在OpenCV和C++中从轮廓中心寻找最近的黑色像素

Question

我想从轮廓的中心找到一个点，它是最接近黑色像素或非轮廓像素的点。我想创建一条从中心到那一点的直线。这里的是我当前的代码：

#include<iostream> 
#include<opencv2\highgui\highgui.hpp> 
#include<opencv2\imgproc\imgproc.hpp> 
#include<opencv2\core\core.hpp> 
#include<opencv2/opencv.hpp> 
#include<opencv2/core/core.hpp> 
#include<opencv2/imgproc/imgproc.hpp> 
#include<opencv2/highgui/highgui.hpp> 
#include<opencv2/objdetect/objdetect.hpp> 
#include<Windows.h> 
#include <sstream> 
using namespace cv; 
using namespace std; 

void on_trackbar(int, void*); 
void createTrackbars(); 
void morphit(Mat &img); 
void toggle(int); 

const int MAX_NUM_OBJECTS = 500; 

const int FRAME_WIDTH = 900; 
const int FRAME_HEIGHT = 600; 

const int MIN_OBJECT_AREA = 20 * 20; 
const int MAX_OBJECT_AREA = FRAME_HEIGHT*FRAME_WIDTH/1.5; 

Point middle; 

int l_MIN = 30; 
int l_MAX = 165; 
int a_MIN = 139; 
int a_MAX = 165; 
int b_MIN = 136; 
int b_MAX = 172; 

int kerode = 2; 
int kdilate = 5; 

bool domorph = true; 
bool showchangedframe = true; 

int main(int argc, char** argv) 
{ 
    createTrackbars(); 
    on_trackbar(0, 0); 

    int x, y; 
    Mat frame, labframe, rangeframe; 
    Mat newframe, newrf; 
    int key; 
    //VideoCapture cap(0); 

    while ((key = waitKey(30)) != 27) 
    { 
     toggle(key); 
     frame = imread(argv[1], 1); 
     newframe = imread(argv[1], 1); 
     //cap >> frame; 
     //cap >> newframe; 
     //flip(frame, frame, 180); 
     //flip(newframe, newframe, 180); 
     newframe = Scalar(0, 0, 0); 
     cvtColor(frame, labframe, COLOR_BGR2Lab); 
     inRange(labframe, Scalar(l_MIN, a_MIN, b_MIN), Scalar(l_MAX, a_MAX, b_MAX), rangeframe); 
     newrf = rangeframe.clone(); 

     int largest_area = 0; 
     int largest_contour_index = 0; 
     vector<vector<Point> > contours; 

     findContours(newrf, contours, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE); 

     vector<Moments> mu(contours.size()); //get moments 
     for (int i = 0; i < contours.size(); i++) 
     { 
      mu[i] = moments(contours[i], false); 
     } 

     vector<Point2f> mc(contours.size()); //get centers 
     for (int i = 0; i < contours.size(); i++) 
     { 
      mc[i] = Point2f(mu[i].m10/mu[i].m00, mu[i].m01/mu[i].m00); 
     } 

     for (int i = 0; i < contours.size(); i++) //iterate through each contour. 
     { 
      double a = contourArea(contours[i], false); //Find the area of contour 

      if (a>largest_area) 
      { 
       largest_area = a; 
       largest_contour_index = i;    //Store the index of largest contour 
      } 
     } 

     drawContours(newframe, contours, largest_contour_index, CV_RGB(255, 0, 0), CV_FILLED); 
     circle(newframe, mc[largest_contour_index], 5, CV_RGB(255, 255, 0), -1, 8, 0); 

     imshow("Detected", newframe); 

     if (showchangedframe) 
      imshow("Camera", frame); 

     else 
      imshow("Camera", rangeframe); 
    } 
} 

void on_trackbar(int, void*) 
{ 
    if (kerode == 0) 
     kerode = 1; 
    if (kdilate == 0) 
     kdilate = 1; 
} 

void createTrackbars() 
{ 
    String trackbarWindowName = "TrackBars"; 
    namedWindow(trackbarWindowName, WINDOW_NORMAL); 
    createTrackbar("l_MIN", trackbarWindowName, &l_MIN, l_MAX, on_trackbar); 
    createTrackbar("l_MAX", trackbarWindowName, &l_MAX, l_MAX, on_trackbar); 
    createTrackbar("a_MIN", trackbarWindowName, &a_MIN, a_MAX, on_trackbar); 
    createTrackbar("a_MAX", trackbarWindowName, &a_MAX, a_MAX, on_trackbar); 
    createTrackbar("b_MIN", trackbarWindowName, &b_MIN, b_MAX, on_trackbar); 
    createTrackbar("b_MAX", trackbarWindowName, &b_MAX, b_MAX, on_trackbar); 
    createTrackbar("Erosion", trackbarWindowName, &kerode, 31, on_trackbar); 
    createTrackbar("Dilation", trackbarWindowName, &kdilate, 31, on_trackbar); 
} 

void morphit(Mat &img) 
{ 
    erode(img, img, getStructuringElement(MORPH_RECT, Size(kerode, kerode))); 
    dilate(img, img, getStructuringElement(MORPH_RECT, Size(kdilate, kdilate))); 
} 

void toggle(int key) 
{ 
    if (key == 'r') 
     showchangedframe = !showchangedframe; 
} 

我的输出变为由以下 Output

我想是创建如下：在advance..much帮助 Desired Output

由于是必要的。

Answer 1

您应该看看distance transform主题。

您将获得一张地图，其中包含每个像素使用所需的距离类型（L1，L2，...）最近的轮廓的坐标。

在OpenCV中有一个教程distanceTransform()：Image Segmentation with Distance Transform and Watershed Algorithm。