rlsa 0.0.2

Run Length Smoothing Algorithm

RLSA

C implementation of RLSA for use in python.

Usage

Requirements

• python version >= 3.8

Install

Install with:

pip install rlsa

Usage

The main function is rlsa.
It takes as input a black and white image (as a uint8 numpy array), and the hvs, vsv and 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.

Usage example

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 so:

out_img = rlsa_horizontal(binary_img, hsv)
out_img = rlsa_vertical(binary_img, vsv)

Results

Input image	After RLSA

Horizontal only	Vertical only

Included scripts

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.

Test

python -m tests.test assets/rlsa_test_image.jpg

Benchmark

python -m tests.benchmark assets/rlsa_test_image.jpg

--> C version is around 400 times faster than the naive python one.

Misc

Note: the numpy tutorial/doc here is (it seems) outdated (be carefull when using it).