Search Results for "gpu image" - Page 6
Sort By:
Showing 158 open source projects for "gpu image"
View related business solutions-
Secure File Transfer for Windows with Cerberus by RedwoodCerberus supports unlimited users and connections on a single IP, with built-in encryption, 2FA, and a browser-based web client — all deployable in under 15 minutes with a 25-day free trial.
-
Our Free Plans just got better! | Auth0You asked, we delivered! Auth0 is excited to expand our Free and Paid plans to include more options so you can focus on building, deploying, and scaling applications without having to worry about your security. Auth0 now, thank yourself later.
-
1
NiftyNet
An open-source convolutional neural networks platform for research
An open-source convolutional neural networks platform for medical image analysis and image-guided therapy. NiftyNet is a TensorFlow-based open-source convolutional neural networks (CNNs) platform for research in medical image analysis and image-guided therapy. NiftyNet’s modular structure is designed for sharing networks and pre-trained models. Using this modular structure you can get started with established pre-trained networks using built-in tools. Adapt existing networks to your imaging... -
2
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. -
3
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. ... -
4
Imogen
GPU Texture Generator
Imogen is a real-time, node-based procedural texture generation tool aimed at artists, developers, and shader enthusiasts. It allows users to build complex material textures using a graph-based interface, combining operations like blending, noise, filters, and color correction in a non-destructive workflow. Built with Vulkan and ImGui, Imogen provides immediate visual feedback and supports GPU acceleration for high-resolution texture output. It's particularly useful in game development, VFX,... -
Stop vibe-debugging.AppSignal's MCP server hands Claude, Cursor, or Zed your real errors, traces, and the deploy that shipped them. AI writes the fix; you review the diff.
-
5
DIGITS
Deep Learning GPU training system
The NVIDIA Deep Learning GPU Training System (DIGITS) puts the power of deep learning into the hands of engineers and data scientists. DIGITS can be used to rapidly train the highly accurate deep neural network (DNNs) for image classification, segmentation and object detection tasks. DIGITS simplifies common deep learning tasks such as managing data, designing and training neural networks on multi-GPU systems, monitoring performance in real-time with advanced visualizations, and selecting the best performing model from the results browser for deployment. ... -
6
Face Recognition
World's simplest facial recognition api for Python & the command line
Face Recognition is the world's simplest face recognition library. It allows you to recognize and manipulate faces from Python or from the command line using dlib's (a C++ toolkit containing machine learning algorithms and tools) state-of-the-art face recognition built with deep learning. Face Recognition is highly accurate and is able to do a number of things. It can find faces in pictures, manipulate facial features in pictures, identify faces in pictures, and do face recognition on a... -
7
Intel neon
Intel® Nervana™ reference deep learning framework
neon is Intel's reference deep learning framework committed to best performance on all hardware. Designed for ease of use and extensibility. See the new features in our latest release. We want to highlight that neon v2.0.0+ has been optimized for much better performance on CPUs by enabling Intel Math Kernel Library (MKL). The DNN (Deep Neural Networks) component of MKL that is used by neon is provided free of charge and downloaded automatically as part of the neon installation. The gpu... -
8
Deepo
Set up deep learning environment in a single command line
Deepo is a series of Docker images that allows you to quickly set up your deep learning research environment, supports almost all commonly used deep learning frameworks, supports GPU acceleration (CUDA and cuDNN included), also works in CPU-only mode, and works on Linux (CPU version/GPU version), Windows (CPU version) and OS X (CPU version). Their Dockerfile generator that allows you to customize your own environment with Lego-like modules, and automatically resolves the dependencies for you. For users in China who may suffer from slow speeds when pulling the image from the public Docker registry, you can pull deepo images from the China registry mirror by specifying the full path, including the registry, in your docker pull command. ... -
9
Starling Filters
A collection of filters for use with the Starling AS3 framework
Starling-Filters is an open-source collection of filter effects for the Starling AS3 framework. These filters allow developers using Starling (a GPU accelerated 2D rendering framework in Flash/AIR) to apply image processing / visual effects (e.g. blur, glow, etc.) in their Starling-based applications. The repo has versions for Starling 2.0 (on master) and older filters archived for Starling 1.x. A collection of filters for use with the Starling AS3 framework. The master branch contains filters for use with Starling 2.0. -
99.99% Uptime for MySQL and PostgreSQL DatabasesCloud SQL Enterprise Plus delivers near-zero downtime with 35 days of point-in-time recovery. Supports MySQL, PostgreSQL, and SQL Server.
