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RoScenes dataset devkit

Project description

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

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[!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

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