diff options
| author | CoprDistGit <infra@openeuler.org> | 2023-04-12 01:47:33 +0000 |
|---|---|---|
| committer | CoprDistGit <infra@openeuler.org> | 2023-04-12 01:47:33 +0000 |
| commit | 936ca3da7ac63a3208604725179d65120b89e4b0 (patch) | |
| tree | ba33b9d915b291cda2065c9d29013f813660ff7e | |
| parent | 770a8ab5c5b4307fc8a042b54172a8c0454bfbaf (diff) | |
automatic import of python-voxel51-eta
| -rw-r--r-- | .gitignore | 1 | ||||
| -rw-r--r-- | python-voxel51-eta.spec | 830 | ||||
| -rw-r--r-- | sources | 1 |
3 files changed, 832 insertions, 0 deletions
@@ -0,0 +1 @@ +/voxel51-eta-0.9.0.tar.gz diff --git a/python-voxel51-eta.spec b/python-voxel51-eta.spec new file mode 100644 index 0000000..a7c0a95 --- /dev/null +++ b/python-voxel51-eta.spec @@ -0,0 +1,830 @@ +%global _empty_manifest_terminate_build 0 +Name: python-voxel51-eta +Version: 0.9.0 +Release: 1 +Summary: Extensible Toolkit for Analytics +License: Apache +URL: https://github.com/voxel51/eta +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/88/98/a6785b618d4a2e2c058bdb76a691a3de9578b902397c4792756eea900ee7/voxel51-eta-0.9.0.tar.gz +BuildArch: noarch + +Requires: python3-argcomplete +Requires: python3-dill +Requires: python3-future +Requires: python3-glob2 +Requires: python3-jsonlines +Requires: python3-numpy +Requires: python3-packaging +Requires: python3-Pillow +Requires: python3-py7zr +Requires: python3-dateutil +Requires: python3-pytz +Requires: python3-rarfile +Requires: python3-requests +Requires: python3-retrying +Requires: python3-six +Requires: python3-scikit-image +Requires: python3-sortedcontainers +Requires: python3-tabulate +Requires: python3-tzlocal +Requires: python3-urllib3 +Requires: python3-opencv-python-headless +Requires: python3-importlib-metadata +Requires: python3-blockdiag +Requires: python3-Sphinx +Requires: python3-sphinxcontrib-napoleon +Requires: python3-azure-identity +Requires: python3-azure-storage-blob +Requires: python3-boto3 +Requires: python3-google-api-python-client +Requires: python3-google-cloud-storage +Requires: python3-httplib2 +Requires: python3-pysftp + +%description +<div align="center"> + +<h1> + ETA: Extensible Toolkit for Analytics +</h1> + +**An open and extensible computer vision, machine learning and video analytics +infrastructure.** + +[](https://pypi.org/project/voxel51-eta) +[](https://pypi.org/project/voxel51-eta) +[](https://github.com/pre-commit/pre-commit) +[](LICENSE) +[](https://twitter.com/voxel51) + +<img src="https://user-images.githubusercontent.com/25985824/78944107-2d766c80-7a8b-11ea-8863-fcb4897eecb5.png" alt="eta-infrastructure.png" width="75%"/> + +</div> + +## Requirements + +ETA is very portable: + +- Installable on Mac or Linux +- Supports Python 3.6 or later +- Supports TensorFlow 1.X and 2.X +- Supports OpenCV 2.4+ and OpenCV 3.0+ +- Supports CPU-only and GPU-enabled installations +- Supports CUDA 8, 9 and 10 for GPU installations + +## Installation + +You can install the latest release of ETA via `pip`: + +```shell +pip install voxel51-eta +``` + +This will perform a [lite installation of ETA](#lite-installation). If you use +an ETA feature that requires additional dependencies (e.g., `ffmpeg` or +`tensorflow`), you will be prompted to install the relevant packages. + +## Docker Installation + +If you prefer to operate via Docker, see the +[Docker Build Guide](https://github.com/voxel51/eta/blob/develop/docs/docker_build_guide.md) +for simple instructions for building a Docker image with an ETA environment +installed. + +## Installation from source + +#### Step 0: Setup your Python environment + +It is assumed that you already have +[Python installed](https://www.python.org/downloads) on your machine. + +> **IMPORTANT:** ETA assumes that the version of Python that you intend to use +> is accessible via `python` and `pip` on your path. In particular, for Python +> 3 users, this means that you may need to alias `python3` and `pip3` to +> `python` and `pip`, respectively. + +We strongly recommend that you install ETA +[in a virtual environment](https://github.com/voxel51/eta/blob/develop/docs/virtualenv_guide.md) +to maintain a clean workspace. + +#### Step 1: Clone the repository + +```shell +git clone https://github.com/voxel51/eta +cd eta +``` + +#### Step 2: Run the install script + +```shell +bash install.bash +``` + +Note that the install script supports flags that control