Mailman 3 March 2015 - scikit-image

Range of beta values in segmentation algorithm?
by Yuta Sato 12 Mar '15

12 Mar '15

In the following skimage.segmentation.random_walker algorithm: What is the range of 'beta' values that can be supplied? I am working with a single band 8bit unsigned image. Is it 0 to 255? skimage.segmentation.random_walker(data, labels, beta=130, mode='bf', tol=0.001, copy=True,multichannel=False, return_full_prob=False, spacing=None) beta : float [Penalization coefficient for the random walker motion (the greater beta, the more difficult the diffusion)] Thanks for your support.

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Re: Equivalent of watershed for cutting connected components of an image of particles?
by Thomas Caswell 11 Mar '15

11 Mar '15

Jumping in from the peanut gallery, can you reliable identify when the segmentation has gone sideways? Looking at the second moment, area to bounding box area, or some other compactness measure? If you can get away with it, you could just drop the offending cells. If not, then you can try eroding the joined cells until they split into multiple segments. Tom On Wed, Mar 11, 2015, 17:15 Claiborne Morton <claiborne.morton(a)gmail.com> wrote: > Hey thanks for the help, here are a few other issues we are running into. > When a sickle cell is in contact with a regular cell, we cannot find a way > to separate the two. Also bottom-middle circle is of a healthy blood cell > that is on its side. The watershed function tends to break these cells into > two or more partitions when the should not be separated. > Any idea on how to fix these problems? > > > On Tue, Mar 10, 2015 at 2:12 PM, Claiborne Morton < > claiborne.morton(a)gmail.com> wrote: > >> Hey guys, Im following up on Adam's behalf, but this is an example of an >> image we are working with in trying to separate cells that are touching >> each other. >> Also you can see the top middle particle has a crescent shape, but is >> actually a healthy red blood cell that has been segmented incorrectly >> because of glare. Is that a way to connect the two tips of the shape so >> that I could then run "binary_fill_holes()" to correctly segment the cell. >> Thanks! >> >> >> On Wednesday, February 18, 2015 at 7:04:10 PM UTC-5, Adam Hughes wrote: >> >>> Hi, >>> >>> In ImageJ, one can select watershedding to break up connected regions of >>> particles. Are there any examples of using watershed in this capacity in >>> scikit image? All of the examples I see seem to use watershedding to do >>> segmentation, not to break connected particles in an already-segmented >>> black and white image. >>> >>> Also, is there a straightforward way to remove particles on a the edge >>> of an image? Sorry, googling is failing me, but I know this is possible. >>> >>> Thanks >>> >> -- >> 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/VL6SZTWvAz8/unsubscribe. >> To unsubscribe from this group and all its topics, send an email to >> scikit-image+unsubscribe(a)googlegroups.com. > > >> For more options, visit https://groups.google.com/d/optout. >> > -- > 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/d/optout. >

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Re: Equivalent of watershed for cutting connected components of an image of particles?
by Claiborne Morton 11 Mar '15

11 Mar '15

Hey guys, Im following up on Adam's behalf, but this is an example of an image we are working with in trying to separate cells that are touching each other. Also you can see the top middle particle has a crescent shape, but is actually a healthy red blood cell that has been segmented incorrectly because of glare. Is that a way to connect the two tips of the shape so that I could then run "binary_fill_holes()" to correctly segment the cell. Thanks! On Wednesday, February 18, 2015 at 7:04:10 PM UTC-5, Adam Hughes wrote: > Hi, > > In ImageJ, one can select watershedding to break up connected regions of > particles. Are there any examples of using watershed in this capacity in > scikit image? All of the examples I see seem to use watershedding to do > segmentation, not to break connected particles in an already-segmented > black and white image. > > Also, is there a straightforward way to remove particles on a the edge of > an image? Sorry, googling is failing me, but I know this is possible. > > Thanks >

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Re: SciPy 2015 Conference: Call for proposals
by Stéfan van der Walt 11 Mar '15

