Wide-angle Image Correction

This package is an attempt to process images taken with a wide-angle
lens and deduce the approximate angle of distortion using a genetic
algorithm. Once the angle of distortion is deduced then the image can
be corrected using a conformal mapping.

At a high level the approach works as follows:

1. From initial image, perform a Hough Transform to detect all
   straight lines
2. Create an initial set of line pairs by randomly shuffling all
   detected lines and pairing neighbors together
3. Compute the slope of each line, and then compute the angle
   between slopes of each pair (loss function)
4. Create parent sets of line pairs determined by the pairs with
   the smallest angles
5. From the set of parents, create children pairs with random
   mutations of the start/stop points
6. Compute the new loss function
7. Recurse on steps 4 - 6

The interpretation of the entire algorithm is that once recursion has
terminated then all of the pairs of lines are either parallel or
quasi-parallel.

If the original image had a wide-angle distortion
then the final pairs of lines will not be exactly parallel, and will
instead have a slight angular separation between them. This average
angular separation is interpreted as the angle of distortion from the
photo lens. Once this angle is determined then the original image
can have the distortion removed by some conformal mapping (TBD).

Note: This is intended as an experiment and learning experience
with genetic algorithms and is not intended as a definite solution to
determining arbitrary wide-angle lens distortions. If you would like
to contribute to this project or offer insight please open a ticket or
contact me directly. Any contributions are greatly appreciated.

Setup

Checkout the package and install dependencies by running

git clone https://github.com/brainsqueeze/Image_correction.git
cd ${HOME}/Image_correction
pip install -r requirements.txt

Run algorithm on demo image

The learning algorithm can be run on the provided sample image by
running

python -m src.find_parallel

To do

1. The final conformal mapping on the original image is not yet
   implemented.
2. There are improvements needed for pruning poorly
   performing species during each generational epoch.