In a previous project on Face Detection, I have used a library to detect faces in images. This library took away the heavy lifting required to find the faces. This time I wanted to implement the detection part on my own and I chose to build a detector for traffic signs.
Cryptocurrencies such as Bitcoin (BTC) or Ethereum (ETH) and the underlying Blockchain technology is getting more and more traction. Special attention is given to so-called Smart Contracts: programmable contracts that execute actions depending on user interactions or other events. Decentralized Finance (DeFi) applications heavily rely on Smart Contracts to offer their goods in an environment not requiring trust: as the contracts are public and the code can be verified by everyone, DeFi aims to resolve trust issues with Centralized Finance (CeFi), represented by banks or other financial actors in the off-chain world.
When working on a new project, having to build separate frontend and backend system can delay progress significantly. Although the architecture will be cleaner (Separaton of Concerns) and not be a single monolith (especially when using a micro service architecture), having to build several systems including deploying each of them can be tedious. This also includes managing databases and hostong the final product.
Building on top of Firebase can ease the pain of having a database (Firestore) and how to host the system.
In an attempt to refresh the look and feel of my website I have updated the design to look less 90s style and more modern. The new website is fully responsive and makes it also easier to add new projects and blog entries.
Face detection systems are being used more and more frequently, I wanted to explore how it is working behind the scenes. My goal was to develop a system that reacts on images being emitted by webcams, processes the images (extract faces and recognize the persons behind them) and displays the information to the owner of the webcam.
A few month ago, I have built a Readlist Application to keep track of interesting reads. Back then I used Python with Flask to build the backend of the service.
Previous blog post: https://www.hagen-schupp.me/2020/05/18/ReadlistApplication.html
Indoor cycling can become quite boring after several sessions as there is no connection to the real world. The cycling training is purely simulated and the data (speed and power) have no physical connection.
On the other hand, when cycling outdoor, most cyclists track their rides, e.g on Strava. These tracks contain geocoordinates as well as information about the altitude. The goal is to combine the data collected during an indoor session and project them in a real-world cycling track.
When stumpling across interesting read such as projects to implement, blog posts or in general any type of information I want to categorize, I always have been struggling with keeping these notes together. Usually I would make them on different devices and want to bring them together into a single collated list.
Thus I decided to build a small web application to accomplish this: having a common place for all links that I want to keep for reading or follow ups. 🤨
Readlist: https://brakidreadlist.herokuapp.com/ (requires a Facebook account)
A while ago I stumpled across a blog post of buiding a simple visual question answering system that took an image and a question and answered the question wrt. the image. I decided to build such a system on my own detecting 3 basic shapes: squares, circles and triangles in three different color.
The complete Jupyter notebook can be found: here
In order to present my current projects better than just by documenting them in Github, I decided to start writing them down in a blog to share these resources more easily.