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take a look at the code below.

i used this way : firstly i found the smallest blob's area (minimum_area)

and later used contourArea / minimum_areafor each blob to increase total blobs count.

(it is not perfect but just to show a way you can improve this approach for better results)

#include <opencv2/highgui.hpp>
#include <opencv2/imgproc.hpp>

using namespace cv;
using namespace std;

int main( int argc, const char** argv )
{
    Mat img = imread(argv[1]);
    if(img.empty())
    {
        return -1;
    }

    vector<vector<Point> > contours;

    Mat bw,bgr[3];
    split( img,bgr );

    int red_blobs_count = 0;

    cvtColor(bgr[1],bw,COLOR_GRAY2BGR);
    bw = img-bw;
    cvtColor(bw,bw,COLOR_BGR2GRAY);

    bw = bw > 10;

    findContours( bw, contours, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE );

    int minimum_area = img.cols * img.rows;
    for( size_t i = 0; i< contours.size(); i++ )
    {
        int _contourArea = contourArea(contours[i]);
        if( _contourArea > 10 & minimum_area > _contourArea )
        {
            minimum_area = _contourArea ;
        }
    }

    for( size_t i = 0; i< contours.size(); i++ )
    {
        int _contourArea = contourArea(contours[i]);
        if( _contourArea > 10 )
        {
            Rect minRect = boundingRect( Mat(contours[i]) );
            rectangle(img,minRect,Scalar(0,255,0));
            red_blobs_count += _contourArea / minimum_area;
            putText(img, format("%.02f",(float)_contourArea / minimum_area), minRect.tl(),FONT_HERSHEY_SIMPLEX, 0.5, Scalar(255,255,0), 2);
            putText(img, format("%d",red_blobs_count), minRect.br(),FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0,127,255), 2);
        }
    }
    putText(img, format("red_blobs_count = %d",red_blobs_count), Point(50, 30),FONT_HERSHEY_SIMPLEX, 0.8, Scalar(0,255,0), 2);

    imshow( "red_blobs_count", img );
    waitKey(0);

    return 0;
}

you can check results of this image. you can see some groups not calculated right but you can improve calculation algorithm.