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https://github.com/andraniksargsyan/pylsd-nova
Python bindings for Line Segment Detector (LSD)
https://github.com/andraniksargsyan/pylsd-nova
image-processing line-segment-detector
Last synced: about 1 month ago
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Python bindings for Line Segment Detector (LSD)
- Host: GitHub
- URL: https://github.com/andraniksargsyan/pylsd-nova
- Owner: AndranikSargsyan
- License: other
- Created: 2020-04-03T19:19:39.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2023-04-19T00:20:50.000Z (over 1 year ago)
- Last Synced: 2024-11-13T17:49:47.459Z (about 1 month ago)
- Topics: image-processing, line-segment-detector
- Language: C++
- Homepage:
- Size: 1.4 MB
- Stars: 32
- Watchers: 1
- Forks: 5
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
pylsd-nova
=============
### 1. Introduction
pylsd-nova is a python binding for [LSD - Line Segment Detector](http://www.ipol.im/pub/art/2012/gjmr-lsd/).
This is a fork from original [pylsd binding](https://github.com/primetang/pylsd/). This fork works for Python 3 and adds the ability to change lsd parameters from python call.
### 2. Install
This package uses distutils, which is the default way of installing python modules. For installing by cloning the repository you can run the following commands:
```
git clone https://github.com/AndranikSargsyan/pylsd-nova.git
cd pylsd-nova
pip install .
```
Or install directly through `pip`:
```
pip install pylsd-nova
```
### 3. Usage
You can use the package by importing like `from pylsd import lsd`, and calling `segments = lsd(img)` by optionally passing other lsd parameters mentioned below. `img` is a Grayscale Image (`H x W` numpy.ndarray), and the return value `segments` contains detected line segments.
`segments` is an `N x 5` numpy.ndarray. Each row represents a straight line. The 5-dimensional row format is:
```
[point1.x, point1.y, point2.x, point2.y, width]
```
These are the parameters of lsd, which can be changed through keyword arguments of lsd call:
* `scale (float)`: Scale the image by Gaussian filter to 'scale'.
* `sigma_scale (float)`: Sigma for Gaussian filter is computed as `sigma = sigma_scale / scale`.
* `quant (float)`: Bound to the quantization error on the gradient norm.
* `ang_th (float)`: Gradient angle tolerance in degrees.
* `eps (float)`: Detection threshold, `-log10(NFA)`.
* `density_th (float)`: Minimal density of region points in rectangle.
* `n_bins (int)`: Number of bins in pseudo-ordering of gradient modulus.
* `max_grad (float)`: Gradient modulus in the highest bin. The default value corresponds to the highest gradient modulus on images with gray levels in [0,255].
You can use it just like the following code ([here is the link to examples](https://github.com/AndranikSargsyan/pylsd-nova/tree/master/example)):
* by using cv2 module
```python
import cv2
import numpy as np
import os
from pylsd import lsd
full_name = 'car.jpg'
folder, img_name = os.path.split(full_name)
img = cv2.imread(full_name, cv2.IMREAD_COLOR)
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
segments = lsd(img_gray, scale=0.5)
for i in range(segments.shape[0]):
pt1 = (int(segments[i, 0]), int(segments[i, 1]))
pt2 = (int(segments[i, 2]), int(segments[i, 3]))
width = segments[i, 4]
cv2.line(img, pt1, pt2, (0, 0, 255), int(np.ceil(width / 2)))
cv2.imwrite(os.path.join(folder, 'cv2_' + img_name.split('.')[0] + '.jpg'), img)
```
* by using PIL(Image) module
```python
import numpy as np
import os
from PIL import Image, ImageDraw
from pylsd import lsd
full_name = 'house.png'
folder, img_name = os.path.split(full_name)
img = Image.open(full_name)
img_gray = np.asarray(img.convert('L'))
segments = lsd(img_gray, scale=0.5)
draw = ImageDraw.Draw(img)
for i in range(segments.shape[0]):
pt1 = (int(segments[i, 0]), int(segments[i, 1]))
pt2 = (int(segments[i, 2]), int(segments[i, 3]))
width = segments[i, 4]
draw.line((pt1, pt2), fill=(0, 0, 255), width=int(np.ceil(width / 2)))
img.save(os.path.join(folder, 'PIL_' + img_name.split('.')[0] + '.jpg'))
```
The following is the result:
* car.jpg by using cv2 module
* house.png by using PIL(Image) module
