Image processing is notoriously a CPU intensive task. To do it in realtime, you need to implement your algorithm in a fast language, hence trying to do it in Python is foolish: Python is clearly not fast enough for this task. Is it? :-)
Actually, it turns out that the PyPy JIT compiler produces code which is fast enough to do realtime video processing using two simple algorithms implemented by Håkan Ardö.
sobel.py implements a classical way of locating edges in images, the Sobel operator. It is an approximation of the magnitude of the image gradient. The processing time is spend on two convolutions between the image and 3x3-kernels.
magnify.py implements a pixel coordinate transformation that rearranges the pixels in the image to form a magnifying effect in the center. It consists of a single loop over the pixels in the output image copying pixels from the input image.
You can try by yourself by downloading the appropriate demo:
pypy-image-demo.tar.bz2: this archive contains only the source code, use this is you have PyPy already installed
pypy-image-demo-full.tar.bz2: this archive contains both the source code and prebuilt PyPy binaries for linux 32 and 64 bits
To run the demo, you need to have mplayer installed on your system. The demo has been tested only on linux, it might (or not) work also on other systems:
$ pypy pypy-image-demo/sobel.py
$ pypy pypy-image-demo/magnify.py
By default, the two demos uses an example AVI file. To have more fun, you can use your webcam by passing the appropriate mplayer parameters to the scripts, e.g:
$ pypy demo/sobel.py tv://
By default magnify.py uses nearest-neighbor interpolation. By adding the option -b, bilinear interpolation will be used instead, which gives smoother result:
$ pypy demo/magnify.py -b
There is only a single implementation of the algorithm in magnify.py. The two different interpolation methods are implemented by subclassing the class used to represent images and embed the interpolation within the pixel access method. PyPy is able to achieve good performance with this kind of abstractions because it can inline the pixel access method and specialize the implementation of the algorithm. In C++ that kind of pixel access method would be virtual and you'll need to use templates to get the same effect without incurring in runtime overhead.
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