Project CV-Interface: Real-Time Hardware Modulation

7/5/2026 THIS IS STILL IN PROGRESS just uncommitted to github due to technical issues and figuring out hardware wiring lol.

Overview

This repository contains a framework for a high-performance Computer Vision (CV) system designed to bridge software-based image analysis with hardware-level input execution. The system utilizes a Python-based backend to interpret a live video stream and generate corresponding data packets for external hardware interfaces.

System Architecture

The implementation relies on a specific hardware-in-the-loop (HITL) configuration to ensure low-latency data transmission and signal integrity.

1. Vision Engine

The primary processing unit (Mac/PC) runs the Python environment. It utilizes OpenCV for real-time frame analysis and pixel-coordinate tracking.

2. Capture Interface

An HDMI capture device facilitates the transfer of the primary video signal to the Vision Engine. For optimal recognition accuracy, a consistent 1080p 60fps feed is required.

3. Hardware Chain

• Logic Controller: A Titan Two or Raspberry Pi serves as the central hub for receiving data from the Vision Engine via a high-speed serial connection (Prog port).
• Input Translator: A XIM Matrix or Arduino-based bridge handles the final translation of modulated data into the target system's native protocol.

Technical Specifications

• Backend: Python 3.x
• Dependencies: OpenCV, NumPy, Serial Library
• Communication Protocol: Serial/HID Passthrough
• Latency Target: < 10ms frame-to-output

Configuration Requirements

To ensure the reliability of the vision algorithms, the environment must adhere to the following parameters:

• Display Ratio: 16:9
• Color Space: Standard Dynamic Range (SDR)
• Interface Scaling: 100% (Default)

Installation and Deployment

1. Initialize the Python environment on the Vision Engine.
2. Install necessary libraries via the provided requirements file.
3. Deploy the hardware-specific script to the Logic Controller.
4. Establish the physical connection between the Vision Engine and the Logic Controller via USB.
5. Calibrate the capture window to align with the defined coordinate system in the software.

Usage

The system is activated by initializing the main Python script while the capture feed is active. It will automatically begin monitoring the defined regions of interest (ROI) and modulating output based on pre-defined data patterns stored in the local library.