Search Results for "idl image processing"
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Showing 28 open source projects for "idl image processing"
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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. -
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Benthos
Fancy stream processing made operationally mundane
Benthos is a high performance and resilient stream processor, able to connect various sources and sinks in a range of brokering patterns and perform hydration, enrichments, transformations and filters on payloads. It comes with a powerful mapping language, is easy to deploy and monitor, and ready to drop into your pipeline either as a static binary, docker image, or serverless function, making it cloud native as heck. Delivery guarantees can be a dodgy subject. Benthos processes and... -
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Posturr
A macOS app that blurs your screen when you slouch
...Running locally on the Mac, the app accesses the built-in camera to detect when you slouch or sit incorrectly, and when it recognizes sustained slouching, it applies a progressive visual blur to the screen as a subtle but effective cue to straighten up. This approach turns posture correction into an interactive feedback loop that doesn’t require wearables or external sensors, prioritizing privacy because all image processing happens on the device with no cloud transmission. It’s helpful for people who spend long hours working at a desk and want a gentle reminder to maintain ergonomic alignment without intrusive notifications. -
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KubeEdge
Kubernetes Native Edge Computing Framework (project under CNCF)
...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 recognition, event processing, and other high-level applications to the Edge. With business logic running at the Edge, much larger volumes of data can be secured & processed locally where the data is produced. With data processed at the Edge, the responsiveness is increased dramatically and data privacy is protected. -
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threeddonut
3D donut. Example of frojasg1.com libraries usage
The application shows a 3D donut, that can be rotated with two sliders in both axis. It is a simple example of what can be done with frojasg1.com platform libraries: - Zoom option for components - Multi language - Dark mode option - Automatic Undo-Redo for text components, with popup menu included - Text Search/Replace window prepared to be used. - Base components for auto-completion windows. - Automatic component relocation after redimensioning a... -
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IM is a toolkit for Digital Imaging. IM is based on 4 concepts: Image Representation, Storage, Processing and Capture. The main goal of the library is to provide a simple API and abstraction of images for scientific applications.
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libCIGI
C++ Library for the Common Image Generator Interface
libCIGI (library for Common Image Generator Interface) is a lightweight header only library that provides interfaces for communication between CIGI hosts and image generators. Currently versions 3.0, 3.2 and 3.3 are supported as well as support for the *draft* V4.0 CIGI standard. Additional functionality to the base packet interfaces is provided through external classes so that to the packet headers have no further dependencies. -
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MegEngine
Easy-to-use deep learning framework with 3 key features
MegEngine is a fast, scalable and easy-to-use deep learning framework with 3 key features. You can represent quantization/dynamic shape/image pre-processing and even derivation in one model. After training, just put everything into your model and inference it on any platform at ease. Speed and precision problems won't bother you anymore due to the same core inside. In training, GPU memory usage could go down to one-third at the cost of only one additional line, which enables the DTR algorithm. ... -
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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
...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. Please read getting_started for the basic usage of MMDeploy. -
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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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MetalPetal
A GPU accelerated image and video processing framework built on Metal
MetalPetal is an image processing framework based on Metal designed to provide real-time processing for still images and video with easy-to-use programming interfaces. This chapter covers the key concepts of MetalPetal, and will help you to get a better understanding of its design, implementation, performance implications, and best practices. A MTIImage object is a representation of an image to be processed or produced. -
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ImageProcessor
Wrapper around System.Drawing for the processing of image files
A .NET library for on-the-fly processing of images. ImageProcessor is a collection of lightweight libraries written in C# that allows you to manipulate images on-the-fly using .NET 4.5+. It consists of two main libraries ImageProcessor, for desktop and application use and ImageProcessor.Web, a dynamic image processing extension built for ASP.NET. It’s lighting fast, extensible, easy to use, comes bundled with some great features and is fully open source. -
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WePY
Framework that allows programs to support componentized development
...Through polyfill, the mini program perfectly supports Promise and solves callback troubles. Features such as Generator Function / Class / Async Function can be used to greatly improve development efficiency. Support a variety of plug-in processing, such as file compression, image compression, content replacement, etc., easy to expand and easy to use. -
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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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Accord.NET Framework
Machine learning, computer vision, statistics and computing for .NET
The Accord.NET Framework is a .NET machine learning framework combined with audio and image processing libraries completely written in C#. It is a complete framework for building production-grade computer vision, computer audition, signal processing and statistics applications even for commercial use. A comprehensive set of sample applications provide a fast start to get up and running quickly, and extensive documentation and a wiki help fill in the details. -
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Starling Filters
A collection of filters for use with the Starling AS3 framework
Starling-Filters is an open-source collection of filter effects for the Starling AS3 framework. These filters allow developers using Starling (a GPU accelerated 2D rendering framework in Flash/AIR) to apply image processing / visual effects (e.g. blur, glow, etc.) in their Starling-based applications. The repo has versions for Starling 2.0 (on master) and older filters archived for Starling 1.x. A collection of filters for use with the Starling AS3 framework. The master branch contains filters for use with Starling 2.0. -
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CognitionMaster
object-based image analysis framework
Wienert S, Heim D, Kotani M, Lindequist B, Stenzinger A, Ishii M, Hufnagl P, Beil M, Dietel M, Denkert C, Klauschen F. CognitionMaster: an object-based image analysis framework. Diagn Pathol 2013, 8:34 -
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Gallop
A framework for build smooth asynchronous iOS APP
...Easily draw text frame bouding, drawing the hollow words, sets the text vertical Alignment property, etc Easy to parse the expression in the text, like http(s) link, @ user, # theme #, phone number. Fast to sets the image corner radius property and blur processing, can be processed directly after the image. -
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MARF is a general cross-platform framework with a collection of algorithms for audio (voice, speech, and sound) and natural language text analysis and recognition along with sample applications (identification, NLP, etc.) of its use, implemented in Java.
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UPDATE March 14, 2018 Repository and tracking officially moved to github. https://github.com/opticks-org/opticks Sourceforge mailing lists are still in use. Opticks is an open source remote sensing application and development framework.
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GPUImage
iOS framework for GPU-based image and video processing
The GPUImage framework is a BSD-licensed iOS library that lets you apply GPU-accelerated filters and other effects to images, live camera video, and movies. In comparison to Core Image (part of iOS 5.0), GPUImage allows you to write your own custom filters, supports deployment to iOS 4.0, and has a slightly simpler interface. However, it currently lacks some of the more advanced features of Core Image, such as facial detection. GPUImage uses OpenGL ES 2.0 shaders to perform image and video... -
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HIPAcc
Heterogeneous Image Processing Acceleration (HIPACC) Framework
HIPAcc development has moved to github: https://github.com/hipacc HIPAcc allows to design image processing kernels and algorithms in a domain-specific language (DSL). From this high-level description, low-level target code for GPU accelerators is generated using source-to-source translation. As back ends, the framework supports CUDA, OpenCL, and Renderscript. HIPAcc allows programmers to develop imaging applications while providing high productivity, flexibility and portability as well as competitive performance: the same algorithm description serves as basis for targeting different GPU accelerators and low-level languages. -
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The Flexible Registration and Evaluation Engine (f.r.e.e.) allows the composition, evaluation and optimization of image processing/registration algorithms. It also aims to boost the exchangeability and comparability of data and algorithms in research.
