Search Results for "image processing netbeans project"
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Showing 34 open source projects for "image processing netbeans project"
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Point Cloud Library
A standalone, large scale, open project for 2D/3D image processing
The Point Cloud Library (PCL) is a standalone, large scale, open project for 2D/3D image and point cloud processing. PCL is released under the terms of the BSD license, and thus free for commercial and research use. Whether you’ve just discovered PCL or you’re a long time veteran, this page contains links to a set of resources that will help consolidate your knowledge on PCL and 3D processing. An additional Wiki resource for developers is available too. ... -
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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. -
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WebP Codec
Library to encode and decode images in WebP format
libwebp is the reference codec library for Google’s WebP image format, providing both encoding and decoding along with command-line tools. It supplies cwebp to compress images into WebP and dwebp to decompress them back, making it easy to test quality/size trade-offs across presets and tuning parameters. The GitHub repository is a mirror; the canonical source of truth lives on Chromium’s git, and developer docs are hosted on WebP’s portal. The project underpins WebP support across browsers,... -
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ComfyUI SUPIR
SUPIR upscaling wrapper for ComfyUI
...It is particularly useful for workflows that require upscaling or restoring images before further processing. The project leverages modern generative models to produce sharp, detailed outputs while preserving the original structure of the image. It can be combined with other ComfyUI nodes for tasks such as stylization or animation. The system is designed to balance quality and performance, making it suitable for both experimentation and production use. -
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ComfyUI Essentials
Essential nodes that are weirdly missing from ComfyUI core
ComfyUI_essentials is a ComfyUI custom node collection that adds practical nodes the author considers missing from the ComfyUI core. The project focuses on useful workflow building blocks rather than generic duplicates, with nodes for image handling, mask processing, sampling, segmentation, conditioning, text, and miscellaneous operations. Its image tools include functions for batching, cropping, flipping, resizing, compositing, background removal, color matching, LUT application, sharpening, tiling, and latent previewing. ... -
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Practical-RIFE
More practical frame interpolation approach
...Users can increase frame rates by two or four times through Python inference scripts. Configurable scaling options help process high-resolution footage while managing GPU memory requirements. Additional scripts support image sequence processing, comparison previews, and experimental video enhancement using SAFA. Lightweight model variants reduce computational demands. It is primarily intended for developers and engineers integrating frame interpolation into video processing workflows. -
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SageMaker Spark Container
Docker image used to run data processing workloads
Apache Spark™ is a unified analytics engine for large-scale data processing. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing. The SageMaker Spark Container is a Docker image used to run batch data... -
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ComfyUI Examples
Examples of ComfyUI workflows
ComfyUI_examples is the companion repository for ComfyUI that collects ready-made example workflows, nodes, and compositions to help users learn the node-based interface for AI image generation. Instead of starting from an empty graph, you can open an example and see how prompts, samplers, models, and image processing steps are wired together. This makes ComfyUI more approachable for people coming from “one text box” generators, because they can reverse-engineer complex pipelines visually.... -
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The Algorithms Python
All Algorithms implemented in Python
The Algorithms-Python project is a comprehensive collection of Python implementations for a wide range of algorithms and data structures. It serves primarily as an educational resource for learners and developers who want to understand how algorithms work under the hood. Each implementation is designed with clarity in mind, favoring readability and comprehension over performance optimization. The project covers various domains including mathematics, cryptography, machine learning, sorting,... -
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wx-cli
WeChat local data CLI with daemon architecture
...The tool can inspect conversations, chat history, unread messages, contacts, group members, group nicknames, favorites, statistics, exports, SNS feed items, public account articles, and image attachments. It is designed to be AI-agent friendly, with YAML output by default and optional JSON output for automation or downstream processing. The project keeps data local, decrypts in real time, and avoids full pre-decryption workflows. It is useful for users who need searchable, scriptable access to their own WeChat records while preserving local control over the data. -
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KubeEdge
Kubernetes Native Edge Computing Framework (project under CNCF)
KubeEdge is built upon Kubernetes and extends native containerized application orchestration and device management to hosts at the Edge. It consists of a cloud part and an edge part, and provides core infrastructure support for networking, application deployment, and metadata synchronization between the cloud and edge. It also supports MQTT which enables edge devices to access through edge nodes. With KubeEdge it is easy to get and deploy existing complicated machine learning, image... -
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Flutter Rust Bridge
Rust binding generator, feature-rich, but seamless and simple
flutter_rust_bridge is a toolchain that generates ergonomic bindings between Rust and Dart/Flutter so you can write performance-critical logic in Rust while building UI in Flutter. It abstracts away the low-level FFI details, letting developers define Rust functions and data structures that are safely callable from Dart. The project supports passing complex types, handling async operations and streams, and integrating with Flutter across mobile and desktop targets. By leaning on Rust’s memory safety and zero-cost abstractions, it enables compute-heavy tasks—parsing, crypto, image/audio processing, and more—without sacrificing Flutter’s developer experience. ... -
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GPUImage 3
GPUImage 3 is a BSD-licensed Swift framework for GPU-accelerated video
