Is-Now-Illegal - 🚫 A NERD protest against Trump's Immigration ban

  •        4

The server costs are too high and we will shutdown very soon if we don't get enough donations. For real. 😔 Please click to Donate via Patreon or contact us below.See full list of contributors.


@google-cloud/storage : ^0.6.1
async : ^2.1.4
express : ^4.14.1
firebase : ^3.6.7
firebase-admin : ^4.0.6
firebase-queue : ^1.6.1
lodash : ^4.17.4
python-shell : ^0.4.0



Related Projects

OpenCV - Open Source Computer Vision

  •    C++

OpenCV (Open Source Computer Vision) is a library of programming functions for real time computer vision. The library has more than 500 optimized algorithms. It is used to interactive art, to mine inspection, stitching maps on the web on through advanced robotics.

iPhone-OCR-Tesseract-and-OpenCV - Simple academic project made using OpenCV and Tesseract

  •    Objective-C

This is a sample project created by me (@PablosPoject) and @_AJ_R for academic purpose. It use the OpenCV framework and tutorial made by BloodAxe( and some other utilities class made by Aptogo ( It also uses the Tesseract OCR engine to read the text processed with openCV. I also build a simple user interface that permit to take a photo or choose one from library, and also permit to apply to the image every single step in the image processing, or to apply directly all the processing.

OpenCV-for-PHP - An OpenCV binding for PHP

  •    C++

This is a PHP extension wrapping the OpenCV library for image processing. It lets you use the OpenCV library for image recognition and modification tasks. It requires PHP 5.3, and OpenCV 2.0 or above.

CloudCV - Large-Scale Distributed Computer Vision As A Cloud Service

  •    NodeJS

An example of using OpenCV library in server environment using Node.js. Here you will see that it's really simple to perform CPU-intense image processing routines in the cloud. A Node.js server handle client requests and calls C++ back-end.

caire - Content aware image resize library

  •    Go

Caire is a content aware image resize library based on Seam Carving for Content-Aware Image Resizing paper. The library is capable detecting human faces prior resizing the images via, which does not require to have OpenCV installed.

CoreAR - AR(Augmented reality) framework for iOS, based on a visual code like ARToolKit

  •    C

CoreAR.framework is open source AR framework. You can make an AR application using visual code like ARToolKit using this framework. CoreAR.framework does not depend on the other computer vision library like OpenCV. Considered portability, this framework is written only C or C++. The pixel array of an image is passed to CoreAR.framework and then visual code's identification number, rotation and translation matrix are obtained from the image including a visual code. Image processing speed of this framework is about 15 fps on iPhone4. Take notice that CoreAR.framework depends on Quartz Help Library and Real time image processing framework for iOS. You have to download these libraries and put on them at the path where CoreAR.framework has been installed.

libface - Face Recognition Library

  •    C++

Libface is a cross platform framework for developing face recognition algorithms and testing its performance. The library uses OpenCV 2.0 and aims to be a middleware for developers that don’t have to include any OpenCV code in order to use face recognition and face detection detection.

rnpm - :iphone: React Native Package Manager

  •    Javascript

Last November me (@Kureev) and Mike (@grabbou) started RNPM. We aimed to bring you a better developer experience and bridge the tooling gap we had back then. Now, as you may know, RNPM is merged into React Native core. It means that from now on you don't need to install a third-party software to use your favorite linking functionality (just use a react-native cli). We'd like to say a big "Thank you!" to everybody who supported us, filed new issues, composed PRs and helped us to review them. Now, when RNPM is a part of React Native, we're going to seal this repository and keep working on React Native tooling inside the core. That said, I kindly ask you to file all new issues / prs in react-native repo and cc us. This repo (and other rnpm plugins) will be a available for a few more months in a read-only mode.

opencv-processing - OpenCV for Processing

  •    Java

A Processing library for the OpenCV computer vision library. OpenCV for Processing is based on OpenCV's official Java bindings. It attempts to provide convenient wrappers for common OpenCV functions that are friendly to beginners and feel familiar to the Processing environment.

