PictoPy

PictoPy is an advanced desktop gallery application that combines the power of Tauri, React, and Rust for the frontend with a Python backend for sophisticated image analysis and management.

Architecture

Frontend

• Tauri: Enables building the desktop application
• React: Used for creating the user interface
• Rust: Powers the backend, which the frontend communicates with through Tauri's API

Backend (Python)

• FastAPI: Serves as the API framework
• SQLite: Database for storing metadata and embeddings
• YOLO: Used for object detection
• FaceNet: Generates face embeddings
• ONNX Runtime: Runs the models efficiently
• DBSCAN: Performs clustering for face embeddings

Backend (Rust via Tauri)

Handles file system operations and provides a secure bridge between the frontend and local system.

Features

• Smart tagging of photos based on detected objects, faces, and their recognition
• Traditional gallery features of album management
• Advanced image analysis with object detection and facial recognition
• Privacy-focused design with offline functionality
• Efficient data handling and parallel processing
• Smart search and retrieval
• Cross-platform compatibility

Technical Stack

Component	Technology
Frontend	React
Desktop Framework	Tauri
Rust Backend	Rust
Python Backend	Python
Database	SQLite
Image Processing	OpenCV, ONNX Runtime
Object Detection	YOLOv8
Face Recognition	FaceNet
API Framework	FastAPI
State Management	React Hooks
Styling	Tailwind CSS
Routing	React Router
UI Components	Radix UI
Build Tool	Vite
Type Checking	TypeScript

Setup

Frontend Setup

Prerequisites

• Node.js (LTS version recommended)
• npm (comes with Node.js)
• Rust (latest stable version)
• Tauri CLI

Installation

1. Clone the repository to your local system:

   git clone git@github.com:AOSSIE-Org/PictoPy.git

   cd PictoPy

2. Navigate to the frontend directory:

   cd frontend

3. Install dependencies:

   npm install

    cd scripts

   On Debian/Ubuntu:

   ./setup_env.sh

   On Windows:

   ./setup_win.ps1
   

Running the Application

npm run tauri dev

Building for Production

Create Signing Keys for tauri using the command:

npm run tauri signer generate

Set the public key in tauri.conf.json as pubkey and private key and password in Enviroment Variables as TAURI_SIGNING_PRIVATE_KEY and TAURI_SIGNING_PRIVATE_KEY_PASSWORD

There is a preset pubkey in tauri.conf.json ; private key and password for it is:

TAURI_SIGNING_PRIVATE_KEY=dW50cnVzdGVkIGNvbW1lbnQ6IHJzaWduIGVuY3J5cHRlZCBzZWNyZXQga2V5ClJXUlRZMEl5NlF2SjE3cWNXOVlQQ0JBTlNITEpOUVoyQ3ZuNTdOSkwyNE1NN2RmVWQ1a0FBQkFBQUFBQUFBQUFBQUlBQUFBQU9XOGpTSFNRd0Q4SjNSbm5Oc1E0OThIUGx6SS9lWXI3ZjJxN3BESEh1QTRiQXlkR2E5aG1oK1g0Tk5kcmFzc0IvZFZScEpubnptRkxlbDlUR2R1d1Y5OGRSYUVmUGoxNTFBcHpQZ1dSS2lHWklZVHNkV1Byd1VQSnZCdTZFWlVGOUFNVENBRlgweUU9Cg==

TAURI_SIGNING_PRIVATE_KEY_PASSWORD=pass

npm run tauri build

Python Backend Setup

Note: For backend setup makes sure that you have Python version 3.12 or lower. Additionally, for Windows, make sure that you are using powershell for the setup, not command prompt.

Installation Steps

1. Navigate to the Backend Directory: Open your terminal and use cd to change directories:

   Bash

   cd backend
   
   

2. Set Up a Virtual Environment (Highly Recommended): Virtual environments isolate project dependencies. Create one using:

   Bash(Linux/MacOS)

   python3 -m venv .env
   
   

   Powershell(Windows)

   python -m venv .env
   
   

3. Activate the Virtual Environment:

   Bash(Linux/MacOS)

   source .env/bin/activate
   
   

   Powershell(Windows)

   .env\Scripts\activate.ps1
   
   

   After activating, you should be able to see the virtual environment's name before the current path. Something like this:

   

4. Install Dependencies: The requirements.txt file lists required packages. Install them using pip:

   Bash

   pip install -r requirements.txt
   
   

5. Missing System Dependencies: Some dependencies might need system-level libraries like libGL.so.1 (often needed by OpenCV). Install the appropriate packages based on your distribution:

   Debian/Ubuntu:

   Bash

   sudo apt update
   sudo apt install -y libglib2.0-dev libgl1-mesa-glx
   
   

   Other Systems: Consult your distribution's documentation for installation instructions.

6. gobject-2.0 Not Found Error: Resolve this error by installing libglib2.0-dev (Debian/Ubuntu):

   Bash

   sudo apt install -y libglib2.0-dev pkg-config
   
   

   For other systems, consult your distribution's documentation.

7. Running the backend:: To start the backend in development mode, run this command while being in the backend folder and the virtual environment activated:

   Bash/Powershell

   fastapi dev
   

   The server will start on http://localhost:8000 by default. In test mode, the server will automatically restart if any errors are detected or if source files are modified.

   

Docker Compose Setup

• Docker Compose

Setup using Dockerfile

• For setting up the frontend, follow the instructions in the Frontend Setup Guide.
  
• For setting up the backend, follow the instructions in the Backend Setup Guide.

Testing

Frontend

cd frontend
npm test

Backend'

• FastAPI

  cd backend
  pytest

• Tauri

  cd frontend/src-tauri/
  cargo test

Additional Resources

• Tauri Documentation
• React Documentation
• FastAPI Documentation

Troubleshooting

If you encounter any issues, please check the respective documentation for Tauri, React, and FastAPI. For persistent problems, feel free to open an issue in the project repository.