Circle detection with OpenCV
up vote
2
down vote
favorite
How can I improve the performance of the following circle-detection code
from matplotlib.pyplot import imshow, scatter, show
import cv2
image = cv2.imread('points.png', 0)
_, image = cv2.threshold(image, 254, 255, cv2.THRESH_BINARY)
image = cv2.Canny(image, 1, 1)
imshow(image, cmap='gray')
circles = cv2.HoughCircles(image, cv2.HOUGH_GRADIENT, 2, 32)
x = circles[0, :, 0]
y = circles[0, :, 1]
scatter(x, y)
show()
with the following source image:
I have tried adjusting the parameters of the HoughCircles function but they result in either too many false positives or too many false negatives. In particular, I am having trouble with spurious circles being detected in the gap between the two blobs:
python opencv geometry computer-vision feature-detection
asked Mar 7 '17 at 21:26
user76284
401515
add a comment |
up vote
2
down vote
favorite
How can I improve the performance of the following circle-detection code
from matplotlib.pyplot import imshow, scatter, show
import cv2
image = cv2.imread('points.png', 0)
_, image = cv2.threshold(image, 254, 255, cv2.THRESH_BINARY)
image = cv2.Canny(image, 1, 1)
imshow(image, cmap='gray')
circles = cv2.HoughCircles(image, cv2.HOUGH_GRADIENT, 2, 32)
x = circles[0, :, 0]
y = circles[0, :, 1]
scatter(x, y)
show()
with the following source image:
I have tried adjusting the parameters of the HoughCircles function but they result in either too many false positives or too many false negatives. In particular, I am having trouble with spurious circles being detected in the gap between the two blobs:
python opencv geometry computer-vision feature-detection
asked Mar 7 '17 at 21:26
user76284
401515
Consider accepting the answer if you think it was helpful.
– m3h0w
Mar 10 '17 at 11:25
add a comment |
up vote
2
down vote
favorite
up vote
2
down vote
favorite
How can I improve the performance of the following circle-detection code
from matplotlib.pyplot import imshow, scatter, show
import cv2
image = cv2.imread('points.png', 0)
_, image = cv2.threshold(image, 254, 255, cv2.THRESH_BINARY)
image = cv2.Canny(image, 1, 1)
imshow(image, cmap='gray')
circles = cv2.HoughCircles(image, cv2.HOUGH_GRADIENT, 2, 32)
x = circles[0, :, 0]
y = circles[0, :, 1]
scatter(x, y)
show()
with the following source image:
I have tried adjusting the parameters of the HoughCircles function but they result in either too many false positives or too many false negatives. In particular, I am having trouble with spurious circles being detected in the gap between the two blobs:
python opencv geometry computer-vision feature-detection
asked Mar 7 '17 at 21:26
user76284
401515
How can I improve the performance of the following circle-detection code
from matplotlib.pyplot import imshow, scatter, show
import cv2
image = cv2.imread('points.png', 0)
_, image = cv2.threshold(image, 254, 255, cv2.THRESH_BINARY)
image = cv2.Canny(image, 1, 1)
imshow(image, cmap='gray')
circles = cv2.HoughCircles(image, cv2.HOUGH_GRADIENT, 2, 32)
x = circles[0, :, 0]
y = circles[0, :, 1]
scatter(x, y)
show()
with the following source image:
I have tried adjusting the parameters of the HoughCircles function but they result in either too many false positives or too many false negatives. In particular, I am having trouble with spurious circles being detected in the gap between the two blobs:
python opencv geometry computer-vision feature-detection
python opencv geometry computer-vision feature-detection
asked Mar 7 '17 at 21:26
user76284
401515
asked Mar 7 '17 at 21:26
user76284
401515
asked Mar 7 '17 at 21:26
user76284
401515
asked Mar 7 '17 at 21:26
user76284
401515
asked Mar 7 '17 at 21:26
user76284
401515
401515
Consider accepting the answer if you think it was helpful.
– m3h0w
Mar 10 '17 at 11:25
add a comment |
Consider accepting the answer if you think it was helpful.
– m3h0w
Mar 10 '17 at 11:25
Consider accepting the answer if you think it was helpful.
– m3h0w
Mar 10 '17 at 11:25
Consider accepting the answer if you think it was helpful.
