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-0.2.3.tar.gz
(441.6 kB
view details)
Built Distribution
heyvi-0.2.3-py3-none-any.whl
(129.9 kB
view details)
File details
Details for the file heyvi-0.2.3.tar.gz
.
File metadata
- Download URL: heyvi-0.2.3.tar.gz
- Upload date:
- Size: 441.6 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 | 36615a875218cbcc3c3e7dfc6f2fbeb871734fcc3e10ea25658629b76640706f |
|
MD5 | ea160174e7aeb0c388af982447f6be25 |
|
BLAKE2b-256 | 3535294d165cbf661ee5f736f101ae4f6739f333c5a7e3a89a3c8178e47f53a8 |
File details
Details for the file heyvi-0.2.3-py3-none-any.whl
.
File metadata
- Download URL: heyvi-0.2.3-py3-none-any.whl
- Upload date:
- Size: 129.9 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 | 665bcb8722af5ce6bf59354f4f69ba65a6fa95aed49513d2e57ae96ef6cd12b0 |
|
MD5 | a1c65d8a4f5c3ed50e2ec948257cab98 |
|
BLAKE2b-256 | b5d6a3fd2166c0375f60cfefc4cb27c258604d3ea27a985ec84843509d66f826 |