С++ (Boost + OpenMP) to Python

[RAZhdanov]

Hi!
I have a C++ project where Boost library and OpenMP #pragmas are used. I have several c++ files

Is it possible to convert this C++ project to Python? And if it is possible, what should I do in this case?

[sisrfeng]

Hi, if you successfully run the code, would you mind trying converting this to python?

#include "stdafx.h"
#include "cv.h"
#include "highgui.h"
 
int _tmain(int argc, _TCHAR* argv[])
{
	CvCapture *capture=cvCreateFileCapture("test.avi");
	IplImage *mframe,*current,*frg,*test;
	int *fg,*bg_bw,*rank_ind;
	double *w,*mean,*sd,*u_diff,*rank;
	int C,M,sd_init,i,j,k,m,rand_temp=0,rank_ind_temp=0,min_index=0,x=0,y=0,counter_frame=0;
	double D,alph,thresh,p,temp;
	CvRNG state;
	int match,height,width;
	mframe=cvQueryFrame(capture);
 
	frg = cvCreateImage(cvSize(mframe->width,mframe->height),IPL_DEPTH_8U,1);
	current = cvCreateImage(cvSize(mframe->width,mframe->height),IPL_DEPTH_8U,1);
	test = cvCreateImage(cvSize(mframe->width,mframe->height),IPL_DEPTH_8U,1);
	
	C = 4;						//number of gaussian components (typically 3-5)
	M = 4;						//number of background components
	sd_init = 6;				//initial standard deviation (for new components) var = 36 in paper
	alph = 0.01;				//learning rate (between 0 and 1) (from paper 0.01)
	D = 2.5;					//positive deviation threshold
	thresh = 0.25;				//foreground threshold (0.25 or 0.75 in paper)
	p = alph/(1/C);			//initial p variable (used to update mean and sd)
 
	height=current->height;width=current->widthStep;
	
	fg = (int *)malloc(sizeof(int)*width*height);					//foreground array
	bg_bw = (int *)malloc(sizeof(int)*width*height);				//background array
	rank = (double *)malloc(sizeof(double)*1*C);					//rank of components (w/sd)
	w = (double *)malloc(sizeof(double)*width*height*C);			//weights array
	mean = (double *)malloc(sizeof(double)*width*height*C);			//pixel means
	sd = (double *)malloc(sizeof(double)*width*height*C);			//pixel standard deviations
	u_diff = (double *)malloc(sizeof(double)*width*height*C);		//difference of each pixel from mean
	
	// initialize mean, standard deviation and weights for model updating of Gussian model  
 
	for (i=0;i<height;i++)
	{
		for (j=0;j<width;j++)
		{
			for(k=0;k<C;k++)
			{
				mean[i*width*C+j*C+k] = cvRandReal(&state)*255;
				w[i*width*C+j*C+k] = (double)1/C;
				sd[i*width*C+j*C+k] = sd_init;
			}
		}
	}
 
	while(1){
		rank_ind = (int *)malloc(sizeof(int)*C);
		cvCvtColor(mframe,current,CV_BGR2GRAY);
		// calculate difference of pixel values from mean
		for (i=0;i<height;i++)
		{
			for (j=0;j<width;j++)
			{
				for (m=0;m<C;m++)
				{
					u_diff[i*width*C+j*C+m] = abs((uchar)current->imageData[i*width+j]-mean[i*width*C+j*C+m]);     //every pixel of the current frame has to be compared with the mean of the established Gaussian model, 
																																																						//and the differences updates here.
				}
			}
		}
		//update gaussian components for each pixel
		for (i=0;i<height;i++)
		{
			for (j=0;j<width;j++)
			{
				match = 0;
				temp = 0;
				for(k=0;k<C;k++)   //for each Gaussian model, this demo is set to be 4 independent models
				{
					if (abs(u_diff[i*width*C+j*C+k]) <= D*sd[i*width*C+j*C+k])      //pixel matches component. If the difference between the current frame and the model mean is bigger than the standard deviation based tolerance,																																					
					{																														//this pixel is determined to be unsuccessful matching. 						
						 match = 1;													// variable to signal component match
						 
