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|
%global _empty_manifest_terminate_build 0
Name: python-lyft-dataset-sdk
Version: 0.0.8
Release: 1
Summary: SDK for Lyft dataset.
License: Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
URL: https://github.com/lyft/nuscenes-devkit
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/ae/89/4713e7e8dbdf91ddf25276ccd3781b7558f06cfdc1c6b0e6ec7a614083de/lyft_dataset_sdk-0.0.8.tar.gz
BuildArch: noarch
Requires: python3-flake8
Requires: python3-numpy
Requires: python3-opencv-python
Requires: python3-Pillow
Requires: python3-pyquaternion
Requires: python3-scikit-learn
Requires: python3-tqdm
Requires: python3-scipy
Requires: python3-cachetools
Requires: python3-Shapely
Requires: python3-fire
Requires: python3-pytest
Requires: python3-black
Requires: python3-matplotlib
Requires: python3-pandas
Requires: python3-plotly
Requires: python3-pytest
%description
# Lyft Dataset SDK
Welcome to the devkit for the [Lyft Level 5 AV dataset](https://level5.lyft.com/dataset/)! This devkit shall help you to visualise and explore our dataset.
## Release Notes
This devkit is based on a version of the [nuScenes devkit](https://www.nuscenes.org).
## Getting Started
### Installation
You can use pip to install [lyft-dataset-sdk](https://pypi.org/project/lyft-dataset-sdk/):
```bash
pip install -U lyft_dataset_sdk
```
If you want to get the latest version of the code before it is released on PyPI you can install the library from GitHub:
```bash
pip install -U git+https://github.com/lyft/nuscenes-devkit
```
### Dataset Download
Go to <https://level5.lyft.com/dataset/> to download the Lyft Level 5 AV Dataset.
The dataset is also availible as a part of the [Lyft 3D Object Detection for Autonomous Vehicles Challenge](https://www.kaggle.com/c/3d-object-detection-for-autonomous-vehicles).
### Tutorial and Reference Model
Check out the [tutorial and reference model README](notebooks/README.md).
# Dataset structure
The dataset contains of json files:
1. `scene.json` - 25-45 seconds snippet of a car's journey.
2. `sample.json` - An annotated snapshot of a scene at a particular timestamp.
3. `sample_data.json` - Data collected from a particular sensor.
4. `sample_annotation.json` - An annotated instance of an object within our interest.
5. `instance.json` - Enumeration of all object instance we observed.
6. `category.json` - Taxonomy of object categories (e.g. vehicle, human).
7. `attribute.json` - Property of an instance that can change while the category remains the same.
8. `visibility.json` - (currently not used)
9. `sensor.json` - A specific sensor type.
10. `calibrated_sensor.json` - Definition of a particular sensor as calibrated on a particular vehicle.
11. `ego_pose.json` - Ego vehicle poses at a particular timestamp.
12. `log.json` - Log information from which the data was extracted.
13. `map.json` - Map data that is stored as binary semantic masks from a top-down view.
With [the schema](schema.md).
# Data Exploration Tutorial
To get started with the Lyft Dataset SDK, run the tutorial using [Jupyter Notebook](notebooks/tutorial_lyft.ipynb).
# Contributing
We would be happy to accept issue reports and pull requests from the community.
For creating pull requests follow our [contributing guide](docs/CONTRIBUTING.md).
%package -n python3-lyft-dataset-sdk
Summary: SDK for Lyft dataset.
Provides: python-lyft-dataset-sdk
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-lyft-dataset-sdk
# Lyft Dataset SDK
Welcome to the devkit for the [Lyft Level 5 AV dataset](https://level5.lyft.com/dataset/)! This devkit shall help you to visualise and explore our dataset.
## Release Notes
This devkit is based on a version of the [nuScenes devkit](https://www.nuscenes.org).
## Getting Started
### Installation
You can use pip to install [lyft-dataset-sdk](https://pypi.org/project/lyft-dataset-sdk/):
```bash
pip install -U lyft_dataset_sdk
```
If you want to get the latest version of the code before it is released on PyPI you can install the library from GitHub:
```bash
pip install -U git+https://github.com/lyft/nuscenes-devkit
```
### Dataset Download
Go to <https://level5.lyft.com/dataset/> to download the Lyft Level 5 AV Dataset.
