C implementation of RLSA for use in python.
- Python: 3.8+
Install with:
pip install rlsa
The main function is rlsa.
It takes as input a black and white image (as a uint8 numpy array), and the hvs, vsv and (optionally) ahvs values.
The function returns a new black and white image, leaving the original one intact.
You can also import the rlsa_horizontal and rlsa_vertical functions to apply only one of the RLSA components.
A full example would be:
import cv2
from rlsa import rlsa
img = cv2.imread("assets/rlsa_test_image.jpg", cv2.IMREAD_GRAYSCALE)
_, binary_img = cv2.threshold(img, 190, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
hsv = vsv = 25
out_img = rlsa(binary_img, hsv, vsv, hsv//10)
With a similar setup, the other functions can be used like this:
out_img = rlsa_horizontal(binary_img, hsv)
out_img = rlsa_vertical(binary_img, vsv)
| Input image | After RLSA |
|---|---|
 |
 |
| Horizontal only | Vertical only |
|---|---|
 |
 |
A few scripts are included in the tests folder. One is a python implementation of rlsa, serving as reference. The other two compare the result and speed of the implementations.
To run the those scripts, you need to install opencv.
python -m tests.test assets/rlsa_test_image.jpg
python -m tests.benchmark assets/rlsa_test_image.jpg
--> C version is around 400 times faster than the naive python one.