– m3h0w
Mar 10 '17 at 11:25
add a comment |
1 Answer
1
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oldest
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up vote
7
down vote
accepted
@Carlos, I'm not really a big fan of Hough Circles in situations like the one you've described. To be honest, I find this algorithm very unintuitive. What I would recommend in your case is using findContour() function and then calculating mass centers. Thus said, I tuned the Hough's parameters a bit to get reasonable results. I also used a different method for preprocessing before Canny, since I don't see how that thresholding would work in any other case than that particular one.
Hough method:
Finding mass centers:
And the code:
from matplotlib.pyplot import imshow, scatter, show, savefig
import cv2
image = cv2.imread('circles.png', 0)
#_, image = cv2.threshold(image, 254, 255, cv2.THRESH_BINARY)
image = cv2.GaussianBlur(image.copy(), (27, 27), 0)
image = cv2.Canny(image, 0, 130)
cv2.imshow("canny", image)
cv2.waitKey(0)
imshow(image, cmap='gray')
circles = cv2.HoughCircles(image, cv2.HOUGH_GRADIENT, 22, minDist=1, maxRadius=50)
x = circles[0, :, 0]
y = circles[0, :, 1]
scatter(x, y)
show()
savefig('result1.png')
cv2.waitKey(0)
_, cnts, _ = cv2.findContours(image.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
# loop over the contours
for c in cnts:
# compute the center of the contour
M = cv2.moments(c)
cX = int(M["m10"] / M["m00"])
cY = int(M["m01"] / M["m00"])
#draw the contour and center of the shape on the image
cv2.drawContours(image, [c], -1, (125, 125, 125), 2)
cv2.circle(image, (cX, cY), 3, (255, 255, 255), -1)
cv2.imshow("Image", image)
cv2.imwrite("result2.png", image)
cv2.waitKey(0)
Both methods require some more fine tuning but I hope that gives you something more to work with.
Sources: this answer and pyimagesearch.
answered Mar 8 '17 at 0:24
m3h0w
1,02011027
1
Hough transform gets unreliable when there are many circles close together, because parts of different circles combine to give the likelihood of a new circle. As @m3h0w has shown, you can tun the Hough parameters and also use more advanced features like contours and moments. The key thing is to either preprocess images before Hough transform, or post-process the results after the transform, or do both.
– Totoro
Mar 8 '17 at 2:13
@m3h0w Aha another master stroke. Wish there was an option to favorite the answer as well.
– Jeru Luke
Mar 10 '17 at 13:35
add a comment |
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1 Answer
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1 Answer
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oldest
votes
active
oldest
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active
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up vote
7
down vote
accepted
@Carlos, I'm not really a big fan of Hough Circles in situations like the one you've described. To be honest, I find this algorithm very unintuitive. What I would recommend in your case is using findContour() function and then calculating mass centers. Thus said, I tuned the Hough's parameters a bit to get reasonable results. I also used a different method for preprocessing before Canny, since I don't see how that thresholding would work in any other case than that particular one.
Hough method:
Finding mass centers:
And the code:
from matplotlib.pyplot import imshow, scatter, show, savefig
import cv2
image = cv2.imread('circles.png', 0)
#_, image = cv2.threshold(image, 254, 255, cv2.THRESH_BINARY)
image = cv2.GaussianBlur(image.copy(), (27, 27), 0)
image = cv2.Canny(image, 0, 130)
cv2.imshow("canny", image)
cv2.waitKey(0)
imshow(image, cmap='gray')
circles = cv2.HoughCircles(image, cv2.HOUGH_GRADIENT, 22, minDist=1, maxRadius=50)
x = circles[0, :, 0]
y = circles[0, :, 1]
scatter(x, y)
show()
savefig('result1.png')
cv2.waitKey(0)
_, cnts, _ = cv2.findContours(image.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
# loop over the contours
for c in cnts:
# compute the center of the contour
M = cv2.moments(c)
cX = int(M["m10"] / M["m00"])
cY = int(M["m01"] / M["m00"])
#draw the contour and center of the shape on the image
cv2.drawContours(image, [c], -1, (125, 125, 125), 2)
cv2.circle(image, (cX, cY), 3, (255, 255, 255), -1)
cv2.imshow("Image", image)
cv2.imwrite("result2.png", image)
cv2.waitKey(0)
Both methods require some more fine tuning but I hope that gives you something more to work with.
Sources: this answer and pyimagesearch.
answered Mar 8 '17 at 0:24
m3h0w
1,02011027
1
Hough transform gets unreliable when there are many circles close together, because parts of different circles combine to give the likelihood of a new circle. As @m3h0w has shown, you can tun the Hough parameters and also use more advanced features like contours and moments. The key thing is to either preprocess images before Hough transform, or post-process the results after the transform, or do both.
