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"""TODO: Add a description here.""" |
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import csv |
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import json |
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import os |
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import math |
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import requests |
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from io import BytesIO |
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from zipfile import ZipFile |
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from urllib.request import urlopen |
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import pandas as pd |
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import datasets |
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_CITATION = """\ |
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@InProceedings{huggingface:dataset, |
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title = {A great new dataset}, |
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author={huggingface, Inc. |
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}, |
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year={2020} |
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} |
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""" |
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_DESCRIPTION = """\ |
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This new dataset is designed to solve this great NLP task and is crafted with a lot of care. |
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""" |
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_HOMEPAGE = "" |
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_LICENSE = "" |
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_LILA_SAS_URLS = pd.read_csv("https://lila.science/wp-content/uploads/2020/03/lila_sas_urls.txt") |
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_LILA_SAS_URLS.rename(columns={"# name": "name"}, inplace=True) |
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_METADATA_BASE_URL = "https://huggingface.co/datasets/NimaBoscarino/LILA/resolve/main/data/" |
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_LILA_URLS = { |
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"Caltech Camera Traps": "Caltech_Camera_Traps.jsonl.zip", |
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"ENA24": "ENA24.jsonl.zip", |
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"Missouri Camera Traps": "Missouri_Camera_Traps.jsonl.zip", |
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"NACTI": "NACTI.jsonl.zip", |
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"WCS Camera Traps": "WCS_Camera_Traps.jsonl.zip", |
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"Wellington Camera Traps": "Wellington_Camera_Traps.jsonl.zip", |
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"Island Conservation Camera Traps": "Island_Conservation_Camera_Traps.jsonl.zip", |
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"Channel Islands Camera Traps": "Channel_Islands_Camera_Traps.jsonl.zip", |
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"Idaho Camera Traps": "Idaho_Camera_Traps.jsonl.zip", |
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"Snapshot Serengeti": "Snapshot_Serengeti.jsonl.zip", |
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"Snapshot Karoo": "Snapshot_Karoo.jsonl.zip", |
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"Snapshot Kgalagadi": "Snapshot_Kgalagadi.jsonl.zip", |
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"Snapshot Enonkishu": "Snapshot_Enonkishu.jsonl.zip", |
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"Snapshot Camdeboo": "Snapshot_Camdeboo.jsonl.zip", |
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"Snapshot Mountain Zebra": "Snapshot_Mountain_Zebra.jsonl.zip", |
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"Snapshot Kruger": "Snapshot_Kruger.jsonl.zip", |
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"SWG Camera Traps": "SWG_Camera_Traps.jsonl.zip", |
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"Orinoquia Camera Traps": "Orinoquia_Camera_Traps.jsonl.zip", |
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} |
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_TAXONOMY = { |
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"kingdom": datasets.ClassLabel(num_classes=1, names=["animalia"]), |
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"phylum": datasets.ClassLabel(num_classes=2, names=["chordata", "arthropoda"]), |
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"subphylum": datasets.ClassLabel(num_classes=5, names=[ |
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'vertebrata', 'hexapoda', 'crustacea', 'chelicerata', |
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'myriapoda' |
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]), |
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"superclass": datasets.ClassLabel(num_classes=1, names=["multicrustacea"]), |
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"class": datasets.ClassLabel(num_classes=8, names=[ |
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'mammalia', 'aves', 'insecta', 'reptilia', 'malacostraca', |
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'arachnida', 'diplopoda', 'amphibia' |
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]), |
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"subclass": datasets.ClassLabel(num_classes=3, names=[ |
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'theria', 'pterygota', 'eumalacostraca' |
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]), |
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"infraclass": datasets.ClassLabel(num_classes=2, names=[ |
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'placentalia', 'marsupialia' |
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]), |
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"superorder": datasets.ClassLabel(num_classes=5, names=[ |
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'laurasiatheria', 'euarchontoglires', 'eucarida', 'xenarthra', |
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'afrotheria' |
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]), |
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"order": datasets.ClassLabel(num_classes=53, names=[ |
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'carnivora', 'chiroptera', 'artiodactyla', 'squamata', |
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'didelphimorphia', 'lagomorpha', 'rodentia', 'primates', |
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'passeriformes', 'galliformes', 'perissodactyla', |
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'accipitriformes', 'caprimulgiformes', 'lepidoptera', |
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'strigiformes', 'piciformes', 'falconiformes', 'charadriiformes', |
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'decapoda', 'columbiformes', 'pelecaniformes', 'procellariiformes', |
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'gruiformes', 'testudines', 'araneae', 'tinamiformes', 'cingulata', |
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'coraciiformes', 'hymenoptera', 'pilosa', 'cathartiformes', |
