예제 #1
0
static GstFlowReturn
gst_segmentation_transform_ip (GstVideoFilter * btrans, GstVideoFrame * frame)
{
  GstSegmentation *filter = GST_SEGMENTATION (btrans);
  int j;

  /*  get image data from the input, which is RGBA */
  filter->cvRGBA->imageData = (char *) GST_VIDEO_FRAME_COMP_DATA (frame, 0);
  filter->cvRGBA->widthStep = GST_VIDEO_FRAME_COMP_STRIDE (frame, 0);
  filter->framecount++;

  /*  Image preprocessing: color space conversion etc */
  cvCvtColor (filter->cvRGBA, filter->cvRGB, CV_RGBA2RGB);
  cvCvtColor (filter->cvRGB, filter->cvYUV, CV_RGB2YCrCb);

  /* Create and update a fg/bg model using a codebook approach following the 
   * opencv O'Reilly book [1] implementation of the algo described in [2].
   *
   * [1] Learning OpenCV: Computer Vision with the OpenCV Library by Gary 
   * Bradski and Adrian Kaehler, Published by O'Reilly Media, October 3, 2008
   * [2] "Real-time Foreground-Background Segmentation using Codebook Model", 
   * Real-time Imaging, Volume 11, Issue 3, Pages 167-256, June 2005. */
  if (METHOD_BOOK == filter->method) {
    unsigned cbBounds[3] = { 10, 5, 5 };
    int minMod[3] = { 20, 20, 20 }, maxMod[3] = {
    20, 20, 20};

    if (filter->framecount < 30) {
      /* Learning background phase: update_codebook on every frame */
      for (j = 0; j < filter->width * filter->height; j++) {
        update_codebook ((unsigned char *) filter->cvYUV->imageData + j * 3,
            (codeBook *) & (filter->TcodeBook[j]), cbBounds, 3);
      }
    } else {
      /*  this updating is responsible for FG becoming BG again */
      if (filter->framecount % filter->learning_interval == 0) {
        for (j = 0; j < filter->width * filter->height; j++) {
          update_codebook ((uchar *) filter->cvYUV->imageData + j * 3,
              (codeBook *) & (filter->TcodeBook[j]), cbBounds, 3);
        }
      }
      if (filter->framecount % 60 == 0) {
        for (j = 0; j < filter->width * filter->height; j++)
          clear_stale_entries ((codeBook *) & (filter->TcodeBook[j]));
      }

      for (j = 0; j < filter->width * filter->height; j++) {
        if (background_diff
            ((uchar *) filter->cvYUV->imageData + j * 3,
                (codeBook *) & (filter->TcodeBook[j]), 3, minMod, maxMod)) {
          filter->cvFG->imageData[j] = 255;
        } else {
          filter->cvFG->imageData[j] = 0;
        }
      }
    }

    /* 3rd param is the smallest area to show: (w+h)/param , in pixels */
    find_connected_components (filter->cvFG, 1, 10000,
        filter->mem_storage, filter->contours);

  }
  /* Create the foreground and background masks using BackgroundSubtractorMOG [1], 
   *  Gaussian Mixture-based Background/Foreground segmentation algorithm. OpenCV 
   * MOG implements the algorithm described in [2].
   * 
   * [1] http://opencv.itseez.com/modules/video/doc/motion_analysis_and_object_tracking.html#backgroundsubtractormog
   * [2] P. KadewTraKuPong and R. Bowden, "An improved adaptive background 
   * mixture model for real-time tracking with shadow detection", Proc. 2nd 
   * European Workshop on Advanced Video-Based Surveillance Systems, 2001
   */
  else if (METHOD_MOG == filter->method) {
    run_mog_iteration (filter);
  }
  /* Create the foreground and background masks using BackgroundSubtractorMOG2
   * [1], Gaussian Mixture-based Background/Foreground segmentation algorithm. 
   * OpenCV MOG2 implements the algorithm described in [2] and [3].
   * 
   * [1] http://opencv.itseez.com/modules/video/doc/motion_analysis_and_object_tracking.html#backgroundsubtractormog2
   * [2] Z.Zivkovic, "Improved adaptive Gausian mixture model for background 
   * subtraction", International Conference Pattern Recognition, UK, Aug 2004.
   * [3] Z.Zivkovic, F. van der Heijden, "Efficient Adaptive Density Estimation 
   * per Image Pixel for the Task of Background Subtraction", Pattern 
   * Recognition Letters, vol. 27, no. 7, pages 773-780, 2006.   */
  else if (METHOD_MOG2 == filter->method) {
    run_mog2_iteration (filter);
  }

