Mailman 3 September 2013 - scikit-image

Re: Get border pixels of labelled region
by Juan Nunez-Iglesias 11 Sep '13

11 Sep '13

Hi Payal, Please explain clearly what you are trying to do. We can't help you without that. For example, what are the images you're using? What are you trying to find within them? Your code is very confused: image = io.imread("IMG_2.jpg") > What is this image? > thresh = filter.threshold_otsu(image) > binary = image > thresh > Why are you thresholding here? > imag= color.colorconv.rgb2grey(binary) > img = array(imag) > Converting a binary image using rgb2grey makes no sense. > edge_canny = canny(img) > edge_sobel = sobel(img) > contours = measure.find_contours(imag,0.9,'high') > I don't think this function is what you want. If I'm not mistaken, find_countours is used for isocline contour plots, such as this: https://en.wikipedia.org/wiki/File:Contour2D.svg print contours > fig, (ax0, ax1) = plt.subplots(ncols=2) > ax0.imshow(imag ,cmap=plt.cm.gray) > ax0.set_title('Roberts Edge Detection') > ax0.axis('off') > ax1.imshow(edge_canny, cmap=plt.cm.gray) > ax1.set_title('Sobel Edge Detection') > ax1.axis('off') Did you attach the images produced by this plot? Juan.

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Re: Get border pixels of labelled region
by Payal Gupta 11 Sep '13

