Someone suggested an adaptive threshold for me to get a desired result, however after messing with the parameters and settings I cannot get a similar result.
Here is the base image:
How can I get from that image to this image using an adaptive threshold?
Here is a snippet of what I've been trying
adaptiveThreshold(thr, thr, 255, ADAPTIVE_THRESH_MEAN_C, THRESH_BINARY, 101, 16);
First of all, read this documentation and see how the result look: DOCS
You should deeply analyse the result from chessboard - there you can see, that adaptive threshold is sth like edge detector. It will detect (set maximum value in you case) only the pixels, which in their local neighborhood has bigger value that "MEAN" + "C". So, the value from pixels ex. (0,0) and (1000,1000) are independent. As I say, this function will detect you edges, not blobs.
In this situation I suggest do sth like to detect blobs: - adaptiveThreshold: find edges in image - findContur: make connected component algorithm (you may to use OPEN or CLOSE morphological operation morphology to delete singe pixels or to connect to edges) - find biggest contour (by area or number of points) and fillPoly it with white color
After this operation you should get sth like you want.
Asked: 2014-03-21 21:44:03 -0600
Seen: 1,288 times
Last updated: Mar 23 '14
Why are you using such a large block size (101) and why are you subtracting 16 from the weighted threshold? Both of those values seem excessive.
That code snippet probably wan't the best example, but it was just mean trying to learn how these values effect the end result.
Try removing the last two parameters and let them go by default.