CODEWITHSUNDEEP
python-3.xopencvpython
haley
haley

Reputation: 909

opencv2: Circle detection not detecting the obvious ones

Problem

I'm trying to use opencv2 to detect PlayStation Move Motion Controllers in still images. In an attempt to increase the contrast between the orbs and the backgrounds, I decided to modify the input image to automatically scale the brightness level between the image's mean level and 96 above for each channel, then when converting to grayscale, taking the maximum value instead of the default transform, since some orbs are saturated but not "bright".

However, my best attempts at adjusting the parameters seems to not work well, detecting circles that aren't there over the obvious ones.

What can I do to improve the accuracy of the detection? What other improvements or algorithms do you think I could use?

Samples

In order of best to worst:

2 Wands, 1 Wand detected (showing all 2 detected circles) enter image description here

2 Wands, 1 Wand detected with many nonexistent circles (showing top 4 circles) Come on, those hands aren't that round.

1 Wand (against a dark background), 6 total circles, the lowest-ranked of which is the correct one (showing all 6 circles) You know what's rounder than a ball? A foot.

1 Wand (against a dark background), 44 total circles detected, none of which are that Wand (showing all 44 circles) ...Why?

I am using this function call:

cv2.HoughCircles(img_gray,cv2.HOUGH_GRADIENT,
    dp=1, minDist=24, param1=90, param2=25,
    minRadius=2, maxRadius=48)

All images are resized and cropped to 640x480 (the resolution of the PS3 Eye). No blur is performed.

Upvotes: 1

Views: 816

Answers (2)

Ta946
Ta946

Reputation: 1412

as the other user said, hough circles arent the best approach here because hough circles look for perfect circles only. whereas your target is "circular" but not a circle (due to motion blur, light bleed/reflection, noise etc)

I suggest converting the image to HSV then filtering by hue/color and intensities to get a binary threshold instead of using grayscale directly (that will help remove background & noise and limit the search area)

then using findContours() (faster than blob detection), check for contours of high circularity and expected size/area range and maybe even solidity.

area = cv2.contourArea(contour)
perimeter = cv2.arcLength(contour,True)
circularity = 4*np.pi*area / (perimeter**2)
solidity = area/cv2.contourArea(cv2.convexHull(contour))

your biggest problem will be the orb contour merging with the background due to low contrast. so maybe some adaptive threshold could help

Upvotes: 2

user3272574
user3272574

Reputation: 3097

I think hough circles is the wrong approach for you, as you are not really looking for circles. You are looking for circular areas with strong intensity. Use e.g. blob detection instead, I linked a guide:

https://www.learnopencv.com/blob-detection-using-opencv-python-c/

In the blob detection, you need to set the parameters to get a proper high-intensity circular area.

Upvotes: 4

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