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'tubulidentata', 'otidiformes', 'struthioniformes', 'proboscidea', |
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'crocodylia', 'pholidota', 'scandentia', 'trogoniformes', |
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'bucerotiformes', 'anseriformes', 'eulipotyphla', 'psittaciformes', |
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'cuculiformes', 'ciconiiformes', 'musophagiformes', 'hyracoidea', |
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'eurypygiformes', 'afrosoricida', 'galbuliformes', 'macroscelidea', |
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'anura', 'rheiformes' |
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]), |
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"suborder": datasets.ClassLabel(num_classes=17, names=[ |
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'ruminantia', 'suina', 'sciuromorpha', 'haplorhini', |
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'hystricomorpha', 'pleocyemata', 'sauria', 'myomorpha', |
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'castorimorpha', 'apocrita', 'vermilingua', 'anomaluromorpha', |
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'whippomorpha', 'serpentes', 'tylopoda', 'strepsirrhini', |
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'tenrecomorpha' |
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]), |
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"infraorder": datasets.ClassLabel(num_classes=9, names=[ |
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'simiiformes', 'hystricognathi', 'brachyura', 'anomura', |
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'aculeata', 'ancodonta', 'chiromyiformes', 'lemuriformes', |
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'lorisiformes' |
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]), |
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"superfamily": datasets.ClassLabel(num_classes=12, names=[ |
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'hominoidea', 'erethizontoidea', 'paguroidea', 'muroidea', |
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'chelonioidea', 'cavioidea', 'formicoidea', 'octodontoidea', |
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'lemuroidea', 'chinchilloidea', 'cheirogaleoidea', 'papilionoidea' |
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]), |
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"family": datasets.ClassLabel(num_classes=159, names=[ |
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'mustelidae', 'felidae', 'bovidae', 'canidae', 'cervidae', |
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'didelphidae', 'suidae', 'leporidae', 'procyonidae', 'mephitidae', |
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'sciuridae', 'hominidae', 'ursidae', 'corvidae', 'phasianidae', |
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'equidae', 'turdidae', 'accipitridae', 'trochilidae', |
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'erethizontidae', 'antilocapridae', 'sittidae', 'parulidae', |
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'cardinalidae', 'picidae', 'falconidae', 'strigidae', 'laridae', |
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'columbidae', 'ardeidae', 'calcinidae', 'iguanidae', |
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'megapodiidae', 'mimidae', 'varanidae', 'procellariidae', |
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'rallidae', 'muridae', 'phocidae', 'hydrobatidae', 'dasyproctidae', |
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'tayassuidae', 'tinamidae', 'cuniculidae', 'odontophoridae', |
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'dasypodidae', 'passerellidae', 'troglodytidae', 'cricetidae', |
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'geomyidae', 'momotidae', 'formicidae', 'caviidae', 'cracidae', |
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'myrmecophagidae', 'chlamyphoridae', 'tapiridae', 'cebidae', |
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'pitheciidae', 'cathartidae', 'atelidae', 'caprimulgidae', |
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'orycteropodidae', 'hyaenidae', 'cercopithecidae', 'otididae', |
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'gruidae', 'viverridae', 'pedetidae', 'herpestidae', |
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'struthionidae', 'hystricidae', 'sagittariidae', 'testudinidae', |
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'elephantidae', 'giraffidae', 'hippopotamidae', 'rhinocerotidae', |
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'crocodylidae', 'numididae', 'manidae', 'irenidae', 'echimyidae', |
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'pittidae', 'leiothrichidae', 'muscicapidae', 'tragulidae', |
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'scolopacidae', 'hylobatidae', 'timaliidae', 'stenostiridae', |
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'tupaiidae', 'trogonidae', 'bucerotidae', 'prionodontidae', |
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'acrocephalidae', 'pycnonotidae', 'anatidae', 'anhimidae', |
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'anomaluridae', 'aramidae', 'erinaceidae', 'brachypteraciidae', |
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'threskiornithidae', 'psittacidae', 'buphagidae', 'burhinidae', |
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'camelidae', 'sarothruridae', 'cuculidae', 'ciconiidae', |
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'furnariidae', 'cisticolidae', 'apodidae', 'musophagidae', |
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'nesomyidae', 'eupleridae', 'daubentoniidae', 'procaviidae', |
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'dicaeidae', 'dicruridae', 'lemuridae', 'laniidae', 'vangidae', |
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'eurypygidae', 'formicariidae', 'galagidae', 'grallariidae', |
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'charadriidae', 'tenrecidae', 'scotocercidae', 'chinchillidae', |
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'sturnidae', 'malaconotidae', 'macrosphenidae', 'cheirogaleidae', |
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'alaudidae', 'icteridae', 'bucconidae', 'motacillidae', |
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'nandiniidae', 'nectariniidae', 'estrildidae', 'bernieridae', |
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'alligatoridae', 'macroscelididae', 'ploceidae', 'indriidae', |
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'psophiidae', 'ramphastidae', 'ranidae', 'rheidae', 'spalacidae', |
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'scincidae', 'soricidae', 'monarchidae', 'thryonomyidae', |
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'teiidae', 'tytonidae' |
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]), |
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"subfamily": datasets.ClassLabel(num_classes=69, names=[ |
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'taxidiinae', 'felinae', 'bovinae', 'capreolinae', |
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'didelphinae', 'suinae', 'sciurinae', 'homininae', 'ursinae', |
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'xerinae', 'mephitinae', 'antilopinae', 'cervinae', 'mustelinae', |
