opencv——对象计数

 思路：

1、通过形态学操作、阈值处理、距离变换等方法，使得各个轮廓分开

2、计算轮廓数量

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

 using namespace cv;
 using namespace std;

 int main(int argc, char** argv)
 {
     Mat src = imread("计数.jpg");
     //medianBlur(src, src,5);//中值滤波，去除椒盐噪声
     imshow("src", src);

     Mat src_gray,binary;

     cvtColor(src,src_gray,COLOR_BGR2GRAY);

     threshold(src_gray,binary,,,THRESH_BINARY|THRESH_TRIANGLE);//颜色单一时，使用THRESH_TRIANGLE比OTSU好
     imshow("binary", binary);

     //形态学操作
     Mat kernel = getStructuringElement(MORPH_RECT,Size(,));
     dilate(binary, binary, kernel,Point(-,-),);

     //距离变换
     Mat dist;
     bitwise_not(binary, binary);//取反
     distanceTransform(binary,dist,CV_DIST_L2,);
     normalize(dist, dist,,1.0,NORM_MINMAX);
     imshow("dist", dist);

     //阈值化二值分割
     //threshold(dist, dist,0.7,1.0,THRESH_BINARY);//对距离进行筛选，去除边缘部分
     //normalize(dist, dist, 0, 255, NORM_MINMAX);
     Mat dist_8U;
     dist.convertTo(dist_8U,CV_8U);
     adaptiveThreshold(dist_8U, dist_8U, , ADAPTIVE_THRESH_GAUSSIAN_C, THRESH_BINARY, , 0.0);//自适应阈值，代替上面的阈值操作
     //形态学操作，使得断开部分连接
     kernel = getStructuringElement(MORPH_RECT, Size(, ), Point(-, -));
     dilate(dist_8U, dist_8U, kernel, Point(-, -),);

     imshow("dist_8U", dist_8U);

     // 连通区域计数
     vector<vector<Point>> contours;
     findContours(dist_8U, contours, CV_RETR_EXTERNAL, CHAIN_APPROX_SIMPLE);

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

     waitKey();

     return ;
 }

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

using namespace cv;
using namespace std;

int main(int argc, char** argv)
{
    Mat src = imread("计数.jpg");
    //medianBlur(src, src,5);//中值滤波，去除椒盐噪声
    imshow("src", src);

Mat src_gray,binary;
    
    cvtColor(src,src_gray,COLOR_BGR2GRAY);
    
    threshold(src_gray,binary,0,255,THRESH_BINARY|THRESH_TRIANGLE);//颜色单一时，使用THRESH_TRIANGLE比OTSU好
    imshow("binary", binary);

//形态学操作
    Mat kernel = getStructuringElement(MORPH_RECT,Size(5,5));
    dilate(binary, binary, kernel,Point(-1,-1),4);

//距离变换
    Mat dist;
    bitwise_not(binary, binary);//取反
    distanceTransform(binary,dist,CV_DIST_L2,3);
    normalize(dist, dist,0,1.0,NORM_MINMAX);
    imshow("dist", dist);

//阈值化二值分割
    //threshold(dist, dist,0.7,1.0,THRESH_BINARY);//对距离进行筛选，去除边缘部分
    //normalize(dist, dist, 0, 255, NORM_MINMAX);
    Mat dist_8U;
    dist.convertTo(dist_8U,CV_8U);
    adaptiveThreshold(dist_8U, dist_8U, 255, ADAPTIVE_THRESH_GAUSSIAN_C, THRESH_BINARY, 85, 0.0);//自适应阈值，代替上面的阈值操作
    //形态学操作，使得断开部分连接
    kernel = getStructuringElement(MORPH_RECT, Size(5, 5), Point(-1, -1));
    dilate(dist_8U, dist_8U, kernel, Point(-1, -1),3);

imshow("dist_8U", dist_8U);

// 连通区域计数
    vector<vector<Point>> contours;
    findContours(dist_8U, contours, CV_RETR_EXTERNAL, CHAIN_APPROX_SIMPLE);

// 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<int>(t), Scalar(rng.uniform(0, 255), rng.uniform(0, 255), rng.uniform(0, 255)),
            -1, 8, Mat());
    }
    printf("number of corns : %d", contours.size());
    imshow("Final result", markers);

waitKey(0);

return 0;
}