GitHub - Staneering/Vehicle_Detection_and

Vehicle Detection and Classification System

Project Overview

This project implements a vehicle detection and classification system using traditional machine learning techniques such as Histogram of Oriented Gradients (HOG) and Support Vector Machines (SVM). The system is designed to detect and classify vehicles from images, and it is deployed via a Flask API for easy integration and use.

Key Features

Vehicle Detection: Using HOG and SVM to detect vehicles in images.
Vehicle Classification: Classifies vehicles into categories such as cars, trucks, and bikes using machine learning models like Random Forest.
Flask API: A simple Flask-based API to serve predictions and integrate with other applications.
Documentation: A comprehensive guide on how to contribute to the project, including coding standards, testing, and branch management.

Installation

To get started with this project, follow the steps below to set it up on your local machine.

Prerequisites

Python 3.x (recommend 3.6+)
pip (Python package manager)

Clone the repository

Clone this repository to your local machine:

git clone https://github.com/your-username/vehicle-detection-classification.git

Install dependencies

Navigate to the project directory and install the required dependencies:

cd vehicle-detection-classification
pip install -r requirements.txt

Set up the environment

It's recommended to use a virtual environment to manage the dependencies:

python3 -m venv venv
source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

Then, install the dependencies:

pip install -r requirements.txt

Usage

Running the Application

To run the Flask API locally, follow these steps:

Start the Flask API server:

The server will start running at http://127.0.0.1:5000/.
API Endpoint: The /predict endpoint accepts image files and returns vehicle detection and classification results.

Example request using curl:
```
curl -X POST -F "file=@path/to/your/image.jpg" http://127.0.0.1:5000/predict
```
Example response:
```
{
    "vehicle_type": "car",
    "confidence": 0.92
}
```

Testing

To run the unit tests for the project:

python -m unittest discover tests/

This will execute all the test cases and provide feedback on whether everything is working as expected.

Project Structure

The project is organized as follows:

vehicle-detection-classification/
├── .github/                  # GitHub workflows, templates, etc.
│   ├── ISSUE_TEMPLATE/
│   ├── PULL_REQUEST_TEMPLATE.md
├── app.py                     # Main Flask app for serving predictions
├── data_collection/           # Scripts for collecting and organizing datasets
├── preprocessing/             # Scripts for data preprocessing
├── feature_extraction/        # Scripts for extracting features like HOG
├── model_training/            # Scripts for training machine learning models
├── evaluation/                # Scripts for evaluating model performance
├── deployment/                # Flask API code and deployment files
├── docs/                      # Documentation, including the contributing guide
├── tests/                     # Unit tests for validating the code
├── .gitignore                 # Git ignore file for unnecessary files
├── requirements.txt           # Project dependencies
├── README.md                  # Project overview and setup instructions
└── CONTRIBUTING.md            # Guidelines for contributing to the project

`docs/`

CONTRIBUTING.md: Guide for contributing to the project, including Git workflow, commit messages, and pull requests.

`tests/`

Contains unit tests for ensuring the functionality of each module in the project.

Contributing

We welcome contributions! If you want to contribute to the project, please follow these steps:

Fork the repository.
Clone your forked repository.
Create a new feature branch.
Implement your changes.
Commit your changes with a clear and concise message.
Push your changes to your forked repository.
Open a Pull Request (PR) from your feature branch to the develop branch.

Please refer to our CONTRIBUTING.md file for more detailed instructions and guidelines on contributing.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Conclusion

This README.md provides all the necessary instructions to get the project up and running. It includes details about installation, usage, project structure, testing, and contributing to the project.