741', 'n04596742', 'n04597913', 'n04599235', 'n04604644', 'n04606251', 'n04612504', 'n04613696', 'n06359193', 'n06596364', 'n06785654', 'n06794110', 'n06874185', 'n07248320', 'n07565083', 'n07579787', 'n07583066', 'n07584110', 'n07590611', 'n07613480', 'n07614500', 'n07615774', 'n07684084', 'n07693725', 'n07695742', 'n07697313', 'n07697537', 'n07711569', 'n07714571', 'n07714990', 'n07715103', 'n07716358', 'n07716906', 'n07717410', 'n07717556', 'n07718472', 'n07718747', 'n07720875', 'n07730033', 'n07734744', 'n07742313', 'n07745940', 'n07747607', 'n07749582', 'n07753113', 'n07753275', 'n07753592', 'n07754684', 'n07760859', 'n07768694', 'n07802026', 'n07831146', 'n07836838', 'n07860988', 'n07871810', 'n07873807', 'n07875152', 'n07880968', 'n07892512', 'n07920052', 'n07930864', 'n07932039', 'n09193705', 'n09229709', 'n09246464', 'n09256479', 'n09288635', 'n09332890', 'n09399592', 'n09421951', 'n09428293', 'n09468604', 'n09472597', 'n09835506', 'n10148035', 'n10565667', 'n11879895', 'n11939491', 'n12057211', 'n12144580', 'n12267677', 'n12620546', 'n12768682', 'n12985857', 'n12998815', 'n13037406', 'n13040303', 'n13044778', 'n13052670', 'n13054560', 'n13133613', 'n15075141']

# print(category[836])

# # path="/home/notebook/code/personal/S9052995/syn_image/ImageNet_sdxl_ood_2/guidance_scale7.5/"
# # save_path="/home/notebook/code/personal/S9052995/syn_pro/OpenOOD/data/benchmark_imglist/imagenet/train_imagenet_idood11k_syn2.txt"

# # path="/home/notebook/code/personal/S9052995/syn_image/ImageNet_sdxl_ood_32/guidance_scale7.5/"
# # save_path="/home/notebook/code/personal/S9052995/syn_pro/OpenOOD/data/benchmark_imglist/imagenet/train_imagenet_idood11k_syn32.txt"

# special_chars_list = [
#     ' ',    # 空格
#     '\t',   # 制表符
#     '\n',   # 换行符
#     '\r',   # 回车符
#     ',',    # 逗号
#     '.',    # 句号
#     ';',    # 分号
#     ':',    # 冒号
#     "'",    # 单引号
#     '"',    # 双引号
#     '`',    # 反引号/重音符
#     '-',    # 破折号
#     # '_',    # 下划线
#     '(',    # 左圆括号
#     ')',    # 右圆括号
#     '[',    # 左方括号
#     ']',    # 右方括号
#     '{',    # 左花括号
#     '}',    # 右花括号
#     '<',    # 小于符号
#     '>',    # 大于符号
#     '/',    # 斜杠
#     '\\',   # 反斜杠
#     '|',    # 竖线
#     '!',    # 感叹号
#     '?',    # 问号
#     '@',    # at符号
#     '#',    # 井号
#     '$',    # 美元符号
#     '%',    # 百分号
#     '^',    # 插入符号
#     '&',    # 和号
#     '*',    # 星号
#     '+',    # 加号
#     '=',    # 等号
#     '~',    # 波浪号
# ]

# path="/home/notebook/code/personal/S9052995/syn_image/ImageNet_sdxl_ood_32/guidance_scale7.5/"
# save_path="/home/notebook/code/personal/S9052995/syn_pro/OpenOOD/data/benchmark_imglist/imagenet/train_imagenet_idood_last1k_syn32_dedup.txt"


# prename = '/home/notebook/code/personal/S9052995/syn_pro/OpenOOD/data/images_largescale/' ## pre_image name
# len_pre = len(prename)

# foot_path = '/home/notebook/code/personal/S9052995/syn_pro/OpenOOD/data/txtfiles/'
# selected_adj_noun_path = foot_path + 'selected_adj_noun_0.95_1k_dedup.pth'  ############### note the file here.
# selected_adj_noun = torch.load(selected_adj_noun_path)
# selected_adj = selected_adj_noun['adj']
# selected_noun = selected_adj_noun['noun']

# selected_adj = [name.replace("_", " ").replace("(", " ").replace(")", " ").replace("[", " ").replace("]", " ").replace("/", " ") for name in selected_adj]
# selected_adj = [whitespace_clean(basic_clean(name)).lower() for name in selected_adj]
# selected_adj = [name.replace(" ", "_") for name in selected_adj]
# for char in special_chars_list:
#     selected_adj = [name.replace(char, "_") for name in selected_adj]

