Search Results for "gpu image"
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Showing 21 open source projects for "gpu image"
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rembg
Rembg is a tool to remove images background
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 -
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darktable
darktable is an open source photography workflow application
darktable is an open source photography workflow application and non-destructive raw developer. A virtual lighttable and darkroom for photographers. It manages your digital negatives in a database, lets you view them through a zoomable lighttable and enables you to develop raw images, enhance them and export them on local or remote storage. -
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Kornia
Open Source Differentiable Computer Vision Library
Kornia is a differentiable computer vision library for PyTorch. It consists of a set of routines and differentiable modules to solve generic computer vision problems. At its core, the package uses PyTorch as its main backend both for efficiency and to take advantage of the reverse-mode auto-differentiation to define and compute the gradient of complex functions. Inspired by existing packages, this library is composed by a subset of packages containing operators that can be inserted within... -
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ArrayFire
ArrayFire, a general purpose GPU library
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... -
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Waifu2x-Extension-GUI
Photo/Video/GIF enlargement using machine learning
Image & GIF & Video Super-Resolution using Deep Convolutional Neural Networks. Built-in image processing algorithm: Waifu2x / SRMD / RealSR / Anime4K / ACNet Built-in image processing engine: Waifu2x-caffe / Waifu2x-converter / Waifu2x-ncnn-vulkan / SRMD-ncnn-vulkan / RealSR-ncnn-vulkan / Anime4KCPP Github: https://github.com/AaronFeng753/Waifu2x-Extension-GUI -
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Imaging Instruments Lite
Image processing App for Windows Desktop
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. -
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Imaging Instruments
High-performance image processing C++ application for Windows Desktop
...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. Fueled by copious amounts of yerba mate. website: https://agustintortolero.pythonanywhere.com/ Source code: https://github.com/agustinTortolero/Imaging-Instruments -
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PyMapStitcher-3---Cuda-Maps-Downloader
A low ram maps downloader to keep your ram free.
PyMapStitcher 3 is a desktop application for downloading, stitching, and exporting very large satellite map areas as GeoTIFF/BigTIFF files. The software supports GPU acceleration with NVIDIA CUDA and CuPy, direct GeoTIFF georeferencing, WebView-based map selection, and high-performance tile processing for large-scale mapping workflows. -
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AwesomeBump
Open Source graphic tool and alternative to Insane Bump
...It was made as an alternative to known gimp plugin Insane Bump or the commercial tool Crazy Bump. It is designed to generate normal, height, specular or ambient occlusion, metallic, roughness and other textures from a single image. Most of the image processing is done on GPU so the program runs very fast and all the parameters can be changed in real time. -
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GPUImage 2
Framework for GPU-accelerated video and image processing
GPUImage 2 is the second generation of the GPUImage framework, an open source project for performing GPU-accelerated image and video processing on Mac, iOS, and now Linux. The original GPUImage framework was written in Objective-C and targeted Mac and iOS, but this latest version is written entirely in Swift and can also target Linux and future platforms that support Swift code. The objective of the framework is to make it as easy as possible to set up and perform realtime video processing or machine vision against image or video sources. ... -
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blitz-image
GPU-accelerated image processing for java
blitz-image is a library to support extremely fast image processing in java, harnessing GPU acceleration via OpenCL. Although more features will be added in time, the initial focus is on very fast high-quality image resizing, including bicubic resizing (both upscale and downscale). The performance of a typical bicubic upscale operation is typically in the order of 10 times faster than via the standard AWT mechanism using RenderingHints.VALUE_INTERPOLATION_BICUBIC. ... -
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A GUI interface to a tool for generating SSBumps (Self Shadowed Bump Maps). Includes a CUDA GPU rendering extension.
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GLIP-LIB
OpenGL Image Processing LIBrary
GLIP-Lib is an OpenGL image processing library written in C++. It features all the necessary tools to quickly build texture filters and pipelines and operate them on the GPU. -
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gpuip
Framework for Image Processing on the GPU
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HIPAcc
Heterogeneous Image Processing Acceleration (HIPACC) Framework
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. -
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LBP in multiple platforms
LBP implementation in multiple computing platforms (ARM,GPU, DSP...)
...When selecting a suitable LBP implementation platform, the specific application and its requirements in terms of performance, size, energy efficiency, cost and developing time has to be carefully considered. This is a software toolbox that collects software implementations of the Local Binary Pattern operator in several platforms: - OpenCL for CPU & GPU - OpenCL for GPU (branchless) - C code optimized for ARM - OpenGL ES 2.0 shaders mobile GPUs - C code for TI C64x DSP core (branchless) - C code for TTA processor synthesis If you use the code somewhere, please cite: Bordallo López M., Nieto A., Boutellier J., Hannuksela J., and Silvén O. "Evaluation of real-time LBP computing in multiple architectures," Journal of Real Time Image Processing, 2014 -
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A simple rendering, image processing engine. Includes a framework width (multiplatform) event handling, plugin management, a templated based math library and some exotic features like general linear camera, gpu based (OpenGL) image process...
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A gathering of state-of-the-art tools for GPU based image processing. They are the sourcecode for related research articles, and provide the basis for own experimentation. All are being implemented forNVidia GPUs in Linux, hence the name "nvision" ;)
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GLEWpy aims to bring advanced OpenGL extensions to Python. This allows the Python OpenGL developer to use features such as fragment/vertex shaders and image processing on the GPU. It serves as a compliment to PyOpenGL and toolkits such as GLUT and SDL.
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Jorik is about using graphics processing units in general purpose (GPGPU) computations: parallel sorting, dynamic PDE and image processing. Jorik is written on C++/DirectX/HSLS, supports both ATI and nVidia cards. It suits for not-gaming GPU benchmarking.
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GPUVision is a framework for creating GPU based general purpose programs, image processing programs, and computer vision programs in C++. Supported libraries include matrix operations, graph partitioning, kernels, corner detection, edge detection etc.
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