What does bradlarson/gpuimage2 do?

GPUImage2 هو إطار عمل Swift لتطبيق المرشحات والتأثيرات في الوقت الفعلي على الصور والفيديو باستخدام GPU. يوفر مكتبة مرشحات فيديو في الوقت الفعلي، ومحرك معالجة هندسة الصور، وخط أنابيب تظليل OpenGL لمعالجة البيانات المرئية على أجهزة الرسومات.

What are the main features of bradlarson/gpuimage2?

The main features of bradlarson/gpuimage2 are: Real-Time Video Filtering, Real-Time Image Processing, Custom Shader Programming, GPU-Accelerated Image Processing Frameworks, GPU Accelerated Rendering, Vertex and Fragment Shading, Camera Pipeline Effects, Shader-Based Image Rendering.

What are some open-source alternatives to bradlarson/gpuimage2?

Open-source alternatives to bradlarson/gpuimage2 include: bradlarson/gpuimage — GPUImage is a GPU-accelerated image processing framework for iOS designed to apply real-time filters and effects to… pistondevelopers/image — This project is an image processing library designed for manipulating pixel data, applying visual filters, and… anthonynsimon/bild — Bild is an image processing library implemented in the Go programming language. It provides a collection of… openmoonray/openmoonray — OpenMoonray is a production-grade physically based rendering system and path-tracing engine. It simulates the physical… imazen/imageflow — Imageflow is a high-performance image manipulation library and composition engine available as a C-compatible library,… jimp-dev/jimp — Jimp is a zero-dependency JavaScript image processing library and programmatic editor designed for manipulating,…

GPUImage2 - تطبيق فلاتر صور GPU في الوقت الفع…

GPUImage2 - تطبيق فلاتر صور GPU في الوقت الفع… | Awesome Repos

Features

Real-Time Video Filtering - Applies complex visual filters and effects to live video streams using GPU acceleration for real-time performance.
Real-Time Image Processing - Executes real-time image and video processing tasks on the GPU to increase performance over CPU code.
Custom Shader Programming - Enables the authoring of custom fragment and vertex shader code to implement bespoke image processing.
GPU-Accelerated Image Processing Frameworks - Provides a Swift framework for applying real-time filters and effects to images and video using the GPU.
GPU Accelerated Rendering - Utilizes graphics hardware to accelerate pixel manipulation and rendering for real-time video processing speeds.
Vertex and Fragment Shading - Implements a system for building custom fragment and vertex shaders to process visual data on graphics hardware.
Camera Pipeline Effects - Chains multiple image processing operations into a sequence of transformations before outputting the result.
Shader-Based Image Rendering - Uses custom fragment and vertex shaders to perform visual manipulations directly on the graphics hardware.
Custom Image Filters - Allows the creation of bespoke visual effects by writing shaders to manipulate pixels and colors on the GPU.
Image Processing Pipelines - Enables the construction of sequential pipelines by chaining image sources to consumers for real-time processing.
Image Visual Filters - Implements a wide array of artistic visual effects including pixellation, halftone, and various image distortions.
GPU-Accelerated Processors - Performs heavy image transformations and geometry adjustments on the GPU to maintain high performance.
Pipeline Chaining Frameworks - Implements a system for chaining multiple image processing steps into sequential, reusable pipelines.
Filter Operation Groupings - Bundles individual filters into reusable units to simplify the management of complex processing sequences.
Image Geometric Transformations - Executes transformations, cropping, resampling, and blurring operations to alter the physical geometry of images.
Image Layer Compositing - Blends multiple image sources together using alpha channels and chroma keying to create final composite visuals.
Image Geometry Toolkits - Provides a toolset for performing cropping, resampling, and transformations on image data via graphics acceleration.
Image Blending - Combines multiple image layers using mask-based transparency and blending logic to create composite frames.
Operation Groupings - Bundles a series of individual processing operations into a single unit to simplify pipeline management and reuse across projects.
Color Adjustments - Modifies brightness, contrast, saturation, and hue using built-in filters and color matrices.
Computer Vision and Processing - High-performance framework for real-time image and video filtering.
Image Processing - GPU-accelerated image and video processing.

بدائل مفتوحة المصدر لـ GPUImage2

مشاريع مفتوحة المصدر مشابهة، مرتبة حسب عدد الميزات المشتركة مع GPUImage2.

bradlarson/gpuimage
BradLarson/GPUImage
20,299عرض على GitHub
GPUImage is a GPU-accelerated image processing framework for iOS designed to apply real-time filters and effects to images and video. It functions as a processing engine and fragment shader library that manages textures and shaders for efficient visual data manipulation. The framework utilizes a chainable filter architecture and a texture-based data pipeline to pass image data between processing stages without expensive memory transfers. It enables the creation of bespoke visual effects through the authoring of custom fragment shaders and provides mechanisms to synchronize texture data with e
Objective-C
عرض على GitHub20,299
pistondevelopers/image
PistonDevelopers/image
5,805عرض على GitHub
This project is an image processing library designed for manipulating pixel data, applying visual filters, and adjusting image appearance. It functions as a low-level tool for processing raw pixel buffers and managing image data. The library provides a toolkit for image geometry manipulation, including resizing, cropping, flipping, and rotating images. It also serves as an image format converter for encoding and decoding data between common binary file formats and as a metadata extractor for retrieving dimensions and color profiles from file headers. The capabilities cover low-level image pr
Rust
عرض على GitHub5,805
anthonynsimon/bild
anthonynsimon/bild
4,192عرض على GitHub
Bild is an image processing library implemented in the Go programming language. It provides a collection of algorithmic engines for image manipulation, including a convolution kernel engine for filtering, an image blending tool for layer composition, and a procedural noise generator for creating synthetic textures. The project is distinguished by its procedural generation capabilities, implementing Perlin, Gaussian, binary, and uniform noise algorithms to produce random pixel distributions and organic patterns. It also features a command-line interface that allows users to apply visual effect
Go
عرض على GitHub4,192
openmoonray/openmoonray
OpenMoonRay/openmoonray
4,693عرض على GitHub
OpenMoonray is a production-grade physically based rendering system and path-tracing engine. It simulates the physical behavior of light to generate high-fidelity photorealistic images and is compatible with Universal Scene Description files for visualizing complex 3D scenes and production pipelines. The system is designed as a distributed rendering framework, capable of splitting heavy computational workloads across multiple machines or cloud clusters to accelerate image generation. It integrates OpenColorIO for consistent color management across different display devices and color spaces.
CMake
عرض على GitHub4,693

عرض جميع البدائل الـ 30 لـ GPUImage2

BradLarsonGPUImage2

GPUImage2

Features

بدائل مفتوحة المصدر لـ GPUImage2

BradLarson/GPUImage

PistonDevelopers/image

anthonynsimon/bild

OpenMoonRay/openmoonray

Frequently asked questions

سجل النجوم

بدائل مفتوحة المصدر لـ GPUImage2

BradLarson/GPUImage

PistonDevelopers/image

anthonynsimon/bild

OpenMoonRay/openmoonray

Frequently asked questions