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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>>
>> --
>> --
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>
>
>
> --
> Regards,
>    Rashad
>
> --
> --
> Check the OTB FAQ at
> http://www.orfeo-toolbox.org/FAQ.html
>
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