voxel51-eta 0.13.0

Description:

voxel51eta 0.13.0

ETA: Extensible Toolkit for Analytics

An open and extensible computer vision, machine learning and video analytics
infrastructure.







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:
pip install voxel51-eta

This will perform a lite installation of ETA. 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
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 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
to maintain a clean workspace.
Step 1: Clone the repository
git clone https://github.com/voxel51/eta
cd eta

Step 2: Run the install script
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:



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
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:
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:
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:
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 for
complete information.
Quickstart
Get your feet wet with ETA by running some of examples in the
examples folder.
Also, see the docs folder
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.



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 for more information about ETA's model registry


eta/modules
library of video processing/analytics modules. See the Module developer's guide for more information about ETA modules


eta/pipelines
library of video processing/analytics pipelines. See the Pipeline developer's guide 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 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:
bash sphinx/generate_docs.bash

To view the documentation, open the sphinx/build/html/index.html file in your
browser.
Uninstallation
pip uninstall voxel51-eta

Acknowledgements
This project was gratefully supported by the
NIST Public Safety Innovation Accelerator Program.
Citation
If you use ETA in your research, feel free to cite the project (but only if you
love it 😊):
@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}
}