The dataset is also availible as a part of the [Lyft 3D Object Detection for Autonomous Vehicles Challenge](https://www.kaggle.com/c/3d-object-detection-for-autonomous-vehicles).
### Tutorial and Reference Model
Check out the [tutorial and reference model README](notebooks/README.md).
# Dataset structure
The dataset contains of json files:
1. `scene.json` - 25-45 seconds snippet of a car's journey.
2. `sample.json` - An annotated snapshot of a scene at a particular timestamp.
3. `sample_data.json` - Data collected from a particular sensor.
4. `sample_annotation.json` - An annotated instance of an object within our interest.
5. `instance.json` - Enumeration of all object instance we observed.
6. `category.json` - Taxonomy of object categories (e.g. vehicle, human).
7. `attribute.json` - Property of an instance that can change while the category remains the same.
8. `visibility.json` - (currently not used)
9. `sensor.json` - A specific sensor type.
10. `calibrated_sensor.json` - Definition of a particular sensor as calibrated on a particular vehicle.
11. `ego_pose.json` - Ego vehicle poses at a particular timestamp.
12. `log.json` - Log information from which the data was extracted.
13. `map.json` - Map data that is stored as binary semantic masks from a top-down view.
With [the schema](schema.md).
# Data Exploration Tutorial
To get started with the Lyft Dataset SDK, run the tutorial using [Jupyter Notebook](notebooks/tutorial_lyft.ipynb).
# Contributing
We would be happy to accept issue reports and pull requests from the community.
For creating pull requests follow our [contributing guide](docs/CONTRIBUTING.md).
%package help
Summary: Development documents and examples for lyft-dataset-sdk
Provides: python3-lyft-dataset-sdk-doc
%description help
# Lyft Dataset SDK
Welcome to the devkit for the [Lyft Level 5 AV dataset](https://level5.lyft.com/dataset/)! This devkit shall help you to visualise and explore our dataset.
## Release Notes
This devkit is based on a version of the [nuScenes devkit](https://www.nuscenes.org).
## Getting Started
### Installation
You can use pip to install [lyft-dataset-sdk](https://pypi.org/project/lyft-dataset-sdk/):
```bash
pip install -U lyft_dataset_sdk
```
If you want to get the latest version of the code before it is released on PyPI you can install the library from GitHub:
```bash
pip install -U git+https://github.com/lyft/nuscenes-devkit
```
### Dataset Download
Go to <https://level5.lyft.com/dataset/> to download the Lyft Level 5 AV Dataset.
The dataset is also availible as a part of the [Lyft 3D Object Detection for Autonomous Vehicles Challenge](https://www.kaggle.com/c/3d-object-detection-for-autonomous-vehicles).
### Tutorial and Reference Model
Check out the [tutorial and reference model README](notebooks/README.md).
# Dataset structure
The dataset contains of json files:
1. `scene.json` - 25-45 seconds snippet of a car's journey.
2. `sample.json` - An annotated snapshot of a scene at a particular timestamp.
3. `sample_data.json` - Data collected from a particular sensor.
4. `sample_annotation.json` - An annotated instance of an object within our interest.
5. `instance.json` - Enumeration of all object instance we observed.
6. `category.json` - Taxonomy of object categories (e.g. vehicle, human).
7. `attribute.json` - Property of an instance that can change while the category remains the same.
8. `visibility.json` - (currently not used)
9. `sensor.json` - A specific sensor type.
10. `calibrated_sensor.json` - Definition of a particular sensor as calibrated on a particular vehicle.
11. `ego_pose.json` - Ego vehicle poses at a particular timestamp.
12. `log.json` - Log information from which the data was extracted.
13. `map.json` - Map data that is stored as binary semantic masks from a top-down view.
With [the schema](schema.md).
# Data Exploration Tutorial
To get started with the Lyft Dataset SDK, run the tutorial using [Jupyter Notebook](notebooks/tutorial_lyft.ipynb).
# Contributing
We would be happy to accept issue reports and pull requests from the community.
For creating pull requests follow our [contributing guide](docs/CONTRIBUTING.md).
%prep
%autosetup -n lyft-dataset-sdk-0.0.8
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-lyft-dataset-sdk -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 0.0.8-1
- Package Spec generated
|
