|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
"""Sun397 loading script.""" |
|
|
|
|
|
|
|
|
import csv |
|
|
import json |
|
|
import os |
|
|
from pathlib import Path |
|
|
|
|
|
import datasets |
|
|
|
|
|
_CITATION = """\ |
|
|
@INPROCEEDINGS{Xiao:2010, |
|
|
author={J. {Xiao} and J. {Hays} and K. A. {Ehinger} and A. {Oliva} and A. {Torralba} }, |
|
|
booktitle={2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition}, |
|
|
title={SUN database: Large-scale scene recognition from abbey to zoo}, |
|
|
year={2010}, |
|
|
volume={}, |
|
|
number={}, |
|
|
pages={3485-3492}, |
|
|
keywords={computer vision;human factors;image classification;object recognition;visual databases;SUN database;large-scale scene recognition;abbey;zoo;scene categorization;computer vision;scene understanding research;scene category;object categorization;scene understanding database;state-of-the-art algorithms;human scene classification performance;finer-grained scene representation;Sun;Large-scale systems;Layout;Humans;Image databases;Computer vision;Anthropometry;Bridges;Legged locomotion;Spatial databases}, |
|
|
doi={10.1109/CVPR.2010.5539970}, |
|
|
ISSN={1063-6919}, |
|
|
month={June},} |
|
|
|
|
|
""" |
|
|
|
|
|
_DESCRIPTION = """\ |
|
|
Scene UNderstanding (SUN) database contains 899 categories. The number of images varies across categories, but there are at least 100 images per category, and 108,754 images in total. Images are in jpg, png, or gif format. The images provided here are for research purposes only. |
|
|
""" |
|
|
|
|
|
_HOMEPAGE = "https://vision.princeton.edu/projects/2010/SUN/" |
|
|
|
|
|
|
|
|
_LICENSE = "" |
|
|
|
|
|
_URLs = { |
|
|
"images": "http://vision.princeton.edu/projects/2010/SUN/SUN397.tar.gz", |
|
|
"partitions": "http://vision.princeton.edu/projects/2010/SUN/download/Partitions.zip", |
|
|
} |
|
|
|
|
|
_VERSION = datasets.Version("1.0.0") |
|
|
|
|
|
_NAMES = [ |
|
|
"abbey", |
|
|
"airplane_cabin", |
|
|
"airport_terminal", |
|
|
"alley", |
|
|
"amphitheater", |
|
|
"amusement_arcade", |
|
|
"amusement_park", |
|
|
"anechoic_chamber", |
|
|
"apartment_building/outdoor", |
|
|
"apse/indoor", |
|
|
"aquarium", |
|
|
"aqueduct", |
|
|
"arch", |
|
|
"archive", |
|
|
"arrival_gate/outdoor", |
|
|
"art_gallery", |
|
|
"art_school", |
|
|
"art_studio", |
|
|
"assembly_line", |
|
|
"athletic_field/outdoor", |
|
|
"atrium/public", |
|
|
"attic", |
|
|
"auditorium", |
|
|
"auto_factory", |
|
|
"badlands", |
|
|
"badminton_court/indoor", |
|
|
"baggage_claim", |
|
|
"bakery/shop", |
|
|
"balcony/exterior", |
|
|
"balcony/interior", |
|
|
"ball_pit", |
|
|
"ballroom", |
|
|
"bamboo_forest", |
|
|
"banquet_hall", |
|
|
"bar", |
|
|
"barn", |
|
|
"barndoor", |
|
|
"baseball_field", |
