2011/7/10 Stéfan van der Walt <stefan@sun.ac.za>

On Thu, Jul 7, 2011 at 6:30 PM, Tony Yu <tsyu80@gmail.com> wrote:

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I added a reference and tried to expand on the description, but the description is still poor. Even in the papers I've read, the descriptions are quite difficult for me to understand (the figures in those papers really help). I've also added an example to the docstring, but it's quite ... verbose.

I guess the question then becomes whether such a specialised algorithm is of enough common interest to include it. In the end, every piece of code requires maintenance, and our aim is to provide a fairly generic set of tools that can be used to construct image processing experiments.

I don't know this algorithm specifically, so I can't say, but it is an aspect worth considering.

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I'd like to add a longer example in the docs, but that will have to wait until I have a bit more time. When I do add it, where should put it? I'd like for it to be more like a tutorial than a function-specific example (specifically, I'd like to compare reconstruction and top-hat for peak detection).

We are eagerly awaiting tutorial contributions for the docs, but no one's volunteered so far :) I'd love to have more of these, as it shows common patterns (such as how to use io, displaying images, etc.).

Regards Stéfan

Hi again, I'm still interested in getting greyscale reconstruction into scikits.image. To that end, I've updated my branch with a tutorial demonstrating peak detection using reconstruction ( https://github.com/tonysyu/scikits.image/tree/reconstruction), with comparisons to thresholding and white top hat. This example should demonstrate reconstruction better than I was able to explain it earlier. Let me know if you have trouble building the docs. There's still some changes I want to make, but first I want to know whether this example is enough to convince you that the reconstruction algorithm is worth including ;) Best, - Tony A couple of random notes: * This branch adds the ipython_directive (and ipython_console_hightlighting) sphinx extension, because it allows you to show code examples with text in-between (and still be able to access variables from previous blocks). These extensions require the latest ipython (i.e. 0.11). * morphology.greyscale_white_top_hat has issues with underflow because there's a subtraction in the algorithm and it requires uint8 images (I think). A similar issue came up in this thread: http://groups.google.com/group/scikits-image/browse_thread/thread/c081f18d9e.... But I guess, that applied specifically to functions using ndimage.convolve. * the morphology names seem a bit clunky to work with. For example, white top hat is called with "morphology.greyscale_white_top_hat". The greyscale routines are already isolated to morphology.grey, so maybe morphology/__init__.py can just call "import grey" (instead of "from grey import *") and then the "greyscale_" prefix can be removed from the functions?