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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. Navigate to the frontend directory: 
    cd frontend
  2. Install dependencies: 
    npm install

Running the Application

npm run tauri dev

Building for Production

npm run tauri build

Python Backend Setup

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

    python -m venv venv  # Replace "venv" with your desired environment name

    Activate it for Linux/macOS:

    Bash

    source venv/bin/activate

    Activate it for Windows:

    Bash

    venv\Scripts\activate.bat

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

    Bash

    pip install -r requirements.txt

  4. 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.

  5. Permission Errors with run.sh: If you encounter a "Permission denied" error when running run.sh, grant execute permissions:

    Bash

    chmod +x ./run.sh

  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.

Running the Backend

Once installation and dependency resolution are complete, you can start the backend server:

UNIX-based Systems (Linux, macOS):

Bash

./run.sh  # To run in production mode
./run.sh --test  # To run in testing mode (if a test script exists)

Windows:

Bash

run.bat  # To run in production mode
run.bat --test  # To run in testing mode (if a test script exists)

Us The backend should now be running on port 8000 by default.

Additional Resources

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.