Arduino & Computer Vision: Control LCD & Robotic Arm with Facial Expressions

Course Introduction and Setup

• 💡 This course teaches building computer vision projects with Arduino, combining deep learning, MediaPipe, and OpenCV.
• 🚀 Two main projects are covered: controlling an LCD with facial expressions and a robotic arm using computer vision.
• 🛠️ Essential software installations include Python, PyCharm IDE, OpenCV, MediaPipe, and the Arduino IDE.

Project 1: Facial Expression Control with LCD

• 📸 Webcam and MediaPipe are used to detect facial landmarks, specifically focusing on lip movements.
• 💬 Python scripts process these landmarks to interpret facial expressions (smiling, sad).
• 📲 A serial communication bridge (PySerial) sends signals from Python to Arduino.
• 🖥️ The Arduino receives signals and displays corresponding text (e.g., "Smiling", "Sad") on an I2C LCD display.
• 💡 Custom characters can be created and displayed on the LCD for enhanced visual feedback.

Project 2: Robotic Arm Control with Computer Vision

• 🦾 A robotic arm is constructed using servo motors, controlled by Arduino.
• 🎯 Computer vision, using MediaPipe's pose detection, identifies key body landmarks: wrist, elbow, and shoulder.
• 📏 The distance between the wrist and shoulder is calculated using the distance formula.
• 🧠 This distance value determines the arm's movement: closing, halfway, or opening, based on predefined thresholds.
• 📡 Signals are sent from Python to Arduino via serial communication to control the servo motor's position (0-180 degrees) accordingly.
• ⚙️ The project demonstrates controlling physical mechanisms based on real-time computer vision analysis.