README.md

Aura

Aura is a comprehensive image processing toolkit designed to provide a wide range of image manipulation capabilities. It offers an intuitive interface and robust performance, making it a powerful tool for anyone interested in image processing, from hobbyists to professionals.

Table of Contents

• Features
• Getting Started
• Technologies Used
• Contributors

Features

Aura comes with a wide range of features:

• Noise Addition: Aura allows users to add various types of noise to images, including salt & pepper noise, uniform noise, and Gaussian noise.

• Image Filtering: Aura supports several image filtering techniques, including average filtering, Gaussian filtering, and median filtering.

• Edge Detection: Aura can apply various edge detection algorithms to images, including Sobel, Roberts, Prewitt, and Canny.

• Frequency Domain Filters: Aura supports applying frequency domain filters to images, including low pass and high pass filters. It can also create hybrid images using frequency domain.

• Image Processing: Aura can apply grayscale, normalization, and equalization to images, and it provides a histogram for visualizing the image's color distribution.

• Thresholding: Aura supports both global and local thresholding, allowing users to adjust the intensity levels in an image to separate objects from the background. It includes Otsu, Optimal, and Spectral thresholding methods.

• Hough Transform: Aura can perform Hough Transform for line, circles, and ellipses detection.

• Active Contouring: Aura supports active contouring for calculating perimeter and area of detected shapes.

• Corner Detection: Aura can detect corners in an image using Harris and Lambda operators.

• SIFT Descriptors: Aura uses Scale-Invariant Feature Transform (SIFT) descriptors to detect keypoints and apply image matching using Sum of Squared Differences (SSD) and Normalized Cross Correlation (NCC).

• Image Segmentation: Aura supports various image segmentation techniques, including K-Means Clustering, Mean Shift Segmentation, Agglomerative Segmentation, and Region Growing Segmentation.

• Face Detection and Recognition: Aura can detect faces in an image and recognize them using Principal Component Analysis (PCA) and Support Vector Machine (SVM) classifier.

These features make Aura a versatile tool for a wide range of image processing tasks.

Getting Started

Prerequisites

• Node.js
• npm
• Python 3.6 or higher

Installation

1. Clone this repository to your local machine.

2. Install the required front-end dependencies.

cd client
npm install

3. Run the front-end.

npm run dev

3. Open new trminal, and install the required server dependencies.

cd server
python -m venv venv
.\venv\Scripts\activate
pip install -r requirements.txt

4. Run the back-end.

uvicorn main:app --reload

5. Enjoy working with Aura :)

Technologies Used

This project leverages a variety of modern technologies to deliver a robust and efficient application. Here's a brief overview of the key technologies used:

• Desktop Application

  • Electron.js
  • React.js
  • TypeScript
  • Tanstack Router
  • Tailwind CSS
  • Shadcn/UI
  • React Hook Form
  • Zustand

• Backend

  • FastAPI
  • OpenCV
  • NumPy
  • scikit-learn

• Landing Page

  • React.js
  • Next.js
  • TypeScript
  • Tailwind CSS
  • Framer Motion
  • Three.js
  • React Three Fiber

Contributors

Abdallah Magdy

Habiba Mohsen

Hazem Raafat

Merna Abdelmoez

Raghda Tarek

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