Orbital Survey

Transform photographs into sci-fi planetary survey visualizations with technical HUD overlays.

Features

• 🎨 6 Analysis Layers: Contours, gradients, FFT spectrum, color extraction, feature detection, HUD overlay
• 📐 Adaptive Layouts: Auto-detects portrait/landscape/panorama and adjusts panel placement
• 🔍 Resolution Preservation: Maintains full input resolution (no downsampling)
• ⚙️ Highly Configurable: Adjust overlay intensity, detail levels, colors
• 🖼️ Professional Output: High-quality PNG/JPEG with white background theme

Examples

Input: Regular photograph Output: Sci-fi technical analysis visualization with:

• Luminance contours (topographic-style lines)
• Gradient vector field (directional arrows)
• 2D FFT frequency spectrum
• Dominant color extraction
• Feature constellation mesh (Harris corners + Delaunay triangulation)
• Coordinate grid and HUD elements

Quick Start

Installation

# Clone the repository
git clone https://github.com/YOUR_USERNAME/orbital-survey.git
cd orbital-survey

# Create virtual environment
python -m venv venv

# Activate virtual environment
source venv/bin/activate  # Linux/Mac
venv\Scripts\activate     # Windows

# Install dependencies
pip install -r requirements.txt

Usage

Recommended: Use V3 for analysis-focused output

# Interactive mode (file dialog)
python orbital_survey_v3.py

# Command line
python orbital_survey_v3.py input.jpg output.png

# Auto-naming
python orbital_survey_v3.py input.jpg

V3 features (analysis-focused):

• Panels are 50% of canvas (analysis is primary)
• Color analysis is largest section (50% of panel)
• Frequency shown as informative spectral energy graph
• 8 colors with temperature indicators
• Image is smaller (reference, not focus)

Or use V2 for balanced presentation:

python orbital_survey_v2.py input.jpg

• Balanced image/analysis split
• Radial FFT visualization
• 40% panel, 60% image

Configuration

Edit the Config class in orbital_survey_enhanced.py:

class Config:
    # Resolution
    SCALE_FACTOR = 1.0          # 1.0 = preserve, 2.0 = double
    MAX_OUTPUT_WIDTH = 3840     # 4K max

    # Overlays
    BASE_IMAGE_FADE = 0.15      # Image fade amount
    OVERLAY_ALPHA_BOOST = 1.0   # Overlay brightness

    # Detail
    CONTOUR_LEVELS = 20         # Number of contour lines
    MAX_FEATURES = 200          # Feature points
    VECTOR_GRID_SPACING = 30    # Vector density

Version Comparison

V3 (Recommended for Analysis) ⭐

• ✅ Analysis-focused - panels are 50% of canvas
• ✅ Color analysis is primary - 50% of panel space
• ✅ Spectral energy graph - informative frequency analysis
• ✅ 8 colors with temperature indicators (warm/cool)
• ✅ Image reduced (reference, not focus)
• ✅ Full resolution preservation
• ✅ Adaptive layouts

V2 (Balanced Presentation)

• ✅ Balanced image/analysis split (60/40)
• ✅ Radial FFT visualization (decorative)
• ✅ 40% panel with clean design
• ✅ Full resolution preservation
• ✅ Adaptive layouts

Enhanced Version

• ✅ Full resolution preservation
• ✅ Adaptive layouts
• ✅ Better error handling
• 30% side panel

Original Version

• Fixed output width (1920px)
• Side panel only
• Basic configuration

Requirements

• Python 3.8+
• numpy >= 1.24.0
• opencv-python >= 4.8.0
• Pillow >= 10.0.0
• scipy >= 1.11.0
• scikit-learn >= 1.3.0

Documentation

• QUICK_START.md - Setup and usage guide
• README_ENHANCED.md - Feature comparison and details
• ENHANCEMENT_PLAN.md - Future improvements roadmap
• SUMMARY.md - Complete analysis summary

How It Works

1. Contours: Gaussian smoothing + marching squares for topographic lines
2. Gradients: Sobel operators for directional intensity changes
3. FFT: 2D Fast Fourier Transform with log scaling
4. Colors: K-means clustering for dominant color extraction
5. Features: Harris corner detection + Delaunay triangulation
6. HUD: Coordinate grids, brackets, technical readouts

License

MIT License - feel free to use and modify!

Contributing

Contributions welcome! See issues for planned improvements.

Credits

Created with Python, OpenCV, NumPy, SciPy, and scikit-learn.