Gap Filling Contours / Lines
image-morphology, image-segmentation, opencv, python, scikit-image
Solution
In the following snippet I calculate the distance map of the inverse image. I threshold it to obtain a large outline of the current object, which I then skeletonize to get the central line. This may already be enough for your purposes. But to make it consistent with the line thickness given, I dilate the skeleton and add it to the original, thereby closing any gaps. I also remove the one remaining object touching the boundary.
from skimage import io, morphology, img_as_bool, segmentation
from scipy import ndimage as ndi
import matplotlib.pyplot as plt
image = img_as_bool(io.imread('/tmp/gaps.png'))
out = ndi.distance_transform_edt(~image)
out = out < 0.05 * out.max()
out = morphology.skeletonize(out)
out = morphology.binary_dilation(out, morphology.selem.disk(1))
out = segmentation.clear_border(out)
out = out | image
plt.imshow(out, cmap='gray')
plt.imsave('/tmp/gaps_filled.png', out, cmap='gray')
plt.show()
Problem
I have the following image: and I would like to fill in its contours (i.e. I would like to gap fill the lines in this image). I have tried a morphological closing, but using a rectangular kernel of size `3x3` with `10` iterations does not fill in the entire border. I have also tried a `21x21` kernel with `1` iteration and also not had luck. UPDATE: I have tried this in OpenCV (Python) using: ``` cv2.morphologyEx(img, cv2.MORPH_CLOSE, cv2.getStructuringElement(cv2.MORPH_RECT, (21,21))) ``` and ``` cv2.morphologyEx(img, cv2.MORPH_CLOSE, cv2.getStructuringElement(cv2.MORPH_RECT, (3,3)), iterations=10) ``` and scikit-image: ``` closing(img, square(21)) ``` My end goal is to a have a filled version of that entire image without distorting the area covered.