(opencv) merge contours together - merge

Question

I am doing a real time motion detection program. I find that there are a lot of contour made in my different image after i used background subtraction method . i would like to ask is there any method that can merge these contour together or make a larger rect contain all the contours?
the case now i have been done
http://singhgaganpreet.files.wordpress.com/2012/07/motioncolour.jpg
My code is here
#include <iostream>
#include <OpenCV/cv.h>
#include <OPenCV/highgui.h>
using namespace cv;
using namespace std;
CvRect rect;
CvSeq* contours = 0;
CvMemStorage* storage = NULL;
CvCapture *cam;
IplImage *currentFrame, *currentFrame_grey, *differenceImg, *oldFrame_grey;
bool first = true;
int main(int argc, char* argv[])
{
//Create a new movie capture object.
cam = cvCaptureFromCAM(0);
//create storage for contours
storage = cvCreateMemStorage(0);
//capture current frame from webcam
currentFrame = cvQueryFrame(cam);
//Size of the image.
CvSize imgSize;
imgSize.width = currentFrame->width;
imgSize.height = currentFrame->height;
//Images to use in the program.
currentFrame_grey = cvCreateImage( imgSize, IPL_DEPTH_8U, 1);
while(1)
{
currentFrame = cvQueryFrame( cam );
if( !currentFrame ) break;
//Convert the image to grayscale.
cvCvtColor(currentFrame,currentFrame_grey,CV_RGB2GRAY);
if(first) //Capturing Background for the first time
{
differenceImg = cvCloneImage(currentFrame_grey);
oldFrame_grey = cvCloneImage(currentFrame_grey);
cvConvertScale(currentFrame_grey, oldFrame_grey, 1.0, 0.0);
first = false;
continue;
}
//Minus the current frame from the moving average.
cvAbsDiff(oldFrame_grey,currentFrame_grey,differenceImg);
//bluring the differnece image
cvSmooth(differenceImg, differenceImg, CV_BLUR);
//apply threshold to discard small unwanted movements
cvThreshold(differenceImg, differenceImg, 25, 255, CV_THRESH_BINARY);
//find contours
cvFindContours( differenceImg, storage, &contours );
//draw bounding box around each contour
for(; contours!=0; contours = contours->h_next)
{
rect = cvBoundingRect(contours, 0); //extract bounding box for current contour
//drawing rectangle
cvRectangle(currentFrame,
cvPoint(rect.x, rect.y),
cvPoint(rect.x+rect.width, rect.y+rect.height),
cvScalar(0, 0, 255, 0),
2, 8, 0);
}
//display colour image with bounding box
cvShowImage("Output Image", currentFrame);
//display threshold image
cvShowImage("Difference image", differenceImg);
//New Background
cvConvertScale(currentFrame_grey, oldFrame_grey, 1.0, 0.0);
//clear memory and contours
cvClearMemStorage( storage );
contours = 0;
//press Esc to exit
char c = cvWaitKey(33);
if( c == 27 ) break;
}
// Destroy the image & movies objects
cvReleaseImage(&oldFrame_grey);
cvReleaseImage(&differenceImg);
cvReleaseImage(&currentFrame);
cvReleaseImage(&currentFrame_grey);
//cvReleaseCapture(&cam);
return 0;
}

Answer 1

Did you try this?
std::vector<cv::Point> points;
points.insert(points.end(), contour1.begin(), contour1.end());
points.insert(points.end(), contour2.begin(), contour2.end());
convexHull(cv::Mat(points), contour);
PS. For some applications, it may be better to use approxPoly() rather than convexHull(). Just try both.
PPS. Try smoothing the resulting contour with gaussian. It also can be helpful.

Answer 2

I came across a similar problem. In my case I created an empty sequence then I filled it with the points of each contour, after that I fitted a bounding ellipse with that sequence.
Here is my code segment...
CvMemStorage *storage = cvCreateMemStorage ();
CvMemStorage *storage1 = cvCreateMemStorage ();
CvSeq *contours = 0;
//find contour in BInv
cvFindContours (BInv, storage, &contours, sizeof(CvContour), CV_RETR_LIST,CV_CHAIN_APPROX_NONE ,cvPoint(0,0));
//creating empty sequence of CvPoint
CvSeq* seq = cvCreateSeq(CV_SEQ_ELTYPE_POINT/*| CV_SEQ_KIND_SET | CV_SEQ_FLAG_SIMPLE*/,sizeof(CvSeq),sizeof(CvPoint),storage1);
//populating seq with all contours
for(; contours!=0; contours = contours->h_next)
for(int i=0;i<contours->total;i++)
{
CvPoint* p;
p = (CvPoint*)cvGetSeqElem (contours, i );
cvSeqPush(seq,p);
}
//bounding box and drawing
CvBox2D bbox=cvMinAreaRect2(seq, NULL );
cvEllipseBox(color,bbox,cvScalarAll(0),5,8,0);
hope this helps.

