Tech-A-Thon Project: Tech4Stack

Welcome to the Tech4Stack project! This repository contains all the code, data, and resources needed to deploy and understand the solutions presented at the hackathon.

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Table of Contents

• Project Overview
• Features
• Folder Structure
• Setup Instructions
• Usage
• Dependencies
• Contributing
• Screenshots and Visuals

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Project Overview

The Tech4Stack project aims to solve challenges in object detection, pathfinding, and warehouse management through:

• Box Detection: Identifying and analyzing object properties.
• Pathfinding Algorithms: Implementing efficient algorithms to navigate mazes or solve spatial problems.
• Warehouse Management System: Providing tools for inventory management and task tracking.

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Features

1. Object Detection and Analysis
   • Detect and annotate objects in images.
   • Generate detailed reports for detections.
2. Pathfinding Algorithms
   • Implementations of A*, BFS, and Dijkstra’s algorithms.
   • Interactive maze-solving tools.
3. Warehouse Management System
   • Frontend and backend for managing inventory, tasks, and path planning.
   • Dashboard with visual metrics and summaries.
4. Combined Deployment
   • Integrated deployment scripts for real-world application.

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Key Folder Descriptions

• Deployment: Contains deployable scripts for the Box Detection and Path Finder features.

  • Box Detection: Includes app.py for backend and frontend.py for the user interface.
  • Path Finder: Includes app.py for pathfinding logic and frontend.py for visualization.

• Task1: Hosts scripts, input images, and output data for object detection.

  • Scripts: Contains the primary Python scripts (one.py, four.ipynb) for detection tasks.
  • Images: Holds the input images for processing (e.g., obj1.jpg, obj2.jpg).
  • Outputs: Stores processed results, including annotated images and a CSV report (box_properties.csv).

• Path: Focuses on pathfinding algorithms and their visualizations.

  • Algorithms: Includes scripts for various algorithms like A*, BFS, and Dijkstra's.
  • Mazes: Contains input maze images and solved outputs.
  • Notebooks: Contains Jupyter notebooks (e.g., maze3.ipynb) for interactive exploration.

• Warehouse: A complete system for warehouse management.

  • Backend: Includes server-side logic and database configuration.
  • Frontend: Contains components and public files for the user interface.

• Miscellaneous Files:

  • requirements.txt: Python dependencies.
  • maze5.py: Additional maze-related script.
  • .gitignore: Specifies files and folders to ignore in version control.

Setup Instructions

Prerequisites

1. Python 3.8+
2. Node.js and npm
3. Pipenv (for virtual environment management)

Steps

1. Clone the repository: bash git clone https://github.com/your-repo/Tech-A-Thon_Tech4Stack.git cd Tech-A-Thon_Tech4Stack

2. Install Python dependencies: bash pip install -r requirements.txt

3. Setup the warehouse frontend: bash cd Warehouse/frontend npm install npm start

4. Run backend servers for deployment: bash python Deployment/Box\ Detection/app.py python Deployment/Path\ Finder/app.py

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Usage

Object Detection

1. Place images in the Task1/ folder.
2. Run detection scripts (e.g., one.py).
3. Check annotated images and reports in Task1/output/.

Pathfinding

1. Use maze images in the Path/ folder.
2. Execute pathfinding scripts (e.g., astar.py).
3. View solved maze outputs.

Warehouse Management System

1. Access the frontend via http://localhost:3000.
2. Add tasks, manage inventory, and analyze metrics.

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Dependencies

Key packages and tools:

• Python: Flask, OpenCV, numpy
• Node.js: React, Axios
• Others: Pandas, Matplotlib

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Contributing

Contributions are welcome! Follow these steps:

1. Fork the repository.
2. Create a new branch.
3. Commit your changes.
4. Open a pull request.

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Screenshots and Visuals

Example Maze Solution

![Solved Maze]

Annotated Object Detection

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For any questions or issues, feel free to open an issue or contact the contributors.

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