Skip to main content

RoScenes dataset devkit

Project description

RoScenes large-scale multi-view dataset for roadside perception RoScenes large-scale multi-view dataset for roadside perception

arXiv Github stars Project Download



[!CAUTION] Commercial use of RoScenes is strictly forbidden.

๐Ÿ“ฐ Release Note

[2024-07-14] You can now download the dataset at ModelScope.

[2024-07-13] Devkit for RoScenes released.

[2024-07-01] Paper accepted to ECCV 2024! ๐Ÿฅณ

[2024-05-28] Please stay tuned for the updates! We are doing final checks on data privacy.

๐Ÿ™๏ธ Features

RoScenes

๐Ÿ”– Table of Contents

๐Ÿ”ฅ Quick Start

Download

[!NOTE] Please refer to ModelScope for downloading the dataset.

After download and extract, the dataset folder should be organized as follows:

. [DATA_ROOT] # Dataset root folder
โ”œโ”€โ”€ ๐Ÿ“‚train # training set
โ”‚   โ”œโ”€โ”€ ๐Ÿ“‚s001_split_train_difficulty_mixed_ambience_day # scene 001's data
โ”‚   โ”‚   โ”œโ”€โ”€ ๐Ÿ“‚database # annotations, grouped by clip
โ”‚   โ”‚   โ”‚   โ”œโ”€โ”€ ๐Ÿ“‚0076fd69_clip_[0000000000000-0000000029529] # a clip's database, please use our devkit to read
โ”‚   โ”‚   โ”‚   โ””   ...
โ”‚   โ”‚   โ””โ”€โ”€ ๐Ÿ“‚images # images, grouped by clips
โ”‚   โ”‚       โ”œโ”€โ”€ ๐Ÿ“‚0076fd69
โ”‚   โ”‚       โ””   ...
โ”‚   โ”œโ”€โ”€ ๐Ÿ“‚s002_split_train_difficulty_mixed_ambience_day
โ”‚   โ”œโ”€โ”€ ๐Ÿ“‚s003_split_train_difficulty_mixed_ambience_day
โ”‚   โ”œโ”€โ”€ ๐Ÿ“‚s004_split_train_difficulty_mixed_ambience_day
โ”‚   โ””โ”€โ”€ ๐Ÿ“‚night_split_train_difficulty_mixed_ambience_night
โ”‚
โ”‚
โ”œโ”€โ”€ ๐Ÿ“‚validation # validation set
โ”‚   โ”œโ”€โ”€ ๐Ÿ“‚s001_split_validation_difficulty_mixed_ambience_day # scene 001's data
โ”‚   โ”œโ”€โ”€ ๐Ÿ“‚s002_split_validation_difficulty_mixed_ambience_day
โ”‚   โ”œโ”€โ”€ ๐Ÿ“‚s003_split_validation_difficulty_mixed_ambience_day
โ”‚   โ”œโ”€โ”€ ๐Ÿ“‚s004_split_validation_difficulty_mixed_ambience_day
โ”‚   โ””โ”€โ”€ ๐Ÿ“‚night_split_validation_difficulty_mixed_ambience_night
โ”‚
โ”‚
โ””โ”€โ”€ ๐Ÿ“‚test # test set
    โ”œโ”€โ”€ ๐Ÿ“‚NO_GTs005_split_test_difficulty_mixed_ambience_day # scene 005's data
    โ”œโ”€โ”€ ๐Ÿ“‚NO_GTs006_split_test_difficulty_mixed_ambience_day
    โ”œโ”€โ”€ ๐Ÿ“‚NO_GTs007_split_test_difficulty_mixed_ambience_day
    โ”œโ”€โ”€ ๐Ÿ“‚NO_GTs008_split_test_difficulty_mixed_ambience_day
    โ”œโ”€โ”€ ๐Ÿ“‚NO_GTs009_split_test_difficulty_mixed_ambience_day
    โ”œโ”€โ”€ ๐Ÿ“‚NO_GTs010_split_test_difficulty_mixed_ambience_day
    โ”œโ”€โ”€ ๐Ÿ“‚NO_GTs011_split_test_difficulty_mixed_ambience_day
    โ”œโ”€โ”€ ๐Ÿ“‚NO_GTs012_split_test_difficulty_mixed_ambience_day
    โ”œโ”€โ”€ ๐Ÿ“‚NO_GTs013_split_test_difficulty_mixed_ambience_day
    โ””โ”€โ”€ ๐Ÿ“‚NO_GTs014_split_test_difficulty_mixed_ambience_day

Install via PyPI

Use PyPI to directly install RoScenes devkit:

pip install roscenes

Install Manually (for dev)

Also, you can clone this repository and install roscenes manually for developing.

git clone https://github.com/roscenes/RoScenes.git

cd RoScenes

pip install -e .

Start Using the Dataset

import roscenes as ro

# load the training set
dataset = ro.load('[DATA_ROOT]/train/*')
# number of total frames
print(len(dataset))

Then, we can iterate over the dataset, in two ways:

You can use indexing:

# use integer indexing
index = 10
# a Frame instance
print(type(dataset[index]))

for i in range(len(dataset)):
    # print num of objects for every frame
    print(len(dataset[index].boxes3D))

OR, you can directly iterate it:

# a frame instance
for frame in dataset:
    print(len(frame.boxes3D))

[!IMPORTANT] Please refer to frame.py, camera.py for the detailed comments on box format, instrinsic and extrinsic definition, etc.

๐Ÿ”Ž Explore the Dataset

python -m roscenes.visualizer [DATA_ROOT]/train/s001_split_train_difficulty_mixed_ambience_day 0 vis_result

TBD.

๐Ÿ‘ฉโ€๐Ÿ’ป Examples

  • 1. Read all boxes in a frame, and convert them from global 3D coordinates to camera's perspective coordinates.

๐Ÿ“ˆ Evaluation

TBD.

๐ŸŽฏ To-do List

  • Devkit release
  • Dataset release
  • Example dataset loader based on MMDetection3D
  • 3D detection task and evaluation suite
  • 3D tracking task and evaluation suite


This repo is licensed under

The Apache Software Foundation The Apache Software Foundation

Apache License
Version 2.0

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distribution

roscenes-0.0.1-py3-none-any.whl (53.3 kB view hashes)

Uploaded Python 3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page