Mailman 3 August 2015 - scikit-image

Re: skimage and opencv
by Emmanuelle Gouillart 22 Sep '16

22 Sep '16

Hi FranÃ¯Â¿Â½ois, that's an excellent question, and not a troll :-). Opencv is a very powerful library, but it focuses primarily on computer vision (feature detection and extraction, classification, ...), as opposed to image processing in general (with other tasks such as denoising, segmentation, ...). The other big difference is that skimage builds on numpy ndarrays, and uses the full power of the numpy API (including of course the basic facilities for processing arrays as images that come with numpy), as well as some of scipy functions (you could have added scipy.ndimage to your list -- a few functions in skimage are wrappers around scipy.ndimage, that exist for the sake of completeneness). One important consequence is that algorithms working for 3-d or even n-d images can be easily implemented in 3-d/n-d in skimage, whereas opencv is restricted to 2-D images (as far as I know). Thanks to the use of numpy arrays, the API of skimage is also quite pleasant for a numpy user, more than the API of opencv. A related difference is that skimage is written in python and cython, whereas opencv is a C++ library. The two libraries attract a different crowd of developers, and a Python/Cython toolkit based on numpy arrays is easier to develop and maintain inside the Scientific Python ecosystem. I'm sure that other devs/users will have things to add to this discussion! Cheers, Emmanuelle On Thu, Dec 27, 2012 at 02:06:08PM -0800, FranÃ¯Â¿Â½ois wrote: > Hi users and devs, > It came to my knowledge that another python library (based on C++ and C > codes) for image processing exists too : opencv > I understand that numpy intregrates some basic features and we need some > advanced features but I have the feeling that skimages is redoundant with > opencv in some ways. > What's the position of skimage about that? (Don't read this question as a > troll but like a real question). > I mean that similar features exist in both. Would not be possible to > reuse/integrate opencv or merge? what's the reason for keeping them apart? > My observation is there is 4 libraries to manipulate images: > * PIL > * numpy > * skimages > * opencv > That's a lot. > Cheers,

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Improving HoG
by Martin Savc 17 Nov '15

17 Nov '15

I've been implementing my own HoG transform looking at different sources. While the implementation in scikit-image seems to lack certain features (multiple normalization schemes, general block overlap, Gaussian block window, trillinear interpolation/weighting of bin assignments,...) these don't seem to be that important, at least when applied to my current problem (eye blink analysis). Most of these would increase complexity, giving the implementation a complicated look, with little gain. I've also been looking into some practical improvements - integral histogram, separating the cell-block histogram feature to use it with other dense feature transforms such as LBP, a HoG visualization function that would render the visualization at higher resolutions that the original image. Would any of these be welcomed additions to scikit-image? Regards, Martin Savc PuppySaturation

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Problems with coordinate ranges when doing conversion from LCH to LAB to RGB
by Matteo 16 Nov '15

16 Nov '15

A while ago I wrote a post on my blog with code on how to import in Python a colour palette in an ASCII file and convert it to a Matplotlib colormap: mycarta.wordpress.com/2014/04/25/convert-color-palettes-to-python-matplotli… Following that post I wrote a tutorial on a geoscience magazine on how to evaluate and compare colormaps using Python: wiki.seg.org/wiki/How_to_evaluate_and_compare_color_maps In the accompanying notebook I show how to convert a 256x3 RGB colormap to a 256x256x3 RGB image, then convert it to CIELAB using scikit-image's rgb2lab, then plot the 256x1 lightness (L) array to evaluate the colormaps perceptually. You can read the relevant extract of the notebook using this nbviewer link: http://nbviewer.ipython.org/urls/dl.dropbox.com/s/54zoax2qesb71wn/evaluate_… In that case scikit-image worked really well for me. Now I am trying to follow up with a new tutorial and I run into problems with the color space conversions. You can follow what I am trying to do in this other notebook extract: http://nbviewer.ipython.org/urls/dl.dropbox.com/s/noli66nzrlk0676/make_colo… The goal of this new tutorial is to show how to build colormaps from scratch using perceptual principles. I design a color palette in LCH (polar version of CIELAB) by keeping Chroma and Lightness fixed and interpolating Hue around the circle, then convert to LAB, then to RGB. As far as I know the code I wrote should work, but the result is a black colormap. I am thinking I got wrong one or more of the ranges for the LCH coordinates. I assumed L between (0,1), Ch between (0,1), and H between (0, 2*pi). Is that wrong, and if that's the case, what are the ranges? Many of them are not stated clearly in the documentation in here: http://scikit-image.org/docs/dev/api/skimage.color.html Is it possible to update the documentation to clearly state all ranges for all colour spaces. Thanks for your help. Matteo

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Issue with morphological filters
by Matteo 16 Nov '15

