Project Vi
Project description
"Hey Vi!"
HEYVI: Visym Analytics for Visual AI
docs: https://developer.visym.com/heyvi
HEYVI is a python package for visual AI that provides systems and trained models for activity detection and object tracking in videos.
HEYVI provides:
- Real time activity detection of the MEVA activity classes
- Real time visual object tracking in long duration videos
- Live streaming of annotated videos to youtube live
- Visual AI from RTSP cameras
Requirements
python 3.*
ffmpeg (required for videos)
vipy, torch, pytorch_lightning (for training)
Installation
pip install heyvi
Quickstart
v = heyvi.sensor.rtsp().framerate(5)
T = heyvi.system.Tracker()
with heyvi.system.YoutubeLive(fps=5, encoder='480p') as s:
T(v, frame_callback=lambda im: s(im.pixelize().annotate()))
Create a default RTSP camera and GPU enabled object tracker, then stream the privacy preserving annotated video (e.g. pixelated bounding boxes with captions) to a YouTube live stream.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
heyvi-0.2.9.tar.gz
(442.2 kB
view details)
Built Distribution
heyvi-0.2.9-py3-none-any.whl
(136.9 kB
view details)
File details
Details for the file heyvi-0.2.9.tar.gz
.
File metadata
- Download URL: heyvi-0.2.9.tar.gz
- Upload date:
- Size: 442.2 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.6.0 importlib_metadata/4.8.2 pkginfo/1.7.1 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.55.2 CPython/3.6.5
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 9dced9dcab4881938004d57612dc7eb47afeeffbbcbdcd7c7ff181d04a075bce |
|
MD5 | c11d77cc46a62b23a0da76a1452213af |
|
BLAKE2b-256 | d657f6865c8946ad128c40a7d3207ce39b3ca8edac18310343375fd0041378aa |
File details
Details for the file heyvi-0.2.9-py3-none-any.whl
.
File metadata
- Download URL: heyvi-0.2.9-py3-none-any.whl
- Upload date:
- Size: 136.9 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.6.0 importlib_metadata/4.8.2 pkginfo/1.7.1 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.55.2 CPython/3.6.5
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 5f983e6bde7e6d61f61f7ef74e4bcb241528f587a71fbbabd890d9cf9975307d |
|
MD5 | 44b7c1034b7088c9c998255cdfbac99f |
|
BLAKE2b-256 | d494ae7c02fb679f10cfa2cf9aa610a9e35d42ba07122bb453add30c84950898 |