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Signed-off-by: Bubbles The Dev <[email protected]>

Author: KernFerm

Run-Application/readme.md

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+<p align="center">
+  <img src="https://github.com/FNBUBBLES420-ORG/game-vision-aid/blob/main/banner/Game_Vision_Aid.png" alt="Game Vision Aid Banner" width="300"/>
+</p>
+
+## 🎯 Real-Time Object Detection Overlay with YOLO, TensorRT, and BetterCam
+
+This project enables **real-time object detection** using **YOLOv5/YOLOv8**, with support for **PyTorch**, **ONNX**, and **TensorRT** models. Bounding boxes are rendered using a **transparent overlay** that displays on top of your game or any screen — perfect for assistive tools, AI research, and accessibility-focused applications.
+
+---
+
+## 🧠 Components
+
+| File        | Description |
+|-------------|-------------|
+| `main.py`   | Core logic that captures frames, runs detection using the selected model, and displays bounding boxes via an overlay. |
+| `config.py` | User-configurable settings: choose model type, set paths, screen dimensions, GPU support, overlay transparency, and more. |
+| `overlay.py`| Creates a transparent, always-on-top, click-through overlay window that displays detection bounding boxes over your game or screen. |
+
+---
+
+## ⚙️ Features
+
+✅ Supports **YOLOv5 / YOLOv8** via Ultralytics  
+✅ Use **PyTorch (.pt)**, **ONNX (.onnx)**, or **TensorRT (.engine)** models  
+✅ Automatic GPU detection: **NVIDIA (CUDA)**, **AMD (DirectML)**, or **CPU fallback**  
+✅ Seamless integration with **BetterCam** for real-time screen capture  
+✅ Transparent overlay using **Win32 API** (minimal FPS impact)  
+✅ Multi-monitor support and full resolution control  
+✅ Smooth, real-time bounding box rendering  
+✅ Fully configurable through `config.py`
+
+---
+
+## 🛠️ Requirements
+
+Install dependencies:
+
+```bash
+pip install ultralytics opencv-python numpy torch torchvision torchaudio torch-directml onnx onnxruntime onnxruntime-directml onnx-simplifier pycuda tensorrt colorama customtkinter requests pandas cupy bettercam
+```
+- ⚠️ `BetterCam` must be installed or available in your environment. If it's a custom module, ensure it's in your Python path.
+
+## 🧩 Configuration (config.py)
+### ✨ Model Type
+
+- Choose the model engine to use:
+```bash
+modelType = 'torch'  # Options: 'torch', 'onnx', 'engine'
+```
+
+## 🧠 Model Paths
+
+- Point to your desired model files:
+
+```bash
+torchModelPath = 'models/yolov8n.pt'
+onnxModelPath = 'models/yolov8n.onnx'
+tensorrtModelPath = 'models/yolov8n.engine'
+```
+
+## 🎥 Screen & Overlay Settings
+
+- Adjust `resolution`, `transparency`, and `monitor index`:
+
+```bash
+screenWidth = 640
+screenHeight = 640
+overlayWidth = 1920
+overlayHeight = 1080
+overlayAlpha = 200  # 0–255 (higher = more opaque)
+```
+
+## ⚡ GPU Support
+
+- These values are used primarily for logic awareness — GPU detection is automatic:
+```bash
+useCuda = True         # CUDA (NVIDIA)
+useDirectML = True     # DirectML (AMD)
+```
+
+## 🚀 Running the Program
+
+- Once your configuration is set, launch the overlay:
+
+```bash
+python main.py
+```
+
+### You’ll be prompted to start your game. After pressing Enter:
+
+- Your screen will begin real-time capture
+
+- The selected model will run detection
+
+- Bounding boxes will appear in the transparent overlay
+
+- Press `Q` anytime to exit safely
+
+📸 Supported Hardware
+
+| GPU Type    | Engine Used                  |
+|-------------|------------------------------|
+| NVIDIA      | CUDA / TensorRT              |
+| AMD Radeon  | DirectML (ONNX + PyTorch DML)|
+| CPU Only    | ONNX CPU Execution           |
+
+
+# ❤️ Credits
+- Created with purpose by `Bubbles The Dev` 🫧
+- Supporting accessible gaming, AI innovation, and empowering every player.
+- Need extra features like FPS display, audio alerts, model switching GUI, or OBS integration?
+
+### Hit me up — I got you! 😎
+
+
+
+---
+---
+# 🚀 NVIDIA CUDA Installation Guide
+
+### 1. **Download the NVIDIA CUDA Toolkit 11.8**
+
+First, download the CUDA Toolkit 11.8 from the official NVIDIA website:
+
+👉 [Nvidia CUDA Toolkit 11.8 - DOWNLOAD HERE](https://developer.nvidia.com/cuda-11-8-0-download-archive)
+
+### 2. **Install the CUDA Toolkit**
+
+- After downloading, open the installer (`.exe`) and follow the instructions provided by the installer.
+- Make sure to select the following components during installation:
+  - CUDA Toolkit
+  - CUDA Samples
+  - CUDA Documentation (optional)
+
+### 3. **Verify the Installation**
+
+- After the installation completes, open the `cmd.exe` terminal and run the following command to ensure that CUDA has been installed correctly:
+  ```
+  nvcc --version
+  ```
+This will display the installed CUDA version.
+
+### **4. Install Cupy**
+Run the following command in your terminal to install Cupy:
+  ```
+  pip install cupy-cuda11x
+  ```
+
+```
+@echo off
+echo MAKE SURE TO HAVE THE WHL DOWNLOADED BEFORE YOU CONTINUE!!!
