[scikit-image] fill closed contour

[Juan Nunez-Iglesias]

Ah, your issue with the thickening was that label by default does not consider diagonally-adjancent pixels as adjacent. You need to pass connectivity=2 for this to be the case. But actually labelling the holes rather than the edges is a rather better option. =) The final image looks very pretty and could make a nice company logo. =P

On 9 Jan 2018, 1:03 PM +1100, Randy Heiland <randy.heiland at gmail.com>, wrote:
> Argh. Nevermind... need to flip black/white on canny edges:  1-(edges*1)
>
>
>
> > On Mon, Jan 8, 2018 at 8:55 PM, Randy Heiland <randy.heiland at gmail.com> wrote:
> > > Thanks Juan. I understand better what the  ndi.measurements.label can do for me now. I've tweaked my previous script and attached the resulting output. Does it make sense that I need to "thicken" the contours in order to get the desired features/regions, or is there something I'm still missing?
> > >
> > > ------------
> > > from skimage.morphology import disk
> > > from skimage.feature import canny
> > > from skimage.filters import rank
> > > from scipy import ndimage as ndi
> > > import matplotlib.pyplot as plt
> > > import numpy as np
> > >
> > > image = disk(100)
> > > for ix in range(200):
> > >   for iy in range(200):
> > >     xdel=ix-100
> > >     ydel=iy-100
> > >     if (xdel*xdel/50 + ydel*ydel/10) < 110:
> > >       image[iy,ix]=0
> > >     elif (xdel*xdel/10 + ydel*ydel/50) < 110:
> > >       image[iy,ix]=0
> > >
> > > edges = canny(image*255.)  # canny expect grayscale, i.e. 0-255 ??!
> > >
> > > thicken = rank.gradient(edges, disk(1)) < 5
> > > bdy = thicken.astype(np.uint8)*255
> > >
> > > labeled_array, num_features = ndi.measurements.label(edges*1)
> > > print("num_features (edges*1)=",num_features)
> > > labeled_array2, num_features2 = ndi.measurements.label(bdy)
> > > print("num_features (thick)=",num_features2)
> > >
> > > fill = ndi.binary_fill_holes(edges)
> > >
> > > fig, axes = plt.subplots(ncols=2, nrows=2, figsize=(6, 7))
> > > ax = axes.ravel()
> > >
> > > ax[0].imshow(edges*1, cmap=plt.cm.gray, interpolation='nearest')
> > > ax[0].set_title('Canny edges')
> > > ax[1].imshow(labeled_array, cmap=plt.cm.spectral, interpolation='nearest')
> > > ax[1].set_title('labeled_array')
> > >
> > > ax[2].imshow(bdy, cmap=plt.cm.gray, interpolation='nearest')
> > > ax[2].set_title('bdy')
> > > ax[3].imshow(labeled_array2, cmap=plt.cm.spectral, interpolation='nearest')
> > > ax[3].set_title('labeled_array2')
> > >
> > > plt.axis('off')
> > > plt.show()
> > >
> > > -->
> > > num_features (edges*1)= 216
> > > num_features (thick)= 6
> > >
> > > -Randy
> > >
> > >
> > > > On Sun, Jan 7, 2018 at 11:36 PM, Juan Nunez-Iglesias <jni.soma at gmail.com> wrote:
> > > > > Oh, I see what's happening. So, in your case, both void spaces are actually holes from the perspective of the binary_fill_holes algorithm, so they both get filled. I suggest you
> > > > >
> > > > > a) label both contours using ndi.label
> > > > > b) use binary_fill_holes on each label separately
> > > > > c) subtract the filled inner hole from the filled outer hole (you can optionally add back in the inner contour if you care about that single-pixel precision)
> > > > >
> > > > > This requires being able to robustly identify the inner and outer contours, but I don't think that should be too hard? If you only have two, you can certainly find them by finding the "larger" of the two bounding boxes. You can use skimage.measure.regionprops for this.
> > > > >
> > > > > I hope that helps!
> > > > >
> > > > > Juan.
> > > > >
> > > > > On 8 Jan 2018, 12:21 PM +1100, Randy Heiland <randy.heiland at gmail.com>, wrote:
> > > > > > Sure - thanks.
> > > > > >
> > > > > > from skimage.morphology import disk
> > > > > > from skimage.feature import canny
> > > > > > from scipy import ndimage as ndi
> > > > > > import matplotlib.pyplot as plt
> > > > > >
> > > > > > image = disk(100)
> > > > > > for ix in range(200):
> > > > > >   for iy in range(200):
> > > > > >     xdel=ix-100
> > > > > >     ydel=iy-100
> > > > > >     if (xdel*xdel/50 + ydel*ydel/10) < 110:
> > > > > >       image[iy,ix]=0
> > > > > >     elif (xdel*xdel/10 + ydel*ydel/50) < 110:
> > > > > >       image[iy,ix]=0
> > > > > >
> > > > > > edges = canny(image*255.)  # canny expect grayscale, i.e. 0-255 ??!
> > > > > >
> > > > > > fill = ndi.binary_fill_holes(edges)   # I don't understand the params; can I seed a region to fill?
> > > > > >
> > > > > > fig, axes = plt.subplots(ncols=3, figsize=(9, 3))
> > > > > > ax = axes.ravel()
> > > > > >
> > > > > > ax[0].imshow(image, cmap=plt.cm.gray, interpolation='nearest')
> > > > > > #ax[0].imshow(invert_img, cmap=plt.cm.gray)
> > > > > > #ax[0].set_title('Inverted image')
> > > > > > ax[0].set_title('Original image')
> > > > > >
> > > > > > ax[1].imshow(edges*1, cmap=plt.cm.gray, interpolation='nearest')
> > > > > > ax[1].set_title('Canny edges')
> > > > > >
> > > > > > ax[2].imshow(fill, cmap=plt.cm.spectral, interpolation='nearest')
> > > > > > ax[2].set_title('Fill')
> > > > > >
> > > > > > plt.show()
> > > > > >
> > > > > >
> > > > > >
> > > > > > > On Sun, Jan 7, 2018 at 6:57 PM, Juan Nunez-Iglesias <jni.soma at gmail.com> wrote:
> > > > > > > > Hi Randy, I was going to suggest binary fill holes. Do you mind posting your image and the code you’ve tried so we can troubleshoot?
> > > > > > > >
> > > > > > > > Thanks,
> > > > > > > >
> > > > > > > > Juan.
> > > > > > > >
> > > > > > > > On 8 Jan 2018, 9:48 AM +1100, Randy Heiland <randy.heiland at gmail.com>, wrote:
> > > > > > > > > If I have a binary image with, say, just a contour boundary (simple example: a white background with a black circle, i.e. an "o"), how can I fill the inside of the contour? I've played with both the watershed segmentation and the scipy.ndimage.binary_fill_holes, without success.
> > > > > > > > >
> > > > > > > > > thanks, Randy
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