Simple Challenges Call for Simple Solutions
In this project, I explored the use of OpenCV and Pillow to create a lightweight, real-time color tracker. The idea was to detect a specific color shown on camera and draw a bounding box around it. While seemingly straightforward, this approach can be highly effective in certain scenarios.
Why OpenCV and Pillow?
I used OpenCV and Pillow for their simplicity and ease of integration. Together, they provided all the tools needed for quick image processing and fast real-time results.
In this example I’m tracking yellow converted from BGR to a HSV range, allowing for variations in the color.
BGR Value: [0, 255, 255]
Lower HSV boundary: - 10, 150, 100
Upper HSV boundary: + 10, 255, 255
Limitations and Strengths
This simple color tracker works best in controlled conditions—consistent lighting, a clear background, and distinct color separation. When these conditions are met, it proves to be robust, fast, and highly efficient. Sometimes, the best solution is also the simplest.
GitHub: https://github.com/BrandtBrandtBrandt/color_tracking.git
This project is part of the “Computer Vision in 30 Days | 30 Days Coding Challenge” created by Felipe, known as Computer Vision Engineer on YouTube. I am participating in this challenge to expand my capabilities and gain deeper hands-on experience in computer vision. I would like to express my gratitude to Felipe for providing such an invaluable resource. His channel offers incredible insights and practical guidance, making it a must-watch for anyone exploring computer vision. You can expect to see a lot more computer vision projects from this coding challenge in my portfolio, as I will be tackling them over the next 30 days.