smart_asset_analyser 0.1.3

A Flutter package to detect visually identical and similar assets using Deep Visual Embeddings (CLIP). Supports images, SVGs, and Lottie animations with interactive HTML reports.

Smart Asset Analyser

A powerful Flutter package that detects visually identical and similar assets in your Flutter project using Deep Visual Embeddings (CLIP).

Features

• 🔍 Deep Visual Embeddings: Uses CLIP model for accurate visual similarity detection
• 🖼️ Multiple Asset Types: Supports images (PNG, JPG, WebP), SVGs, and Lottie JSON animations
• 📊 Interactive HTML Report: Beautiful, filterable HTML report with similarity percentages
• ⚡ Fast Processing: Efficient batch processing with caching
• 🎯 Configurable: Customizable similarity thresholds and filters

Quick Start

1. Install Python Dependencies (One-time)

🎯 Easy Way - Automatic Detection:

The package automatically finds requirements.txt for you! Just run:

# In your Flutter project, run:
dart run smart_asset_analyser analyse assets --show-requirements

This will display:

• ✅ Exact path to requirements.txt
• ✅ Ready-to-use installation command

Then simply copy and run the shown command:

pip install -r "<path-shown>"

💡 Pro Tip: If you try to run the analyser without Python dependencies, it will automatically show you where requirements.txt is located and provide the exact installation command!

Alternative - Manual Installation:

pip install torch transformers pillow numpy clip-by-openai cairosvg python-lottie

Note: The first run will automatically download the CLIP model (~150MB).

2. Add to Your Flutter Project

Add to your Flutter project's pubspec.yaml as a dev dependency:

dev_dependencies:
  smart_asset_analyser: ^0.1.1

Then:

cd /path/to/your/flutter/project
flutter pub get

Note: This is a dev dependency because it's a development tool for analyzing assets, not needed at runtime.

3. Run the Analyser

dart run smart_asset_analyser analyse assets

Note: The correct command is dart run smart_asset_analyser analyse assets (space, not colon).

4. View Results

Open asset_report.html in your browser!

Usage Examples

# Basic analysis
dart run smart_asset_analyser analyse assets

# Find requirements.txt location
dart run smart_asset_analyser analyse assets --show-requirements

# Only images
dart run smart_asset_analyser analyse assets --types images

# Higher similarity threshold
dart run smart_asset_analyser analyse assets --threshold 0.95 --min-similarity 95

# Custom output location
dart run smart_asset_analyser analyse assets --output reports/duplicates.html

# Exclude test assets
dart run smart_asset_analyser analyse assets --exclude "**/test/**"

Command Options

dart run smart_asset_analyser analyse assets [options]

Options:
  --show-requirements          Show location of requirements.txt file
  --threshold <0.0-1.0>      Similarity threshold (default: 0.85)
  --min-similarity <0-100>    Minimum similarity percentage (default: 85)
  --output <path>             Output HTML file path (default: asset_report.html)
  --types <types>              Asset types: images,svgs,lottie (comma-separated, default: all)
  --exclude <pattern>          Exclude files matching pattern (glob)
  --project-path <path>        Flutter project path (default: current directory)
  --python-path <path>         Path to Python executable (default: python3)
  --use-server                 Use HTTP server mode for Python bridge (faster)
  --server-port <port>         HTTP server port (default: 8000)
  --cache-embeddings           Cache embeddings to disk (default: true)

Finding requirements.txt

Easy Discovery:

dart run smart_asset_analyser analyse assets --show-requirements

What you'll see:

🔍 Finding requirements.txt...

✅ Found requirements.txt at:
   /path/to/smart_asset_analyser-0.1.1/requirements.txt

📦 Install Python dependencies with:
   pip install -r "/path/to/smart_asset_analyser-0.1.1/requirements.txt"

Automatic Detection: The tool automatically finds requirements.txt when dependencies are missing, so you don't need to search for it manually!

How It Works

1. Asset Discovery: Scans pubspec.yaml and assets/ folders to find all assets
2. Processing:
   • Images: Direct processing
   • SVGs: Rasterized to PNG images
   • Lottie: Key frames extracted and processed
3. Embedding Generation: Uses CLIP model (via Python) to generate visual embeddings
4. Similarity Calculation: Compares embeddings using cosine similarity
5. Report Generation: Creates an interactive HTML report with filtering options

Report Features

The generated HTML report includes:

• Statistics Dashboard: Total assets, groups, pairs, potential savings
• Interactive Filters:
  • Similarity percentage slider (0-100%)
  • Asset type filter (Images, SVGs, Lottie)
  • Search by filename
• Visual Comparison: Side-by-side comparison with similarity percentage
• Group View: All similar assets grouped together

Supported Asset Types

• Images: PNG, JPG, JPEG, WebP, GIF, BMP
• SVGs: Scalable Vector Graphics (rasterized for comparison)
• Lottie: JSON animation files (key frames extracted and averaged)

Prerequisites

• Python 3.8+ with pip
• Dart SDK 3.0+
• Flutter project with assets

Installation Details

Python Dependencies

The package requires:

• torch - PyTorch for CLIP model
• transformers or clip-by-openai - CLIP implementation
• pillow - Image processing
• cairosvg - SVG rasterization
• python-lottie - Lottie frame extraction

Easy Installation (Recommended):

The package automatically detects requirements.txt location. Just run:

dart run smart_asset_analyser analyse assets --show-requirements

This will show you:

• 📄 Exact path to requirements.txt
• 📦 Ready-to-use pip install command

Simply copy and run the command shown in the output!

What happens automatically:

• When dependencies are missing, the tool shows the path automatically
• No need to manually search for the file
• Works whether installed from pub.dev or used locally

Manual Installation:

pip install torch transformers pillow numpy clip-by-openai cairosvg python-lottie

Example Project

An example Flutter project is included in the example/ folder. To try it:

cd example
flutter pub get
dart run smart_asset_analyser analyse assets

This demonstrates how to use the package in a real Flutter project.

Troubleshooting

Python Not Found

dart run smart_asset_analyser analyse assets --python-path /usr/local/bin/python3

Missing Dependencies

Automatic Help: When you run the analyser without Python dependencies, you'll see:

⚠️  Python dependencies not found!

📄 Found requirements.txt at:
   /path/to/smart_asset_analyser-0.1.1/requirements.txt

✅ Install Python dependencies with:
   pip install -r "/path/to/smart_asset_analyser-0.1.1/requirements.txt"

Simply copy and run the shown command!

Manual Discovery: If you want to find requirements.txt before running:

dart run smart_asset_analyser analyse assets --show-requirements

This will show you the exact path and installation command.

CLIP Model Download Issues

• Check internet connection
• Ensure sufficient disk space (~200MB)
• Try running Python service manually to see detailed errors

Documentation

• QUICK_START.md - Quick setup guide
• USAGE.md - Detailed usage instructions
• INSTALLATION.md - Installation guide
• EXAMPLE.md - Complete walkthrough example
• SETUP.md - Detailed setup guide
• IMPLEMENTATION_PLAN.md - Technical details

Performance Tips

1. Use Caching: Embeddings are cached by default for faster subsequent runs
2. HTTP Server Mode: Use --use-server for large projects (keeps model in memory)
3. Filter Types: Only analyze what you need with --types
4. Exclude Patterns: Skip unnecessary files with --exclude

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

MIT License