right,&nbsp; extending the example at the bottom of the houghtf.py,<br>i do:<br><br>&nbsp;&nbsp;&nbsp; import scipy as S<br>&nbsp;&nbsp;&nbsp; largevals = S.where(out + delta &gt;&nbsp; max(out.flatten()));<br>&nbsp;&nbsp;&nbsp; largevals = N.array(zip(ss[0],ss[1]))<br><br>which gives an array of r,thetas that are within delta of the maximum. now, to find img coordinates that match those values...
<br><br><br><div><span class="gmail_quote">On 8/17/06, <b class="gmail_sendername">stephen emslie</b> &lt;<a href="mailto:stephenemslie@gmail.com">stephenemslie@gmail.com</a>&gt; wrote:</span><blockquote class="gmail_quote" style="border-left: 1px solid rgb(204, 204, 204); margin: 0pt 0pt 0pt 0.8ex; padding-left: 1ex;">
<div>I'm busy doing just that at the moment (in fact literally right now), and I'll be happy to post any results here.<br><br>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.
<br><br>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.<br><br>The second to last part of this document is a good read on the hough transform: <a href="http://homepages.inf.ed.ac.uk/rbf/BOOKS/VERNON/Chap006.pdf" target="_blank" onclick="return top.js.OpenExtLink(window,event,this)">

http://homepages.inf.ed.ac.uk/rbf/BOOKS/VERNON/Chap006.pdf</a><br><br><div><span class="gmail_quote">Stephen</span></div><div><span class="e" id="q_10d1de764eab31af_1"><br><br>On 8/17/06, <b class="gmail_sendername">Brent Pedersen
</b> &lt;<a href="mailto:bpederse@gmail.com" target="_blank" onclick="return top.js.OpenExtLink(window,event,this)">bpederse@gmail.com
</a>&gt; wrote:</span></div><div><blockquote class="gmail_quote" style="border-left: 1px solid rgb(204, 204, 204); margin: 0pt 0pt 0pt 0.8ex; padding-left: 1ex;"></blockquote></div><div><span class="e" id="q_10d1de764eab31af_3">
<div>hi, i found this in recent archives and the script is useful. <br>
<a href="http://projects.scipy.org/pipermail/scipy-user/2006-August/008841.html" target="_blank" onclick="return top.js.OpenExtLink(window,event,this)">http://projects.scipy.org/pipermail/scipy-user/2006-August/008841.html
</a><br>
<br>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.
<br>thanks.
<br>-brent
<br>[please include my email in the reply, i've subscribed to scipy-users, but not sure if it went through yet]<br><br><br><br><br><br><br><br>



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