Opencv选取目标颜色最大轮廓并框出

最新推荐文章于 2025-06-21 16:49:51 发布

Maybemust

最新推荐文章于 2025-06-21 16:49:51 发布

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分类专栏：数字媒体技术文章标签： opencv

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本篇介绍如何使用OpenCV实现基于直方图反投影的颜色识别与定位，包括读取图片、定义感兴趣区域、计算颜色直方图、设置阈值、进行反向投影等步骤，并展示如何找到并突出显示最大连通区域。

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本讲实现：给定原始图片，通过直方图反投影画出目标颜色所在的所有区域中的最大区域的轮廓，并用矩形框出。
依赖的类文件见文章《Opencv直方图反投影识别颜色》
以下是实现的主程序

#include <opencv2\opencv.hpp>
#include "Histogram1D.h"//定义1维灰度直方图的获取与绘制方法类：class Histogram1D  
#include "ColorHistogram.h"//定义3维彩色直方图的获取与绘制方法类：class ColorHistogram   
#include "ContentFinder.h"//定义反向投影直方图检测方法的类：class ContentFinder  
using namespace std;
using namespace cv;
int main()
{
    // Read input image
    cv::Mat image = cv::imread("E:\\ProgramProject\\1.png");
    if (!image.data)
        return 0;
    // define image ROI
    cv::Mat imageROI;
    imageROI = image(cv::Rect(0, 0, 30, 24)); 

    ContentFinder finder;

    cv::Mat result1;
    cv::Mat color;
    ColorHistogram hc;
    hc.setSize(4); 
    cv::Mat shist = hc.getHistogram(imageROI);
    finder.setHistogram(shist);
    finder.setThreshold(0.09f);
    VideoCapture cap(1);
    while (true) {
        cap >> color;
        namedWindow("1");
        imshow("1", color);
        result1 = finder.find(color);
        Mat res;
        Mat element5(3, 3, CV_8U, Scalar(1));
        morphologyEx(result1, res, MORPH_CLOSE, element5);
        blur(res, res, Size(2, 2));
        vector<std::vector<Point> >contours;
        findContours(res, contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_NONE);
        unsigned __int64 cmin = 20;

        vector<vector<Point> >::const_iterator itc = contours.begin();
        int cnt = 0;
        int store = -1;
        while (itc != contours.end())
        {
            if (itc->size() > cmin)
            {
                cmin = itc->size();
                store = cnt;
            }
            itc++;
            cnt++;

        }
        if (store == -1) { 
            Mat black(res.size(), CV_8U, Scalar(255));
            cv::namedWindow("Color Detection Result");
            cv::imshow("Color Detection Result",black ); 
            continue;
        }
        else {
            Mat result1(res.size(), CV_8U, Scalar(255));
            drawContours(result1, contours, store, Scalar(0), 2);
            vector<Point> retan = contours[store];
            Rect r0 = boundingRect(Mat(retan));
            rectangle(result1, r0, Scalar(0), 2);

            /*float radius;
            Point2f center;
            minEnclosingCircle(Mat(contours[store]), center, radius);
            circle(result1, Point(center), static_cast<int>(radius), Scalar(0), 2);*/


            /*vector<Point> poly;
            approxPolyDP(Mat(contours[store]), poly, 5, true);
            vector<Point>::const_iterator itp = poly.begin();
            while (itp != (poly.end() - 1))
            {
                line(result1, *itp, *(itp + 1), Scalar(0), 2);
                ++itp;
            }
            line(result1, *(poly.begin()), *(poly.end() - 1), Scalar(20), 2);
            vector<Point> hull;
            convexHull(Mat(contours[store]), hull);*/


            /*itc = contours.begin();
            while (itc != contours.end())
            {
                Moments mom = cv::moments(Mat(*itc++));
                circle(result1, Point(mom.m10 / mom.m00, mom.m01 / mom.m00), 2, Scalar(2));
            }*/


            cv::namedWindow("Color Detection Result");
            cv::imshow("Color Det`
ction Result", result1);
        }
        char c = waitKey(33);
        if (c == 27) break;
    }
    return 0;
}