Python Computer Vision Developer – Real-Time Tracking Loop for Titan Two (Gtuner IV)

[SECRET WORD REQUIREMENT: Please see the bottom of this job post before applying] We are seeking an experienced Python developer with a strong background in computer vision (CV) and real-time game automation to develop a low-latency target tracking control loop. The project involves taking a live stream from a capture card, running it through a pre-trained YOLOv8/YOLOv11 object detection model, calculating spatial target offsets, and routing those coordinates instantly to console hardware. The catch: This script must be written specifically to integrate with the Titan Two hardware emulator inside Gtuner IV's Computer Vision (GCV) panel. Standard PC mouse-injection libraries (like PyAutoGUI or Win32 API) will not work for this project. Core Technical Requirements: Language & Stack: Python 3.8 - 3.11, OpenCV, Ultralytics (YOLO). Hardware Interface: Deep understanding of the native Gtuner Python API. You must be comfortable writing a modular script formatted as a ⁠GCVWorker⁠ class that outputs data directly via ⁠gtuner.gcv_set_val()⁠ to the hardware analog stick registers (⁠STICK_1_X⁠ and ⁠STICK_1_Y⁠). Optimization: Expertise in minimizing latency. You must know how to export or compile raw PyTorch ⁠.pt⁠ weights into an optimized execution format like NVIDIA TensorRT or ONNX Runtime to keep frame inference times under 5ms. Personal & Performance Requirements: FPS Game Knowledge: You must be well-versed in competitive FPS games and understand design choices like deadzones, linear vs. exponential thumbstick scaling, and coordinate normalization (translating a bounding box from a 640x640 vision matrix back to an analog controller scale of -100 to 100). Collaborative & Adaptive Mindset: We are looking for someone who treats this as an active partnership. The tracking loop will likely require iterative tweaking (balancing acceleration, smoothing functions, and target-switching filters). You must be open to brainstorming features, listening to gameplay feedback, and working collaboratively to fix bugs or tracking faults. Deliverables (Phase 1): 1. A clean, well-documented ⁠.py⁠ script implementing the ⁠GCVWorker⁠ interface for Gtuner IV. 2. Implementation of target prioritization logic (e.g., locking onto the entity closest to the center crosshair). 3. Smoothness and sensitivity scaling adjustments exposed as variables at the top of the script for easy user calibration. Mandatory Screening Questions: 1. Have you ever written a Python script that explicitly interacts with Gtuner IV or hardware input controllers? If so, briefly outline how you handled joystick clamping and stick drift variables. 2. If our capture card inputs a 1080p stream at 60 FPS, but our YOLO model runs inference on a 640x640 frame, how will your script calculate the target pixel offset and scale it accurately to the controller's axis values? 3. To eliminate automated bot proposals, what is the secret word hidden at the bottom of this post? How to Apply: To prove you read this entire post and understand the project scope, please start the very first line of your proposal with the secret word "TITAN".