Reputation: 13
GOAL: Locate ball in OpenCV
ISSUE: I have a mask I obtained with hsv bounds for my ball but some of the background is bleeding into my mask. How can I segment the circle I want from the background I don't? I suspect the issue is that I'm using minEnclosingCircle, but I don't know what else to use
I've tried eroding my mask to separate the circle, but it doesn't do me much good
Mask
Resulting Circle
def draw_circle(orig,mask,name):
cnts=cv2.findContours(mask.copy(),cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
cnts=imutils.grab_contours(cnts)
center=None
if len(cnts)>0:
c=max(cnts,key=cv2.contourArea)
((x,y),radius)=cv2.minEnclosingCircle(c)
M=cv2.moments(c)
center = (int(M["m10"] / M["m00"]), int(M["m01"] / M["m00"]))
if radius > 10:
cv2.circle(orig, (int(x), int(y)), int(radius),
(0, 255, 255), 2)
cv2.circle(orig, center, 5, (0, 0, 255), -1)
#d=int(name.split(".JPG")[0])
#expected_R=(231*1.6)/(2*d)
#error=1.0-(radius/expected_R)
#print "error is "+str(error)
else:
print "oops"
cv2.imshow(str(name)+" circled",orig)
cv2.imshow(str(name)+" mask",mask)
cv2.waitKey(0)
Upvotes: 0
Views: 295
Reputation: 76
First of all, I would recommend you to try using an edge detection algorithm, a simple Sobel Filter should be enough to provide you distinct features of the ball and afterwards perform thresholding to eliminate noise. You can use fitting ellipse or a minimum enclosing circle as you do now to better locate the ball. More details in this
Another approach would be a Hough transform for circles, an algorithm customised for detecting circles, but in comparison to the prior presented approach, it is more computationally expensive. See the following link:
As a note, if the scene presented in your image is just a mock up, not a dependency or a real use case, I would recommend you to use a different colour of the background, this ay your job would be much easier.
Upvotes: 1