11 Sep '13

hello everyone... i used find_contour function which returns a 2D ndarray . i can't understand the return array so any body help me. *program :* image = io.imread("IMG_2.jpg") thresh = filter.threshold_otsu(image) binary = image > thresh imag= color.colorconv.rgb2grey(binary) img = array(imag) edge_canny = canny(img) edge_sobel = sobel(img) contours = measure.find_contours(imag,0.9,'high') print contours fig, (ax0, ax1) = plt.subplots(ncols=2) ax0.imshow(imag ,cmap=plt.cm.gray) ax0.set_title('Roberts Edge Detection') ax0.axis('off') ax1.imshow(edge_canny, cmap=plt.cm.gray) ax1.set_title('Sobel Edge Detection') ax1.axis('off') *returns array:* [array([[ 135. , 72.9 ], [ 134. , 72.9 ], [ 133. , 72.9 ], ..., [ 1. , 21.52941176], [ 0.47058824, 21. ], [ 0. , 20.9 ]]), array([[ 0. , 104.35137034], [ 1. , 104.35137034], [ 1.64862966, 105. ], [ 2. , 105.1 ], [ 3. , 105.35137034], [ 3.1 , 105. ], [ 3.1 , 104. ], [ 3.1 , 103. ], [ 4. , 102.1 ], [ 4.1 , 102. ], [ 5. , 101.1 ], [ 5.9 , 102. ], [ 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[ 132. , 48.13129412], [ 132.64862966, 49. ], [ 132.9 , 50. ], [ 133. , 50.1 ], [ 134. , 50.1 ], [ 135. , 50.1 ]])] >>> Click here to Reply or Forward Why this ad?Ads – Best CTET Coaching Delhi<http://www.googleadservices.com/pagead/aclk?sa=L&ai=CXWpUwCYwUq__JrOoiga6x4…> Classroom Coaching by Experts with Study Material & Mock Tests. 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Free Shipping, COD, Shop Now! jabong.com/Sandals/Fastest_Delivery<http://www.googleadservices.com/pagead/aclk?sa=L&ai=CsfKtwCYwUq__JrOoiga6x4…> ICICI™ Car Insurance<http://www.googleadservices.com/pagead/aclk?sa=L&ai=CenrHwCYwUq__JrOoiga6x4…> Free Quick Quotes In 30 secs .Now, Also Use Fb to login & Insure ICICILombard.com/Car_Insurance<http://www.googleadservices.com/pagead/aclk?sa=L&ai=CenrHwCYwUq__JrOoiga6x4…> Jobs In TCS<http://www.googleadservices.com/pagead/aclk?sa=L&ai=CqOzuwCYwUq__JrOoiga6x4…> Apply today - Upload your profile A Unique 4th Generation Job Portal www.wisdomjobs.com<http://www.googleadservices.com/pagead/aclk?sa=L&ai=CqOzuwCYwUq__JrOoiga6x4…> <http://www.googleadservices.com/pagead/aclk?sa=L&ai=CXWpUwCYwUq__JrOoiga6x4…> On Thu, Sep 5, 2013 at 12:19 PM, Juan Nunez-Iglesias <jni.soma(a)gmail.com>wrote: > Hi Payal, > > I think you're misunderstanding the purpose of "find_boundaries"... Not > quite your fault since the documentation is pretty sparse there. > > I'll use my favourite fuzzball as an example: > > [image: Inline image 1] > > "find_boundaries" is meant to take in a segmentation, which is an image in > which each region has a different integer label, like this one: > > [image: Inline image 2] > > Then, find_boundaries merely looks for pixels where the neighbouring > labels are different: > > [image: Inline image 3] > > It's not doing any edge detection per se, it's just a tool for displaying > segmentations when it is easier to look at edges than at regions (above). I > think what you are after is edge detection? Maybe an image like this one: > > [image: Inline image 4] > ? > > For that, you should directly use the edge detection algorithms, such as > Sobel or Canny, without previously thresholding your image. Have a close > look at the examples gallery to figure out if any are close to what you are > after, and try to apply the code there to your own image: > > http://scikit-image.org/docs/dev/auto_examples/ > > Some of the more pertinent ones, I think: > http://scikit-image.org/docs/dev/auto_examples/plot_canny.html > http://scikit-image.org/docs/dev/auto_examples/plot_edge_filter.html > http://scikit-image.org/docs/dev/auto_examples/plot_join_segmentations.html > http://scikit-image.org/docs/dev/auto_examples/plot_label.html > http://scikit-image.org/docs/dev/auto_examples/plot_marked_watershed.html > > http://scikit-image.org/docs/dev/auto_examples/plot_random_walker_segmentat… > http://scikit-image.org/docs/dev/auto_examples/plot_watershed.html > > http://scikit-image.org/docs/dev/auto_examples/applications/plot_coins_segm… > > Note that in the edge detection examples, Canny/Sobel is applied to the > image *before* any thresholding — unlike the example code you provided. > > Juan. > > > On Wed, Sep 4, 2013 at 5:31 PM, Payal Gupta <erpayal2010(a)gmail.com> wrote: > >> hello everyone... >> i will try this code,in this i want to do read a image then do edge >> detection after that find the boundaries of image object. but >> find_boundaries return me a bool array but i need a integer array. is it >> any solution of this. >> >> >> code:: >> >> import Image >> >> from skimage import segmentation as seg,io,filter >> >> imge = io.imread("IMG_1.jpg") >> >> thresh = filter.threshold_otsu(imge) >> >> binary = imge> thresh >> >> edge_canny = filter.canny(binary) >> >> bimg =seg.find_boundaries(binary) >> >> print(bimg) >> >> fig, (ax0, ax1) = plt.subplots(ncols=2) >> >> ax0.imshow(bimg,cmap=plt.cm.gray) >> ax1.imshow(binary,cmap=plt.cm.gray) >> >> >> On Tue, Sep 3, 2013 at 7:42 PM, Stéfan van der Walt <stefan(a)sun.ac.za>wrote: >> >>> >>> On 3 Sep 2013 11:45, "Payal Gupta" <erpayal2010(a)gmail.com> wrote: >>> > >>> > thanks it working but i confused when i use function of skimage >>> library. i want to change rgb image to a graylevel image but inbuilt >>> function rgb2gray not convert gray level image. >>> > reply. >>> >>> If you want help on this list, please provide code examples of what you >>> are attempting. >>> >>> Stéfan >>> >>> -- >>> You received this message because you are subscribed to a topic in the >>> Google Groups "scikit-image" group. >>> To unsubscribe from this topic, visit >>> https://groups.google.com/d/topic/scikit-image/rXRS0KT2PdU/unsubscribe. >>> To unsubscribe from this group and all of its topics, send an email to >>> scikit-image+unsubscribe(a)googlegroups.com. >>> For more options, visit https://groups.google.com/groups/opt_out. >>> >> >> -- >> 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. >> > > -- > You received this message because you are subscribed to a topic in the > Google Groups "scikit-image" group. > To unsubscribe from this topic, visit > https://groups.google.com/d/topic/scikit-image/rXRS0KT2PdU/unsubscribe. > To unsubscribe from this group and all of its topics, 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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Re: Dividing a large image into smaller overlapping blocks for parallel processing
by Johannes Schönberger 11 Sep '13

