scikit-image October 2009

scikit-image@python.org

11 participants
43 discussions

Request for review / progress updates
by Stéfan van der Walt 01 Nov '09

01 Nov '09

Hey guys, I finally got around to writing a draft of the proposed plugin system. You'll find this work in the 'io' branch at http://github.com/stefanv/scikits.image It's still a work in progress, but I welcome all comments. Example plugins are http://github.com/stefanv/scikits.image/blob/io/scikits/image/io/matplotlib… http://github.com/stefanv/scikits.image/blob/io/scikits/image/io/pil_plugin… I've merged Ralf's colour space conversions, as well as his code coverage enhancements. Ralf is now documenting the coverage process and will also look at improving the execution time of the slow filter tests. For OpenCV, Chris wrote a docs-decorator that now provides us with links to the OpenCV online reference: http://stefanv.github.com/scikits.image/api/scikits.image.opencv.html#cvlap… He added more functions to the cv module, and is busy implementing some checks to make sure that even slices out of ndarray's may be passed to OpenCV, whenever possible. Tony Yu (welcome!) did some work on loading paletted images. I'll be sure to review those changes tomorrow. Have yourselves a splendid weekend! Cheers Stéfan

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reading images in palette mode
by Tony S Yu 01 Nov '09

01 Nov '09

Hi, Currently, imread doesn't properly handle palette images since PIL palette images can't be converted directly to numpy arrays. Well, you can convert it, but the output is garbage since the array values correspond to the image palette, which you don't have access. Flattening the image seems to be an OK work around, but if the image had a color palette, this information is lost. Also, this work around requires you to know that an image is in palette mode before calling imread. Below is a short patch that checks if an image is in palette mode; if it is, grayscale images are converted to luminance mode and color images are converted to RGB. I'm not sure if these conversions are appropriate, but at least it's an improvement. I hope this is helpful to somebody. Cheers, -Tony P.S. `palette_is_grayscale` is a convoluted function to check whether the palette is grayscale. PIL may provide a simpler check; if so, I couldn't find it. P.P.S. The following link has examples of palette images if you want to test: http://homepages.inf.ed.ac.uk/rbf/HIPR2/libfce.htm (NOTE: the thumbnails are actually not palette images, but the linked images are) ~~~~~~~ diff --git a/scikits/image/io/pil_imread.py b/scikits/image/io/ pil_imread.py index 421f45c..946d00c 100644 --- a/scikits/image/io/pil_imread.py +++ b/scikits/image/io/pil_imread.py @@ -34,6 +34,34 @@ def imread(fname, flatten=False, dtype=None): " instructions.") im = Image.open(fname) + if im.mode == 'P': + if palette_is_grayscale(im): + im = im.convert('L') + else: + im = im.convert('RGB') if flatten and not im.mode in ('1', 'L', 'I', 'F', 'I;16', 'I; 16L', 'I;16B'): im = im.convert('F') return np.array(im, dtype=dtype) + + +def palette_is_grayscale(pil_image): + """Return True if PIL image is grayscale. + + Parameters + ---------- + pil_image : PIL image + PIL Image that is in Palette mode. + + Returns + ------- + is_grayscale : bool + True if all colors in image palette are gray. + """ + assert pil_image.mode == 'P' + # get palette as an array with R, G, B columns + palette = np.asarray(pil_image.getpalette()).reshape((256, 3)) + # Not all palette colors are used; unused colors have junk values. + start, stop = pil_image.getextrema() + valid_palette = palette[start:stop] + # Image is grayscale if channel differences (R - G and G - B) are all zero. + return np.allclose(np.diff(valid_palette), 0)

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color spaces
by Ralf Gommers 23 Oct '09

23 Oct '09

Hi all, I'm working on the color spaces, and would like some feedback. Doing color space conversions is very simple, but finding the right references and making sure things are correct is a real pain. So I'm wondering what spaces are useful and unambiguously defined. So far I have ----------------- - RGB : sRGB with D65 whitepoint, the "central" color space in the API. - HSV : unambiguous - XYZ : unambiguous - CIE RGB : unambiguous - RGB NTSC : from Travis' code, not sure what it is. Not equal to YIQ, which is the color space used for NTSC. Will delete again unless I figure it out. Some code, seems clearly defined ---------------------------------------------- - CIE LAB - CIE LUV Some code, but not sure about ----------------------------------------- - RGBP : gamma corrected sRGB. I am usually interested in intensities / photon counts, so I have a natural aversion to this one. Anyone care? - YIQ : see RGB NTSC above - RGB SB : Stiles and Burch (1955) 2-degree RGB color space. seems obsolete. - UVW : seems obsolete - YUV / YCbCr / YCC / YPbPr / 8-bit YCbCr : these are all similar and often confused. A mess. No code yet, but commonly used -------------------------------------------- - HSL - CMYK The code I have so far lives here: http://github.com/rgommers/scikits.image/tree/color I'm afraid adding color spaces that are not clearly defined does more harm than good, so please let me know which color spaces are useful for you. Cheers, Ralf

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isocontours, marching squares, patent issue
by Ralf Gommers 22 Oct '09