11 Mar '15

On Mon, Mar 9, 2015 at 5:19 PM, Tony Yu <tsyu80(a)gmail.com> wrote: > SciPy 2015 is requesting proposals for tutorials, talks, and posters for > this year's conference. Note that there's a mini-symposium on > "Visualization, Vision and Imaging", so this year will be particularly > relevant for people here. I am interested in presenting a tutorial this year, but will be gone most of June, so I need a partner in crime to share in the fun. Any volunteers? Stéfan

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Re: hough ellipse fit inaccurate?
by Johannes Schoenberger 11 Mar '15

11 Mar '15

@Stefan, good idea! I am curious to know whether this solves your problem. > On Mar 10, 2015, at 2:49 PM, Stéfan van der Walt <stefanv(a)berkeley.edu> wrote: > > It may be a good idea to upsample your image before doing canny, > because edges lie in between pixels, and can only be accurately marked > with enough resolution. > > -- > 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/d/optout.

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extract_edges_after_segmentation
by GiorgosR 10 Mar '15

10 Mar '15

<https://lh5.googleusercontent.com/-HJwNDOP7D1U/VP7KITX1mFI/AAAAAAAACXQ/zn1P…> Hi there, First of all, many thanks for this very nice and useful package! I just started using the skimage library and I do some image processing in very noisy angiographic data. After some image enhancement in the obtained images, I applied a canny edge detection algorithm implemented in skimage and I came up with the attached image (after dilation and erosion). Do you have any advice on how could I extract only the vessel edges automatically from the image? I looked in examples with labels or binary hole filling but it does not work well for my problem. Many thanks in advance, Giorgos

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Re: hough ellipse fit inaccurate?
by Johannes Schoenberger 10 Mar '15

10 Mar '15

Well, there is not reason to use `fit ellipse` from OpenCV, you can use `skimage.measure.EllipseModel`. > On Mar 5, 2015, at 5:38 PM, Kevin Keraudren <kevin.keraudren(a)googlemail.com> wrote: > > Hi Arno, > > Looking at the code, I would ask: Did your score improve by setting accuracy=1 ? > > https://github.com/scikit-image/scikit-image/blob/master/skimage/transform/… > > Considering that you are asking for accuracy below half a pixel, I would not be surprised if the voting process of the Hough transform is not that accurate. A least-square fitting (Opencv fitellipse) might be more accurate than a voting process for a perfect ellipse. > > Aren't the eyeball and the pupil both balls? If you slice them in any way, wouldn't you obtain disks? So why detecting elllipses and not circles? Maybe hough_circle will be more accurate. > > Sorry I cannot provide any proof or certitude on how accurate hough_ellipse is. > > Kind regards, > > Kevin > > > -- > 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/d/optout.

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SciPy 2015 Conference: Call for proposals
by Tony Yu 09 Mar '15

