LLVM based HLS library for HWToolkit (hardware devel. toolkit)

Record&Replay HLS streams

In this Project, our main aim is to accelerate the image recognition of CNN (Convolution Neural Network) with the help of a platform deployable on FPGA. CNN focuses on image classification, speech recognition, and video analysis. CNN is accelerated by using GPU (Graphical Processing Unit), which is relatively slow and consumes a high amount of power as CNN requires 20 GFLOPS/image. Also, the CPU acceleration being cheaper as it is readily available on most x86 machines is proportional to power. The modern Application-Specific Chips(ASICS) and the capability of a Field Programmable Gate Array( FPGA ) have power efficiency and faster computation rate over the GPU. With FPGA as a reconfigurable base and parallel architecture, we decided to target the CNN acceleration with an FPGA using Pipe CNN- an algorithm that gets synthesized via HLS (Hardware Level Synthesis Tools) like Intel's Quartus, and Open CL toolkit. Modern Large scale FPGA's like Stratix 10 and Arria 10 have shown a 10 percent less power consumption than GPU's, and it has an added advantage of pipeline parallel architecture and dedicated DSP for faster and efficient computations. The main goal of the Project is to design an OpenCL accelerator that is generic and yet powerful means of improving throughput in inference computations

A powerful command-line toolkit written in Go for streaming professionals. Test, analyze, and download RTMP and HLS streams with ease.

A Python toolkit for downloading and processing satellite data (HLS and SAR) for hail and wind damage analysis using NASA and ESA data sources.

A lightweight toolkit to extract, download, and convert HLS streams to MP4 files for offline viewing. Features browser-based TS segment extraction and FFmpeg-powered conversion, handling CORS restrictions and blob URLs with ease. Educational purposes only.

A cross-platform toolkit of Python scripts to convert videos into HLS format. Includes both automated and manual options for flexible video processing, simplifying HLS generation for all media projects.

Professional video processing toolkit for Nextcloud. Automatically generates HLS streams with m3u8 playlists, enables server-side video cropping, and provides adaptive streaming playback. Designed for videographers who need to process and preview large footage libraries without streaming and downloading terabytes of raw video files.

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Record and replay HLS streams

C++-based video codec toolkit The toolkit includes configurations for real-time streaming, including RTMP, HLS, and CDN.

Python toolkit for video segmentation and HLS streaming with live simulation and quality monitoring

A Go toolkit for packaging MP4 into M3U8, decrypting AES-encrypted TS segments, and rebuilding MP4 from HLS playlists.

VidVerse-frontend is a video streaming web app built with Next.js, featuring HLS streaming, state management using Redux Toolkit, and a modern UI styled with Tailwind CSS. It integrates Axios for API calls and React-Redux for global state management, hosted on Vercel for seamless deployment.