you need add only to filters and not applications.

see example,
https://github.com/jmichel-otb/GKSVM/blob/master/include/otbChangeProfileKernelFunctor.h#L44



On Thu, Aug 13, 2015 at 10:38 AM, Rémi Cresson <[email protected]> wrote:

> Thank you Rashad,
>
> I am trying to use otb baseline in the (fixed) new test.
> About doxygen, I will look into existing otb code to mimic. Do you mean I
> must add the \ingroup to each filters, app, or both?
>
>
>
> 2015-08-13 9:59 GMT+02:00 Rashad M <[email protected]>:
>
>>
>>
>> On Wed, Aug 12, 2015 at 7:58 PM, Rémi Cresson <[email protected]>
>> wrote:
>>
>>> Hello Rashad,
>>>
>>> Thank you for testing it.
>>> I made the changes you suggest, except I put sources in include folder
>>> (instead of src).
>>>
>>
>> yes that is correct. it was my bad mentioning src if you dont have .cxx
>> in library
>>
>>
>>> I had to read the dashboard output, because it compiled fine on my
>>> system without any error...
>>>
>>
>> I have the new build without errors. But however tests are failing. But
>> that can be fixed easily. Have a look at -
>> http://dash.orfeo-toolbox.org/testDetails.php?test=32229576&build=195008
>>
>> For doxygen, it is required to mention the ingroup in the header as
>> doxygen comments.
>>
>>
>> Dasboard submission:
>> http://dash.orfeo-toolbox.org/testDetails.php?test=32229575&build=195008
>>
>>
>>
>> you have test images which is now in test folder. But usually OTB keeps
>> all baseline and example data in OTB-Data repository.
>>  So if you could run the test on some images already existing in
>> OTB-Data, I think that would be nice. Also I am not sure about adding new
>> baseline for remote modules which are not official yet.
>>
>> OTB-Data -https://git.orfeo-toolbox.org/otb-data.git
>>
>>>
>>> Waiting for your feedback !
>>>
>>> Rémi
>>>
>>>
>>> 2015-08-12 17:15 GMT+02:00 Rashad M <[email protected]>:
>>>
>>>> Hi,
>>>>
>>>> On Mon, Aug 10, 2015 at 6:56 PM, remicres <[email protected]> wrote:
>>>>
>>>>> Hi,
>>>>>
>>>>> I've just commited a remote module on GitHub wich deals with image
>>>>> mosaics generation (https://github.com/remicres/otb-mosaic.git).
>>>>> Provided are mosaicing filters and one application.
>>>>> All filters support streaming. The application performs the mosaic
>>>>> generation of multiple images, whith some color/radiometric harmonization
>>>>> features, and feathering features (Details are provided above*)
>>>>>
>>>>> I hope that you will try and enjoy it. Keep me updated about issues !
>>>>> Feel free to ask questions, if any.
>>>>>
>>>>
>>>> I tried to setup nightly build for your remote module but didnt work.
>>>>
>>>> here is the dasboard output:
>>>> http://dash.orfeo-toolbox.org/buildSummary.php?buildid=194873
>>>>
>>>> From first look,
>>>>
>>>> you have all code in app folder include .h and .hxx.
>>>>
>>>> but otb remote module needs:
>>>>
>>>> all library code in src/*.h *.txx (  .txx is because all other modules
>>>> use it that way. you need to confirm with someone else in the list)
>>>>
>>>> all application code in app/
>>>>
>>>> Also please add a file like the below in your git repo.
>>>>
>>>> https://github.com/jmichel-otb/GKSVM/blob/master/GKSVM.remote.cmake
>>>>
>>>>
>>>>>
>>>>> Cheers,
>>>>>
>>>>> Rémi
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>> * The application:
>>>>>
>>>>>    - From a given input images list, a mosaic is produced. The
>>>>>    pipeline supports streaming, so there is no restriction on images 
>>>>> number or
>>>>>    size.
