PROPIX GUI Analysis

A lightweight Python library to load, inspect, summarize, and visualize JSON frames generated by the PROPIX GUI.

This package is designed to work directly with PROPIX GUI outputs, including:

• RGB images
• Segmentation masks
• Hyperspectral data (wavelengths and spectra)

It provides a clean, NumPy-friendly API for analysis, debugging, and visualization.

✨ Features

• Load PROPIX GUI JSON frames into Python structures
• Decode base64-encoded RGB images and masks
• Normalize and handle multiple segmentation masks
• Visualize:
  • RGB images
  • Masks (overlayed or individual)
  • RGB + masks
  • Spectral curves
  • RGB + masks + spectra (side-by-side)
• Print a human-readable frame summary:
  • Image resolution
  • Mask coverage (percentage of pixels)
  • Spectral range
  • Number of spectral bands
  • Spectral resolution (Δλ)

📦 Installation

pip install propix_gui_analysis

Or for development:

pip install -e .

🚀 Quick Start

from propix_gui_analysis.propix_json_analysis import (
    load_frame,
    frame_summary,
    plot_rgb,
    plot_rgb_with_masks,
    plot_masks_only,
    plot_spectra,
    plot_frame_rgb_and_spectra,
)

frame = load_frame("frame.json")
frame_summary(frame)

📂 Frame Structure

frame = {
    "frame_id": int,
    "task_finished": bool,

    "rgb": np.ndarray,          # [H, W, 3]
    "masks": List[np.ndarray],  # list of [H, W] masks (values in [0,1])
    "mask_colors": List[List[float]],  # normalized RGBA colors

    "wavelengths": np.ndarray,  # [N]
    "spectra": {
        "values": np.ndarray,   # [num_masks, N]
        "labels": List[str]
    }
}

🖨️ Frame Summary

frame_summary(frame)

📊 Visualization

• plot_rgb(frame)
• plot_rgb_with_masks(frame)
• plot_masks_only(frame)
• plot_spectra(frame)
• plot_frame_rgb_and_spectra(frame)

🔬 Spectral Resolution

Defined as the average wavelength spacing (Δλ) between consecutive bands.

🧰 Dependencies

• Python ≥ 3.8
• NumPy
• Pillow
• Matplotlib

📄 License

MIT License.