PNG2SVG Converter

🚀 Revolutionary PNG/JPG to SVG converter with THREE advanced algorithms - pixel rectangles, contour paths, and smart polygon simplification using Douglas-Peucker algorithm.

✨ Features

• 🎯 Three Conversion Methods: Choose the best algorithm for your needs
• 🔥 Smart Polygons: Douglas-Peucker algorithm for optimal shape simplification
• 📐 Pixel-Perfect Accuracy: Every black pixel preserved as SVG rectangles
• 🎨 Smooth Contours: Vector paths following original image shapes
• ⚡ High Performance: Optimized algorithms with progress bars
• 🎛️ Flexible Options: Resolution scaling, negative inversion, custom thresholds
• 💻 Cross-Platform: Windows EXE and Python script versions

🚀 Quick Start

Method 1: Download EXE (Windows)

# Download png2svg.exe from releases
png2svg.exe image.png --smart              # Smart polygons (recommended)
png2svg.exe image.png --contours           # Smooth contours  
png2svg.exe image.png                      # Pixel rectangles (default)

Method 2: Python Script

git clone https://github.com/ussdeveloper/png2svg.git
cd png2svg
pip install -r requirements.txt
python png2svg.py image.png --smart

🧠 Conversion Methods

🔥 1. SMART POLYGONS (Recommended)

Best for: Logos, graphics, geometric shapes

png2svg.exe image.png output.svg --smart --resolution 300

• Algorithm: Douglas-Peucker polygon simplification
• Process: Find contours → Simplify vertices → Merge close points → Smooth curves
• Result: Minimal SVG elements with maximum quality
• Example: 2000+ rectangles → 4 smart polygons (99.8% reduction!)

🎨 2. CONTOUR PATHS

Best for: Organic shapes, illustrations

png2svg.exe image.png output.svg --contours --resolution 300

• Algorithm: OpenCV contour detection
• Process: Edge detection → Path tracing → SVG conversion
• Result: Vector paths following image boundaries
• Example: Complex shape → 5-15 smooth SVG paths

📐 3. PIXEL RECTANGLES (Default)

Best for: Pixel art, absolute precision needed

png2svg.exe image.png output.svg --resolution 400

• Algorithm: Pixel grouping and rectangle optimization
• Process: Scan pixels → Group adjacent → Create rectangles → Optimize
• Result: 100% accurate pixel reproduction
• Example: Every black pixel becomes SVG rectangle

📊 Performance Comparison

Method	Elements	File Size	Quality	Speed	Best For
Smart Polygons	4-10	Smallest	Excellent	Fastest	Logos, Graphics
Contour Paths	5-50	Small	Very Good	Fast	Illustrations
Pixel Rectangles	100-5000+	Large	Perfect	Slower	Pixel Art

🎛️ Command Line Options

png2svg.exe input.png [output.svg] [options]

Positional Arguments:
  input                 Input PNG/JPG file
  output                Output SVG file (optional)

Conversion Methods:
  --smart               Smart polygons (Douglas-Peucker + smoothing)
  --contours            Contour paths (original shapes)
  (default)             Pixel rectangles (absolute precision)

Quality Options:
  --resolution [100-1000]    SVG resolution % (default: 100%)
  --scale [1-10]             Image scale factor (default: 5)
  --threshold [1-255]        Binarization threshold (default: 128)

Image Processing:
  --no-negative              Disable negative inversion (enabled by default)

💡 Usage Examples

Basic Conversion

# Smart polygons (recommended for most cases)
png2svg.exe logo.png logo.svg --smart

# High resolution with smart polygons
png2svg.exe image.png output.svg --smart --resolution 500

# Contour paths for illustrations
png2svg.exe drawing.png vector.svg --contours --resolution 300

# Pixel-perfect conversion
png2svg.exe pixelart.png precise.svg --resolution 200

Advanced Options

# Disable negative inversion (for black backgrounds)
png2svg.exe image.png --smart --no-negative

# Custom threshold for fine-tuning
png2svg.exe image.png --contours --threshold 100 --resolution 400

# Maximum quality with smart polygons
png2svg.exe logo.png --smart --resolution 1000 --scale 8

🔬 Technical Details

Douglas-Peucker Algorithm (Smart Polygons)

The smart polygon method implements the Douglas-Peucker line simplification algorithm:

