PyFibreBundle

PyFibreBundle is a Python package for processing of images captured through optical fibre bundles.

The project is hosted on github. The latest stable release can be installed via pip:

pip install PyFibreBundle

The package supports fibre core pattern removal by filtering and triangular linear interpolation, background correction and flat fielding, as well as automatic bundle location, cropping and masking. Both monochrome and colour images can be processed. The PyBundle class is the preferred way to access this functionality, but the lower level functions can also be used directly for greater customisation. The Mosaic class provides mosaicing via normalised cross correlation, and the SuperRes class allows multiple shifted images to be combined to improve resolution.

The package is designed to be fast enough for use in imaging GUIs as well as for offline research - frame rates of over 100 fps can be achieved on mid-level hardware, including core removal and mosaicing. The Numba just-in-time compiler is used to accelerate key portions of code (particularly triangular linear interpolation) and OpenCV is used for fast mosaicing. If the Numba package is not installed then PyFibreBundle falls back on Python interpreted code.

PyFibreBundle is developed mainly by Mike Hughes’s lab in the Applied Optics Group, School of Physics and Astronomy, University of Kent. Bug reports, contributions and pull requests are welcome.

Contents

• Installation
  • Dependencies
• Basic Image Processing
  • Getting Started
  • Filtering
  • Background and Normalisation
  • Cropping
  • Masking
  • Image Type and Autocontrast
  • Examples
• Linear Interpolation
  • Object Oriented Approach
  • Lower level functions
  • Example
• PyBundle class
  • Instantiatation
  • Methods
• Mosaicing
  • Getting Started
  • Methods
  • Usage Notes
  • Low Level Functions
  • Example
• Super Resolution
  • Getting Started
  • Using Known Shifts
  • Correcting for Intensity Difference Between Images
  • Using Background/Normalisation Stack
  • Using Lower Level Functions
  • Parameterised Shifts
  • Calibration Look-up-table
  • Function Reference
  • Implementation Details
  • Examples
• Using the Low-Level Functions
• Function Reference
  • Classes
  • Low-Level Functions for Bundle finding, cropping, masking
  • Low Level Functions for Spatial Filtering
  • Functions for Triangular Linear Interpolation
  • Utility Functions

• Index

Acknowledgements: Cheng Yong Xin, Joseph, contributed to triangular linear interpolation; Petros Giataganas who developed some of the Matlab code that parts of this library were ported from. Funding to Mike Hughes’s lab from EPSRC (Ultrathin fluorescence microscope in a needle, EP/R019274/1), Royal Society (Ultrathin Inline Holographic Microscopy).