  /*  if we want to test_mode, just overwrite the output */
  if (filter->test_mode) {
    cvCvtColor (filter->cvFG, filter->cvRGB, CV_GRAY2RGB);

    cvSplit (filter->cvRGB, filter->ch1, filter->ch2, filter->ch3, NULL);
  } else
    cvSplit (filter->cvRGBA, filter->ch1, filter->ch2, filter->ch3, NULL);

  /*  copy anyhow the fg/bg to the alpha channel in the output image */
  cvMerge (filter->ch1, filter->ch2, filter->ch3, filter->cvFG, filter->cvRGBA);


  return GST_FLOW_OK;
}
예제 #2
0
int main(int argc, char** argv)
{
	IplImage* temp1 = NULL;
    IplImage* temp2 = NULL;
    IplImage* result = NULL;
    IplImage* result1 = NULL;
    IplImage* result2 = NULL;

	CvBGStatModel* bg_model=0;
	CvBGStatModel* bg_model1=0;

    IplImage* rawImage = 0; 
	IplImage* yuvImage = 0; 
	IplImage* rawImage1 = 0;
    IplImage* pFrImg = 0;
	IplImage* pFrImg1= 0;
	IplImage* pFrImg2= 0;
	IplImage* ImaskCodeBookCC = 0;
    CvCapture* capture = 0;

	int c,n;

	maxMod[0] = 25; 
	minMod[0] = 35;
	maxMod[1] = 8;
	minMod[1] = 8;
	maxMod[2] = 8;
	minMod[2] = 8;

    argc=2;
    argv[1]="intelligentroom_raw.avi";
    if( argc > 2 )
	{
		fprintf(stderr, "Usage: bkgrd [video_file_name]\n");
		return -1;
	}
 
    if (argc ==1)
		if( !(capture = cvCaptureFromCAM(-1)))
		{
			fprintf(stderr, "Can not open camera.\n");
			return -2;
		}

    if(argc == 2)
		if( !(capture = cvCaptureFromFile(argv[1])))
		{
			fprintf(stderr, "Can not open video file %s\n", argv[1]);
			return -2;
		}

	bool pause = false;
	bool singlestep = false;

    if( capture )
    {
        cvNamedWindow( "原视频序列图像", 1 );
		cvNamedWindow("不实时更新的Codebook算法[本文]",1);
		cvNamedWindow("实时更新的Codebook算法[本文]",1);
		cvNamedWindow("基于MOG的方法[Chris Stauffer'2001]",1);
		cvNamedWindow("三帧差分", 1);
		cvNamedWindow("基于Bayes decision的方法[Liyuan Li'2003]", 1);
        
		cvMoveWindow("原视频序列图像", 0, 0);
		cvMoveWindow("不实时更新的Codebook算法[本文]", 360, 0);
		cvMoveWindow("实时更新的Codebook算法[本文]", 720, 350);
		cvMoveWindow("基于MOG的方法[Chris Stauffer'2001]", 0, 350);
		cvMoveWindow("三帧差分", 720, 0);
        cvMoveWindow("基于Bayes decision的方法[Liyuan Li'2003]",360, 350);
        int nFrmNum = -1;
        for(;;)
        {
    		if(!pause)
			{
				rawImage = cvQueryFrame( capture );
				++nFrmNum;
				printf("第%d帧\n",nFrmNum);
				if(!rawImage) 
					break;
			}
			if(singlestep)
			{
				pause = true;
			}
			if(0 == nFrmNum) 
			{
				printf(". . . wait for it . . .\n"); 
				
				temp1 = cvCreateImage(cvGetSize(rawImage), IPL_DEPTH_8U, 3);
				temp2 = cvCreateImage(cvGetSize(rawImage), IPL_DEPTH_8U, 3);
				result1 = cvCreateImage(cvGetSize(rawImage), IPL_DEPTH_8U, 1);
				result2 = cvCreateImage(cvGetSize(rawImage), IPL_DEPTH_8U, 1);
				result = cvCreateImage(cvGetSize(rawImage), IPL_DEPTH_8U, 1);

				bg_model = cvCreateGaussianBGModel(rawImage);
                bg_model1 = cvCreateFGDStatModel(rawImage);
				rawImage1 = cvCreateImage( cvGetSize(rawImage), IPL_DEPTH_8U, 3 );

				yuvImage = cvCloneImage(rawImage);
				pFrImg  = cvCreateImage( cvGetSize(rawImage), IPL_DEPTH_8U, 1 );
				pFrImg1 = cvCreateImage( cvGetSize(rawImage), IPL_DEPTH_8U, 1 );
				pFrImg2 = cvCreateImage( cvGetSize(rawImage), IPL_DEPTH_8U, 1 );
				ImaskCodeBookCC = cvCreateImage( cvGetSize(rawImage), IPL_DEPTH_8U, 1 );

				imageLen = rawImage->width*rawImage->height;