– Totoro
Mar 8 '17 at 2:13
@m3h0w Aha another master stroke. Wish there was an option to favorite the answer as well.
– Jeru Luke
Mar 10 '17 at 13:35
add a comment |
up vote
7
down vote
accepted
@Carlos, I'm not really a big fan of Hough Circles in situations like the one you've described. To be honest, I find this algorithm very unintuitive. What I would recommend in your case is using findContour() function and then calculating mass centers. Thus said, I tuned the Hough's parameters a bit to get reasonable results. I also used a different method for preprocessing before Canny, since I don't see how that thresholding would work in any other case than that particular one.
Hough method:
Finding mass centers:
And the code:
from matplotlib.pyplot import imshow, scatter, show, savefig
import cv2
image = cv2.imread('circles.png', 0)
#_, image = cv2.threshold(image, 254, 255, cv2.THRESH_BINARY)
image = cv2.GaussianBlur(image.copy(), (27, 27), 0)
image = cv2.Canny(image, 0, 130)
cv2.imshow("canny", image)
cv2.waitKey(0)
imshow(image, cmap='gray')
circles = cv2.HoughCircles(image, cv2.HOUGH_GRADIENT, 22, minDist=1, maxRadius=50)
x = circles[0, :, 0]
y = circles[0, :, 1]
scatter(x, y)
show()
savefig('result1.png')
cv2.waitKey(0)
_, cnts, _ = cv2.findContours(image.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
# loop over the contours
for c in cnts:
# compute the center of the contour
M = cv2.moments(c)
cX = int(M["m10"] / M["m00"])
cY = int(M["m01"] / M["m00"])
#draw the contour and center of the shape on the image
cv2.drawContours(image, [c], -1, (125, 125, 125), 2)
cv2.circle(image, (cX, cY), 3, (255, 255, 255), -1)
cv2.imshow("Image", image)
cv2.imwrite("result2.png", image)
cv2.waitKey(0)
Both methods require some more fine tuning but I hope that gives you something more to work with.
Sources: this answer and pyimagesearch.
answered Mar 8 '17 at 0:24
m3h0w
1,02011027
1
Hough transform gets unreliable when there are many circles close together, because parts of different circles combine to give the likelihood of a new circle. As @m3h0w has shown, you can tun the Hough parameters and also use more advanced features like contours and moments. The key thing is to either preprocess images before Hough transform, or post-process the results after the transform, or do both.
– Totoro
Mar 8 '17 at 2:13
@m3h0w Aha another master stroke. Wish there was an option to favorite the answer as well.
– Jeru Luke
Mar 10 '17 at 13:35
add a comment |
up vote
7
down vote
accepted
up vote
7
down vote
accepted
@Carlos, I'm not really a big fan of Hough Circles in situations like the one you've described. To be honest, I find this algorithm very unintuitive. What I would recommend in your case is using findContour() function and then calculating mass centers. Thus said, I tuned the Hough's parameters a bit to get reasonable results. I also used a different method for preprocessing before Canny, since I don't see how that thresholding would work in any other case than that particular one.
Hough method:
Finding mass centers:
And the code:
from matplotlib.pyplot import imshow, scatter, show, savefig
import cv2
image = cv2.imread('circles.png', 0)
#_, image = cv2.threshold(image, 254, 255, cv2.THRESH_BINARY)
image = cv2.GaussianBlur(image.copy(), (27, 27), 0)
image = cv2.Canny(image, 0, 130)
cv2.imshow("canny", image)
cv2.waitKey(0)
imshow(image, cmap='gray')
circles = cv2.HoughCircles(image, cv2.HOUGH_GRADIENT, 22, minDist=1, maxRadius=50)
x = circles[0, :, 0]
y = circles[0, :, 1]
scatter(x, y)
show()
savefig('result1.png')
cv2.waitKey(0)
_, cnts, _ = cv2.findContours(image.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
# loop over the contours
for c in cnts:
# compute the center of the contour
M = cv2.moments(c)
cX = int(M["m10"] / M["m00"])
cY = int(M["m01"] / M["m00"])
#draw the contour and center of the shape on the image
cv2.drawContours(image, [c], -1, (125, 125, 125), 2)
cv2.circle(image, (cX, cY), 3, (255, 255, 255), -1)
cv2.imshow("Image", image)
cv2.imwrite("result2.png", image)
cv2.waitKey(0)
Both methods require some more fine tuning but I hope that gives you something more to work with.
