umut-academy

Hand Tracking with Python and MediaPipe
Source code and full explanation article:
https://umut-academy.com/computer-vision-hand-tracking/

In this post, we explored how to build a hand-tracking application using Python, OpenCV, and MediaPipe. The aim is to simplify the process for beginners, especially those not fluent in English or AI, and help them understand how to track hand landmarks in real-time using a we**am.

✏️📚 What We Covered:

📝 Hand Tracking Basics: We started by explaining what hand landmarks are and how we can use them to track hand movements.

📝 Setting Up Environment: We discussed setting up the work environment using PyCharm Community Edition (free), highlighting that we used procedural programming (without OOP) for simplicity.

📝 Capturing Video with OpenCV: We then showed how to use OpenCV (`cv2.VideoCapture()`) to capture video from a we**am and convert it into RGB format to be processed by MediaPipe.

📝 Detecting Hand Landmarks with MediaPipe: Using MediaPipe’s pre-trained hand-tracking models, we explained how to detect landmarks in real-time video and render visual effects on the hand.

📝 Simplifying Code with Functions: To make the code easier to understand and reuse, we created a `findPosition()` function that helps identify specific hand landmarks, converting their coordinates into pixel values.

📝 Finger Detection Logic: We implemented logic to detect whether fingers are open or closed based on their landmark positions. We also used a unique method to differentiate between the left and right hand, simplifying the thumb detection process.

Our website: https://umut-academy.com/