I am working on a voxel coloring implementation with scipy and have used (in the beginning) hough transformation to detect lines the algroithm i used is quite simple 1. get the PIL library, which is quite good imo 2. use the build in filter function for edge detection imagename.filter(ImageFilter.FIND_EDGES) 3. make a scipy array from the image 4. to eliminate noise do a closing operation on the edge image 5. set a threshold on edge value (i.e pixel value > t is a point on the edge). I used a double threshold whith 2 runs 6. for each of the pixels found in 6 calculate the p value found for a discreat number of tetas (i used 360 values between 0 and pi) as in x cos tet + y sin tet = p where x,y and tet are know. and store for each pair of tet and p how often they come out 7. set a threshhold for the value to determine if its a real line or not ( i u sed max value found /2) and all p and tetas tuple with a total value bigger then this are your lines. I eventually dropped this in favour of radon transform as the results are better imo. Kfir Breger On Aug 17, 2006, at 10:56 PM, Brent Pedersen wrote:
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@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@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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