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