In my previous tutorial , I outlined how to build the OpenCV 4 library on Microsoft Windows 10. In that tutorial, however, I did not mention how to use that library in an actual project. This time around I will be showing you how to create an OpenCV 4 project within Microsoft Visual Studio 2017.

Please note that I am going to assume that your OpenCV install is set up in the way I outlined in my previous tutorial. That is to say that the necessary files are located within an OpenCV folder in your C:\ drive. If this is not the case you’ll have to make some minor modifications to the steps outlined here to suit your setup.

Let’s get started!

I love working with the OpenCV library, but it can be a royal pain in the butt to get it installed and running on your machine. This is especially true for Microsoft Windows. Despite a Google search turning up plenty of results for articles promising to show you how to do it, they all seem to have issues or be incomplete. This results in a rather frustrating experience when you just want to get things up and running.

The purpose of this tutorial is to walk you through, step-by-step, how to install OpenCV version 4 on your Microsoft Windows 10 system. So, let’s quit talking about it and jump right in!

Typically my weekends are spent hiking with my camera , but this past weekend I was knocked down by a pretty nasty cold. As a result, I found myself toying around with various programming projects I’ve had laying around. Here for a while, I’ve been curious how emulators work, so I took this opportunity to start venturing down that rabbit hole. During this excursion, I decided to start my foray into emulators by writing one for the game Space Invaders, which ran on an Intel 8080 microprocessor. It seemed only natural to get started by writing a disassembler for the 8080 to get things started.

What I came up with is a pretty simple (albeit long) bit of C code to disassemble binary code compiled for the Intel 8080. So, without further ado, let’s jump on in and see how I did it!

MBTiles is a database format, developed by Mapbox , for storing tiled data. It’s a relatively simple database format that allows for a convenient, portable way to store map tile data.

Here recently, I’ve been developing code that works with tiled map data, including data contained within an MBTiles database. As part of this, I’ve needed an easy way to dump the map tiles from an MBTiles database to my local disk. It turns out that we can do this quite easily with a little bit of Python, so let’s dig in!

Welcome to what will be the first in an ongoing series aimed at reviewing computer science topics. Today we will be diving into binary number representation. Understanding binary is an important step in gaining a better understanding of how the computer works on a lower level.

Recall that a computer is made up a whole bunch of integrated circuits, which are in turn made up of a bunch of transistors. These transitors can have one of two states: on or off. We represent these states as a 1 or a 0, respectively. This means that a computer can only work with two different values, 1s and 0s. This is where binary comes in.

Have you ever written code to interface with a webcam? Well, if you have then you know that it can be a royal pain in the ass. And God forbid you want it to be a cross-platform solution! The good news is that there is a ready-made solution that can help us out: OpenCV. Yes, you heard me right. Not only is OpenCV and amazing computer vision library, but it also provides a handy, cross-platform way of interfacing with webcams. Let’s take a look at how simple OpenCV makes this. I’ll be using Python for these examples, but the API is similar in other languages.

The uses for prime numbers in computer science are nearly endless. They are useful for everything from hashing, cryptology, factorization, and all sorts of applications in-between.

There exists a great number of algorithms that allow us to quickly generate primes, but today we are going to take a look at a popular method known as a prime sieve. There are a number of different implementations of prime sieves, but one of the simplest to implement is known as the Sieve of Eratosthenes . This algorithm is great for quickly generating smaller prime numbers (but it may not be the best choice for generating very large primes).

I’ve recently been experimenting on a new project to scrape data from webpages located on the Tor network. For simplicity’s sake, I decided to write this bit of code in Python and use the handy urllib2 library to handle the HTTP requests.

For those that don’t know, Tor runs a SOCKS5 proxy, which, by default, runs on I thought things would be as simple as telling urllib2 to use a proxy located at IP and port 9050, but I quickly found that this doesn’t work.