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.heil...@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.heil...@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.s...@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.heil...@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.s...@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.heil...@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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