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'guloninae', 'erethizontinae', 'sterninae', 'ardeinae', 'murinae', |
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'lutrinae', 'melinae', 'neotominae', 'hydrochoerinae', |
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'tigriornithinae', 'tolypeutinae', 'pantherinae', 'cebinae', |
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'callicebinae', 'alouattinae', 'saimiriinae', 'protelinae', |
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'cercopithecinae', 'genettinae', 'mungotinae', 'herpestinae', |
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'ictonychinae', 'hyaeninae', 'mellivorinae', 'echimyinae', |
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'paradoxurinae', 'ratufinae', 'helictidinae', 'colobinae', |
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'viverrinae', 'hemigalinae', 'callosciurinae', 'erinaceinae', |
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'atelinae', 'camelinae', 'caviinae', 'furnariinae', 'criniferinae', |
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'cricetomyinae', 'euplerinae', 'deomyinae', 'nesomyinae', |
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'euphractinae', 'galidiinae', 'tenrecinae', 'oryzorictinae', |
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'musophaginae', 'myadinae', 'macroscelidinae', 'rhizomyinae', |
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'rhynchocyoninae', 'scincinae', 'crocidurinae', 'tremarctinae', |
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'tupinambinae' |
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]), |
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"tribe": datasets.ClassLabel(num_classes=46, names=[ |
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'bovini', 'odocoileini', 'didelphini', 'suini', 'sciurini', |
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'tamiini', 'marmotini', 'caprini', 'cervini', 'alceini', 'rattini', |
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'capreolini', 'apodemini', 'reithrodontomyini', 'neotomini', |
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'papionini', 'alcelaphini', 'potamochoerini', 'cephalophini', |
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'tragelaphini', 'hippotragini', 'oreotragini', 'cercopithecini', |
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'reduncini', 'antilopini', 'aepycerotini', 'phacochoerini', |
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'xerini', 'echimyini', 'pteromyini', 'presbytini', 'muntiacini', |
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'callosciurini', 'camelini', 'colobini', 'praomyini', |
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'protoxerini', 'arvicanthini', 'malacomyini', 'metachirini', |
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'murini', 'neotragini', 'macroscelidini', 'myocastorini', |
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'rhizomyini', 'lamini' |
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]), |
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"genus": datasets.ClassLabel(num_classes=476, names=[ |
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'taxidea', 'lynx', 'felis', 'bos', 'canis', 'odocoileus', |
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'urocyon', 'puma', 'didelphis', 'sus', 'procyon', 'sciurus', |
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'homo', 'ursus', 'corvus', 'gallus', 'tamias', 'sylvilagus', |
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'equus', 'vulpes', 'mephitis', 'meleagris', 'marmota', 'ovis', |
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'sialia', 'nucifraga', 'cervus', 'mustela', 'pekania', 'neogale', |
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'pica', 'alces', 'erethizon', 'antilocapra', 'sitta', 'ixoreus', |
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'piranga', 'falco', 'strix', 'anous', 'athene', 'nasua', 'capra', |
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'ardea', 'butorides', 'calcinus', 'iguana', 'caloenas', 'rattus', |
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'calonectris', 'asio', 'hydrobates', 'zenaida', 'nyctanassa', |
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'turdus', 'dasyprocta', 'pecari', 'lepus', 'tinamus', 'leopardus', |
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'cuniculus', 'mazama', 'tamiasciurus', 'capreolus', 'apodemus', |
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'callipepla', 'cyanocitta', 'dasypus', 'dendragapus', 'junco', |
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'lontra', 'martes', 'meles', 'otospermophilus', 'perisoreus', |
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'troglodytes', 'peromyscus', 'neotoma', 'momotus', 'speothos', |
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'hydrochoerus', 'cerdocyon', 'mitu', 'tigrisoma', 'myrmecophaga', |
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'priodontes', 'pteronura', 'panthera', 'herpailurus', 'tapirus', |
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'sapajus', 'plecturocebus', 'tamandua', 'penelope', 'eira', |
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'cathartes', 'alouatta', 'saimiri', 'tayassu', 'orycteropus', |
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'proteles', 'papio', 'damaliscus', 'syncerus', 'potamochoerus', |
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'ardeotis', 'caracal', 'anthropoides', 'sylvicapra', 'tragelaphus', |
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'dama', 'otocyon', 'oryx', 'genetta', 'pedetes', 'alcelaphus', |
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'lupulella', 'oreotragus', 'suricata', 'herpestes', 'cynictis', |
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'chlorocebus', 'struthio', 'hystrix', 'redunca', 'pelea', |
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'sagittarius', 'antidorcas', 'raphicerus', 'connochaetes', |
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'ictonyx', 'acinonyx', 'madoqua', 'cephalophus', 'loxodonta', |
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'nanger', 'eudorcas', 'giraffa', 'hippopotamus', 'crocuta', |
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'aepyceros', 'ourebia', 'phacochoerus', 'kobus', 'neotis', |
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'parahyaena', 'bunolagus', 'diceros', 'mellivora', 'crocodylus', |
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'pronolagus', 'hippotragus', 'leptailurus', 'lycaon', 'xerus', |
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'ceratotherium', 'hyaena', 'nesolagus', 'irena', 'atherurus', |
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'macaca', 'dactylomys', 'hydrornis', 'macropygia', 'varanus', |
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'arctictis', 'ratufa', 'pterorhinus', 'cinclidium', 'myophonus', |
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'moschiola', 'capricornis', 'cissa', 'paradoxurus', 'urva', |
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'rheinardia', 'spilornis', 'chalcophaps', 'scolopax', 'melogale', |
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'enicurus', 'trachypithecus', 'petaurista', 'cyanoderma', |
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'catopuma', 'garrulax', 'culicicapa', 'polyplectron', 'arctonyx', |
