https://github.com/romanjuranek/pclines-python
PCLines for Python
https://github.com/romanjuranek/pclines-python
hough-transform line-detection pclines pclines-python python
Last synced: 4 months ago
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PCLines for Python
- Host: GitHub
- URL: https://github.com/romanjuranek/pclines-python
- Owner: RomanJuranek
- License: bsd-3-clause
- Created: 2020-08-01T09:35:21.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2020-09-03T14:23:38.000Z (over 4 years ago)
- Last Synced: 2024-12-15T17:27:00.108Z (4 months ago)
- Topics: hough-transform, line-detection, pclines, pclines-python, python
- Language: Jupyter Notebook
- Homepage:
- Size: 492 KB
- Stars: 13
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: ChangeLog
- License: LICENSE
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README
# `pclines` package for Python
This package implements a PCLines transform for line detection in images.
```bibtex
@INPROCEEDINGS{dubska2011pclines,
author={M. {Dubská} and A. {Herout} and J. {Havel}},
booktitle={CVPR 2011},
title={PClines — Line detection using parallel coordinates},
year={2011},
}
```
# Requrements
* Python 3.6+
* numpy
* numba
* scikit-image
# Installation
The package is on [PyPI](https://pypi.org/project/pclines/), so just run following command and install the package.
```shell
> pip install pclines
```
Alternatively, you can download this repository and install manually.
# Example
1. Import package
```python
import pclines as pcl
```
2. Data and observations
The observations are 2D weighted coordinates enclosed by a known bounding box. As an example we extract edge points from an image.
```python
image = imread("doc/test.png", as_gray=True)
edges = sobel(image)
r,c = np.nonzero(edges > 0.5)
x = np.array([c,r],"i").T
weights = edges[r,c]
```
 
3. Accumulation in PCLines space
```python
h,w = image.shape[:2]
bbox=(0,0,w,h) # Bounding box of observations
d = 1024 # Accumulator resolution
P = PCLines(bbox, d) # Create new accumulator
P.insert(x, weights) # Insert observations
p, w = P.find_peaks(min_dist=10, prominence=1.3, t=0.1) # Find local maxima
```
4. Detected lines
```python
h = P.inverse(p) # (a,b,c) parameters of lines
X,Y = utils.line_segments_from_homogeneous(h, bbox) # Convert to line segments for plotting
```
# Contribute
If you have a suggestion for improvement, let us know by filling an issue. Or you can fork the project and submit a pull request.