mirror of https://github.com/opencv/opencv.git
Open Source Computer Vision Library
https://opencv.org/
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
84 lines
2.2 KiB
84 lines
2.2 KiB
#!/usr/bin/python |
|
|
|
''' |
|
This example illustrates how to use cv2.HoughCircles() function. |
|
''' |
|
|
|
# Python 2/3 compatibility |
|
from __future__ import print_function |
|
|
|
import cv2 |
|
import numpy as np |
|
import sys |
|
from numpy import pi, sin, cos |
|
|
|
from tests_common import NewOpenCVTests |
|
|
|
def circleApproximation(circle): |
|
|
|
nPoints = 30 |
|
dPhi = 2*pi / nPoints |
|
contour = [] |
|
for i in range(nPoints): |
|
contour.append(([circle[0] + circle[2]*cos(i*dPhi), |
|
circle[1] + circle[2]*sin(i*dPhi)])) |
|
|
|
return np.array(contour).astype(int) |
|
|
|
def convContoursIntersectiponRate(c1, c2): |
|
|
|
s1 = cv2.contourArea(c1) |
|
s2 = cv2.contourArea(c2) |
|
|
|
s, _ = cv2.intersectConvexConvex(c1, c2) |
|
|
|
return 2*s/(s1+s2) |
|
|
|
class houghcircles_test(NewOpenCVTests): |
|
|
|
def test_houghcircles(self): |
|
|
|
fn = "samples/data/board.jpg" |
|
|
|
src = self.get_sample(fn, 1) |
|
img = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY) |
|
img = cv2.medianBlur(img, 5) |
|
|
|
circles = cv2.HoughCircles(img, cv2.HOUGH_GRADIENT, 1, 10, np.array([]), 100, 30, 1, 30)[0] |
|
|
|
testCircles = [[38, 181, 17.6], |
|
[99.7, 166, 13.12], |
|
[142.7, 160, 13.52], |
|
[223.6, 110, 8.62], |
|
[79.1, 206.7, 8.62], |
|
[47.5, 351.6, 11.64], |
|
[189.5, 354.4, 11.64], |
|
[189.8, 298.9, 10.64], |
|
[189.5, 252.4, 14.62], |
|
[252.5, 393.4, 15.62], |
|
[602.9, 467.5, 11.42], |
|
[222, 210.4, 9.12], |
|
[263.1, 216.7, 9.12], |
|
[359.8, 222.6, 9.12], |
|
[518.9, 120.9, 9.12], |
|
[413.8, 113.4, 9.12], |
|
[489, 127.2, 9.12], |
|
[448.4, 121.3, 9.12], |
|
[384.6, 128.9, 8.62]] |
|
|
|
matches_counter = 0 |
|
|
|
for i in range(len(testCircles)): |
|
for j in range(len(circles)): |
|
|
|
tstCircle = circleApproximation(testCircles[i]) |
|
circle = circleApproximation(circles[j]) |
|
if convContoursIntersectiponRate(tstCircle, circle) > 0.6: |
|
matches_counter += 1 |
|
|
|
self.assertGreater(float(matches_counter) / len(testCircles), .5) |
|
self.assertLess(float(len(circles) - matches_counter) / len(circles), .75) |
|
|
|
|
|
if __name__ == '__main__': |
|
NewOpenCVTests.bootstrap()
|
|
|