From jni.soma at gmail.com  Fri Jul  1 10:35:39 2016
From: jni.soma at gmail.com (Juan Nunez-Iglesias)
Date: Fri, 1 Jul 2016 10:35:39 -0400
Subject: Import error when importing from skimage.transform
In-Reply-To: <98b2c001-1f06-482f-b800-6e09438e6b86@googlegroups.com>
References: <d7a8d058-2202-424e-b6b4-b4cd85c1811e@googlegroups.com> <98b2c001-1f06-482f-b800-6e09438e6b86@googlegroups.com>
Message-ID: <CA+JHcKRCNj_QmKHj4u70hJ9DLEdG=mApftOBtqV4-uELYn3EJA@mail.gmail.com>

Hi Barbara,

Can you look at my response to the original email and post your results?
https://groups.google.com/d/msgid/scikit-image/d7a8d058-2202-424e-b6b4-b4cd85c1811e%40googlegroups.com?utm_medium=email&utm_source=footer

Sometimes skimage installs to a path that is *not* the path where Python
looks for packages. Or sometimes you install the C extensions to the Python
path but then try to import the local copy of scikit-image. It's hard to
know what's happening without the diagnostics above...

On Thu, Jun 30, 2016 at 4:19 PM, Barbara Collignon <
barbara.collignon at gmail.com> wrote:

> same issue here with python2.7+
>
> On Thursday, June 23, 2016 at 8:30:36 PM UTC-4, sja... at nyu.edu wrote:
>>
>> The environment I'm using is a Jupyter notebook running python 3.5. I can import other packages, but importing anything from skimage.transform results in the following error:
>>
>>
>> ImportError                               Traceback (most recent call last)<ipython-input-19-741b6a154664> in <module>()----> 1 from skimage.transform import resize
>> /home/jupyter/anaconda3/envs/py35/lib/python3.5/site-packages/skimage/transform/__init__.py in <module>()----> 1 from .hough_transform import (hough_line, hough_line_peaks,      2                               probabilistic_hough_line, hough_circle,      3                               hough_ellipse)      4 from .radon_transform import radon, iradon, iradon_sart      5 from .finite_radon_transform import frt2, ifrt2
>> /home/jupyter/anaconda3/envs/py35/lib/python3.5/site-packages/skimage/transform/hough_transform.py in <module>()      2 from scipy import ndimage as ndi      3 from .. import measure----> 4 from ._hough_transform import (_hough_circle,      5                                hough_ellipse as _hough_ellipse,      6                                hough_line as _hough_line,
>> ImportError: cannot import name '_hough_circle'
>>
>>
>> We've already tried uninstalling skimage, updating cython and then re-installing. Anyone else encounter this problem?
>>
>> --
> You received this message because you are subscribed to the Google Groups
> "scikit-image" group.
> To unsubscribe from this group and stop receiving emails from it, send an
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> To view this discussion on the web, visit
> https://groups.google.com/d/msgid/scikit-image/98b2c001-1f06-482f-b800-6e09438e6b86%40googlegroups.com
> <https://groups.google.com/d/msgid/scikit-image/98b2c001-1f06-482f-b800-6e09438e6b86%40googlegroups.com?utm_medium=email&utm_source=footer>
> .
>
> For more options, visit https://groups.google.com/d/optout.
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From stefanv at berkeley.edu  Fri Jul  1 20:38:01 2016
From: stefanv at berkeley.edu (=?UTF-8?Q?St=C3=A9fan_van_der_Walt?=)
Date: Fri, 1 Jul 2016 17:38:01 -0700
Subject: DOI in references?
In-Reply-To: <fdbfec75-3e13-adfa-ff6a-ba876ff7ae32@sciunto.org>
References: <fdbfec75-3e13-adfa-ff6a-ba876ff7ae32@sciunto.org>
Message-ID: <CABDkGQn5numF+q5JXxgS-Q2ymt3scLiLE7sbQ=mx+bDYCDYATw@mail.gmail.com>

By all means, add them in!
On Jul 1, 2016 16:05, "Fran?ois Boulogne" <fboulogne at sciunto.org> wrote:

> Hi,
>
>
> I have a question about the references. Is it a choice to do not put DOI
> pointers when available? For my usage, I have to use a search engine to
> find papers referenced in our documentation and it would be more simple
> to add and use the DOI. It's also relatively short, the length is not
> really a problem.
>
> Any comment on this?
> Thanks.
>
> --
> Fran?ois Boulogne.
> http://www.sciunto.org
> GPG: 32D5F22F
>
>
> --
> You received this message because you are subscribed to the Google Groups
> "scikit-image" group.
> To unsubscribe from this group and stop receiving emails from it, send an
> email to scikit-image+unsubscribe at googlegroups.com.
> To post to this group, send an email to scikit-image at googlegroups.com.
> To view this discussion on the web, visit
> https://groups.google.com/d/msgid/scikit-image/fdbfec75-3e13-adfa-ff6a-ba876ff7ae32%40sciunto.org
> .
> For more options, visit https://groups.google.com/d/optout.
>
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From fboulogne at sciunto.org  Fri Jul  1 19:05:24 2016
From: fboulogne at sciunto.org (=?UTF-8?Q?Fran=c3=a7ois_Boulogne?=)
Date: Sat, 2 Jul 2016 01:05:24 +0200
Subject: DOI in references?
Message-ID: <fdbfec75-3e13-adfa-ff6a-ba876ff7ae32@sciunto.org>

Hi,


I have a question about the references. Is it a choice to do not put DOI
pointers when available? For my usage, I have to use a search engine to
find papers referenced in our documentation and it would be more simple
to add and use the DOI. It's also relatively short, the length is not
really a problem.

Any comment on this?
Thanks.

-- 
Fran?ois Boulogne.
http://www.sciunto.org
GPG: 32D5F22F




From takowl at gmail.com  Mon Jul  4 05:38:50 2016
From: takowl at gmail.com (Thomas Kluyver)
Date: Mon, 4 Jul 2016 02:38:50 -0700 (PDT)
Subject: Dropping support for 2.7 in 2020
In-Reply-To: <etPan.5742793c.5c6c742c.141c@MacBook.local>
References: <20160518064719.GA3031017@phare.normalesup.org>
 <CABDkGQkZaHayUW8aL6xNwicCHbg7guZr9aEEOai=xS-uBxzttQ@mail.gmail.com>
 <20160518074002.GC3031017@phare.normalesup.org>
 <CABDkGQm_SE8fjU1ZiUg1kWne1eLTicjvtV14Bhp1x8fOupOQNA@mail.gmail.com>
 <CANpJW-URfDAKKzmbeLQz5=ksxjJ8QbFCU2Hdm4bedZ2zx1AAAw@mail.gmail.com>
 <CAA48SF_0zEveSw7H-WL2aObma+m7xaM1B=S5dv+9Aaj_DsMafQ@mail.gmail.com>
 <20160518175455.GA4080630@phare.normalesup.org>
 <b971d22e-7340-41b9-a41a-bc11fd50f896@googlegroups.com>
 <20160520105049.GA3583487@phare.normalesup.org>
 <c30471be-47e7-e526-4e73-854c3ea389df@cadair.com>
 <20160520141800.GD3583487@phare.normalesup.org>
 <CAB9hrOqEXPFQDKwPAyZ6LArGHq3paZ1nDgyHikNycr5KpOWqaA@mail.gmail.com>
 <CAPJVwBni1W9CfQ5fzj1512Ojsdy80u-YrTB=gbpL87fFF5sU7g@mail.gmail.com>
 <CAPJVwBkcaoKPj4YqrjGvss3iq2+w6YntdSuJwC1EyzLxdD+amA@mail.gmail.com>
 <CAPJVwB=jRQtHn9Xj0xwv3hxtDsjBvKYtJTB38ewBuZ84cNR4xw@mail.gmail.com>
 <etPan.5742793c.5c6c742c.141c@MacBook.local>
Message-ID: <ba5c98fa-d413-4ba3-bcee-92031977df6c@googlegroups.com>

I'd like to revive this discussion with a bit of added context. We 
(principally people from IPython/Jupyter, Matplotlib, Sympy and Scikit-bio) 
are putting together a statement for Scientific Python community projects 
to signal that we're not planning to maintain Python 2 support forever. 
We're saying that we will end Python 2 support in or before 2020, to 
correspond with the end of support for Python 2.7 itself.

http://python3statement.github.io/

The more projects that get on board with this, the better we can make the 
case that researchers and users need Python 3 to be available. Then we can 
all benefit from a reduced maintenance burden. So scikit-image wouldn't be 
making a lone stand on this - there are a number of projects already 
agreeing to drop Python 2 support by 2020, and we hope there will be more 
soon.