						 //if matched, update weights, mean, sd, p 
						 w[i*width*C+j*C+k] = (1-alph)*w[i*width*C+j*C+k] + alph;   //update the weight of each pixel according to the former weight and the learning rate
						 p = alph/w[i*width*C+j*C+k];                  
						 mean[i*width*C+j*C+k] = (1-p)*mean[i*width*C+j*C+k] + p*(uchar)current->imageData[i*width+j];   //update the mean of each pixel accoring to the former mean and the current frame
						 sd[i*width*C+j*C+k] =sqrt((1-p)*(sd[i*width*C+j*C+k]*sd[i*width*C+j*C+k]) + p*(pow((uchar)current->imageData[i*width+j] - mean[i*width*C+j*C+k],2)));   
																																													//update the standard deviation according to the former sd and the difference between the current frame and the new mean
					}else{
						w[i*width*C+j*C+k] = (1-alph)*w[i*width*C+j*C+k];			// weight slighly decreases. If not matched, decreasing the weight means increasing p for updating the mean, which means updating the model 
																																		//by paying more attention to the current frame rather than the former model, beause match faliure means great changes  
					}
					temp += w[i*width*C+j*C+k];                                      //sum of the weights of the same pixel in 4 models
				}
				
				for(k=0;k<C;k++)
				{
					w[i*width*C+j*C+k] = w[i*width*C+j*C+k]/temp;                //normalize the 4 weights
				}
			
				temp = w[i*width*C+j*C];
				bg_bw[i*width+j] = 0;
				for (k=0;k<C;k++)
				{
					bg_bw[i*width+j] = bg_bw[i*width+j] + mean[i*width*C+j*C+k]*w[i*width*C+j*C+k];                  //background pixel value is determined by the weighted sum of the Gaussian mean of the 4 models
					if (w[i*width*C+j*C+k]<=temp)
					{
						min_index = k;
						temp = w[i*width*C+j*C+k];
					}
					rank_ind[k] = k;
				}                                                                                                    // find the model which has the smallest weight
 
				test->imageData[i*width+j] = (uchar)bg_bw[i*width+j];
 
				//if no components match, create new component, replace the parameters of the model which has the smallest weight by the current frame and the initial sd.
				if (match == 0)
				{
					mean[i*width*C+j*C+min_index] = (uchar)current->imageData[i*width+j];
					//printf("%d ",(uchar)bg->imageData[i*width+j]);
					sd[i*width*C+j*C+min_index] = sd_init;
				}
				for (k=0;k<C;k++)
				{
					rank[k] = w[i*width*C+j*C+k]/sd[i*width*C+j*C+k];
					//printf("%f ",w[i*width*C+j*C+k]);
				}
 
				//sort rank values
				for (k=1;k<C;k++)
				{
					for (m=0;m<k;m++)
					{
						if (rank[k] > rank[m])
						{
							//swap max values
							rand_temp = rank[m];
							rank[m] = rank[k];
							rank[k] = rand_temp;
 
							//swap max index values
							rank_ind_temp = rank_ind[m];
							rank_ind[m] = rank_ind[k];
							rank_ind[k] = rank_ind_temp;
						}
					}
				}                     // sort rank from the biggest to the smallest
 
				//calculate foreground
				match = 0;k = 0;
				//frg->imageData[i*width+j]=0;
				while ((match == 0)&&(k<M)){
					if (w[i*width*C+j*C+rank_ind[k]] >= thresh)
						if (abs(u_diff[i*width*C+j*C+rank_ind[k]]) <= D*sd[i*width*C+j*C+rank_ind[k]]){
							frg->imageData[i*width+j] = 0;                                                                  //determine the background from the biggest rank by comparing the differece and the sd based tolerance
							match = 1;																										     //once matched, background is determined and while procedure will break
						}
						else
							frg->imageData[i*width+j] = (uchar)current->imageData[i*width+j];     //only if the all 4 models cannot be matched successfully, the pixel can be determined to be a foreground pixel 
					k = k+1;
				}
			}
		}		
 
		mframe = cvQueryFrame(capture);
		cvShowImage("fore",frg);
		cvShowImage("back",test);
		char s=cvWaitKey(33);
		if(s==27) break;
		free(rank_ind);
	}
	
	free(fg);free(w);free(mean);free(sd);free(u_diff);free(rank);
	cvNamedWindow("back",0);
	cvNamedWindow("fore",0);
	cvReleaseCapture(&capture);
	cvDestroyWindow("fore");
	cvDestroyWindow("back");
	return 0;
}