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 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())

# 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))

imshow(skeleton_th, cmap='bone_r', interpolation='none')

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

# threshold using using Otsu method

thresh_thdr = threshold_otsu(cfthdr)

binary_thdr = cfthdr > thresh_thdr

# plot it

fig = plt.figure(figsize=(5, 5))

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-object

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

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