22 Oct '09

Hi all, I was just looking at the iso-contouring code of Zach Pincus ( http://mail.scipy.org/pipermail/scipy-user/2009-July/021719.html), and saw it is implemented with the marching squares algorithm. On the task list that algorithm is listed as well, with a note to investigate patent issues. So I had a look at that. The patent issue was earlier raised in this thread in 2004: http://osdir.com/ml/python.matplotlib.devel/2004-10/msg00066.html. The open question was whether marching squares was covered under the patent for marching cubes or not. This is a 1985 patent and has expired in the meantime, as noted here: http://en.wikipedia.org/wiki/Marching_cubes. IANAL, but I think there are no patent issues anymore. Separate question to Zach: the code you sent to the SciPy list in July was GPL licensed. You noted you were willing to send it again under a different license. Could you please send it to the list with a BSD/MIT/similar license? Cheers, Ralf

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Re: Bug in io.ImageCollection
by Ralf Gommers 22 Oct '09

22 Oct '09

On Wed, Oct 21, 2009 at 1:32 PM, SirVer <sirver(a)gmx.de> wrote: > > On a side note, can't we get a real bug tracker somewhere? > > This one should do the job right? At least for now. http://github.com/stefanv/scikits.image/issues > --- > I have a selection of grayscale (8bit) tifs in a directory. > > In [14]: k = io.ImageCollection("calib/*.tif", True) > > In [15]: k[0].dtype > Out[15]: dtype('uint8') > > In [16]: k = io.ImageCollection("calib/*.tif", as_grey=True) > > In [17]: k[0].dtype > Out[17]: dtype('float32') > --- > But the docs say: > as_grey : bool, optional > If True, convert the input images to grey-scale. This does not > affect images that are already in a grey-scale format. > > Obviously the image data type gets converted though. > Thanks for testing Holger. I wrote the docstring based on what I thought pil_imread was doing. Obviously I was wrong. This is the desired behavior imho, so I'll come up with a patch for pil_imread. Cheers, Ralf > Cheers, > Holger > >

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Re: Separation of tests into unit test and functional tests
by Ralf Gommers 21 Oct '09

21 Oct '09

Hi Holger, That's a good point. On Mon, Oct 19, 2009 at 6:55 PM, SirVer <sirver(a)gmx.de> wrote: > > Hi, > > I have something of a lot of importance to mention. We should start to > separate our tests into unit and integration tests. For what reason? > The 40 tests need 3.256 seconds on my box to run; that's approximately > the time it takes to compile the opencv module here. So compiling + > tests = 2 * compiling. That's still acceptable, but 40 tests is > nothing. 400 tests will need 30 seconds which is too much to run after > each edit. The main thing I think is to be careful with images. The slowest one by far now are the tests for lpi_filter, because they do a lot of things with a *large* image. This is why I originally put my test images for io under io/tests. I thought data_dir should only contain images for examples, useful for users. Images for testing algorithms can often be small. For the color conversion I now use images of size (4, 2, 3), which means they take no time at all. > But that is was unittests are for. The reason why I come up > with this now is that it is important to make this separation while it > is still easy and possible. The best way is to group tests into many > groups ('opencv', 'fast', 'slow', 'need_camera'). What is lacking when you apply decorators like @opencv_skip and @slow? Note also that if you run nosetests from some folder you run only tests in subfolders. This is a natural separation already. > A short blog post > concerning this is: > > http://beust.com/weblog/archives/000319.html > > That's mostly semantics. > I have no idea how to achieve this with pynose in a simple way > though, > > Once again: I think scikit_image will grow fast and therefore test > will grow fast. Users will stop running the tests if they take to > long. Too long for an interactive coding session is > 10s . > True. The @slow decorator should take care of this. What is the approximate limit again when it should be used? Cheers, Ralf > > Cheers, > Holger

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Bug in io.ImageCollection
by SirVer 21 Oct '09

21 Oct '09

On a side note, can't we get a real bug tracker somewhere? --- I have a selection of grayscale (8bit) tifs in a directory. In [14]: k = io.ImageCollection("calib/*.tif", True) In [15]: k[0].dtype Out[15]: dtype('uint8') In [16]: k = io.ImageCollection("calib/*.tif", as_grey=True) In [17]: k[0].dtype Out[17]: dtype('float32') --- But the docs say: as_grey : bool, optional If True, convert the input images to grey-scale. This does not affect images that are already in a grey-scale format. Obviously the image data type gets converted though. Cheers, Holger

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Re: That's awesome))
by David Warde-Farley 21 Oct '09

21 Oct '09

And so it begins... Time to reevaluate the posting permissions? David On 20-Oct-09, at 1:13 PM, FunGuy wrote: > > Muhahahhaaa )))awesome!!))) still can't believe this)).... > http://villaworld.com/pictures/

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That's awesome))
by FunGuy 20 Oct '09

20 Oct '09

Muhahahhaaa )))awesome!!))) still can't believe this)).... http://villaworld.com/pictures/

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Re: Image acquisition (via camera) in scikit-image
by Stéfan van der Walt 20 Oct '09

20 Oct '09

2009/10/19 SirVer <sirver(a)gmx.de>: > Stéfan, which is the solution (1 or 2) you'd like to have for sckit- > images? Personally, I'd prefer 1(b) [Cython wrapper] with a second choice 2 [close collaboration with an existing project], simply because it is easier to manage code under our own roof. In order to get this implemented as soon as possible, we should build on the work done by yourself and Andrew. His code is also BSD licensed, so no problems there. Also have a look at his higher-level Python wrapper: http://code.astraw.com/projects/motmot/cam_iface.html Cheers Stéfan

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scikit-image October 2009