All Projects → nicholaskajoh → Ivy

nicholaskajoh / Ivy

Licence: mit
Video-based object counting software.

Programming Languages

python
139335 projects - #7 most used programming language

Projects that are alternatives of or similar to Ivy

video autoencoder
Video lstm auto encoder built with pytorch. https://arxiv.org/pdf/1502.04681.pdf
Stars: ✭ 32 (-90.36%)
Mutual labels:  video-processing
meltsub
Convert hardsub to softsub
Stars: ✭ 19 (-94.28%)
Mutual labels:  video-processing
Amazon Rekognition Video Analyzer
A working prototype for capturing frames off of a live MJPEG video stream, identifying objects in near real-time using deep learning, and triggering actions based on an objects watch list.
Stars: ✭ 309 (-6.93%)
Mutual labels:  video-processing
video-audio-tools
To process/edit video and audio with Python+FFmpeg. [简单实用] 基于Python+FFmpeg的视频和音频的处理/剪辑。
Stars: ✭ 164 (-50.6%)
Mutual labels:  video-processing
nebula
Media asset management and broadcast automation system
Stars: ✭ 103 (-68.98%)
Mutual labels:  video-processing
Tensor Stream
A library for real-time video stream decoding to CUDA memory
Stars: ✭ 277 (-16.57%)
Mutual labels:  video-processing
VideoSplice
Video splice effects.
Stars: ✭ 56 (-83.13%)
Mutual labels:  video-processing
Video Enhancement
A list of resources for video enhancement, including video super-resolutio, interpolation, denoising, compression artifact removal et al..
Stars: ✭ 336 (+1.2%)
Mutual labels:  video-processing
eloquent-ffmpeg
High-level API for FFmpeg's Command Line Tools
Stars: ✭ 71 (-78.61%)
Mutual labels:  video-processing
Libva
Libva is an implementation for VA-API (Video Acceleration API)
Stars: ✭ 305 (-8.13%)
Mutual labels:  video-processing
CustomVideo
Customize video, photo, animation, music, border etc...
Stars: ✭ 71 (-78.61%)
Mutual labels:  video-processing
tssi2
tssi2 is a header-only library for parsing MPEG-2 and DVB Transport Streams in the domain of multimedia processing applications.
Stars: ✭ 18 (-94.58%)
Mutual labels:  video-processing
Awesome Action Recognition
A curated list of action recognition and related area resources
Stars: ✭ 3,202 (+864.46%)
Mutual labels:  video-processing
video2gif
Programmatically generate animated GIF from video in Python(convert video to gif)
Stars: ✭ 18 (-94.58%)
Mutual labels:  video-processing
Toflow
TOFlow: Video Enhancement with Task-Oriented Flow
Stars: ✭ 314 (-5.42%)
Mutual labels:  video-processing
tracker
CRS Ticket Tracker
Stars: ✭ 15 (-95.48%)
Mutual labels:  video-processing
Optimizedimageenhance
Several image/video enhancement methods, implemented by Java, to tackle common tasks, like dehazing, denoising, backscatter removal, low illuminance enhancement, featuring, smoothing and etc.
Stars: ✭ 272 (-18.07%)
Mutual labels:  video-processing
Filter.js
Filter.js: Video and Image Processing and Computer Vision Library in pure JavaScript (Browser and Node.js)
Stars: ✭ 335 (+0.9%)
Mutual labels:  video-processing
Vectorhub
Vector Hub - Library for easy discovery, and consumption of State-of-the-art models to turn data into vectors. (text2vec, image2vec, video2vec, graph2vec, bert, inception, etc)
Stars: ✭ 317 (-4.52%)
Mutual labels:  video-processing
Server
The Kaltura Platform Backend. To install Kaltura, visit the install packages repository.
Stars: ✭ 293 (-11.75%)
Mutual labels:  video-processing

Ivy

Ivy is an open-source video-based object counting software for tallying pretty much anything (vehicles, people, animals — you name it).

Requirements

  • Python 3 (tested with versions 3.5, 3.6 and 3.7)

Setup

  • Clone this repo [email protected]:nicholaskajoh/ivy.git.
  • Create and/or use a virtual environment (optional but recommended).
  • Install the dependencies in requirements.txt pip install -r requirements.txt.
  • Choose a detector and install its dependencies where necessary (if you're not sure what to pick, we recommend you start with yolo).
Detector Description Dependencies
yolo Perform detection using models created with the YOLO (You Only Look Once) neural net. https://pjreddie.com/darknet/yolo/
tfoda Perform detection using models created with the Tensorflow Object Detection API. https://github.com/tensorflow/models/tree/master/research/object_detection CPU: pip install tensorflow-cpu
GPU: pip install tensorflow-gpu
detectron2 Perform detection using models created with FAIR's Detectron2 framework. https://github.com/facebookresearch/detectron2 python -m pip install 'git+https://github.com/facebookresearch/detectron2.git' (https://github.com/facebookresearch/detectron2/blob/master/INSTALL.md)
haarcascade Perform detection using Haar feature-based cascade classifiers. https://docs.opencv.org/3.4/db/d28/tutorial_cascade_classifier.html

Run

  • Create a .env file (based on .env.example) in the project's root directory and edit as appropriate.
  • Run python -m main.

Demo

Download ivy_demo_data.zip and unzip its contents in the data directory. It contains detection models and a sample video.

Test

python -m pytest

Debug

By default, Ivy runs in "debug mode" which provides you a window to monitor the object counting process. You can:

  • press the p key to pause/play the counting process
  • press the s key to capture a screenshot
  • press the q key to quit the program
  • click any point on the window to log the coordinates of the pixel in that position

Community

Got questions, contributions, suggestions, concerns? Join our community on Spectrum! Also follow us on Twitter @CountWithIvy to get notified about new features, fixes and initiatives.

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].