本案例主要完成对玉米颗粒数量的计算，由于颗粒存在重叠的现象，因此需要对这种情况进行一定的图像处理，分离出每个颗粒的轮廓。

代码：

#include <iostream>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/opencv.hpp>
#include <math.h>
#include <iostream>

using namespace std;
using namespace cv;

//double threshold = 200.0;


int main()
{
	Mat grayimg, BinaryImg, morhImage;
	Mat img = imread("../img/44.png");
	imshow("原图", img);
	//二值化操作
	cvtColor(img, grayimg, COLOR_BGR2GRAY);
	threshold(grayimg, BinaryImg ,245 ,255,THRESH_BINARY );
	imshow("二值化",BinaryImg);

	//形态学腐蚀
	Mat kernel = getStructuringElement(MORPH_RECT, Size(15,15));
	dilate(BinaryImg ,morhImage,kernel);
	imshow("morphology", morhImage);

	//距离变换：用于二值化图像中的每一个非零点距自己最近的零点的距离，距离变换图像上越亮的点，代表了这一点距离零点的距离越远。
	Mat dist;
	bitwise_not(morhImage, morhImage);
	distanceTransform(morhImage, dist, CV_DIST_L2, 3);
	normalize(dist, dist, 0, 1.0, NORM_MINMAX);   //范围在0~1之间
	imshow("distance", dist);

	//形态学处理
	Mat MorphImg;
	dist.convertTo(MorphImg, CV_8U);
	threshold(MorphImg, MorphImg, 0.99, 255, THRESH_BINARY);  //上面像素值在0~1之间
	kernel = getStructuringElement(MORPH_RECT, Size(11, 11), Point(-1, -1));
	morphologyEx(MorphImg, MorphImg, MORPH_OPEN, kernel);  //开操作
	imshow("t-distance" , MorphImg);

	//找到种子的轮廓区域
	vector<vector<Point>> contours;
	findContours(MorphImg, contours, CV_RETR_EXTERNAL, CHAIN_APPROX_SIMPLE);
	// draw result
	Mat markers = Mat::zeros(img.size(), CV_8UC3);
	RNG rng(12345);     //随机产生颜色
	for (int i = 0; i < contours.size(); i++)
	{
		drawContours(markers, contours, i, Scalar(rng.uniform(0, 255), rng.uniform(0, 255), rng.uniform(0, 255)), -1, 8, Mat());
	}
	cout << "number of corns: " << contours.size() << endl;
	imshow("result Img", markers);
	
	waitKey(0);
}

结果：