Thanks,
Thomas

On Monday, 23 May 2016 04:30:21 UTC+1, Juan Nunez-Iglesias wrote:
>
> I would drop 2.7 sooner. NumPy/SciPy dropping it is the absolute cutoff, 
> but there?s no reason why we can?t jump the gun (and lead rather than 
> follow). Py2.7 users can make do with older releases. As St?fan mentioned, 
> this is not about erasing Py2.7 support, but not releasing new features on 
> Py2.7. Very different. I envision that we stop producing Py2.7 releases 
> after 0.13, but we call that an ?LTS? release which will get bugfix 
> backports until 2020.
>
> imho, with Py3.4 and especially Py3.5, Py3-only suddenly became very 
> attractive. My three favourite features are type annotations, keyword-only 
> arguments (these two together make it much easier to produce correct code 
> and debug), and the @ matrix multiplication operator. The latter makes 
> linear algebra code *so* *much* nicer to read and write, and we have our 
> fair share in scikit-image. For my own projects, I am now Py3.5-only, 
> always.
>
> Finally, as others have mentioned, but is worth restating:
> - Conda allows user-space installs of Python versions and packages, so you 
> don?t ever depend on your sysadmin-controlled environment.
> - It is *absolutely false* that conda packages are only useful on Windows 
> ? I have failed to compile scipy both on OSX and Ubuntu boxes.
> - It is also *not* a requirement to download the monolithic Anaconda 
> distro ? with miniconda (which should be the default), you just install 
> precisely what you need.
> - With conda-forge we now have a community-run repository of binary conda 
> packages.
>
> So, in short, the switch to Py3 is easier than ever for *all* users, some 
> might just not realise it yet ;), and switching to a Py3-only programming 
> environment is a boon to all the scikit-image devs? though again some might 
> not realise it yet ;).
>
> Juan.
>
> On 21 May 2016 at 12:20:38 PM, Nathaniel Smith (n... at vorpus.org 
> <javascript:>) wrote:
>
> On May 20, 2016 07:30, "Michael Sarahan" <msar... at gmail.com <javascript:>> 
> wrote:
> > I don't think this is a concern for cython or scikit-image, but many 
> people at bumping into the language support limit in the C++11 sense with 
> Python 2.7 on Windows.  Since VS 2008 is the de-facto standard compiler for 
> Python 2.7, people are unable to use C++11 code in modules for Python 2.7.  
> Some people use newer compilers anyway, which sometimes works, but is 
> mixing runtimes, and can lead to bugs or crashes.  Many people would like 
> to support Python 2.7 using a different compiler for the whole ecosystem.  
> One example is Ilastik, by folks at HHMI, using VS 2012 to have a custom 
> stack: https://github.com/ilastik/ilastik-build-conda
>
> I think getting mingwpy finished and polished is probably an easier 
> solution for this problem than forking the entire py27-on-windows ecosystem 
> :-)
>
> -n
> --
> You received this message because you are subscribed to the Google Groups 
> "scikit-image" group.
> To unsubscribe from this group and stop receiving emails from it, send an 
> email to scikit-image... at googlegroups.com <javascript:>.
> To post to this group, send email to scikit... at googlegroups.com 
> <javascript:>.
> To view this discussion on the web, visit 
> https://groups.google.com/d/msgid/scikit-image/CAPJVwB%3DjRQtHn9Xj0xwv3hxtDsjBvKYtJTB38ewBuZ84cNR4xw%40mail.gmail.com 
> <https://groups.google.com/d/msgid/scikit-image/CAPJVwB%3DjRQtHn9Xj0xwv3hxtDsjBvKYtJTB38ewBuZ84cNR4xw%40mail.gmail.com?utm_medium=email&utm_source=footer>
> .
> For more options, visit https://groups.google.com/d/optout.
>
>
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From takowl at gmail.com  Mon Jul  4 05:55:04 2016
From: takowl at gmail.com (Thomas Kluyver)
Date: Mon, 4 Jul 2016 10:55:04 +0100
Subject: Dropping support for 2.7 in 2020
In-Reply-To: <d8b02fc1-e65c-949b-d093-58cfdc5fecdc@sciunto.org>
References: <20160518064719.GA3031017@phare.normalesup.org>
 <20160518074002.GC3031017@phare.normalesup.org> <CABDkGQm_SE8fjU1ZiUg1kWne1eLTicjvtV14Bhp1x8fOupOQNA@mail.gmail.com>
 <CANpJW-URfDAKKzmbeLQz5=ksxjJ8QbFCU2Hdm4bedZ2zx1AAAw@mail.gmail.com>
 <CAA48SF_0zEveSw7H-WL2aObma+m7xaM1B=S5dv+9Aaj_DsMafQ@mail.gmail.com>
 <20160518175455.GA4080630@phare.normalesup.org> <b971d22e-7340-41b9-a41a-bc11fd50f896@googlegroups.com>
 <20160520105049.GA3583487@phare.normalesup.org> <c30471be-47e7-e526-4e73-854c3ea389df@cadair.com>
 <20160520141800.GD3583487@phare.normalesup.org> <CAB9hrOqEXPFQDKwPAyZ6LArGHq3paZ1nDgyHikNycr5KpOWqaA@mail.gmail.com>
 <CAPJVwBni1W9CfQ5fzj1512Ojsdy80u-YrTB=gbpL87fFF5sU7g@mail.gmail.com>
 <CAPJVwBkcaoKPj4YqrjGvss3iq2+w6YntdSuJwC1EyzLxdD+amA@mail.gmail.com>
 <CAPJVwB=jRQtHn9Xj0xwv3hxtDsjBvKYtJTB38ewBuZ84cNR4xw@mail.gmail.com>
 <etPan.5742793c.5c6c742c.141c@MacBook.local> <ba5c98fa-d413-4ba3-bcee-92031977df6c@googlegroups.com>
 <d8b02fc1-e65c-949b-d093-58cfdc5fecdc@sciunto.org>
Message-ID: <CAOvn4qj1iseuWac7c47Guk_Tv8P7gUoL25srbm=7My+FDcdRJg@mail.gmail.com>

On 4 July 2016 at 10:47, Fran?ois Boulogne <fboulogne at sciunto.org> wrote:

> Does it mean that all new version of those libraries will support
> python2.7 until 2020 or just that you can release a bugfix to already
> released version supporting 2.7?
>
> If it's the first option, it means we can't use python3 features before
> 2020 and we will be "in late" with these features.
>

The statement doesn't commit projects to support 2.7 until any specific
date - scikit-bio already went Python 3 only with its release last month,
for instance. We're agreeing to end Python 2 support by 2020, but projects
are welcome to do it earlier than that.

For IPython, we're planning for the 6.0 feature release (some time in 2017)
to be Python 3 only, while we'll make bugfix releases of the 5.x series for
longer than normal to support users on Python 2.
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From fboulogne at sciunto.org  Mon Jul  4 05:47:30 2016
From: fboulogne at sciunto.org (=?UTF-8?Q?Fran=c3=a7ois_Boulogne?=)
Date: Mon, 4 Jul 2016 11:47:30 +0200
Subject: Dropping support for 2.7 in 2020
In-Reply-To: <ba5c98fa-d413-4ba3-bcee-92031977df6c@googlegroups.com>
References: <20160518064719.GA3031017@phare.normalesup.org>
 <20160518074002.GC3031017@phare.normalesup.org>
 <CABDkGQm_SE8fjU1ZiUg1kWne1eLTicjvtV14Bhp1x8fOupOQNA@mail.gmail.com>
 <CANpJW-URfDAKKzmbeLQz5=ksxjJ8QbFCU2Hdm4bedZ2zx1AAAw@mail.gmail.com>
 <CAA48SF_0zEveSw7H-WL2aObma+m7xaM1B=S5dv+9Aaj_DsMafQ@mail.gmail.com>
 <20160518175455.GA4080630@phare.normalesup.org>
 <b971d22e-7340-41b9-a41a-bc11fd50f896@googlegroups.com>
 <20160520105049.GA3583487@phare.normalesup.org>
 <c30471be-47e7-e526-4e73-854c3ea389df@cadair.com>
 <20160520141800.GD3583487@phare.normalesup.org>
 <CAB9hrOqEXPFQDKwPAyZ6LArGHq3paZ1nDgyHikNycr5KpOWqaA@mail.gmail.com>
 <CAPJVwBni1W9CfQ5fzj1512Ojsdy80u-YrTB=gbpL87fFF5sU7g@mail.gmail.com>
 <CAPJVwBkcaoKPj4YqrjGvss3iq2+w6YntdSuJwC1EyzLxdD+amA@mail.gmail.com>
 <CAPJVwB=jRQtHn9Xj0xwv3hxtDsjBvKYtJTB38ewBuZ84cNR4xw@mail.gmail.com>
 <etPan.5742793c.5c6c742c.141c@MacBook.local>
 <ba5c98fa-d413-4ba3-bcee-92031977df6c@googlegroups.com>
Message-ID: <d8b02fc1-e65c-949b-d093-58cfdc5fecdc@sciunto.org>



Le 04/07/2016 ? 11:38, Thomas Kluyver a ?crit :
> I'd like to revive this discussion with a bit of added context. We
> (principally people from IPython/Jupyter, Matplotlib, Sympy and
> Scikit-bio) are putting together a statement for Scientific Python
> community projects to signal that we're not planning to maintain
> Python 2 support forever. We're saying that we will end Python 2
> support in or before 2020, to correspond with the end of support for
> Python 2.7 itself.
>
> http://python3statement.github.io/
>
> The more projects that get on board with this, the better we can make
> the case that researchers and users need Python 3 to be available.
> Then we can all benefit from a reduced maintenance burden. So
> scikit-image wouldn't be making a lone stand on this - there are a
> number of projects already agreeing to drop Python 2 support by 2020,
> and we hope there will be more soon.
>

Thanks for your note.

