YOLOv8 Real-Time Object Detection using USB Camera

About

This repository contains a Python script that demonstrates real-time object detection using the YOLOv8 model with a USB camera. YOLOv8, developed by Ultralytics, is the latest version of the YOLO series, offering high accuracy and performance for a variety of object detection tasks.

The script provides a user-friendly interface, allowing users to choose specific objects for detection from a predefined list of classes. It also automatically detects whether a GPU is available and downloads the YOLOv8 model if it is not already present.

Features

• Real-Time Object Detection: Uses OpenCV to capture video feed from the USB camera and YOLOv8 to detect objects in real-time.
• User-Selectable Classes: Users can choose which objects to detect from an available list of 80 classes.
• Automatic Model Download: If the YOLOv8 model weights file is not present, the script will automatically download it from the official Ultralytics repository.
• GPU Acceleration: Automatically utilizes CUDA if available, otherwise falls back to CPU for inference.

Requirements

• Python 3.8 or above
• OpenCV (opencv-python)
• PyTorch (with CUDA support for GPU acceleration)
• Ultralytics YOLO library (ultralytics)
• CUDA Toolkit (required for GPU acceleration)

CUDA Installation

Windows 11

1. Download CUDA Toolkit:

   • Visit the NVIDIA CUDA Toolkit download page.
   • Select your operating system as "Windows" and version as "Windows 11".
   • Choose the installer (e.g., exe file) and follow the installation instructions to install CUDA and the appropriate drivers.

2. Install cuDNN:

   • Visit the NVIDIA cuDNN page and download the cuDNN library that matches your CUDA version.
   • Extract the contents and copy the bin, include, and lib directories into your CUDA Toolkit installation path (e.g., C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\vXX.X).

3. Verify CUDA Installation:

   • Open a command prompt and run:

     nvcc --version

   • This should display the version of CUDA installed.

4. Install the Correct Version of PyTorch:

   • Visit the PyTorch Get Started page.
   • Select the CUDA version that matches your installed CUDA Toolkit, and copy the installation command.
   • Example installation command:

     pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu117

   • Replace cu117 with the appropriate version based on your CUDA installation.

Ubuntu (Latest Version)

1. Update System Packages:

   • Open a terminal and run:

     sudo apt update
     sudo apt upgrade

2. Install CUDA Toolkit:

   • Visit the NVIDIA CUDA Toolkit download page.
   • Select your operating system as "Linux" and version as "Ubuntu".
   • Follow the instructions provided for adding the NVIDIA package repository and installing CUDA.

   Example commands for installing CUDA on Ubuntu:

   wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu$(lsb_release -sr | tr -d '.')/x86_64/cuda-ubuntu$(lsb_release -sr | tr -d '.').pin
   sudo mv cuda-ubuntu$(lsb_release -sr | tr -d '.').pin /etc/apt/preferences.d/cuda-repository-pin-600
   sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu$(lsb_release -sr | tr -d '.')/x86_64/7fa2af80.pub
   sudo add-apt-repository "deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu$(lsb_release -sr | tr -d '.')/x86_64/ /"
   sudo apt update
   sudo apt install -y cuda

3. Install cuDNN:

   • Visit the NVIDIA cuDNN page and download the cuDNN library that matches your CUDA version.
   • Extract the downloaded file and copy the lib, include, and bin directories to your /usr/local/cuda/ directory.

4. Set Up Environment Variables:

   • Add the following lines to your .bashrc file:

     export PATH=/usr/local/cuda/bin:$PATH
     export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH

   • Save the file and update your terminal:

     source ~/.bashrc

5. Verify CUDA Installation:

   • Run:

     nvcc --version

   • This should display the version of CUDA installed.

6. Install the Correct Version of PyTorch:

   • Visit the PyTorch Get Started page.
   • Select the CUDA version that matches your installed CUDA Toolkit, and copy the installation command.
   • Example installation command:

     pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu117

   • Replace cu117 with the appropriate version based on your CUDA installation.

Installation

Step 1: Clone the Repository

Clone this repository to your local machine:

git clone https://github.com/KD5VMF/Yolov8.git
cd Yolov8

Step 2: Create a Virtual Environment (Recommended)

Creating a virtual environment helps keep dependencies organized:

python -m venv yolo_env

Activate the virtual environment:

• On Windows:

  yolo_env\Scripts\activate

• On macOS/Linux:

  source yolo_env/bin/activate

Step 3: Install Dependencies

Install the required dependencies using pip:

pip install -r requirements.txt

If the requirements.txt is not available, you can manually install the dependencies:

pip install opencv-python torch ultralytics screeninfo

Usage

To run the object detection script:

Step 1: Run the Script

python detect.py

Step 2: Follow On-Screen Instructions

• The script will start by displaying a welcome message and loading the YOLOv8 model.
• You will be prompted to choose which objects to detect from a list of available classes.
• The available classes are displayed in a multi-column format for easy readability.
• Enter the numbers of the classes you want to detect, separated by commas, or type ALL to detect all classes.

Example Usage

1. Select Specific Classes for Detection:

   Enter the numbers of the classes you want to detect (comma-separated) or type 'ALL' for all classes: 1, 3, 7
   

   This will enable detection of "person," "car," and "train."

2. Detect All Classes:

   Enter the numbers of the classes you want to detect (comma-separated) or type 'ALL' for all classes: ALL
   

   This will enable detection of all available objects.

Output

• The USB camera feed will be displayed with bounding boxes drawn around the detected objects.

• The labels and confidence scores for each detected object will also be displayed.

• Press the q key to exit the real-time object detection.

Troubleshooting

1. Camera Not Opening:

   • Ensure that your USB camera is connected properly.
   • Verify that the camera is not being used by another application.

2. Model Download Issues:

   • If the model weights file fails to download, manually download yolov8x.pt from the Ultralytics GitHub releases page and place it in the same directory as the script.

3. CUDA Not Available:

   • If CUDA is not detected, the script will default to CPU for inference. Ensure you have installed the correct version of PyTorch with GPU support as described above.

License

This project is licensed under the MIT License. See the LICENSE file for more details.

Acknowledgments

• Ultralytics YOLO for providing the YOLOv8 model.
• OpenCV for real-time image processing.
• PyTorch for enabling deep learning model inference.

Author

ChatGTP 4o

Feel free to reach out for any questions or contributions to improve this project further.