CODEWITHSUNDEEP
opencv
jnnnnn
jnnnnn

Reputation: 4448

How to remove the local average color from an image with OpenCV

I have an image with a gentle gradient background and sharp foreground features that I want to detect. I wish to find green arcs in the image. However, the green arcs are only green relative to the background (they are semitransparent). The green arcs are also only one or two pixels wide.

As a result, for each pixel of the image, I want to subtract the average color of the surrounding pixels. The surrounding 5x5 or 10x10 pixels would be sufficient. This should leave the foreground features relatively untouched but remove the soft background, and I can then select the green channel for further processing.

Is there any way to do this efficiently? It doesn't have to be particularly accurate, but I want to run it at 30Hz on a 1080p image.

Image with soft background and sharp foreground

Upvotes: 1

Views: 2803

Answers (2)

Mark Setchell
Mark Setchell

Reputation: 208107

You can achieve this by doing a simple box blur on the image and then subtracting that from the original image:

blur(img,blurred,Size(15,15));
diff=img-blurred;

I tried this on a 1920x1080 image and the blur took 25ms and the subtract took 15ms on a decent spec iMac.

If your background is not changing fast, you could calculate the blur over the space of a few frames in a second thread and keep re-using it till you recalculate it again a few frames later then you would only have 15ms subtraction to do for each of your 30fps rather than 45ms of processing.

Upvotes: 2

Saransh Kejriwal
Saransh Kejriwal

Reputation: 2533

Depending on how smooth the background gradient is, edge detection followed by dilation might work well for you.

Edge detection will output the 3 lines that you've shown in the sample and discard the background gradient (again, depending on smoothness). Dilating the image then will thicken the edges so that your line contours are more significant.

You can superimpose this edge map on your original image as:

Mat src,edges;
//perform edge detection on src and output in edges. 
Mat result;
cvtColor(edges,edges,cv2.COLOR_GRAY2BGR);//change mask to a 3 channel image 
subtract(edges,src,result);
subtract(edges,result);

The result should contain only the 3 lines with 3 colours. From here on you can apply color filtering to select the green one.

Upvotes: 1

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