Does it mean that all new version of those libraries will support
python2.7 until 2020 or just that you can release a bugfix to already
released version supporting 2.7?

If it's the first option, it means we can't use python3 features before
2020 and we will be "in late" with these features.


From silvertrumpet999 at gmail.com  Mon Jul  4 20:12:16 2016
From: silvertrumpet999 at gmail.com (Josh Warner)
Date: Mon, 4 Jul 2016 17:12:16 -0700 (PDT)
Subject: Dropping support for 2.7 in 2020
In-Reply-To: <CAOvn4qj1iseuWac7c47Guk_Tv8P7gUoL25srbm=7My+FDcdRJg@mail.gmail.com>
References: <20160518064719.GA3031017@phare.normalesup.org>
 <20160518074002.GC3031017@phare.normalesup.org> <CABDkGQm_SE8fjU1ZiUg1kWne1eLTicjvtV14Bhp1x8fOupOQNA@mail.gmail.com>
 <CANpJW-URfDAKKzmbeLQz5=ksxjJ8QbFCU2Hdm4bedZ2zx1AAAw@mail.gmail.com>
 <CAA48SF_0zEveSw7H-WL2aObma+m7xaM1B=S5dv+9Aaj_DsMafQ@mail.gmail.com>
 <20160518175455.GA4080630@phare.normalesup.org> <b971d22e-7340-41b9-a41a-bc11fd50f896@googlegroups.com>
 <20160520105049.GA3583487@phare.normalesup.org> <c30471be-47e7-e526-4e73-854c3ea389df@cadair.com>
 <20160520141800.GD3583487@phare.normalesup.org> <CAB9hrOqEXPFQDKwPAyZ6LArGHq3paZ1nDgyHikNycr5KpOWqaA@mail.gmail.com>
 <CAPJVwBni1W9CfQ5fzj1512Ojsdy80u-YrTB=gbpL87fFF5sU7g@mail.gmail.com>
 <CAPJVwBkcaoKPj4YqrjGvss3iq2+w6YntdSuJwC1EyzLxdD+amA@mail.gmail.com>
 <CAPJVwB=jRQtHn9Xj0xwv3hxtDsjBvKYtJTB38ewBuZ84cNR4xw@mail.gmail.com>
 <etPan.5742793c.5c6c742c.141c@MacBook.local> <ba5c98fa-d413-4ba3-bcee-92031977df6c@googlegroups.com>
 <d8b02fc1-e65c-949b-d093-58cfdc5fecdc@sciunto.org>
 <CAOvn4qj1iseuWac7c47Guk_Tv8P7gUoL25srbm=7My+FDcdRJg@mail.gmail.com>
Message-ID: <dc739979-e63f-4ce0-849b-66f8a659c8fc@googlegroups.com>

I think we should support 2.7 through end-of-life. 2020 sounds right.

There are a tremendous amount of systems out there still running 2.7. I 
know more than one lab which standardized on 2.7 years ago, and while we're 
evaluating the move to 3.x it won't be without major pains. Small, niche 
packages which work perfectly under 2.7 break in 3.x, and they may or may 
not be maintained. It only takes a couple of these to render lab equipment 
with price tags in the 6 figures buggy, problematic, or useless in 3.x. 
This is problematic when you want a consistent Python experience across a 
lab or team, but certain pieces just won't work in 3.x. 

Sounding the trumpets is appropriate, as 2.7 won't be around forever. 
Quarterly or bi-annually it would be reasonable to send out a reminder. 
Three years sounds like a long time, but that's about right for this kind 
of transition.

On Monday, July 4, 2016 at 4:55:34 AM UTC-5, Thomas Kluyver wrote:
>
> On 4 July 2016 at 10:47, Fran?ois Boulogne <fboulogne at sciunto.org> wrote:
>
>> Does it mean that all new version of those libraries will support
>> python2.7 until 2020 or just that you can release a bugfix to already
>> released version supporting 2.7?
>>
>> If it's the first option, it means we can't use python3 features before
>> 2020 and we will be "in late" with these features.
>>
>
> The statement doesn't commit projects to support 2.7 until any specific 
> date - scikit-bio already went Python 3 only with its release last month, 
> for instance. We're agreeing to end Python 2 support by 2020, but projects 
> are welcome to do it earlier than that.
>
> For IPython, we're planning for the 6.0 feature release (some time in 
> 2017) to be Python 3 only, while we'll make bugfix releases of the 5.x 
> series for longer than normal to support users on Python 2.
>
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From parul1sethi at gmail.com  Wed Jul  6 08:54:55 2016
From: parul1sethi at gmail.com (parul sethi)
Date: Wed, 6 Jul 2016 05:54:55 -0700 (PDT)
Subject: Implementing Kadir and Brady's saliency detector in skimage?
Message-ID: <7132cb6e-e66f-45be-a71a-327a2e5ff7d9@googlegroups.com>

Hi, 

I required to use Kadir and Brady's saliency detector for feature 
extraction process, and didn't found it's python implementation. So, i was 
in process of implementing the same.
Will it be of interest to skimage too? I can use some advice from skimage 
maintainers to build it with specific suitability for skimage module.

Thank you.
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From sid.zidane at gmail.com  Thu Jul  7 03:37:16 2016
From: sid.zidane at gmail.com (Sid)
Date: Thu, 7 Jul 2016 00:37:16 -0700 (PDT)
Subject: Warping images using control points
Message-ID: <e51ad552-af63-49e0-afbc-2d4b5597d5e4@googlegroups.com>

Hi

I have two images of faces, the source image and the destination image. For 
each face, I have a set of x and y coordinates stored in two separate numpy 
arrays. These coordinates correspond to various facial features, as well as 
the four corners of the image, and points on half the length of the borders.

I want to warp the features of the source image onto the destination, such 
that the corresponding control points in each image are aligned. I used the 
PiecewiseAffineTransform method, as in the example here 
<http://scikit-image.org/docs/dev/auto_examples/transform/plot_piecewise_affine.html>
.

My code is:

src_pts = # numpy array of coordinates for the source image
dst_pts = # numpy array of coordinates for the destination image

image = # image of dimensions (550, 420, 3)

tform = skimage.transform.PiecewiseAffineTransform()
tform.estimate(src_pts, dst_pts)

out = skimage.transform.warp(image, tform)
 

I get an image that is warped but also cropped. To see if I could get the 
code to work (in case there were errors in my control points), I tried 
warping the image using the same control points for the source and 
destination.

tform.estimate(src_pts, src_pts)

The output is still cropped and is also warped slightly, as can be seen in 
the attached images. Logically, it doesn't make sense as there should not 
be any warping since I am using the same points. A big difference between 
the example and my implementation is that the example has uniformly spaced 
points, whereas I have 76 points which are much more sparse. Is this the 
right function to achieve what I want or should I be using something else? 


Thanks

--
Sid





<https://lh3.googleusercontent.com/-noDhRC6CbkE/V34EvUCZgtI/AAAAAAAACFc/8-Q5D7-2EO0S_E0EVlFHxwTtusPd5wcDwCLcB/s1600/image.jpg>
<https://lh3.googleusercontent.com/-7PTIVXGCCg8/V34E2M6yEUI/AAAAAAAACFg/z2zoGOPHTNss6n7hTaRRkiWToqlhwZtPgCLcB/s1600/out.jpg>
<https://lh3.googleusercontent.com/-7PTIVXGCCg8/V34E2M6yEUI/AAAAAAAACFg/z2zoGOPHTNss6n7hTaRRkiWToqlhwZtPgCLcB/s1600/out.jpg>
<https://lh3.googleusercontent.com/-7PTIVXGCCg8/V34E2M6yEUI/AAAAAAAACFg/z2zoGOPHTNss6n7hTaRRkiWToqlhwZtPgCLcB/s1600/out.jpg>

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From stefanv at berkeley.edu  Sun Jul 10 18:41:08 2016
From: stefanv at berkeley.edu (=?UTF-8?Q?St=C3=A9fan_van_der_Walt?=)
Date: Sun, 10 Jul 2016 15:41:08 -0700
Subject: Implementing Kadir and Brady's saliency detector in skimage?
In-Reply-To: <7132cb6e-e66f-45be-a71a-327a2e5ff7d9@googlegroups.com>
References: <7132cb6e-e66f-45be-a71a-327a2e5ff7d9@googlegroups.com>
Message-ID: <CABDkGQ=+cAy=Lzc5MOR-CzummOuWiyDWrvoN_mXNX+NBh868Ew@mail.gmail.com>

Hi Parul

We are happy to evaluate any PR for inclusion. Saliency detection is within
scope.

