Open Source Computer Vision Library https://opencv.org/
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#!/usr/bin/python
'''
This example illustrates how to use Hough Transform to find lines
'''
# Python 2/3 compatibility
from __future__ import print_function
import cv2 as cv
import numpy as np
import sys
import math
from tests_common import NewOpenCVTests
def linesDiff(line1, line2):
norm1 = cv.norm(line1 - line2, cv.NORM_L2)
line3 = line1[2:4] + line1[0:2]
norm2 = cv.norm(line3 - line2, cv.NORM_L2)
return min(norm1, norm2)
class houghlines_test(NewOpenCVTests):
def test_houghlines(self):
fn = "/samples/data/pic1.png"
src = self.get_sample(fn)
dst = cv.Canny(src, 50, 200)
lines = cv.HoughLinesP(dst, 1, math.pi/180.0, 40, np.array([]), 50, 10)[:,0,:]
eps = 5
testLines = [
#rect1
[ 232, 25, 43, 25],
[ 43, 129, 232, 129],
[ 43, 129, 43, 25],
[232, 129, 232, 25],
#rect2
[251, 86, 314, 183],
[252, 86, 323, 40],
[315, 183, 386, 137],
[324, 40, 386, 136],
#triangle
[245, 205, 377, 205],
[244, 206, 305, 278],
[306, 279, 377, 205],
#rect3
[153, 177, 196, 177],
[153, 277, 153, 179],
[153, 277, 196, 277],
[196, 177, 196, 277]]
matches_counter = 0
for i in range(len(testLines)):
for j in range(len(lines)):
if linesDiff(testLines[i], lines[j]) < eps:
matches_counter += 1
self.assertGreater(float(matches_counter) / len(testLines), .7)
lines_acc = cv.HoughLinesWithAccumulator(dst, rho=1, theta=np.pi / 180, threshold=150, srn=0, stn=0)
self.assertEqual(lines_acc[0,0,2], 192.0)
self.assertEqual(lines_acc[1,0,2], 187.0)
if __name__ == '__main__':
NewOpenCVTests.bootstrap()