[Solved] find all the cards that are above and their center

edit

Well gfx, I had some free time and I worked a bit on your problem. I considered that you wanted to detect all the individual cards on the table both full/partially occluded or not. Here are my results:

#include <opencv2/opencv.hpp>
#include <iostream>

int main()
{
    // Load your image
    cv::Mat src = cv::imread("cards1.png");

    // Check if everything was fine
    if (!src.data)
        return -1;

    // Show source image
    cv::imshow("Source Image", src);

// Change the background from white to black, since that will help later to extract
// better results during the use of Distance Transform
for( int x = 0; x < src.rows; x++ ) {
  for( int y = 0; y < src.cols; y++ ) {
      if ( src.at<cv::Vec3b>(x, y) == cv::Vec3b(255,255,255) ) {
        src.at<cv::Vec3b>(x, y)[0] = 0;
        src.at<cv::Vec3b>(x, y)[1] = 0;
        src.at<cv::Vec3b>(x, y)[2] = 0;
      }
    }
}

// Show output image
cv::imshow("Black Background Image", src);

// Create a kernel that we will use for accuting/sharpening our image
cv::Mat kernel = (cv::Mat_<float>(3,3) <<
        1,  1, 1,
        1, -8, 1,
        1,  1, 1); // an approximation of second derivative, quite strong

// do the laplacian filtering as it is
// well, we need to convert everything in something more deeper then CV_8U
// because the kernel has some negative values,
// and we can expect in general to have a Laplacian image with negative values
// BUT a 8bits unsigned int (the one we are working with) can contain values from 0 to 255
// so the possible negative number will be truncated
cv::Mat imgLaplacian;
cv::Mat sharp = src;
cv::filter2D(sharp, imgLaplacian, CV_32F, kernel);
src.convertTo(sharp, CV_32F);
cv::Mat imgResult = sharp - imgLaplacian;

// convert back to 8bits gray scale
imgResult.convertTo(imgResult, CV_8UC3);
imgLaplacian.convertTo(imgLaplacian, CV_8UC3);

// imshow( "laplacian", imgLaplacian );
imshow( "New Sharped Image", imgResult );

// Create binary image from source image
cv::Mat bw;
src = imgResult;
cv::cvtColor(src, bw, CV_BGR2GRAY);
cv::threshold(bw, bw, 40, 255, CV_THRESH_BINARY | CV_THRESH_OTSU);
cv::imshow("Binary Image", bw);

// Perform the distance transform algorithm
cv::Mat dist;
cv::distanceTransform(bw, dist, CV_DIST_L2, 3);

// Normalize the distance image for range = {0.0, 1.0}
// so we can visualize and threshold it
cv::normalize(dist, dist, 0, 1., cv::NORM_MINMAX);
cv::imshow("Distance Transform Image", dist);

// Threshold to obtain the peaks
// This will be the markers for the foreground objects
cv::threshold(dist, dist, .4, 1., CV_THRESH_BINARY);

// Dilate a bit
cv::Mat kernel1 = cv::Mat::ones(3, 3, CV_8UC1);
cv::dilate(dist, dist, kernel1);
cv::imshow("Peaks", dist);

// Create the CV_8U version of the distance image
// It is needed for cv::findContours()
cv::Mat dist_8u;
dist.convertTo(dist_8u, CV_8U);

// Find total markers
std::vector<std::vector<cv::Point> > contours;
cv::findContours(dist_8u, contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE);
int ncomp = contours.size();

// Create the marker image for the watershed algorithm
cv::Mat markers = cv::Mat::zeros(dist.size(), CV_32SC1);

// Draw the foreground markers
for (int i = 0; i < ncomp; i++)
    cv::drawContours(markers, contours, i, cv::Scalar::all(i+1), -1 ...