步骤如下：

1.图片灰化；

2.中值滤波 去噪

3.求图片的光影（自动光学检测）

4.除法去光影

5.阈值操作

6.实现了三种目标检测方法

主要分两种连通区域和findContours

过程遇到了错误主要是图片忘了灰化处理，随机颜色的问题。下面代码都已经进行了解决

这是findContours的效果

下面是连通区域的结果

#include <opencv2\core\utility.hpp>#include <opencv2\imgproc.hpp>#include <opencv2\highgui.hpp>#include<opencv2\opencv.hpp>#include <opencv2\core\core.hpp>#include <opencv2\core\matx.hpp>#include<string>#include <iostream>#include <limits>using namespace std;using namespace cv;Mat img = imread("C:\\Users\\hasee\\Desktop\\luosi.jpg",0);Mat removeLight(Mat imge, Mat pattern, int method);Mat calculateLightPattern(Mat img);static Scalar randomColor(RNG& rng);void ConnectedComponents(Mat img);void ConnectedComponetsStats(Mat img);void FindContoursBasic(Mat img);void main(){Mat img_noise;medianBlur(img,img_noise,3);Mat pattern = calculateLightPattern(img_noise);Mat re_light = removeLight(img_noise, pattern, 1);Mat img_thr;threshold(re_light,img_thr,30,255,THRESH_BINARY);//ConnectedComponents(img_thr);ConnectedComponetsStats(img_thr);//FindContoursBasic(img_thr);waitKey(0);}Mat removeLight(Mat imge, Mat pattern, int method) {Mat aux;if (method == 1) {Mat img32, pattern32;imge.convertTo(img32, CV_32F);pattern.convertTo(pattern32, CV_32F);aux = 1 - (img32 / pattern32);aux = aux * 255;aux.convertTo(aux, CV_8U);}else {aux = pattern - imge;}return aux;}Mat calculateLightPattern(Mat img) {Mat pattern;blur(img, pattern, Size(img.cols / 3, img.cols / 3));return pattern;}static Scalar randomColor(RNG& rng){int icolor = (unsigned)rng;return Scalar(icolor & 255, (icolor >> 8) & 255, (icolor >> 16) & 255);}void ConnectedComponents(Mat img) {Mat lables;int num_objects = connectedComponents(img, lables);if (num_objects < 2) {cout << "未检测到目标" << endl;return;}else {cout << "检测到的目标数量： " << num_objects - 1 << endl;}Mat output = Mat::zeros(img.rows,img.cols,CV_8UC3);RNG rng(0xFFFFFFFF);for (int i = 1; i < num_objects;i++) {Mat mask = lables == i;output.setTo(randomColor(rng),mask);}imshow("Result",output);}void ConnectedComponetsStats(Mat img) {Mat labels, stats, centroids;int num_objects = connectedComponentsWithStats(img,labels,stats,centroids);if (num_objects<2) {cout << "未检测到目标" << endl;return;}else {cout << "检测到的目标数量： " << num_objects - 1 << endl;}Mat output = Mat::zeros(img.rows, img.cols, CV_8UC3);RNG rng(0xFFFFFFFF);for (int i = 1; i < num_objects; i++) {Mat mask = labels == i;output.setTo(randomColor(rng), mask);stringstream ss;ss << "area: " << stats.at<int>(i,CC_STAT_AREA);putText(output,ss.str(), centroids.at<Point2d>(i),FONT_HERSHEY_SIMPLEX,0.4,Scalar(255,255,255));}imshow("Result", output);}void FindContoursBasic(Mat img) {vector<vector<Point>> contours;findContours(img, contours, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE);Mat output = Mat::zeros(img.rows, img.cols, CV_8UC3);if (contours.size()==0) {cout << "未检测到对象" << endl;return;}else{cout << "检测到对象数量： " << contours.size() << endl;}RNG rng(0xFFFFFFFF);for (int i = 0; i < contours.size(); i++)drawContours(output,contours,i,randomColor(rng));imshow("Result", output);}

补充知识：SURF特征点检测与匹配之误匹配点删除

SURF特征点检测与匹配之误匹配点删除

SURF（SpeededUp Robust Feature）是加速版的具有鲁棒性的算法，是SIFT算法的加速版。

但是SURF特征匹配之后有大量的误匹配点，需要对这些误匹配点进行删除。

这里不从理论上讲解SURF原理等，直接说用法。

特征匹配的步骤分为三步：

1、找出特征点

2、描述特征点

3、特征点匹配

具体基本代码见最后。具体的可以看毛星云的书籍，但是个人认为其编程风格不严谨，自己有做改动。

但是匹配出来的结果如下：

有很多的误匹配点，如何对误匹配点进行删除呢。

双向匹配加距离约束。

实验结果如下：效果还是非常好的。

#include "stdafx.h" #include <opencv2\opencv.hpp> #include <opencv2\nonfree\nonfree.hpp> #include <opencv2\legacy\legacy.hpp> #include <iostream> int _tmain(int argc, _TCHAR* argv[]) { //读取图片 cv::Mat srcImg1 = cv::imread("1.jpg", 1); cv::Mat srcImg2 = cv::imread("2.jpg", 1); if (srcImg1.empty() || srcImg2.empty()) { std::cout << "Read Image ERROR!" << std::endl; return 0; } //SURF算子特征点检测 int minHessian = 700; cv::SurfFeatureDetector detector(minHessian);//定义特征点类对象 std::vector<cv::KeyPoint> keyPoint1, keyPoint2;//存放动态数组，也就是特征点 detector.detect(srcImg1, keyPoint1); detector.detect(srcImg2, keyPoint2); //特征向量 cv::SurfDescriptorExtractor extrator;//定义描述类对象 cv::Mat descriptor1, descriptor2;//描述对象 extrator.compute(srcImg1, keyPoint1, descriptor1); extrator.compute(srcImg2, keyPoint2, descriptor2); //BruteForce暴力匹配 cv::BruteForceMatcher <cv::L2<float>>matcher;//匹配器 std::vector <cv::DMatch> matches; matcher12.match(descriptor1, descriptor2, matches); //绘制关键点 cv::Mat imgMatch; cv::drawMatches(srcImg1, keyPoint1, srcImg2, keyPoint2, matches, imgMatch); cv::namedWindow("匹配图", CV_WINDOW_AUTOSIZE); cv::imshow("匹配图", imgMatch); cv::imwrite("匹配图.jpg", imgMatch); cv::waitKey(10); return 0; }

以上这篇浅谈opencv自动光学检测、目标分割和检测(连通区域和findContours)就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持。