11 Sep '13

Yes that should be very useful. Nevertheless, I think a function like Matlab's blockproc would be a really good addition. Am 01.09.2013 um 11:34 schrieb Stéfan van der Walt <stefan(a)sun.ac.za>: > On Sat, Aug 31, 2013 at 8:17 PM, Johannes Schönberger <jsch(a)demuc.de> wrote: >> - pad image with skimage.util.pad, which allows a large number of padding methods >> - spawn a pool of processes using Python's multiprocessing package in the standard library >> - use shared memory to provide read access to complete image >> - define slices of image blocks and add them to a processing queue > > How about we add an `overlap` parameter to > `skimage.utill.view_as_windows`? That should solve this problem. > > 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. >

3 6

Sift on GPU
by Jérôme Kieffer 10 Sep '13

10 Sep '13

Dear Pythonistas, We are porting the SIFT keypoints extraction algorithm (available from IPOL) to GPU using PyOpenCL. For the moment, the keypoint location works and shows a speed-up of 5 to 10x (without tuning so far, vs C++). A lot of work is remaining, especially: * limit the memory footprint (700MB/10Mpix image currently) * calculate the descriptor for each descriptor * keypoint matching and image alignment. * best interleave of IO/CPU/GPU but we managed to port the most trickiest part to OpenCL (without using textures, which makes it running also on multi-core). I would like to thank the people who published their algorithm on IPOL; making unit testing possible. Last but not least, the code is open source and should have a BSD licence (even if there is a patent on the algorithm in the USA). https://github.com/pierrepaleo/sift_pyocl Cheers, -- Jérôme Kieffer <google(a)terre-adelie.org>

4 5

morphology.is_local_maximum --> feature.peak_local_max
by Sigmund 07 Sep '13

07 Sep '13

Hey! I noticed that there is no 2D array support anymore in the 0.8 release. Neither in morphology.is_local_max() nor in the feature.peak_local_max() Is this on purpose? If yes, a meaningful error massage would be cool. Thanks Siggi

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Re: uniform_filter not working with NANs present?
by Michael Aye 06 Sep '13

06 Sep '13

On Wednesday, September 4, 2013 10:44:21 PM UTC-7, Juan Nunez-Iglesias wrote: > > Hi Michael, > > It makes a lot of sense that this would fail, but skimage.filter.rank.mean<http://scikit-image.org/docs/dev/api/skimage.filter.rank.html#mean>has a `mask` keyword argument that can do what you want. However, it only > works for uint8 or uint16 images, *and* if I remember correctly, it will > be much slower if values exceed 12 bits, that is, your image intensity > values should be in [0, 4096). > > Hi! Thanks for your reply! Could you explain why it makes sense that the uniform_filter fails in my example? Using a window of 3, I expect the following in this simple 1d example: input: [nan, 1, 2, 3, 4, 5, nan] output: [nan, nan, 2, 3, 4, nan, nan] Why should this not work? Michael >>> from skimage.filter import rank > >>> arr = array([[np.nan, 1, 2, 3, 4, 5, np.nan]]) # make sure this is 2D > >>> selem = np.ones((3, 3)) > >>> arr_mean1 = rank.mean(arr.astype(np.uint8), selem) > >>> arr_mean1 > array([[0, 1, 2, 3, 4, 3, 2]], dtype=uint8) > > >>> arr.astype(np.uint8) # nan's are actually converted to 0... Not good! > array([[0, 1, 2, 3, 4, 5, 0]], dtype=uint8) > > >>> nan_mask = True - np.isnan(arr) > >>> arr_mean2 = rank.mean(arr.astype(np.uint8), selem, mask=nan_mask) > >>> arr_mean2 # using the nan mask produces the correct output > array([[1, 1, 2, 3, 4, 4, 5]], dtype=uint8) > > > Hope this helps! > > Juan. > > > On Mon, Sep 2, 2013 at 10:12 PM, Michael Aye <kmicha...(a)gmail.com<javascript:> > > wrote: > >> Hi! >> >> I am trying to use ndimage's uniform_filter (for a simple local mean >> filtering) on a map-projected image that has NAN's at the border (basically >> the corners where the rotated map projected image does not fit into the >> rectangular grid). >> Is there a way, maybe in skimage, to use a uniform_filter on an array >> that contains NANs? ndimage' version does not cope with it correctly: >> >> > arr = array([np.nan, 1,2,3,4,5,np.nan]) >> > arr >> >> array([ nan, 1., 2., 3., 4., 5., nan]) >> >> > nd.filters.uniform_filter(arr, 3) >> >> array([ nan, nan, nan, nan, nan, nan, nan]) >> >> >> Cheers, >> >> Michael >> >> >> -- >> 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...(a)googlegroups.com <javascript:>. >> For more options, visit https://groups.google.com/groups/opt_out. >> > >

