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

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ocrd-fork-pylsd 0.0.8

pylsd

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SHA256	`93f05d7e22873973fed1ee3f7183f1e53d48db2d57bbdfb19db9402040a8c053`
MD5	`6a4a46db5456e8d834492199c28c3205`
BLAKE2b-256	`053ddfb070682c39f02b2da4c08a15f20d7429d9f09a42f337f91d0f969f6a26`

Algorithm	Hash digest
SHA256	`cd47c141fe390d3694ea4f323b4329a76cfd84597eb1ac91c40f0d60d437e819`
MD5	`cb1db2de37f647eaa4a19ca5a79c778c`
BLAKE2b-256	`1ba7f0607326c00e616820ab79c1c8df9627006f3e4819bcbe6c615103b41a86`

Algorithm	Hash digest
SHA256	`0ee734019ca1a66b34ff916c7cbd7c30e1827c308e730793716965c4e8c49054`
MD5	`0379b56ac707c7eb13ce8b14eb96fd32`
BLAKE2b-256	`7ad534ca26e18a5d2417e21604805d986cf5527b6443f3d5c936f8165b844050`

Algorithm	Hash digest
SHA256	`88d3720b546a5dfac8aa5b2879761d6583ed865ebb43b2393195732a9bdeba50`
MD5	`30fa27801140bfa4d610bcf2e7538f57`
BLAKE2b-256	`8ec6791a47575342b5f6e5e941d3b22180378adc4c2e87a02438acfd268e9eaa`

Algorithm	Hash digest
SHA256	`f9636ba0689c672f5e71203d05a360052d4d5cb8be63266fa8c3ce59ed626bca`
MD5	`21918dd5322adb40f3907c1128ec010d`
BLAKE2b-256	`05f96e2d96f70d40ccd0bef1c22a0ea06bdc9b973ec4c5e99df35d960bfc7495`

Algorithm	Hash digest
SHA256	`586f158dcea52fea95b1928701ca1d534fac6bdc73faba46b6560c09aac81913`
MD5	`7ec24236312539235ccf9ce188b69407`
BLAKE2b-256	`1b7f9d28aa9e366283f94148e855cc6a8bafcf9c6a8c57a3fb7b80bded12dcf1`

Algorithm	Hash digest
SHA256	`acda728ec970b227d930c11e8f41dff4b3acf9ef5ade347c4e72f30d599f71e4`
MD5	`c354b3ff790f9114cf394f4b35583906`
BLAKE2b-256	`5efd96e9029456d6d63fdcd4d9276ba1a99c42b80b0167bc931bd6e444b060c0`

Algorithm	Hash digest
SHA256	`c0bca39543c5f037ed3039a949a480cebdac7978378edd8f8bd591bb0091de07`
MD5	`755b3cea95976b1d19dccada6515128e`
BLAKE2b-256	`001c1bcd4940de4e0e14d126f5fa70e16fcd7e08b98f5abcf8e090cf166fb2ac`