by Akshay Bhat
Deep Video Analytics is a platform for indexing and extracting information from videos and images. With latest version of docker installed correctly, you can run Deep Video Analytics in minutes locally (even without a GPU) using a single command.
For installation instructions & demo please visit https://www.deepvideoanalytics.com
- For a quick overview we strongly recommend going through the presentation in readme.pdf
- OCR example has been moved to /docs/experiments/ocr directory.
- More experiments coming soon!
We provide instructions for developing, testing and deploying DVA.
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deploy/compose/dev contains docker-compose files for interactively developing DVA by using host server directory mapped as a volume.
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deploy/compose/test contains docker-compose files for testing cloud filesystem (s3, gcs) support.
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deploy/compose/cpu contains docker-compose files for non-GPU single machine deployments on Linode, AWS, GCP etc.
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deploy/compose/gpu contains docker-compose files for GPU single machine deployments on GCP, AWS etc.
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deploy/kube contains files used for launching DVA in a scalable GKE + GCS setup, with and without GPUs.
- /client : Python client using DVA REST API
- /configs : ngnix config + defaults.py defining models + processing pipelines (can be replaced by mounting a volume)
- /deploy : Dockerfiles + Instructions for development, single machine deployment and scalable deployment with Kubernetes
- /docs : Documentation, tutorial and experiments
- /tests : Files required for testing
- /repos : Code copied from third party repos, e.g. Yahoo LOPQ, TF-CTPN etc.
- /server : dvalib + django server contains contains bulk of the code for UI, App and models.
- /logs : Empty dir for storing logs
| Library | Link to the license |
|---|---|
| YAD2K | MIT License |
| AdminLTE2 | MIT License |
| FabricJS | MIT License |
| Facenet | MIT License |
| JSFeat | MIT License |
| MTCNN | MIT License |
| Insight Face | MIT License |
| CRNN.pytorch | MIT License |
| Original CRNN code by Baoguang Shi | MIT License |
| Object Detector App using TF Object detection API | MIT License |
| Plotly.js | MIT License |
| CRF as RNN | MIT License |
| Text Detection CTPN | MIT License |
| SphereFace | MIT License |
| Segment annotator | BSD 3-clause |
| TF Object detection API | Apache 2.0 |
| TF models/slim | Apache 2.0 |
| TF models/delf | Apache 2.0 |
| Youtube 8M feature extractor | Apache 2.0 |
| CROW | Apache 2.0 |
| LOPQ | Apache 2.0 |
| Open Images Pre-trained network | Apache 2.0 |
| Library | Link to the license |
|---|---|
| pqkmeans | MIT License |
| faiss | BSD + PATENTS License |
- FFmpeg (not linked, called via a Subprocess)
- Tensorflow
- OpenCV
- Numpy
- Pytorch
- Docker
- Nvidia-docker
- Docker-compose
- All packages in requirements.txt
- All dependancies installed in CPU Dockerfile & GPU Dockerfile
Copyright 2016-2018, Akshay Bhat, All rights reserved.
Deep Video Analytics is nearing stable 1.0, we expect to release in Summer 2018. The license will be relaxed once a stable release version is reached. Please contact me for more information.