|
|
"basement", |
|
|
"basilica", |
|
|
"basketball_court/outdoor", |
|
|
"bathroom", |
|
|
"batters_box", |
|
|
"bayou", |
|
|
"bazaar/indoor", |
|
|
"bazaar/outdoor", |
|
|
"beach", |
|
|
"beauty_salon", |
|
|
"bedroom", |
|
|
"berth", |
|
|
"biology_laboratory", |
|
|
"bistro/indoor", |
|
|
"boardwalk", |
|
|
"boat_deck", |
|
|
"boathouse", |
|
|
"bookstore", |
|
|
"booth/indoor", |
|
|
"botanical_garden", |
|
|
"bow_window/indoor", |
|
|
"bow_window/outdoor", |
|
|
"bowling_alley", |
|
|
"boxing_ring", |
|
|
"brewery/indoor", |
|
|
"bridge", |
|
|
"building_facade", |
|
|
"bullring", |
|
|
"burial_chamber", |
|
|
"bus_interior", |
|
|
"butchers_shop", |
|
|
"butte", |
|
|
"cabin/outdoor", |
|
|
"cafeteria", |
|
|
"campsite", |
|
|
"campus", |
|
|
"canal/natural", |
|
|
"canal/urban", |
|
|
"candy_store", |
|
|
"canyon", |
|
|
"car_interior/backseat", |
|
|
"car_interior/frontseat", |
|
|
"carrousel", |
|
|
"casino/indoor", |
|
|
"castle", |
|
|
"catacomb", |
|
|
"cathedral/indoor", |
|
|
"cathedral/outdoor", |
|
|
"cavern/indoor", |
|
|
"cemetery", |
|
|
"chalet", |
|
|
"cheese_factory", |
|
|
"chemistry_lab", |
|
|
"chicken_coop/indoor", |
|
|
"chicken_coop/outdoor", |
|
|
"childs_room", |
|
|
"church/indoor", |
|
|
"church/outdoor", |
|
|
"classroom", |
|
|
"clean_room", |
|
|
"cliff", |
|
|
"cloister/indoor", |
|
|
"closet", |
|
|
"clothing_store", |
|
|
"coast", |
|
|
"cockpit", |
|
|
"coffee_shop", |
|
|
"computer_room", |
|
|
"conference_center", |
|
|
"conference_room", |
|
|
"construction_site", |
|
|
"control_room", |
|
|
"control_tower/outdoor", |
|
|
"corn_field", |
|
|
"corral", |
|
|
"corridor", |
|
|
"cottage_garden", |
|
|
"courthouse", |
|
|
"courtroom", |
|
|
"courtyard", |
|
|
"covered_bridge/exterior", |
|
|
"creek", |
|
|
"crevasse", |
|
|
"crosswalk", |
|
|
"cubicle/office", |
|
|
"dam", |
|
|
"delicatessen", |
|
|
"dentists_office", |
|
|
"desert/sand", |
|
|
"desert/vegetation", |
|
|
"diner/indoor", |
|
|
"diner/outdoor", |
|
|
"dinette/home", |
|
|
"dinette/vehicle", |
|
|
"dining_car", |
|
|
"dining_room", |
|
|
"discotheque", |
|
|
"dock", |
|
|
"doorway/outdoor", |
|
|
"dorm_room", |
|
|
"driveway", |
|
|
"driving_range/outdoor", |
|
|
"drugstore", |
|
|
"electrical_substation", |
|
|
"elevator/door", |
|
|
"elevator/interior", |
|
|
"elevator_shaft", |
|
|
"engine_room", |
|
|
"escalator/indoor", |
|
|
"excavation", |
|
|
"factory/indoor", |
|
|
"fairway", |
|
|
"fastfood_restaurant", |
|
|
"field/cultivated", |
|
|
"field/wild", |
|
|
"fire_escape", |
|
|
"fire_station", |
|
|
"firing_range/indoor", |
|
|
"fishpond", |
|
|
"florist_shop/indoor", |
|
|
"food_court", |
|
|
"forest/broadleaf", |
|
|
"forest/needleleaf", |
|
|
"forest_path", |
|
|
"forest_road", |