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'muntiacus', 'viverra', 'erythrogenys', 'prionailurus', 'picus', |
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'pardofelis', 'paguma', 'nisaetus', 'ducula', 'tupaia', |
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'harpactes', 'geokichla', 'chrotogale', 'callosciurus', 'manis', |
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'dremomys', 'pygathrix', 'trochalopteron', 'ianthocincla', |
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'aceros', 'rusa', 'zoothera', 'leiothrix', 'lophura', 'prionodon', |
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'helarctos', 'pitta', 'tamiops', 'myiomela', 'urocissa', |
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'accipiter', 'acrocephalus', 'acryllium', 'agamia', 'alectoris', |
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'chamaetylas', 'alophoixus', 'alopochen', 'stelgidillas', |
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'eurillas', 'anhima', 'anomalurus', 'aonyx', 'aquila', 'aramides', |
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'aramus', 'arborophila', 'arctogalidia', 'ardeola', 'argusianus', |
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'arremonops', 'atelerix', 'ateles', 'atelocynus', 'atelornis', |
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'atilax', 'balearica', 'bambusicola', 'baryphthengus', 'bdeogale', |
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'blastocerus', 'bostrychia', 'brachypteracias', 'brotogeris', |
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'bubo', 'bubulcus', 'buphagus', 'burhinus', 'butastur', 'buteo', |
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'buteogallus', 'bycanistes', 'cabassous', 'cairina', 'caloperdix', |
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'camelus', 'mentocrex', 'caprimulgus', 'caracara', 'carpococcyx', |
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'hylocichla', 'catharus', 'cavia', 'cebus', 'cercocebus', |
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'cercopithecus', 'allochrocebus', 'cercotrichas', 'ortalis', |
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'chelonoidis', 'ciconia', 'cinclodes', 'circus', 'cisticola', |
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'civettictis', 'claravis', 'cochlearius', 'coendou', 'collocalia', |
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'colobus', 'colomys', 'columba', 'columbina', 'conepatus', |
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'copsychus', 'coragyps', 'corythaixoides', 'cossypha', 'coturnix', |
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'coua', 'crax', 'cricetomys', 'cryptoprocta', 'crypturellus', |
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'cuon', 'cyanoptila', 'cyornis', 'daptrius', 'daubentonia', |
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'dendrocitta', 'dendrohyrax', 'ortygornis', 'deomys', 'dicaeum', |
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'dicerorhinus', 'dicrurus', 'melaenornis', 'egretta', 'elephas', |
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'eliurus', 'larvivora', 'erythrocebus', 'eulemur', 'euphractus', |
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'eupleres', 'eupodotis', 'eurocephalus', 'euryceros', 'eurypyga', |
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'eutriorchis', 'ficedula', 'formicarius', 'fossa', 'scleroptila', |
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'pternistis', 'francolinus', 'funisciurus', 'galago', 'galictis', |
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'galidia', 'galidictis', 'geotrygon', 'grallaria', 'guttera', |
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'haliaeetus', 'vanellus', 'harpia', 'heliosciurus', 'helogale', |
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'hemicentetes', 'hemigalus', 'urosphena', 'heterohyrax', |
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'hippocamelus', 'hybomys', 'hylomyscus', 'hylopetes', 'hypogeomys', |
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'ichneumia', 'arundinax', 'jynx', 'lagidium', 'lamprotornis', |
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'laniarius', 'lanius', 'lariscus', 'lemur', 'leptotila', |
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'lissotis', 'litocranius', 'lophotibis', 'lutreolina', 'lycalopex', |
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'malacomys', 'melierax', 'melocichla', 'mesembrinibis', |
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'chloropicus', 'metachirus', 'micrastur', 'microcebus', |
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'microgale', 'microsciurus', 'mirafra', 'molothrus', 'monasa', |
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'morphnus', 'motacilla', 'mungos', 'mus', 'musophaga', 'mydaus', |
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'myoprocta', 'mystacornis', 'nandinia', 'cyanomitra', 'oressochen', |
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'neocossyphus', 'neofelis', 'neomorphus', 'delacourella', |
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'streptopelia', 'nesomys', 'nesotragus', 'niltava', 'nothocrax', |
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'numida', 'nyctidromus', 'odontophorus', 'oenomys', 'oenanthe', |
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'otolemur', 'otus', 'oxylabes', 'paleosuchus', 'pan', 'paraxerus', |
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'pernis', 'petrodromus', 'phaethornis', 'philander', 'philantomba', |
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'pilherodius', 'xanthomixis', 'pipile', 'ploceus', 'poecilogale', |
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'pogonocichla', 'potos', 'praomys', 'presbytis', 'procavia', |
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'piliocolobus', 'proechimys', 'propithecus', 'protoxerus', |
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'psophia', 'pteroglossus', 'ramphastos', 'rana', 'rhea', |
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'rhizomys', 'rhynchocyon', 'rollulus', 'rupornis', 'ruwenzorornis', |
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'salanoia', 'saxicola', 'setifer', 'sheppardia', 'plestiodon', |
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'spilogale', 'spizaetus', 'stephanoaetus', 'stigmochelys', |
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'amazona', 'suncus', 'sundasciurus', 'tauraco', 'tenrec', |
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'terpsiphone', 'thamnomys', 'thryonomys', 'tockus', 'tolypeutes', |
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'tragulus', 'tremarctos', 'trichys', 'tupinambis', 'turtur', |
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'tyto', 'vicugna', 'viverricula', 'xenoperdix', 'euxerus', |
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'zonotrichia', 'erinaceus' |
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]), |
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"species": datasets.ClassLabel(num_classes=668, names=[ |
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'taxidea taxus', 'lynx rufus', 'felis catus', 'bos taurus', |
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'canis latrans', 'canis familiaris', 'urocyon cinereoargenteus', |