1. Contour Detection: Find shape boundaries using OpenCV
2. Polygon Simplification: Reduce vertices while preserving shape accuracy
3. Point Merging: Combine nearby vertices for cleaner output
4. Smooth Path Generation: Create optimized SVG paths with curves

Benefits:

• 95-99% reduction in SVG elements
• Maintains visual fidelity
• Creates scalable vector graphics
• Optimized for geometric shapes

Pixel Rectangle Method

Advanced rectangle packing algorithm:

1. Horizontal Scanning: Find maximum width rectangles
2. Vertical Extension: Extend rectangles downward when possible
3. Optimization: Minimize total rectangle count
4. Pixel Accuracy: Every source pixel represented

Contour Path Method

OpenCV-based vector tracing:

1. Edge Detection: Identify shape boundaries
2. Hierarchy Processing: Handle nested shapes correctly
3. Path Conversion: Transform contours to SVG paths
4. Hole Support: Proper handling of complex shapes with holes

🛠️ Installation

Prerequisites

• Python 3.8+ (for script version)
• Windows 10+ (for EXE version)

Install Dependencies

pip install opencv-python numpy svgwrite

Build from Source

git clone https://github.com/ussdeveloper/png2svg.git
cd png2svg
pip install -r requirements.txt

# Run directly
python png2svg.py image.png output.svg --smart

# Build EXE (Windows)
./build.bat

# Build on Linux/macOS  
./build.sh

📈 Build System

Windows Build

build.bat                    # Automatic build with dependency check

Linux/macOS Build

chmod +x build.sh
./build.sh                   # Cross-platform build script

Build Features:

• ✅ Automatic dependency installation
• ✅ Clean build process (removes old artifacts)
• ✅ Executable testing and validation
• ✅ PyInstaller optimization for smaller file size

📝 Examples and Results

Logo Conversion (Smart Polygons)

Input:  company_logo.png (complex logo)
Method: --smart --resolution 300
Result: 6 SVG polygons instead of 3,247 rectangles
Size:   89% smaller file, vector scalable

Illustration (Contour Paths)

Input:  drawing.png (hand-drawn illustration)  
Method: --contours --resolution 200
Result: 23 smooth SVG paths
Size:   Vector-perfect curves, artistic quality preserved

Pixel Art (Rectangle Method)

Input:  sprite.png (16x16 pixel character)
Method: default --resolution 800  
Result: 94 optimized rectangles
Size:   100% pixel accuracy, 8x scale factor

🤝 Contributing

1. Fork the repository
2. Create feature branch (git checkout -b feature/amazing-feature)
3. Make changes and test thoroughly
4. Commit changes (git commit -m 'Add amazing feature')
5. Push to branch (git push origin feature/amazing-feature)
6. Open Pull Request

Development Setup

git clone https://github.com/ussdeveloper/png2svg.git
cd png2svg
python -m venv .venv
source .venv/bin/activate  # Windows: .venv\Scripts\activate
pip install -r requirements.txt
python png2svg.py --help

📄 License

MIT License - see LICENSE file for details.

🔗 Links

• Repository: https://github.com/ussdeveloper/png2svg
• Issues: https://github.com/ussdeveloper/png2svg/issues
• Releases: https://github.com/ussdeveloper/png2svg/releases

🏆 Algorithm Comparison

Feature	Smart Polygons	Contour Paths	Pixel Rectangles
Element Count	⭐⭐⭐⭐⭐ (Minimal)	⭐⭐⭐⭐ (Low)	⭐⭐ (High)
File Size	⭐⭐⭐⭐⭐ (Smallest)	⭐⭐⭐⭐ (Small)	⭐⭐ (Large)
Accuracy	⭐⭐⭐⭐ (Excellent)	⭐⭐⭐⭐ (Very Good)	⭐⭐⭐⭐⭐ (Perfect)
Speed	⭐⭐⭐⭐⭐ (Fastest)	⭐⭐⭐⭐ (Fast)	⭐⭐⭐ (Moderate)
Scalability	⭐⭐⭐⭐⭐ (Infinite)	⭐⭐⭐⭐⭐ (Infinite)	⭐⭐⭐⭐⭐ (Infinite)

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🚀 Transform your images into perfect vectors with PNG2SVG!