09 Mar '15

SciPy 2015 is requesting proposals for tutorials, talks, and posters for this year's conference. Note that there's a mini-symposium on "Visualization, Vision and Imaging", so this year will be particularly relevant for people here. Cheers! -Tony ---- *SciPy 2015 Conference (Scientific Computing with Python) Call for Proposals is Open: Submit Your Tutorial and Talk Ideas ASAP <http://scipy2015.scipy.org/>!* SciPy 2015, the fourteenth annual Scientific Computing with Python conference, will be held July 6-12, 2015 in Austin, Texas. SciPy is a community dedicated to the advancement of scientific computing through open source Python software for mathematics, science, and engineering. The annual SciPy Conference brings together over 500 participants from industry, academia, and government to showcase their latest projects, learn from skilled users and developers, and collaborate on code development. The full program will consist of two days of tutorials by followed by three days of presentations, and concludes with two days of developer sprints. More info available on the conference website:http://www.scipy2015.scipy.org <http://www.scipy2015.scipy.org/ehome/115969/259266/?&>; you can also sign up for mailing list updates <http://scipy2015.scipy.org/>. Registration is expected to open at the end of March. We encourage you to submit tutorial or talk proposals in the categories below <http://scipy2015.scipy.org/>; please also share with others who you’d like to see participate! *SCIPY TUTORIAL SESSION PROPOSALS – requested by March 16, 2015* The SciPy experience kicks off with two days of tutorials. These sessions provide extremely affordable access to expert training, and consistently receive fantastic feedback from participants. We're looking for submissions on topics from introductory to advanced - we'll have attendees across the gamut looking to learn. Whether you are a major contributor to a scientific Python library or an expert-level user, this is a great opportunity to share your knowledge and stipends are available. *Submit Your Tutorial Proposal <http://www.scipy2015.scipy.org/eselectv2/frontend/index/115969>*on the SciPy 2015 website:http://scipy2015.scipy.org *SCIPY TALK AND POSTER SUBMISSIONS – requested by April 1, 2015* SciPy 2015 will include 3 major topic tracks and 7 mini-symposia tracks. *Submit Your Talk Proposal <http://submit%20your%20talk%20proposal%20/>*on the SciPy 2015 website: http://scipy2015.scipy.org Major topic tracks include: · Scientific Computing in Python (General track) · Python in Data Science · and Quantitative and Computational Social Sciences. Mini-symposia will include the applications of Python in: · Astronomy and astrophysics · Computational life and medical sciences · Engineering · Geographic information systems (GIS) · Geophysics · Oceanography and meteorology · Visualization, vision and imaging

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Extracting/Isolating Segmented Objects in scikit-image
by Sumesh K.C. 09 Mar '15

09 Mar '15

Hello Everyone, I've just started working with scikit-image python package for image processing and segmentation. Its cool to learn. I'm getting problem in extracting or simply isolating the segmented objects after running segmentation like quickshift, watershed, slic. How can i do this? Need your help! Thank you, in advance!

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Re: Extracting/Isolating Segmented Objects in scikit-image
by Emmanuelle Gouillart 09 Mar '15

09 Mar '15

Hi Sumesh, also see http://scikit-image.org/docs/dev/auto_examples/plot_label.html#example-plot… with the use of "label". Cheers, Emma On Sun, Mar 08, 2015 at 04:52:32PM -0700, StÃ©fan van der Walt wrote: > Hi Sumesh > On Sun, Mar 8, 2015 at 8:17 AM, Sumesh K.C. <rockingsumes(a)gmail.com> wrote: > > I'm getting problem in extracting or simply isolating the segmented objects after running segmentation like quickshift, watershed, slic. > Please have a look at the tutorial at > https://github.com/scikit-image/skimage-tutorials > Regards > StÃ©fan

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Re: Hessian-Laplace blob detector.
by Vighnesh Birodkar 09 Mar '15

09 Mar '15

Hey Daniil A good technical discussion is always welcome. Its never considered as spamming. Thanks Vighnesh On Monday, March 9, 2015 at 6:29:28 AM UTC+5:30, Daniil Pakhomov wrote: > > Thanks. > I've sent it. > > 2015-03-09 1:39 GMT+01:00 Josh Warner <silvertr...(a)gmail.com <javascript:> > >: > >> We'd welcome this as a PR on GitHub. That would be the ideal place for >> code review, etc. >> >> On Sunday, March 8, 2015 at 4:01:12 PM UTC-5, Daniil Pakhomov wrote: >>> >>> Really sorry for spamming you with questions. >>> No more need to answer. >>> I implemented this detector and it works as fast as your determinant of >>> Hessian approach implementation. >>> It passes all you tests and works better with coin() images (it doesn't >>> detect a false coin as determinant of Hessian does in the example). >>> >>> May I ask you to do a review of my code later? >>> >>> Thank you. >>> >>> понедельник, 2 марта 2015 г., 18:05:09 UTC+1 пользователь Daniil >>> Pakhomov написал: >>>> >>>> Hello, >>>> >>>> I want to try to implement Hessian-Laplace blob detector (as mentioned >>>> in requested features on github page). >>>> >>>> Can someone give me the list of corresponding papers, using which I can >>>> implement it. >>>> >>>> Thank you. >>>> >>> -- >> 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/ghIYwQFubEU/unsubscribe. >> To unsubscribe from this group and all its topics, send an email to >> scikit-image...(a)googlegroups.com <javascript:>. >> For more options, visit https://groups.google.com/d/optout. >> > >

