Determining the average distance of pixels (to the centre of an image) in OpenCV
I'm trying to figure out how to do the following calculation in OpenCV.
Assuming a binary image (black/white):
Average distance of white pixels from the centre of the image. An image with most of its white pixels near the edges will have a high score, whereas an image with most white pixels near the centre will have a low score.
I know how to do this manually with loops, but since I'm working Java I'd rather offload it to a set of high-performance OpenCV calls which are native.
Thanks
distanceTransform() is almost what you want. Unfortunately, it only calculates distance to the nearest black pixel, which means the data must be massaged a little bit. The image needs to contain only a single black pixel at the center for distanceTransform() to work properly.
My method is as follows:
distanceTransform() on the modified image mean() , using the white pixels in the binary image as a mask Example code is below. It's in C++, but you should be able to get the idea:
cv::Mat img; // binary image
img.setTo(128, img == 0);
img.at<uchar>(img.rows/2, img.cols/2) = 0; // Set center point to zero
cv::Mat dist;
cv::distanceTransform(img, dist, CV_DIST_L2, 3); // Can be tweaked for desired accuracy
cv::Scalar val = cv::mean(dist, img == 255);
double mean = val[0];
With that said, I recommend you test whether this method is actually any faster than iterating in a loop. This method does a fair bit more processing than necessary to accommodate the API call.
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