Best regards
St?fan
On Jul 6, 2016 05:54, "parul sethi" <parul1sethi at gmail.com> wrote:

> Hi,
>
> I required to use Kadir and Brady's saliency detector for feature
> extraction process, and didn't found it's python implementation. So, i was
> in process of implementing the same.
> Will it be of interest to skimage too? I can use some advice from skimage
> maintainers to build it with specific suitability for skimage module.
>
> Thank you.
>
> --
> You received this message because you are subscribed to the Google Groups
> "scikit-image" group.
> To unsubscribe from this group and stop receiving emails from it, send an
> email to scikit-image+unsubscribe at googlegroups.com.
> To post to this group, send email to scikit-image at googlegroups.com.
> To view this discussion on the web, visit
> https://groups.google.com/d/msgid/scikit-image/7132cb6e-e66f-45be-a71a-327a2e5ff7d9%40googlegroups.com
> <https://groups.google.com/d/msgid/scikit-image/7132cb6e-e66f-45be-a71a-327a2e5ff7d9%40googlegroups.com?utm_medium=email&utm_source=footer>
> .
> For more options, visit https://groups.google.com/d/optout.
>
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From stefanv at berkeley.edu  Mon Jul 11 15:35:14 2016
From: stefanv at berkeley.edu (=?UTF-8?Q?St=C3=A9fan_van_der_Walt?=)
Date: Mon, 11 Jul 2016 12:35:14 -0700
Subject: Warping images using control points
In-Reply-To: <e51ad552-af63-49e0-afbc-2d4b5597d5e4@googlegroups.com>
References: <e51ad552-af63-49e0-afbc-2d4b5597d5e4@googlegroups.com>
Message-ID: <CABDkGQm=23n8J6Ys3ath2jWp=AHiFaAZvu2pmkrECGJxXT4s-w@mail.gmail.com>

Hi Sid

Sorry, I think you've been a victim of a known
inconsistency---unfortunately, one I'm not sure how to address.

The transforms module uses a (x, y) coordinate convention to be consistent
with most of the warping literature out there.  But the rest of
scikit-image uses  a (row, column) convention.

The following code works for me:

from skimage import io, transform
import numpy as np

image = io.imread('image.jpg')
h, w = image.shape[:2]

rng = np.random.RandomState(0)
xs = rng.randint(0, w - 1, 76)
ys = rng.randint(0, h - 1, 76)

src_pts = np.column_stack([xs, ys])
dst_pts = src_pts

tform = transform.PiecewiseAffineTransform()
tform.estimate(src_pts, dst_pts)

out = transform.warp(image, tform)

import matplotlib.pyplot as plt
plt.imshow(out)
plt.show()

Best regards
St?fan


On 7 July 2016 at 00:37, Sid <sid.zidane at gmail.com> wrote:

> Hi
>
> I have two images of faces, the source image and the destination image.
> For each face, I have a set of x and y coordinates stored in two separate
> numpy arrays. These coordinates correspond to various facial features, as
> well as the four corners of the image, and points on half the length of the
> borders.
>
> I want to warp the features of the source image onto the destination, such
> that the corresponding control points in each image are aligned. I used the
> PiecewiseAffineTransform method, as in the example here
> <http://scikit-image.org/docs/dev/auto_examples/transform/plot_piecewise_affine.html>
> .
>
> My code is:
>
> src_pts = # numpy array of coordinates for the source image
> dst_pts = # numpy array of coordinates for the destination image
>
> image = # image of dimensions (550, 420, 3)
>
> tform = skimage.transform.PiecewiseAffineTransform()
> tform.estimate(src_pts, dst_pts)
>
> out = skimage.transform.warp(image, tform)
>
>
> I get an image that is warped but also cropped. To see if I could get the
> code to work (in case there were errors in my control points), I tried
> warping the image using the same control points for the source and
> destination.
>
> tform.estimate(src_pts, src_pts)
>
> The output is still cropped and is also warped slightly, as can be seen in
> the attached images. Logically, it doesn't make sense as there should not
> be any warping since I am using the same points. A big difference between
> the example and my implementation is that the example has uniformly spaced
> points, whereas I have 76 points which are much more sparse. Is this the
> right function to achieve what I want or should I be using something else?
>
>
> Thanks
>
> --
> Sid
>
>
>
>
>
>
> <https://lh3.googleusercontent.com/-noDhRC6CbkE/V34EvUCZgtI/AAAAAAAACFc/8-Q5D7-2EO0S_E0EVlFHxwTtusPd5wcDwCLcB/s1600/image.jpg>
>
> <https://lh3.googleusercontent.com/-7PTIVXGCCg8/V34E2M6yEUI/AAAAAAAACFg/z2zoGOPHTNss6n7hTaRRkiWToqlhwZtPgCLcB/s1600/out.jpg>
>
> <https://lh3.googleusercontent.com/-7PTIVXGCCg8/V34E2M6yEUI/AAAAAAAACFg/z2zoGOPHTNss6n7hTaRRkiWToqlhwZtPgCLcB/s1600/out.jpg>
>
> <https://lh3.googleusercontent.com/-7PTIVXGCCg8/V34E2M6yEUI/AAAAAAAACFg/z2zoGOPHTNss6n7hTaRRkiWToqlhwZtPgCLcB/s1600/out.jpg>
>
> --
> You received this message because you are subscribed to the Google Groups
> "scikit-image" group.
> To unsubscribe from this group and stop receiving emails from it, send an
> email to scikit-image+unsubscribe at googlegroups.com.
> To post to this group, send email to scikit-image at googlegroups.com.
> To view this discussion on the web, visit
> https://groups.google.com/d/msgid/scikit-image/e51ad552-af63-49e0-afbc-2d4b5597d5e4%40googlegroups.com
> <https://groups.google.com/d/msgid/scikit-image/e51ad552-af63-49e0-afbc-2d4b5597d5e4%40googlegroups.com?utm_medium=email&utm_source=footer>
> .
> For more options, visit https://groups.google.com/d/optout.
>
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From jni.soma at gmail.com  Mon Jul 11 15:09:16 2016
From: jni.soma at gmail.com (Juan Nunez-Iglesias)
Date: Mon, 11 Jul 2016 14:09:16 -0500
Subject: Dropping support for 2.7 in 2020
In-Reply-To: <dc739979-e63f-4ce0-849b-66f8a659c8fc@googlegroups.com>
References: <20160518064719.GA3031017@phare.normalesup.org>
 <20160518074002.GC3031017@phare.normalesup.org> <CABDkGQm_SE8fjU1ZiUg1kWne1eLTicjvtV14Bhp1x8fOupOQNA@mail.gmail.com>
 <CANpJW-URfDAKKzmbeLQz5=ksxjJ8QbFCU2Hdm4bedZ2zx1AAAw@mail.gmail.com>
 <CAA48SF_0zEveSw7H-WL2aObma+m7xaM1B=S5dv+9Aaj_DsMafQ@mail.gmail.com>
 <20160518175455.GA4080630@phare.normalesup.org> <b971d22e-7340-41b9-a41a-bc11fd50f896@googlegroups.com>
 <20160520105049.GA3583487@phare.normalesup.org> <c30471be-47e7-e526-4e73-854c3ea389df@cadair.com>
 <20160520141800.GD3583487@phare.normalesup.org> <CAB9hrOqEXPFQDKwPAyZ6LArGHq3paZ1nDgyHikNycr5KpOWqaA@mail.gmail.com>
 <CAPJVwBni1W9CfQ5fzj1512Ojsdy80u-YrTB=gbpL87fFF5sU7g@mail.gmail.com>
 <CAPJVwBkcaoKPj4YqrjGvss3iq2+w6YntdSuJwC1EyzLxdD+amA@mail.gmail.com>
 <CAPJVwB=jRQtHn9Xj0xwv3hxtDsjBvKYtJTB38ewBuZ84cNR4xw@mail.gmail.com>
 <etPan.5742793c.5c6c742c.141c@MacBook.local> <ba5c98fa-d413-4ba3-bcee-92031977df6c@googlegroups.com>
 <d8b02fc1-e65c-949b-d093-58cfdc5fecdc@sciunto.org> <CAOvn4qj1iseuWac7c47Guk_Tv8P7gUoL25srbm=7My+FDcdRJg@mail.gmail.com>
 <dc739979-e63f-4ce0-849b-66f8a659c8fc@googlegroups.com>
Message-ID: <CA+JHcKRwRfjgYfuouzf63J083JSU2SzMorEQ5F4LntieWCtwPA@mail.gmail.com>

For what it's worth, I strongly support the statement, and would prefer to
drop 2.7 support much earlier. This does *not* mean ignoring people
submitting 2.7 bug reports. It means that, like Python itself, *new*
versions would not support Py2.7. Bug fixes would be ported to e.g. the
0.13.x branch. This in no way interferes people with systems that "just
work" and who understandably don't want to risk breaking them.