				cA = new codeBook [imageLen];
				cC = new codeBook [imageLen];
				cD = new codeBook [imageLen];

				for(int f = 0; f<imageLen; f++)
				{
					cA[f].numEntries = 0; cA[f].t = 0;
					cC[f].numEntries = 0; cC[f].t = 0;
					cD[f].numEntries = 0; cD[f].t = 0;
				}
				for(int nc=0; nc<nChannels;nc++)
				{
					cbBounds[nc] = 10;
				}
				ch[0] = true;
				ch[1] = true;
				ch[2] = true;
			}
             
        	if( rawImage )
        	{
				if(!pause)
				{					
					cvSmooth(rawImage, rawImage1, CV_GAUSSIAN,3,3);

					cvChangeDetection(temp1, temp2, result1);
					cvChangeDetection(rawImage1, temp1, result2);
					cvAnd(result1, result2, result, NULL);
					cvCopy(temp1,temp2, NULL);
					cvCopy(rawImage,temp1, NULL);

					
					cvUpdateBGStatModel( rawImage, bg_model );
					cvUpdateBGStatModel( rawImage, bg_model1 );
				}

				cvCvtColor( rawImage1, yuvImage, CV_BGR2YCrCb );
				if( !pause && nFrmNum >= 1 && nFrmNum < T  )
				{
					pColor = (uchar *)((yuvImage)->imageData);
					for(int c=0; c<imageLen; c++)
					{
						update_codebook_model(pColor, cA[c],cbBounds,nChannels);
					    trainig_codebook(pColor, cC[c],cbBounds,nChannels);
						pColor += 3;
					}
				}

				if( nFrmNum == T)
				{
					for(c=0; c<imageLen; c++)
					{
						clear_stale_entries(cA[c]);
						training_clear_stale_entries(cC[c]);
					}
				}

				if(nFrmNum > T) 
				{
					pColor = (uchar *)((yuvImage)->imageData);
					uchar maskPixelCodeBook;
					uchar maskPixelCodeBook1;
					uchar maskPixelCodeBook2;
					uchar *pMask = (uchar *)((pFrImg)->imageData);
					uchar *pMask1 = (uchar *)((pFrImg1)->imageData);
					uchar *pMask2 = (uchar *)((pFrImg2)->imageData);
					for(int c=0; c<imageLen; c++)
					{
						//本文中不带自动背景更新的算法输出
						maskPixelCodeBook1=background_Diff(pColor, cA[c],nChannels,minMod,maxMod);
                        *pMask1++ = maskPixelCodeBook1;
						
						//本文中带自动背景更新的算法输出
						if ( !pause && det_update_codebook_cC(pColor, cC[c],cbBounds,nChannels))
						{	
							det_update_codebook_cD(pColor, cD[c],cbBounds,nChannels, nFrmNum); 
							realtime_clear_stale_entries_cD(cD[c], nFrmNum);
							cD_to_cC(cD[c], cC[c], (nFrmNum - T)/5);
							
						}
						else
						{
							realtime_clear_stale_entries_cC(cC[c], nFrmNum);
						
						} 

						maskPixelCodeBook2=background_Diff(pColor, cC[c],nChannels,minMod,maxMod);
						*pMask2++ = maskPixelCodeBook2;  
						pColor += 3;
					}

					cvCopy(pFrImg2,ImaskCodeBookCC);
					if(!pause)
					{
						count_Segmentation(cC,yuvImage,nChannels,minMod,maxMod);
						forgratio = (float) (fgcount)/ imageLen;
					}
				}
				bg_model1->foreground->origin=1;
				bg_model->foreground->origin=1;				
				pFrImg->origin=1;
                pFrImg1->origin=1;
				pFrImg2->origin=1;
				ImaskCodeBookCC->origin=1;
				result->origin=1;
				//connected_Components(pFrImg1,1,40);
				//connected_Components(pFrImg2,1,40);
				
                cvShowImage("基于MOG的方法[Chris Stauffer'2001]", bg_model->foreground);
           		cvShowImage( "原视频序列图像", rawImage );
				cvShowImage("三帧差分", result);
 				cvShowImage( "不实时更新的Codebook算法[本文]",pFrImg1);
				cvShowImage("实时更新的Codebook算法[本文]",pFrImg2);
				cvShowImage("基于Bayes decision的方法[Liyuan Li'2003]", bg_model1->foreground);