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'puma concolor', 'didelphis virginiana', 'sus scrofa', |
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'procyon lotor', 'urocyon littoralis', 'homo sapiens', |
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'ursus americanus', 'corvus brachyrhynchos', 'gallus gallus', |
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'tamias striatus', 'sylvilagus floridanus', 'sciurus niger', |
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'sciurus carolinensis', 'equus caballus', 'vulpes vulpes', |
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'mephitis mephitis', 'odocoileus virginianus', |
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'meleagris gallopavo', 'marmota monax', 'ovis canadensis', |
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'nucifraga columbiana', 'cervus canadensis', 'mustela erminea', |
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'pekania pennanti', 'neogale frenata', 'pica hudsonia', |
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'alces alces', 'erethizon dorsatum', 'antilocapra americana', |
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'corvus corax', 'sitta canadensis', 'ixoreus naevius', |
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'piranga ludoviciana', 'canis lupus', 'falco sparverius', |
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'strix varia', 'anous stolidus', 'athene cunicularia', |
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'nasua nasua', 'equus asinus', 'capra hircus', 'ardea herodias', |
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'butorides virescens', 'calcinus tubularis', 'falco tinnunculus', |
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'caloenas nicobarica', 'asio flammeus', 'hydrobates pelagicus', |
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'zenaida asiatica', 'nyctanassa violacea', 'dasyprocta coibae', |
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'pecari tajacu', 'didelphis marsupialis', 'lepus europaeus', |
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'tinamus major', 'ovis ammon', 'leopardus pardalis', |
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'mazama americana', 'cervus elaphus', 'tamiasciurus hudsonicus', |
|
'rattus praetor', 'nasua narica', 'apodemus sylvaticus', |
|
'callipepla californica', 'cyanocitta stelleri', |
|
'dasypus novemcinctus', 'dendragapus obscurus', 'equus africanus', |
|
'equus ferus', 'junco hyemalis', 'lepus americanus', |
|
'lepus californicus', 'lontra canadensis', 'marmota flaviventris', |
|
'martes americana', 'meles meles', 'odocoileus hemionus', |
|
'otospermophilus beecheyi', 'perisoreus canadensis', |
|
'rattus rattus', 'troglodytes aedon', 'zenaida macroura', |
|
'momotus momota', 'dasyprocta fuliginosa', 'speothos venaticus', |
|
'hydrochoerus hydrochaeris', 'iguana iguana', 'cerdocyon thous', |
|
'mitu tomentosum', 'tigrisoma fasciatum', |
|
'myrmecophaga tridactyla', 'priodontes maximus', |
|
'pteronura brasiliensis', 'panthera onca', |
|
'herpailurus yagouaroundi', 'tapirus terrestris', 'sapajus apella', |
|
'leopardus wiedii', 'lontra longicaudis', 'sciurus igniventris', |
|
'dasyprocta guamara', 'plecturocebus ornatus', 'mitu salvini', |
|
'tamandua tetradactyla', 'penelope jacquacu', 'cuniculus paca', |
|
'eira barbara', 'cathartes aura', 'penelope jacucaca', |
|
'tayassu pecari', 'orycteropus afer', 'proteles cristatus', |
|
'damaliscus pygargus', 'syncerus caffer', 'potamochoerus larvatus', |
|
'ardeotis kori', 'caracal caracal', 'anthropoides paradiseus', |
|
'sylvicapra grimmia', 'tragelaphus oryx', 'dama dama', |
|
'otocyon megalotis', 'oryx gazella', 'lepus saxatilis', |
|
'pedetes capensis', 'alcelaphus buselaphus', 'lupulella mesomelas', |
|
'oreotragus oreotragus', 'tragelaphus strepsiceros', |
|
'suricata suricatta', 'herpestes ichneumon', |
|
'cynictis penicillata', 'chlorocebus pygerythrus', |
|
'struthio camelus', 'hystrix africaeaustralis', |
|
'redunca fulvorufula', 'pelea capreolus', |
|
'sagittarius serpentarius', 'antidorcas marsupialis', |
|
'raphicerus campestris', 'connochaetes gnou', 'equus zebra', |
|
'ictonyx striatus', 'tragelaphus scriptus', 'acinonyx jubatus', |
|
'loxodonta africana', 'nanger granti', 'eudorcas thomsonii', |
|
'giraffa camelopardalis', 'lepus victoriae', |
|
'hippopotamus amphibius', 'crocuta crocuta', 'aepyceros melampus', |
|
'panthera pardus', 'panthera leo', 'ourebia ourebi', |
|
'hystrix cristata', 'damaliscus lunatus', 'phacochoerus africanus', |
|
'kobus ellipsiprymnus', 'connochaetes taurinus', 'equus quagga', |
|
'neotis ludwigii', 'vulpes chama', 'parahyaena brunnea', |
|
'herpestes pulverulentus', 'bunolagus monticularis', |
|
'diceros bicornis', 'felis lybica', 'lepus capensis', |
|
'mellivora capensis', 'crocodylus niloticus', |
|
'cephalophus natalensis', 'lupulella adusta', |
|
'tragelaphus angasii', 'pronolagus randensis', |
|
'hippotragus equinus', 'leptailurus serval', 'lycaon pictus', |
|
'ceratotherium simum', 'hyaena hyaena', 'nesolagus timminsi', |
|
'irena puella', 'ursus thibetanus', 'atherurus macrourus', |
|
'mustela strigidorsa', 'hydrornis elliotii', 'macropygia unchall', |
|
'varanus bengalensis', 'arctictis binturong', 'ratufa bicolor', |
|
'pterorhinus chinensis', 'cinclidium frontale', |
|
'hydrornis cyaneus', 'myophonus caeruleus', 'strix leptogrammica', |
|
'moschiola meminna', 'capricornis sumatraensis', 'cissa chinensis', |
|
'paradoxurus hermaphroditus', 'urva urva', 'rheinardia ocellata', |
|
'spilornis cheela', 'chalcophaps indica', 'scolopax rusticola', |
|
'turdus obscurus', 'trachypithecus francoisi', |
|
'cyanoderma chrysaeum', 'catopuma temminckii', 'garrulax maesi', |
|
'culicicapa ceylonensis', 'polyplectron bicalcaratum', |
|
'trachypithecus hatinhensis', 'arctonyx collaris', |
|
'cissa hypoleuca', 'turdus cardis', 'muntiacus vuquangensis', |
|
'viverra zibetha', 'erythrogenys hypoleucos', |
|
'prionailurus bengalensis', 'picus chlorolophus', |
|
'hystrix brachyura', 'pardofelis marmorata', 'paguma larvata', |
|
'nisaetus nipalensis', 'ducula badia', 'pterorhinus pectoralis', |
|
'tupaia belangeri', 'harpactes oreskios', 'geokichla citrina', |
|
'chrotogale owstoni', 'callosciurus erythraeus', |
|
'trachypithecus phayrei', 'macaca nemestrina', |
|
'dremomys rufigenis', 'picus rabieri', 'muntiacus muntjak', |
|
'pygathrix nemaeus', 'trochalopteron milnei', |
|
'muntiacus rooseveltorum', 'garrulax castanotis', |
|
'ianthocincla konkakinhensis', 'aceros nipalensis', |
|
'rusa unicolor', 'zoothera dauma', 'geokichla sibirica', |
|
'leiothrix argentauris', 'lophura nycthemera', |
|
'prionodon pardicolor', 'butorides striata', 'macaca arctoides', |
|
'helarctos malayanus', 'enicurus leschenaulti', 'myiomela leucura', |
|
'urocissa whiteheadi', 'mustela kathiah', 'martes flavigula', |
|
'accipiter madagascariensis', 'accipiter melanoleucus', |
|
'acrocephalus baeticatus', 'acryllium vulturinum', 'agamia agami', |
|
'alectoris rufa', 'chamaetylas poliophrys', 'alophoixus bres', |
|
'alopochen aegyptiaca', 'alouatta sara', |
|
'stelgidillas gracilirostris', 'eurillas latirostris', |
|
'eurillas virens', 'anhima cornuta', 'anomalurus derbianus', |
|
'aonyx cinereus', 'aquila heliaca', 'aquila rapax', |
|
'aramides cajaneus', 'aramus guarauna', |
|
'arborophila brunneopectus', 'arborophila rubrirostris', |
|