Juan.

On Mon, Jul 4, 2016 at 7:12 PM, Josh Warner <silvertrumpet999 at gmail.com>
wrote:

> I think we should support 2.7 through end-of-life. 2020 sounds right.
>
> There are a tremendous amount of systems out there still running 2.7. I
> know more than one lab which standardized on 2.7 years ago, and while we're
> evaluating the move to 3.x it won't be without major pains. Small, niche
> packages which work perfectly under 2.7 break in 3.x, and they may or may
> not be maintained. It only takes a couple of these to render lab equipment
> with price tags in the 6 figures buggy, problematic, or useless in 3.x.
> This is problematic when you want a consistent Python experience across a
> lab or team, but certain pieces just won't work in 3.x.
>
> Sounding the trumpets is appropriate, as 2.7 won't be around forever.
> Quarterly or bi-annually it would be reasonable to send out a reminder.
> Three years sounds like a long time, but that's about right for this kind
> of transition.
>
> On Monday, July 4, 2016 at 4:55:34 AM UTC-5, Thomas Kluyver wrote:
>>
>> On 4 July 2016 at 10:47, Fran?ois Boulogne <fboulogne at sciunto.org> wrote:
>>
>>> Does it mean that all new version of those libraries will support
>>> python2.7 until 2020 or just that you can release a bugfix to already
>>> released version supporting 2.7?
>>>
>>> If it's the first option, it means we can't use python3 features before
>>> 2020 and we will be "in late" with these features.
>>>
>>
>> The statement doesn't commit projects to support 2.7 until any specific
>> date - scikit-bio already went Python 3 only with its release last month,
>> for instance. We're agreeing to end Python 2 support by 2020, but projects
>> are welcome to do it earlier than that.
>>
>> For IPython, we're planning for the 6.0 feature release (some time in
>> 2017) to be Python 3 only, while we'll make bugfix releases of the 5.x
>> series for longer than normal to support users on Python 2.
>>
> --
> You received this message because you are subscribed to the Google Groups
> "scikit-image" group.
> To unsubscribe from this group and stop receiving emails from it, send an
> email to scikit-image+unsubscribe at googlegroups.com.
> To post to this group, send email to scikit-image at googlegroups.com.
> To view this discussion on the web, visit
> https://groups.google.com/d/msgid/scikit-image/dc739979-e63f-4ce0-849b-66f8a659c8fc%40googlegroups.com
> <https://groups.google.com/d/msgid/scikit-image/dc739979-e63f-4ce0-849b-66f8a659c8fc%40googlegroups.com?utm_medium=email&utm_source=footer>
> .
>
> For more options, visit https://groups.google.com/d/optout.
>
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From stefanv at berkeley.edu  Wed Jul 13 02:25:38 2016
From: stefanv at berkeley.edu (=?UTF-8?Q?St=C3=A9fan_van_der_Walt?=)
Date: Tue, 12 Jul 2016 23:25:38 -0700
Subject: Licensing tutorial material
Message-ID: <CABDkGQmrcXpEExNu7vXQL-czpDpTeMqTMj2Y56iMNoWHkR8HEA@mail.gmail.com>

Hi, everyone

Currently, our tutorial repo has no license.  Everyone who has contributed,
is it OK if I license it under CC-BY 4.0?

Thanks!
St?fan
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From silvertrumpet999 at gmail.com  Wed Jul 13 19:10:03 2016
From: silvertrumpet999 at gmail.com (Josh Warner)
Date: Wed, 13 Jul 2016 16:10:03 -0700 (PDT)
Subject: Licensing tutorial material
In-Reply-To: <EFFF2B8F-FF1A-4CD9-BB30-E60B9D916B14@demuc.de>
References: <CABDkGQmrcXpEExNu7vXQL-czpDpTeMqTMj2Y56iMNoWHkR8HEA@mail.gmail.com> <CA+JHcKQ+jk3Fcv5A8QEBjW_Nf+r6d_yRAk-dy0JOfPDfez-CqA@mail.gmail.com>
 <EFFF2B8F-FF1A-4CD9-BB30-E60B9D916B14@demuc.de>
Message-ID: <9534f453-5d9b-4558-96d7-81be919bae2c@googlegroups.com>

Yes from me.

(The panorama images are/were already under that exact license 
<https://github.com/scikit-image/skimage-tutorials/blob/master/images/pano/LICENSE.txt>
).

On Wednesday, July 13, 2016 at 5:21:02 PM UTC-5, Johannes Sch?nberger wrote:
>
> If there is any content from me in the tutorial, feel free to use CC-BY 
> 4.0. 
>
> > On Jul 13, 2016, at 3:11 PM, Juan Nunez-Iglesias <jni.soma at gmail.com> 
> wrote: 
> > 
> > Yes from me. 
> > 
> > On Wed, Jul 13, 2016 at 1:25 AM, St?fan van der Walt <
> stefanv at berkeley.edu> wrote: 
> > Hi, everyone 
> > 
> > Currently, our tutorial repo has no license.  Everyone who has 
> contributed, is it OK if I license it under CC-BY 4.0? 
> > 
> > Thanks! 
> > St?fan 
> > 
> > -- 
> > You received this message because you are subscribed to the Google 
> Groups "scikit-image" group. 
> > To unsubscribe from this group and stop receiving emails from it, send 
> an email to scikit-image+unsubscribe at googlegroups.com. 
> > To post to this group, send email to scikit-image at googlegroups.com. 
> > To view this discussion on the web, visit 
> https://groups.google.com/d/msgid/scikit-image/CABDkGQmrcXpEExNu7vXQL-czpDpTeMqTMj2Y56iMNoWHkR8HEA%40mail.gmail.com. 
>
> > For more options, visit https://groups.google.com/d/optout. 
> > 
> > 
> > -- 
> > You received this message because you are subscribed to the Google 
> Groups "scikit-image" group. 
> > To unsubscribe from this group and stop receiving emails from it, send 
> an email to scikit-image+unsubscribe at googlegroups.com. 
> > To post to this group, send email to scikit-image at googlegroups.com. 
> > To view this discussion on the web, visit 
> https://groups.google.com/d/msgid/scikit-image/CA%2BJHcKQ%2Bjk3Fcv5A8QEBjW_Nf%2Br6d_yRAk-dy0JOfPDfez-CqA%40mail.gmail.com. 
>
> > For more options, visit https://groups.google.com/d/optout. 
>
>
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From jni.soma at gmail.com  Wed Jul 13 17:11:50 2016
From: jni.soma at gmail.com (Juan Nunez-Iglesias)
Date: Wed, 13 Jul 2016 16:11:50 -0500
Subject: Licensing tutorial material
In-Reply-To: <CABDkGQmrcXpEExNu7vXQL-czpDpTeMqTMj2Y56iMNoWHkR8HEA@mail.gmail.com>
References: <CABDkGQmrcXpEExNu7vXQL-czpDpTeMqTMj2Y56iMNoWHkR8HEA@mail.gmail.com>
Message-ID: <CA+JHcKQ+jk3Fcv5A8QEBjW_Nf+r6d_yRAk-dy0JOfPDfez-CqA@mail.gmail.com>

Yes from me.

