Search Results for "cuda gpu"

Rembg is a powerful tool that utilizes AI (specifically U^2-Net) to automatically remove backgrounds from images, offering a streamlined command-line interface and Docker support. It's ideal for batch processing and integrates smoothly into workflows

ArrayFire is a general-purpose tensor library that simplifies the process of software development for the parallel architectures found in CPUs, GPUs, and other hardware acceleration devices. The library serves users in every technical computing market. Data structures in ArrayFire are smartly managed to avoid costly memory transfers and to take advantage of each performance feature provided by the underlying hardware. The community of ArrayFire developers invites you to build with us if...

Imaging Instruments is a high-performance image processing application crafted for efficiency and designed to deliver powerful and reliable image manipulation capabilities. Developed using the Model-View-Controller (MVC) architecture, Imaging Instruments harnesses the full potential of modern technologies, combining C++, Qt6, CUDA-C, and OpenCV to deliver a powerful and seamless experience for both professionals and enthusiasts. With multi-threading support via OpenMP and GPU acceleration through CUDA-C, Imaging Instruments provides lightning-fast image processing, unlocking the full capabilities of modern hardware. Built and designed by Agustin Tortolero. ...

Imaging Instruments lite is a comprehensive image processing application developed following the Model-View-Controller (MVC) design pattern, utilizing Python, Tkinter, and OpenCV. It provides users with image manipulation capabilities, leveraging multi-threading with OpenMP and GPU acceleration using CUDA-C. Fueled by yerba mate and a passion for coding. Created by Agustin Tortolero. website: https://agustintortolero.pythonanywhere.com/ Source code: https://github.com/agustinTortolero/Imaging-Instruments-lite

A GUI interface to a tool for generating SSBumps (Self Shadowed Bump Maps). Includes a CUDA GPU rendering extension.

HIPAcc development has moved to github: https://github.com/hipacc HIPAcc allows to design image processing kernels and algorithms in a domain-specific language (DSL). From this high-level description, low-level target code for GPU accelerators is generated using source-to-source translation. As back ends, the framework supports CUDA, OpenCL, and Renderscript. HIPAcc allows programmers to develop imaging applications while providing high productivity, flexibility and portability as well as competitive performance: the same algorithm description serves as basis for targeting different GPU accelerators and low-level languages.