3D TIFF Viewer

A modular Python application for viewing, analyzing, and processing 3D TIFF images with color-based spine analysis and metadata management.

File Structure

The application has been broken down into the following modular files:

Core Modules

• data_in_image.py - Contains the DataInImage class for handling metadata of 3D figure properties
• estimator.py - Contains the Estimator class with all estimation functions for spine analysis
• tiff_viewer_ui.py - Contains the TIFFViewerUI class that handles all UI tasks and interactions
• tiff_viewer_3d.py - Contains the TIFFViewer3D main class for loading and processing TIFF images

Application Entry Points

• main.py - Main entry point to run the application
• Stable_version_see_through.py - Original monolithic file (kept for reference)

Configuration Files

• requirements.txt - Python dependencies
• README.md - This file

Installation

1. Clone or download the project files
2. Install dependencies:

   pip install -r requirements.txt

Usage

Running the Application

python main.py

Using Individual Modules

You can also import and use individual classes:

from data_in_image import DataInImage
from estimator import Estimator
from tiff_viewer_ui import TIFFViewerUI
from tiff_viewer_3d import TIFFViewer3D

# Example usage
data = DataInImage()
# ... configure data ...

estimator = Estimator(image_data, rgb_colors)
# ... run estimations ...

Features

• 3D TIFF Visualization: Interactive 3D plotting of TIFF images
• Color-based Analysis: Select and analyze specific colors/spines
• Performance Optimization: Configurable downsampling, quality settings, and chunked processing
• Metadata Management: Comprehensive metadata handling and export
• Memory Efficiency: Optimized for large datasets with garbage collection and memory mapping

Module Dependencies

main.py
└── tiff_viewer_3d.py
    ├── data_in_image.py
    └── tiff_viewer_ui.py
        ├── data_in_image.py
        └── estimator.py

Performance Settings

The application includes several performance optimization features:

• Downsample Factor: Reduce data size for faster processing
• Render Quality: Adjust rendering quality vs. speed
• Max Points: Limit maximum points for visualization
• Chunk Size: Control memory usage during processing

Notes

• The original Stable_version_see_through.py file is preserved for reference
• All functionality from the original file has been maintained
• The modular structure improves maintainability and allows for easier testing and extension
• Each module has clear separation of concerns and minimal dependencies