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Hessian-Laplace blob detector.
by Daniil Pakhomov 08 Mar '15

08 Mar '15

Hello, I want to try to implement Hessian-Laplace blob detector (as mentioned in requested features on github page). Can someone give me the list of corresponding papers, using which I can implement it. Thank you.

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ANN: scikit-image 0.11
by Stefan van der Walt 05 Mar '15

05 Mar '15

Announcement: scikit-image 0.11.0 ================================= We're happy to announce the release of scikit-image v0.11.0! scikit-image is an image processing toolbox for SciPy that includes algorithms for segmentation, geometric transformations, color space manipulation, analysis, filtering, morphology, feature detection, and more. For more information, examples, and documentation, please visit our website: http://scikit-image.org Highlights ---------- For this release, we merged over 200 pull requests with bug fixes, cleanups, improved documentation and new features. Highlights include: - Region Adjacency Graphs - Color distance RAGs (#1031) - Threshold Cut on RAGs (#1031) - Similarity RAGs (#1080) - Normalized Cut on RAGs (#1080) - RAG drawing (#1087) - Hierarchical merging (#1100) - Sub-pixel shift registration (#1066) - Non-local means denoising (#874) - Sliding window histogram (#1127) - More illuminants in color conversion (#1130) - Handling of CMYK images (#1360) - `stop_probability` for RANSAC (#1176) - Li thresholding (#1376) - Signed edge operators (#1240) - Full ndarray support for `peak_local_max` (#1355) - Improve conditioning of geometric transformations (#1319) - Standardize handling of multi-image files (#1200) - Ellipse structuring element (#1298) - Multi-line drawing tool (#1065), line handle style (#1179) - Point in polygon testing (#1123) - Rotation around a specified center (#1168) - Add `shape` option to drawing functions (#1222) - Faster regionprops (#1351) - `skimage.future` package (#1365) - More robust I/O module (#1189) API Changes ----------- - The ``skimage.filter`` subpackage has been renamed to ``skimage.filters``. - Some edge detectors returned values greater than 1--their results are now appropriately scaled with a factor of ``sqrt(2)``. Contributors to this release ---------------------------- (Listed alphabetically by last name) - Fedor Baart - Vighnesh Birodkar - François Boulogne - Nelson Brown - Alexey Buzmakov - Julien Coste - Phil Elson - Adam Feuer - Jim Fienup - Geoffrey French - Emmanuelle Gouillart - Charles Harris - Jonathan Helmus - Alexander Iacchetta - Ivana Kajić - Kevin Keraudren - Almar Klein - Gregory R. Lee - Jeremy Metz - Stuart Mumford - Damian Nadales - Pablo Márquez Neila - Juan Nunez-Iglesias - Rebecca Roisin - Jasper St. Pierre - Jacopo Sabbatini - Michael Sarahan - Salvatore Scaramuzzino - Phil Schaf - Johannes Schönberger - Tim Seifert - Arve Seljebu - Steven Silvester - Julian Taylor - Matěj Týč - Alexey Umnov - Pratap Vardhan - Stefan van der Walt - Joshua Warner - Tony S Yu

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GSoC - Dynamic Time Warping
by Mudit Jain 05 Mar '15

