Mailman 3 January 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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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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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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Example: Scikit-image and trackpy (bubble tracking in foams)
by François Boulogne March 25, 2015

March 25, 2015

Hi, Just for your information, I wrote an example based on my research to show the use of scikit-image with trackpy for feature detection and tracking. http://nbviewer.ipython.org/github/soft-matter/trackpy-examples/blob/master… Hopefully, this might be helpful for future workshops or presentations. Best, -- François Boulogne. http://www.sciunto.org GPG: 32D5F22F

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Image viewer plugins - multiple parameters from different functions
by Marcel Gutsche Jan. 29, 2015

Jan. 29, 2015

Hi folks, kind of a follow up question of my last one (see here <https://groups.google.com/forum/#%21topic/scikit-image/WXrwvxFd8xA>). <https://groups.google.com/forum/#%21topic/scikit-image/WXrwvxFd8xA> I still have a chain of image processing tasks, which I want to visualize with the image viewer. Certain parameters should be changed on the fly, where i can see the results in the image viwer immediately by the use of plugins. Something like this basically: a, b = previous_function(image, parameter1 = 0.002 parameter = 0.02) new_image = next_function(image, a, b, another_parameter = 3) I managed to pass the values a and b, generated by a previous function using Juan's hint with functools. next_function which was fine as long as I don't want to change parameter1 and parameter2 interactively. What do I do now, when I want to change them? Using functools here won't get me far, since now I want to be able to change parameter1 and parameter2 with a slider, as well as another_parameter. Maybe there is still a way to achive this, I saw something like a callback argument for the widgets, but I am not sure how to use them or whether they are related to my problem at all. I would also be fine with writing my own Plugin class, but could not get my head around the given examples. I feel that something like this is quite common, so I hope there are already ways to achieve this. Thanks a lot for the great work, Marcel

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Starting with Scikit-Image
by FARS Research Group Jan. 29, 2015

Jan. 29, 2015

Hello everyone, As you'll see the dumb questions I'll make, I'm starting with scikit-image. I started studying but I wanted to run the tests with my own images. But I tried to import them and there was no way to do it. Is there any documentation or tutorial that could help me with this issue? All I want is to run the algorithms in my own image. Thanks

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Viewer Plugins -> Passing precalculated arguments
by Marcel Gutsche Jan. 28, 2015

Jan. 28, 2015

Hi everyone, I'm working on an image processing project involving several consecutive steps. For parameter optimization I would like to create a viewer plugin which is capable to take these previous outputs into account. So basically the introductory example found in the docs of denoise_plugin = Plugin(image_filter=denoise_tv_bregman) would be sufficient, if I could pass additional fixed arguments (like an additional numpy array). I hope I made myself clear, and someone can give me hint at where to look at. Best regards, Marcel

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