|
|
"formal_garden", |
|
|
"fountain", |
|
|
"galley", |
|
|
"game_room", |
|
|
"garage/indoor", |
|
|
"garbage_dump", |
|
|
"gas_station", |
|
|
"gazebo/exterior", |
|
|
"general_store/indoor", |
|
|
"general_store/outdoor", |
|
|
"gift_shop", |
|
|
"golf_course", |
|
|
"greenhouse/indoor", |
|
|
"greenhouse/outdoor", |
|
|
"gymnasium/indoor", |
|
|
"hangar/indoor", |
|
|
"hangar/outdoor", |
|
|
"harbor", |
|
|
"hayfield", |
|
|
"heliport", |
|
|
"herb_garden", |
|
|
"highway", |
|
|
"hill", |
|
|
"home_office", |
|
|
"hospital", |
|
|
"hospital_room", |
|
|
"hot_spring", |
|
|
"hot_tub/outdoor", |
|
|
"hotel/outdoor", |
|
|
"hotel_room", |
|
|
"house", |
|
|
"hunting_lodge/outdoor", |
|
|
"ice_cream_parlor", |
|
|
"ice_floe", |
|
|
"ice_shelf", |
|
|
"ice_skating_rink/indoor", |
|
|
"ice_skating_rink/outdoor", |
|
|
"iceberg", |
|
|
"igloo", |
|
|
"industrial_area", |
|
|
"inn/outdoor", |
|
|
"islet", |
|
|
"jacuzzi/indoor", |
|
|
"jail/indoor", |
|
|
"jail_cell", |
|
|
"jewelry_shop", |
|
|
"kasbah", |
|
|
"kennel/indoor", |
|
|
"kennel/outdoor", |
|
|
"kindergarden_classroom", |
|
|
"kitchen", |
|
|
"kitchenette", |
|
|
"labyrinth/outdoor", |
|
|
"lake/natural", |
|
|
"landfill", |
|
|
"landing_deck", |
|
|
"laundromat", |
|
|
"lecture_room", |
|
|
"library/indoor", |
|
|
"library/outdoor", |
|
|
"lido_deck/outdoor", |
|
|
"lift_bridge", |
|
|
"lighthouse", |
|
|
"limousine_interior", |
|
|
"living_room", |
|
|
"lobby", |
|
|
"lock_chamber", |
|
|
"locker_room", |
|
|
"mansion", |
|
|
"manufactured_home", |
|
|
"market/indoor", |
|
|
"market/outdoor", |
|
|
"marsh", |
|
|
"martial_arts_gym", |
|
|
"mausoleum", |
|
|
"medina", |
|
|
"moat/water", |
|
|
"monastery/outdoor", |
|
|
"mosque/indoor", |
|
|
"mosque/outdoor", |
|
|
"motel", |
|
|
"mountain", |
|
|
"mountain_snowy", |
|
|
"movie_theater/indoor", |
|
|
"museum/indoor", |
|
|
"music_store", |
|
|
"music_studio", |
|
|
"nuclear_power_plant/outdoor", |
|
|
"nursery", |
|
|
"oast_house", |
|
|
"observatory/outdoor", |
|
|
"ocean", |
|
|
"office", |
|
|
"office_building", |
|
|
"oil_refinery/outdoor", |
|
|
"oilrig", |
|
|
"operating_room", |
|
|
"orchard", |
|
|
"outhouse/outdoor", |
|
|
"pagoda", |
|
|
"palace", |
|
|
"pantry", |
|
|
"park", |
|
|
"parking_garage/indoor", |
|
|
"parking_garage/outdoor", |
|
|
"parking_lot", |
|
|
"parlor", |
|
|
"pasture", |
|
|
"patio", |
|
|
"pavilion", |
|
|
"pharmacy", |
|
|
"phone_booth", |
|
|
"physics_laboratory", |
|
|
"picnic_area", |
|
|
"pilothouse/indoor", |
|
|
"planetarium/outdoor", |
|
|
"playground", |
|
|
"playroom", |
|
|
"plaza", |
|
|
"podium/indoor", |
|
|
"podium/outdoor", |
|
|
"pond", |
|
|
"poolroom/establishment", |
|
|
"poolroom/home", |
|
|
"power_plant/outdoor", |
|
|
