Datasets:
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import csv
import json
import os
from typing import List
import datasets
import logging
# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
}
"""
# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
This dataset contains traffic images from traffic signal cameras of singapore. The images are captured at 1.5 minute interval from 6 pm to 7 pm everyday for the month of January 2024.
"""
# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = "https://beta.data.gov.sg/collections/354/view"
# TODO: Add the licence for the dataset here if you can find it
_LICENSE = ""
# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_URL = "https://github.com/Sayali-pingle/HuggingFace--Traffic-Image-Dataset/blob/main/camera_data.csv"
_URLS = {
"train": _URL + "train-v1.1.json",
"dev": _URL + "dev-v1.1.json",
}
# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
class TrafficImages(datasets.GeneratorBasedBuilder):
"""TODO: Short description of my dataset."""
_URLS = _URLS
VERSION = datasets.Version("1.1.0")
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"timestamp": datasets.Value("string"),
"camera_id": datasets.Value("string"),
"latitude": datasets.Value("float"),
"longitude": datasets.Value("float"),
"image_url": datasets.Value("string"),
"image_metadata": datasets.Value("string")
}
),
homepage=_HOMEPAGE,
citation=_CITATION,
)
def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
urls_to_download = self._URLS
downloaded_files = dl_manager.download_and_extract(urls_to_download)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
]
def _generate_examples(self, file_path):
# This method will yield examples from your dataset
start_date = datetime(2024, 1, 1, 18, 0, 0)
end_date = datetime(2024, 1, 31, 19, 0, 0)
interval_seconds = 240
date_time_strings = [
(current_date + timedelta(seconds=seconds)).strftime('%Y-%m-%dT%H:%M:%S+08:00')
for current_date in pd.date_range(start=start_date, end=end_date, freq='D')
for seconds in range(0, 3600, interval_seconds)
]
url = 'https://api.data.gov.sg/v1/transport/traffic-images'
camera_data = []
for date_time in date_time_strings:
params = {'date_time': date_time}
response = requests.get(url, params=params)
if response.status_code == 200:
data = response.json()
camera_data.extend([
{
'timestamp': item['timestamp'],
'camera_id': camera['camera_id'],
'latitude': camera['location']['latitude'],
'longitude': camera['location']['longitude'],
'image_url': camera['image'],
'image_metadata': camera['image_metadata']
}
for item in data['items']
for camera in item['cameras']
])
else:
print(f"Error: {response.status_code}")
for idx, example in enumerate(camera_data):
yield idx, {
"timestamp": example["timestamp"],
"camera_id": example["camera_id"],
"latitude": example["latitude"],
"longitude": example["longitude"],
"image_url": example["image_url"],
"image_metadata": example["image_metadata"]
}
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