Datasets:

Sayali9141
/

traffic_signal_images

	import csv
	import json
	import os
	from typing import List
	import datasets
	import logging
	from datetime import datetime, timedelta
	import pandas as pd
	import requests
	# 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://raw.githubusercontent.com/Sayali-pingle/HuggingFace--Traffic-Image-Dataset/main/camera_data.csv"

	# 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.Image(),
	"image_metadata": datasets.Value("string")
	}
	),
	homepage=_HOMEPAGE,
	citation=_CITATION,
	)

	# def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
	# urls_to_download = self._URL
	# 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 _split_generators(self, dl_manager: datasets.DownloadManager):
	# The URLs should be the paths to the raw files in the Hugging Face dataset repository
	urls_to_download = {
	"csv_file": "https://raw.githubusercontent.com/Sayali-pingle/HuggingFace--Traffic-Image-Dataset/main/camera_data.csv"
	}
	downloaded_files = dl_manager.download_and_extract(urls_to_download['csv_file'])

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"csv_file_path": downloaded_files,
	},
	),
	]

	def _generate_examples(self, csv_file_path):
	# This method will yield examples from your dataset
	# start_date = datetime(2024, 1, 1, 18, 0, 0)
	# end_date = datetime(2024, 1, 2, 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}")

	camera_data= pd.read_csv(csv_file_path)

	for idx, example in camera_data.iterrows():
	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"]
	}