Hey Alex, Do an imshow on your distance transform, ie `inv_dist`. You’ll see that it’s incorrect — it has the peaks outside of the bubbles. Then inv_peak has as the “maxima” the entire bubble — not good! So you should run your distance transform on `img_bin` rather than `inv_bin` and I think it’ll be a better result!
Juan. On 6 Oct 2017, 1:00 AM +1100, Thomas Walter via scikit-image <scikit-image@python.org>, wrote: > Hello Alex, > > the ultimate erosion coincides with the local maxima of the distance map (or > the local minima of the inverse distance map). > > In skimage, you can get these by: > > from skimage.morphology import extrema > local_maxima = extrema.local_maxima(distance_map) > > peak_local_max is not equivalent to the UEP, but can be preferable in certain > situations. > > I did not check your example in practice as I do not have the image, but I > was a bit confused by the inversions, as you start by applying the threshold > the other way around. > > Best, > > Thomas. > > P.S.: The ImageJ description is not correct, which I have always found > disturbing. The Watershed algorithm is of course not limited to applications > to the inverse distance map. > > > On 10/5/17 3:22 PM, Alexandre Fioravante de Siqueira wrote: > > Dear all, > > I am trying to write a that mimics the behavior of ImageJ's flooding > > watershed. This is how they describe it > > (https://imagej.nih.gov/ij/docs/menus/process.html#watershed): > > > > Watershed > > Watershed segmentation is a way of automatically separating or cutting > > apart particles that touch. It first calculates the Euclidian distance map > > (EDM) and finds the ultimate eroded points (UEPs). It then dilates each of > > the UEPs (the peaks or local maxima of the EDM) as far as possible - either > > until the edge of the particle is reached, or the edge of the region of > > another (growing) UEP. Watershed segmentation works best for smooth convex > > objects that don't overlap too much. > > > > I tried to implement that (more or less), using the local minima (as > > pointed in https://www.mathworks.com/help/images/ref/bwulterode.html). Of > > course, there is something wrong :) > > > > from scipy.ndimage import binary_fill_holes > > from scipy.ndimage.morphology import distance_transform_edt > > from skimage.feature import peak_local_max > > from skimage.filters import threshold_otsu > > from skimage.io import imread > > from skimage.measure import label > > from skimage.morphology import disk, watershed > > from skimage.util import invert > > > > import matplotlib.pyplot as plt > > > > image = imread('K90_incid4,5min_1.bmp') > > img_bin = binary_fill_holes(image < threshold_otsu(image)) > > > > inv_bin = invert(img_bin) > > inv_dist = distance_transform_edt(inv_bin) > > inv_peak = peak_local_max(-inv_dist, indices=False) > > markers = label(peak) > > labels = watershed(-distance, markers, mask=img_bin, watershed_line=True) > > > > plt.imshow(labels, cmap='nipy_spectral'), plt.show() > > > > We don't have a function for UEPs in skimage yet, right? > > How could I implement this flooding watershed? > > Thank you very much. Kind regards, > > > > Alex > > > > -- > > -------------------------------------------------- > > Dr. Alexandre 'Jaguar' Fioravante de Siqueira > > Grupo de Cronologia - Sala 13 > > Departamento de Raios Cósmicos e Cronologia - DRCC > > Instituto de Física "Gleb Wataghin" - IFGW > > Unicamp - Universidade Estadual de Campinas > > Rua Sérgio Buarque de Holanda, 777 > > Cidade Universitária Zeferino Vaz - CEP 13083-859 > > Campinas - SP - Brazil > > > > Phone: +55(19)3521-5362 > > Lattes curriculum: 3936721630855880 > > ORCID: 0000-0003-1320-4347 > > Personal site: programmingscience.org > > Github: alexandrejaguar > > Skype: alexandrejaguar > > Twitter: alexdesiqueira > > -------------------------------------------------- > > > > > > _______________________________________________ > > scikit-image mailing list > > scikit-image@python.org > > https://mail.python.org/mailman/listinfo/scikit-image > > > -- > Thomas Walter > 27 rue des Acacias > 75017 Paris > _______________________________________________ > scikit-image mailing list > scikit-image@python.org > https://mail.python.org/mailman/listinfo/scikit-image
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