13 مستودعات
Tools and algorithms for isolating a foreground subject from its background to create transparency.
Distinct from Image Removal: None of the candidates provide a general image-based background removal tag that is not video-centric or PDF-centric
Explore 13 awesome GitHub repositories matching graphics & multimedia · Image Background Removal. Refine with filters or upvote what's useful.
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
Uses machine learning to isolate specific objects from a live camera feed by removing the surrounding background.
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
Separates the main object from its surroundings to create transparent cutouts.
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
Removes backgrounds from single image files using AI segmentation, supporting JPG, PNG, and HEIC formats.
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
Removes image backgrounds entirely in the browser using on-device AI, eliminating server costs and preserving privacy.
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
Isolates foreground subjects from high-resolution images and videos to remove the background.
A command line toolkit to generate maps, point clouds, 3D models and DEMs from drone, balloon or kite images. 📷
Filters out sky or other non-relevant areas from input photos before processing to improve reconstruction quality.
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
Separates foreground subjects from backgrounds using pre-trained neural network models in a visual pipeline.
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。
Performs all image editing operations locally in the browser without sending data to any server.
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
Extracts people from photos by generating a per-pixel alpha matte for transparency.
This project is a web-based graphic design editor and online poster designer. It provides a browser-based environment for creating professional visual layouts, e-commerce graphics, and social media covers using a canvas with drag-and-drop elements. The toolkit includes a specialized PSD template converter that parses Photoshop design files into editable web templates. It also features a custom QR code generator capable of producing styled codes with gradients and embedded logos, alongside a browser-based image manipulation tool for cropping assets and removing backgrounds. The editor covers
Features a background removal tool with cutout capabilities and manual brush repairs for image isolation.
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
Generates alpha mattes to isolate foreground subjects from backgrounds using only standard RGB input.
photo2cartoon هو أداة برمجية قائمة على الرؤية وإطار عمل تدريب مصمم لتحويل صور البورتريه البشرية الحقيقية إلى صور كرتونية منمقة. يستخدم شبكات الخصومة التوليدية (GANs) لترجمة الصور من مجال العالم الحقيقي إلى نمط كرتوني. يتضمن المشروع إطار عمل تدريب لهذه النماذج يدعم الإشراف بالبيانات المزدوجة والتدريب الموزع متعدد الـ GPU. ويستخدم وظائف خسارة تحافظ على الهوية لضمان أن المخرجات الكرتونية الناتجة تحتفظ بالسمات الوجهية الأصلية للموضوع. يدمج النظام خط أنابيب معالجة كامل يتعامل مع اكتشاف الوجه، ومحاذاة النقاط الرئيسية، وإزالة الخلفية لإعداد الصور الشخصية لنقل النمط.
Provides a pipeline to isolate the primary subject from the image background to improve cartoonization quality.
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
Extracts the foreground subject from an image and replaces the background with transparency, solid colors, blur effects, or custom background images.