Hello Juan Thanks so much for your suggestions. Once I convertedthe image as you suggested: # import back image cfthdr=io.imread('filled_contour_THDR.png') cfthdr = color.rgb2gray(cfthdr) > 0.5 I get good results with opening: # clean it up with opening selem17 = disk(17) opened_thdr = opening(cfthdr, selem17)/255 # plot it fig = plt.figure(figsize=(5, 5)) ax = fig.add_subplot(1, 1, 1) ax.set_xticks([]) ax.set_yticks([]) plt.imshow(opened_thdr,cmap='bone') plt.show() # not bad With remove_small_objects the advantage is that it does not join blobs in the original: cfthdr_inv = ~cfthdr test=remove_small_objects(cfthdr,10000) # plot it fig = plt.figure(figsize=(5, 5)) ax = fig.add_subplot(1, 1, 1) ax.set_xticks([]) ax.set_yticks([]) plt.imshow(test,cmap='bone') plt.show() but with reconstruction done as this: # filling holes with morphological reconstruction seed = np.copy(cfthdr_inv) seed[1:-1, 1:-1] = cfthdr_inv.max() mask = cfthdr_inv filled = reconstruction(seed, mask, method='erosion') # plot it fig = plt.figure(figsize=(5, 5)) ax = fig.add_subplot(1, 1, 1) ax.set_xticks([]) ax.set_yticks([]) plt.imshow(filled,cmap='bone',vmin=cfthdr_inv.min(), vmax=cfthdr_inv.max()) plt.show() I get a completely white image. Do you have any suggestions as to why? Thank again. Cheers, Matteo On Wednesday, March 25, 2015 at 6:29:43 PM UTC-6, Juan Nunez-Iglesias wrote:
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....@gmail.com <javascript:>> 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
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