opencv实践::对象计数

 

问题描述
　　真实案例，农业领域经常需要计算对象个数 或者在其它领域拍照自动计数，可以提供效率，减低成本

 

解决思路 
　　通过二值分割+形态学处理+距离变换+连通区域计算 

 

 

 

 

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

using namespace cv;
using namespace std;

int main(int argc, char** argv) {
    Mat gray_src, binary, dst;
    Mat src = imread("D:/case5.png");
    if (src.empty()) {
        printf("could not load image...\n");
        return -1;
    }
    namedWindow("input image", CV_WINDOW_AUTOSIZE);
    imshow("input image", src);
    cvtColor(src, gray_src, COLOR_BGR2GRAY);

    // 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
    dilate(binary, binary, kernel, Point(-1, -1), 3);
    imshow("dilate image", binary);

    // 距离变换
    Mat dist;
    // 取反
    bitwise_not(binary, binary);
    distanceTransform(binary, dist, CV_DIST_L2, 3);
    // 归一化
    normalize(dist, dist, 0, 1.0, NORM_MINMAX);
    imshow("dist image", dist);

    // 阈值化二值分割
    Mat dist_8u;
    dist.convertTo(dist_8u, CV_8U);
    // 高斯阈值 101  必须是奇数
    adaptiveThreshold(dist_8u, dist_8u, 255, ADAPTIVE_THRESH_GAUSSIAN_C, THRESH_BINARY, 101, 0.0);
    kernel = getStructuringElement(MORPH_RECT, Size(3, 3), Point(-1, -1));
    dilate(dist_8u, dist_8u, kernel, Point(-1, -1), 2);
    imshow("dist-binary", 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;
}