Search Results for "x-windows"
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Showing 13 open source projects for "x-windows"
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SwiftShader
SwiftShader is a high-performance CPU-based implementation
...It acts as a drop-in replacement for Vulkan drivers, allowing existing applications to run seamlessly by redirecting API calls through its software-based rendering engine. The project supports Windows, Linux, macOS, Android, and ChromeOS, offering broad cross-platform compatibility. SwiftShader’s design emphasizes both performance and compliance, enabling robust software rendering for testing, virtualization, and cloud-based graphics workloads. -
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PhotoPrism
AI-Powered Photos App for the Decentralized Web 🌈💎✨
PhotoPrism® is an AI-Powered Photos App for the Decentralized Web. It makes use of the latest technologies to tag and find pictures automatically without getting in your way. You can run it at home, on a private server, or in the cloud. Our mission is to provide the most user- and privacy-friendly solution to keep your pictures organized and accessible. That's why PhotoPrism was built from the ground up to run wherever you need it, without compromising freedom, privacy, or functionality. -
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Milvus
Vector database for scalable similarity search and AI applications
Milvus is an open-source vector database built to power embedding similarity search and AI applications. Milvus makes unstructured data search more accessible, and provides a consistent user experience regardless of the deployment environment. Milvus 2.0 is a cloud-native vector database with storage and computation separated by design. All components in this refactored version of Milvus are stateless to enhance elasticity and flexibility. Average latency measured in milliseconds on trillion... -
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gowitness
Golang, web screenshot utility using Chrome Headless
A golang, web screenshot utility using Chrome Headless. gowitness is a website screenshot utility written in Golang, that uses Chrome Headless to generate screenshots of web interfaces using the command line, with a handy report viewer to process results. Both Linux and macOS is supported, with Windows support mostly working. Inspiration for gowitness comes from Eyewitness. If you are looking for something with lots of extra features, be sure to check it out along with these other projects. For installation information and other documentation, please refer to the wiki. The primary of purpose that gowitness serves is to take screenshots of websites and do that well. ... -
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imagor
Fast, secure image processing server and Go library, using libvips
Imagor is a fast, Docker-ready image processing server built in Go, ideal for on-the-fly image resizing, cropping, filtering, and optimization. It supports a wide variety of image formats and integrates seamlessly with cloud storage backends like AWS S3 and Google Cloud Storage. With support for HTTP and gRPC APIs, Imagor can be used in production environments to serve optimized images dynamically with high performance and low latency. -
6
bild
Image processing algorithms in pure Go
A collection of parallel image processing algorithms in pure Go. The aim of this project is simplicity in use and development over absolute high performance, but most algorithms are designed to be efficient and make use of parallelism when available. It uses packages from the standard library whenever possible to reduce dependency use and development abstractions. All operations return image types from the standard library. Package convolution provides the functionality to create and apply a... -
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Amazon EC2 Metadata Mock
A tool to simulate Amazon EC2 instance metadata
Instance metadata is data about your instance that you can use to configure or manage the running instance. Instance metadata is divided into categories, for example, hostname, events, and security groups. You can also use instance metadata to access user data that you specified when launching your instance. For example, you can specify parameters for configuring your instance, or include a simple script. You can build generic AMIs and use user data to modify the configuration files supplied... -
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Simple-PNG-2-ICON-Convert
Windows GUI desktop for convert PNG to ICON files
This is a simple Windows UI desktop program that does not require installation and runs locally. It currently supports PNG to ICON file conversion. Other image formats will be added later. -
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Simple-HEIC-Convert
Windows GUI desktop app for convert HEIC to PNG and JPEG
This is a windows x64 GUI reference program that can convert HEIC file formats to PNG or JPEG, and supports multi-task conversion. It is an offline free program. No need to install, just run it directly. -
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ascii-image-converter
A cross-platform command-line tool to convert images into ascii art
ascii-image-converter is a command-line tool that converts images into ASCII art and prints them out onto the console. Available on Windows, Linux, and macOS. -
11
GoCity
Code City metaphor for visualizing Go source code in 3D
GoCity is an implementation of the Code City metaphor for visualizing Go source code. The UI is built with React and uses babylon.js to plot 3D structures. The front-end source code is available in the front-end branch. The Number of Lines of Source Code (LOC) represents the build color (high values makes the building dark) The Number of Variables (NOV) correlates to the building's base size. The Number of methods (NOM) correlates to the building height. -
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Pixelizer
An efficient image pixelizer written in go
Pixelizer is a command-line tool written in Go that transforms images into pixelated versions. Designed for simplicity and efficiency, it allows users to apply pixelation effects with customizable parameters directly from the terminal. Pixelizer supports various output options, including animated GIFs and ANSI-rendered previews, making it a versatile tool for developers and artists working with pixel art. -
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Primitive Pictures
Reproducing images with geometric primitives
Primitive Pictures is an image processing command-line tool written in Go that reproduces images using geometric primitives (triangles, rectangles, ellipses, polygons, etc.). The core algorithm is iterative and “hill-climbing”: given a target image, it repeatedly finds the best single shape to add that will reduce the error between the current approximation and the target image, then draws that shape. Over time (e.g., adding 50-200 shapes) the output becomes an abstracted version of the...
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