HSE Computer Vision Project

This project implements an object detection system using the YOLOv3 model. It processes video files to detect objects and displays the results in real-time.

Installation

You can install this package directly from the repository or by cloning and building it locally. Below are the instructions for both methods.

Direct Installation from GitHub

To install the package directly from GitHub, run:

pip install git+https://github.com/makarovNick/hse-cv-project.git

Cloning and Installing Locally

Alternatively, you can clone the repository and either use poetry or build the package manually with Python:

1. Clone the repository:

   git clone https://github.com/makarovNick/hse-cv-project.git
   cd hse-cv-project

2. Install using Poetry:

   If you have Poetry installed, you can set up the project and its dependencies by running:

   poetry install

   This will create a virtual environment and install all required dependencies.

3. Build and Install with Python:

   To build the project manually and install it, run:

   python -m build
   pip install dist/*.whl

   This creates a wheel distribution in the dist/ directory, which can then be installed with pip.

Usage

After installation, you can run the application using the yolov3 command followed by optional arguments for specifying the input and output video files.

yolov3 --input path/to/input/video.mp4 --output path/to/output/video.mp4

If no arguments are provided, the application will default to processing road.mp4 and saving the result to out.mp4.