%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 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). ![](notebooks/media/001.gif) # 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 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). ![](notebooks/media/001.gif) # 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 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). ![](notebooks/media/001.gif) # 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 - 0.0.8-1 - Package Spec generated