Thanks Juan Nunez-Iglesias for the information! On Sat, Apr 4, 2015 at 10:02 PM, Juan Nunez-Iglesias <jni.soma@gmail.com> wrote:
Hey Yuta,
I'm not sure how much h5py mirrors the numpy array interface, but I suspect not sufficiently to allow C/Cython functions to work. I think you'll need to find a clever way to partition your image to get the right result, because although you can use numpy.memmap [1] to use an on-disk array, I think that might be far too slow (since binary fill holes uses quite a few iterations...
Juan.
[1] http://docs.scipy.org/doc/numpy/reference/generated/numpy.memmap.html
On Sat, Apr 4, 2015 at 12:54 AM, Yuta Sato <yutaxsato@gmail.com> wrote:
Dear Juan Nunez-Iglesias and Josh Warner:
Thanks for your kind responses. Lets take a more simpler case, e.g., binary_fill_holes. I want to apply to the WHOLE image at once, because if I apply it in the parts of image and later combine, the result differs.
Does putting the image into hdf5 and applying the binary_fill_holes solve my problem? Can I really apply binary_fill_holes to hdf5 file?
Thanks for your helps.
Yuta
On Sun, Mar 15, 2015 at 10:18 AM, Juan Nunez-Iglesias <jni.soma@gmail.com
wrote:
Josh, you might be thinking of morphology.remove_small_objects, but that is O(image.size), rather than O(sum(image == label)), which is what you are after. In fact we would need a flood-fill algorithm, which we don't have... That would be a fantastic addition.
On Sun, Mar 15, 2015 at 10:24 AM, Josh Warner < silvertrumpet999@gmail.com> wrote:
Would it be possible to generalize / refactor `clear_border` to a function which removes all points connected to a specific pixel/voxel? That would greatly simplify the work needed here.
I thought we had some sort of `remove_object` functionality like this, but I don't see it.
Josh
On Friday, March 13, 2015 at 9:04:12 PM UTC-5, Juan Nunez-Iglesias wrote:
Hey Yuta,
You'll need to do some stitching out-of-core. That's a really tricky problem and I don't have any ready-made solutions for you. The solution will depend on the nature of your segments. The only thing I would recommend is that you use a format such as HDF5 (you can use the excellent h5py library) that allows random access into the underlying disk data.
Other than that, as I said, to my knowledge you'll have to develop your own stitching: segment *overlapping* tiles independently in memory, and when it comes time to write to disk, load the tile and overlapping tiles that have already been segmented, and resolve label mapping then...
Generally, think of it this way: tile i has already been segmented and written out. We now want to write out tile j, which overlaps tile i. Labels from tile i that intersect labels from tile j in the overlap region should be matched. labels in tile j that *don't* intersect tile i should be relabelled to ensure they are unique with respect to tile i.
Of course this gets a bit more complicated in 2D or 3D...
Juan.
On Fri, Mar 13, 2015 at 7:20 PM, Yuta Sato <yutaxsato@gmail.com> wrote:
Dear SKIMAGE Developers and Users:
I want to use the following algorithm in a large binary image that does not fit into my PC memory. So, I am thinking to split my large image into tiles and apply algorithm one by one. However, the original border definition change when I do it in parts. I need the result as applied in original full image. How can I do it efficiently?
skimage.segmentation.clear_border(image, buffer_size=0, bgval=0)
Thanks for your ideas.
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