Search Results for "batch image processor"

...Datachain can persist features of Python objects returned by AI models, and enables vectorized analytical operations over them. The typical use cases are data curation, LLM analytics and validation, image segmentation, pose detection, and GenAI alignment. Datachain is especially helpful if batch operations can be optimized – for instance, when synchronous API calls can be parallelized or where an LLM API offers batch processing.

Scanopy is a powerful multi-modal data capture and analysis toolkit that enables users to collect, process, and visualize structured and unstructured information from a variety of sources in a flexible pipeline. It is built to handle complex scanning tasks — such as OCR, document analysis, audio transcription, network data capture, and image extraction — while providing unified APIs and workflows that make managing heterogeneous data sources seamless. Developers can compose custom pipelines...

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.

...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 processing workloads on Amazon SageMaker using the Apache Spark framework. The container images in this repository are used to build the pre-built container images that are used when running Spark jobs on Amazon SageMaker using the SageMaker Python SDK. The pre-built images are available in the Amazon Elastic Container Registry (Amazon ECR), and this repository serves as a reference for those wishing to build their own customized Spark containers for use in Amazon SageMaker.

Luminance HDR is a complete suite for HDR imaging workflow. It provides a wide range of functionalities, during both the fusion stage and the tonemapping stage. Its graphical user interface, based on Qt5, runs on a variety of platforms, such as Microsoft Windows, Mac OS X 10.9 and later and several Unix flavors (Linux, FreeBSD and others). Input images can be supplied in multiple formats, from JPEG to RAW files. In the same way, output can be saved in many different formats as well, from...

SIVIC is an open-source, standards-based software framework and application suite for processing and visualization of DICOM MR Spectroscopy data. Through the use of DICOM, SIVIC aims to facilitate the application of MRS in medical imaging studies.

Dicom Image Reader is opensource medical image viewer built with JavaScript, HTML5, NodeJS and Electron framework. It can load data in DICOM format (single image dcm) and provides standard tools for its manipulation such as contrast, zoom, drag, possibility to draw regions on top of the image and imaging filters such as threshold and sharpening.

JPIV is a platform independent, graphical stand-alone application for Particle Image Velocimetry (PIV) written in Java. PIV is an optical technique for measuring fluid flow velocities. JPIV moved to GitHub. Please visit us at: https://eguvep.github.io/jpiv/

CCDLAB is a FITS image data viewer, reducer, and UVIT Data Pipeline. The latest CCDLAB installer can be downloaded here: https://github.com/user29A/CCDLAB/releases The Visual Studio 2017 project files can be found here: https://github.com/user29A/CCDLAB/ Those may not be the latest code files as code is generally updated a few times a week. If you want the latest project files then let me know.

Processor and realtime viewer for the program VXFlow (http://www.morgenthal.org/vxflow/), an numerical flow field simulator, written by Prof. Dr. Guido Morgenthal (http://www.uni-weimar.de/Bauing/MSK/?lang=en). This project uses the gloost framework.