Mailman 3 September 2013 - scikit-image

Re: Get border pixels of labelled region
by Juan Nunez-Iglesias Sept. 11, 2013

Sept. 11, 2013

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 Sept. 11, 2013

Sept. 11, 2013

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. >

1 0

Re: Dividing a large image into smaller overlapping blocks for parallel processing
by Johannes Schönberger Sept. 11, 2013

Sept. 11, 2013

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 Sept. 10, 2013

Sept. 10, 2013

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 Sept. 6, 2013

Sept. 6, 2013

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

4 6

Re: uniform_filter not working with NANs present?
by Michael Aye Sept. 6, 2013

Sept. 6, 2013

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 Sept. 5, 2013

Sept. 5, 2013

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>

1 0

Re: GSoC Blog updated
by Jerome Kieffer Sept. 5, 2013

Sept. 5, 2013

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 Sept. 5, 2013

Sept. 5, 2013

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 Sept. 5, 2013

Sept. 5, 2013

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

Re: Get border pixels of labelled region
by Juan Nunez-Iglesias Sept. 5, 2013

Sept. 5, 2013

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. >

1 0

edge_detetection
by Payal Gupta Sept. 5, 2013

Sept. 5, 2013

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. also edge detection not work properly. please reply me ... 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)

1 0

uniform_filter not working with NANs present?
by Michael Aye Sept. 5, 2013

Sept. 5, 2013

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

2 1

Re: Get border pixels of labelled region
by Payal Gupta Sept. 4, 2013

Sept. 4, 2013

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. >

1 0

Re: Request: usage examples, demos, testimonials
by Stuart Mumford Sept. 4, 2013

Sept. 4, 2013

Hello, Do you still have the copy of that Sun warp code? Stuart On 1 Sep 2013 09:07, "Stéfan van der Walt" <stefan(a)sun.ac.za> wrote: > Hello, everyone > > I'd like to give a talk on scikit-image at the upcoming PyCon ZA [1]. > If you have any examples of the use of scikit-image in research or > industry, I'd love to hear about it. Also, impressive demo scripts > (like Stuart Mumford's sun warping), even if you can just share > snapshots, would be very valuable. > > Thanks! > Stéfan > > [1] http://za.pycon.org/ > > -- > 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. >

1 0

Re: Request: usage examples, demos, testimonials
by Jostein Bø Fløystad Sept. 3, 2013

Sept. 3, 2013

A version of random_walker which conserved memory better would be nice, sure! But I have high hopes for SLIC :) So far it has only been used in unpblished work, and unfortunately, I cannot share that work at the moment. But I will use the SART reconstruction code in a publication some time during the next few months, along with several other skimage functions. 2013/9/2 Stéfan van der Walt <stefan(a)sun.ac.za> > On Mon, Sep 2, 2013 at 9:51 PM, Jostein Bø Fløystad > <jostein.floystad(a)gmail.com> wrote: > > However, the memory requirements of the function were too high to allow > > processing the data set all in one piece, so I'm not sure I'd recommend > this > > as a showcase. > > But perhaps a good thing to put on our TODO :) What was the radon > transform code you submitted used in? > > 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. >

1 0

Re: Get border pixels of labelled region
by Stéfan van der Walt Sept. 3, 2013

Sept. 3, 2013

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

1 0

Re: Get border pixels of labelled region
by Guillaume Gay Sept. 3, 2013

Sept. 3, 2013

Ok, I responded a bit quickly yesterday, So here's the doc from find_boundaries (you can get that by running `find_boundaries?` in an ipython) Definition:find_boundaries(label_img) Docstring:Return bool array where boundaries between labeled regions are True. So you see that find_boundaries work on a labeled image, so my guess is you first need to threshold your image, and only then find the boundaries. It's not clear from what you say what exactly you want to segment out of the image, but maybe your work flow would include some colorspace conversion (for exemple RGB to HSV), then a thresholding, and then find_boundaries)... Hope this helps, Guillaume Le 03/09/2013 09:02, Payal Gupta a écrit : > . i want to find the boundary of this fig

2 1

Re: Get border pixels of labelled region
by Guillaume Gay Sept. 3, 2013

Sept. 3, 2013

You should use `io.imread('IMG_1.jpg')` instead of `Image.open`, which returns an `Image` object, not a numpy array. Le 02/09/2013 18:25, Payal Gupta a écrit : > > code: > > import Image > > from skimage import segmentation as seg,io > > img = Image.open("IMG_1.jpg" , 'r') > > bimg =seg.find_boundaries(img) > > io.imshow(bimg) > > > error: > > Traceback (most recent call last): > > File "<stdin>", line 1, in <module> > > File > "C:\Anaconda\lib\site-packages\spyderlib\widgets\externalshell\sitecustomize.py", > line 523, in runfile > > execfile(filename, namespace) > > File "C:\Users\Dell\Documents\Python Scripts\final_try.py", line 12, > in <module> > > bimg =seg.find_boundaries(img) > > File > "C:\Anaconda\lib\site-packages\skimage\segmentation\boundaries.py", > line 10, in find_boundaries > > boundaries = np.zeros(label_img.shape, dtype=np.bool) > > File "C:\Anaconda\lib\site-packages\PIL\Image.py", line 512, in > __getattr__ > > raise AttributeError(name) > > AttributeError: shape > > > > On Mon, Sep 2, 2013 at 8:27 PM, Stéfan van der Walt <stefan(a)sun.ac.za > <mailto:stefan@sun.ac.za>> wrote: > > On Mon, Sep 2, 2013 at 4:48 PM, Payal Gupta <erpayal2010(a)gmail.com > <mailto:erpayal2010@gmail.com>> wrote: > > but this syntax segmentation.find_boundaries(img) not work > > where img is an image > > Unless you send us a code snippet (e.g. on https://gist.github.com), > there is nothing we can do to help. > > 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 > <mailto:scikit-image%2Bunsubscribe@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.