16 Nov '15

*Issues with morphological filters when trying to remove white holes in black objects in a binary images. Using opening or filling holes on inverted (or complement) of the original binary.* Hi there I have a series of derivatives calculated on geophysical data. Many of these derivatives have nice continuous maxima, so I treat them as images on which I do some cleanup with morphological filter. Here's one example of operations that I do routinely, and successfully: # threshold theta map using Otsu method thresh_th = threshold_otsu(theta) binary_th = theta > thresh_th # clean up small objects label_objects_th, nb_labels_th = sp.ndimage.label(binary_th) sizes_th = np.bincount(label_objects_th.ravel()) mask_sizes_th = sizes_th > 175 mask_sizes_th[0] = 0 binary_cleaned_th = mask_sizes_th[label_objects_th] # further enhance with morphological closing (dilation followed by an erosion) to remove small dark spots and connect small bright cracks # followed by an extra erosion selem = disk(1) closed_th = closing(binary_cleaned_th, selem)/255 eroded_th = erosion(closed_th, selem)/255 # Finally, extract lienaments using skeletonization skeleton_th=skeletonize(binary_th) skeleton_cleaned_th=skeletonize(binary_cleaned_th) # plot to compare fig = plt.figure(figsize=(20, 7)) ax = fig.add_subplot(1, 2, 1) imshow(skeleton_th, cmap='bone_r', interpolation='none') ax2 = fig.add_subplot(1, 3, 2) imshow(skeleton_cleaned_th, cmap='bone_r', interpolation='none') ax.set_xticks([]) ax.set_yticks([]) ax2.set_xticks([]) ax2.set_yticks([]) Unfortunately I cannot share the data as it is proprietary, but I will for the next example, which is the one that does not work. There's one derivative that shows lots of detail but not continuous maxima. As a workaround I created filled contours in Matplotlib exported as an image. The image is attached. Now I want to import back the image and plot it to test: # import back image cfthdr=io.imread('filled_contour.png') # threshold using using Otsu method thresh_thdr = threshold_otsu(cfthdr) binary_thdr = cfthdr > thresh_thdr # plot it fig = plt.figure(figsize=(5, 5)) ax = fig.add_subplot(1, 1, 1) ax.set_xticks([]) ax.set_yticks([]) plt.imshow(binary_thdr, cmap='bone') plt.show() The above works without issues. Next I want to fill the white holes inside the black blobs. I thought of 2 strategies. The first would be to use opening; the second to invert the image, and then fill the holes as in here: http://scikit-image.org/docs/dev/auto_examples/plot_holes_and_peaks.html By the way, I found a similar example for opencv here http://stackoverflow.com/questions/10316057/filling-holes-inside-a-binary-o… Let's start with opening. When I try: selem = disk(1) opened_thdr = opening(binary_thdr, selem) or: selem = disk(1) opened_thdr = opening(cfthdr, selem) I get an error message like this: --------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-49-edc0d01ba327> in <module>() 1 #binary_thdr=img_as_float(binary_thdr,force_copy=False) ----> 2 opened_thdr = opening(binary_thdr, selem)/255 3 4 # plot it 5 fig = plt.figure(figsize=(5, 5)) C:\...\skimage\morphology\grey.pyc in opening(image, selem, out) 160 shift_y = True if (h % 2) == 0 else False 161 --> 162 eroded = erosion(image, selem) 163 out = dilation(eroded, selem, out=out, shift_x=shift_x, shift_y=shift_y) 164 return out C:\...\skimage\morphology\grey.pyc in erosion(image, selem, out, shift_x, shift_y) 58 selem = img_as_ubyte(selem) 59 return cmorph._erode(image, selem, out=out, ---> 60 shift_x=shift_x, shift_y=shift_y) 61 62 C:\...\skimage\morphology\cmorph.pyd in skimage.morphology.cmorph._erode (skimage\morphology\cmorph.c:2658)() ValueError: Buffer has wrong number of dimensions (expected 2, got 3) --------------------------------------------------------------------------- Any idea of what is going on and how I can fix it? As for inverting (or finding the complement) and then hole filling, that would be my preferred option. However, I have not been able to invert the image. I tried numpy.invert, adapting the last example from here: http://docs.scipy.org/doc/numpy/reference/generated/numpy.invert.html I tried something like this: http://stackoverflow.com/a/16724700 and this: http://stackoverflow.com/a/2498909 But none of these methods worked. Is there a way in scikit.image to do that, and if not, do you have any suggestions? Thank you, Matteo

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Finding pairs of images (homologous chromosomes)
by Jean-Patrick Pommier 15 Nov '15

