evadb 0.1.0

EVA Video Database System (Think MySQL for videos).

EVA Video Database System: Where SQL meets Deep Learning

🌟 Try It Out! 🌟

Links

• Documentation
• Tutorials
• Slack
• Quick Demo

EVA Video Database System

What is EVA?

EVA is a new database system tailored for video analytics -- think PostgreSQL for videos. It supports a SQL-like language for querying videos (e.g., finding frames in a movie with your favorite actor or finding touchdowns in a football game). It comes with a wide range of commonly used computer vision models.

Why EVA?

👀 Easily query videos in user-facing applications with a SQL-like interface for commonly used computer vision models.

🚅 Speed up queries and save money spent on model inference using in-built sampling, caching, and filtering optimizations.

✨ Hit your target accuracy using state-of-the-art model selection and query optimization algorithms.

QuickStart

1. EVA requires Python . To install EVA, we recommend using an virtual environment and the pip package manager:

pip install evadb

1. Start the EVA server and the client programs

eva_server &   # launch server
eva_client     # launch client

2. LOAD a video using the client (we use ua_detrac.mp4 video as an example):

LOAD FILE "data/ua_detrac/ua_detrac.mp4" INTO MyVideo;

3. That's it. You can now start issuing queries over the loaded video:

SELECT id, data FROM MyVideo WHERE id < 5;

More Interesting Queries

1. Search for frames in the video that contain a car

SELECT id, data FROM MyVideo WHERE ['car'] <@ FastRCNNObjectDetector(data).labels;

Source Video	Query Result

2. Search for frames in the video that contain a pedestrian and a car

SELECT id, data FROM MyVideo WHERE ['pedestrian', 'car'] <@ FastRCNNObjectDetector(data).labels;

3. Search for frames in the video with more than 3 cars

SELECT id, data FROM MyVideo WHERE Array_Count(FastRCNNObjectDetector(data).labels, 'car') > 3;

4. Create your own user-defined function (UDF) that wraps around a vision model like FastRCNN

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';

EVA Applications

MNIST Digit Recognition

Source Video	Query Result

Emotion Detection

Source Video	Query Result

Contributing to EVA

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.

License

Copyright (c) 2018-2022 Georgia Tech Database Group Licensed under Apache License.