As mentioned in earlier answers, Python is slower compared to C++ or C. Python is built for its simplicity, portability and moreover, creativity where users need to worry only about their algorithm, not programming troubles.
But here in OpenCV, there is something different. Python-OpenCV is just a wrapper around the original C/C++ code. It is normally used for combining best features of both the languages, Performance of C/C++ & Simplicity of Python.
So when you call a function in OpenCV from Python, what actually run is underlying C/C++ source. So there won’t be much difference in performance.( I remember I read somewhere that performance penalty is <1%, don’t remember where. A rough estimate with some basic functions in OpenCV shows a worst-case penalty of <4%. ie penalty = [maximum time taken in Python - minimum time taken in C++]/minimum time taken in C++ ).
The problem arises when your code has a lot of native python codes.For eg, if you are making your own functions that are not available in OpenCV, things get worse. Such codes are ran natively in Python, which reduces the performance considerably.
But new OpenCV-Python interface has full support to Numpy. Numpy is a package for scientific computing in Python. It is also a wrapper around native C code. It is a highly optimized library which supports a wide variety of matrix operations, highly suitable for image processing. So if you can combine both OpenCV functions and Numpy functions correctly, you will get a very high speed code.
Thing to remember is, always try to avoid loops and iterations in Python. Instead, use array manipulation facilities available in Numpy (and OpenCV). Simply adding two numpy arrays using C = A+B is a lot times faster than using double loops.
For eg, you can check these articles :
- Fast Array Manipulation in Python
- Performance comparison of OpenCV-Python interfaces, cv and cv2