05 Mar '15

Hello everyone, I am Mudit, a third year undergraduate student from Birla Institute of Technology and Sciences, India. I had come across the idea of implementing a Dynamic Time Warping Library on the Ideas Page and would like to pursue the project for GSOC-2015. I have read the paper regarding Adaptive Feature Based Dynamic Time Warping Library, that was attached in the Ideas page and have a thorough understanding of the system to be implemented. I have attached a summary regarding the same. I have also given a procedure to implement the methodology stated in the paper. I understand that the implementation of dynamic programming is similar to that of the edit distance problem.The summary also has a description of the workflow for the algorithm used. I am presently going through the DTW package is R Some links that I am following are: http://www.jstatsoft.org/v31/i07/paper It would be highly appreciated if more insight regarding the project and what would be the role of a person if he/she is selected for this project. I would also appreciate links that can better my understanding regarding various topics. Cheers Mudit

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Re: hough ellipse fit inaccurate?
by Kevin Keraudren 05 Mar '15

05 Mar '15

By the way, your ground truth is the values you used when drawing the ellipse, not the values you detect with a different detection method. On Thu, Mar 5, 2015 at 10:07 AM, Kevin Keraudren < kevin.keraudren(a)googlemail.com> wrote: > Hi Arno, > In order to stay on the safe side, why don't you post your actual code, > with a test case highlighting the error you measure between the true centre > of the ellipse and the detected one? > Kind regards, > Kevin > > On Thu, Mar 5, 2015 at 9:53 AM, Arno Dietz <arnodietz86(a)googlemail.com> > wrote: > >> Thank you very much Kevin and Johannes. >> >> I see the rounding Problem, but it is just for the ellipse drawing. In my >> actual code I just use the Ellipse center like best [1] and best[2] without >> rounding. This still produces much more inaccurate ellipse center results >> than other methods like center of mass for example, althoug >> <http://www.dict.cc/englisch-deutsch/although.html>h I also use >> the anti-aliased input image. So is there any possibility to get more >> accurate results from the hough ellipse fit approach? If not, this is also >> ok, I just want to be on the safe side that it's not my fault. In that >> case I will have a look at the suggested approach from Johannes. >> >> Am Donnerstag, 5. März 2015 01:21:48 UTC+1 schrieb Kevin Keraudren: >>> >>> Hi Arno, >>> >>> The first source of inaccuracy comes from your code, you need to round >>> the values instead of truncating them: >>> >>> #yc = int(best[1]) >>> >>> >>> #xc = int(best[2]) >>> >>> >>> #a = int(best[3]) >>> >>> >>> #b = int(best[4]) >>> >>> >>> >>> yc = int(round(best[1])) >>> >>> xc = int(round(best[2])) >>> >>> a = int(round(best[3])) >>> >>> b = int(round(best[4])) >>> >>> See resulting image attached. >>> >>> Kind regards, >>> >>> Kevin >>> >>> >>> A second source of inaccuracy comes from your input ellipse: it is not a >>> perfect ellipse because you drew it using anti-aliasing. >> >> >> >> Third, you could fit an ellipse using RANSAC. How does this approach work >>> for you: http://stackoverflow.com/questions/28281742/fitting-a- >>> circle-to-a-binary-image/28289147#28289147 >> >> -- >> 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/d/optout. >> > >

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Re: hough ellipse fit inaccurate?
by Arno Dietz 05 Mar '15

05 Mar '15

Thank you very much Kevin and Johannes. I see the rounding Problem, but it is just for the ellipse drawing. In my actual code I just use the Ellipse center like best [1] and best[2] without rounding. This still produces much more inaccurate ellipse center results than other methods like center of mass for example, althoug <http://www.dict.cc/englisch-deutsch/although.html>h I also use the anti-aliased input image. So is there any possibility to get more accurate results from the hough ellipse fit approach? If not, this is also ok, I just want to be on the safe side that it's not my fault. In that case I will have a look at the suggested approach from Johannes. Am Donnerstag, 5. März 2015 01:21:48 UTC+1 schrieb Kevin Keraudren: > > Hi Arno, > > The first source of inaccuracy comes from your code, you need to round the > values instead of truncating them: > > #yc = int(best[1]) > > > #xc = int(best[2]) > > > #a = int(best[3]) > > > #b = int(best[4]) > > > > yc = int(round(best[1])) > > xc = int(round(best[2])) > > a = int(round(best[3])) > > b = int(round(best[4])) > > See resulting image attached. > > Kind regards, > > Kevin > > > A second source of inaccuracy comes from your input ellipse: it is not a > perfect ellipse because you drew it using anti-aliasing. Third, you could fit an ellipse using RANSAC. How does this approach work > for you: > http://stackoverflow.com/questions/28281742/fitting-a-circle-to-a-binary-im… >

