Mailman 3 April 2013 - scikit-image

Re: Coordinate overlap in two labelled images
by Stuart Mumford 04 Apr '13

04 Apr '13

Hello, Thanks for your replies everyone, I am looking through them but this is a reasonably low-priority project. I am sure I will have a follow up question or two soon!! Thanks again Stuart

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Re: Coordinate overlap in two labelled images
by Juan Nunez-Iglesias 02 Apr '13

02 Apr '13

Suggestion number 3. I'll deal with each of three interpretations of your question. Suppose your images are im1 and im2. Now, you might want 1) of the regions in im1, the ones that intersect over at least 1 pixel with regions of im2; 2) the regions that are part of regions in both im1 and im2 (ie the intersection of regions in im1 and im2); or 3) the union of regions of im1 and im2 that have at least one pixel intersection with the other image. The last example assumes the union'ed regions don't touch each other. Then: In [1]: import scipy.ndimage as nd In [2]: im1 = np.array([[0, 0, 0, 0], [1, 1, 0, 2], [0, 0, 0, 2]]) In [3]: im1 Out[3]: array([[0, 0, 0, 0], [1, 1, 0, 2], [0, 0, 0, 2]]) In [4]: im2 = np.array([[0, 1, 0, 0], [0, 1, 0, 0], [0, 1, 0, 0]]) In [5]: im2 Out[5]: array([[0, 1, 0, 0], [0, 1, 0, 0], [0, 1, 0, 0]]) In [6]: intersect = im1.astype(bool) * im2.astype(bool) In [7]: intersect Out[7]: array([[False, False, False, False], [False, True, False, False], [False, False, False, False]], dtype=bool) In [8]: overlap_labels1 = np.unique(im1[intersect]) In [9]: selector = np.zeros(im1.max() + 1, int) In [10]: selector[overlap_labels1] = arange(1, len(overlap_labels1) + 1) In [11]: reg1 = selector[im1] In [12]: reg1 Out[12]: array([[0, 0, 0, 0], [1, 1, 0, 0], [0, 0, 0, 0]]) In [13]: reg2 = nd.label(intersect)[0] In [14]: reg2 Out[14]: array([[0, 0, 0, 0], [0, 1, 0, 0], [0, 0, 0, 0]], dtype=int32) In [15]: overlap_labels2 = np.unique(im2[intersect]) In [16]: selector2 = np.zeros(im2.max() + 1, int) In [17]: selector2[overlap_labels2] = 1 In [18]: nonzero_pixels = reg1 + selector2[im2] In [19]: reg3 = nd.label(nonzero_pixels)[0] In [20]: reg3 Out[20]: array([[0, 1, 0, 0], [1, 1, 0, 0], [0, 1, 0, 0]], dtype=int32) On Wed, Apr 3, 2013 at 5:52 AM, Stéfan van der Walt <stefan(a)sun.ac.za>wrote: > Hi Stuart > > On Tue, Apr 2, 2013 at 7:04 PM, Stuart Mumford <littlemumford(a)gmail.com> > wrote: > > I am using your (amazingly fast) label image routines and regionprops to > extract the coordinates of the regions in two images. I need to filter > these objects out if they do not overlap in both the images, then renumber > all the remaining regions. At the moment I am doing something unspeakable > with nested loops and sets which is pretty damn slow; I was hoping someone > here would have a really clever idea on how to solve this. Failing that > does anyone know how I can find the number of coordinates (output from > regionprops) that overlap in two numpy arrays? > > You can use join_segmentations to find an intersection of your two > label maps. Then, walk through the newly formed regions and check at > each point whether the object exists in both images (that way, you > don't have so many comparisons to do). > > http://scikit-image.org/docs/dev/auto_examples/plot_join_segmentations.html > > If that's not exactly what you had in mind, you can use this code to > find all the connected regions in an image: > > https://github.com/stefanv/lulu/blob/master/lulu/base.pyx#L21 > > These regions are encoded sparsely, and testing their intersection is > very fast (see the algorithm section of this paper: > https://scholar.sun.ac.za/handle/10019.1/4883). > > I hope that helps! > Stéfan > > -- > 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(a)googlegroups.com. > For more options, visit https://groups.google.com/groups/opt_out. > > >

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Coordinate overlap in two labelled images
by Stuart Mumford 02 Apr '13

02 Apr '13

Hello, I have a problem I was hoping someone here would be able to help me with! I am using your (amazingly fast) label image routines and regionprops to extract the coordinates of the regions in two images. I need to filter these objects out if they do not overlap in both the images, then renumber all the remaining regions. At the moment I am doing something unspeakable with nested loops and sets which is pretty damn slow; I was hoping someone here would have a really clever idea on how to solve this. Failing that does anyone know how I can find the number of coordinates (output from regionprops) that overlap in two numpy arrays? Thanks a lot Stuart

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Re: SciPy 2013 Tutorial
by Emmanuelle Gouillart 01 Apr '13

01 Apr '13

Hi Tony, I'm not going to SciPy 2013, but I've agreed to give a 1h30-long tutorial on image processing with scikit-image at Euroscipy 2013 (August 21-24). I'd happy to share course materials if you're interested. I copy below the abstract I've given to the organizers. Cheers, Emmanuelle Image processing with scikit-image and the SciPy stack ====================================================== Numerical image processing involves the manipulation and transformation of images, often in order to extract information of interest from the images. NumPy nd-arrays provide an efficient container for image data, that can therefore be processed using the SciPy toolstack. In this tutorial, we will first briefly see how simple operations on images (e.g. cropping, framing) can be performed using NumPy. More complex image processing operations will be handled principally by the scikit-image module (occasionally by the ndimage submodule of SciPy). Compared to other image processing modules for Python, scikit-image is designed to work transparently with numpy nd-arrays, and is written in pure Python and some Cython in order to promote readability and maintainability. The different subtopics of this tutorial include: * input and output of images (file formats...) * image enhancing / denoising * image segmentation (separating an image in labeled regions) * extraction of geometrical features (edges, lines, spheres, skeleton...) * extraction of features for image classification This tutorial will consist mostly of hands-on examples; no mathematical justification of the algorithms will be given during the tutorial. Besides the main image processing tasks, the tutorial will demonstrate how NumPy advanced features (masks, broadcasting, manipulation of subsets of indices) and scikit-image utilities functions make image processing easier. The tutorial will also address the visualization of image processing results (contours, etc.). On Thu, Mar 28, 2013 at 09:06:04PM -0500, Tony Yu wrote: > I was wondering if anyone has plans to submit a [1]tutorial proposal for > SciPy 2013. The proposal is due on Monday (Apr. 1st). It'd be great to > have scikit-image represented there; especially since medical imaging is > one of the mini-symposia topics. > If no one else is able, I'll probably submit something, but I have a > feeling that many of you have more (i.e. any) experience teaching image > processing than I do. > Cheers, > -Tony

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Google Summer of Code 2013
by Stéfan van der Walt 01 Apr '13

01 Apr '13

Hi everyone It looks like we will be able to participate in Google Summer of Code 2013 under the PSF umbrella. If you have any project ideas, please add details here: https://github.com/scikit-image/scikit-image/wiki/GSoC-2013 *Prospective GSoC students: *We follow the same policy as scikit-learn, in that you have to have at least one patch contributed to the project *before*applying for a summer of code project. Regards Stéfan

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