>>>>>    - It implements a color harmonization method explained on our
>>>>>    paper: Cresson R., Saint-Geours N., "Natural Color Satellite Image
>>>>>    Mosaicking Using Quadratic Programming in Decorrelated Color Space"IEEE
>>>>>    Journal of Selected Topics in Applied Earth Observations and Remote 
>>>>> Sensing
>>>>>    (Volume:PP ,  Issue: 99) July 2015
>>>>>    
>>>>> <http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7154397&filter%3DAND%28p_IS_Number%3A4609444%29>.
>>>>>    In brief, it performs the global colorimetric harmonization of 
>>>>> overlapping
>>>>>    images in natural colors (i.e. input images must have 3 bands, RGB). 
>>>>> You
>>>>>    can set the application parameter "-harmo.method" to "rgb" to use this
>>>>>    feature. However, the global radiometric harmonization can also be
>>>>>    performed on each band individually by setting the application 
>>>>> parameter
>>>>>    "-harmo.method" to "band" (it can be useful if the color space is not 
>>>>> truly
>>>>>    RGB like raw Spot6/7, or if you want to perform a kind of radiometric
>>>>>    equalized mosaic). Various statistical cost functions can be used (e.g.
>>>>>    rmse) to find a correction model (which is linear with zero-y 
>>>>> intercept:
>>>>>    input images are scaled). We have developed a richer harmonization 
>>>>> solver,
>>>>>    wich can deal with general linear correction models (i.e. scale, and 
>>>>> shift
>>>>>    scale of images) but the maths tools brought by VNL were not 
>>>>> sufficient,
>>>>>    and we had to make use of external libraries to make it work (OOQP,
>>>>>    QuadProg++). That is why we brought only simple zero-y intercept (i.e.
>>>>>    scale) correction model in this current module. In future release, I 
>>>>> think
>>>>>    we should propose OOQP based (or QuadProg++ based) solver because it 
>>>>> is far
>>>>>    more robust than VNL routines (Sometimes VNL's routines throw 
>>>>> unexpected
>>>>>    errors, obviously coming from numerical instabilities). Waiting what 
>>>>> will
>>>>>    be the OTB framework for third parties...
>>>>>    - As the harmonization method rely of local image statistics,
>>>>>    those must be exempt from perturbations (e.g. clouds, ice, brutal 
>>>>> landcover
>>>>>    change between two images, ...). That's why it is possible to input 
>>>>> some
>>>>>    vector data to mask valid areas (need one vector data for one input 
>>>>> image,
>>>>>    in the same order as appearing in input).
>>>>>    - It is possible to input some vector data for cutline (need one
>>>>>    vector data for one input image, in the same order as appearing in 
>>>>> input).
>>>>>    - Another cool feature is the feathering/blending of input images,
>>>>>    which allows to produce seamless mosaics. The current limitation is 
>>>>> that we
>>>>>    use a distance map image for each input, computed by
>>>>>    itk::DanielssonDistanceMapImageFilter wich is not a streamable filter.
>>>>>    That's why the current process first generates all distance map images 
>>>>> in
>>>>>    temporary .tif files, from resampled input images (the decimation 
>>>>> factor
>>>>>    can be set using the "-alphamasks.spacing"). Then, the filters use 
>>>>> these
>>>>>    previously computed distance map images to perform the 
>>>>> feathering/blending
>>>>>    of the input images. At the moment, 2 feathering methods are 
>>>>> implemented
>>>>>    (slim: blends the last image over earlier ones in areas of overlap, on 
>>>>> a
>>>>>    given transition distance / large: blends all images on largests 
>>>>> possible
>>>>>    overlapping areas)
>>>>>
>>>>> We tested the application successfully on big images collections:
>>>>> RapidEye, France coverage from 2011 (~130 images), Spot6/7, France 
>>>>> coverage
>>>>> from 2014 (~190 images), both with a reasonable processing time. 