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Re: hough ellipse fit inaccurate?
by Johannes Schoenberger 04 Mar '15

04 Mar '15

Third, you could fit an ellipse using RANSAC. How does this approach work for you: http://stackoverflow.com/questions/28281742/fitting-a-circle-to-a-binary-im… > On Mar 4, 2015, at 7:24 PM, Kevin Keraudren <kevin.keraudren(a)googlemail.com> wrote: > > A second source of inaccuracy comes from your input ellipse: it is not a perfect ellipse because you drew it using anti-aliasing. > > On Thu, Mar 5, 2015 at 12:21 AM, Kevin Keraudren <kevin.keraudren(a)googlemail.com> wrote: > Hi Arno, > > The first source of inaccuracy comes from your code, you need to round the values instead of truncating them: > > #yc = int(best[1]) > #xc = int(best[2]) > #a = int(best[3]) > #b = int(best[4]) > > yc = int(round(best[1])) > xc = int(round(best[2])) > a = int(round(best[3])) > b = int(round(best[4])) > > See resulting image attached. > > Kind regards, > > Kevin > > > > On Wed, Mar 4, 2015 at 11:49 PM, Arno Dietz <arnodietz86(a)googlemail.com> wrote: > > Ok sorry. Here is my code: > > from skimage import color > from skimage.filter import canny > from skimage.transform import hough_ellipse > from skimage.draw import ellipse_perimeter > from skimage import io > from skimage.viewer import ImageViewer > # load image > img = io.imread('ellipse.png') > cimg = color.gray2rgb(img) > # edges and ellipse fit > edges = canny(img, sigma=0.1, low_threshold=0.55, high_threshold=0.8) > result = hough_ellipse(edges, accuracy=4, threshold=25, min_size=47, max_size=60) > result.sort(order='accumulator') > # Estimated parameters for the ellipse > best = result[-1] > yc = int(best[1]) > xc = int(best[2]) > a = int(best[3]) > b = int(best[4]) > orientation = best[5] > # Draw the ellipse on the original image > cy, cx = ellipse_perimeter(yc, xc, a, b, orientation) > cimg[cy, cx] = (0, 0, 255) > # Draw the edge (white) and the resulting ellipse (red) > edges = color.gray2rgb(edges) > edges[cy, cx] = (250, 0, 0) > viewer = ImageViewer(edges) > viewer.show() > > I noticed, that the ellipse center is detected only in half pixel accuracy. Maybe this is the Problem? Is there a possibility to get the ellipse center with sub-pixel accuracy? > > regards Arno > > -- > 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/d/optout. > > > > -- > 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/d/optout.

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Re: hough ellipse fit inaccurate?
by Kevin Keraudren 04 Mar '15

