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6 مستودعات

Awesome GitHub RepositoriesProcessing APIs

Web-based interfaces and endpoints for performing automated image transformations and analysis.

Distinct from Image Processing: Distinct from Image Processing: focuses on the HTTP API layer for remote service integration rather than the underlying pixel-level transformation logic.

Explore 6 awesome GitHub repositories matching graphics & multimedia · Processing APIs. Refine with filters or upvote what's useful.

Awesome Processing APIs GitHub Repositories

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  • danielgatis/rembgالصورة الرمزية لـ danielgatis

    danielgatis/rembg

    21,911عرض على GitHub↗

    Rembg is a machine learning-based toolkit designed for automated image background removal and subject segmentation. It functions as a versatile engine that identifies and extracts subjects from images, supporting diverse input methods including individual files, directory-based batch processing, and live binary data streams. The project distinguishes itself through its flexible integration options, offering a command-line interface for local automation, a library for programmatic access, and an HTTP service for remote requests. It utilizes deep learning architectures to classify pixels and ge

    Exposes an HTTP API endpoint for integrating background removal into external applications.

    Pythonbackground-removalimage-processingpython
    عرض على GitHub↗21,911
  • imgly/background-removal-jsالصورة الرمزية لـ imgly

    imgly/background-removal-js

    7,192عرض على GitHub↗

    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

    Drives all editing operations programmatically, enabling batch processing and automated creative workflows on the server.

    TypeScriptbackground-removalimage-mattingimage-segmentation
    عرض على GitHub↗7,192
  • vladmandic/sdnextالصورة الرمزية لـ vladmandic

    vladmandic/sdnext

    7,139عرض على GitHub↗

    SD.Next is an all-in-one web interface and multi-backend inference engine for generating, editing, and processing images and videos using diffusion models. It functions as a comprehensive tool for diffusion model management and an automated image processing pipeline for bulk operations. The project is distinguished by its hardware-backend abstraction layer, which provides automatic detection and acceleration for NVIDIA CUDA, AMD ROCm, Intel OpenVINO, and DirectML. It features a headless generative API and a programmatic command interface, allowing users to trigger tasks via REST API or CLI wi

    Offers a headless REST API to trigger image and video generation without a graphical interface.

    Pythonai-artcaptiondiffusers
    عرض على GitHub↗7,139
  • opendronemap/opendronemapالصورة الرمزية لـ OpenDroneMap

    OpenDroneMap/OpenDroneMap

    6,196عرض على GitHub↗

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

    Exposes the processing pipeline over a network so remote programs can submit and manage tasks.

    Python
    عرض على GitHub↗6,196
  • h2non/imaginaryالصورة الرمزية لـ h2non

    h2non/imaginary

    6,017عرض على GitHub↗

    Imaginary is a self-hosted HTTP server for image processing that applies transformations like resizing, cropping, rotating, and format conversion through URL parameters. It operates as a stateless request-response pipeline, processing images fetched from remote URLs or served from a local directory without requiring client-side dependencies. The server distinguishes itself through its security and access control capabilities, offering optional API key validation, HMAC-signed URL verification, and remote origin whitelisting to restrict which image sources are permitted. It also provides a heal

    Exposes image transformation operations like resize, crop, rotate, and watermark through a simple HTTP API.

    Gocrop-imagedockergif
    عرض على GitHub↗6,017
  • philz1337x/clarity-upscalerالصورة الرمزية لـ philz1337x

    philz1337x/clarity-upscaler

    5,079عرض على GitHub↗

    Clarity-upscaler هو أداة ذكاء اصطناعي لتحسين دقة الصور (Upscaler) تستخدم نماذج التعلم العميق لزيادة دقة الصور واستعادة التفاصيل البصرية. يعمل كمحرك استنتاج فائق الدقة (Super-resolution) يستخدم الشبكات العصبية للتنبؤ بالبكسلات المفقودة وتوليد تفاصيل عالية التردد من مصادر منخفضة الدقة. يتم تقديم المشروع كواجهة برمجة تطبيقات (API) قابلة للبرمجة، مما يسمح بدمج معالجة الصور عالية الدقة المؤتمتة وتحسين حدتها في التطبيقات وسير العمل الخارجية. تتيح هذه الواجهة رفع دقة الصور برمجياً لإنشاء أصول عالية الجودة. يوفر النظام إمكانيات لتحسين الصور تلقائياً، وإزالة الضوضاء، وتعزيز الوضوح لإنتاج نسخ أكثر حدة من الصور منخفضة الجودة. يعالج النظام هذه المهام من خلال بنية مفصولة بين العميل والخادم (Client-Server) لإدارة طبيعة استنتاج التعلم الآلي المكثفة حوسبياً.

    Provides web-based endpoints for performing automated image transformations and high-resolution upscaling.

    Pythonaiai-artimage-upscale
    عرض على GitHub↗5,079
  1. Home
  2. Graphics & Multimedia
  3. Image Processing & Editing
  4. Image Processing
  5. Processing APIs

استكشف الوسوم الفرعية

  • Headless APIsAn API that drives all editing operations programmatically, enabling batch processing and automated creative workflows on the server. **Distinct from Processing APIs:** Distinct from Processing APIs: specifies headless (no GUI) programmatic control for batch and automated workflows.
  • Pipeline Submission APIsWeb endpoints for remotely submitting and managing multi-stage processing pipeline tasks. **Distinct from Processing APIs:** Distinct from Processing APIs: focuses on submitting and managing multi-stage pipeline tasks rather than single image transformations.