I have implemented an iterative gaussian smoothing approach that is
working well for my purposes. My approach uses a median filter to
populate the initial values and then runs a few passes with gaussian
smoothing. This works very well for the missing values that I care
about within the face region.
I also came across an error when I tried to use the Rbf class. I was
hoping that I could just input all of the data that I have and have a
quick and easy solution. I expect this would work if ran the Rbf on
just a small image tile near the missing data region. I am not sure
if this is worthy of a bug report.
When I tried to create an RBF from the full image I got this error:
Traceback (most recent call last):
File "/Users/bolme/Documents/workspace/pyvision/src/pyvision/types/
RangeImage.py", line 258, in <module>
ri.populateMissingData()
File "/Users/bolme/Documents/workspace/pyvision/src/pyvision/types/
RangeImage.py", line 184, in populateMissingData
it.Rbf(x[mask],y[mask],z[mask])
File "/Library/Python/2.5/site-packages/scipy-0.7.0.dev4645-py2.5-
macosx-10.3-i386.egg/scipy/interpolate/rbf.py", line 129, in __init__
r = self._call_norm(self.xi, self.xi)
File "/Library/Python/2.5/site-packages/scipy-0.7.0.dev4645-py2.5-
macosx-10.3-i386.egg/scipy/interpolate/rbf.py", line 144, in _call_norm
return self.norm(x1, x2)
File "/Library/Python/2.5/site-packages/scipy-0.7.0.dev4645-py2.5-
macosx-10.3-i386.egg/scipy/interpolate/rbf.py", line 54, in
_euclidean_norm
return sqrt( ((x1 - x2)**2).sum(axis=0) )
ValueError: broadcast dimensions too large.
This is probably because I tried to input the full 640X480 image. Too
much data. x[mask], y[mask], and z[mask] are a one dimensional arrays
with approximately 100,000 elements. I am trying to predict z. It
would be nice to have a more descriptive error message.
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