How can I merge blobs / contours - c++

Question

I use findContours for blob detection. Now I would merge close and similar blobs together.
Here are some sample images:
Is that possible with normal Opencv?

Answer 1

The input images you gave us are pretty easy to work with:
The first step is isolate the yellow blobs from everything else and a simple color segmentation technique can accomplish this task. You can take a look at Segmentation & Object Detection by color or Tracking colored objects in OpenCV to have an idea on how to do it.
Then, it's time to merge the blobs. One technique in particular that can be useful is the bounding box, to put all the blobs inside a rectangle. Notice in the images below, that there is a green rectangle surrounding the blobs:
After that, all you need to do is fill the rectangle with the color of your choice, thus connecting all the blobs. I'm leaving this last as homework for you.
This is the quickest and most simple approach I could think of. The following code demonstrates how to achieve what I just described:
#include <cv.h>
#include <highgui.h>
#include <iostream>
#include <vector>
int main(int argc, char* argv[])
{
cv::Mat img = cv::imread(argv[1]);
if (!img.data)
{
std::cout "!!! Failed to open file: " << argv[1] << std::endl;
return 0;
}
// Convert RGB Mat into HSV color space
cv::Mat hsv;
cv::cvtColor(img, hsv, CV_BGR2HSV);
// Split HSV Mat into HSV components
std::vector<cv::Mat> v;
cv::split(hsv,v);
// Erase pixels with low saturation
int min_sat = 70;
cv::threshold(v[1], v[1], min_sat, 255, cv::THRESH_BINARY);
/* Work with the saturated image from now on */
// Erode could provide some enhancement, but I'm not sure.
// cv::Mat element = cv::getStructuringElement(cv::MORPH_RECT, cv::Size(3, 3));
// cv::erode(v[1], v[1], element);
// Store the set of points in the image before assembling the bounding box
std::vector<cv::Point> points;
cv::Mat_<uchar>::iterator it = v[1].begin<uchar>();
cv::Mat_<uchar>::iterator end = v[1].end<uchar>();
for (; it != end; ++it)
{
if (*it) points.push_back(it.pos());
}
// Compute minimal bounding box
cv::RotatedRect box = cv::minAreaRect(cv::Mat(points));
// Display bounding box on the original image
cv::Point2f vertices[4];
box.points(vertices);
for (int i = 0; i < 4; ++i)
{
cv::line(img, vertices[i], vertices[(i + 1) % 4], cv::Scalar(0, 255, 0), 1, CV_AA);
}
cv::imshow("box", img);
//cv::imwrite(argv[2], img);
cvWaitKey(0);
return 0;
}

Answer 2

i think i did it, thanks to your program details i found this solution: (comments are welcome)
vector<vector<Point> > contours;
vector<vector<Point> > tmp_contours;
findContours(detectedImg, tmp_contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE);
vector<vector<Point> >::iterator it1;
it1 = tmp_contours.begin();
Mat test;
test = Mat(FImage.size(), CV_32FC3);
while (it1 != tmp_contours.end()) {
vector<Point> approx1;
approxPolyDP(Mat(*it1), approx1, 3, true);
Rect box1 = boundingRect(approx1);
float area1 = contourArea(approx1);
if ((area1 > 50) && (area1 < 13000) && (box1.width < 100) && (box1.height < 120)) {
vector<vector<Point> >::iterator it2;
it2 = tmp_contours.begin();
while (it2 != tmp_contours.end()) {
vector<Point> approx2;
approxPolyDP(Mat(*it2), approx2, 3, true);
Moments m1 = moments(Mat(approx1), false);
Moments m2 = moments(Mat(approx2), false);
float x1 = m1.m10 / m1.m00;
float y1 = m1.m01 / m1.m00;
float x2 = m2.m10 / m2.m00;
float y2 = m2.m01 / m2.m00;
vector<Point> dist;
dist.push_back(Point(x1, y1));
dist.push_back(Point(x2, y2));
float d = arcLength(dist, false);
Rect box2 = boundingRect(approx2);
if (box1 != box2) {
if (d < 25) {
//Method to merge the vectors
approx1 = mergePoints(approx1, approx2);
}
}
++it2;
}
Rect b = boundingRect(approx1);
rectangle(test, b, CV_RGB(125, 255, 0), 2);
contours.push_back(approx1);
}
++it1;
}