Projects sharing features with React Native Vision Camera

Darknet is a high-performance C-based inference engine and computer vision library designed for real-time object identification and localization. It serves as a neural network framework for training and deploying detection models using the YOLO architecture, providing a toolset for deep learning training and deployment. The project differentiates itself through a C and CUDA implementation that enables hardware acceleration for matrix multiplication and inference speed optimization. It provides a shared library interface for embedding detection capabilities into external applications and suppo

GoCV is a computer vision library and Go language binding for OpenCV. It serves as an image processing toolkit and deep learning inference engine, providing programmatic access to a wide range of algorithms for image manipulation, object detection, and video analysis. The project differentiates itself through high-performance native bindings and hardware acceleration. It utilizes a foreign function interface to map Go calls to C++ functions and includes a hardware-agnostic backend dispatch to route neural network tasks to computation engines such as CUDA and OpenVINO. The library covers a br

RT-DETR is a real-time object detection model based on the detection transformer architecture. It is implemented as a computer vision model for both the PyTorch and PaddlePaddle deep learning platforms, designed to identify and locate multiple objects in images and video streams. The model eliminates the need for anchor generation and non-maximum suppression by utilizing a transformer-based approach. It focuses on high-performance detection, balancing precision and low latency for live environment deployment. The system employs a hybrid encoder and multi-scale feature fusion to extract globa

CameraView is a high-level Android camera library and hardware wrapper designed for capturing photos and videos. It provides an abstraction layer for managing camera hardware and a media capture API for recording high-resolution video and RAW photos with configurable bitrates and resolutions. The project features a real-time camera filter framework and a preview manager. These systems allow for the application of custom shaders and visual effects to live camera streams and the rendering of previews with customizable aspect ratios, overlays, and composition grids. The library covers a wide ra

YOLOv7 is a PyTorch vision library and real-time inference engine designed for object detection, human pose estimation, and instance segmentation. It provides a framework for detecting and locating multiple objects within images or video streams using neural networks. The system includes tools for custom model training and fine-tuning, allowing pre-trained weights to be adapted to specialized datasets via transfer learning. It also supports model weight export and format conversion to facilitate deployment on production servers and embedded edge devices.

This is an Android barcode scanning library designed to detect and decode barcode data from a live camera feed. It provides the core infrastructure for translating visual patterns into text on mobile devices. The library includes a camera preview manager that adjusts aspect ratios and feed sizes to fit various screen dimensions, as well as a hardware controller for managing flash, autofocus, and sensor selection. It also features a barcode format filter to restrict scanning to specific barcode types to increase detection speed and accuracy. The project covers camera hardware control, live ba

PaddleDetection is an object detection framework designed for the end-to-end development, training, and deployment of computer vision models. It provides a comprehensive library of modular neural network architectures and pipelines that support object detection, instance segmentation, and multi-object tracking tasks. The project distinguishes itself through a configuration-driven approach that decouples model components like backbones and heads, allowing for the flexible assembly of custom vision workflows. It incorporates advanced techniques such as anchor-free detection logic, joint detecti

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A collection of reference implementations and code samples for integrating Android camera hardware and software APIs. The project provides demonstrations for using both the Jetpack CameraX library and the low-level Camera2 API to implement photo and video capture features. The repository includes specialized implementations for high-performance recording, such as high-frame-rate slow motion and high-dynamic-range video. It also features examples of machine learning vision, demonstrating how to analyze live camera frames for object detection and QR code scanning. The project covers broad imag

html5-qrcode is a client-side JavaScript library that enables QR code and barcode scanning directly in a web browser, processing live video from a device camera or decoding codes from uploaded image files without any server-side involvement. The library handles real-time scanning from continuous camera feeds with adjustable frame rates and scanning regions, while also supporting file-based decoding for static images. The scanner offers configurable behavior through runtime settings, allowing developers to adjust scanning speed, viewfinder region, aspect ratio, and restrict decoding to specifi

CefSharp is a .NET binding for the Chromium Embedded Framework that allows developers to embed a full web browser into desktop applications. It provides an embedded browser control for rendering HTML, CSS, and JavaScript content within a native host window. The project features a bidirectional JavaScript bridge interface that enables the execution of scripts and the exposure of native host classes and methods to the browser environment. It also includes a headless browser automation tool for executing web tasks and capturing page screenshots without a graphical user interface. The library co

Ultralight is a GPU-accelerated HTML UI renderer and C++ framework designed for embedding web interfaces within native applications. It functions as a lightweight web browser and cross-platform UI framework that renders HTML, CSS, and JavaScript directly to CPU pixel buffers or GPU textures. The project distinguishes itself by allowing developers to emit raw geometry and draw calls via a custom GPU driver interface, eliminating intermediate CPU bitmaps. It provides deep integration between native logic and web environments through a C++ web interface library that binds native functions and ob

This repository is a comprehensive collection of reference implementations and sample libraries for the Universal Windows Platform. It provides practical examples of how to use Windows Runtime APIs to build cross-device applications, including detailed guidance on XAML-based declarative user interfaces and DirectX-integrated rendering. The project distinguishes itself by providing a wide array of hardware integration suites, covering low-level communication with USB, Serial, I2C, SPI, and GPIO peripherals. It includes specialized implementations for mixed reality holographic rendering, advanc

