Awesome GitHub RepositoriesImage Background Removal

ar-cutpaste is an augmented reality asset extraction tool and prototype designed to isolate objects from a live camera feed and transfer them into image editing software. It functions as a mobile-to-desktop bridge that uses machine learning to remove backgrounds from live images, creating digital cutouts for use in image composition. The system establishes a local server connection to transmit image data and spatial coordinates from a mobile device to a design application. This bridge uses a remote socket mechanism and a secure password to inject captured assets directly into a desktop worksp

U-2-Net is a PyTorch image segmentation framework and computer vision saliency model designed to generate high-resolution foreground-background masks. It functions as an AI background removal tool that identifies and isolates the most visually prominent objects within an image. The model utilizes a nested U-structure design to detect salient objects, creating precise cutouts by predicting saliency maps. These capabilities enable the separation of main subjects from their surroundings to create transparent images. The framework covers several image processing workflows, including automatic ba

Backgroundremover is an AI-powered tool that removes backgrounds from both images and videos, accessible through a command-line interface and a Python API. At its core, it uses a pre-trained deep learning model to classify each pixel as foreground or background, producing a binary mask for removal. The tool distinguishes itself through multiple integration methods and output capabilities. It can process images and videos via Unix pipeline data streams, operate as an HTTP API server, or be called programmatically within Python scripts. Users can choose among different AI models to balance proc

Background Removal JS is a client-side neural matting library that runs a lightweight AI model directly in the browser to compute alpha mattes, removing image backgrounds without uploading any data to a server. It functions as a browser-based image background removal SDK and client-side image matting library, keeping all processing on the user's device to eliminate server costs and preserve privacy. The project provides a cross-platform creative editing engine that produces identical image and video output across web, mobile, desktop, and server environments from a single codebase. It offers

BackgroundMattingV2 is a deep learning background matting tool and real-time image segmentation framework. It provides a system for isolating foreground subjects from high-resolution images and video feeds in real time. The project includes a deep learning model trainer for optimizing matting models through base convergence and end-to-end refinement. It also functions as a cross-runtime model exporter, converting trained neural networks into interchangeable formats for deployment across different software environments and hardware runtimes. The framework supports streaming processed webcam f

A command line toolkit to generate maps, point clouds, 3D models and DEMs from drone, balloon or kite images. 📷

chaiNNer is a GPU-accelerated AI image upscaling application that uses a visual node-based interface for constructing image processing pipelines. At its core, it provides a node-based visual programming environment where users connect processing nodes in a directed acyclic graph, with a graph execution scheduler that traverses the pipeline in topological order. The application includes an iterator-based batch processing system that automatically applies the same pipeline to multiple files, and a model format conversion pipeline that transforms neural network models between PyTorch, ONNX, and N

A free and open-source inpainting & image-upscaling tool powered by webgpu and wasm on the browser。| 基于 Webgpu 技术和 wasm 技术的免费开源 inpainting & image-upscaling 工具, 纯浏览器端实现。

This project is a deep learning image matting framework and computer vision tool designed to separate people from their backgrounds. It functions as a real-time video matting engine and a trainable foreground isolation model that generates per-pixel alpha mattes to isolate subjects from photos and videos. The system utilizes reference-based alpha matting, incorporating a specific background image to simulate green screen effects without a physical screen. This approach allows for the removal and replacement of backgrounds in high-resolution footage, including live video streams. The framewor

Dieses Projekt ist ein webbasierter Grafikdesign-Editor und Online-Poster-Designer. Es bietet eine browserbasierte Umgebung für die Erstellung professioneller visueller Layouts, E-Commerce-Grafiken und Social-Media-Cover unter Verwendung einer Arbeitsfläche mit Drag-and-Drop-Elementen. Das Toolkit enthält einen spezialisierten PSD-Template-Konverter, der Photoshop-Designdateien in editierbare Web-Templates parst. Es bietet zudem einen benutzerdefinierten QR-Code-Generator, der stilisierte Codes mit Verläufen und eingebetteten Logos erstellen kann, sowie ein browserbasiertes Bildbearbeitungstool zum Zuschneiden von Assets und Entfernen von Hintergründen. Der Editor deckt breite Grafikdesign-Funktionen ab, einschließlich Artboard- und Ebenenverwaltung, Typografie-Formatierung mit CSS-Effekten sowie Unterstützung bei der räumlichen Ausrichtung durch Fanglinien und Lineale. Eine High-Fidelity-Ausgabe wird durch einen hybriden Frontend- und serverseitigen Rendering-Prozess erreicht, um eine präzise Bildwiedergabe zu gewährleisten. Das System unterstützt die Verwaltung von Benutzervorlagen zum Speichern und Aktualisieren eigener Design-Layouts.

MODNet is a deep learning image segmenter and portrait matting model designed to isolate human subjects from backgrounds. It generates high-quality alpha mattes for images and video using only standard RGB input, removing the requirement for manual trimap guides. The framework is optimized for real-time inference and provides utilities to export pre-trained model weights into specialized formats for deployment on target hardware. The project covers the full workflow for portrait isolation, including supervised matting model training on labeled datasets, real-time video background removal, an

photo2cartoon ist ein vision-basiertes Software-Tool und Trainings-Framework, das darauf ausgelegt ist, echte menschliche Porträtfotos in stilisierte Cartoon-Bilder umzuwandeln. Es nutzt generative gegnerische Netzwerke (GANs), um Bilder aus einer realen Domäne in einen Cartoon-Stil zu übersetzen. Das Projekt enthält ein Trainings-Framework für diese Modelle, das überwachtes Lernen mit gepaarten Daten und verteiltes Multi-GPU-Training unterstützt. Es setzt identitätserhaltende Loss-Funktionen ein, um sicherzustellen, dass die resultierenden Cartoon-Ausgaben die ursprünglichen Gesichtszüge des Subjekts beibehalten. Das System integriert eine vollständige Vorverarbeitungs-Pipeline, die Gesichtserkennung, Keypoint-Ausrichtung und Hintergrundentfernung handhabt, um Porträts für den Style-Transfer vorzubereiten.

This software is a computer vision utility designed for automated subject isolation and background removal. It provides a graphical desktop interface that allows users to extract foreground subjects from static images, video files, and live webcam streams without requiring command-line interaction. The application leverages deep learning models to generate high-fidelity alpha masks, enabling the creation of transparent backgrounds or the application of custom replacements. By utilizing hardware-accelerated tensor processing, the system performs real-time segmentation on live camera feeds and