Introducing CloudCV bootstrap

Here’s an open-source ready to use bootstrap project written in Node.js that lets you to quickly build a REST service to host your image processing and computer vision code in a cloud environment. Please welcome: cloudcv-bootstrap. I made this project aside of CloudCV to keep it simple but functionaly. It is self-contained Node.js project that helps you to get quick results on building and deploying your first server-based image processing service.

How to debug node.js addons in Visual Studio

While working on CloudCV I encountered problems in node.js addon written in native code. For CloudCV I use node.js with C++ Addon to separate high-performance algorithms (C++) from high-level networking API which node provides. In this tutorial I’m going to reveal best practices on debugging C++ Addons for Node.js (0.12) using Visual Studio 2013. Continue reading if you want to read in details why this works. This article is valid for Node.

Image processing in your browser - Unit Test automation

JavaScript. Do you like debug JavaScript code? I hate it. Literally. What what if you have to? In this post I’m going to show you how to simplify your life by automating unit testing of the JavaScript code for the browser. To get things more interesting - let’s automate unit-testing of the image processing library called JSFeat. JSFeat provides a JavaScript implementation of the basic image processing operations that let you to process images in your browser and build sophisticated algorithms.

Argument checking for native addons for Node.js. Do it right!

![NanCheck](logo.jpg) During development of CloudCV I came to the problem on converting v8::Arguments to native C++ data types in my Node.js native module. If you are new to C++ and Node.js, I suggest you to read how to write C++ modules for Node.js and connecting OpenCV and Node.js first. Mapping V8 data types to native C++ equivalents is trivial, but somewhat wordy. One should take the argument at given index, check whether it is defined, then check it’s type and finally cast to C++ type.

Using Travis-CI for continuous testing your projects

img.img-thumbnail.pull-left(src=“travis-logo.png”) p | In this post i will show you how i implemented continuous integration and testing in my a(href=“http://cloudcv.io") CloudCV | project. Healthy unit tests and easy and continuous integration workflow is a must in any project goes beyound “Hello, world” application. | Today software is a mixture of technologies of all kind. Therefore it can break literally everywhere. Each integration point is a place of risk. | The CloudCV has a C++ backend that is using OpenCV library, it’s front-end is written in Node.

CloudCV - Cloud image processing platform

| Hey everyone! I continue to play with clouds and today it’s time to reveal the CloudCV - a cloud-based image processing project. | Based on my previous posts i host a server in the Digital Ocean’s cloud. | I have to say, everything is working like a charm. | The cheapest 5$/month plan gives me whatever i may need for this project. | All the source-code is already sits on Github and you are more than welcome to study it.

Connecting OpenCV and Node.js inside Cloud9 IDE

Vacation time is over, and now i’m on my way from Tartu, Estonia where i participated in 48 km. inline speedskating marathon to Odessa. My bus have Wi-Fi onboard, so i decided to write a short success-story how i managed to build a C++ addon module for Node.js and run it on the real server inside the Cloud9 IDE. You may also want to check the first tutorial since this guid relies on it.

Cloud image processing using OpenCV and Node.js

A long time ago i was playing with cloud-based image processing. The first reason why i didn’t shared a reciple how to compile OpenCV as native app for windows azure cloud was trycky build process. It was too complicated and this tutorial will become outdated very quickly. The second one - Azure hosting wants a lot of money. So i put my research in this area on hold for better times.