'arborophila rufogularis', 'arctogalidia trivirgata', |
|
'arctonyx hoevenii', 'ardea alba', 'ardea cocoi', |
|
'ardea melanocephala', 'ardeola grayii', 'argusianus argus', |
|
'arremonops chloronotus', 'asio madagascariensis', |
|
'atelerix albiventris', 'ateles chamek', 'atelocynus microtis', |
|
'atelornis pittoides', 'atherurus africanus', 'atilax paludinosus', |
|
'balearica regulorum', 'bambusicola fytchii', |
|
'baryphthengus martii', 'bdeogale crassicauda', |
|
'bdeogale jacksoni', 'blastocerus dichotomus', 'bos gaurus', |
|
'bostrychia hagedash', 'brachypteracias squamiger', |
|
'bubulcus ibis', 'burhinus capensis', 'butastur indicus', |
|
'buteo ridgwayi', 'buteogallus urubitinga', 'bycanistes brevis', |
|
'cabassous centralis', 'cabassous unicinctus', 'cairina moschata', |
|
'callosciurus notatus', 'caloperdix oculeus', |
|
'camelus dromedarius', 'mentocrex kioloides', 'capra aegagrus', |
|
'caracara plancus', 'carpococcyx renauldi', |
|
'cathartes burrovianus', 'cathartes melambrotus', |
|
'hylocichla mustelina', 'catharus ustulatus', 'cavia aperea', |
|
'cebus albifrons', 'cephalophus harveyi', 'cephalophus nigrifrons', |
|
'cephalophus silvicultor', 'cephalophus spadix', |
|
'cercocebus sanjei', 'cercopithecus erythrogaster', |
|
'allochrocebus lhoesti', 'cercopithecus mitis', 'ortalis vetula', |
|
'chelonoidis carbonarius', 'ciconia maguari', |
|
'cinclodes atacamensis', 'cinclodes fuscus', 'circus cyaneus', |
|
'cisticola cherina', 'civettictis civetta', 'claravis pretiosa', |
|
'cochlearius cochlearius', 'coendou bicolor', 'collocalia linchi', |
|
'colobus angolensis', 'colomys goslingi', 'columba arquatrix', |
|
'columba larvata', 'columbina talpacoti', 'conepatus chinga', |
|
'conepatus semistriatus', 'copsychus albospecularis', |
|
'copsychus malabaricus', 'copsychus saularis', 'coragyps atratus', |
|
'corythaixoides leucogaster', 'cossypha archeri', |
|
'coturnix delegorguei', 'coua caerulea', 'coua ruficeps', |
|
'coua serriana', 'crax alector', 'crax rubra', |
|
'cricetomys gambianus', 'cryptoprocta ferox', |
|
'crypturellus atrocapillus', 'crypturellus boucardi', |
|
'crypturellus cinereus', 'crypturellus cinnamomeus', |
|
'crypturellus erythropus', 'crypturellus bartletti', |
|
'crypturellus soui', 'crypturellus undulatus', |
|
'crypturellus variegatus', 'cuniculus taczanowskii', |
|
'cuon alpinus', 'cyanoptila cyanomelana', 'cyornis banyumas', |
|
'daptrius ater', 'dasyprocta punctata', 'dasyprocta leporina', |
|
'dasypus kappleri', 'daubentonia madagascariensis', |
|
'dendrocitta occipitalis', 'dendrohyrax arboreus', |
|
'ortygornis sephaena', 'deomys ferrugineus', |
|
'dicaeum trigonostigma', 'dicerorhinus sumatrensis', |
|
'dicrurus adsimilis', 'didelphis imperfecta', 'didelphis pernigra', |
|
'melaenornis fischeri', 'egretta thula', 'elephas maximus', |
|
'eliurus penicillatus', 'eliurus petteri', 'eliurus webbi', |
|
'enicurus schistaceus', 'equus grevyi', 'larvivora cyane', |
|
'erythrocebus patas', 'eudorcas rufifrons', 'eulemur albifrons', |
|
'euphractus sexcinctus', 'eupleres goudotii', |
|
'eupodotis senegalensis', 'eurocephalus ruppelli', |
|
'euryceros prevostii', 'eurypyga helias', 'eutriorchis astur', |
|
'felis chaus', 'felis silvestris', 'ficedula mugimaki', |
|
'ficedula tricolor', 'formicarius analis', 'formicarius colma', |
|
'fossa fossana', 'scleroptila afra', 'pternistis nobilis', |
|
'funisciurus carruthersi', 'funisciurus pyrropus', |
|
'galago senegalensis', 'galictis vittata', 'galidia elegans', |
|
'galidictis fasciata', 'genetta genetta', 'genetta maculata', |
|
'genetta servalina', 'genetta tigrina', 'geokichla gurneyi', |
|
'geotrygon montana', 'geotrygon saphirina', 'grallaria andicolus', |
|
'guttera pucherani', 'haliaeetus vociferoides', 'vanellus cayanus', |
|
'harpia harpyja', 'buteogallus solitarius', |
|
'heliosciurus rufobrachium', 'heliosciurus ruwenzorii', |
|
'helogale parvula', 'hemicentetes semispinosus', |
|
'hemigalus derbyanus', 'urosphena neumanni', |
|
'herpestes sanguineus', 'urva semitorquata', 'heterohyrax brucei', |
|
'hippocamelus antisensis', 'hybomys univittatus', |
|
'hydrornis oatesi', 'hylomyscus stella', 'hylopetes alboniger', |
|
'hypogeomys antimena', 'ichneumia albicauda', 'arundinax aedon', |
|
'jynx torquilla', 'lagidium viscacia', 'lamprotornis chalybaeus', |
|
'lamprotornis hildebrandti', 'lamprotornis superbus', |
|
'laniarius funebris', 'lanius collaris', 'lariscus insignis', |
|
'leopardus tigrinus', 'leptotila plumbeiceps', |
|
'leptotila rufaxilla', 'leptotila verreauxi', |
|
'lissotis hartlaubii', 'lissotis melanogaster', |
|
'litocranius walleri', 'lophotibis cristata', 'eupodotis gindiana', |
|
'lophura diardi', 'lophura erythrophthalma', 'lophura ignita', |
|
'lophura inornata', 'lutreolina crassicaudata', |
|
'lycalopex culpaeus', 'macaca assamensis', 'macaca fascicularis', |
|
'macaca mulatta', 'madoqua guentheri', 'malacomys longipes', |
|
'manis javanica', 'mazama temama', 'mazama chunyi', |
|
'mazama gouazoubira', 'odocoileus pandora', |
|
'melaenornis ardesiacus', 'melaenornis pammelaina', |
|
'meleagris ocellata', 'melierax poliopterus', |
|
'melocichla mentalis', 'melogale everetti', 'melogale personata', |
|
'mesembrinibis cayennensis', 'chloropicus griseocephalus', |
|
'metachirus nudicaudatus', 'microcebus murinus', |
|
'microsciurus flaviventer', 'microsciurus mimulus', |
|
'mitu tuberosum', 'molothrus oryzivorus', 'monasa morphoeus', |
|
'morphnus guianensis', 'motacilla flava', 'motacilla flaviventris', |
|
'mungos mungo', 'mus minutoides', 'musophaga rossae', |
|
'mustela lutreolina', 'mydaus javanensis', 'myophonus glaucinus', |
|
'myophonus melanurus', 'myoprocta pratti', 'mystacornis crossleyi', |
|
'nandinia binotata', 'cyanomitra cyanolaema', 'oressochen jubatus', |
|
'neocossyphus rufus', 'neofelis diardi', 'neofelis nebulosa', |
|
'neomorphus geoffroyi', 'neomorphus rufipennis', |
|
'delacourella capistrata', 'streptopelia picturata', |
|
'nesolagus netscheri', 'nesomys audeberti', 'nesotragus moschatus', |
|
'caprimulgus europaeus', 'niltava sumatrana', 'nisaetus nanus', |
|
'nothocrax urumutum', 'numida meleagris', 'nyctidromus albicollis', |
|
'odontophorus balliviani', 'odontophorus erythrops', |
|
'odontophorus gujanensis', 'oenomys hypoxanthus', |
|
'ortalis guttata', 'oryx beisa', 'otolemur garnettii', |
|
'otus spilocephalus', 'ovis aries', 'oxylabes madagascariensis', |
|
'pan troglodytes', 'panthera tigris', 'papio anubis', |
|
'papio cynocephalus', 'paraxerus boehmi', 'paraxerus cepapi', |
|
'paraxerus lucifer', 'paraxerus ochraceus', |
|
'paraxerus vexillarius', 'penelope purpurascens', |
|
'penelope superciliaris', 'pernis ptilorhynchus', |
|
'petrodromus tetradactylus', 'philander opossum', |
|
'philantomba monticola', 'pilherodius pileatus', |
|
'xanthomixis apperti', 'pipile cumanensis', 'pipile pipile', |
|
'hydrornis guajanus', 'hydrornis schneideri', 'ploceus alienus', |
|
'ploceus baglafecht', 'poecilogale albinucha', |
|
'pogonocichla stellata', 'polyplectron chalcurum', |
|
'erythrogenys mcclellandi', 'potos flavus', 'praomys tullbergi', |
|
'presbytis femoralis', 'presbytis thomasi', 'prionodon linsang', |
|
'procavia capensis', 'piliocolobus gordonorum', |
|
'procyon cancrivorus', 'propithecus candidus', |
|
'protoxerus stangeri', 'psophia crepitans', 'psophia leucoptera', |
|
'pternistis hildebrandti', 'pternistis leucoscepus', |
|
