Object counting using ultralytics

This project utilizes Ultralytics to perform object counting, incorporating advanced tracking algorithms for enhanced accuracy and efficiency. The supported tracking algorithms include BOTSORT and BYTETRACK.

Features

• Object counting using state-of-the-art deep learning models
• Counts object moving in and out
• Flexible configuration options for model customization
• Object tracking for improved accuracy and robustness
• Support for BOTSORT and BYTETRACK algorithms

Installation

• Create a virtual environment: (recommended)

Linux/macOS:

  # Create a virtual environment
    python3 -m venv venv

  # Activate the virtual environment
    source venv/bin/activate
  

Windows:

  # Create a virtual environment
    python -m venv venv

  # Activate the virtual environment
    venv\Scripts\activate
  

• Clone the repository:

  git clone https://github.com/Anandukc/object_counting.git

• Install ultralytics: note: Install the latest version

  pip install ultralytics

• Set the video path

  Modify the line cap = cv2.VideoCapture("path to input image")" in the detect.py file to the path of bottle.mp4

• Run the script:

  python detect.py

note: users can set the classes of object they want to detect by using the argument classes=[0,1,2]

output result