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" # 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._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 _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"] }