GPUImage3 is a Swift framework for GPU-accelerated image and video processing on macOS and iOS. It is the third generation of the GPUImage project and replaces the earlier OpenGL-based approach with Apple’s Metal framework. The library aims to make real-time video processing and machine vision easier by hiding much of the GPU rendering boilerplate. It supports image and video sources, custom shader-based processing, and filter pipelines. ... -
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MMDeploy
OpenMMLab Model Deployment Framework
MMDeploy is an open-source deep learning model deployment toolset. It is a part of the OpenMMLab project. Models can be exported and run in several backends, and more will be compatible. All kinds of modules in the SDK can be extended, such as Transform for image processing, Net for Neural Network inference, Module for postprocessing and so on. Install and build your target backend. ONNX Runtime is a cross-platform inference and training accelerator compatible with many popular ML/DNN frameworks. ... -
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Emgu CV
Emgu CV is a cross platform .Net wrapper for OpenCV
Emgu CV is a cross platform .Net wrapper to the OpenCV image processing library. Allowing OpenCV functions to be called from .NET compatible languages such as C#, VB, VC++, IronPython etc. The wrapper can be compiled in Mono and run on Windows, Linux, Mac OS X, iPhone, iPad and Android devices. -
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Real-ESRGAN
Real-ESRGAN aims at developing Practical Algorithms
Real-ESRGAN is a highly popular open-source project that provides practical algorithms for general image and video restoration using deep learning-based super-resolution techniques. It extends the original Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) approach by training on synthetic degradations to make results more robust on real-world images, effectively enhancing resolution, reducing noise/artifacts, and reconstructing fine detail in low-quality imagery. The... -
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Unet
Source code for unet-pytorch, which can train its own model
...The repository is built around training, prediction, and mIoU evaluation for VOC-style segmentation data and medical-style datasets. It includes scripts for general training, medical dataset training, prediction, annotation handling, model summaries, and evaluation. The project supports multiple backbones, data processing utilities, extensive comments, and adjustable training parameters. Its README notes that U-Net is better suited to datasets with fewer features and shallow visual structures, such as medical image segmentation, rather than complex VOC-style scenes. It is useful for developers and students who want a clear U-Net implementation for segmentation experiments, custom masks, and biomedical-style image analysis. -
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blind-watermark
Blind&Invisible Watermark, image blind watermark, extract watermark
...Multiprocessing can use several CPU cores during processing. The project is intended for copyright marking, traceability, and image steganography experiments. -
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Paperless-ng
A supercharged version of paperless, scan, index and archive docs
Paperless is a simple Django application running in two parts, a Consumer (the thing that does the indexing) and a Web server (the part that lets you search & download already-indexed documents). Paper is a nightmare. Environmental issues aside, there’s no excuse for it in the 21st century. It takes up space, collects dust, doesn’t support any form of a search feature, indexing is tedious, it’s heavy and prone to damage & loss. I wrote this to make “going paperless” easier. I do not have to... -
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GPUImage 2
Framework for GPU-accelerated video and image processing
GPUImage 2 is the second generation of the GPUImage framework, an open source project for performing GPU-accelerated image and video processing on Mac, iOS, and now Linux. The original GPUImage framework was written in Objective-C and targeted Mac and iOS, but this latest version is written entirely in Swift and can also target Linux and future platforms that support Swift code. The objective of the framework is to make it as easy as possible to set up and perform realtime video processing or machine vision against image or video sources. ... -
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iText®, a JAVA PDF library
PDF Library for Developers
iText is an open-source PDF library available for Java and .NET (C#). iText allows you to effortlessly generate and manipulate standards-compliant PDF documents with a powerful and feature-rich SDK. With iText, you can create archivable and accessible PDFs, split and merge documents, fill and flatten forms, digitally sign documents, and more. iText add-ons enable additional functionality, such as PDF creation from HTML templates, secure redaction, OCR, and much more. The latest... -
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Accord.NET Framework
Machine learning, computer vision, statistics and computing for .NET
The Accord.NET project provides machine learning, statistics, artificial intelligence, computer vision and image processing methods to .NET. It can be used on Microsoft Windows, Xamarin, Unity3D, Windows Store applications, Linux or mobile. After merging with the AForge.NET project, the framework now offers a unified API for learning/training machine learning models that is both easy to use and extensible. -
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fast-neural-style
Feedforward style transfer
The fast-neural-style project is an implementation of neural style transfer techniques optimized for real-time image processing. It uses convolutional neural networks to apply artistic styles to images, enabling users to transform photos into stylized outputs inspired by famous artworks. Unlike earlier approaches that required expensive optimization per image, this project leverages feed-forward networks to achieve fast inference, making style transfer practical for real-world applications. ... -
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The Camellia library is an open source and cross-platform image processing library, written in plain C. It includes a lot of speed-optimized imaging functions (filtering, morpho, labeling, motion detection, warping, color conversion, project/backproject)
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CSSgram
CSS library for Instagram filters
...What we're doing is adding filters to the images, as well as applying color and/or gradient overlays via various blending techniques to mimic filter effects. This means less manual image processing and more fun filter effects on the web! We are using pseudo-elements (i.e. :before and :after) to create the filter effects, so you must apply these filters on a containing element (i.e. not a content-block like <img>. The recommendation is to wrap your images in a <figure> tag. If you use custom naming in your CSS architecture, you can add the .scss files for the provided styles within your project and then @extend the filter effects within your style definitions. ...
