int cluster_detections(int a[], int rs[], int cs[], int ss[], int qs[], int n)
{
int ncc, cc, idx;
//
ncc = find_connected_components(a, rs, cs, ss, n);
if(!ncc)
return 0;
//
idx = 0;
for(cc=1; cc<=ncc; ++cc)
{
int i, k;
int sumrs=0, sumcs=0, sumss=0, sumqs=0;
//
k = 0;
for(i=0; i<n; ++i)
if(a[i] == cc)
{
sumrs += rs[i];
sumcs += cs[i];
sumss += ss[i];
sumqs += qs[i];
++k;
}
///for(i=0; i<n; ++i)
/// if(a[i] == cc)
/// qs[i] = sumqs;
//
qs[idx] = sumqs; // accumulated confidence measure
//
rs[idx] = sumrs/k;
cs[idx] = sumcs/k;
ss[idx] = sumss/k;
//
++idx;
}
//
return idx;
}
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;
}