04 Mar '15

A second source of inaccuracy comes from your input ellipse: it is not a perfect ellipse because you drew it using anti-aliasing. On Thu, Mar 5, 2015 at 12:21 AM, Kevin Keraudren < kevin.keraudren(a)googlemail.com> wrote: > Hi Arno, > > The first source of inaccuracy comes from your code, you need to round the > values instead of truncating them: > > #yc = int(best[1]) > > > #xc = int(best[2]) > > > #a = int(best[3]) > > > #b = int(best[4]) > > > > yc = int(round(best[1])) > > xc = int(round(best[2])) > > a = int(round(best[3])) > > b = int(round(best[4])) > > See resulting image attached. > > Kind regards, > > Kevin > > > > On Wed, Mar 4, 2015 at 11:49 PM, Arno Dietz <arnodietz86(a)googlemail.com> > wrote: > >> >> Ok sorry. Here is my code: >> >> from skimage import color >>> from skimage.filter import canny >>> from skimage.transform import hough_ellipse >>> from skimage.draw import ellipse_perimeter >>> from skimage import io >>> from skimage.viewer import ImageViewer >>> # load image >>> img = io.imread('ellipse.png') >>> cimg = color.gray2rgb(img) >>> # edges and ellipse fit >>> edges = canny(img, sigma=0.1, low_threshold=0.55, high_threshold=0.8) >>> result = hough_ellipse(edges, accuracy=4, threshold=25, min_size=47, >>> max_size=60) >>> result.sort(order='accumulator') >>> # Estimated parameters for the ellipse >>> best = result[-1] >>> yc = int(best[1]) >>> xc = int(best[2]) >>> a = int(best[3]) >>> b = int(best[4]) >>> orientation = best[5] >>> # Draw the ellipse on the original image >>> cy, cx = ellipse_perimeter(yc, xc, a, b, orientation) >>> cimg[cy, cx] = (0, 0, 255) >>> # Draw the edge (white) and the resulting ellipse (red) >>> edges = color.gray2rgb(edges) >>> edges[cy, cx] = (250, 0, 0) >>> viewer = ImageViewer(edges) >>> viewer.show() >> >> >> I noticed, that the ellipse center is detected only in half pixel >> accuracy. Maybe this is the Problem? Is there a possibility to get the >> ellipse center with sub-pixel accuracy? >> >> regards Arno >> >>> -- >> 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/d/optout. >> > >

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hough ellipse fit inaccurate?
by Arno Dietz 04 Mar '15

04 Mar '15

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Re: GSoC 2015 mentors
by Johannes Schoenberger 04 Mar '15

04 Mar '15

Done. > On Mar 4, 2015, at 5:56 PM, Stéfan van der Walt <stefanv(a)berkeley.edu> wrote: > > On Wed, Mar 4, 2015 at 2:21 PM, Johannes Schoenberger <jsch(a)demuc.de> wrote: > Sorry, I’ll be too busy over the summer myself, but I am happy to function as a backup mentor, if occasional reviews of PRs and sporadic participation in discussions are okay with you. > > Good enough! Please fill out the mentor signup form here: > > http://goo.gl/forms/PMXVM1CUAS > > 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/d/optout.

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GSoC 2015 mentors
by Stefan van der Walt 04 Mar '15

04 Mar '15

Hi everyone Who would be interested in mentoring Google Summer of Code for scikit-image this year? Projects include porting ndimage to Cython, dynamic time warping, and possibly face detection. Regards Stéfan

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Re: GSoC 2015 mentors
by Stefan van der Walt 04 Mar '15

04 Mar '15

Hi Jaime On 2015-03-04 08:59:22, Jaime Fernández del Río <jaime.frio(a)gmail.com> wrote: > I wouldn't mind getting involved in the ndimage port to Cython, > as in co-mentoring it or helping out any other way you see fit, > . > > I haven't done much in skimage, but am a numpy developer and > have done some stuff in ndimage. If nothing else, I actually > understand what most of the ndimage C code is doing, which is > probably a useful skill for the job! I've been following your work on NumPy, and I would love to have you on board! I'll send you the signup form offline. Stéfan

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Wikipedia Page
by Steven Silvester 04 Mar '15

04 Mar '15

Hi all, I created a Wikipedia page for us today: https://en.wikipedia.org/wiki/Scikit-image. Any updates are welcome! I was not able to upload our logo, as I am not a "confirmed user". Anyone in that camp? Regards, Steve

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Re: Face detection
by Daniil Pakhomov 04 Mar '15

