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Add HoughCirclesWithAccumulator binding #27389 ### Pull Request Readiness Checklist Fix #27377 See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [x] I agree to contribute to the project under Apache 2 License. - [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [x] The PR is proposed to the proper branch - [x] There is a reference to the original bug report and related work - [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [x] The feature is well documented and sample code can be built with the project CMake
162 lines
4.5 KiB
Python
162 lines
4.5 KiB
Python
#!/usr/bin/python
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'''
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This example illustrates how to use cv.HoughCircles() function.
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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 as cv
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import numpy as np
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import sys
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from numpy import pi, sin, cos
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from tests_common import NewOpenCVTests
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def circleApproximation(circle):
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nPoints = 30
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dPhi = 2*pi / nPoints
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contour = []
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for i in range(nPoints):
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contour.append(([circle[0] + circle[2]*cos(i*dPhi),
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circle[1] + circle[2]*sin(i*dPhi)]))
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return np.array(contour).astype(int)
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def convContoursIntersectiponRate(c1, c2):
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s1 = cv.contourArea(c1)
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s2 = cv.contourArea(c2)
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s, _ = cv.intersectConvexConvex(c1, c2)
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return 2*s/(s1+s2)
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class houghcircles_test(NewOpenCVTests):
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def test_houghcircles(self):
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fn = "samples/data/board.jpg"
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src = self.get_sample(fn, 1)
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img = cv.cvtColor(src, cv.COLOR_BGR2GRAY)
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img = cv.medianBlur(img, 5)
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circles = cv.HoughCircles(img, cv.HOUGH_GRADIENT, 1, 10, np.array([]), 100, 30, 1, 30)[0]
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testCircles = [[38, 181, 17.6],
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[99.7, 166, 13.12],
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[142.7, 160, 13.52],
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[223.6, 110, 8.62],
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[79.1, 206.7, 8.62],
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[47.5, 351.6, 11.64],
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[189.5, 354.4, 11.64],
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[189.8, 298.9, 10.64],
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[189.5, 252.4, 14.62],
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[252.5, 393.4, 15.62],
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[602.9, 467.5, 11.42],
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[222, 210.4, 9.12],
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[263.1, 216.7, 9.12],
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[359.8, 222.6, 9.12],
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[518.9, 120.9, 9.12],
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[413.8, 113.4, 9.12],
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[489, 127.2, 9.12],
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[448.4, 121.3, 9.12],
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[384.6, 128.9, 8.62]]
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matches_counter = 0
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for i in range(len(testCircles)):
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for j in range(len(circles)):
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tstCircle = circleApproximation(testCircles[i])
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circle = circleApproximation(circles[j])
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if convContoursIntersectiponRate(tstCircle, circle) > 0.6:
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matches_counter += 1
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self.assertGreater(float(matches_counter) / len(testCircles), .5)
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self.assertLess(float(len(circles) - matches_counter) / len(circles), .75)
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circles_acc = cv.HoughCirclesWithAccumulator(
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image=img,
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method=cv.HOUGH_GRADIENT,
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dp=1,
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minDist=10,
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circles=np.array([]),
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param1=150,
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param2=45,
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minRadius=1,
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maxRadius=30)
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self.assertEqual(circles_acc.shape, (1, 2, 4))
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self.assertEqual(circles_acc[0, 0, 3], 66.)
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self.assertEqual(circles_acc[0, 1, 3], 62.)
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def test_houghcircles_alt(self):
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fn = "samples/data/board.jpg"
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src = self.get_sample(fn, 1)
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img = cv.cvtColor(src, cv.COLOR_BGR2GRAY)
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img = cv.medianBlur(img, 5)
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circles = cv.HoughCircles(img, cv.HOUGH_GRADIENT_ALT, 1, 10, np.array([]), 300, 0.9, 1, 30)
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self.assertEqual(circles.shape, (1, 18, 3))
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circles = circles[0]
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testCircles = [[38, 181, 17.6],
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[99.7, 166, 13.12],
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[142.7, 160, 13.52],
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[223.6, 110, 8.62],
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[79.1, 206.7, 8.62],
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[47.5, 351.6, 11.64],
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[189.5, 354.4, 11.64],
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[189.8, 298.9, 10.64],
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[189.5, 252.4, 14.62],
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[252.5, 393.4, 15.62],
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[602.9, 467.5, 11.42],
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[222, 210.4, 9.12],
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[263.1, 216.7, 9.12],
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[359.8, 222.6, 9.12],
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[518.9, 120.9, 9.12],
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[413.8, 113.4, 9.12],
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[489, 127.2, 9.12],
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[448.4, 121.3, 9.12],
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[384.6, 128.9, 8.62]]
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matches_counter = 0
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for i in range(len(testCircles)):
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for j in range(len(circles)):
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tstCircle = circleApproximation(testCircles[i])
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circle = circleApproximation(circles[j])
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if convContoursIntersectiponRate(tstCircle, circle) > 0.6:
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matches_counter += 1
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self.assertGreater(float(matches_counter) / len(testCircles), .5)
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self.assertLess(float(len(circles) - matches_counter) / len(circles), .75)
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circles_acc = cv.HoughCirclesWithAccumulator(
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image=img,
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method=cv.HOUGH_GRADIENT_ALT,
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dp=1,
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minDist=10,
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circles=np.array([]),
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param1=300,
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param2=0.9,
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minRadius=13,
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maxRadius=15)
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self.assertEqual(circles_acc.shape, (1, 3, 4))
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self.assertEqual(circles_acc[0, 0, 3], 62.)
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self.assertEqual(circles_acc[0, 1, 3], 59.)
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self.assertEqual(circles_acc[0, 2, 3], 47.)
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if __name__ == '__main__':
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NewOpenCVTests.bootstrap()
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