things like (on macOS) +whether `port` or `brew` is used to install packages. Run +`bash install.bash -h` for more information. + +For Linux installs, the script inspects your system to see if CUDA is installed +via the `lspci` command. If CUDA is available, TensorFlow is installed with GPU +support. + +The table below lists the version of TensorFlow that will be installed by the +installer, as recommended by the +[tested build configurations](https://www.tensorflow.org/install/source#tested_build_configurations): + +| CUDA Version Found | TensorFlow Version Installed | +| ------------------ | ---------------------------- | +| CUDA 8 | `tensorflow-gpu~=1.4` | +| CUDA 9 | `tensorflow-gpu~=1.12` | +| CUDA 10 | `tensorflow-gpu~=1.15` | +| Other CUDA | `tensorflow-gpu~=1.15` | +| No CUDA | `tensorflow~=1.15` | + +> Note that ETA also supports TensorFlow 2.X. The only problems you may face +> when using ETA with TensorFlow 2 are when trying to run inference with +> [ETA models](https://github.com/voxel51/eta/blob/develop/eta/models/manifest.json) +> that only support TensorFlow 1. A notable case here are TF-slim models. In +> such cases, you should see an informative error message alerting you of the +> requirement mismatch. + +### Lite installation + +Some ETA users are only interested in using the core ETA library defined in the +`eta.core` package. In such cases, you can perform a lite installation using +the `-l` flag of the install script: + +```shell +bash install.bash -l +``` + +Lite installation omits submodules and other large dependencies that are not +required in order for the core library to function. If you use an ETA feature +that requires additional dependencies (e.g., `ffmpeg` or `tensorflow`), you +will be prompted to install the relevant packages. + +### Developer installation + +If you are interested in contributing to ETA or generating its documentation +from source, you should perform a developer installation using the `-d` flag of +the install script: + +```shell +bash install.bash -d +``` + +## Setting up your execution environment + +When the root `eta` package is imported, it tries to read the `eta/config.json` +file to configure various package-level constants. Many advanced ETA features +such as pipeline building, model management, etc. require a properly configured +environment to function. + +To setup your environment, create a copy the example configuration file: + +```shell +cp config-example.json eta/config.json +``` + +If desired, you can edit your config file to customize the various paths, +change default constants, add environment variables, customize your default +`PYTHONPATH`, and so on. You can also add additional paths to the +`module_dirs`, `pipeline_dirs`, and `models_dirs` sections to expose custom +modules, pipelines, and models to your system. + +Note that, when the config file is loaded, any `{{eta}}` patterns in directory +paths are replaced with the absolute path to the `eta/` directory on your +machine. + +The default config includes the `modules/`, `pipelines/`, and `models/` +directories on your module, pipeline, and models search paths, respectively. +These directories contain the necessary information to run the standard +analytics exposed by the ETA library. In addition, the relative paths +`./modules/`, `./pipelines/`, and `./models/` are added to their respective +paths to support the typical directory structure that we adopt for our custom +projects. + +### CLI + +Installing ETA automatically installs `eta`, a command-line interface (CLI) for +interacting with the ETA Library. This utility provides access to many useful +features of ETA, including building and running pipelines, downloading models, +and interacting with remote storage. + +To explore the CLI, type `eta --help`, and see the +[CLI Guide](https://github.com/voxel51/eta/blob/develop/docs/cli_guide.md) for +complete information. + +## Quickstart + +Get your feet wet with ETA by running some of examples in the +[examples folder](https://github.com/voxel51/eta/tree/develop/eta/examples). + +Also, see the [docs folder](https://github.com/voxel51/eta/tree/develop/docs) +for more documentation about the various components of the ETA library. + +## Organization + +The ETA package is organized as described below. For more information about the +design and function of the various ETA components, read the