(Python, openCV study), k-means example source code of python and C++, and processing time comparing

Example source code of K-means algorithm in OpenCV,
The source code are two version, one is python and other is C++.
And I compare processing time, I do same condition such as same image, same parameter, and I checked same result.

The winner of processing speed is C++.
Despite C++ contain many "for" syntax but faster than python.

Check example source code.

This is input image

C++ version.
...

#include <  stdio.h>   
#include <  iostream>   
#include <  opencv2\opencv.hpp>   


#ifdef _DEBUG           
#pragma comment(lib, "opencv_core247d.lib")   
#pragma comment(lib, "opencv_imgproc247d.lib")   //MAT processing   
#pragma comment(lib, "opencv_highgui247d.lib")   
#else   
#pragma comment(lib, "opencv_core247.lib")   
#pragma comment(lib, "opencv_imgproc247.lib")   
#pragma comment(lib, "opencv_highgui247.lib")   
#endif  

using namespace cv;
using namespace std;

void main()
{

 unsigned long AAtime=0, BBtime=0; //check processing time   
 unsigned long inAtime=0, inBtime=0;
 AAtime = getTickCount(); //check processing time   

 inAtime = getTickCount(); //check processing time  
 Mat src = imread( "mare-08.jpg", 1 );
 Mat samples(src.rows * src.cols, 3, CV_32F);
 for( int y = 0; y < src.rows; y++ )
  for( int x = 0; x < src.cols; x++ )
   for( int z = 0; z < 3; z++)
    samples.at< float>(y + x*src.rows, z) = src.at< Vec3b>(y,x)[z];
 inBtime = getTickCount(); //check processing time    
 printf("in Data preparing %.2lf sec \n",  (inBtime - inAtime)/getTickFrequency() ); //check processing time   

 inAtime = getTickCount(); //check processing time  
 int clusterCount = 5;
 Mat labels;
 int attempts = 10;
 Mat centers;
 kmeans(samples, clusterCount, labels, TermCriteria(CV_TERMCRIT_ITER|CV_TERMCRIT_EPS, 10, 1.0), attempts, KMEANS_RANDOM_CENTERS, centers );
 inBtime = getTickCount(); //check processing time    
 printf("K mean processing %.2lf sec \n",  (inBtime - inAtime)/getTickFrequency() ); //check processing time   


 inAtime = getTickCount(); //check processing time  
 Mat new_image( src.size(), src.type() );
 for( int y = 0; y < src.rows; y++ )
 {
  for( int x = 0; x < src.cols; x++ )
  { 
   int cluster_idx = labels.at< int>(y + x*src.rows,0);
   new_image.at< Vec3b>(y,x)[0] = centers.at< float>(cluster_idx, 0);
   new_image.at< Vec3b>(y,x)[1] = centers.at< float>(cluster_idx, 1);
   new_image.at< Vec3b>(y,x)[2] = centers.at< float>(cluster_idx, 2);
  }
 }
 inBtime = getTickCount(); //check processing time    
 printf("out Data Preparing processing %.2lf sec \n",  (inBtime - inAtime)/getTickFrequency() ); //check processing time   

 BBtime = getTickCount(); //check processing time    
 printf("Total processing %.2lf sec \n",  (BBtime - AAtime)/getTickFrequency() ); //check processing time   
  
 //imshow( "clustered image", new_image );
 imwrite("clustered_image.jpg", new_image);
 //waitKey( 0 );
 
}

...
result image and processing time of C++ version

Python Version
...

import numpy as np
import cv2
from matplotlib import pyplot as plt




e1 = cv2.getTickCount()

inA = cv2.getTickCount()
img = cv2.imread('mare-08.jpg')
Z = img.reshape((-1,3))
# convert to np.float32
Z = np.float32(Z)
inB = cv2.getTickCount()
print("in data preparing", (inB-inA)/cv2.getTickFrequency())

# define criteria, number of clusters(K) and apply kmeans()
inA = cv2.getTickCount()
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)
K = 5
ret,label,center = cv2.kmeans(Z,K,criteria,10,cv2.KMEANS_RANDOM_CENTERS)
inB = cv2.getTickCount()
print("K-means ", (inB-inA)/cv2.getTickFrequency())


# Now convert back into uint8, and make original image
inA = cv2.getTickCount()
center = np.uint8(center)
res = center[label.flatten()]
res2 = res.reshape((img.shape))
inB = cv2.getTickCount()
print("out data preparing", (inB-inA)/cv2.getTickFrequency())

#print(center)
#print(label)

e2 = cv2.getTickCount()
time = (e2 - e1)/cv2.getTickFrequency()
print("total time", time, 1/time)

cv2.imshow('res2',res2)
cv2.waitKey(0)
cv2.destroyAllWindows()

...

The result of python