Datasets:
Languages:
English
Size:
10K<n<100K
import os | |
import datasets | |
_CITATION = """\ | |
@InProceedings{huggingface:dataset, | |
title = {Unsplash Lite Dataset 1.2.0 Photos}, | |
author={Unsplash}, | |
year={2022} | |
} | |
""" | |
_DESCRIPTION = """\ | |
This is a dataset that streams photos data from the Unsplash 25K servers. | |
""" | |
_HOMEPAGE = "https://github.com/unsplash/datasets/" | |
_LICENSE = "" | |
_URL = "https://unsplash.com/data/lite/latest" | |
class Unsplash(datasets.GeneratorBasedBuilder): | |
"""The Unsplash 25K dataset for photos""" | |
def _info(self): | |
return datasets.DatasetInfo( | |
description=_DESCRIPTION, | |
features=datasets.Features( | |
{ | |
'photo_id': datasets.Value("string"), | |
'photo_url': datasets.Value("string"), | |
'photo_image_url': datasets.Value("string"), | |
'photo_submitted_at': datasets.Value("string"), | |
'photo_featured': datasets.Value("string"), | |
'photo_width': datasets.Value("int32"), | |
'photo_height': datasets.Value("int32"), | |
'photo_aspect_ratio': datasets.Value("float32"), | |
'photo_description': datasets.Value("string"), | |
'photographer_username': datasets.Value("string"), | |
'photographer_first_name': datasets.Value("string"), | |
'photographer_last_name': datasets.Value("string"), | |
'exif_camera_make': datasets.Value("string"), | |
'exif_camera_model': datasets.Value("string"), | |
'exif_iso': datasets.Value("string"), | |
'exif_aperture_value': datasets.Value("string"), | |
'exif_focal_length': datasets.Value("string"), | |
'exif_exposure_time': datasets.Value("string"), | |
'photo_location_name': datasets.Value("string"), | |
'photo_location_latitude': datasets.Value("string"), | |
'photo_location_longitude': datasets.Value("string"), | |
'photo_location_country': datasets.Value("string"), | |
'photo_location_city': datasets.Value("string"), | |
'stats_views': datasets.Value("int32"), | |
'stats_downloads': datasets.Value("int32"), | |
'ai_description': datasets.Value("string"), | |
'ai_primary_landmark_name': datasets.Value("string"), | |
'ai_primary_landmark_latitude': datasets.Value("string"), | |
'ai_primary_landmark_longitude': datasets.Value("string"), | |
'ai_primary_landmark_confidence': datasets.Value("string"), | |
'blur_hash': datasets.Value("string"), | |
} | |
), | |
supervised_keys=None, | |
homepage="https://github.com/unsplash/datasets/", | |
citation=_CITATION, | |
) | |
def _split_generators(self, dl_manager): | |
new_url = dl_manager.download_and_extract(_URL) | |
# remove extra files | |
for file in os.listdir(new_url): | |
if os.path.isfile(new_url+"/"+file): | |
if file != 'photos.tsv000': | |
os.remove(new_url+'/'+file) | |
return [ | |
datasets.SplitGenerator( | |
name=datasets.Split.TRAIN, | |
gen_kwargs={"filepath": os.path.join(new_url, "photos.tsv000")} | |
), | |
] | |
def _generate_examples(self, filepath): | |
"""This function returns the examples in the raw (text) form.""" | |
with open(filepath, "r") as f: | |
id_ = 0 | |
for line in f: | |
if id_ == 0: | |
cols = line.strip().split("\t") | |
id_ += 1 | |
else: | |
values = line.strip().split("\t") | |
if len(values) != len(cols): | |
values.extend(['']*(len(cols)-len(values))) | |
yield id_, {cols[i]: values[i] for i in range(len(cols))} | |
id_ += 1 |