On Wed, Jul 13, 2016 at 1:25 AM, St?fan van der Walt <stefanv at berkeley.edu>
wrote:

> Hi, everyone
>
> Currently, our tutorial repo has no license.  Everyone who has
> contributed, is it OK if I license it under CC-BY 4.0?
>
> Thanks!
> St?fan
>
> --
> You received this message because you are subscribed to the Google Groups
> "scikit-image" group.
> To unsubscribe from this group and stop receiving emails from it, send an
> email to scikit-image+unsubscribe at googlegroups.com.
> To post to this group, send email to scikit-image at googlegroups.com.
> To view this discussion on the web, visit
> https://groups.google.com/d/msgid/scikit-image/CABDkGQmrcXpEExNu7vXQL-czpDpTeMqTMj2Y56iMNoWHkR8HEA%40mail.gmail.com
> <https://groups.google.com/d/msgid/scikit-image/CABDkGQmrcXpEExNu7vXQL-czpDpTeMqTMj2Y56iMNoWHkR8HEA%40mail.gmail.com?utm_medium=email&utm_source=footer>
> .
> For more options, visit https://groups.google.com/d/optout.
>
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From jsch at demuc.de  Wed Jul 13 18:20:59 2016
From: jsch at demuc.de (=?utf-8?Q?Johannes_Sch=C3=B6nberger?=)
Date: Wed, 13 Jul 2016 16:20:59 -0600
Subject: Licensing tutorial material
In-Reply-To: <CA+JHcKQ+jk3Fcv5A8QEBjW_Nf+r6d_yRAk-dy0JOfPDfez-CqA@mail.gmail.com>
References: <CABDkGQmrcXpEExNu7vXQL-czpDpTeMqTMj2Y56iMNoWHkR8HEA@mail.gmail.com> <CA+JHcKQ+jk3Fcv5A8QEBjW_Nf+r6d_yRAk-dy0JOfPDfez-CqA@mail.gmail.com>
Message-ID: <EFFF2B8F-FF1A-4CD9-BB30-E60B9D916B14@demuc.de>

If there is any content from me in the tutorial, feel free to use CC-BY 4.0.

> On Jul 13, 2016, at 3:11 PM, Juan Nunez-Iglesias <jni.soma at gmail.com> wrote:
> 
> Yes from me.
> 
> On Wed, Jul 13, 2016 at 1:25 AM, St?fan van der Walt <stefanv at berkeley.edu> wrote:
> Hi, everyone
> 
> Currently, our tutorial repo has no license.  Everyone who has contributed, is it OK if I license it under CC-BY 4.0?
> 
> Thanks!
> St?fan
> 
> -- 
> You received this message because you are subscribed to the Google Groups "scikit-image" group.
> To unsubscribe from this group and stop receiving emails from it, send an email to scikit-image+unsubscribe at googlegroups.com.
> To post to this group, send email to scikit-image at googlegroups.com.
> To view this discussion on the web, visit https://groups.google.com/d/msgid/scikit-image/CABDkGQmrcXpEExNu7vXQL-czpDpTeMqTMj2Y56iMNoWHkR8HEA%40mail.gmail.com.
> For more options, visit https://groups.google.com/d/optout.
> 
> 
> -- 
> You received this message because you are subscribed to the Google Groups "scikit-image" group.
> To unsubscribe from this group and stop receiving emails from it, send an email to scikit-image+unsubscribe at googlegroups.com.
> To post to this group, send email to scikit-image at googlegroups.com.
> To view this discussion on the web, visit https://groups.google.com/d/msgid/scikit-image/CA%2BJHcKQ%2Bjk3Fcv5A8QEBjW_Nf%2Br6d_yRAk-dy0JOfPDfez-CqA%40mail.gmail.com.
> For more options, visit https://groups.google.com/d/optout.



From remi.cura at gmail.com  Thu Jul 21 09:26:34 2016
From: remi.cura at gmail.com (=?UTF-8?Q?R=C3=A9mi_Cura?=)
Date: Thu, 21 Jul 2016 06:26:34 -0700 (PDT)
Subject: Active contour : snake balloon?
Message-ID: <fb202508-b0af-4af6-a2fc-15d7db6317a1@googlegroups.com>

Hello everybody.
I'm a real fan of skimage,
and use it for numerous image processing stuf.

I'm stuck in a segmentation problem: I have an old Paris map, and I try to 
separate the streets and the buildings.
I have rough street axis.
This looks like a watershed problem, but sme of the building footprints are 
not closed, so the watershed starting in streets is bleeding in the 
buildings.

To avoid that, I was thinking of snake, starting conservatively in the 
streets, then make them balloon .

I can't find the parameters so the snake balloons.
Is this possible,
would you have any advice?

Cheers
R?mi-C

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From jni.soma at gmail.com  Sat Jul 23 03:13:49 2016
From: jni.soma at gmail.com (Juan Nunez-Iglesias)
Date: Sat, 23 Jul 2016 17:13:49 +1000
Subject: Active contour : snake balloon?
In-Reply-To: <fb202508-b0af-4af6-a2fc-15d7db6317a1@googlegroups.com>
References: <fb202508-b0af-4af6-a2fc-15d7db6317a1@googlegroups.com>
Message-ID: <CA+JHcKSjohw-dK9ou1BOO3988--=e_TZgpKutH=nhvR=2WApnQ@mail.gmail.com>

Hi R?mi!

This is a cool problem but I'm afraid I don't have a solution for you. I'm
not familiar with snakes so I can't help you with the parameters.

One thing I will say is that all methods will benefit from a ground truth
dataset, where you have manually painted the result you're looking for.
Once you have that you can really expand your parameter and method search
dramatically because you don't have to look at each answer. Do you have
such a dataset?

Good luck!

Juan.

On Thu, Jul 21, 2016 at 11:26 PM, R?mi Cura <remi.cura at gmail.com> wrote:

> Hello everybody.
> I'm a real fan of skimage,
> and use it for numerous image processing stuf.
>
> I'm stuck in a segmentation problem: I have an old Paris map, and I try to
> separate the streets and the buildings.
> I have rough street axis.
> This looks like a watershed problem, but sme of the building footprints
> are not closed, so the watershed starting in streets is bleeding in the
> buildings.
>
> To avoid that, I was thinking of snake, starting conservatively in the
> streets, then make them balloon .
>
> I can't find the parameters so the snake balloons.
> Is this possible,
> would you have any advice?
>
> Cheers
> R?mi-C
>
>
> <https://lh3.googleusercontent.com/-nYRJ7LGyDZs/V5DMlLC00QI/AAAAAAAABFg/kYaLf0XkaXMlzUIkaOh3zu-h94i1i-ShQCLcB/s1600/eextract_jacoubet.png>
> <https://lh3.googleusercontent.com/-nYRJ7LGyDZs/V5DMlLC00QI/AAAAAAAABFg/kYaLf0XkaXMlzUIkaOh3zu-h94i1i-ShQCLcB/s1600/eextract_jacoubet.png>
>
> --
> You received this message because you are subscribed to the Google Groups
> "scikit-image" group.
> To unsubscribe from this group and stop receiving emails from it, send an
> email to scikit-image+unsubscribe at googlegroups.com.
> To post to this group, send email to scikit-image at googlegroups.com.
> To view this discussion on the web, visit
> https://groups.google.com/d/msgid/scikit-image/fb202508-b0af-4af6-a2fc-15d7db6317a1%40googlegroups.com
> <https://groups.google.com/d/msgid/scikit-image/fb202508-b0af-4af6-a2fc-15d7db6317a1%40googlegroups.com?utm_medium=email&utm_source=footer>
> .
> For more options, visit https://groups.google.com/d/optout.
>
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From mob.irl at gmail.com  Sun Jul 24 09:59:04 2016
From: mob.irl at gmail.com (Michael O'Brien)
Date: Sun, 24 Jul 2016 06:59:04 -0700 (PDT)
Subject: HoG Descriptors and Bag of Words
Message-ID: <e1b83557-9418-475e-a5c3-33d7d4ff7a20@googlegroups.com>

Hi all,

I'm new to computer vision/machine learning and I was hoping I could ask 
the community for some advise. I've calculated HoG descriptors for frames 
in a video but I'm not sure how best to group/join/??? them so I can then 
run Kmeans clustering on them. I'm hoping to use the (Visual) Bag of Words 
method to classify using random forrests but I'm a novice when it comes to 
ndarrays and not sure of the correct terminology.

I know the HoG descriptors are flattened arrays but in order to cluster the 
frames/image descriptors I would need to group all the descriptors 
together. What is the best way to create a data structure suitable for 
kmeans when you have 100,000's of individual descriptors and do I need to 
pre-process the ndarrays ?

