Camera Setup and Calibration- Embed

1. Project Overview This project builds a computer vision system that tracks whether a handheld object is inside or outside a user defined region on a person's head in real time. The system observes the user through a webcam, allows the user to define a region on their head once at the start, tracks that region as the user moves, and outputs a binary signal whenever a tracked object is inside or outside the region. The signal is transmitted to an Arduino over USB serial and can be used to control any device, such as turning a flashlight on when the object is in the correct region and off when it leaves. The previous developer completed Milestone 1, which covers webcam capture, frame rate monitoring, multi camera support, and a basic overlay system. The remaining work is organized into five milestones over an estimated nine weeks. 2. System Architecture The system runs as a single Python application on the user's computer. It captures frames from the webcam, runs all computer vision processing, and sends commands to the Arduino over USB serial. The Arduino runs a minimal firmware that reads those commands and switches an output pin accordingly. That output pin can drive any device the client chooses. The processing pipeline has six stages that run on every captured frame. First, the frame is read from the camera and corrected for lens distortion using stored calibration parameters. Second, MediaPipe Face Mesh detects 468 facial landmarks on the user's head. Third, the previously stored region anchor points are reprojected into the current frame using the new landmark positions, which is how the region follows the head as it moves. Fourth, a color based tracker locates the handheld object in the frame. Fifth, the system computes whether the object is inside or outside the region using a point in polygon test. Sixth, the resulting on or off signal is sent over serial. A separate safety supervisor runs alongside the main pipeline. It forces the output signal to off if the face cannot be detected, if the object cannot be tracked, or if the serial connection is lost. This guarantees the output is only on when the system is confident about the object's position This WHAT Im looking to be Completed a simple part of the project: Milestone 1: Camera Setup & Calibration Goal: Set up the camera and calibrate the system for accurate real world distance measurements. Steps: Set up and test the camera for real time video capture. Correct camera distortion using OpenCV and a checkerboard pattern. Calibrate the system using a reference object of known size and adjust dynamically based on the user's inter pupillary distance. Success Criteria: Achieve real world measurements with an accuracy within 5%. Correct camera distortion and dynamically adjust scale during use. I do have existing code that does cover this part:with basic webcam capture, FPS display, multi camera preview and overlay logic It is however missing this:calibration, head anchored region tracking, smoothing, object detection, Kalman filtering, Arduino communication and safety logic are missing.