'pteroglossus beauharnaisii', 'ramphastos tucanus', |
|
'rattus tiomanicus', 'rhea americana', 'rhizomys sumatrensis', |
|
'rhynchocyon cirnei', 'rhynchocyon petersi', |
|
'rhynchocyon udzungwensis', 'rollulus rouloul', |
|
'rupornis magnirostris', 'ruwenzorornis johnstoni', |
|
'saimiri boliviensis', 'salanoia concolor', 'saxicola tectes', |
|
'sciurus deppei', 'sciurus granatensis', 'sciurus ignitus', |
|
'sciurus spadiceus', 'setifer setosus', 'sheppardia lowei', |
|
'spilogale putorius', 'spizaetus ornatus', |
|
'stephanoaetus coronatus', 'stigmochelys pardalis', |
|
'streptopelia capicola', 'streptopelia lugens', |
|
'streptopelia senegalensis', 'amazona oratrix', 'suncus murinus', |
|
'sundasciurus hippurus', 'sus barbatus', 'sylvilagus brasiliensis', |
|
'tamandua mexicana', 'tapirus bairdii', 'tapirus indicus', |
|
'tauraco livingstonii', 'tenrec ecaudatus', 'terpsiphone mutata', |
|
'thamnomys venustus', 'thryonomys gregorianus', |
|
'thryonomys swinderianus', 'tigrisoma lineatum', |
|
'tigrisoma mexicanum', 'tinamus guttatus', 'tinamus tao', |
|
'tockus deckeni', 'tockus flavirostris', 'tolypeutes matacus', |
|
'tragelaphus imberbis', 'tragulus javanicus', 'tragulus kanchil', |
|
'tragulus napu', 'tremarctos ornatus', 'trichys fasciculata', |
|
'tupaia glis', 'tupinambis teguixin', 'turdus ignobilis', |
|
'turdus olivaceus', 'turdus tephronotus', 'turtur chalcospilos', |
|
'turtur tympanistria', 'tyto alba', 'vanellus coronatus', |
|
'varanus salvator', 'vicugna pacos', 'viverricula indica', |
|
'xenoperdix udzungwensis', 'euxerus erythropus', 'xerus rutilus', |
|
'zonotrichia capensis', 'erinaceus europaeus', 'rattus norvegicus' |
|
]), |
|
"subspecies": datasets.ClassLabel(num_classes=8, names=[ |
|
'sciurus niger cinereus', 'alces alces americanus', |
|
'sapajus apella margaritae', 'damaliscus pygargus phillipsi', |
|
'alcelaphus buselaphus caama', 'damaliscus lunatus jimela', |
|
'equus quagga burchellii', 'zoothera dauma dauma' |
|
]), |
|
"variety": datasets.ClassLabel(num_classes=1, names=[ |
|
'gallus gallus domesticus' |
|
]), |
|
} |
|
|
|
|
|
class LILAConfig(datasets.BuilderConfig): |
|
"""Builder Config for LILA""" |
|
|
|
def __init__(self, image_base_url, metadata_url, **kwargs): |
|
"""BuilderConfig for LILA. |
|
Args: |
|
**kwargs: keyword arguments forwarded to super. |
|
""" |
|
super(LILAConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs) |
|
self.image_base_url = image_base_url |
|
self.metadata_url = metadata_url |
|
|
|
|
|
class LILA(datasets.GeneratorBasedBuilder): |
|
"""TODO: Short description of my dataset.""" |
|
|
|
VERSION = datasets.Version("1.1.0") |
|
|
|
BUILDER_CONFIGS = [ |
|
LILAConfig( |
|
name=row.name, |
|
|
|
image_base_url=row.image_base_url, |
|
metadata_url=_METADATA_BASE_URL + _LILA_URLS[row.name] |
|
) for row in _LILA_SAS_URLS.itertuples() |
|
] |
|
|
|
def _get_features(self) -> datasets.Features: |
|
|
|
|
|
|
|
if self.config.name == 'Caltech Camera Traps': |
|
return datasets.Features({ |
|
"id": datasets.Value("string"), "file_name": datasets.Value("string"), |
|
"width": datasets.Value("int32"), "height": datasets.Value("int32"), |
|
"seq_num_frames": datasets.Value("int32"), |
|
"date_captured": datasets.Value("string"), |
|
"seq_id": datasets.Value("string"), |
|
"location": datasets.Value("string"), |
|
"rights_holder": datasets.Value("string"), |
|
"frame_num": datasets.Value("int32"), |
|
"annotations": datasets.Sequence({ |
|
"id": datasets.Value("string"), |
|
"category_id": datasets.Value("int32"), |
|
"taxonomy": _TAXONOMY, |
|
}), |
|
"bboxes": datasets.Sequence({ |
|
"id": datasets.Value("string"), |
|
"category_id": datasets.Value("int32"), |
|
"bbox": datasets.Sequence(datasets.Value("float32"), length=4), |
|
}), |
|
"image": datasets.Image(decode=False), |
|
}) |
|
elif self.config.name == 'ENA24': |
|
return datasets.Features({ |
|
"id": datasets.Value("string"), "file_name": datasets.Value("string"), |
|
"width": datasets.Value("int32"), "height": datasets.Value("int32"), |
|
"annotations": datasets.Sequence({ |
|
"id": datasets.Value("string"), |
|
"category_id": datasets.Value("int32"), |
|
"bbox": datasets.Sequence(datasets.Value("float32"), length=4), |
|
"taxonomy": _TAXONOMY, |
|
}), |
|
"image": datasets.Image(decode=False), |
|
}) |
|
elif self.config.name == 'Missouri Camera Traps': |
|
return datasets.Features({ |
|
"id": datasets.Value("string"), "file_name": datasets.Value("string"), |
|
"width": datasets.Value("int32"), "height": datasets.Value("int32"), |
|
"seq_id": datasets.Value("string"), "seq_num_frames": datasets.Value("int32"), |
|
"frame_num": datasets.Value("int32"), |
|
"annotations": datasets.Sequence({ |
|
"id": datasets.Value("string"), |
|
"category_id": datasets.Value("int32"), |
|
"sequence_level_annotation": datasets.Value("bool"), |
|
"bbox": datasets.Sequence(datasets.Value("float32"), length=4), |
|
"taxonomy": _TAXONOMY, |
|
}), |
|
"image": datasets.Image(decode=False), |
|
}) |
|
elif self.config.name == 'NACTI': |
|
return datasets.Features({ |
|
"id": datasets.Value("string"), "file_name": datasets.Value("string"), |
|
"width": datasets.Value("int32"), "height": datasets.Value("int32"), |
|
"study": datasets.Value("string"), "location": datasets.Value("string"), |
|
"annotations": datasets.Sequence({ |
|
"id": datasets.Value("string"), |
|
"category_id": datasets.Value("int32"), |
|
"taxonomy": _TAXONOMY, |
|
}), |
|
"bboxes": datasets.Sequence({ |
|
"id": datasets.Value("string"), |
|
"category_id": datasets.Value("int32"), |
|
"bbox": datasets.Sequence(datasets.Value("float32"), length=4), |
|
}), |
|
"image": datasets.Image(decode=False), |
|
}) |
|
elif self.config.name == 'WCS Camera Traps': |
|
return datasets.Features({ |
|
"id": datasets.Value("string"), "file_name": datasets.Value("string"), |
|
"width": datasets.Value("int32"), "height": datasets.Value("int32"), |
|
"wcs_id": datasets.Value("string"), "location": datasets.Value("string"), |
|
"frame_num": datasets.Value("int32"), "match_level": datasets.Value("int32"), |
|
"seq_id": datasets.Value("string"), "country_code": datasets.Value("string"), |
|
"seq_num_frames": datasets.Value("int32"), |
|
"status": datasets.Value("string"), |
|
"datetime": datasets.Value("string"), |
|
"corrupt": datasets.Value("bool"), |
|
"annotations": datasets.Sequence({ |
|
"id": datasets.Value("string"), |
|
"category_id": datasets.Value("int32"), |
|
"count": datasets.Value("int32"), |
|
"sex": datasets.Value("string"), |
|
"age": datasets.Value("string"), |
|
"taxonomy": _TAXONOMY, |
|
}), |
|
"bboxes": datasets.Sequence({ |
|
"id": datasets.Value("string"), |
|
"category_id": datasets.Value("int32"), |
|
"bbox": datasets.Sequence(datasets.Value("float32"), length=4), |
|
}), |
|
"image": datasets.Image(decode=False), |
|
}) |
|
elif self.config.name == 'Wellington Camera Traps': |
|
return datasets.Features({ |
|
"id": datasets.Value("string"), "file_name": datasets.Value("string"), |
|
"width": datasets.Value("int32"), "height": datasets.Value("int32"), |
|
"frame_num": datasets.Value("int32"), "seq_id": datasets.Value("string"), |
|
"site": datasets.Value("string"), "camera": datasets.Value("string"), |
|
"datetime": datasets.Value("string"), |
|
"annotations": datasets.Sequence({ |
|
"id": datasets.Value("string"), |
|
"category_id": datasets.Value("int32"), |
|
"taxonomy": _TAXONOMY, |
|
}), |
