With the release of the Segment Anything Model¹ (SAM) released by Meta AI Research last year, the lie of the land changed quite substantially in Computer Vision, as now images could be segmented easily, with great results even zero-shot. With the release of SAM2² earlier this year, I wanted to get hands on and experiment with these models myself.

This post walks you through how SAM2 could be used in practice, provides a mini analysis of segmentation results and will be released with code so that you can explore further if you want to. This could be expanded to interesting use cases, such as facilitating object grasping in robotic systems, branded product addition or removal in marketing images, or mapping changes in forested areas from satellite imagery over time for environmental monitoring.

In this project a stunning timelapse video was created from an image stock of over 3,000 photos of a construction site, tracking the progress of a new residential building from breaking ground to completion, a process lasting more than three years. Those images were taken without a tripod, so the variability in camera positions and angles was of course high. To correct for this, Computer Vision techniques were used to predict key points in the images, that could then be used to straighten them and produce the final, steady timelapse.

I’m very excited to launch my new website! Going forward, I will be available for contract work, and will use this space, zl-labs.tech, to showcase my work and connect.

If you have a spare moment, please take a look around and check out my articles. I value your feedback! If you have any improvement suggestions (or proposed projects), please reach out via the contact page or via traditional channels.

Keep an eye for exciting new content coming your way in the coming weeks and months 🚀

This is an article I wrote for Verv during their Seedrs fundraising campaign in March 2024. Link to article.

This January, we finished the installation of the first Verv Smart Isolator with our partner Nido, a Spanish heat pump provider. A Verv Smart Isolator was attached to an air-to-water heat pump and in less than a week, we provided actionable insights for the customer.

One of the tools that helps us understand the energy usage patterns is the energy-utilization heatmap. The heatmap visualizes the hourly energy consumption over different days (vertical axis) and times of day (horizontal axis). The intensity of colour - marked in the legend - represents the energy expenditure during each hour, with darker colours indicating higher usage.

Like to parkrun? I certainly do! This project helps you to visualize where you’ve been running, and where you could go next. Simply run the app locally with Python, and enter an athlete ID (e.g. your own one).

I’ve used Plotly Dash to render the parkrun participation on a map for a given athlete. The map initially starts out centred on your home parkrun course - the one you’ve visited most - just zoom out if you’ve gone farther afield.

This is an article I wrote for Verv in the August / September 2023 edition of the ACR Journal. Link to publication.

Having faced a winter of record-high energy prices and soaring inflation, minimizing energy bills is front of mind for many businesses around the UK. Energy monitoring and management is crucial in the fight against these high energy costs—by understanding how much energy your business uses, you can make informed decisions about how to reduce your energy consumption and save money on your utility bills.

Duplicate photos are annoying and unwanted. Wouldn’t you rather make those post-holiday rundowns with the family as impressive (short) as possible? The burden of boiling your photo set down to the most memorable and ones with the best angle is greatly reduced by this app. It works because you can visualize all images together in the order they were taken.

I now use my Dash app routinely after every holiday, as it makes the deduplication process much faster, yielding a cleaner set of photos without maxing out your hard drive!

This is a short post to describe my practical introduction to Reinforcement Learning (RL), where I trained a simple agent to play the classic Atari game Pong via a Deep Q-Network.

In English, this means we teach a novice computer to play the classic paddle game by allowing it to observe what happens when it performs various movements at different times and stages of gameplay (against the same, fairly strong opponent). Then, after making a sequence of movement choices, our agent either gets a point (reward of +1) or loses one (reward of -1). After a lot of trial and error, the agent will have observed enough situations to learn what is a good move to make at a given moment in the game.