"promenade_deck", |
|
|
"pub/indoor", |
|
|
"pulpit", |
|
|
"putting_green", |
|
|
"racecourse", |
|
|
"raceway", |
|
|
"raft", |
|
|
"railroad_track", |
|
|
"rainforest", |
|
|
"reception", |
|
|
"recreation_room", |
|
|
"residential_neighborhood", |
|
|
"restaurant", |
|
|
"restaurant_kitchen", |
|
|
"restaurant_patio", |
|
|
"rice_paddy", |
|
|
"riding_arena", |
|
|
"river", |
|
|
"rock_arch", |
|
|
"rope_bridge", |
|
|
"ruin", |
|
|
"runway", |
|
|
"sandbar", |
|
|
"sandbox", |
|
|
"sauna", |
|
|
"schoolhouse", |
|
|
"sea_cliff", |
|
|
"server_room", |
|
|
"shed", |
|
|
"shoe_shop", |
|
|
"shopfront", |
|
|
"shopping_mall/indoor", |
|
|
"shower", |
|
|
"skatepark", |
|
|
"ski_lodge", |
|
|
"ski_resort", |
|
|
"ski_slope", |
|
|
"sky", |
|
|
"skyscraper", |
|
|
"slum", |
|
|
"snowfield", |
|
|
"squash_court", |
|
|
"stable", |
|
|
"stadium/baseball", |
|
|
"stadium/football", |
|
|
"stage/indoor", |
|
|
"staircase", |
|
|
"street", |
|
|
"subway_interior", |
|
|
"subway_station/platform", |
|
|
"supermarket", |
|
|
"sushi_bar", |
|
|
"swamp", |
|
|
"swimming_pool/indoor", |
|
|
"swimming_pool/outdoor", |
|
|
"synagogue/indoor", |
|
|
"synagogue/outdoor", |
|
|
"television_studio", |
|
|
"temple/east_asia", |
|
|
"temple/south_asia", |
|
|
"tennis_court/indoor", |
|
|
"tennis_court/outdoor", |
|
|
"tent/outdoor", |
|
|
"theater/indoor_procenium", |
|
|
"theater/indoor_seats", |
|
|
"thriftshop", |
|
|
"throne_room", |
|
|
"ticket_booth", |
|
|
"toll_plaza", |
|
|
"topiary_garden", |
|
|
"tower", |
|
|
"toyshop", |
|
|
"track/outdoor", |
|
|
"train_railway", |
|
|
"train_station/platform", |
|
|
"tree_farm", |
|
|
"tree_house", |
|
|
"trench", |
|
|
"underwater/coral_reef", |
|
|
"utility_room", |
|
|
"valley", |
|
|
"van_interior", |
|
|
"vegetable_garden", |
|
|
"veranda", |
|
|
"veterinarians_office", |
|
|
"viaduct", |
|
|
"videostore", |
|
|
"village", |
|
|
"vineyard", |
|
|
"volcano", |
|
|
"volleyball_court/indoor", |
|
|
"volleyball_court/outdoor", |
|
|
"waiting_room", |
|
|
"warehouse/indoor", |
|
|
"water_tower", |
|
|
"waterfall/block", |
|
|
"waterfall/fan", |
|
|
"waterfall/plunge", |
|
|
"watering_hole", |
|
|
"wave", |
|
|
"wet_bar", |
|
|
"wheat_field", |
|
|
"wind_farm", |
|
|
"windmill", |
|
|
"wine_cellar/barrel_storage", |
|
|
"wine_cellar/bottle_storage", |
|
|
"wrestling_ring/indoor", |
|
|
"yard", |
|
|
"youth_hostel", |
|
|
] |
|
|
|
|
|
|
|
|
class Sun397Config(datasets.BuilderConfig): |
|
|
def __init__(self, partition, **kwargs): |
|
|
super(Sun397Config, self).__init__(**kwargs) |
|
|
self.partition = partition |
|
|
|
|
|
|
|
|
class Sun397Dataset(datasets.GeneratorBasedBuilder): |
|
|
|
|
|
BUILDER_CONFIGS = [ |
|
|
Sun397Config( |
|
|
name=f"standard-part{partition:d}-120k", |
