[SciPy-user] hough transform again

Brent Pedersen bpederse at gmail.com
Thu Aug 17 15:56:55 CDT 2006


right,  extending the example at the bottom of the houghtf.py,
i do:

    import scipy as S
    largevals = S.where(out + delta >  max(out.flatten()));
    largevals = N.array(zip(ss[0],ss[1]))

which gives an array of r,thetas that are within delta of the maximum. now,
to find img coordinates that match those values...


On 8/17/06, stephen emslie <stephenemslie at gmail.com> wrote:
>
> I'm busy doing just that at the moment (in fact literally right now), and
> I'll be happy to post any results here.
>
> My understanding is that you'll need to search the output of the hough
> transform for cells with a high count as those will be the most likely to
> correspond to lines in the main image. The output is a matrix relating
> combinations of rho and theta to the number of feature points that that line
> passes through - so the combinations of rho and theta with that go through
> the most feature points will be the strongest lines.
>
> Or something like that - I'm also really new to this stuff so I'd be happy
> to be corrected by someone that knows more.
>
> The second to last part of this document is a good read on the hough
> transform: http://homepages.inf.ed.ac.uk/rbf/BOOKS/VERNON/Chap006.pdf
>
> Stephen
>
>
> On 8/17/06, Brent Pedersen <bpederse at gmail.com > wrote:
>
> > hi, i found this in recent archives and the script is useful.
> http://projects.scipy.org/pipermail/scipy-user/2006-August/008841.html
>
> has anyone written the code to go from the hough transform back to the
> image with the lines/edges enhanced or with non-lines removed? it's bending
> my mind a bit so if someone's already done it, i'd be glad of it--or any
> pointers.
> thanks.
> -brent
> [please include my email in the reply, i've subscribed to scipy-users, but
> not sure if it went through yet]
>
>
>
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