Open Source JavaScript Software Development Software - Page 3

The OpenHealhPractice Simulation Environment is a web framework intended to concurrently manage and launch generalized models. Models can be uploaded by the users and also can be connected to other models in order to simulate them in a row. These simulations are stored and can be reviewed or deleted at anytime. The Graphical Visualization Environment (GVE) provides the graphical visualization of the simulation results. Results can also be exported to different formats (xls, txt or csv). This simulation environment is built using Spring Framework and Java technologies. For more information on how to deploy and use the tool, check the installation and user manuals. This tool was developed within the Synergy project. You can view our publication about the Simulation Environment here: http://www.translational-medicine.com/content/12/S2/S7 The rest of the publication outcomes of the project can be found here: http://www.translational-medicine.com/supplements/12/S2

Pipeless is an open-source computer vision framework to create and deploy applications without the complexity of building and maintaining multimedia pipelines. It ships everything you need to create and deploy efficient computer vision applications that work in real-time in just minutes. Pipeless is inspired by modern serverless technologies. It provides the development experience of serverless frameworks applied to computer vision. You provide some functions that are executed for new video frames and Pipeless takes care of everything else. You can easily use industry-standard models, such as YOLO, or load your custom model in one of the supported inference runtimes. Pipeless ships some of the most popular inference runtimes, such as the ONNX Runtime, allowing you to run inference with high performance on CPU or GPU out-of-the-box. You can deploy your Pipeless application with a single command to edge and IoT devices or the cloud.

The smallest, simplest and fastest JavaScript pixel-level image comparison library, originally created to compare screenshots in tests. Features accurate anti-aliased pixels detection and perceptual color difference metrics. Inspired by Resemble.js and Blink-diff. Unlike these libraries, pixelmatch is around 150 lines of code, has no dependencies, and works on raw typed arrays of image data, so it's blazing fast and can be used in any environment (Node or browsers). Compares two images, writes the output diff and returns the number of mismatched pixels.

Bots are special Telegram accounts designed to handle messages automatically. Users can interact with bots by sending them command messages in private or group chats. These accounts serve as an interface for code running somewhere on your server. Telegraf is a library that makes it simple for you to develop your own Telegram bots using JavaScript or TypeScript. You can see in every example is a Context instance. Telegraf creates one for each incoming update and passes it to your middleware. It contains the update, botInfo, and telegram for making arbitrary Bot API requests, as well as shorthand methods and getters. Telegraf is written in TypeScript and therefore ships with declaration files for the entire library. Moreover, it includes types for the complete Telegram API via the typegram package. While most types of Telegraf's API surface are self-explanatory, there's some notable things to keep in mind.

The tracking.js library brings different computer vision algorithms and techniques into the browser environment. By using modern HTML5 specifications, we enable you to do real-time color tracking, face detection and much more, all that with a lightweight core (~7 KB) and intuitive interface. To get started, download the project. This project includes all of the tracking.js examples, source code dependencies you'll need to get started. Unzip the project somewhere on your local drive. The package includes an initial version of the project you'll be working with. While you're working, you'll need a basic HTTP server to serve your pages. Test out the web server by loading the finished version of the project. The main goal of tracking.js is to provide those complex techniques in a simple and intuitive way on the web. We believe computer vision is important to improve people's life, bringing it to the web will make this future a reality a lot faster.