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这篇文章主要讲解了opencv自动光学检测、目标分割和检测的详细分析,内容清晰明了,对此有兴趣的小伙伴可以学习一下,相信大家阅读完之后会有帮助。
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#include #include #include #include #include #include #include #include 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 (i,CC_STAT_AREA); putText(output,ss.str(), centroids.at (i),FONT_HERSHEY_SIMPLEX,0.4,Scalar(255,255,255)); } imshow("Result", output); } void FindContoursBasic(Mat img) { vector > 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); }
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