Search Results for "flash image software"

JuliaImages (source code) hosts the major Julia packages for image processing. Julia is well-suited to image processing because it is a modern and elegant high-level language that is a pleasure to use, while also allowing you to write "inner loops" that compile to efficient machine code (i.e., it is as fast as C). Julia supports multithreading and, through add-on packages, GPU processing. JuliaImages is a collection of packages specifically focused on image processing. It is not yet as...

This package provides an interface to Qt6 QML (and to Qt5 for older versions). It uses the CxxWrap package to expose C++ classes. Current functionality allows interaction between QML and Julia using Observables, JuliaItemModels and function calling. There is also a generic Julia display, as well as specialized integration for image drawing, GR plots and Makie.

Julia is, in general, a "just-barely-ahead-of-time" compiled language. When you call a function for the first time, Julia compiles it for precisely the types of arguments given. This can take some time. All subsequent calls within that same session use this fast compiled function, but if you restart Julia you lose all the compiled work. PackageCompiler allows you to do this work upfront — further ahead of time — and store the results for a lower latency startup. You can save loaded packages...

Knet.jl is a deep learning package implemented in Julia, so you should be able to run it on any machine that can run Julia. It has been extensively tested on Linux machines with NVIDIA GPUs and CUDA libraries, and it has been reported to work on OSX and Windows. If you would like to try it on your own computer, please follow the instructions on Installation. If you would like to try working with a GPU and do not have access to one, take a look at Using Amazon AWS or Using Microsoft Azure. If...

A fast library for increasing the number of training images by applying various transformations. Augmentor is a real-time image augmentation library designed to render the process of artificial dataset enlargement more convenient, less error prone, and easier to reproduce. It offers the user the ability to build a stochastic image-processing pipeline (or simply augmentation pipeline) using image operations as building blocks. In other words, an augmentation pipeline is little more but a...