Markut Given the VOD of the stream and the markers that are exported as a CSV file, generate a video using ffmpeg that cuts out part of the VOD according to the provided markers. Quick Start $ ./markut.py -c marks.csv -i vod.mp4 -d 4 GitHub https://github.com/tsoding/markut    

Face-Every-Day-Maker-Studio This project is a tool for making a everyday video, which is timelapse video or slides video, of images but for face of a person, this if you take a picture of yourself everyday and you want to appreciate how you are getting old. In this readme file there’re the instructions for running the dev-working environment and the Motivation I made this repo to explain some of my students the basic concepts of git in practice, removing, and modifying […]

This is the official implementation for the ECCV 2020 spotlight paper by Davis Rempe, Leonidas J. Guibas, Aaron Hertzmann, Bryan Russell, Ruben Villegas, and Jimei Yang. For more information, see the project webpage. Environment Setup Note: the code in this repo has only been tested on Ubuntu 16.04. First create and activate a virtual environment to install dependencies for the code in this repo. For example with conda: conda create -n contact_dynamics_env python=3.6 conda activate contact_dynamics_env pip install -r requirements.txt […]

PyTorchVideo PyTorchVideo is a deeplearning library with a focus on video understanding work. PytorchVideo provides resusable, modular and efficient components needed to accelerate the video understanding research. PyTorchVideo is developed using PyTorch and supports different deeplearning video components like video models, video datasets, and video-specific transforms. A PyTorchVideo-accelerated X3D model running on a Samsung Galaxy S10 phone. The model runs ~8x faster than real time, requiring roughly 130 ms to process one second of video. A PyTorchVideo-based SlowFast model performing […]

Text2Video Text2Video’s purpose is to help people create videos quickly and easily by simply typing out the video’s script and a description of images to include in the video. Usage Just upload a script and an optional audio file of you saying the script To start the server run this in the terminal docker start gentle python app.py To stop the server open the same terminal and press CTRL-C. After that run docker stop gentle For instructions on how to […]

VidGear VidGear is a High-Performance Video Processing Python Library that provides an easy-to-use, highly extensible, thoroughly optimised Multi-Threaded + Asyncio Framework on top of many state-of-the-art specialized libraries like OpenCV, FFmpeg, ZeroMQ, picamera, starlette, streamlink, pafy, pyscreenshot, aiortc and python-mss serving at its backend, and enable us to flexibly exploit their internal parameters and methods, while silently delivering robust error-handling and real-time performance fire VidGear primarily focuses on simplicity, and thereby lets programmers and software developers to easily integrate and […]

Activity Recognition TensorRT Perform video classification using 3D ResNets trained on Kinetics-400 dataset and accelerated with TensorRT. TensorRT Installation Assuming you have CUDA already installed, go ahead and download TensorRT from here. Follow instructions of installing the system binaries and python package for tensorrt here. Python dependencies Install the necessary python dependencies by running the following command – pip3 install -r requirements.txt Clone the repository This is a straightforward step, however, if you are new to git recommend glancing threw […]