Son Hua wrote:
I'm quite new Python here. Recently I wrote an image mosaicking
application in Python (using Numpy library). It turns out that the
performance is quite terrible. For example, a mosaic of 7 images, each
800x600, the output size is about 8000x2000. Each output pixel is
sampled from the source images using bilinear interpolation according to
the inverse homography. The running time is nearly 1 hour.
State-of-the-art commercial mosaicking software (written in C) runs the
above examples in less than 10 seconds!!!
Note that "State-of-the-art" image processing software is likely not
only written in C, but also highly optimized -- taking into account
cache sizes, using special instructions, etc, so you've not going to get
close with naively written code. Still a factor of ten or so maybe.
I wonder how is other Python image processing applications' performance?
Do we always need to implement slow functions in C?
in a word, yes -- if you are looping trough pixels in python, it's going
to be painfully slow - python simply is not designed for that kind of thing.
E.g., I suppose to
reimplement the bilinear interpolation in C, as it is called million
times in Python, which is slow.
have you looked at the ndarray package? or PIL's interpolation for that
matter? both are written in C.
If they don't have the algorithm you need, as a another poster pointed
out, smart use of numpy will help a lot (essentially,l you are getting
the inner loop in c via numpy)
If you have not idea how to do that, read up a bit on scipy.org, and
then post a question to the numpy list.
Option 3 is Cython -- it lets you write C code in an almost-python
syntax, and it understand numpy arrays, to it's really easy to re-write
that inner loop in C.
-Chris
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