04 Mar '15

Thank you for really fast response :) Great! Yes, I know about this and I am working on it right now: https://groups.google.com/forum/?utm_source=digest&utm_medium=email#!topic/… When is the deadline for getting the PR? четверг, 28 марта 2013 г., 10:35:31 UTC+1 пользователь Stefan van der Walt написал: > > Hi everyone > > I've been interested in getting face detection into skimage for a > while. This morning, Nathan Faggian reminded me that the highly > popular Viola-Jones detector is patent encumbered (yes, if you're not > careful you can use patented code in packages like OpenCV). However, > the following link seems to suggest that we can work around that by > training our own classifier with different features: > > > http://rafaelmizrahi.blogspot.com/2007/02/intel-opencv-face-detection-licen… > > If there's any interest in working on this, or if you already have an > algorithm available, please get in touch. > > Stéfan >

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Re: Face detection
by Daniil Pakhomov 04 Mar '15

04 Mar '15

Hello, Is there still interest in the implementation? I have experience with face detection. And I can do it as a GSOC project. Thank you. четверг, 28 марта 2013 г., 10:35:31 UTC+1 пользователь Stefan van der Walt написал: > > Hi everyone > > I've been interested in getting face detection into skimage for a > while. This morning, Nathan Faggian reminded me that the highly > popular Viola-Jones detector is patent encumbered (yes, if you're not > careful you can use patented code in packages like OpenCV). However, > the following link seems to suggest that we can work around that by > training our own classifier with different features: > > > http://rafaelmizrahi.blogspot.com/2007/02/intel-opencv-face-detection-licen… > > If there's any interest in working on this, or if you already have an > algorithm available, please get in touch. > > Stéfan >

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Hessian-Laplace blob detector.
by Daniil Pakhomov 04 Mar '15

04 Mar '15

Hello, I would like to implement this detector. Can someone give me a list of papers that may help. Thank you.

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Building 0.11dev - issues
by Benjamin Cichy 03 Mar '15

03 Mar '15

Hi all, I am attempting this install on Windows with >pip install . as per the instructions. The last compiler I have access to, is Visual Studio 10, so according to scikit-learn, and digging through the compiler script, it should be the last one recognized for .dll compilation before Python 3.5. I have the following errors right at the end of the build. This is under a fresh Anaconda install, with all the packages updated. creating build\temp.win-amd64-3.4\Release\skimage\_shared C:\Program Files (x86)\Microsoft Visual Studio 10.0\VC\BIN\amd64\cl.exe /c /nologo /Ox /MD /W3 /GS- /DNDEBUG -IC:\Anaconda3\lib\core\include -IC:\Anaconda3\include -IC:\Anaconda3\include /Tcskimage\_shared\geometry.c /Fobuild\temp.win-amd64-3.4\Release\skimagej Found executable C:\Program Files (x86)\Microsoft Visual Studio 10.0\VC\BIN\ amd64\cl.exe C:\Anaconda3\lib\site-packages\setuptools-12.2-py3.4.egg\setuptools\dist.py: 282: UserWarning: Normalizing '0.11dev' to '0.11.dev error: Command "C:\Program Files (x86)\Microsoft Visual Studio 10.0\VC\BIN\amd64\cl.exe /c /nologo /Ox /MD /W3 /GS- /DNDEBUG -IC-packages\numpy\core\include -IC:\Anaconda3\include -IC:\Anaconda3\include /Tcskimage\_shared\geometry.c /Fobuild\temp.win-amd64-3.4ared\geometry.obj" failed with exit status 2 geometry.c C:\Anaconda3\include\pyconfig.h(68) : fatal error C1083: Cannot open include file: 'io.h': No such file or directory ---------------------------------------- Rolling back uninstall of scikit-image Command "C:\Anaconda3\python.exe -c "import setuptools, tokenize;__file__='C:\\cygwin64\\tmp\\pip-jkks4ooy-build\\setup.py';execenize, 'open', open)(__file__).read().replace('\r\n', '\n'), __file__, 'exec'))" install --record C:\cygwin64\tmp\pip-xqr2l87y-recor --single-version-externally-managed --compile" failed with error code 1 in C:\cygwin64\tmp\pip-jkks4ooy-build Any suggestions? -Ben

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