SciPy is an effective tool suite for prototyping new algorithms. We share some of our experiences using it for the first time to support our research in object detection. SciPy makes it easy to integrate C code, which is essential when algorithms operating on large data sets cannot be vectorized. The universality of Python, the language in which SciPy was written, gives the researcher access to a broader set of non-numerical libraries to support GUI development, interface with databases, manipulate graph structures, render 3D graphics, unpack binary files, etc. Python's extensive support for operator overloading makes SciPy's syntax as succinct as its competitors, MATLAB, Octave, and R. More profoundly, we found it easy to rework research code written with SciPy into a production application, deployable on numerous platforms.

D Eads, E Rosten, Experiences Using SciPy for Computer Vision Research in Proceedings of the 7th Python in Science conference (SciPy 2008), G Varoquaux, T Vaught, J Millman (Eds.), pp. 22-26

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