Sources: this answer and pyimagesearch.
answered Mar 8 '17 at 0:24
m3h0w
1,02011027
@Carlos, I'm not really a big fan of Hough Circles in situations like the one you've described. To be honest, I find this algorithm very unintuitive. What I would recommend in your case is using findContour() function and then calculating mass centers. Thus said, I tuned the Hough's parameters a bit to get reasonable results. I also used a different method for preprocessing before Canny, since I don't see how that thresholding would work in any other case than that particular one.
Hough method:
Finding mass centers:
And the code:
from matplotlib.pyplot import imshow, scatter, show, savefig
import cv2
image = cv2.imread('circles.png', 0)
#_, image = cv2.threshold(image, 254, 255, cv2.THRESH_BINARY)
image = cv2.GaussianBlur(image.copy(), (27, 27), 0)
image = cv2.Canny(image, 0, 130)
cv2.imshow("canny", image)
cv2.waitKey(0)
imshow(image, cmap='gray')
circles = cv2.HoughCircles(image, cv2.HOUGH_GRADIENT, 22, minDist=1, maxRadius=50)
x = circles[0, :, 0]
y = circles[0, :, 1]
scatter(x, y)
show()
savefig('result1.png')
cv2.waitKey(0)
_, cnts, _ = cv2.findContours(image.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
# loop over the contours
for c in cnts:
# compute the center of the contour
M = cv2.moments(c)
cX = int(M["m10"] / M["m00"])
cY = int(M["m01"] / M["m00"])
#draw the contour and center of the shape on the image
cv2.drawContours(image, [c], -1, (125, 125, 125), 2)
cv2.circle(image, (cX, cY), 3, (255, 255, 255), -1)
cv2.imshow("Image", image)
cv2.imwrite("result2.png", image)
cv2.waitKey(0)
Both methods require some more fine tuning but I hope that gives you something more to work with.
Sources: this answer and pyimagesearch.
answered Mar 8 '17 at 0:24
m3h0w
1,02011027
edited Nov 11 at 22:11
answered Mar 8 '17 at 0:24
m3h0w
1,02011027
answered Mar 8 '17 at 0:24
m3h0w
1,02011027
answered Mar 8 '17 at 0:24
m3h0w
1,02011027
1,02011027
1
Hough transform gets unreliable when there are many circles close together, because parts of different circles combine to give the likelihood of a new circle. As @m3h0w has shown, you can tun the Hough parameters and also use more advanced features like contours and moments. The key thing is to either preprocess images before Hough transform, or post-process the results after the transform, or do both.
– Totoro
Mar 8 '17 at 2:13
@m3h0w Aha another master stroke. Wish there was an option to favorite the answer as well.
– Jeru Luke
Mar 10 '17 at 13:35
add a comment |
1
Hough transform gets unreliable when there are many circles close together, because parts of different circles combine to give the likelihood of a new circle. As @m3h0w has shown, you can tun the Hough parameters and also use more advanced features like contours and moments. The key thing is to either preprocess images before Hough transform, or post-process the results after the transform, or do both.
– Totoro
Mar 8 '17 at 2:13
@m3h0w Aha another master stroke. Wish there was an option to favorite the answer as well.
– Jeru Luke
Mar 10 '17 at 13:35
1
1
Hough transform gets unreliable when there are many circles close together, because parts of different circles combine to give the likelihood of a new circle. As @m3h0w has shown, you can tun the Hough parameters and also use more advanced features like contours and moments. The key thing is to either preprocess images before Hough transform, or post-process the results after the transform, or do both.
– Totoro
Mar 8 '17 at 2:13
Hough transform gets unreliable when there are many circles close together, because parts of different circles combine to give the likelihood of a new circle. As @m3h0w has shown, you can tun the Hough parameters and also use more advanced features like contours and moments. The key thing is to either preprocess images before Hough transform, or post-process the results after the transform, or do both.
– Totoro
Mar 8 '17 at 2:13
@m3h0w Aha another master stroke. Wish there was an option to favorite the answer as well.
– Jeru Luke
Mar 10 '17 at 13:35
@m3h0w Aha another master stroke. Wish there was an option to favorite the answer as well.
– Jeru Luke
Mar 10 '17 at 13:35
add a comment |
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