Michael
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From mob.irl at gmail.com  Sun Jul 24 11:13:30 2016
From: mob.irl at gmail.com (Michael O'Brien)
Date: Sun, 24 Jul 2016 08:13:30 -0700 (PDT)
Subject: HoG Descriptors and Bag of Words
In-Reply-To: <e1b83557-9418-475e-a5c3-33d7d4ff7a20@googlegroups.com>
References: <e1b83557-9418-475e-a5c3-33d7d4ff7a20@googlegroups.com>
Message-ID: <0481fceb-29d9-4ffe-975c-1a6a2b2193ee@googlegroups.com>

Some properties of the individual ndarray (aka HoG descriptor)
('NDarry Size', 251328)
('NDarray Number of Dimensions', 1)
('NDarray length of 1 array element in Bytes', 8)
('NDarray Total byes consumed by elements', 2010624)
('NDarray DataType', dtype('float64'))

On Sunday, 24 July 2016 14:59:04 UTC+1, Michael O'Brien wrote:
>
> Hi all,
>
> I'm new to computer vision/machine learning and I was hoping I could ask 
> the community for some advise. I've calculated HoG descriptors for frames 
> in a video but I'm not sure how best to group/join/??? them so I can then 
> run Kmeans clustering on them. I'm hoping to use the (Visual) Bag of Words 
> method to classify using random forrests but I'm a novice when it comes to 
> ndarrays and not sure of the correct terminology.
>
> I know the HoG descriptors are flattened arrays but in order to cluster 
> the frames/image descriptors I would need to group all the descriptors 
> together. What is the best way to create a data structure suitable for 
> kmeans when you have 100,000's of individual descriptors and do I need to 
> pre-process the ndarrays ?
>
> Michael
>
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From mob.irl at gmail.com  Tue Jul 26 15:06:41 2016
From: mob.irl at gmail.com (Michael O'Brien)
Date: Tue, 26 Jul 2016 12:06:41 -0700 (PDT)
Subject: HoG Descriptors and Bag of Words
In-Reply-To: <e1b83557-9418-475e-a5c3-33d7d4ff7a20@googlegroups.com>
References: <e1b83557-9418-475e-a5c3-33d7d4ff7a20@googlegroups.com>
Message-ID: <ff083d85-52fb-4cfe-a122-664abae572f2@googlegroups.com>

And is there a way to select the top N HoG Descriptors from the flattened 
array?

On Sunday, 24 July 2016 14:59:04 UTC+1, Michael O'Brien wrote:
>
> Hi all,
>
> I'm new to computer vision/machine learning and I was hoping I could ask 
> the community for some advise. I've calculated HoG descriptors for frames 
> in a video but I'm not sure how best to group/join/??? them so I can then 
> run Kmeans clustering on them. I'm hoping to use the (Visual) Bag of Words 
> method to classify using random forrests but I'm a novice when it comes to 
> ndarrays and not sure of the correct terminology.
>
> I know the HoG descriptors are flattened arrays but in order to cluster 
> the frames/image descriptors I would need to group all the descriptors 
> together. What is the best way to create a data structure suitable for 
> kmeans when you have 100,000's of individual descriptors and do I need to 
> pre-process the ndarrays ?
>
> Michael
>
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From winecoding at gmail.com  Wed Jul 27 22:59:58 2016
From: winecoding at gmail.com (wine lover)
Date: Wed, 27 Jul 2016 19:59:58 -0700 (PDT)
Subject: the underlying cause of getting the error message of  Reason:
 "broken data stream when reading image file"
Message-ID: <fe62dc05-e818-4352-a236-3fb10593bc1a@googlegroups.com>

Hi, 

I am trying to test the skimage by running the following python script

from skimage import io
data = io.imread("./test_data/tiger.jpeg")print(data.shape)


It gives the following error messages. What can be the reason? Thank you 
very much for the help.


bash-4.1$ python test.py

Traceback (most recent call last): File 
"/test/tfw/lib/python3.4/site-packages/scikit_image-0.12.3-py3.4-linux-x86_64.egg/skimage/io/_plugins/pil_plugin.py", 
line 53, in pil_to_ndarray im.getdata()[0] File 
"/test/tfw/lib/python3.4/site-packages/Pillow-3.2.0-py3.4-linux-x86_64.egg/PIL/Image.py", 
line 1151, in getdata File 
"/test/tfw/lib/python3.4/site-packages/Pillow-3.2.0-py3.4-linux-x86_64.egg/PIL/ImageFile.py", 
line 235, in load File 
"/test/tfw/lib/python3.4/site-packages/Pillow-3.2.0-py3.4-linux-x86_64.egg/PIL/ImageFile.py", 
line 59, in raise_ioerror OSError: broken data stream when reading image 
file

During handling of the above exception, another exception occurred:

Traceback (most recent call last): File "test.py", line 2, in data = 
io.imread("./test_data/tiger.jpeg") File 
"/test/tfw/lib/python3.4/site-packages/scikit_image-0.12.3-py3.4-linux-x86_64.egg/skimage/io/_io.py", 
line 61, in imread img = call_plugin('imread', fname, plugin=plugin, 
**plugin_args) File 
"/test/tfw/lib/python3.4/site-packages/scikit_image-0.12.3-py3.4-linux-x86_64.egg/skimage/io/manage_plugins.py", 
line 211, in call_plugin return func(*args, **kwargs) File 
"/test/tfw/lib/python3.4/site-packages/scikit_image-0.12.3-py3.4-linux-x86_64.egg/skimage/io/_plugins/pil_plugin.py", 
line 37, in imread return pil_to_ndarray(im, dtype=dtype, img_num=img_num) 
File 
"/test/tfw/lib/python3.4/site-packages/scikit_image-0.12.3-py3.4-linux-x86_64.egg/skimage/io/_plugins/pil_plugin.py", 
line 61, in pil_to_ndarray raise ValueError(error_message) ValueError: 
Could not load "" Reason: "broken data stream when reading image file" 
Please see documentation at:
http://pillow.readthedocs.org/en/latest/installation.html#external-libraries
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From stefanv at berkeley.edu  Thu Jul 28 04:27:01 2016
From: stefanv at berkeley.edu (=?UTF-8?Q?St=C3=A9fan_van_der_Walt?=)
Date: Thu, 28 Jul 2016 01:27:01 -0700
Subject: the underlying cause of getting the error message of Reason:
 "broken data stream when reading image file"
In-Reply-To: <fe62dc05-e818-4352-a236-3fb10593bc1a@googlegroups.com>
References: <fe62dc05-e818-4352-a236-3fb10593bc1a@googlegroups.com>
Message-ID: <CABDkGQnoZ=VTUFum_E4HfkCR1r5CZKQ9jYKX0eu6j1k-7XA-Hw@mail.gmail.com>

Hi,

It sounds like the image might be corrupt. Can you load it using other
image viewers? Also, did you try different plugins?

St?fan

On Jul 27, 2016 20:00, "wine lover" <winecoding at gmail.com> wrote:

> Hi,
>
> I am trying to test the skimage by running the following python script
>
> from skimage import io
> data = io.imread("./test_data/tiger.jpeg")print(data.shape)
>
>
> It gives the following error messages. What can be the reason? Thank you
> very much for the help.
>
>
> bash-4.1$ python test.py
>
> Traceback (most recent call last): File
> "/test/tfw/lib/python3.4/site-packages/scikit_image-0.12.3-py3.4-linux-x86_64.egg/skimage/io/_plugins/pil_plugin.py",
> line 53, in pil_to_ndarray im.getdata()[0] File
> "/test/tfw/lib/python3.4/site-packages/Pillow-3.2.0-py3.4-linux-x86_64.egg/PIL/Image.py",
> line 1151, in getdata File
> "/test/tfw/lib/python3.4/site-packages/Pillow-3.2.0-py3.4-linux-x86_64.egg/PIL/ImageFile.py",
> line 235, in load File
> "/test/tfw/lib/python3.4/site-packages/Pillow-3.2.0-py3.4-linux-x86_64.egg/PIL/ImageFile.py",
> line 59, in raise_ioerror OSError: broken data stream when reading image
> file
>
> During handling of the above exception, another exception occurred:
>
> Traceback (most recent call last): File "test.py", line 2, in data =
> io.imread("./test_data/tiger.jpeg") File
> "/test/tfw/lib/python3.4/site-packages/scikit_image-0.12.3-py3.4-linux-x86_64.egg/skimage/io/_io.py",
> line 61, in imread img = call_plugin('imread', fname, plugin=plugin,
> **plugin_args) File
> "/test/tfw/lib/python3.4/site-packages/scikit_image-0.12.3-py3.4-linux-x86_64.egg/skimage/io/manage_plugins.py",
> line 211, in call_plugin return func(*args, **kwargs) File
> "/test/tfw/lib/python3.4/site-packages/scikit_image-0.12.3-py3.4-linux-x86_64.egg/skimage/io/_plugins/pil_plugin.py",
> line 37, in imread return pil_to_ndarray(im, dtype=dtype, img_num=img_num)
> File
> "/test/tfw/lib/python3.4/site-packages/scikit_image-0.12.3-py3.4-linux-x86_64.egg/skimage/io/_plugins/pil_plugin.py",
> line 61, in pil_to_ndarray raise ValueError(error_message) ValueError:
> Could not load "" Reason: "broken data stream when reading image file"
> Please see documentation at:
> http://pillow.readthedocs.org/en/latest/installation.html#external-libraries
>
> --
> You received this message because you are subscribed to the Google Groups
> "scikit-image" group.
> To unsubscribe from this group and stop receiving emails from it, send an
> email to scikit-image+unsubscribe at googlegroups.com.
> To post to this group, send email to scikit-image at googlegroups.com.
> To view this discussion on the web, visit
> https://groups.google.com/d/msgid/scikit-image/fe62dc05-e818-4352-a236-3fb10593bc1a%40googlegroups.com
> <https://groups.google.com/d/msgid/scikit-image/fe62dc05-e818-4352-a236-3fb10593bc1a%40googlegroups.com?utm_medium=email&utm_source=footer>
> .
> For more options, visit https://groups.google.com/d/optout.
>
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From winecoding at gmail.com  Thu Jul 28 10:08:40 2016
From: winecoding at gmail.com (wine lover)
Date: Thu, 28 Jul 2016 07:08:40 -0700 (PDT)
Subject: the underlying cause of getting the error message of Reason:
 "broken data stream when reading image file"
In-Reply-To: <CABDkGQnoZ=VTUFum_E4HfkCR1r5CZKQ9jYKX0eu6j1k-7XA-Hw@mail.gmail.com>
References: <fe62dc05-e818-4352-a236-3fb10593bc1a@googlegroups.com>
 <CABDkGQnoZ=VTUFum_E4HfkCR1r5CZKQ9jYKX0eu6j1k-7XA-Hw@mail.gmail.com>
Message-ID: <e6e27555-9009-4ce3-8896-0d8a225693a3@googlegroups.com>

