05 OpenCV Optimisation.md

OpenCV Python Optimisation Cheatsheet

Author: methylDragon
Contains a syntax reference and code snippets for OpenCV for Python!
Note that this document is more or less based on the tutorials on https://docs.opencv.org
With some personal notes from me!

---

Pre-Requisites

Required

• Python knowledge, this isn't a tutorial!
• OpenCV installed

1. Introduction

Not much of an introduction here. OpenCV is just really great!

Since this is a work in progress, it's not going to be very well organised.

# These will have been assumed to have been run

import cv2 as cv2, cv
import numpy as np

If you need additional help or need a refresher on the parameters, feel free to use:

help(cv.FUNCTION_YOU_NEED_HELP_WITH)

2. Optimisation

Enabling Optimisation

cv.useOptimized() # Returns True if optimisation is enabled
cv.setUseOptimized(True) # Set it to True

Measuring Performance

Timing your code is important!

Tick comparison

cv.getTickCount() # Current clock cycles
cv.getTickFrequency() # Number of clock cycles per second

# Example use
e1 = cv.getTickCount()
# your code execution
e2 = cv.getTickCount()
time = (e2 - e1)/ cv.getTickFrequency()

Using Timeit

Example

import timeit

def performSearch(array):
    array.sort()

arrayTest = ["X"]*1000

if __name__ == "__main__":
    print(timeit.timeit("performSearch(arrayTest)",
                        "from __main__ import performSearch, arrayTest",
                        repeat=3,
                        number=10))

Cython

• Cython code compiles to C, making it way faster than just pure Python code!
• Install Cython with pip install cython or conda install cython

Note: Using Cython to optimise your OpenCV script will only work generally for the for loops, since the OpenCV Python API is actually a Python wrapper for already fairly optimised C++ code.

Ok!

So.... You're going to need to check the Cython reference I made, or know how to use Cython. But generally...

You need a

setup.py

from distutils.core import setup
from Cython.Build import cythonize

setup(
    ext_modules = cythonize('script_file.pyx'
)

script_file.pyx

# Source: https://www.pyimagesearch.com/2017/08/28/fast-optimized-for-pixel-loops-with-opencv-and-python/

import cython
 
@cython.boundscheck(False)
cpdef unsigned char[:, :] threshold_fast(int T, unsigned char [:, :] image):
    # set the variable extension types
    cdef int x, y, w, h
    
    # grab the image dimensions
    h = image.shape[0]
    w = image.shape[1]
    
    # loop over the image
    for y in range(0, h):
        for x in range(0, w):
            # threshold the pixel
            image[y, x] = 255 if image[y, x] >= T else 0
    
    # return the thresholded image
    return image

And then compile it with

$ python3 setup.py build_ext --inplace

Then import it!

from script_file import threshold_fast

# Now you can use it!

                            .     .
                         .  |\-^-/|  .    
                        /| } O.=.O { |\

---