|
|
version=_VERSION, |
|
|
partition=partition, |
|
|
description=f"Train and test splits from the official partition number {partition:d}.", |
|
|
) |
|
|
for partition in range(1, 10 + 1) |
|
|
] |
|
|
|
|
|
|
|
|
|
|
|
def _info(self): |
|
|
features = datasets.Features( |
|
|
{ |
|
|
"image": datasets.Image(), |
|
|
"label": datasets.features.ClassLabel(names=_NAMES), |
|
|
} |
|
|
) |
|
|
return datasets.DatasetInfo( |
|
|
description=_DESCRIPTION, |
|
|
features=features, |
|
|
homepage=_HOMEPAGE, |
|
|
license=_LICENSE, |
|
|
citation=_CITATION, |
|
|
) |
|
|
|
|
|
def _split_generators(self, dl_manager): |
|
|
data_dir = dl_manager.download_and_extract(_URLs) |
|
|
data_dir = {key: Path(path) for key, path in data_dir.items()} |
|
|
data_dir["images"] = data_dir["images"] / "SUN397" |
|
|
subset_images = self._get_partition_subsets_images( |
|
|
data_dir["images"], data_dir["partitions"] |
|
|
) |
|
|
return [ |
|
|
datasets.SplitGenerator( |
|
|
name=datasets.Split.TRAIN, |
|
|
gen_kwargs={ |
|
|
"images_dir": data_dir["images"], |
|
|
"subset_images": subset_images["tr"], |
|
|
}, |
|
|
), |
|
|
datasets.SplitGenerator( |
|
|
name=datasets.Split.TEST, |
|
|
gen_kwargs={ |
|
|
"images_dir": data_dir["images"], |
|
|
"subset_images": subset_images["te"], |
|
|
}, |
|
|
), |
|
|
datasets.SplitGenerator( |
|
|
name="other", |
|
|
gen_kwargs={ |
|
|
"images_dir": data_dir["images"], |
|
|
"subset_images": subset_images["va"], |
|
|
}, |
|
|
), |
|
|
] |
|
|
|
|
|
def _load_image_set_from_file(self, filepath): |
|
|
with open(filepath, mode="r") as f: |
|
|
return set([line.strip() for line in f]) |
|
|
|
|
|
def _get_all_image_paths(self, images_dir): |
|
|
return [ |
|
|
str(path)[len(str(images_dir)) :] for path in images_dir.rglob("sun_*.jpg") |
|
|
] |
|
|
|
|
|
def _get_partition_subsets_images(self, images_dir, partitions_dir): |
|
|
|
|
|
all_images = set(self._get_all_image_paths(images_dir)) |
|
|
|
|
|
partition = self.config.partition |
|
|
filenames = { |
|
|
"tr": f"Training_{partition:02d}.txt", |
|
|
"te": f"Testing_{partition:02d}.txt", |
|
|
} |
|
|
splits_sets = {} |
|
|
for split, filename in filenames.items(): |
|
|
filepath = partitions_dir / filename |
|
|
splits_sets[split] = self._load_image_set_from_file(filepath) |
|
|
|
|
|
splits_sets["va"] = all_images - (splits_sets["tr"] | splits_sets["te"]) |
|
|
return splits_sets |
|
|
|
|
|
def _generate_examples(self, images_dir, subset_images): |
|
|
for image_name in subset_images: |
|
|
label = "/".join(image_name.split("/")[2:-1]) |
|
|
image_path = os.path.join(str(images_dir), *image_name.split("/")) |
|
|
record = {"image": str(image_path), "label": label} |
|
|
yield image_name, record |
|
|
|