|
"image": datasets.Image(decode=False), |
|
}) |
|
elif self.config.name == 'Island Conservation Camera Traps': |
|
return datasets.Features({ |
|
"id": datasets.Value("string"), "file_name": datasets.Value("string"), |
|
"width": datasets.Value("int32"), "height": datasets.Value("int32"), |
|
"annotations": datasets.Sequence({ |
|
"id": datasets.Value("string"), |
|
"category_id": datasets.Value("int32"), |
|
"bbox": datasets.Sequence(datasets.Value("float32"), length=4), |
|
"taxonomy": _TAXONOMY, |
|
}), |
|
"image": datasets.Image(decode=False), |
|
}) |
|
elif self.config.name == 'Channel Islands Camera Traps': |
|
return datasets.Features({ |
|
"id": datasets.Value("string"), "file_name": datasets.Value("string"), |
|
"width": datasets.Value("int32"), "height": datasets.Value("int32"), |
|
"frame_num": datasets.Value("int32"), "seq_id": datasets.Value("string"), |
|
"seq_num_frames": datasets.Value("int32"), |
|
"original_relative_path": datasets.Value("string"), |
|
"location": datasets.Value("string"), |
|
"temperature": datasets.Value("string"), |
|
"annotations": datasets.Sequence({ |
|
"id": datasets.Value("string"), |
|
"category_id": datasets.Value("int32"), |
|
"sequence_level_annotation": datasets.Value("bool"), |
|
"bbox": datasets.Sequence(datasets.Value("float32"), length=4), |
|
"taxonomy": _TAXONOMY, |
|
}), |
|
"image": datasets.Image(decode=False), |
|
}) |
|
elif self.config.name == 'Idaho Camera Traps': |
|
return datasets.Features({ |
|
"id": datasets.Value("string"), "file_name": datasets.Value("string"), |
|
"frame_num": datasets.Value("int32"), "seq_id": datasets.Value("string"), |
|
"seq_num_frames": datasets.Value("int32"), |
|
"original_relative_path": datasets.Value("string"), |
|
"datetime": datasets.Value("string"), |
|
"location": datasets.Value("string"), |
|
"annotations": datasets.Sequence({ |
|
"id": datasets.Value("string"), |
|
"category_id": datasets.Value("int32"), |
|
"sequence_level_annotation": datasets.Value("bool"), |
|
"taxonomy": _TAXONOMY, |
|
}), |
|
"image": datasets.Image(decode=False), |
|
}) |
|
elif self.config.name == 'Snapshot Serengeti': |
|
return datasets.Features({ |
|
"id": datasets.Value("string"), "file_name": datasets.Value("string"), |
|
"frame_num": datasets.Value("int32"), "seq_id": datasets.Value("string"), |
|
"width": datasets.Value("int32"), "height": datasets.Value("int32"), |
|
"seq_num_frames": datasets.Value("int32"), |
|
"datetime": datasets.Value("string"), |
|
"corrupt": datasets.Value("bool"), |
|
"location": datasets.Value("string"), |
|
"annotations": datasets.Sequence({ |
|
"id": datasets.Value("string"), |
|
"category_id": datasets.Value("int32"), |
|
"sequence_level_annotation": datasets.Value("bool"), |
|
"seq_id": datasets.Value("string"), |
|
"season": datasets.Value("string"), |
|
"datetime": datasets.Value("string"), |
|
"subject_id": datasets.Value("string"), |
|
"count": datasets.Value("string"), |
|
"standing": datasets.Value("float32"), |
|
"resting": datasets.Value("float32"), |
|
"moving": datasets.Value("float32"), |
|
"interacting": datasets.Value("float32"), |
|
"young_present": datasets.Value("float32"), |
|
"location": datasets.Value("string"), |
|
"taxonomy": _TAXONOMY, |
|
}), |
|
"bboxes": datasets.Sequence({ |
|
"id": datasets.Value("string"), |
|
"category_id": datasets.Value("int32"), |
|
"bbox": datasets.Sequence(datasets.Value("float32"), length=4), |
|
}), |
|
"image": datasets.Image(decode=False), |
|
}) |
|
elif self.config.name in [ |
|
'Snapshot Karoo', 'Snapshot Kgalagadi', 'Snapshot Enonkishu', 'Snapshot Camdeboo', |
|
'Snapshot Mountain Zebra', 'Snapshot Kruger' |
|
]: |
|
return datasets.Features({ |
|
"id": datasets.Value("string"), "file_name": datasets.Value("string"), |
|
"frame_num": datasets.Value("int32"), "seq_id": datasets.Value("string"), |
|
"width": datasets.Value("int32"), "height": datasets.Value("int32"), |
|
"seq_num_frames": datasets.Value("int32"), |
|
"datetime": datasets.Value("string"), |
|
"corrupt": datasets.Value("bool"), |
|
"location": datasets.Value("string"), |
|
"annotations": datasets.Sequence({ |
|
"id": datasets.Value("string"), |
|
"category_id": datasets.Value("int32"), |
|
"sequence_level_annotation": datasets.Value("bool"), |
|
"seq_id": datasets.Value("string"), |
|
"season": datasets.Value("string"), |
|
"datetime": datasets.Value("string"), |
|
"subject_id": datasets.Value("string"), |
|
"count": datasets.Value("string"), |
|
"standing": datasets.Value("float32"), |
|
"resting": datasets.Value("float32"), |
|
"moving": datasets.Value("float32"), |
|
"interacting": datasets.Value("float32"), |
|
"young_present": datasets.Value("float32"), |
|
"location": datasets.Value("string"), |
|
"taxonomy": _TAXONOMY, |
|
}), |
|
"image": datasets.Image(decode=False), |
|
}) |
|
elif self.config.name == 'SWG Camera Traps': |
|
return datasets.Features({ |
|
"id": datasets.Value("string"), "file_name": datasets.Value("string"), |
|
"width": datasets.Value("int32"), "height": datasets.Value("int32"), |
|
"location": datasets.Value("string"), |
|
"frame_num": datasets.Value("int32"), |
|
"seq_id": datasets.Value("string"), |
|
"seq_num_frames": datasets.Value("int32"), |
|
"datetime": datasets.Value("string"), |
|
"corrupt": datasets.Value("bool"), |
|
"annotations": datasets.Sequence({ |
|
"id": datasets.Value("string"), |
|
"sequence_level_annotation": datasets.Value("bool"), |
|
"category_id": datasets.Value("int32"), |
|
"taxonomy": _TAXONOMY, |
|
}), |
|
"bboxes": datasets.Sequence({ |
|
"id": datasets.Value("string"), |
|
"category_id": datasets.Value("int32"), |
|
"sequence_level_annotation": datasets.Value("bool"), |
|
"bbox": datasets.Sequence(datasets.Value("float32"), length=4), |
|
}), |
|
"image": datasets.Image(decode=False), |
|
}) |
|
elif self.config.name == 'Orinoquia Camera Traps': |
|
return datasets.Features({ |
|
"id": datasets.Value("string"), "file_name": datasets.Value("string"), |
|
"frame_num": datasets.Value("int32"), "seq_id": datasets.Value("string"), |
|
"seq_num_frames": datasets.Value("int32"), "datetime": datasets.Value("string"), |
|
"location": datasets.Value("string"), |
|
"annotations": datasets.Sequence({ |
|
"id": datasets.Value("string"), |
|
"sequence_level_annotation": datasets.Value("bool"), |
|
"category_id": datasets.Value("int32"), |
|
"taxonomy": _TAXONOMY, |
|
}), |
|
"image": datasets.Image(decode=False), |
|
}) |
|
|
|
def _info(self): |
|
features = self._get_features() |
|
|
|
return datasets.DatasetInfo( |
|
|
|
description=_DESCRIPTION, |
|
|
|
features=features, |
|
|
|
|
|
|
|
|
|
homepage=_HOMEPAGE, |
|
|
|
license=_LICENSE, |
|
|
|
citation=_CITATION, |
|
) |
|
|
|
def _split_generators(self, dl_manager): |
|
archive_path = dl_manager.download_and_extract(self.config.metadata_url) |
|
if archive_path.endswith(".zip") or os.path.isdir(archive_path): |
|
archive_path = os.path.join(archive_path, os.listdir(archive_path)[0]) |
|
|
|
return [ |
|
datasets.SplitGenerator( |
|
name=datasets.Split.TRAIN, |
|
gen_kwargs={ |
|
"filepath": archive_path, |
|
"split": "train", |
|
}, |
|
), |
|
] |
|
|
|
def _generate_examples(self, filepath, split): |
|
with open(filepath) as f: |
|
for line in f: |
|
example = json.loads(line) |
|
image_url = f"{self.config.image_base_url}/{example['file_name']}" |
|
yield example["id"], { |
|
**example, |
|
"image": image_url |
|
} |
