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This example shows a rather complex program in Python, it is a JPEG decoder 
that is able to decode (M)JPEG Baseline complain files.

The image to be decoded could be in any chroma sub-sampling configuration, for 
example 4:4:4, 4:2:2, 4:2:0, or any custom one such that each plane is block 
quantity is a power of 2.

Apart from plain JPEG files, this program can demux a .MOV (or .MP4) video 
file, and if it found a JPEG streaming on it, it dumps and decode every frame.

The program has to be invoked as follows:

python decode.py <image.jpg|video.mov>

The procedure to this type of image processing usually require a lot of 
processing, for decoding a JPEG file consist of the following steps:

-setup Huffman tables.
-setup the quantization matrix.
-decode a 8x8 block using Huffman codes and RLE.
-apply a 2D Inverse Discrete Cosine Transformation.
-process the chroma sub-sampling.
-convert from the YCbCr color space to the RGB color space.

Huffman decoding, and IDCT transformation make the JPEG decoding very Python 
unfriendly due to the speed of the Python bytecode execution, moreover, the 
YCbCr to RGB and the blit of the final image is very time consuming when 
applied to each pixel of an image.

Inside this example I provide several examples to demonstrate Python 
performance optimization techniques.
All these techniques are fine tuned with a very carefully profiling of code 
execution, and were tested decoding a video of 1180 frames of 320x240 pixels 
each (ftp://ftp.hp.com/pub/information_storage/software/video/MakeUp.mov) on a 
Core i5 CPU.

The resumed results are shown in the following tables:

CPython 2.7
2155.45 seconds.
(1.0 speed factor)

PyPy 1.7 (JIT)
198.85 seconds.
(10.8 speed factor)

CPython 2.7 modules C Huffman and IDCT modules.
393.75 seconds.
(5.5 speed factor)

Refactoring of Jpeg_decoder class for Cython and Huffman and IDCT C code
4.29 seconds.
(502.4 speed factor)

These experiment results shows that Python code could perform at outstanding 
speeds when it is properly profiled, and the CPU intensive critical parts are 
refactored to a lower level and higher performance implementations.

In this case we decode a video containing 40 seconds of image in ~4.3 seconds, 
enough for realtime player, and ~500 times the speed of the pure Python 
implementation.

Directory description:
pure: pure Python version.
common: C version of IDCT, YCbCr -> RGB, and Huffman algorithms.
mod: CPython Huffman, IDCT and YCbCr C modules.
mod/linx64_p27: Python 2.7 (Linux x86_64) binary modules
mod/win32_p27: Python 2.7 (Windows 32 bits) binary modules
fast: Jpeg_decoder class (jpegdec.py replacement) refoctored for Cython and 
      integrated with native C version of Huffman, IDCT and YCbCr->RGB code.
fast/linx64_p27: Python 2.7 (Linux x64) binary modules
fast/win32_p27: Python 2.7 (Windows 32 bits) binary modules
fast/win32_p32: Python 3.2 (Windows 32 bits) binary modules