>>>>> Processing
>>>>> time is o(n) for both mosaic generation and statistics computation
>>>>> (n=number of input images pixels). Harmonization problem is solved in o(m)
>>>>> with m=number of input images (about some milliseconds for a hundred of
>>>>> input images).
>>>>>
>>>>> The filters:
>>>>>
>>>>>    - otbStreamingMosaicFilterBase: is the base class of all mosaicing
>>>>>    filters
>>>>>    - otbStreamingSimpleMosaicFilter: inherits
>>>>>    otbStreamingMosaicFilterBase, performs simple mosaics with a simple
>>>>>    compositing technique which copies the last image over earlier ones in
>>>>>    areas of overlap
>>>>>    - otbStreamingStatisticsMosaicFilter: inherits
>>>>>    otbStreamingMosaicFilterBase, computes statistics of the input images 
>>>>> in
>>>>>    each overlapping areas (e.g. mean of image i in overlap j, for all i 
>>>>> and j)
>>>>>    - otbStreamingMosaicFilterWithBlendingBase: inherits
>>>>>    otbStreamingMosaicFilterBase, is the base class for all mosaicing 
>>>>> filters
>>>>>    which use a distance map image (feathering filters...)
>>>>>    - otbStreamingFeatherMosaicFilter: inherits
>>>>>    otbStreamingMosaicFilterWithBlendingBase, generates a mosaic with 
>>>>> blends
>>>>>    the last image over earlier ones in areas of overlap, on a given 
>>>>> transition
>>>>>    distance
>>>>>    - otbStreamingLargeFeatherMosaicFilter: inherits
>>>>>    otbStreamingMosaicFilterWithBlendingBase, blends all images on largests
>>>>>    possible overlapping areas
>>>>>    - otbStreamingMultibandFeatherMosaicFilter: inherits
>>>>>    otbStreamingFeatherMosaicFilter, experimental. Multiband blending of 
>>>>> input
>>>>>    image using the laplacian (see Burt & Adelson "A multiresolution spline
>>>>>    with application to image mosaics"). Could use GPU support for gaussian
>>>>>    filtering. One need to fix some issues related to side effects (around 
>>>>> no
>>>>>    data pixels of images).
>>>>>    - otbSummingFilter: utility filter which sums input images.
>>>>>    - otbQuadraticallyConstrainedSimpleSolver: is the solver used for
>>>>>    images color/radiometric harmonization. Works only with zero-y 
>>>>> intercept
>>>>>    correction models (as explained before).
>>>>>
>>>>> We try to make sense of this multilevel inheritance, but it might be
>>>>> perfectible !
>>>>> A possible optimization might be to process every threadedRegion by
>>>>> subdividing it in multiple smaller regions, in order to loop only on
>>>>> relevant images: an actual drawback is where the threadedRegion is large,
>>>>> a lot of input images have to be checked even if they are out of the
>>>>> region. Another one might be to pre-compute areas where interpolators can
>>>>> work, instead of checking every processed pixel if the current point is
>>>>> inside the buffer.
>>>>> On excellent upgrade would be to strengthen the architecture, and
>>>>> implements those optimizations in a general way (currently, we have to do
>>>>> it for each filter... that is why I think this could be better built)
>>>>>
>>>>> --
>>>>> --
>>>>> Check the OTB FAQ at
>>>>> http://www.orfeo-toolbox.org/FAQ.html
>>>>>
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>>>>>
>>>>
>>>>
>>>>
>>>> --
>>>> Regards,
>>>>    Rashad
>>>>
>>>> --
>>>> --
>>>> Check the OTB FAQ at
>>>> http://www.orfeo-toolbox.org/FAQ.html
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>>>
>>> --
>>> --
>>> Check the OTB FAQ at
>>> http://www.orfeo-toolbox.org/FAQ.html
>>>
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>>
>>
>>
>> --
>> Regards,
>>    Rashad
>>
>> --
>> --
>> Check the OTB FAQ at
>> http://www.orfeo-toolbox.org/FAQ.html
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>
> --
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-- 
Regards,
   Rashad

-- 
-- 
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