Build a Live Object Detection App for the Reachy Mini With TensorFlow and PyCharm | The PyCharm Blog
Blog post from JetBrains
In a guest post by Iulia Feroli, founder of the Back To Engineering YouTube community, the process of building a live object detection app using TensorFlow and PyCharm is detailed, specifically for deployment on the Reachy Mini, an open-source robot by Pollen Robotics, Hugging Face, and Seeed Studio. The tutorial outlines how to create an object detection pipeline with SSD MobileNet V2 from TensorFlow Hub, utilizing OpenCV for live webcam inference, and integrating this with the Reachy Mini for real-time object tracking, including head movement and antenna reactions. The project is structured in two stages: first, testing the model on a laptop to ensure functionality without hardware, and second, deploying it on the robot, where the head tracks detected objects and a web dashboard displays live annotated detections. The Reachy Mini, which resembles a physical representation of an AI agent with conversational and camera-based capabilities, highlights its potential for open-source development, allowing users to print their own parts and contribute to an app store. The post serves as a starting point for leveraging TensorFlow object detection in robotics, with all code available in the Reachy-mini-object-detection GitHub repository for further customization and exploration.