Spot Finder

Our goal is to make parking more accessible, convenient, and sustainable for everyone. Spot Finder aims to solve the problem of underutilized parking lots by providing data-driven solutions. Spot Finder will help individuals and businesses optimize their parking spaces, reducing congestion and increasing efficiency.

Project Overview

Spot Finder is a comprehensive solution that integrates computer vision and machine learning to detect empty parking spots and provide real-time updates to users. Our system includes both a server and frontend application to deliver a seamless user experience.

Key Features

• Real-time Parking Detection: Utilizing advanced computer vision techniques to identify available parking spots.
• User-Friendly Dashboard: Visual representation of parking data for easy understanding and decision-making.
• Interactive Mobile App: Allows users to find and book parking spots quickly and efficiently.

Visual Demonstrations

Real-time Parking Detection

Our system accurately detects empty parking spots and highlights them for easy identification.

User Dashboard

The dashboard provides a comprehensive overview of parking data including duration, fare, and parking patterns.

Mobile App Interface

• Our mobile app offers a user-friendly interface to find and book parking spots.
• Users can select available parking spots and confirm their booking through the app.

Technologies Used

• Python
• Flask
• TensorFlow
• OpenCV
• React
• Redux
• Material-UI
• React Native

Tech Stack

• React (Typescript): A popular Javascript library for building user interfaces.
• Next.js (A react framework for server-side rendering and generating static websites.)
• node.js (version 18+ required JavaScript runtime for server-side development node.js)
• yarn/npm (Package manager to handle dependencies)

Getting Started

Prerequisites

Ensure you have the following installed on your development environment:

• Node.js (version 18 or above)
• yarn or npm package manager

Installation

Clone the repository and install the dependencies:

> git clone /~https://github.com/amainnain122/spot-finder.git
> cd spot-finder
> cd client # where the fontend project is located
> yarn install # install packages

Running the Application Locally

Start the local development server

> yarn dev # run the app

Running the Application via Docker

> bash run.sh # or
> docker build -t spot-finder-app:1.0 . # buld app
> docker image ls # list images
> docker run -d -p 3001:3001 --name spot-finder spot-finder-app:1.0
> docker image prune -a --filter "until=24h" -f # clear images

Project Folder Structure

├── public
├── src
   ├── app <- All the pages and app config
   ├── assets <- Images, Fonts used in the project
   ├── atoms <- reusable UI
   ├── compponent <- Reusable components
   ├── components <- Reusable components from shadcn UI
   ├── data <- mock or dummy data
   ├── lib <- Contains helper function and API connections
├── index.html <- Entry Point
├── README.md <- Developer Documentation
├── package.json <- list of library used

Deployment

The project is deployed on vercel. You can access the web application here Web App Link