Comparison of Image Stitching Methods

Evaluation tool for image stitching methods. Some methods included:

• SURF, SIFT, AKAZE
• An area based method by Öfverstedt et al..
• Frequency based method
• Learning based methods (see deep-homography branch and notebooks within).

This is the corresponding repository for the thesis project "Implementation and Evaluation of Image Stitching Methods" (2020) by Tristan Wright

Cloning

This repository contains a submodule for the Öfverstedt method repository. If you would like to pull the code for it when pulling this repo use:

git clone --recursive https://github.com/tristaaan/stitch-eval

If you have already pulled you can update the submodule with:

git submodule update --init

Installing

With Anaconda:

conda create --name stitch-eval --file requirements.txt

Separately you'll need to install imutils with pip:

pip install imutils # not sure why anaconda cannot find this.

Can install these libraries with pip too but much easier to manage with anaconda.

Libraries for machine learning methods not included as they're very system dependent. Safe to say one will need Keras and TensorFlow.

Scripts

im_split.py

$ python im_split.py -h
usage: im_split.py [-h] [-noise NOISE] [-rotation ROTATION] [-overlap OVERLAP] [-blocks BLOCKS] [-downsample DOWNSAMPLE] [-file FILE]

Split an image into blockswith some overlap, rotation,and/or noise

optional arguments:
  -h, --help            show this help message and exit
  -noise NOISE, -n NOISE
                        add noise
  -rotation ROTATION, -r ROTATION
                        add rotation
  -overlap OVERLAP, -o OVERLAP
                        overlap percentage [0...1]
  -blocks BLOCKS, -b BLOCKS
                        number of blocks, must be a square number
  -downsample DOWNSAMPLE, -ds DOWNSAMPLE
                        downsample output images
  -file FILE, -f FILE   image filename

evaluator.py

$ python evaluator.py -h
usage: evaluator.py [-h] [-noise] [-rotation] [-overlap] [-o_range O_RANGE]
                    [-r_range R_RANGE] (-file FILE | -dir DIR)
                    [-method METHOD] [-downsample DOWNSAMPLE] [-output]
                    [-debug] [-record_all] [-tex] [-viz]

Run evaluations with a method

optional arguments:
  -h, --help            show this help message and exit
  -noise                run noise evaluations
  -rotation             run rotation evaluations
  -overlap              run overlap evaluations
  -o_range O_RANGE      range of overlap start:end:stride
  -r_range R_RANGE      range of rotation, start:end:stride, for negative
                        values use -r_range="..."
  -file FILE, -f FILE   image filename
  -dir DIR, -d DIR      run evaluations on all images in a directory
  -method METHOD, -m METHOD
                        method to evaluate
  -downsample DOWNSAMPLE, -ds DOWNSAMPLE
                        downsample images
  -output, -o           output results to csv
  -debug                write the stitched image after each stitch
  -record_all, -r       record all errors and average them regardless if they
                        are under the error threshold
  -tex                  output results to LaTeX table
  -viz, -vis            create a heatmap of the results

visualization.py

$ python visualization.py -h
usage: visualization.py [-h] (-file FILE | -dir DIR) [-o O] [-threshold THRESHOLD] [-save_csvs]

Visualize csv results

optional arguments:
  -h, --help            show this help message and exit
  -file FILE, -f FILE   input csv
  -dir DIR, -d DIR      input directory
  -o O                  output directory
  -threshold THRESHOLD, -t THRESHOLD
                        error threshold
  -save_csvs, -s        save csv's with error and success columns