+pause
+echo Click the link to download the WHL: press ctrl then left click with mouse
+echo https://github.com/cupy/cupy/releases/download/v13.4.1/cupy_cuda11x-13.4.0-cp311-cp311-win_amd64.whl
+pause
+
+echo Installing CuPy from WHL...
+pip install https://github.com/cupy/cupy/releases/download/v13.4.1/cupy_cuda11x-13.4.0-cp311-cp311-win_amd64.whl
+pause
+
+echo All packages installed successfully!
+pause
+```
+
+## 5. CUDNN Installation 🧩
+Download cuDNN (CUDA Deep Neural Network library) from the NVIDIA website:
+
+👉 [Download CUDNN](https://developer.nvidia.com/downloads/compute/cudnn/secure/8.9.6/local_installers/11.x/cudnn-windows-x86_64-8.9.6.50_cuda11-archive.zip/). (Requires an NVIDIA account – it's free).
+
+## 6. Unzip and Relocate 📁➡️
+Open the `.zip` cuDNN file and move all the folders/files to the location where the CUDA Toolkit is installed on your machine, typically:
+
+```
+C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8
+```
+
+
+## 7. Get TensorRT 8.6 GA 🔽
+Download [TensorRT 8.6 GA](https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/secure/8.6.1/zip/TensorRT-8.6.1.6.Windows10.x86_64.cuda-11.8.zip).
+
+## 8. Unzip and Relocate 📁➡️
+Open the `.zip` TensorRT file and move all the folders/files to the CUDA Toolkit folder, typically located at:
+
+```
+C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8
+```
+
+
+## 9. Python TensorRT Installation 🎡
+Once all the files are copied, run the following command to install TensorRT for Python:
+
+```
+pip install "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\python\tensorrt-8.6.1-cp311-none-win_amd64.whl"
+```
+
+🚨 **Note:** If this step doesn’t work, double-check that the `.whl` file matches your Python version (e.g., `cp311` is for Python 3.11). Just locate the correct `.whl` file in the `python` folder and replace the path accordingly.
+
+## 10. Set Your Environment Variables 🌎
+Add the following paths to your environment variables:
+
+```
+C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\lib
+C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\libnvvp
+C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\bin
+```
+
+# Setting Up CUDA 11.8 with cuDNN on Windows
+
+Once you have CUDA 11.8 installed and cuDNN properly configured, you need to set up your environment via `cmd.exe` to ensure that the system uses the correct version of CUDA (especially if multiple CUDA versions are installed).
+
+## Steps to Set Up CUDA 11.8 Using `cmd.exe`
+
+### 1. Set the CUDA Path in `cmd.exe`
+
+You need to add the CUDA 11.8 binaries to the environment variables in the current `cmd.exe` session.
+
+Open `cmd.exe` and run the following commands:
+
+```
+set PATH=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\bin;%PATH%
+```
+```
+set PATH=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\libnvvp;%PATH%
+```
+```
+set PATH=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\extras\CUPTI\lib64;%PATH%
+```
+These commands add the CUDA 11.8 binary, lib, and CUPTI paths to your system's current session. Adjust the paths as necessary depending on your installation directory.
+
+2. Verify the CUDA Version
+After setting the paths, you can verify that your system is using CUDA 11.8 by running:
+```
+nvcc --version
+```
+This should display the details of CUDA 11.8. If it shows a different version, check the paths and ensure the proper version is set.
+
+3. **Set the Environment Variables for a Persistent Session**
+If you want to ensure CUDA 11.8 is used every time you open `cmd.exe`, you can add these paths to your system environment variables permanently:
+
+1. Open `Control Panel` -> `System` -> `Advanced System Settings`.
+Click on `Environment Variables`.
+Under `System variables`, select `Path` and click `Edit`.
+Add the following entries at the top of the list:
+```
+C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\bin
+C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\libnvvp
+C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\extras\CUPTI\lib64
+```
+This ensures that CUDA 11.8 is prioritized when running CUDA applications, even on systems with multiple CUDA versions.
+
+4. **Set CUDA Environment Variables for cuDNN**
+If you're using cuDNN, ensure the `cudnn64_8.dll` is also in your system path:
+```
+set PATH=C:\tools\cuda\bin;%PATH%
+```
+This should properly set up CUDA 11.8 to be used for your projects via `cmd.exe`.
+
+#### Additional Information
+- Ensure that your GPU drivers are up to date.
+- You can check CUDA compatibility with other software (e.g., PyTorch or TensorFlow) by referring to their documentation for specific versions supported by CUDA 11.8.
+
+```
+import torch
+
+print(torch.cuda.is_available())  # This will return True if CUDA is available
+print(torch.version.cuda)  # This will print the CUDA version being used
+print(torch.cuda.get_device_name(0))  # This will print the name of the GPU, e.g., 'NVIDIA GeForce RTX GPU Model'
+```
+run the `get_device.py` to see if you installed it correctly