标记蓝色区域的外部轮廓

这里简单记录一下轮廓的提取，便于以后需要时查找。

#include <opencv2/core/core.hpp>
#include <opencv2/imgcodecs.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/opencv.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <iostream>

using namespace std;

int main(int argc, char **argv)
{
    cv::Mat img = cv::imread("./3.JPG");       //读入图片
    if(img.empty())                                                                              //判断是否为空                                        
    {
        cout << "CANNOT LOAD!" << endl;
        return -1;
    }

    cv::Mat image;
    image = img.clone();                                                                    //深拷贝

    int height = image.rows;
    int width = image.cols;

    for(int row = 0; row < height; row++)                                    //遍历像素，修改颜色
    {
        for(int col = 0; col < width; col++)				     //红色部分修改为蓝色
        {
            //<cv::Vec3b>
            if(image.at<cv::Vec3b>(row, col)[2] >= 50){
                if(image.at<cv::Vec3b>(row, col)[1] <= 50){
                    image.at<cv::Vec3b>(row, col)[0] = 255;
                    image.at<cv::Vec3b>(row, col)[1] = 0;
                    image.at<cv::Vec3b>(row, col)[2] = 0;
                }
            }
        }
    }

    //cv::imshow("image", image);
    //cv::waitKey(1000);

    cv::Mat img_gray, output_bin;                                               //img_gray:灰度图；output_bin:二值图
    cv::cvtColor(image, img_gray, CV_BGR2GRAY);             //转换为灰度图

    //cv::imshow("image", img_gray);
    //cv::waitKey(1000);

    cv::threshold(img_gray, output_bin, 50, 255, CV_THRESH_BINARY_INV);  //转换为二值图

    //cv::imshow("image", output_bin);
    //cv::waitKey(1000);

    vector<vector<cv::Point>> contours;                                 //定义轮廓(多个)
    vector<vector<cv::Point>> contours_max;                      //定义最大轮廓(只存放一个最大的)
    vector<cv::Vec4i> hierarchy;
    cv::findContours(output_bin, contours, hierarchy, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_NONE);
    //4个参数分别为：输入二值图像；包含points的vector的vector；轮廓检索模式；近似方法
    //cv::findContours(output_bin, contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_NONE); 
    
    int size = 0;
    int index = 0;
    int number = 0;
    for(int i = 0; i < contours.size(); i++)
    {
        number = contours[i].size();
        if(number > size){                                                                  //查找各个轮廓中，点最多的轮廓
            size = number;                                                                    //点的个数赋值给size
            index = i;                                                                                //轮廓的索引赋值给index
        }
    }
    cout << "The current largest contours is " << index <<"; Size : "<< size << endl;
    contours_max.push_back(contours[index]);                //将最大的轮廓存储到contours_max，以便后面画出最大的轮廓      

    int itv = 0;
    int rem = size%64;
    if(rem == 0){                                                       
        itv = size/64;                                                                             //计算取样点之间的距离：itv
    }
    if(rem != 0){
        itv = (size - rem)/64;
    }
    cout << "The sample spacing is : "<< itv <<endl;

    int j = 0;
    vector<cv::Point> points;                                                       //定义vector容器points，存储取样的点
    while(j < (itv*64))
    {
        cv::Point P = contours[index][j];
        points.push_back(P);
        j += itv;
    }

    cout << "The number of points : "<< points.size() <<endl;
    cout << points << endl;

    cv::drawContours(img, contours_max, -1, cv::Scalar(0, 0, 255), 2);    //画出轮廓
    
    //cv::imshow("image", img);
    //cv::waitKey(1000);
    
    for(int i = 0; i < points.size(); i++)                                                             //遍历points中的点
     {
         cv::Point P = points[i];
         cv::circle(img, P, 5, cv::Scalar(255, 255, 255));                             //画圈
     }

    cv::imshow("image", img);
    cv::waitKey();

    return 0;
}