ocrd-fork-pylsd 0.0.8

pylsd is the python bindings for LSD - Line Segment Detector

pylsd

1. Introduction

pylsd is the python bindings for LSD - Line Segment Detector.

2. Install

This package uses distutils, which is the default way of installing python modules. To install in your home directory, securely run the following:

git clone https://github.com/primetang/pylsd.git
cd pylsd
python setup.py install

Or directly through pip to install it:

pip install pylsd

3. Usage

We can use the package by using from pylsd.lsd import lsd, and lines = lsd(src) is the call format for the lsd function, where src is a Grayscale Image (H * W numpy.array), and the return value lines is the Detected Line Segment, lines is an N * 5 numpy.array, each row represents a straight line, the 5-dimensional vector is:

[point1.x, point1.y, point2.x, point2.y, width]

According to these presentations, we can use it just like the following code (here is the link):

• by using cv2 module

import cv2
import numpy as np
import os
from pylsd.lsd import lsd
fullName = 'car.jpg'
folder, imgName = os.path.split(fullName)
src = cv2.imread(fullName, cv2.IMREAD_COLOR)
gray = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY)
lines = lsd(gray)
for i in xrange(lines.shape[0]):
    pt1 = (int(lines[i, 0]), int(lines[i, 1]))
    pt2 = (int(lines[i, 2]), int(lines[i, 3]))
    width = lines[i, 4]
    cv2.line(src, pt1, pt2, (0, 0, 255), int(np.ceil(width / 2)))
cv2.imwrite(os.path.join(folder, 'cv2_' + imgName.split('.')[0] + '.jpg'), src)

• by using PIL(Image) module

from PIL import Image, ImageDraw
import numpy as np
import os
from pylsd.lsd import lsd
fullName = 'house.png'
folder, imgName = os.path.split(fullName)
img = Image.open(fullName)
gray = np.asarray(img.convert('L'))
lines = lsd(gray)
draw = ImageDraw.Draw(img)
for i in xrange(lines.shape[0]):
    pt1 = (int(lines[i, 0]), int(lines[i, 1]))
    pt2 = (int(lines[i, 2]), int(lines[i, 3]))
    width = lines[i, 4]
    draw.line((pt1, pt2), fill=(0, 0, 255), width=int(np.ceil(width / 2)))
img.save(os.path.join(folder, 'PIL_' + imgName.split('.')[0] + '.jpg'))

The following is the result:

• car.jpg by using cv2 module

• house.png by using PIL(Image) module