| Re: faster convol on local subsets? [message #78631 is a reply to message #78627] |
Sun, 04 December 2011 19:02   |
Jeremy Bailin
Messages: 618
Registered: April 2008
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Senior Member |
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On 12/4/11 8:19 PM, ben.bighair wrote:
> On Dec 4, 7:37 pm, Andre<note....@gmail.com> wrote:
>> Hello experts,
>>
>> Maybe somebody has an easy solution for this?
>> I have a 2D array (img) and want the filter response from kernels that vary according to the image position. In a second array (loc, same dimensions as img) I have the information which kernel should be used at each pixel. My current approach is to first convolve the full image with the j-th kernel and take the response only at the positions with the current j indexed in the loc array:
>>
>> for j=0, n do begin
>> kernel=kernel_store[*,*,j]
>> response_temp = convol(img, kernel, /edge_zero, /NAN)
>> index=where(loc eq j)
>> if (index[0] gt -1)then response[index]=response_temp[index]
>> endfor
>>
>> I works fine, but it is relatively slow and I wonder if there is a smarter (faster) to apply only the convolutions that are really needed?
>>
>> Thanks in advance for any help!
>
> Hi,
>
> Since the value of loc doesn't change, you might try precomputing the
> values of index in loc for each step, j. That would save a two full
> scans of you loc array at each iteration (one for loc eq j and one for
> WHERE). Instead, use HISTOGRAM with its REVERSE_INDICES to get them
> sorted and tagged. Then for each iteration step use David Fanning's
> REVERSEINDICES to grab the correct indices in loc.
>
> So, it might look like...
>
> locHist = HISTOGRAM(loc, min = 0, REVERSE_INDICES = ri)
>
> for j=0, n do begin
> kernel=kernel_store[*,*,j]
> response_temp = convol(img, kernel, /edge_zero, /NAN)
> index = REVERSEINDICES(ri, j, COUNT =
> count)
> if (count GT 0)then response[index]=response_temp[index]
> endfor
>
> Cheers,
> Ben
>
>
>
> http://www.idlcoyote.com/programs/reverseindices.pro
>
You should probably also move the check on whether any points use that
kernel to before you bother doing the convolution... so I'd modify that to:
lochist = histogram(loc, min=0, reverse_indices=ri)
for j=0,n-1 do if lochist[j] gt 0 then begin
kernel = kernel_store[*,*,j]
response_temp = convol(img, kernel, /edge_zero, /nan)
index = ri[ri[j]:ri[j+1]-1]
response[index] = response_temp[index]
endif
-Jeremy.
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