-
10
waifu2x-cpu-torch-vks
waifu2x fork without CUDA
The original is here: https://github.com/nagadomi/waifu2x Differences: - CPU only, no CUDA needed; - double-precision version; - GPU and CPU-trained models (on the fly conversion in memory); - adapted for Lua v5.2 (works with Torch 7 on x32 Ubuntu). 31-12-2016: - removed unneeded data copies, left from CUDA processing; - removed large duplicate files (see "ReadMe" before use). -
11
openAviToGif
Video to GIF converter
With openAviToGif you can convert video into an animated gif file. What's new in v0.6: -GPU processing -improvements and bugfixes Use your video card to speed up the conversion. Recommended for those who have high-end video cards. -
12
cphcttoolbox
Cph CT Toolbox is a selection of Computed Tomography tools
Copenhagen Computed Tomography Toolbox is a collection of applications and libraries for flexible and efficient CT reconstruction. The toolbox apps generally take a set of projections (X-ray intensity measurements) and filter and back project them in order to recreate the image or volume that the projections represent. The project includes both mostly informative CPU implementations and highly efficient GPU implementations. Regular releases are hosted at the Python Package Index. -
13
Loaders.css
Delightful, performance-focused pure css loading animations
...Animations are crafted to be smooth and performant, relying on GPU-friendly properties to keep frame rates high even on mobile devices. The set covers common loading metaphors (pulsing dots, rotating squares, bouncing bars), giving designers a consistent visual language across a site while keeping bundle size tiny. -
14
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. -
15
reg_stack
For registering stacks of 2D images.
reg_stack is an extension to the open source project, NiftyReg, for the coregistration of a series of 2D images representing a 3D block tissue subjected to histological serial sectioning. The software performs a series of nonlinear image transformations across a kernel of user-specified size to restore data continuity, as the sectioning process results in misalignments and distortions. The NiftyReg package also includes optimizations for parallelization on either the CPU or GPU. reg_stack leverages this feature and extends it to include support for running simultaneous instances of reg_f3d (NiftyReg) on multiple GPUs. -
16
Ezys
Ezys 3D medical image registration program
Ezys is a non-linear 3D medical image registration program. Ezys fully exploits the parallel computing power of inexpensive commercial graphics processing units (GPU), resulting in a very fast and accurate program capable of running on desktop PCs and even some laptops. On these systems, non-linear image registrations take less than a minute to complete. Ezys implements a diffeomorphic inverse consistent image registration algorithm with a demons-style regularization based on a non-parametric free form deformation model. ... -
17
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. -
18
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 -
19
birgHPCC
Rapid CUDA Cluster Deployment
...While retaining the major functions of the original birgHPC, including automated computing cluster conversion and auto slots detection, the new version (birgHPCC) is capable of creating and configuring a compute unified device architecture (CUDA) computing cluster, hence the extra “C” in the name. In addition to the increase in image size (less than 2 gigabytes) and a new Linux base (previously Debian, now Ubuntu), CUDA-capable bioinformatics software programs, such as NAMD, HOOMD-blue, VMD, GPU-HMMER and GPU-BLAST, are pre-installed in birgHPCC, along with the CUDA driver, libraries and software development kit (SDK). In short, birgHPCC is the world's first CUDA-ready, bioinformatics-based, live DVD. -
20
Pixma Frontpanel Library
Library for using Canon Pixma inkjet printer frontpanels
This library makes it possible to reuse the LCD/button frontpanels of Canon Pixma MP620 / MP630 inkjet printers in your own projects. The library makes use of the processing power and RAM that is built into the frontpanels. The library is written for maple / olimexino but might be expanded in the future. Please see the wiki for info on how the display works, the protocol used and how to connect your display to your board: http://sourceforge.net/p/pixmafrontpanel/wiki/Home/ -
21
pngacidbath is the one true PNG image brute force compressor. While other tools simply run down a list of "intelligent" compressor algorithms (some known as zlib methods), pngacidbath tries every byte combination to come up with THE smallest file.
-
22
Renderer for flam3 cosmic recursive fractal flames implemented on GPU. Requires a CUDA-capable graphics card.
-
23
GPUCL is a 100% GPU-based connected component labeling implementation which utilizes the OpenGL API. GPUCL computes bounding boxes of objects and also correctly labels individual pixels in the input image.
-
24
GPUCL is an OpenGL implementation of connected component labeling. All operations are performed fully on the GPU. Every pixel in the source image is correctly labeled with an ID and bounding boxes are computed for each detected blob.
-
25
A fast JPEG2000 encoder running on CUDA-capable GPUs. Supports lossless and lossy encoding. Both commandline tool with GUI and CUDA/C++ library. Visit >> http://cuj2k.sourceforge.net << for further information.
