(三)OpenCV图像处理_04_对象计数

1. 计算对象个数
   通过二值分割+形态学处理+距离变换+形态学变换(具体问题具体分析)+连通区域计算

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

using namespace std;
using namespace cv;

int main(int argc, char** argv)
{
	Mat src,temp,temp_mor,temp_dist,dst;
	src = imread("../path.jpg",IMREAD_GRAYSCALE);
	if (src.empty())
	{
		cout << "could not load image..." << endl;
		return -1;
	}

	namedWindow("src", WINDOW_AUTOSIZE);
	imshow("src", src);
	//通过二值分割
	threshold(src, temp, 0, 255, THRESH_BINARY | THRESH_TRIANGLE);
	//imshow("Threshold", temp);
	
	//形态学处理
	Mat kernel = getStructuringElement(MORPH_RECT, Size(3, 3), Point(-1, -1));
	dilate(temp, temp_mor, kernel, Point(-1, -1), 1);//膨胀
	//imshow("MorphShape", temp_mor);
	
	//距离变换
	bitwise_not(temp_mor, temp_mor);//取反,物体变白,背景变黑
	distanceTransform(temp_mor, temp_dist, DIST_L2, 3);
	normalize(temp_dist, temp_dist, 0, 0.9, NORM_MINMAX);
	imshow("Distance", temp_dist);
	
	// 阈值化二值分割
	Mat temp_dist_8u;
	temp_dist.convertTo(temp_dist_8u, CV_8U);
	//threshold(temp_dist_8u, temp_dist_8u, 0.5, 1, THRESH_BINARY);//使用阈值，再次二值化，得到标记
	adaptiveThreshold(temp_dist_8u, temp_dist_8u, 255, ADAPTIVE_THRESH_GAUSSIAN_C, THRESH_BINARY,81, 0);//使用自适应阈值，再次二值化，得到标记
	//erode(temp_dist_8u, temp_dist_8u, kernel, Point(-1, -1), 1);
	dilate(temp_dist_8u, temp_dist_8u, kernel, Point(-1, -1), 3);//形态学操作,分割各个连通区域

	imshow("Dilate", temp_dist_8u);
	
	//连通区域计算
	vector<vector<Point>> contours;
	findContours(temp_dist_8u, contours, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE);//寻找(外部)轮廓
	
	dst = Mat::zeros(src.size(), CV_8UC3);
	RNG rng;
	for (size_t i = 0; i < contours.size(); i++)
	{
		//绘制轮廓
		drawContours(dst, contours, static_cast<int>(i), Scalar(rng.uniform(0, 255), rng.uniform(0, 255), rng.uniform(0, 255)),-1, 8);
	}
	cout << "共有轮廓:" << contours.size() << endl;
	imshow("Final", dst);

	waitKey(0);
	return 0;
}

src为:

输出结果为: