Metadata-Version: 2.1
Name: evadb
Version: 0.1.2
Summary: EVA Video Database System (Think MySQL for videos).
Home-page: https://github.com/georgia-tech-db/eva
Author: Georgia Tech Database Group
Author-email: georgia.tech.db@gmail.com
License: Apache License 2.0
Download-URL: https://github.com/georgia-tech-db/eva
Description: <div >
          <a href="https://evadb.readthedocs.io/">
            <img src="https://raw.githubusercontent.com/georgia-tech-db/eva/master/api-docs/images/eva/eva-banner.png" alt="EVA" width="1000px" margin-left="-5px">
          </a>
          <div>
                <h3>Try It Out!</h3>
                <a href="https://colab.research.google.com/github/georgia-tech-db/eva/blob/master/tutorials/03-emotion-analysis.ipynb">
                    <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open EVA on Colab"/>
                </a>
                <a href="https://join.slack.com/t/eva-db/shared_invite/zt-1i10zyddy-PlJ4iawLdurDv~aIAq90Dg">
                    <img alt="Slack" src="https://img.shields.io/badge/slack-eva-ff69b4.svg?logo=slack">
                </a>    
                <a href="https://github.com/georgia-tech-db/eva/discussions">
                    <img alt="Discuss on Github!" src="https://img.shields.io/badge/-Discuss%20on%20Github!-blueviolet">
                </a>
                <img alt="PyPI" src="https://img.shields.io/pypi/v/evadb.svg"/>
                <img alt="License" src="https://img.shields.io/badge/license-Apache%202-brightgreen.svg?logo=apache"/>
                <img alt="Python Versions" src="https://img.shields.io/badge/Python--versions-3.7%20|%203.8%20|%203.9%20|%203.10-brightgreen"/>
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        </div>
        
        # EVA Multimedia Database System
        
        EVA is a **database system tailored for video analytics** -- think PostgreSQL for videos. It supports a SQL-like language for querying videos like:
        
         * examining the "emotion palette" of different actors
         * finding gameplays that lead to a touchdown in a football game
        
        EVA comes with a wide range of commonly used computer vision models. It written in Python, and it is licensed under the Apache license. 
        
        If you are wondering why you might need a video database system, start with page on <a href="https://evadb.readthedocs.io/en/latest/source/overview/video.html#">Video Database Systems</a>. It describes how EVA lets users easily make use of deep learning models and how they can reduce money spent on inference on large image or video datasets.
        
        The <a href="https://evadb.readthedocs.io/en/latest/source/overview/installation.html">Getting Started</a> page shows how you can use EVA for different computer vision tasks: image classification, object detection, action recognition, and how you can easily extend EVA to support your custom deep learning model in the form of user-defined functions.
        
        The <a href="https://evadb.readthedocs.io/en/latest/source/tutorials/index.html">User Guides</a> section contains Jupyter Notebooks that demonstrate how to use various features of EVA. Each notebook includes a link to Google Colab, where you can run the code by yourself.
        
        ## Why EVA? ##
        
        <details>
          <summary><b>Easily combine SQL and Deep Learning to build next-generation database applications</b></summary>
          Easily query videos in user-facing applications with a SQL-like interface for commonly used computer vision models.
        </details>
        
        <details>
          <summary><b>Speed up queries and save money spent on model inference</b></summary>
          EVA comes with a collection of built-in sampling, caching, and filtering optimizations inspired by time-tested relational database systems.
        </details>
        
        <details>
          <summary><b>Extensible by design to support custom deep learning models </b></summary>
          EVA has first-class support for user-defined functions that wrap around your deep learning models in PyTorch.
        </details>
        
        ## Links
        * [Documentation](https://evadb.readthedocs.io/en/latest/)
        * [Tutorials](https://github.com/georgia-tech-db/eva/blob/master/tutorials/03-emotion-analysis.ipynb)
        * [Join Slack](https://join.slack.com/t/eva-db/shared_invite/zt-1i10zyddy-PlJ4iawLdurDv~aIAq90Dg)
        * [Demo](https://ada-00.cc.gatech.edu/eva/playground)
        
        ## Quick Start
        
        1. EVA supports Python versions 3.7 through 3.10. To install EVA, we recommend using the pip package manager.
        
        ```shell
        pip install evadb
        ```
        
        1. EVA works on Jupyter notebooks -- illustrative notebooks are available in the [Tutorials](https://github.com/georgia-tech-db/eva/blob/master/tutorials/03-emotion-analysis.ipynb) folder. EVA adopts a client-server architecture and comes with a terminal-based client. To start the EVA server and a terminal-based client, use the following commands:
        ```shell
        eva_server &   # launch server
        eva_client     # launch client
        ```
        
        2. Load a video onto the server using the client (we use [ua_detrac.mp4](data/ua_detrac/ua_detrac.mp4) video as an example):
        
        ```mysql
        LOAD VIDEO "data/ua_detrac/ua_detrac.mp4" INTO MyVideo;
        ```
        
        3. That's it! You can now start running queries over the loaded video:
        
        ```mysql
        SELECT id, data FROM MyVideo WHERE id < 5;
        ```
        
        4. Search for frames in the video that contain a car
        
        ```mysql
        SELECT id, data FROM MyVideo WHERE ['car'] <@ FastRCNNObjectDetector(data).labels;
        ```
        | Source Video  | Query Result |
        |---------------|--------------|
        |<img alt="Source Video" src="https://github.com/georgia-tech-db/eva/releases/download/v0.1.0/traffic-input.webp" width="300"> |<img alt="Query Result" src="https://github.com/georgia-tech-db/eva/releases/download/v0.1.0/traffic-output.webp" width="300"> |
        
        5. Search for frames in the video that contain a pedestrian and a car
        
        ```mysql
        SELECT id, data FROM MyVideo WHERE ['pedestrian', 'car'] <@ FastRCNNObjectDetector(data).labels;
        ```
        
        6. Search for frames in the video with more than 3 cars
        
        ```mysql
        SELECT id, data FROM MyVideo WHERE Array_Count(FastRCNNObjectDetector(data).labels, 'car') > 3;
        ```
        
        7. You can create a new user-defined function (UDF) that wraps around your custom vision model or an off-the-shelf model like FastRCNN:
        ```mysql
        CREATE UDF IF NOT EXISTS MyUDF
        INPUT  (frame NDARRAY UINT8(3, ANYDIM, ANYDIM))
        OUTPUT (labels NDARRAY STR(ANYDIM), bboxes NDARRAY FLOAT32(ANYDIM, 4),
                scores NDARRAY FLOAT32(ANYDIM))
        TYPE  Classification
        IMPL  'eva/udfs/fastrcnn_object_detector.py';
        ```
        
        8. You can combine multiple user-defined functions in a single query to accomplish more complicated tasks.
        ```mysql
           -- Analyse emotions of faces in a video
           SELECT id, bbox, EmotionDetector(Crop(data, bbox)) 
           FROM HAPPY JOIN LATERAL UNNEST(FaceDetector(data)) AS Face(bbox, conf)  
           WHERE id < 15;
        ```
        
        ## Illustrative EVA Applications 
        
        ### :desert_island: Traffic Analysis Application using Object Detection Model
        | Source Video  | Query Result |
        |---------------|--------------|
        |<img alt="Source Video" src="https://github.com/georgia-tech-db/eva/releases/download/v0.1.0/traffic-input.webp" width="300"> |<img alt="Query Result" src="https://github.com/georgia-tech-db/eva/releases/download/v0.1.0/traffic-output.webp" width="300"> |
        
        ### :desert_island: MNIST Digit Recognition using Image Classification Model
        | Source Video  | Query Result |
        |---------------|--------------|
        |<img alt="Source Video" src="https://github.com/georgia-tech-db/eva/releases/download/v0.1.0/mnist-input.webp" width="150"> |<img alt="Query Result" src="https://github.com/georgia-tech-db/eva/releases/download/v0.1.0/mnist-output.webp" width="150"> |
        
        ### :desert_island: Movie Analysis Application using Face Detection + Emotion Classfication Models
        
        | Source Video  | Query Result |
        |---------------|--------------|
        |<img alt="Source Video" src="https://github.com/georgia-tech-db/eva/releases/download/v0.1.0/gangubai-input.webp" width="400"> |<img alt="Query Result" src="https://github.com/georgia-tech-db/eva/releases/download/v0.1.0/gangubai-output.webp" width="400"> |
        
        ## Community
        
        Join the EVA community on [Slack](https://join.slack.com/t/eva-db/shared_invite/zt-1i10zyddy-PlJ4iawLdurDv~aIAq90Dg) to ask questions and to share your ideas for improving EVA.
        
        <a href="https://join.slack.com/t/eva-db/shared_invite/zt-1i10zyddy-PlJ4iawLdurDv~aIAq90Dg">              
            <img src="https://raw.githubusercontent.com/georgia-tech-db/eva/master/api-docs/images/eva/eva-slack.jpg" alt="EVA Slack Channel" width="500">
        </a>
        
        ## Contributing to EVA
        
        [![PyPI Version](https://img.shields.io/pypi/v/evadb.svg)](https://pypi.org/project/evadb)
        [![CI Status](https://circleci.com/gh/georgia-tech-db/eva.svg?style=svg)](https://circleci.com/gh/georgia-tech-db/eva)
        [![Coverage Status](https://coveralls.io/repos/github/georgia-tech-db/eva/badge.svg?branch=master)](https://coveralls.io/github/georgia-tech-db/eva?branch=master)
        [![Documentation Status](https://readthedocs.org/projects/evadb/badge/?version=latest)](https://evadb.readthedocs.io/en/latest/index.html)
        
        To file a bug or request a feature, please use GitHub issues. Pull requests are welcome.
        For more information on installing from source and contributing to EVA, see our
        [contributing guidelines](https://evadb.readthedocs.io/en/latest/source/contribute/index.html).
        
        ## License
        Copyright (c) 2018-2022 [Georgia Tech Database Group](http://db.cc.gatech.edu/)
        Licensed under [Apache License](LICENSE).
        
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3
Classifier: Development Status :: 3 - Alpha
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
Provides-Extra: dev
