Mailman 3 May 2015 - scikit-image

Re: skimage and opencv
by Emmanuelle Gouillart Sept. 22, 2016

Sept. 22, 2016

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 Nov. 16, 2015

Nov. 16, 2015

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 Nov. 16, 2015

Nov. 16, 2015

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 Nov. 16, 2015

Nov. 16, 2015

*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 Nov. 15, 2015

Nov. 15, 2015

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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Pattern and object recognition with COSFIRE
by Almar Klein July 6, 2015

July 6, 2015

Hi all, Just wanted to mention an algorithm which I was very impressed by and which might be an awesome addition to scikit-image. The algorithm is called COSFIRE (Combination of Shifted Filter Responses). I saw a talk by Geore Azzopardi, who presented the use of the algorithm for object and patter recognition. I was very impressed with the quality of segmentation of the shown examples, but also with the wide variety of image data (e.g. retina images, traffic signs, hand-written characters, and more). The filter is inspired by how the brain processes visual information, and according to the author the algorithm is actually quite simple. The algorithm was the topic of his PhD and he's going to continue working on it in his new job. The code (in Matlab) is available, so it might be worth a shot to try and port it to Python. Some links: - PhD thesis: http://www.cs.rug.nl/~george/phd-thesis/ - Matlab code: nl.mathworks.com/matlabcentral/fileexchange/37395 - Paper in MIA: http://www.medicalimageanalysisjournal.com/article/S1361-8415%2814%2900136-… - Almar

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ANN: imageio v1.0
by Almar Klein July 3, 2015

July 3, 2015

Hi all, I'm pleased to announce version 1.0 of imageio - a library for reading and writing images. This library started as a spin-off of the freeimage plugin in skimage, and is now a fully-fledged library with unit tests and all. Imageio provides an easy interface to read and write a wide range of image data, including animated images, volumetric data, and scientific formats. It is cross-platform, runs on Python 2.x and 3.x, and is easy to install. Imageio is plugin-based, making it easy to extend. It could probably use more scientific formats. I welcome anyone who's interested to contribute! install: pip install imageio website: http://imageio.github.io release notes: http://imageio.readthedocs.org/en/latest/releasenotes.html Regards, Almar

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PhD Thesis Proposal
by Edward Ando July 3, 2015

July 3, 2015

Dear All, I'm writing to you with a piece of information that may interest you: Together with some colleagues we have secured funding for a PhD in image analysis applied to Geomaterials in Grenoble (France), starting in October 2015. More information, and the addresses to write to for the application here: https://docs.google.com/document/d/1mGzZT7GRAhdtk1_Y2HwhfVkzU_IvHhyOZVJYkVA… Thanks for your attention, Edward Andò

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Re: Pattern and object recognition with COSFIRE
by George Azzopardi June 26, 2015

June 26, 2015

Dear all, I am pleased to see interest in the COSFIRE approach that I started during my PhD studies. The COSFIRE approach is a trainable pattern recognition approach which can be applied to several applications, including feature detection, object recognition and localization, image classification, contour detection and vessel segmentation. The selectively for a pattern of interest is automatically configured in a training process. The method involves several computations that are independent of each other, and thus it can be easily implemented using parallel programming (e.g. on a GPU). The original paper (http://www.cs.rug.nl/~george/articles/PAMI2013.pdf) combines information about the contours of the concerned pattern. We now have another paper which is currently being reviewed for CVPR2015 where we show that by adding colour information COSFIRE filters become even more robust. Please feel free to send me other ideas on how this work can be developed further. I would be very happy and available to work with an undergraduate or a postgraduate student (or any other person) to have this parallel implementation in Python. I see that you already added it to the Requested-features page. You can also add my contact details (geazzo@gmail) there for the interested readers. All my papers can be freely downloaded from my website: http://www.cs.rug.nl/~george/research-activities/ regards, George On Tuesday, 16 December 2014 15:22:45 UTC+1, Stefan van der Walt wrote: > > On Tue, Dec 16, 2014 at 1:57 PM, Pratap Vardhan <prat...(a)gmail.com > <javascript:>> wrote: > > I found few copies of the paper hosted by universities. I haven't > checked if > > these are the actual pre-prints - However, by the citation it looks like > it. > > Thanks! I've added it to the list: > > https://github.com/scikit-image/scikit-image/wiki/Requested-features > > Stéfan >

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missing kernel or block size in equalize_adapthist
by Kai Wiechen June 6, 2015

June 6, 2015

<https://lh3.googleusercontent.com/-0jVbtyT6ArM/VWNtiWHECgI/AAAAAAAABOg/AKpR…> Hello, I am using equalize_adapthist after color deconvolution and some morphological operations to enhance local contrast of nuclei in histological images. There are different results when applying equalize_adapthist when using small patches and larger size whole images (left small patch, right large size whole image). The CLAHE implementation in ImageJ has a 'block size' parameter and the implementation in Pixinsight has a 'Kernel size' parameter to get results depending on the local context. Is it possible to 'simulate' this behaviour with scikit-image? Best regards, Kai

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