Sorry for my slow reply and I think your question has about the image3
has been answered. If not, ask away. But I wanted to note that after
image3 is installed you should find labs: Image Addon 3, Image Addon 3
Html, Image Addon 3 Movie give a fairly quick introduction to the addon.
Also the image3 addon help is in the subdirectory of the place it was
installed. You can poke around to find it or take a look at
http://ww2.lafayette.edu/~reiterc/j/image3help/index.html
but the help is more detailed and slower to the punch than the labs.
Table of contents for fvj3 is at
http://ww2.lafayette.edu/~reiterc/j/fvj3/index.html
Best, Cliff
Jonathan Lettvin wrote:
> NB. This much is working
> load 'media/image3'
> load 'media/image3/view_m'
> filename1=:'filename.png'
> image1=:read_image filename1
> {image1
> view_image image1
>
>
> Very exciting, but very hard won. Difficulty is anticipated, but help would
> be appreciated.
> First, please recommend the book that accelerates newbies best.
> I have the book APL\360 and actually used the APL system back in the early
> 70s.
>
> It is not clear that 'image3' is cross platform like 'platimg'.
> If switching to platimg is preferable, please show conversion from RGB plane
> to R G B planes.
>
> Then, please help with the following.
> Experiments and extensive reading of documentation has been unfruitful.
> I expect that the learning curve to do this anaided will consume a month or
> two.
> In the "teach a man to fish" spirit give chapter and verse instead of actual
> code.
> I am not a mathematician, but hard work is okay.
> I am also okay with actual code.
>
> Here are the actions I would like to take:
> 1) convert from 0-255 valued triples to 0-1 valued color planes
> 2) take per pixel normalized weighted mean of color planes ((Cr * R) + (Cg *
> G) + (Cb * B)) / (Cr + Cg + Cb)
> 3) local normalized weighted mean (North + South + East + West + (Cc *
> Center)) / ( 4 + Cc )
> 4) distribute pixel from plane into deep bricks with brick face dimensions
> same as an image color plane
>
> Cn is a scalar to be applied to the N plane.
>
> Operation 2 in C++ uses valarray as follows:
> valarray<double> mean( ( Cr * Rsource + Cg * Gsource + Cb * Bsource ) / (Cr
> + Cg + Cb) );
>
> Operation 3 in C++ uses valarray and mask_array to mask out edges and
> calculate as follows:
> target = 0; /// zero the target valarray
> target[ North ] += source[ South ];
> target[ South ] += source[ North ];
> target[ East ] += source[ West ];
> target[ West ] += source[ East ];
> target /= NeighborCount;
> target += Cc * source;
> target /= ( 1.0 + Cc );
>
> Operation 4 (like convolution):
> Construct an index_array specifying relative offsets of a sub-brick within a
> brick.
> Construct a same-sized as index_array valarray containing coefficients.
> Add raveled pixel offset to index_array.
> Add product of coefficient array times pixel value to brick through offset
> index_array.
> I wouldn't mind being able to use negative offseting.
> The brick will be sized to contain the sub-bricks around the image edge.
> This way "convolution" will never be out of bounds.
>
> Apologies if the character of this email is outside etiquette.
>
> Jonathan D. Lettvin
> ----------------------------------------------------------------------
> For information about J forums see http://www.jsoftware.com/forums.htm
>
--
Clifford A. Reiter
Mathematics Department, Lafayette College
Easton, PA 18042 USA, 610-330-5277
http://www.lafayette.edu/~reiterc
----------------------------------------------------------------------
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