Displaying 1 to 7 from 7 results

commands - Java Command Framework - (Bukkit, Spigot, Paper, Sponge, Bungee supported)

  •    Java

This is the Framework created for Empire Minecraft. ACF Started as a Bukkit Command Framework, but has shifted to be platform agnostic and can be used on any Java based application.

yoast-acf-analysis - WordPress plugin that adds the content of all ACF fields to the Yoast SEO score analysis

  •    Javascript

WordPress plugin that adds the content of all ACF fields to the Yoast SEO score analysis. ##Description Yoast WordPress SEO's score analysis does not take in to account the content of a post's Advanced Custom Fields. This plugin uses Yoast WordPress SEO 3.1's plugin system to hook into the analyser in order to add ACF content to the SEO analysis.

Axe - Ax is a simple bare bones WordPress starter theme.

  •    PHP

Axe is a simple bare bones WordPress starter structure. It is a theme meant to be a starting point to get you setup and running as fast as possible. My build workflow might not be very orthodox but I typically review the design, Setup my Custom post types using Custom Post Type UI and setup any page data structures using ACF.

acf-code-field - WordPress ACF Plugin to add a Codemirror powered field

  •    Javascript

WordPress ACF Plugin to add a Codemirror powered field

ACF-Image-Select - Image Select addon for Advanced Custom Fields.

  •    PHP

Adds a new choice field 'Image Select' field type to ACF field list to add image-select field. The 'Image Select' field allows you to add images as radio button.

drishti - Real time eye tracking for embedded and mobile devices.

  •    C++

Native iOS, Android, and "desktop" variants of the real-time facefilter application have been added here: src/examples/facefilter. These applications link against the installed public drishti::drishti package interface, which is designed without external types in the API definition. The facefilter demos are enabled by the DRISHTI_BUILD_EXAMPLES CMake option, and the entire src/examples tree is designed to be relocatable, you can cp -r src/examples ${HOME}/drishti_examples, customize, and build, by simply updating the drishti package details. The iOS facefilter target requires Xcode 9 (beta 4) or above (Swift language requirements) and will be generated directly as a standard CMake add_executable() target as part of the usual top level project build -- if you are using an appropriate CMake iOS toolchain for cross compilation from your macOS + Xcode host for your iOS device. Please see Polly Based Build and iOS Build below for more details.

acf - Aggregated Channel Feature object detection in C++ and OpenGL ES 2

  •    C++

This module is very well suited to running real time object detection on mobile processors, where recent high performing but GPU needy DNN approaches aren't as suitable. The ACF pyramids can be computed with the OpenGL ES 2.0 shaders and retrieved more or less for free (< 1 frame time with 1 frame of latency). For selfie video, the pretrained face detectors (see FACE80 and FACE64) run in a few milliseconds on an iPhone 7. TODO: The Locally Decorrelated Channel Feature addition has not yet been added (see LDCF), but the 5x5 kernels should map well to OpenGL shaders. That should make performance very competitive (see Piotr's references for comparisons). ACF is a CMake based project that uses the Hunter package manager to download and build project dependencies from source as needed. Hunter contains detailed documentation, but a few high level notes and documentation links are provided here to help orient first time users. In practice, some working knowledge of CMake may also be required. Hunter itself is written in CMake, and is installed as part of the build process from a single HunterGate() macro at the top of the root CMakeLists.txt file (typically cmake/Hunter/HunterGate.cmake) (you don't have to build or install it). Each CMake dependency's find_package(FOO) call that is paired with a hunter_add_package(FOO CONFIG REQUIRED) will be managed by Hunter. In most cases, the only system requirement for building a Hunter project is a recent CMake with CURL support and a working compiler correpsonding to the operative toolchain. Hunter will maintain all dependencies in a versioned local cache by default (typically ${HOME}/.hunter) where they can be reused in subsequent builds and shared between different projects. They can also be stored in a server side binary cache -- select toolchains will be backed by a server side binary cache (https://github.com/elucideye/hunter-cache) and will produce faster first time builds (use them if you can!).