Hi Matteo, My guess is that even though you are looking at a "black and white" image, the png is actually an RGB png. Just check the output of "print(cfthdr.shape)". Should be straightforward to make it a binary image: from skimage import color cfthdr = color.rgb2gray(cfthdr) > 0.5 Then you should have a binary image. (And inverting should be as simple as "cfthdr_inv = ~cfthdr") We have morphology.binary_fill_holes to do what you want. btw, there's also morphology.remove_small_objects, which does exactly what you did but as a function call. Finally, it looks like you are not using the latest version of scikit-image (0.11), so you might want to upgrade. Hope that helps! Juan. On Thu, Mar 26, 2015 at 8:48 AM, Matteo <matteo.niccoli@gmail.com> wrote:
*Issues with morphological filters when trying to remove white holes in black objects in a binary images. Using opening or filling holes on inverted (or complement) of the original binary.* Hi there I have a series of derivatives calculated on geophysical data. Many of these derivatives have nice continuous maxima, so I treat them as images on which I do some cleanup with morphological filter. Here's one example of operations that I do routinely, and successfully: # threshold theta map using Otsu method thresh_th = threshold_otsu(theta) binary_th = theta > thresh_th # clean up small objects label_objects_th, nb_labels_th = sp.ndimage.label(binary_th) sizes_th = np.bincount(label_objects_th.ravel()) mask_sizes_th = sizes_th > 175 mask_sizes_th[0] = 0 binary_cleaned_th = mask_sizes_th[label_objects_th] # further enhance with morphological closing (dilation followed by an erosion) to remove small dark spots and connect small bright cracks # followed by an extra erosion selem = disk(1) closed_th = closing(binary_cleaned_th, selem)/255 eroded_th = erosion(closed_th, selem)/255 # Finally, extract lienaments using skeletonization skeleton_th=skeletonize(binary_th) skeleton_cleaned_th=skeletonize(binary_cleaned_th) # plot to compare fig = plt.figure(figsize=(20, 7)) ax = fig.add_subplot(1, 2, 1) imshow(skeleton_th, cmap='bone_r', interpolation='none') ax2 = fig.add_subplot(1, 3, 2) imshow(skeleton_cleaned_th, cmap='bone_r', interpolation='none') ax.set_xticks([]) ax.set_yticks([]) ax2.set_xticks([]) ax2.set_yticks([]) Unfortunately I cannot share the data as it is proprietary, but I will for the next example, which is the one that does not work. There's one derivative that shows lots of detail but not continuous maxima. As a workaround I created filled contours in Matplotlib exported as an image. The image is attached. Now I want to import back the image and plot it to test: # import back image cfthdr=io.imread('filled_contour.png') # threshold using using Otsu method thresh_thdr = threshold_otsu(cfthdr) binary_thdr = cfthdr > thresh_thdr # plot it fig = plt.figure(figsize=(5, 5)) ax = fig.add_subplot(1, 1, 1) ax.set_xticks([]) ax.set_yticks([]) plt.imshow(binary_thdr, cmap='bone') plt.show() The above works without issues.
Next I want to fill the white holes inside the black blobs. I thought of 2 strategies. The first would be to use opening; the second to invert the image, and then fill the holes as in here: http://scikit-image.org/docs/dev/auto_examples/plot_holes_and_peaks.html By the way, I found a similar example for opencv here http://stackoverflow.com/questions/10316057/filling-holes-inside-a-binary-ob...
Let's start with opening. When I try: selem = disk(1) opened_thdr = opening(binary_thdr, selem) or: selem = disk(1) opened_thdr = opening(cfthdr, selem) I get an error message like this: --------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-49-edc0d01ba327> in <module>() 1 #binary_thdr=img_as_float(binary_thdr,force_copy=False) ----> 2 opened_thdr = opening(binary_thdr, selem)/255 3 4 # plot it 5 fig = plt.figure(figsize=(5, 5)) C:\...\skimage\morphology\grey.pyc in opening(image, selem, out) 160 shift_y = True if (h % 2) == 0 else False 161 --> 162 eroded = erosion(image, selem) 163 out = dilation(eroded, selem, out=out, shift_x=shift_x, shift_y=shift_y) 164 return out C:\...\skimage\morphology\grey.pyc in erosion(image, selem, out, shift_x, shift_y) 58 selem = img_as_ubyte(selem) 59 return cmorph._erode(image, selem, out=out, ---> 60 shift_x=shift_x, shift_y=shift_y) 61 62 C:\...\skimage\morphology\cmorph.pyd in skimage.morphology.cmorph._erode (skimage\morphology\cmorph.c:2658)() ValueError: Buffer has wrong number of dimensions (expected 2, got 3) --------------------------------------------------------------------------- Any idea of what is going on and how I can fix it?
As for inverting (or finding the complement) and then hole filling, that would be my preferred option. However, I have not been able to invert the image. I tried numpy.invert, adapting the last example from here: http://docs.scipy.org/doc/numpy/reference/generated/numpy.invert.html I tried something like this: http://stackoverflow.com/a/16724700 and this: http://stackoverflow.com/a/2498909 But none of these methods worked. Is there a way in scikit.image to do that, and if not, do you have any suggestions? Thank you, Matteo -- You received this message because you are subscribed to the Google Groups "scikit-image" group. To unsubscribe from this group and stop receiving emails from it, send an email to scikit-image+unsubscribe@googlegroups.com. For more options, visit https://groups.google.com/d/optout.