OpenCV之图像处理case(四) 对象计数

###代码
#include “…/common/common.hpp”

void main(int argc, char** argv) 
{
	Mat gray_src, binary, dst;
	Mat src = imread(getCVImagesPath("images/case3-4.png"));
	imshow("src3-4", src);
	cvtColor(src, gray_src, COLOR_BGR2GRAY);

	// 二值分割+形态学处理+距离变换+局部自适应阈值+连通区域计算
	// 二值分割
	threshold(gray_src, binary, 0, 255, THRESH_BINARY | THRESH_TRIANGLE);
	imshow("binary image", binary);

	// 形态学操作
	Mat kernel = getStructuringElement(MORPH_RECT, Size(3, 3), Point(-1, -1));
	// 膨胀，尽可能的将挨在一起的对象分隔开来，但不是代表挨着的一定能分开。膨胀的次数很重要，本案例膨胀次数4-5次都行
	dilate(binary, binary, kernel, Point(-1, -1), 4);
	imshow("dilate image", binary);

	// 距离变换
	Mat dist;
	bitwise_not(binary, binary); // 反差颜色值
	distanceTransform(binary, dist, CV_DIST_L2, 3);
	cout << "1 dist depth=" << dist.depth() << ", type=" << dist.type() << endl; // 1 dist depth=5, type=5
	normalize(dist, dist, 0, 1.0, NORM_MINMAX); // 归一化后才好分割山头
	cout << "2 dist depth=" << dist.depth() << ", type=" << dist.type() << endl; // 2 dist depth=5, type=5
	imshow("dist image", dist);

	// 阈值化二值分割
	//threshold(dist, dist, 0.6, 1.0, THRESH_BINARY);
	//normalize(dist, dist, 0, 255, NORM_MINMAX);
	Mat dist_8u;
	dist.convertTo(dist_8u, CV_8U);
	// 这里要使用 局部自适应阈值与ADAPTIVE_THRESH_GAUSSIAN_C， 使用ADAPTIVE_THRESH_MEAN_C 或直接使用 threshold ， 无法达到目的
	adaptiveThreshold(dist_8u, dist_8u, 255, ADAPTIVE_THRESH_GAUSSIAN_C, THRESH_BINARY, 85, 0.0); // 局部自适应阈值，这里使用ADAPTIVE_THRESH_GAUSSIAN_C
	//dilate(dist_8u, dist_8u, kernel, Point(-1, -1),	1); // 再次膨胀一下，膨胀的次数很重要，直接影响最终的对象计数，将两次膨胀次数合并在上面也可以
	imshow("dist-binary", dist_8u);

	// 连通区域计数
	vector> contours;
	findContours(dist_8u, contours, CV_RETR_EXTERNAL, CHAIN_APPROX_SIMPLE); // RETR_EXTERNAL:表示只检测最外层轮廓，对所有轮廓设置hierarchy[i][2]=hierarchy[i][3]=-1
	cout << "contours.size=" << contours.size() << endl; // contours.size=17

	// draw result
	Mat markers = Mat::zeros(src.size(), CV_8UC3);
	RNG rng(12345);
	for (size_t t = 0; t < contours.size(); t++) 
	{
		drawContours(markers, contours, static_cast(t), Scalar(rng.uniform(0, 255), rng.uniform(0, 255), rng.uniform(0, 255)), -1, 8, Mat());
	}
	printf("number of corns : %d", contours.size()); // number of corns : 17
	imshow("Final result", markers);

	waitKey(0);
}

###效果图