Answer 3

If you want to merge contours on the basis of distance apart then you can do something like this:
struct hash_pair {
template <class T1, class T2>
size_t operator()(const pair<T1, T2>& p) const
{
auto hash1 = hash<T1>{}(p.first);
auto hash2 = hash<T2>{}(p.second);
if (hash1 != hash2) {
return hash1 ^ hash2;
}
return hash1;
}
};
void findPixelsNearby(unordered_map<pair<int, int>,bool,hash_pair>&res, Point px,int pxlVal) {
for (int itr1 = (px.x) - pxlVal; itr1 <= (px.x) + pxlVal; itr1++) {
for (int itr2 = (px.y - pxlVal); itr2 <= (px.y) + pxlVal; itr2++) {
res[{itr1, itr2}] = true;
}
}
}
unordered_map<pair<int, int>, bool, hash_pair> createSets(vector<Point2f>Contour, int rect) {
unordered_map<pair<int,int>,bool,hash_pair>res;
for (auto tra : Contour) {
Point px = (Point)tra;
findPixelsNearby(res,px,rect);
}
return res;
}
//void drawContour(Mat& img, vector<Point2f>s1,int px,int py,int pz) {
// for (auto x : s1) {
// line(img, x, x, Scalar(px, py, pz), 4, 0);
//
// }
// resShow("temp",img,1);
//}
bool hasCommon(unordered_map<pair<int,int>,bool,hash_pair>s1, unordered_map<pair<int, int>, bool, hash_pair>s2){
for (auto x : s1) {
if (s2.find(x.first) != s2.end()) {
return true;
}
}
return false;
}
void MergeContours(Mat image, vector<Contour>&usableContours,int distance_considered, vector<Contour>& finalContours) {
int numberContours = usableContours.size();
vector<vector<int>>ids_for_contour_merge(numberContours);
vector<unordered_map<pair<int, int>, bool, hash_pair>>ContourSets;
vector<bool>boolVals(numberContours,false);
for (int i = 0; i < numberContours; i++) {
ContourSets.push_back(createSets(usableContours[i].points, distance_considered/2));
}
for (int i = 0; i < numberContours; i++) {
if (boolVals[i] == false) {
boolVals[i] = true;
for (int j = i+1; j < numberContours; j++) {
if (boolVals[j] == false) {
if(hasCommon(ContourSets[i], ContourSets[j])==true){
ContourSets[i].insert(ContourSets[j].begin(), ContourSets[j].end());
boolVals[j] = true;
ids_for_contour_merge[i].push_back(j);
j = i;
}
}
}
}
}
vector<bool>Visited(ids_for_contour_merge.size(), false);
for (int mr = 0; mr < ids_for_contour_merge.size(); mr++) {
if (Visited[mr] == false) {
vector<Point2f>temp=usableContours[mr].points;
if (ids_for_contour_merge[mr].size() > 0) {
for (int mc = 0; mc < ids_for_contour_merge[mr].size(); mc++) {
int valPtr = ids_for_contour_merge[mr][mc];
copy(usableContours[valPtr].points.begin(), usableContours[valPtr].points.end(), std::back_inserter(temp));
Visited[valPtr] = true;
}
}
else {
Visited[mr] = true;
}
Contour newCtr;
newCtr.points = temp;
finalContours.push_back(newCtr);
}
}
///////////////////////////////////////////////////////////////DRAWING CONTOURS
/*for (auto x : finalContours) {
cout <<"CONTOURS FINAL SIZE IS : " <<x.points.size()<<endl;
int px = 0;
int py = 0;
int pz = 0;
drawContour(image, x.points, ((px+rand())%255), ((py + rand()) % 255), ((pz + rand()) % 255));
}*/
//////////////////////////////////////////////////////////////////////////////
}
More On Github: https://github.com/HimanshuYadav117/Merge-Contours/blob/main/MergeContours.cpp