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

BGAQRCode-Android is a developer toolkit for Android that provides a library for scanning and recognizing QR codes and barcodes. It functions as a camera scanning framework and a barcode generator for creating one-dimensional and two-dimensional codes. The project includes a customizable UI kit for implementing branded scanning interfaces with adjustable dimensions, colors, and animation styles. It also provides tools for creating customized barcodes featuring specific color palettes and embedded logos. The toolkit manages camera hardware settings, including flashlight control, zoom levels,

This project is a collection of reference implementations and demonstration projects covering computer vision, DevOps automation, distributed systems, and Java-based microservices. It provides a programming reference library and practical examples for building server-side applications, containerizing services, and orchestrating clusters. The repository features a comprehensive toolset for DevOps automation, including scripts and playbooks for CI/CD pipelines and automated cluster installation. It includes a computer vision demo project for image object detection and facial analysis, as well a

Camerakit-android is a library and API wrapper that provides a consistent interface for photo and video capture across Android Camera 1 and 2 APIs. It functions as a media capture library that standardizes hardware access and provides a unified system for recording images and video across different operating system versions. The project includes a camera preview component that automatically scales and crops camera output to fit custom view dimensions. It also provides a camera control toolkit for managing continuous autofocus, tap-to-focus interactions, and pinch-to-zoom gestures. The toolki

ImageSharp is a .NET image processing library and manipulation framework used for decoding, encoding, and modifying digital images. It functions as a comprehensive toolkit for resizing, cropping, and applying pixel-level filters while managing color profiles and pixel data across various file formats. The project integrates a 2D vector graphics engine and a typography rendering engine to draw geometric shapes, paths, and complex stylized text onto images. It also includes a geometry boolean operation library for calculating intersections, unions, and differences between complex polygons and c

node-canvas is a server-side 2D vector graphics and image processing library for Node.js. It provides a server-side implementation of the HTML5 Canvas API using the Cairo graphics library as its rendering engine to draw shapes, text, and paths. The library enables the programmatic generation of dynamic images and the creation of scalable vector graphics and PDF documents. It supports the registration of custom and system fonts for typography rendering and allows for the import of image assets via local paths, buffers, data URIs, and remote URLs. Capabilities include low-level pixel manipulat

This project is a Java-based toolkit that integrates the OpenCV computer vision library into the Processing creative coding environment. It provides a programming interface designed to facilitate the inclusion of real-time image analysis and computer vision algorithms within interactive art installations and visual design projects. The library distinguishes itself by wrapping low-level C++ routines into a managed environment, allowing users to perform complex visual tasks through a simplified interface. It supports high-performance operations by sharing raw pixel data between the host environ

This project is a collection of optional, community-contributed algorithms and specialized vision tools that extend the core OpenCV framework. It serves as a comprehensive library of extra modules for computer vision research, providing advanced toolsets for image processing, visual data analysis, and object detection. The library includes specialized frameworks for augmented reality tracking, biometric face recognition, and three-dimensional pose estimation. It provides distinct capabilities for identifying AR markers, tracking 3D object silhouettes, and performing neural network vulnerabili

node-opencv is a high-performance C++ native addon and bridge that connects Node.js applications to the OpenCV library. It serves as an image processing toolkit and computer vision library, allowing JavaScript code to execute vision algorithms and image manipulation operations through native bindings. The project provides specialized capabilities for face and shape detection, as well as face identity recognition using trained models. It includes tools for object motion tracking through optical flow and background subtraction, along with the ability to identify specific patterns and analyze sh

This project is a PyTorch implementation of the YOLOv3 object detection architecture. It functions as a real-time object detector and computer vision framework designed to identify and locate multiple objects within images using bounding boxes and class labels. The system allows for both the use of pretrained weights for immediate image analysis and the training of custom models using datasets with bounding box annotations. It provides a programmatic interface to integrate detection capabilities directly into other software applications. The framework includes tools for model evaluation to m

This project is a Python wrapper for the OpenCV computer vision library, providing a bridge that exposes high-performance C++ functions to the Python programming language. It serves as a collection of tools for real-time image processing, object detection, and machine learning on visual data. The project provides precompiled binary distributions, allowing for the integration of vision capabilities into Python applications without requiring a local C++ compiler. It offers multi-variant package distributions, including headless versions designed for server or cloud environments where a graphica

JavaCV provides a Java-based interface for native computer vision and video processing libraries. It functions as a wrapper for native vision libraries, allowing Java applications to perform image analysis, object detection, and video stream processing. The project integrates comprehensive computer vision capabilities, including facial recognition, image segmentation, and optical flow analysis for motion tracking. It also provides tools for hardware geometry calibration and projector-camera alignment to ensure accurate spatial representation. The system covers high-performance media renderin

This project is a collection of neural network models and geometric tools designed for image feature matching, spatial alignment, and visual localization. It provides a pre-trained neural network model for identifying high-accuracy correspondences between sparse image features without requiring local training. The system utilizes a graph neural network matcher that employs attention mechanisms and message passing to learn spatial relationships between image feature points. It integrates a RANSAC camera pose estimator to filter feature matches and calculate the relative spatial transformation