Hello Rashad,

Thank you for testing it.
I made the changes you suggest, except I put sources in include folder
(instead of src).
I had to read the dashboard output, because it compiled fine on my system
without any error...

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)
>>
>> --
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>
>
>
> --
> Regards,
>    Rashad
>
> --
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