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osv5m

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osv5m / osv5m.py

osv5m

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	import os
	import pandas as pd
	import datasets
	from os.path import join

	# convert these to features
	#id,latitude,longitude,thumb_original_url,country,sequence,captured_at,lon_bin,lat_bin,cell,region,sub-region,city,land_cover,road_index,drive_side,climate,soil,dist_sea,quadtree_10_5000,quadtree_10_25000,quadtree_10_1000,quadtree_10_50000,quadtree_10_12500,quadtree_10_500,quadtree_10_2500,unique_region,unique_sub-region,unique_city,unique_country,creator_username,creator_id
	#3859149887465501,-43.804769384023,-176.61409250805,,8,"(0, 8)",Chatham Islands,,Waitangi,4,4.661764145,1,15,3,0.0068841379890803,0,0,0,0,0,0,0,Chatham Islands_NZ,,Waitangi_NaN_Chatham Islands_NZ,NZ,roadroid,111336221091714.0

	class OSV5M(datasets.GeneratorBasedBuilder):
	def __init__(self, args, *kwargs):
	self.full = kwargs.pop('full', False)
	super().__init__(args, *kwargs)
	print('OSV5M', self.__dict__)

	def _info(self):
	if self.full:
	return datasets.DatasetInfo(
	features=datasets.Features(
	{
	"image": datasets.Image(),
	"latitude": datasets.Value("float32"),
	"longitude": datasets.Value("float32"),
	"thumb_original_url": datasets.Value("string"),
	"country": datasets.Value("string"),
	"sequence": datasets.Value("string"),
	"captured_at": datasets.Value("string"),
	"lon_bin": datasets.Value("float32"),
	"lat_bin": datasets.Value("float32"),
	"cell": datasets.Value("string"),
	"region": datasets.Value("string"),
	"sub-region": datasets.Value("string"),
	"city": datasets.Value("string"),
	"land_cover": datasets.Value("float32"),
	"road_index": datasets.Value("float32"),
	"drive_side": datasets.Value("float32"),
	"climate": datasets.Value("float32"),
	"soil": datasets.Value("float32"),
	"dist_sea": datasets.Value("float32"),
	"quadtree_10_5000": datasets.Value("int32"),
	"quadtree_10_25000": datasets.Value("int32"),
	"quadtree_10_1000": datasets.Value("int32"),
	"quadtree_10_50000": datasets.Value("int32"),
	"quadtree_10_12500": datasets.Value("int32"),
	"quadtree_10_500": datasets.Value("int32"),
	"quadtree_10_2500": datasets.Value("int32"),
	"unique_region": datasets.Value("string"),
	"unique_sub-region": datasets.Value("string"),
	"unique_city": datasets.Value("string"),
	"unique_country": datasets.Value("string"),
	"creator_username": datasets.Value("string"),
	"creator_id": datasets.Value("string"),
	}
	)
	)
	else:
	return datasets.DatasetInfo(
	features=datasets.Features(
	{
	"image": datasets.Image(),
	"latitude": datasets.Value("float32"),
	"longitude": datasets.Value("float32"),
	"country": datasets.Value("string"),
	"region": datasets.Value("string"),
	"sub-region": datasets.Value("string"),
	"city": datasets.Value("string"),
	}
	)
	)

	def df(self, annotation_path):
	if not hasattr(self, 'df_'):
	self.df_ = {}
	if annotation_path not in self.df_:
	df = pd.read_csv(annotation_path, dtype={
	'id': str, 'creator_id': str, 'creator_username': str,
	'unique_country': str, 'unique_city': str, 'unique_sub-region': str, 'unique_region': str,
	'quadtree_10_2500': int, 'quadtree_10_500': int, 'quadtree_10_12500': int, 'quadtree_10_50000': int, 'quadtree_10_1000': int, 'quadtree_10_25000': int, 'quadtree_10_5000': int,
	'dist_sea': float, 'soil': float, 'climate': float, 'drive_side': float, 'road_index': float, 'land_cover': float, 'city': str, 'sub-region': str, 'region': str, 'cell': str, 'lat_bin': float, 'lon_bin': float, 'captured_at': str, 'sequence': str, 'country': str, 'thumb_original_url': str, 'longitude': float, 'latitude': float
	})
	if not self.full:
	df = df[['id', 'latitude', 'longitude', 'country', 'region', 'sub-region', 'city']]

	df = df.set_index('id')
	self.df_[annotation_path] = df.to_dict('index')
	return self.df_[annotation_path]

	def _split_generators(self, dl_manager):
	_URLS = {
	"train": [join('images', 'train', str(i).zfill(2) + '.zip') for i in range(98)],
	"test": [join('images', 'test', str(i).zfill(2) + '.zip') for i in range(5)],
	"train_meta": "train.csv",
	"test_meta": "test.csv",
	}

	data_files = dl_manager.download_and_extract(_URLS)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"image_paths": dl_manager.iter_files(data_files["train"]),
	"annotation_path": data_files["train_meta"],
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={
	"image_paths": dl_manager.iter_files(data_files["test"]),
	"annotation_path": data_files["test_meta"],
	},
	),
	]

	def _generate_examples(self, image_paths, annotation_path):
	"""Generate examples."""
	df = self.df(annotation_path)
	for idx, image_path in enumerate(image_paths):
	info_id = os.path.splitext(os.path.split(image_path)[-1])[0]
	try:
	example = {
	"image": image_path,
	} \| df[info_id]
	except Exception as e:
	print('Exception ' + str(e), info_id, idx, image_path, sep='\n')
	continue

	yield idx, example