Using OpenCV to Detect the Foreground

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Photo by Ostap Senyuk on Unsplash

We live in the era of video calls. Conducted over the internet and using whatever camera that comes with your laptop or computer, we broadcast our lives to our classmates, coworkers, and families.

Sometimes, though, we don’t want to broadcast our space. My office, like many others, has a few perennial pieces of clutter. I also have a guitar on the wall behind me, which doesn’t always scream professionalism.

As a result, Zoom and other video calling software includes a feature to hide your background, usually behind an image of your choice. …

Using OpenCV and Python to track objects by color

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Photo by Kevin Mueller on Unsplash

Among the most demanded features of computer vision is object tracking. Unfortunately, this is also one of the most difficult aspects of computer vision to implement.

Not only does an object need to be identified, it needs to be identified quickly enough to render in real time while it moves. The object may change orientation or change its distance from the camera, which further complicates matters.

While still an adamant challenge in computer vision, the mean shift and cam shift algorithms present a simplified solution. …

Using OpenCV.js to find edges in video

The edges detected around my cat
The edges detected around my cat
The edges detected around my cat. Image produced by author.

Edge detection is a fundamental tool in computer vision. By using sharp contrasts in brightness, general outlines and shapes can be found in an image. Often used as a filter to find distinctions between items in a photo, edge detection offers a first stepping stone into object recognition.

OpenCV offers a wide range of computer vision tools in C++, Java, Python, and JavaScript. Among them include an edge detection algorithm using the Canny method. Built with performance in mind, OpenCV’s implementation of edge detection can be interfaced with real time video with little to no noticeable latency.

While many examples…

Employing NumPy to Optimize Python Code

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Photo by 𝓴𝓘𝓡𝓚 𝕝𝔸𝕀 on Unsplash

While Python remains easy to use, quick to learn, and offers an overabundance of external libraries that can do almost anything, it has one critical weakness: it’s slow.

Of course, to the human eye, its sluggishness seems negligible. Usually Python only lags behind other programming languages by milliseconds; however, when iterating over millions or even billions of data points, it quickly adds up.

NumPy offers a unique solution. While allowing users to still write Python, it converts it into well written C code specifically optimized for numerical analysis.

Using NumPy arrays can increase a script’s performance by one or two…

An Explanation for Splitting Data into Training and Testing Sets

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Photo by Isaac Smith on Unsplash

On my first job out of college, I was tasked with streamlining how a company made purchases. While a big project that encompassed many factors, such as lead times and order quantities, the most challenging part was determining how many units would sell over time.

With lead times in excess of 2 or 3 months, I needed to accurately predict if a certain product would run out before a new shipment came. It was a balancing act, because if I underpredicted how much product would sell, the company would run out and lose sales. …

A Quick Guide on Writing your Own tl;dr

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Photo by Aaron Burden on Unsplash

Project Gutenberg offers over 60,000 full length books. Wikipedia contains over 55 million unique articles. Wattpad has over 400 million short stories. In the age of the internet, there is no shortage of literature to read.

These numbers, however, are completely overwhelming. A person could spend a lifetime attempting to read the entirety of the internet and never scratch more than a fraction of the surface.

The ocean of written material creates a paradoxical problem: because there’s an overabundance of information, finding relevant information becomes more difficult.

Automatically generating text summarizations may help the problem. Instead of leaving users to…

An introduction with a Python example

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Photo by Jon Tyson on Unsplash

K-Nearest Neighbor Algorithm

K-Nearest Neighbor (KNN) is an easy to understand, but essential and broadly applicable supervised machine learning technique. To understand the intuition behind KNN, examine the scatterplot below. The plot shows the relationship between two arbitrary dimensions, x and y. The blue points represent members of group A and the orange points represent the members of group B. This will represent the training data for KNN.

A Primer with a Real World Demonstration

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Photo by Pineapple Supply Co. on Unsplash

Retailers have access to an unprecedented amount of shopper transactions. As shopping habits have become more electronic, records of every purchase are neatly stored in databases, ready to be read and analyzed. With such an arsenal of data at their disposal, they can uncover patterns of consumer behavior.

What is Market Basket Analysis?

A market basket analysis is a set of affinity calculations meant to determine which items sell together. For example, a grocery store may use market basket analysis to determine that consumers typically buy both hot dogs and hot dog buns together.

If you’ve ever gone onto an online retailer’s website, you’ve probably…

How to Create an Electron-Like App with Python

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Photo by Carl Heyerdahl on Unsplash

From web development to data science, Python offers an incredibly diverse set of tools. Its easy-to-read syntax and quick learning curve makes it a popular language but it lacks the diverse and beautiful GUI support of web technologies. Anyone who’s used Flask, a popular and lightweight web framework, has probably wondered if they could take the same principles and apply them to desktop app development. The temptation of combining an HTML and CSS frontend with a Python backend is alluring,

A few libraries attempt to achieve this, but lack the customization options and don’t have a large community. …

Writing a News Article Recommender to Reduce Polarization and Radicalization

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Photo by Matthew Guay on Unsplash

The popular Netflix documentary, The Social Dilemma, outlines many of the fundamental problems of social media. The film explores how the technology used by many platforms don’t necessarily align to user interests.

In theory, social media companies can create data-driven systems to deliver interesting and engaging content to users. Normally, this is a feature meant to filter irrelevant content, but in practice, it ensures nobody is confronted with opposing views if they don’t want to see them.

As people become increasingly polarized by the content they see online, it’s imperative…

Andrew Udell

Data Science Enthusiast | Code Junkie | Lifelong Student

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