简单的形态学处理流程

简单的形态学处理流程–标出图中硬币的位置

通过一个简单的例子——找出图像中的硬币轮廓，来简单的介绍一下形态学处理的流程。

下面是例子用到的图片：

流程

• 灰度化—cvtColor(sourceImage, grayImage, CV_BGR2GRAY);

• 阈值化—threshold(grayImage, thresholdImage, 100, 255, 0);

• 形态学处理 — morphologyEx（）

• 找到并标出轮廓— findContours() 和 drawContours()

代码

#include <iostream>#include <opencv2/opencv.hpp>using namespace cv;using namespace std;void main(){    //【1】     Mat sourceImage = imread("coin.jpg");    if (sourceImage.empty()){         cout << "can't grab a picture " << endl;          return;     }    imshow("source image", sourceImage);    //【2】 转换为灰度图    Mat grayImage;    cvtColor(sourceImage, grayImage, CV_BGR2GRAY);    imshow("gray image", grayImage);    //【3】 阈值化    Mat thresholdImage;    threshold(grayImage, thresholdImage, 100, 255, 0);    imshow("threshold image", thresholdImage);    //【4】 通过开闭运算，进行形态学处理    Mat element1 = getStructuringElement(MORPH_ELLIPSE,Size(15, 15));       //将硬币的内部凸显出来    morphologyEx(thresholdImage, thresholdImage, MORPH_CLOSE, element1);    Mat element2 = getStructuringElement(MORPH_RECT ,Size(5, 5));               //滤掉周围的小点    morphologyEx(thresholdImage, thresholdImage, MORPH_OPEN, element2);    imshow("filtered image", thresholdImage);    //【5】 找到轮廓并标出    vector<vector<Point>> contours;    vector<Vec4i> hierarchy;    findContours(thresholdImage, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, Point(0,0));    RNG g_rng(999999);    for (int i = 0; i< contours.size() ; i++)    {        Scalar color = Scalar(g_rng.uniform(0, 255), g_rng.uniform(0, 255), g_rng.uniform(0, 255));//任意值        drawContours(sourceImage, contours, i, color, 2, 8, hierarchy, 0, Point());    }    imshow("finished", sourceImage);    waitKey(0);    return;}

结果截图：
阈值化后

形态学处理后

最后结果