# selected_noun = [name.replace("_", " ").replace("(", " ").replace(")", " ").replace("[", " ").replace("]", " ").replace("/", " ") for name in selected_noun]
# selected_noun = [whitespace_clean(basic_clean(name)).lower() for name in selected_noun]
# selected_noun = [name.replace(" ", "_") for name in selected_noun]
# for char in special_chars_list:
#     selected_noun = [name.replace(char, "_") for name in selected_noun]

# classnames = [name.replace("_", " ").replace("(", " ").replace(")", " ").replace("[", " ").replace("]", " ").replace("/", " ") for name in classnames]
# classnames = [whitespace_clean(basic_clean(name)).lower() for name in classnames]
# classnames = [name.replace(" ", "_") for name in classnames]
# for char in special_chars_list:
#     classnames = [name.replace(char, "_") for name in classnames]


# classnames_all = classnames + selected_adj + selected_noun
# print(len(classnames_all))
# files=os.listdir(path)
# id_class = 0
# with open(save_path,'w') as f:
#     for test_string in classnames:
#         contained_elements = [s for s in files if s.split('_', 2)[-1] == test_string]
#         if len(contained_elements) != 1:
#             # ipdb.set_trace()
#             # print(contained_elements)
#             contained_elements = [s for s in contained_elements if int(s.split('_')[0]) == id_class]
#             # print(contained_elements)
#         subfile = contained_elements[0]
#         subpath = os.path.join(path, subfile)
#         # print(subpath)
#         images = os.listdir(subpath)
#         for image in images:
#             image = os.path.join(subpath, image)
#             line = image[len_pre:] +' '+str(id_class)+'\n'
#             f.write(line)
#         id_class += 1
#         # print(id_class)

#     for test_string in selected_adj:
#         contained_elements = [s for s in files if s.split('_', 2)[-1] == test_string]
#         if len(contained_elements) == 0:
#             ipdb.set_trace()
#         # if len(contained_elements) != 1:
#             # ipdb.set_trace()
#             # print(contained_elements)
#             # contained_elements = [s for s in contained_elements if int(s.split('_')[0]) > id_class]
#             # print(contained_elements) ### the first is the required adj one.
#         subfile = contained_elements[0]
#         subpath = os.path.join(path, subfile)
#         # print(subpath)
#         images = os.listdir(subpath)
#         for image in images:
#             image = os.path.join(subpath, image)
#             line = image[len_pre:] +' '+str(id_class)+'\n'
#             f.write(line)
#         id_class += 1
#         print(id_class)
#     print(len(selected_noun))
#     for test_string in selected_noun:
#         contained_elements = [s for s in files if s.split('_', 2)[-1] == test_string]
#         if len(contained_elements) == 0:
#             ipdb.set_trace()
#         if len(contained_elements) != 1:
#             # ipdb.set_trace()
#             # print(contained_elements)
#             id_flag = [s for s in contained_elements if int(s.split('_')[0]) < 1000]
#             if len(id_flag) > 0:  ## overlap with ID classes. 
#                 # ipdb.set_trace()
#                 id_class += 1
#                 continue
#             print(contained_elements) ### the first is the required adj one.
#         subfile = contained_elements[-1]
#         subpath = os.path.join(path, subfile)
#         # print(subpath)
#         images = os.listdir(subpath)
#         for image in images:
#             image = os.path.join(subpath, image)
#             line = image[len_pre:] +' '+str(id_class)+'\n'
#             f.write(line)
#         id_class += 1
#         print(id_class)

#         # ipdb.set_trace()

#         # splits=subfile.split("_")
#         # label=splits[0]
#         # subpath = os.path.join(path, subfile)
#         # images = os.listdir(subpath)
#         # ipdb.set_trace()
#         # for image in images:
#         #     image = os.path.join(subpath, image)
#         #     line = image[len_pre:] +' '+str(label)+'\n'
#         #     # f.write(line)
#     f.close()


# # files=os.listdir(path)
# # with open(save_path,'w') as f:
# #     for subfile in files:
# #         # print(subfile)
# #         ipdb.set_trace()

# #         # splits=subfile.split("_")
# #         # label=splits[0]
# #         # subpath = os.path.join(path, subfile)
# #         # images = os.listdir(subpath)
# #         # ipdb.set_trace()
# #         # for image in images:
# #         #     image = os.path.join(subpath, image)
# #         #     line = image[len_pre:] +' '+str(label)+'\n'
# #         #     # f.write(line)
# #     f.close()