Fast auto-montager using ORB

This repo contains an implementation of the method described in this paper. Please cite the paper if you use the code.

@article{Davidson:18,
author = {Benjamin Davidson and Angelos Kalitzeos and Joseph Carroll and Alfredo Dubra and Sebastien Ourselin and Michel Michaelides and Christos Bergeles},
journal = {Biomed. Opt. Express},
keywords = {Image processing; Image quality; Ophthalmic imaging; Optical imaging; Scanning laser ophthalmoscopy; Wavefront aberrations},
number = {9},
pages = {4317--4328},
publisher = {OSA},
title = {Fast adaptive optics scanning light ophthalmoscope retinal montaging},
volume = {9},
month = {Sep},
year = {2018},
url = {http://www.osapublishing.org/boe/abstract.cfm?URI=boe-9-9-4317},
doi = {10.1364/BOE.9.004317},
}

Getting Started

To install and use requires:

• Python 3
• pip

Installing

1. Download the git repository to a folder of your choice, /path/to/code/autoMontager

2. Install Python package using pip. Ubuntu: pip install /path/to/code/autoMontager; Windowspython -m pip install /path/to/code/autoMontager

Using

• Run the tool from the command line after installing via

auto_montage

Features

• Super fast!
• Will output photoshop .jsx scripts which should be run from photoshop
• Will put multiple FOV images into a single montage, after appropriately resizing

To Use

• Enter movie numbers, movie nominal positions, and movie fovs into an .xlsx (excell) file as in the provided template
• Run the tool from the command line
• Use the GUI to enter the required info and run
• Go to photoshop and run the script generated by the tool

Wishlist

• Output disjoint montage pieces to same document
• Replace numpy interpolation with OpenCV for visualisations without Photoshop
• Use multiprocessing for even faster montaging.