Hi St?fan,

The image itself could me opened and viewed using other viewers. Would you 
like to let me know what are the plugins you were referring to? I am new to 
skimage.  

Thanks.

On Thursday, July 28, 2016 at 3:27:04 AM UTC-5, stefanv wrote:
>
> Hi, 
>
> It sounds like the image might be corrupt. Can you load it using other 
> image viewers? Also, did you try different plugins? 
>
> St?fan 
>
> On Jul 27, 2016 20:00, "wine lover" <winec... at gmail.com <javascript:>> 
> wrote:
>
>> Hi, 
>>
>> I am trying to test the skimage by running the following python script
>>
>> from skimage import io
>> data = io.imread("./test_data/tiger.jpeg")print(data.shape)
>>
>>
>> It gives the following error messages. What can be the reason? Thank you 
>> very much for the help.
>>
>>
>> bash-4.1$ python test.py
>>
>> Traceback (most recent call last): File 
>> "/test/tfw/lib/python3.4/site-packages/scikit_image-0.12.3-py3.4-linux-x86_64.egg/skimage/io/_plugins/pil_plugin.py", 
>> line 53, in pil_to_ndarray im.getdata()[0] File 
>> "/test/tfw/lib/python3.4/site-packages/Pillow-3.2.0-py3.4-linux-x86_64.egg/PIL/Image.py", 
>> line 1151, in getdata File 
>> "/test/tfw/lib/python3.4/site-packages/Pillow-3.2.0-py3.4-linux-x86_64.egg/PIL/ImageFile.py", 
>> line 235, in load File 
>> "/test/tfw/lib/python3.4/site-packages/Pillow-3.2.0-py3.4-linux-x86_64.egg/PIL/ImageFile.py", 
>> line 59, in raise_ioerror OSError: broken data stream when reading image 
>> file
>>
>> During handling of the above exception, another exception occurred:
>>
>> Traceback (most recent call last): File "test.py", line 2, in data = 
>> io.imread("./test_data/tiger.jpeg") File 
>> "/test/tfw/lib/python3.4/site-packages/scikit_image-0.12.3-py3.4-linux-x86_64.egg/skimage/io/_io.py", 
>> line 61, in imread img = call_plugin('imread', fname, plugin=plugin, 
>> **plugin_args) File 
>> "/test/tfw/lib/python3.4/site-packages/scikit_image-0.12.3-py3.4-linux-x86_64.egg/skimage/io/manage_plugins.py", 
>> line 211, in call_plugin return func(*args, **kwargs) File 
>> "/test/tfw/lib/python3.4/site-packages/scikit_image-0.12.3-py3.4-linux-x86_64.egg/skimage/io/_plugins/pil_plugin.py", 
>> line 37, in imread return pil_to_ndarray(im, dtype=dtype, img_num=img_num) 
>> File 
>> "/test/tfw/lib/python3.4/site-packages/scikit_image-0.12.3-py3.4-linux-x86_64.egg/skimage/io/_plugins/pil_plugin.py", 
>> line 61, in pil_to_ndarray raise ValueError(error_message) ValueError: 
>> Could not load "" Reason: "broken data stream when reading image file" 
>> Please see documentation at:
>> http://pillow.readthedocs.org/en/latest/installation.html#external-libraries
>>
>> -- 
>> You received this message because you are subscribed to the Google Groups 
>> "scikit-image" group.
>> To unsubscribe from this group and stop receiving emails from it, send an 
>> email to scikit-image... at googlegroups.com <javascript:>.
>> To post to this group, send email to scikit... at googlegroups.com 
>> <javascript:>.
>> To view this discussion on the web, visit 
>> https://groups.google.com/d/msgid/scikit-image/fe62dc05-e818-4352-a236-3fb10593bc1a%40googlegroups.com 
>> <https://groups.google.com/d/msgid/scikit-image/fe62dc05-e818-4352-a236-3fb10593bc1a%40googlegroups.com?utm_medium=email&utm_source=footer>
>> .
>> For more options, visit https://groups.google.com/d/optout.
>>
>
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From parul1sethi at gmail.com  Thu Jul 28 02:03:46 2016
From: parul1sethi at gmail.com (Parul Sethi)
Date: Thu, 28 Jul 2016 11:33:46 +0530
Subject: Implementing Kadir and Brady's saliency detector in skimage?
In-Reply-To: <CABDkGQ=+cAy=Lzc5MOR-CzummOuWiyDWrvoN_mXNX+NBh868Ew@mail.gmail.com>
References: <7132cb6e-e66f-45be-a71a-327a2e5ff7d9@googlegroups.com>
	<CABDkGQ=+cAy=Lzc5MOR-CzummOuWiyDWrvoN_mXNX+NBh868Ew@mail.gmail.com>
Message-ID: <CAFgV4O4USEwFTnoc8aANrUi=QuGcMqMJ=LzudMcv-1oh3KJgyQ@mail.gmail.com>

So sorry for the long pause.

Thankyou for the response!
I'll soon be done with it.
On 11 Jul 2016 4:11 am, "St?fan van der Walt" <stefanv at berkeley.edu> wrote:

> Hi Parul
>
> We are happy to evaluate any PR for inclusion. Saliency detection is
> within scope.
>
> Best regards
> St?fan
> On Jul 6, 2016 05:54, "parul sethi" <parul1sethi at gmail.com> wrote:
>
>> Hi,
>>
>> I required to use Kadir and Brady's saliency detector for feature
>> extraction process, and didn't found it's python implementation. So, i was
>> in process of implementing the same.
>> Will it be of interest to skimage too? I can use some advice from skimage
>> maintainers to build it with specific suitability for skimage module.
>>
>> Thank you.
>>
>> --
>> You received this message because you are subscribed to the Google Groups
>> "scikit-image" group.
>> To unsubscribe from this group and stop receiving emails from it, send an
>> email to scikit-image+unsubscribe at googlegroups.com.
>> To post to this group, send email to scikit-image at googlegroups.com.
>> To view this discussion on the web, visit
>> https://groups.google.com/d/msgid/scikit-image/7132cb6e-e66f-45be-a71a-327a2e5ff7d9%40googlegroups.com
>> <https://groups.google.com/d/msgid/scikit-image/7132cb6e-e66f-45be-a71a-327a2e5ff7d9%40googlegroups.com?utm_medium=email&utm_source=footer>
>> .
>> For more options, visit https://groups.google.com/d/optout.
>>
> --
> You received this message because you are subscribed to a topic in the
> Google Groups "scikit-image" group.
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> https://groups.google.com/d/topic/scikit-image/FzZ1VkJt-Ks/unsubscribe.
> To unsubscribe from this group and all its topics, send an email to
> scikit-image+unsubscribe at googlegroups.com.
> To post to this group, send email to scikit-image at googlegroups.com.
> To view this discussion on the web, visit
> https://groups.google.com/d/msgid/scikit-image/CABDkGQ%3D%2BcAy%3DLzc5MOR-CzummOuWiyDWrvoN_mXNX%2BNBh868Ew%40mail.gmail.com
> <https://groups.google.com/d/msgid/scikit-image/CABDkGQ%3D%2BcAy%3DLzc5MOR-CzummOuWiyDWrvoN_mXNX%2BNBh868Ew%40mail.gmail.com?utm_medium=email&utm_source=footer>
> .
> For more options, visit https://groups.google.com/d/optout.
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