documentation in +the [docs folder](https://github.com/voxel51/eta/tree/develop/docs). + +| Directory | Description | +| ----------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `eta/classifiers` | wrappers for performing inference with common classifiers | +| `eta/core` | the core ETA library, which includes utilities for working with images, videos, embeddings, and much more | +| `eta/detectors` | wrappers for performing inference with common detectors | +| `eta/docs` | documentation about the ETA library | +| `eta/examples` | examples of using the ETA library | +| `eta/models` | library of ML models. The `manifest.json` file in this folder enumerates the models, which are downloaded to this folder as needed. See the [Models developer's guide](https://github.com/voxel51/eta/blob/develop/docs/models_dev_guide.md) for more information about ETA's model registry | +| `eta/modules` | library of video processing/analytics modules. See the [Module developer's guide](https://github.com/voxel51/eta/blob/develop/docs/modules_dev_guide.md) for more information about ETA modules | +| `eta/pipelines` | library of video processing/analytics pipelines. See the [Pipeline developer's guide](https://github.com/voxel51/eta/blob/develop/docs/pipelines_dev_guide.md) for more information about ETA pipelines | +| `eta/resources` | resources such as media, templates, etc | +| `eta/segmenters` | wrappers for performing inference with common semantic segmenters | +| `eta/tensorflow` | third-party TensorFlow repositories that ETA builds upon | + +## Generating Documentation + +This project uses +[Sphinx-Napoleon](https://pypi.python.org/pypi/sphinxcontrib-napoleon) to +generate its documentation from source. + +To generate the documentation, you must install the developer dependencies by +running the `install.bash` script with the `-d` flag. + +Then you can generate the docs by running: + +```shell +bash sphinx/generate_docs.bash +``` + +To view the documentation, open the `sphinx/build/html/index.html` file in your +browser. + +## Uninstallation + +```shell +pip uninstall voxel51-eta +``` + +## Acknowledgements + +This project was gratefully supported by the +[NIST Public Safety Innovation Accelerator Program](https://www.nist.gov/news-events/news/2017/06/nist-awards-385-million-accelerate-public-safety-communications). + +## Citation + +If you use ETA in your research, feel free to cite the project (but only if you +love it 😊): + +```bibtex +@article{moore2017eta, + title={ETA: Extensible Toolkit for Analytics}, + author={Moore, B. E. and Corso, J. J.}, + journal={GitHub. Note: https://github.com/voxel51/eta}, + year={2017} +} +``` + + +%package -n python3-voxel51-eta +Summary: Extensible Toolkit for Analytics +Provides: python-voxel51-eta +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-voxel51-eta +<div align="center"> + +<h1> + ETA: Extensible Toolkit for Analytics +</h1> + +**An open and extensible computer vision, machine learning and video analytics +infrastructure.** + +[](https://pypi.org/project/voxel51-eta) +[](https://pypi.org/project/voxel51-eta) +[](https://github.com/pre-commit/pre-commit) +[](LICENSE) +[](https://twitter.com/voxel51) + +<img src="https://user-images.githubusercontent.com/25985824/78944107-2d766c80-7a8b-11ea-8863-fcb4897eecb5.png" alt="eta-infrastructure.png" width="75%"/> + +</div> + +## Requirements + +ETA is very portable: + +- Installable on Mac or Linux +- Supports Python 3.6 or later +- Supports TensorFlow 1.X and 2.X +- Supports OpenCV 2.4+ and OpenCV 3.0+ +- Supports CPU-only and GPU-enabled installations +- Supports CUDA 8, 9 and 10 for GPU installations + +## Installation + +You can install the latest release of ETA via `pip`: + +```shell +pip install voxel51-eta +``` + +This will perform a [lite installation of ETA](#lite-installation). If you use +an ETA feature that requires additional dependencies (e.g., `ffmpeg` or +`tensorflow`), you will be prompted to install the relevant packages. + +## Docker Installation + +If you prefer to operate via Docker, see the +[Docker Build Guide](https://github.com/voxel51/eta/blob/develop/docs/docker_build_guide.md) +for simple instructions for building a Docker image with an ETA environment +installed. + +## Installation from source + +#### Step 0: Setup your Python environment + +It is assumed that you already have +[Python installed](https://www.python.org/downloads) on