OpenCV
3.4.2
Open Source Computer Vision
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In image processing, since you are dealing with large number of operations per second, it is mandatory that your code is not only providing the correct solution, but also in the fastest manner. So in this chapter, you will learn
Apart from OpenCV, Python also provides a module time which is helpful in measuring the time of execution. Another module profile helps to get detailed report on the code, like how much time each function in the code took, how many times the function was called etc. But, if you are using IPython, all these features are integrated in an user-friendly manner. We will see some important ones, and for more details, check links in Additional Resources section.
cv.getTickCount function returns the number of clock-cycles after a reference event (like the moment machine was switched ON) to the moment this function is called. So if you call it before and after the function execution, you get number of clock-cycles used to execute a function.
cv.getTickFrequency function returns the frequency of clock-cycles, or the number of clock-cycles per second. So to find the time of execution in seconds, you can do following:
We will demonstrate with following example. Following example apply median filtering with a kernel of odd size ranging from 5 to 49. (Don't worry about what will the result look like, that is not our goal):
Many of the OpenCV functions are optimized using SSE2, AVX etc. It contains unoptimized code also. So if our system support these features, we should exploit them (almost all modern day processors support them). It is enabled by default while compiling. So OpenCV runs the optimized code if it is enabled, else it runs the unoptimized code. You can use cv.useOptimized() to check if it is enabled/disabled and cv.setUseOptimized() to enable/disable it. Let's see a simple example.
See, optimized median filtering is2x faster than unoptimized version. If you check its source, you can see median filtering is SIMD optimized. So you can use this to enable optimization at the top of your code (remember it is enabled by default).
Sometimes you may need to compare the performance of two similar operations. IPython gives you a magic command timeit to perform this. It runs the code several times to get more accurate results. Once again, they are suitable to measure single line codes.
For example, do you know which of the following addition operation is better, x = 5; y = x**2, x = 5; y = x*x, x = np.uint8([5]); y = x*x or y = np.square(x) ? We will find it with timeit in IPython shell.
You can see that, x = 5 ; y = x*x is fastest and it is around 20x faster compared to Numpy. If you consider the array creation also, it may reach upto 100x faster. Cool, right? *(Numpy devs are working on this issue)*
We will try one more example. This time, we will compare the performance of cv.countNonZero() and np.count_nonzero() for same image.
See, OpenCV function is nearly 25x faster than Numpy function.
There are several other magic commands to measure the performance, profiling, line profiling, memory measurement etc. They all are well documented. So only links to those docs are provided here. Interested readers are recommended to try them out.
There are several techniques and coding methods to exploit maximum performance of Python and Numpy. Only relevant ones are noted here and links are given to important sources. The main thing to be noted here is that, first try to implement the algorithm in a simple manner. Once it is working, profile it, find the bottlenecks and optimize them.
Even after doing all these operations, if your code is still slow, or use of large loops are inevitable, use additional libraries like Cython to make it faster.