## Install
```
$ pip install localaplace
```
This is an implement of Local Laplace Filter algorithm [1] [[Paper](https://people.csail.mit.edu/hasinoff/pubs/ParisEtAl11-lapfilters-lowres.pdf)]. Just see the effect below.
|Image modal| Original | Enhanced |
|:---:|:------------:|:------------:|
|RGB (800 × 533)| <img src="./images/flower.png" alt="Image 1" style="width: 600px; height: auto;"> | <img src="./images/flower_enchanced.png" alt="Image 2" style="width: 600px; height: auto;"> |
|RGBA (600 × 460)| <img src="./images/anime.png" alt="Image 1" style="width: 600px; height: auto;"> | <img src="./images/anime_enchanced.png" alt="Image 2" style="width: 600px; height: auto;"> |
|CT (3072 × 3072)| <img src="./images/ankle.png" alt="Image 1" style="width: 600px; height: auto;"> | <img src="./images/ankle_enchanced.png" alt="Image 2" style="width: 600px; height: auto;"> |
> This is a boring code when I attempt to finish my computer vision homework, but no available python package is found on PyPi
## Usage
Demo is as follows, the only function is `local_laplace_filter` (Please see my detailed comment of the function to see the meaning of arguments):
```python
import os
import cv2
import localaplace as llp
image = cv2.imread('./images/flower.png', cv2.IMREAD_UNCHANGED)
result = llp.local_laplace_filter(
image, 0.2, 0.5, 0.8, 100,
num_workers=-1,
verbose=1,
return_layers=False
)
# save result to disk
if not os.path.exists('results'):
os.makedirs('results')
cv2.imwrite('./results/flower_enchanced.png', result)
```
If you are interested in the Laplace Pyramid, set `return_layers` as `True` to get layers:
```python
result, layers = llp.local_laplace_filter(
image, 0.2, 0.5, 0.8, 100,
num_workers=-1,
verbose=1,
return_layers=True
)
# save inner layers to disk
for i, layer_image in enumerate(layers):
cv2.imwrite(f'./results/layer_{i + 1}.png', layer_image)
```
## Reference
[1] Paris, Sylvain, Samuel W. Hasinoff, and Jan Kautz. "Local Laplacian filters: edge-aware image processing with a Laplacian pyramid." Communications of the ACM 58.3 (2015): 81-91
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