OpenCV / Emgu services for Robotics Developer Studio


This project provides encapsulation of standard OpenCV routines to allow them to be used as services in Microsoft Robotics Developer Studio. The services utilize the EMGU C# wrapper for the OpenCV libraries. The EMGU version is wrapped to provide standard services in RDS

Sharp - High performance Node.js image processing

  •    Javascript

The typical use case for this high speed Node.js module is to convert large images in common formats to smaller, web-friendly JPEG, PNG and WebP images of varying dimensions.Resizing an image is typically 4x-5x faster than using the quickest ImageMagick and GraphicsMagick settings.

DeepBeliefSDK - The SDK for Jetpac's iOS Deep Belief image recognition framework

  •    Javascript

The SDK for Jetpac's iOS, Android, Linux, and OS X Deep Belief image recognition framework. This is a framework implementing the convolutional neural network architecture described by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton. The processing code has been highly optimized to run within the memory and processing constraints of modern mobile devices, and can analyze an image in under 300ms on an iPhone 5S. It's also easy to use together with OpenCV.

GPUImage2 - GPUImage 2 is a BSD-licensed Swift framework for GPU-accelerated video and image processing

  •    Swift

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. By relying on the GPU to run these operations, performance improvements of 100X or more over CPU-bound code can be realized. This is particularly noticeable in mobile or embedded devices. On an iPhone 4S, this framework can easily process 1080p video at over 60 FPS. On a Raspberry Pi 3, it can perform Sobel edge detection on live 720p video at over 20 FPS.

GPUImage3 - GPUImage 3 is a BSD-licensed Swift framework for GPU-accelerated video and image processing using Metal

  •    Swift

GPUImage 3 is the third generation of the GPUImage framework, an open source project for performing GPU-accelerated image and video processing on Mac and iOS. The original GPUImage framework was written in Objective-C and targeted Mac and iOS, the second iteration rewritten in Swift using OpenGL to target Mac, iOS, and Linux, and now this third generation is redesigned to use Metal in place of OpenGL. 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. Previous iterations of this framework wrapped OpenGL (ES), hiding much of the boilerplate code required to render images on the GPU using custom vertex and fragment shaders. This version of the framework replaces OpenGL (ES) with Metal. Largely driven by Apple's deprecation of OpenGL (ES) on their platforms in favor of Metal, it will allow for exploring performance optimizations over OpenGL and a tighter integration with Metal-based frameworks and operations.

Accord.NET - Machine learning, Computer vision, Statistics and general scientific computing for .NET

  •    CSharp

The Accord.NET project provides machine learning, statistics, artificial intelligence, computer vision and image processing methods to .NET. It can be used on Microsoft Windows, Xamarin, Unity3D, Windows Store applications, Linux or mobile.

node-easyimage - Node.js module for image processing and manipulation

  •    TypeScript

EasyImage is a promise-based image processing module for Node.js, it is built on top of ImageMagick, so make sure ImageMagick is installed on your system. EasyImage 3 is only compatible with NodeJS 4 or greater.

Emgu CV

  •    CSharp

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.

javacv - Java interface to OpenCV, FFmpeg, and more

  •    Java

JavaCV uses wrappers from the JavaCPP Presets of commonly used libraries by researchers in the field of computer vision (OpenCV, FFmpeg, libdc1394, PGR FlyCapture, OpenKinect, librealsense, CL PS3 Eye Driver, videoInput, ARToolKitPlus, and flandmark), and provides utility classes to make their functionality easier to use on the Java platform, including Android.

zimg - A lightweight and high performance image storage and processing system.

  •    C

Project zimg is a lightweight image storage and processing system. It's written in C and it has high performance in image field. The zimg is designed for high concurrency image server. It supports many features for storing and processing images.The concurrent I/O, distributed storage and in time processing ability of zimg is excellent. You needn't nginx in your image server any more. In the benchmark test, zimg can deal with 3000+ image downloading task per second and 90000+ HTTP echo request per second on a high concurrency level. The performance is higher than PHP or other image processing server. More infomation of zimg is in the documents below.