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

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

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

Windows:

Using PowerShell (Recommended):

powershell

.\run-server.ps1  # To run in production mode
.\run-server.ps1 --test  # To run in testing mode

Note: If you encounter a PowerShell execution policy error, run this command first:

powershell

Set-ExecutionPolicy -ExecutionPolicy RemoteSigned -Scope CurrentUser

Alternative using Batch (Legacy): bash

run.bat  # To run in production mode
run.bat --test # To run in testing mode

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.

You can control the number of workers by setting the WORKERS environment variable before running the script. If not set, it defaults to 1 worker.

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.

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.