	         	c = cvWaitKey(1)&0xFF;
				//End processing on ESC, q or Q
				if(c == 27 || c == 'q' || c == 'Q')
					break;
				//Else check for user input
				switch(c)
				{
					case 'h':
						help();
						break;
					case 'p':
						pause ^= 1;
						break;
					case 's':
						singlestep = 1;
						pause = false;
						break;
					case 'r':
						pause = false;
						singlestep = false;
						break;
				//CODEBOOK PARAMS
                case 'y':
                case '0':
                        ch[0] = 1;
                        ch[1] = 0;
                        ch[2] = 0;
                        printf("CodeBook YUV Channels active: ");
                        for(n=0; n<nChannels; n++)
                                printf("%d, ",ch[n]);
                        printf("\n");
                        break;
                case 'u':
                case '1':
                        ch[0] = 0;
                        ch[1] = 1;
                        ch[2] = 0;
                        printf("CodeBook YUV Channels active: ");
                        for(n=0; n<nChannels; n++)
                                printf("%d, ",ch[n]);
                        printf("\n");
                        break;
                case 'v':
                case '2':
                        ch[0] = 0;
                        ch[1] = 0;
                        ch[2] = 1;
                        printf("CodeBook YUV Channels active: ");
                        for(n=0; n<nChannels; n++)
                                printf("%d, ",ch[n]);
                        printf("\n");
                        break;
                case 'a': //All
                case '3':
                        ch[0] = 1;
                        ch[1] = 1;
                        ch[2] = 1;
                        printf("CodeBook YUV Channels active: ");
                        for(n=0; n<nChannels; n++)
                                printf("%d, ",ch[n]);
                        printf("\n");
                        break;
                case 'b':  //both u and v together
                        ch[0] = 0;
                        ch[1] = 1;
                        ch[2] = 1;
                        printf("CodeBook YUV Channels active: ");
                        for(n=0; n<nChannels; n++)
                                printf("%d, ",ch[n]);
                        printf("\n");
                        break;
                case 'z': 
					printf(" Fadd加1 ");
					Fadd += 1;
					printf("Fadd=%.4d\n",Fadd);										
					break;
				case 'x':
					printf(" Fadd减1 "); 
					Fadd -= 1;					
					printf("Fadd=%.4d\n",Fadd);										
					break;
				case 'n': 
					printf(" Tavgstale加1 ");
					Tavgstale += 1;
					printf("Tavgstale=%.4d\n",Tavgstale);										
					break;
				case 'm': 
					printf(" Tavgstale减1 ");
					Tavgstale -= 1;
					printf("Tavgstale=%.4d\n",Tavgstale);										
					break;
				case 'i': //modify max classification bounds (max bound goes higher)
					for(n=0; n<nChannels; n++)
					{
						if(ch[n])
							maxMod[n] += 1;
						printf("%.4d,",maxMod[n]);
					}
					printf(" CodeBook High Side\n");
					break;
				case 'o': //modify max classification bounds (max bound goes lower)
					for(n=0; n<nChannels; n++)
					{
						if(ch[n])
							maxMod[n] -= 1;
						printf("%.4d,",maxMod[n]);
					}
					printf(" CodeBook High Side\n");
					break;
				case 'k': //modify min classification bounds (min bound goes lower)
					for(n=0; n<nChannels; n++)
					{
						if(ch[n])
							minMod[n] += 1;
						printf("%.4d,",minMod[n]);
					}
					printf(" CodeBook Low Side\n");
					break;
				case 'l': //modify min classification bounds (min bound goes higher)
					for(n=0; n<nChannels; n++)
					{
						if(ch[n])
							minMod[n] -= 1;
						printf("%.4d,",minMod[n]);
					}
					printf(" CodeBook Low Side\n");
					break;
				}
            }
		}		
		cvReleaseCapture( &capture );
		cvReleaseBGStatModel((CvBGStatModel**)&bg_model);
		cvReleaseBGStatModel((CvBGStatModel**)&bg_model1);

        cvDestroyWindow( "原视频序列图像" );
		cvDestroyWindow( "不实时更新的Codebook算法[本文]");
		cvDestroyWindow( "实时更新的Codebook算法[本文]");
		cvDestroyWindow( "基于MOG的方法[Chris Stauffer'2001]");
		cvDestroyWindow( "三帧差分" );
		cvDestroyWindow( "基于Bayes decision的方法[Liyuan Li'2003]");

		cvReleaseImage(&temp1);
		cvReleaseImage(&temp2);
		cvReleaseImage(&result);
		cvReleaseImage(&result1);
		cvReleaseImage(&result2);
		cvReleaseImage(&pFrImg);
		cvReleaseImage(&pFrImg1);
		cvReleaseImage(&pFrImg2);

		if(yuvImage) cvReleaseImage(&yuvImage);
		if(rawImage) cvReleaseImage(&rawImage);
		if(rawImage1) cvReleaseImage(&rawImage1);
		if(ImaskCodeBookCC) cvReleaseImage(&ImaskCodeBookCC);
		delete [] cA;
		delete [] cC;
		delete [] cD;
    }
	else
	{ 
		printf("\n\nDarn, Something wrong with the parameters\n\n"); help();
	}
    return 0;
}