your machine. + +> **IMPORTANT:** ETA assumes that the version of Python that you intend to use +> is accessible via `python` and `pip` on your path. In particular, for Python +> 3 users, this means that you may need to alias `python3` and `pip3` to +> `python` and `pip`, respectively. + +We strongly recommend that you install ETA +[in a virtual environment](https://github.com/voxel51/eta/blob/develop/docs/virtualenv_guide.md) +to maintain a clean workspace. + +#### Step 1: Clone the repository + +```shell +git clone https://github.com/voxel51/eta +cd eta +``` + +#### Step 2: Run the install script + +```shell +bash install.bash +``` + +Note that the install script supports flags that control things like (on macOS) +whether `port` or `brew` is used to install packages. Run +`bash install.bash -h` for more information. + +For Linux installs, the script inspects your system to see if CUDA is installed +via the `lspci` command. If CUDA is available, TensorFlow is installed with GPU +support. + +The table below lists the version of TensorFlow that will be installed by the +installer, as recommended by the +[tested build configurations](https://www.tensorflow.org/install/source#tested_build_configurations): + +| CUDA Version Found | TensorFlow Version Installed | +| ------------------ | ---------------------------- | +| CUDA 8 | `tensorflow-gpu~=1.4` | +| CUDA 9 | `tensorflow-gpu~=1.12` | +| CUDA 10 | `tensorflow-gpu~=1.15` | +| Other CUDA | `tensorflow-gpu~=1.15` | +| No CUDA | `tensorflow~=1.15` | + +> Note that ETA also supports TensorFlow 2.X. The only problems you may face +> when using ETA with TensorFlow 2 are when trying to run inference with +> [ETA models](https://github.com/voxel51/eta/blob/develop/eta/models/manifest.json) +> that only support TensorFlow 1. A notable case here are TF-slim models. In +> such cases, you should see an informative error message alerting you of the +> requirement mismatch. + +### Lite installation + +Some ETA users are only interested in using the core ETA library defined in the +`eta.core` package. In such cases, you can perform a lite installation using +the `-l` flag of the install script: + +```shell +bash install.bash -l +``` + +Lite installation omits submodules and other large dependencies that are not +required in order for the core library to function. If you use an ETA feature +that requires additional dependencies (e.g., `ffmpeg` or `tensorflow`), you +will be prompted to install the relevant packages. + +### Developer installation + +If you are interested in contributing to ETA or generating its documentation +from source, you should perform a developer installation using the `-d` flag of +the install script: + +```shell +bash install.bash -d +``` + +## Setting up your execution environment + +When the root `eta` package is imported, it tries to read the `eta/config.json` +file to configure various package-level constants. Many advanced ETA features +such as pipeline building, model management, etc. require a properly configured +environment to function. + +To setup your environment, create a copy the example configuration file: + +```shell +cp config-example.json eta/config.json +``` + +If desired, you can edit your config file to customize the various paths, +change default constants, add environment variables, customize your default +`PYTHONPATH`, and so on. You can also add additional paths to the +`module_dirs`, `pipeline_dirs`, and `models_dirs` sections to expose custom +modules, pipelines, and models to your system. + +Note that, when the config file is loaded, any `{{eta}}` patterns in directory +paths are replaced with the absolute path to the `eta/` directory on your +machine. + +The default config includes the `modules/`, `pipelines/`, and `models/` +directories on your module, pipeline, and models search paths, respectively. +These directories contain the necessary information to run the standard +analytics exposed by the ETA library. In addition, the relative paths +`./modules/`, `./pipelines/`, and `./models/` are added to their respective +paths to support the typical directory structure that we adopt for our custom +projects. + +### CLI + +Installing ETA automatically installs `eta`, a command-line interface (CLI) for +interacting with the ETA Library. This utility provides access to many useful +features of ETA, including building and running pipelines, downloading models, +and interacting with remote storage. + +To explore the CLI, type `eta --help`, and see the +[CLI Guide](https://github.com/voxel51/eta/blob/develop/docs/cli_guide.md) for +complete information. + +## Quickstart + +Get your feet wet with ETA by running some of examples in the +[examples folder](https://github.com/voxel51/eta/tree/develop/eta/examples). + +Also, see the [docs folder](https://github.com/voxel51/eta/tree/develop/docs) +for more documentation about the various components of the ETA library. + +## Organization + +The ETA package is organized as described below. For more information about the +design and function of the various ETA components, read the documentation in +the [docs folder](https://github.com/voxel51/eta/tree/develop/docs). + +| Directory | Description | +| ----------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `eta/classifiers` | wrappers for performing inference with common classifiers | +| `eta/core` | the core ETA library, which includes utilities for working with images, videos, embeddings, and much more | +| `eta/detectors` | wrappers for performing inference with common detectors | +| `eta/docs` | documentation about the ETA library | +| `eta/examples` | examples of using the ETA library | +| `eta/models` | library of ML models. The `manifest.json` file in this folder enumerates the models, which are downloaded to this folder as needed. See the [Models developer's guide](https://github.com/voxel51/eta/blob/develop/docs/models_dev_guide.md) for more information about ETA's model registry | +| `eta/modules` | library of video processing/analytics modules. See the [Module developer's guide](https://github.com/voxel51/eta/blob/develop/docs/modules_dev_guide.md) for more information about ETA modules | +| `eta/pipelines` | library of video processing/analytics pipelines. See the [Pipeline developer's guide](https://github.com/voxel51/eta/blob/develop/docs/pipelines_dev_guide.md) for more information about ETA pipelines | +| `eta/resources` | resources such as media, templates, etc | +| `eta/segmenters` | wrappers for performing inference with common semantic segmenters | +| `eta/tensorflow` | third-party TensorFlow repositories that ETA builds upon | + +## Generating Documentation + +This project uses +[Sphinx-Napoleon](https://pypi.python.org/pypi/sphinxcontrib-napoleon) to +generate its documentation from source. + +To generate the documentation, you must install the developer dependencies by +running the `install.bash` script with the `-d` flag. + +Then you can generate the docs by running: + +```shell +bash sphinx/generate_docs.bash +``` + +To view the documentation, open the `sphinx/build/html/index.html` file in your +browser. + +## Uninstallation + +```shell +pip uninstall voxel51-eta +``` + +## Acknowledgements + +This project was gratefully supported by the +[NIST Public Safety Innovation Accelerator Program](https://www.nist.gov/news-events/news/2017/06/nist-awards-385-million-accelerate-public-safety-communications). + +## Citation + +If you use ETA in your research, feel free to cite the project (but only if you +love it 😊): + +```bibtex +@article{moore2017eta, + title={ETA: Extensible Toolkit for Analytics}, + author={Moore, B. E. and Corso, J. J.}, + journal={GitHub. Note: https://github.com/voxel51/eta}, + year={2017} +} +``` + + +%package help +Summary: Development documents and examples for voxel51-eta +Provides: python3-voxel51-eta-doc +%description help +<div align="center"> + +<h1> + ETA: Extensible Toolkit for Analytics +</h1> + +**An open and extensible computer vision, machine learning and video analytics +infrastructure.** + +[](https://pypi.org/project/voxel51-eta) +[](https://pypi.org/project/voxel51-eta) +[](https://github.com/pre-commit/pre-commit) +[](LICENSE) +[](https://twitter.com/voxel51) + +<img src="https://user-images.githubusercontent.com/25985824/78944107-2d766c80-7a8b-11ea-8863-fcb4897eecb5.png" alt="eta-infrastructure.png" width="75%"/> + +</div> + +## Requirements + +ETA is very portable: + +- Installable on Mac or Linux +- Supports Python 3.6 or later +- Supports TensorFlow 1.X and 2.X +- Supports OpenCV 2.4+ and OpenCV 3.0+ +- Supports CPU-only and GPU-enabled installations +- Supports CUDA 8, 9 and 10 for GPU installations + +## Installation + +You can install the latest release of ETA via `pip`: + +```shell +pip install voxel51-eta +``` + +This will perform a [lite installation of ETA](#lite-installation). If you use +an ETA feature that requires additional dependencies (e.g., `ffmpeg` or +`tensorflow`), you will be prompted to install the relevant packages. + +## Docker Installation + +If you prefer to operate via Docker, see the +[Docker Build Guide](https://github.com/voxel51/eta/blob/develop/docs/docker_build_guide.md) +for simple instructions for building a Docker image with an ETA environment +installed. + +## Installation from source + +#### Step 0: Setup your Python environment + +It is assumed that you already have +[Python installed](https://www.python.org/downloads) on your machine. + +> **IMPORTANT:** ETA assumes that the version of Python that you intend to use +> is accessible via `python` and `pip` on your path. In particular, for Python +> 3 users, this means that you may need to alias `python3` and `pip3` to +> `python` and `pip`, respectively. + +We strongly recommend that you install ETA +[in a virtual environment](https://github.com/voxel51/eta/blob/develop/docs/virtualenv_guide.md) +to maintain a clean workspace. + +#### Step 1: Clone the repository + +```shell +git clone https://github.com/voxel51/eta +cd eta +``` + +#### Step 2: Run the install script + +```shell +bash install.bash +``` + +Note that the install script supports flags that control things like (on macOS) +whether `port` or `brew` is used to install packages. Run +`bash install.bash -h` for more information. + +For Linux installs, the script inspects your system to see if CUDA is installed +via the `lspci` command. If CUDA is available, TensorFlow is installed with GPU +support. + +The table below lists the version of TensorFlow that will be installed by the +installer, as recommended by the +[tested build configurations](https://www.tensorflow.org/install/source#tested_build_configurations): + +| CUDA Version Found | TensorFlow Version Installed | +| ------------------ | ---------------------------- | +| CUDA 8 | `tensorflow-gpu~=1.4` | +| CUDA 9 | `tensorflow-gpu~=1.12` | +| CUDA 10 | `tensorflow-gpu~=1.15` | +| Other CUDA | `tensorflow-gpu~=1.15` | +| No CUDA | `tensorflow~=1.15` | + +> Note that ETA also supports TensorFlow 2.X. The only problems you may face +> when using ETA with TensorFlow 2 are when trying to run inference with +> [ETA models](https://github.com/voxel51/eta/blob/develop/eta/models/manifest.json) +> that only support TensorFlow 1. A notable case here are TF-slim models. In +> such cases, you should see an informative error message alerting you of the +> requirement mismatch. + +### Lite installation + +Some ETA users are only interested in using the core ETA library defined in the +`eta.core` package. In such cases, you can perform a lite installation using +the `-l` flag of the install script: + +```shell +bash install.bash -l +``` + +Lite installation omits submodules and other large dependencies that are not +required in order for the core library to function. If you use an ETA feature +that requires additional dependencies (e.g., `ffmpeg` or `tensorflow`), you +will be prompted to install the relevant packages. + +### Developer installation + +If you are interested in contributing to ETA or generating its documentation +from source, you should perform a developer installation using the `-d` flag of +the install script: + +```shell +bash install.bash -d +``` + +## Setting up your execution environment + +When the root `eta` package is imported, it tries to read the `eta/config.json` +file to configure various package-level constants. Many advanced ETA features +such as pipeline building, model management, etc. require a properly configured +environment to function. + +To setup your environment, create a copy the example configuration file: + +```shell +cp config-example.json eta/config.json +``` + +If desired, you can edit your config file to customize the various paths, +change default constants, add environment variables, customize your default +`PYTHONPATH`, and so on. You can also add additional paths to the +`module_dirs`, `pipeline_dirs`, and `models_dirs` sections to expose custom +modules, pipelines, and models to your system. + +Note that, when the config file is loaded, any `{{eta}}` patterns in directory +paths are replaced with the absolute path to the `eta/` directory on your +machine. + +The default config includes the `modules/`, `pipelines/`, and `models/` +directories on your module, pipeline, and models search paths, respectively. +These directories contain the necessary information to run the standard +analytics exposed by the ETA library. In addition, the relative paths +`./modules/`, `./pipelines/`, and `./models/` are added to their respective +paths to support the typical directory structure that we adopt for our custom +projects. + +### CLI + +Installing ETA automatically installs `eta`, a command-line interface (CLI) for +interacting with the ETA Library. This utility provides access to many useful +features of ETA, including building and running pipelines, downloading models, +and interacting with remote storage. + +To explore the CLI, type `eta --help`, and see the +[CLI Guide](https://github.com/voxel51/eta/blob/develop/docs/cli_guide.md) for +complete information. + +## Quickstart + +Get your feet wet with ETA by running some of examples in the +[examples folder](https://github.com/voxel51/eta/tree/develop/eta/examples). + +Also, see the [docs folder](https://github.com/voxel51/eta/tree/develop/docs) +for more documentation about the various components of the ETA library. + +## Organization + +The ETA package is organized as described below. For more information about the +design and function of the various ETA components, read the documentation in +the [docs folder](https://github.com/voxel51/eta/tree/develop/docs). + +| Directory | Description | +| ----------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `eta/classifiers` | wrappers for performing inference with common classifiers | +| `eta/core` | the core ETA library, which includes utilities for working with images, videos, embeddings, and much more | +| `eta/detectors` | wrappers for performing inference with common detectors | +| `eta/docs` | documentation about the ETA library | +| `eta/examples` | examples of using the ETA library | +| `eta/models` | library of ML models. The `manifest.json` file in this folder enumerates the models, which are downloaded to this folder as needed. See the [Models developer's guide](https://github.com/voxel51/eta/blob/develop/docs/models_dev_guide.md) for more information about ETA's model registry | +| `eta/modules` | library of video processing/analytics modules. See the [Module developer's guide](https://github.com/voxel51/eta/blob/develop/docs/modules_dev_guide.md) for more information about ETA modules | +| `eta/pipelines` | library of video processing/analytics pipelines. See the [Pipeline developer's guide](https://github.com/voxel51/eta/blob/develop/docs/pipelines_dev_guide.md) for more information about ETA pipelines | +| `eta/resources` | resources such as media, templates, etc | +| `eta/segmenters` | wrappers for performing inference with common semantic segmenters | +| `eta/tensorflow` | third-party TensorFlow repositories that ETA builds upon | + +## Generating Documentation + +This project uses +[Sphinx-Napoleon](https://pypi.python.org/pypi/sphinxcontrib-napoleon) to +generate its documentation from source. + +To generate the documentation, you must install the developer dependencies by +running the `install.bash` script with the `-d` flag. + +Then you can generate the docs by running: + +```shell +bash sphinx/generate_docs.bash +``` + +To view the documentation, open the `sphinx/build/html/index.html` file in your +browser. + +## Uninstallation + +```shell +pip uninstall voxel51-eta +``` + +## Acknowledgements + +This project was gratefully supported by the +[NIST Public Safety Innovation Accelerator Program](https://www.nist.gov/news-events/news/2017/06/nist-awards-385-million-accelerate-public-safety-communications). + +## Citation + +If you use ETA in your research, feel free to cite the project (but only if you +love it 😊): + +```bibtex +@article{moore2017eta, + title={ETA: Extensible Toolkit for Analytics}, + author={Moore, B. E. and Corso, J. J.}, + journal={GitHub. Note: https://github.com/voxel51/eta}, + year={2017} +} +``` + + +%prep +%autosetup -n voxel51-eta-0.9.0 + +%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-voxel51-eta -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Wed Apr 12 2023 Python_Bot <Python_Bot@openeuler.org> - 0.9.0-1 +- Package Spec generated @@ -0,0 +1 @@ +06a546ed976e3800bc0d9346f538570e voxel51-eta-0.9.0.tar.gz |