Now, try implement that matrix subtraction or something more complex such as blurring or edge detection in Python, and compare results.
Also, are you sure that doesn't measure disk speed?
Also, are you sure that doesn't measure disk speed?
I agree that in pure Python it'd be slower, but realistically why would you do that? Unless you work somewhere where you're forced to write your own libraries... but even then you could still implement your own.
And you would want to do it in pure Python if you want to answer the question "how fast can we make interpreted Python?". Using C extensions for that is cheating, as it isn't Python and it isn't interpreted. You don't answer the question "how fast can you run?" With "30 km an hour, using a bicycle", either.
If you make those images large enough (and I guess 2k x 2k is large enough), any language that uses OpenCV to do the job will give results in the same ballpark. For example, you can make the difference between Python implementations that can call OpenCV as small as you want it.
I agree that the question is valid - making vanilla Python faster is cool. My point was that this particular example (image processing) was flawed, because it's not something a sane person would ever do in pure Python.
Of course it is about the same. Except for function entry and function exit, which should be a few thousand instructions, at the most, it runs the exact same instruction sequence (if you are using identical versions, compiler and compiler flags)
If you want an easily measurable difference, use way smaller images, and make a few thousand or even a few million calls, or look at the python sources to see how efficiently it calls into C.