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Re: Request: usage examples, demos, testimonials
by Stéfan van der Walt Sept. 2, 2013

Sept. 2, 2013

On Mon, Sep 2, 2013 at 9:51 PM, Jostein Bø Fløystad <jostein.floystad(a)gmail.com> wrote: > However, the memory requirements of the function were too high to allow > processing the data set all in one piece, so I'm not sure I'd recommend this > as a showcase. But perhaps a good thing to put on our TODO :) What was the radon transform code you submitted used in? Stéfan

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Re: Request: usage examples, demos, testimonials
by Jostein Bø Fløystad Sept. 2, 2013

Sept. 2, 2013

I used random_walker for 3D segmentation in the publication below: http://pubs.acs.org/doi/abs/10.1021/ma3021163 However, the memory requirements of the function were too high to allow processing the data set all in one piece, so I'm not sure I'd recommend this as a showcase. Jostein 2013/9/1 Stéfan van der Walt <stefan(a)sun.ac.za> > Hello, everyone > > I'd like to give a talk on scikit-image at the upcoming PyCon ZA [1]. > If you have any examples of the use of scikit-image in research or > industry, I'd love to hear about it. Also, impressive demo scripts > (like Stuart Mumford's sun warping), even if you can just share > snapshots, would be very valuable. > > Thanks! > Stéfan > > [1] http://za.pycon.org/ > > -- > 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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Re: Get border pixels of labelled region
by Payal Gupta Sept. 2, 2013

Sept. 2, 2013

code: import Image from skimage import segmentation as seg,io img = Image.open("IMG_1.jpg" , 'r') bimg =seg.find_boundaries(img) io.imshow(bimg) error: Traceback (most recent call last): File "<stdin>", line 1, in <module> File "C:\Anaconda\lib\site-packages\spyderlib\widgets\externalshell\sitecustomize.py", line 523, in runfile execfile(filename, namespace) File "C:\Users\Dell\Documents\Python Scripts\final_try.py", line 12, in <module> bimg =seg.find_boundaries(img) File "C:\Anaconda\lib\site-packages\skimage\segmentation\boundaries.py", line 10, in find_boundaries boundaries = np.zeros(label_img.shape, dtype=np.bool) File "C:\Anaconda\lib\site-packages\PIL\Image.py", line 512, in __getattr__ raise AttributeError(name) AttributeError: shape On Mon, Sep 2, 2013 at 8:27 PM, Stéfan van der Walt <stefan(a)sun.ac.za>wrote: > On Mon, Sep 2, 2013 at 4:48 PM, Payal Gupta <erpayal2010(a)gmail.com> wrote: > > but this syntax segmentation.find_boundaries(img) not work > > where img is an image > > Unless you send us a code snippet (e.g. on https://gist.github.com), > there is nothing we can do to help. > > 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. >

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Re: Get border pixels of labelled region
by Stéfan van der Walt Sept. 2, 2013

Sept. 2, 2013

On Mon, Sep 2, 2013 at 4:48 PM, Payal Gupta <erpayal2010(a)gmail.com> wrote: > but this syntax segmentation.find_boundaries(img) not work > where img is an image Unless you send us a code snippet (e.g. on https://gist.github.com), there is nothing we can do to help. Stéfan

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find_boundaries
by Payal Gupta Sept. 2, 2013

Sept. 2, 2013

hi all... i m using skimage module of scikit image. in image i wanna find all boundaries of all object in a image but i dont know the syntax of find_boundaries this syntax gives error... anyone know the full syntax of find_boundaries and what the meaning of levei_img plz reply me... payal

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Dividing a large image into smaller overlapping blocks for parallel processing
by Riaan van den Dool Sept. 1, 2013

Sept. 1, 2013

Hi guys I would like to use scikit-image to process large images, for example (5696, 13500). In the interest of speed I need to divide the image into smaller sub-images with the possibility of processing these in parallel. If I define the sub-images so that neighbouring sub-images overlap then edge effects should not be a problem for the algorithm operating on each sub-image. This is probably a specific case of the more general border/edge-effect handling issue as addressed by the mode parameter here: http://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.filters.c… My questions: 1. Is there already a image-division function/strategy implemented in scikit-image? 2. Is this something that might be included in future if an implementation is available? 3. Please share any references to articles or code that deals with this. Riaan

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