2 1

Re: GSoC Blog updated
by Jerome Kieffer 05 Sep '13

05 Sep '13

On Thu, 5 Sep 2013 17:46:03 +0530 Chintak Sheth <chintaksheth(a)gmail.com> wrote: > Hi Jerome > > Could you send me a couple of image samples ? I'd love to see if we can do > better than GIMP. :D here is one: http://forge.epn-campus.eu/attachments/download/1883/Pilatus1M.edf There are some concentric, continuous rings but some parts of the detector is missing. Under debian, to read the data: sudo apt-get install python-fabio ipython >>> import fabio >>> data = fabio.open("Pilatus1M.edf").data >>> mask = data<0 > However, the two posts are essentially for Texture Synthesis in the sense > that it will be able to beautifully reconstruct the damaged portion if it > already exists in the image. Ive also implemented Fast Marching Method > algorithm which is essentially a Structural inpainting algorithm. PR #663 > (in case you want to check it out) My computer is just heating one the problem :) -- Jerome Kieffer <google(a)terre-adelie.org>

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Re: GSoC Blog updated
by Jerome Kieffer 05 Sep '13

05 Sep '13

On Thu, 5 Sep 2013 15:37:17 +0530 Chintak Sheth <chintaksheth(a)gmail.com> wrote: > Hi folks, > > New blog posts: > > http://chintak.github.io/2013/08/texture-synthesis-efros-and-leung-algo/ > > http://chintak.github.io/2013/09/texture-and-structural-inpainting/ > > A > ny feedback welcome. It looks impressive. I wonder how it compares to the resynthetizer plugin in GIMP. Because I just tested Resynthetize this morning on diffraction images where pieces were missing and the results were disappointing. Indeed my problem is strange as I am looking for contiguous regions with a camera containing many holes (about 10%) and most algorithm fail on those image due to those holes. Cheers. -- Jerome Kieffer <google(a)terre-adelie.org>

2 1

Re: GSoC Blog updated
by Johannes Schönberger 05 Sep '13

05 Sep '13

Hi Chintak, The quality of the results looks very impressing! Am 05.09.2013 um 12:07 schrieb Chintak Sheth <chintaksheth(a)gmail.com>: > Hi folks, > > New blog posts: > > http://chintak.github.io/2013/08/texture-synthesis-efros-and-leung-algo/ > > http://chintak.github.io/2013/09/texture-and-structural-inpainting/ > > Any feedback welcome. > Chintak > > -- > 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.

2 3

GSoC Blog updated
by Chintak Sheth 05 Sep '13

05 Sep '13

Hi folks, New blog posts: http://chintak.github.io/2013/08/texture-synthesis-efros-and-leung-algo/ http://chintak.github.io/2013/09/texture-and-structural-inpainting/ A ny feedback welcome. Chintak

1 0