OpenCV YOLO Project

An Object Detection Project using OpenCV and YOLO

This project demonstrates how to build OpenCV from source with GStreamer support, how to use it for real-time object detection using the YOLO model (best.pt), and how to transmit the video stream over a local area network (LAN) using sockets.

Requirements

• OpenCV with GStreamer support
• YOLOv11 or another compatible YOLO model
• GStreamer and its necessary plugins
• Python libraries: socket, opencv-python, numpy, torch, and ultralytics

Features

• GStreamer integration with OpenCV for high-performance video processing
• Real-time object detection using the YOLO model
• Video transmission over LAN using UDP sockets
• Python-based implementation for ease of use and customization
• Dockerized environment for easy deployment across different systems

Installation

Follow the steps below to build OpenCV with GStreamer support, set up the YOLO model for object detection, and enable LAN-based video transmission.

Step 1: Install Dependencies

First, ensure that all necessary dependencies are installed on your system:

sudo apt update
sudo apt install -y build-essential cmake git pkg-config libgtk-3-dev \
    libavcodec-dev libavformat-dev libswscale-dev libv4l-dev \
    libxvidcore-dev libx264-dev libjpeg-dev libpng-dev libtiff-dev \
    gfortran openexr libatlas-base-dev python3-dev python3-numpy \
    libtbb2 libtbb-dev libdc1394-22-dev libgstreamer1.0-dev \
    libgstreamer-plugins-base1.0-dev libgstreamer-plugins-good1.0-dev

Step 2: Clone the OpenCV Repository

Next, clone the OpenCV and OpenCV contrib repositories from GitHub:

cd ~
git clone https://github.com/opencv/opencv.git
git clone https://github.com/opencv/opencv_contrib.git

Step 3: Build OpenCV with GStreamer support

Now, let's build OpenCV from source:

cd ~/opencv
mkdir build
cd build

cmake -D CMAKE_BUILD_TYPE=RELEASE \
      -D CMAKE_INSTALL_PREFIX=/usr/local \
      -D OPENCV_EXTRA_MODULES_PATH=~/opencv_contrib/modules \
      -D WITH_GSTREAMER=ON \
      -D WITH_TBB=ON \
      -D BUILD_EXAMPLES=ON ..

make -j$(nproc)  # Parallel build using the number of available cores
sudo make install
sudo ldconfig  # Update shared library cache

Step 4: Verify the installation

python3 -c "import cv2; print(cv2.getBuildInformation())"

Look for a line that says GStreamer: YES. If it says NO, go back and check if the correct GStreamer libraries were installed.

YOLO Model Setup

This project uses the YOLO model for object detection. You can download the pre-trained weights or train your own model.

To use YOLO, first install the ultralytics package:

pip install torch torchvision ultralytics

Then, make sure you have your best.pt model file ready.

Docker

To easily deploy this project using Docker, you can create a Dockerfile as follows:

FROM ubuntu:20.04

# Install OpenCV dependencies
RUN apt update && apt install -y build-essential cmake git pkg-config \
    libgtk-3-dev libavcodec-dev libavformat-dev libswscale-dev \
    libv4l-dev libxvidcore-dev libx264-dev libjpeg-dev libpng-dev \
    libtiff-dev gfortran openexr libatlas-base-dev python3-dev python3-numpy \
    libtbb2 libtbb-dev libdc1394-22-dev libgstreamer1.0-dev \
    libgstreamer-plugins-base1.0-dev libgstreamer-plugins-good1.0-dev

# Install Python dependencies
RUN pip install torch torchvision ultralytics

# Copy project files
COPY . /usr/src/app
WORKDIR /usr/src/app

CMD ["python3", "your_script.py"]

To build and run the Docker container:

docker build -t my-opencv-yolo .
docker run -it --rm my-opencv-yolo

License

MIT