# embeddings-sort

This program can sort images such that ones with similar motives are close together. This is accomplished by using [AI](https://github.com/minimaxir/imgbeddings) to extract the meaning of the image, and then approximating a travelling-salesperson-tour through all of them.  
As a bonus feature, this program can also sort the images by hue, brightness or color, though the results for this could be improved by using a less generalized algorithm.

The sorting can be accessed by letting the progam print the image paths in order, or by copying/symlinking the images into a new directory.

Detailed usage:

```
Usage: embeddings-sort [OPTIONS] [IMAGES]...

Arguments:
  [IMAGES]...  

Options:
  -e, --embedder <EMBEDDER>        Characteristic to sort by [default: content-angular-distance] [possible values: brightness, hue, color, content-euclidean, content-angular-distance, content-manhatten]
  -s, --symlink-dir <SYMLINK_DIR>  Symlink the sorted images into this directory
  -o, --copy-dir <COPY_DIR>        Copy the sorted images into this directory. Uses COW when available
  -c, --stdout                     Write sorted paths into stdout, one per line
  -0, --stdout0                    Write sorted paths into stdout, null-separated. Overrides -c
  -b, --benchmark                  Output total tour length to stderr
      --tsp-approx <TSP_APPROX>    Algorithm for TSP approximation. Leave as default if unsure [default: christofides] [possible values: mst-dfs, christofides]
  -r <REFINE>                      Number of 2-Opt refinement steps. Has quickly diminishing returns [default: 3]
  -i, --ignore-errors              Ignore failed embeddings
      --hash-seed <HASH_SEED>      Seed for hashing. Random by default
  -h, --help                       Print help
```

## Insides
The christofides implementation uses an approximated min-weight matching algorithm, which may be non-ideal, though I haven't benchmarked how much of a difference it makes (mainly due to the implementation complexity of an exact algorithm, which would also increase the implementations complexity from O(n²) to O(n³) where n is the number of given images).

The 2-Opt refinement algorithm uses a doubly linked list with implicit iteration order to be able to do 2-Opt swaps in O(1), bringing a 2-Opt iteration to O(n²) complexity.

## Compatibility
`embeddings-sort` has, at some point, worked on both linux and windows.