如何合并斑点/轮廓

Question

我使用findContours进行斑点检测。现在我将合并近距离和相似的斑点。

下面是一些样品图片：

这有可能与正常opencv的？你给

Answer 1

输入图像我们是相当容易的工作：

的第一步是从一切隔离黄色斑点和简单的色彩分割技术可以做到这一点任务。你可以看看Segmentation & Object Detection by color或Tracking colored objects in OpenCV有一个想法如何做到这一点。

然后，是时候合并斑点。一种特别有用的技术是bounding box，以将所有斑点放在矩形内。在下面的图片请注意，有一个绿色的长方形周边的斑：

之后，所有你需要做的是填充矩形与您选择的颜色，从而连接所有的斑点。我将最后留给你做作业。

这是我能想到的最快最简单的方法。下面的代码演示了如何实现我刚才所描述的：

#include <cv.h> 
#include <highgui.h> 

#include <iostream> 
#include <vector> 

int main(int argc, char* argv[]) 
{ 
    cv::Mat img = cv::imread(argv[1]); 
    if (!img.data) 
    { 
     std::cout "!!! Failed to open file: " << argv[1] << std::endl; 
     return 0; 
    } 

    // Convert RGB Mat into HSV color space 
    cv::Mat hsv; 
    cv::cvtColor(img, hsv, CV_BGR2HSV); 

    // Split HSV Mat into HSV components 
    std::vector<cv::Mat> v; 
    cv::split(hsv,v); 

    // Erase pixels with low saturation 
    int min_sat = 70; 
    cv::threshold(v[1], v[1], min_sat, 255, cv::THRESH_BINARY); 

    /* Work with the saturated image from now on */ 

// Erode could provide some enhancement, but I'm not sure. 
// cv::Mat element = cv::getStructuringElement(cv::MORPH_RECT, cv::Size(3, 3)); 
// cv::erode(v[1], v[1], element); 

    // Store the set of points in the image before assembling the bounding box 
    std::vector<cv::Point> points; 
    cv::Mat_<uchar>::iterator it = v[1].begin<uchar>(); 
    cv::Mat_<uchar>::iterator end = v[1].end<uchar>(); 
    for (; it != end; ++it) 
    { 
     if (*it) points.push_back(it.pos()); 
    } 

    // Compute minimal bounding box 
    cv::RotatedRect box = cv::minAreaRect(cv::Mat(points)); 

    // Display bounding box on the original image 
    cv::Point2f vertices[4]; 
    box.points(vertices); 
    for (int i = 0; i < 4; ++i) 
    { 
      cv::line(img, vertices[i], vertices[(i + 1) % 4], cv::Scalar(0, 255, 0), 1, CV_AA); 
    } 

    cv::imshow("box", img); 
    //cv::imwrite(argv[2], img); 

    cvWaitKey(0); 

    return 0; 
} 

Answer 2

我想我做到了，感谢你的程序的详细信息，我发现这个解决方案：（评论欢迎）

vector<vector<Point> > contours; 
    vector<vector<Point> > tmp_contours; 
    findContours(detectedImg, tmp_contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE); 

    vector<vector<Point> >::iterator it1; 
    it1 = tmp_contours.begin(); 

    Mat test; 
    test = Mat(FImage.size(), CV_32FC3); 

    while (it1 != tmp_contours.end()) { 
     vector<Point> approx1; 
     approxPolyDP(Mat(*it1), approx1, 3, true); 
     Rect box1 = boundingRect(approx1); 
     float area1 = contourArea(approx1); 



     if ((area1 > 50) && (area1 < 13000) && (box1.width < 100) && (box1.height < 120)) { 

      vector<vector<Point> >::iterator it2; 
      it2 = tmp_contours.begin(); 

      while (it2 != tmp_contours.end()) { 
       vector<Point> approx2; 
       approxPolyDP(Mat(*it2), approx2, 3, true); 

       Moments m1 = moments(Mat(approx1), false); 
       Moments m2 = moments(Mat(approx2), false); 
       float x1 = m1.m10/m1.m00; 
       float y1 = m1.m01/m1.m00; 
       float x2 = m2.m10/m2.m00; 
       float y2 = m2.m01/m2.m00; 

       vector<Point> dist; 
       dist.push_back(Point(x1, y1)); 
       dist.push_back(Point(x2, y2)); 
       float d = arcLength(dist, false); 

       Rect box2 = boundingRect(approx2); 
       if (box1 != box2) { 

        if (d < 25) { 
         //Method to merge the vectors 
         approx1 = mergePoints(approx1, approx2); 
        } 

       } 
       ++it2; 

      } 
      Rect b = boundingRect(approx1); 
      rectangle(test, b, CV_RGB(125, 255, 0), 2); 
      contours.push_back(approx1); 
     } 
     ++it1; 
    }