cv::Scalar refineSegments(const cv::Mat &img, const cv::Mat &mask, cv::Mat &dst, std::vector<cv::Point> &contour, std::vector<cv::Point> &second_contour, cv::Point2i &previous)
{
//int niters = 3;
std::vector<std::vector<cv::Point> > contours;
std::vector<cv::Vec4i> hierarchy;
cv::Mat temp;
//cv::dilate(mask, temp, cv::Mat(), cv::Point(-1, -1), cv::niters);
//cv::erode(temp, temp, cv::Mat(), cv::Point(-1, -1), cv::niters * 2);
//cv::dilate(temp, temp, cv::Mat(), cv::Point(-1, -1), cv::niters);
cv::blur(mask, temp, Size(11, 11));
//cv::imshow("temp", temp);
temp = temp > 95.0;
cv::findContours(temp, contours, /*hierarchy,*/ CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE);
if (dst.data == NULL)
dst = cv::Mat::zeros(img.size(), CV_8UC1);
else
dst.setTo(cv::Scalar(0));
if (contours.size() == 0)
return cv::Scalar(-1, -1);
// iterate through all the top-level contours,
// draw each connected component with its own random color
int idx = 0, largestComp = -1, secondlargest = -1;
double maxWArea = 0, maxJArea = 0;
std::vector<double> justarea(contours.size());
std::vector<double> weightedarea(contours.size());
//for (; idx >= 0; idx = hierarchy[idx][0])
for (; idx < contours.size(); ++idx)
{
const std::vector<cv::Point> &c = contours[idx];
cv::Scalar _mean = cv::mean(cv::Mat(contours[idx]));
justarea[idx] = std::fabs(cv::contourArea(cv::Mat(c)));
weightedarea[idx] = std::fabs(cv::contourArea(cv::Mat(c))) /
((previous.x >- 1) ? (1.0 + cv::norm(cv::Point(_mean[0], _mean[1]) - previous)) : 1.0); // consider distance from last blob
}
for (idx = 0; idx < contours.size(); ++idx)
{
if (weightedarea[idx] > maxWArea)
{
maxWArea = weightedarea[idx];
largestComp = idx;
}
}
for (idx = 0; idx < contours.size(); ++idx)
{
if (justarea[idx] > maxJArea && idx != largestComp)
{
maxJArea = justarea[idx];
secondlargest = idx;
}
}
cv::Scalar color(255);
//std::cout << "largest cc " << largestComp << std::endl;
//cv::drawContours(dst, contours, largestComp, color, CV_FILLED); //, 8, hierarchy);
//for (idx = 0; idx < contours[largestComp].size() - 1; ++idx)
// cv::line(dst, contours[largestComp][idx], contours[largestComp][idx + 1], color, 2);
if (largestComp >= 0) {
int num = contours[largestComp].size();
cv::Point *pts = &(contours[largestComp][0]);
cv::fillPoly(dst, (const Point **)(&pts), &num, 1, color);
cv::Scalar b = cv::mean(cv::Mat(contours[largestComp]));
b[2] = justarea[largestComp];
contour.clear();
contour = contours[largestComp];
second_contour.clear();
if (secondlargest >= 0)
{
second_contour = contours[secondlargest];
b[3] = maxJArea;
}
previous.x = b[0];
previous.y = b[1];
return b;
}
else
return cv::Scalar(-1, -1);
}
Scalar refineSegments(const Mat& img,
Mat& mask,
Mat& dst,
vector<Point>& contour,
vector<Point>& second_contour,
Point2i& previous)
{
// int niters = 3;
vector<vector<Point> > contours;
vector<Vec4i> hierarchy;
Mat temp;
blur(mask, temp, Size(11,11));
temp = temp > 85.0;
findContours( temp, contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE );
if(dst.data==NULL)
dst = Mat::zeros(img.size(), CV_8UC1);
else
dst.setTo(Scalar(0));
if( contours.size() == 0 )
return Scalar(-1,-1);
// iterate through all the top-level contours,
// draw each connected component with its own random color
int idx = 0, largestComp = -1, secondlargest = -1;
double maxWArea = 0, maxJArea = 0;
vector<double> justarea(contours.size());
vector<double> weightedarea(contours.size());
// for( ; idx >= 0; idx = hierarchy[idx][0] )
for (; idx<contours.size(); idx++)
{
const vector<Point>& c = contours[idx];
Scalar _mean = mean(Mat(contours[idx]));
justarea[idx] = fabs(contourArea(Mat(c)));
weightedarea[idx] = fabs(contourArea(Mat(c))) /
((previous.x >- 1) ? (1.0 + norm(Point(_mean[0],_mean[1])-previous)) : 1.0); //consider distance from last blob
}
for (idx = 0; idx<contours.size(); idx++) {
if( weightedarea[idx] > maxWArea )
{
maxWArea = weightedarea[idx];
largestComp = idx;
}
}
for (idx = 0; idx < contours.size(); idx++) {
if ( justarea[idx] > maxJArea && idx != largestComp ) {
maxJArea = justarea[idx];
secondlargest = idx;
}
}
Scalar color( 255 );
// cout << "largest cc " << largestComp << endl;
// drawContours( dst, contours, largestComp, color, CV_FILLED); //, 8, hierarchy );
// for (idx=0; idx<contours[largestComp].size()-1; idx++) {
// line(dst, contours[largestComp][idx], contours[largestComp][idx+1], color, 2);
//
if(largestComp >= 0) {
//find top-left values
int maxx = -INT_MAX,miny = INT_MAX;
int num = contours[largestComp].size();
for (int i=0; i<num; i++) {
if(contours[largestComp][i].x > maxx) maxx = contours[largestComp][i].x;
if(contours[largestComp][i].y < miny) miny = contours[largestComp][i].y;
}
//crop contour to 150x150 "window"
vector<Point> newblob = contours[largestComp];
// int maxxp150 = MAX(maxx-200,0),minyp150 = MIN(miny+170,480);
//
// for (int i=0; i<num; i++) {
// Point _p = contours[largestComp][i];
// if(_p.x > maxxp150 && _p.y < minyp150) newblob.push_back(_p);
// else newblob.push_back(Point(MAX(_p.x,maxxp150),MIN(_p.y,minyp150)));
// }
Point* pts = &(newblob[0]);
num = newblob.size();
fillPoly(dst, (const Point**)(&pts), &num, 1, color);
Scalar b = mean(Mat(newblob)); //center of the blob
b[0] += 40; b[1] -= 40;
b[2] = justarea[largestComp];
contour.clear();
contour = newblob;
second_contour.clear();
if(secondlargest >= 0) {
second_contour = contours[secondlargest];
b[3] = maxJArea;
}
previous.x = b[0]; previous.y = b[1];
return b;
} else
return Scalar(-1,-1);
}