15 Nov '15

Dear All, I am trying to make pairs of images from the following set of images (chromosomes sorted by size after rotation). The idea is to make a feature vector for unsupervised classification (kmeans with 19 clusters) >From each chromosome an integral image was calculated: plt.figure(figsize = (15,15)) gs1 = gridspec.GridSpec(6,8) gs1.update(wspace=0.0, hspace=0.0) # set the spacing between axes. for i in range(38): # i = i + 1 # grid spec indexes from 0 ax1 = plt.subplot(gs1[i]) plt.axis('off') ax1.set_xticklabels([]) ax1.set_yticklabels([]) ax1.set_aspect('equal') image = sk.transform.integral_image(reallysorted[i][:,:,2]) imshow(image , interpolation='nearest') Then each integral image was flatten and combined with the others: Features =[] for i in range(38): Feat = np.ndarray.flatten(sk.transform.integral_image(reallysorted[i][:,:,2])) Features.append(Feat) X = np.asarray(Features) print X.shape The X array contains *38* lines and 9718 features, which is not good. However, I trried to submit these raw features to kmeans classification with sklearn using a direct example <http://scikit-learn.org/stable/modules/neighbors.html> : from sklearn.neighbors import NearestNeighbors nbrs = NearestNeighbors(n_neighbors=*19*, algorithm='ball_tree').fit(X) distances, indices = nbrs.kneighbors(X) connection = nbrs.kneighbors_graph(X).toarray() Ploting the connection graph shows that a chromosomes is similar to more than one ... - Do you think that integral images can be used to discriminate the chromosomes pairs? - If so, how to reduce the number of features to 10~20? (to get a better discrimination) Thanks for your advices. Jean-Patrick

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future.graph.merge_hierarchical
by bricklemacho＠gmail.com 21 Sep '15

21 Sep '15

Hi All, I am looking at generating some detection proposals, see Hosang, Jan, et al. "What makes for effective detection proposals?." /arXiv preprint arXiv:1502.05082/ (2015), http://arxiv.org/pdf/1502.05082.pdf Starting with the Selective Search algorithm, Section 3 of Uijlings, Jasper RR, et al. "Selective search for object recognition." /International journal of computer vision/ 104.2 (2013): 154-171, https://staff.fnwi.uva.nl/th.gevers/pub/GeversIJCV2013.pdf The basic idea is the performing a hierarchical merging of the image, where each new merge get added to the list of regions suspected to contain an object, you can capture objects at all scales. This reduces the search space significantly than say compared to floating window. The output is NOT a image segmentaiton, rather a list of regions (bounding boxes) of potential objects (deteciton proposals). I have looked in the gallery at RAG Merging http://scikit-image.org/docs/dev/auto_examples/plot_rag_merge.html, fairly confident I can setup the callback methods to provided the similarity measure. I am naively hoping that future.graph.hierarchical(), even though it seems to output a segmentation (labels), can be easily adapted to the task. What would be the best way to have future.graph.merge_hierarchica() merge regions with the "highest" similarity measure, rather thana threshold? What would be the best way future.graph.merge_hierarchica() save each merged region? Tried setting "in_place" to false, but didn't notice any difference. Any help appreciated, Brickle. --

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Euroscipy sprint on Friday evening and Sunday
by Emmanuelle Gouillart 27 Aug '15

27 Aug '15

Hello, we will have a sprint on scikit-image at the EuroSciPy conference in Cambridge. It will take place on Friday evening at the Enthought office and will be organized by Juan and myself, and we'll also have a second session on Sunday. For more information, please read https://github.com/scikit-image/scikit-image/wiki/EuroSciPy-2015-sprint If you're not attending EuroSciPy but you wish to participate to the sprint, please tell us on the mailing-list or write your name on the wiki page on Github. Emmanuelle

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image processing usecases and flowchart
by Emmanuelle Gouillart 27 Aug '15

27 Aug '15

Hello list, I was thinking that it would be nice to have an image processing flowchart, using similar ideas as the machine learning flowchart of scikit-learn http://scikit-learn.org/stable/tutorial/machine_learning_map/ I'm not sure whether it's possible to do something that makes sense, but a first step would be to know more typical usecases and workflows that people encounter. I just wrote a short blogpost to encourage people to leave a comment describing their workflow http://emmanuelle.github.io/what-is-your-typical-image-processing-usecase.h… but it would be even more meaningful to discuss it on this mailing-list. I think it would be important for scikit-image to have examples on more diverse applications and to insist on the fact that the package can be used for a large variety of applications. So, what is your typical application :-)? Cheers, Emma

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powerful linear regression based solutions
by samira afzal 24 Aug '15

24 Aug '15

HI all, could you please tell me what are the powerful LR-based solutions (more powerful than linear regression as the baseline )? i have an assignment that i have to perform powerful linear regression based solutions on it. i am using scikit. thanks in advanced

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Re: Continuous documentation
by Steven Silvester 23 Aug '15

23 Aug '15

I've used this workflow on another project: https://medium.com/@nthgergo/publishing-gh-pages-with-travis-ci-53a8270e87db https://github.com/jupyter/jupyter-js-services/blob/master/scripts/travis_a… Do we want to do that? On Sunday, August 23, 2015 at 8:32:19 AM UTC-5, Johannes Schönberger wrote: > > See: https://github.com/icgood/continuous-docs/ >

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