Creating Bounding boxes and circles for contours

Goal

In this tutorial you will learn how to:

• Use the OpenCV function cv::boundingRect
• Use the OpenCV function cv::minEnclosingCircle

Theory

Code

This tutorial code's is shown lines below. You can also download it from here

#include "opencv2/imgcodecs.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/imgproc.hpp"
#include <iostream>

using namespace cv;
using namespace std;

Mat src; Mat src_gray;
int thresh = 100;
int max_thresh = 255;
RNG rng(12345);

void thresh_callback(int, void* );

int main( int argc, char** argv )
{
  CommandLineParser parser( argc, argv, "{@input | ../data/stuff.jpg | input image}" );
  src = imread( parser.get<String>( "@input" ), IMREAD_COLOR );
  if( src.empty() )
    {
      cout << "Could not open or find the image!\n" << endl;
      cout << "usage: " << argv[0] << " <Input image>" << endl;
      return -1;
    }

  cvtColor( src, src_gray, COLOR_BGR2GRAY );
  blur( src_gray, src_gray, Size(3,3) );

  const char* source_window = "Source";
  namedWindow( source_window, WINDOW_AUTOSIZE );
  imshow( source_window, src );

  createTrackbar( " Threshold:", "Source", &thresh, max_thresh, thresh_callback );

  thresh_callback( 0, 0 );

  waitKey(0);

  return(0);
}

void thresh_callback(int, void* )
{
  Mat threshold_output;
  vector<vector<Point> > contours;
  vector<Vec4i> hierarchy;

  threshold( src_gray, threshold_output, thresh, 255, THRESH_BINARY );

  findContours( threshold_output, contours, hierarchy, RETR_TREE, CHAIN_APPROX_SIMPLE, Point(0, 0) );

  vector<vector<Point> > contours_poly( contours.size() );
  vector<Rect> boundRect( contours.size() );
  vector<Point2f>center( contours.size() );
  vector<float>radius( contours.size() );

  for( size_t i = 0; i < contours.size(); i++ )
  {
    approxPolyDP( contours[i], contours_poly[i], 3, true );
    boundRect[i] = boundingRect( contours_poly[i] );
    minEnclosingCircle( contours_poly[i], center[i], radius[i] );
  }

  Mat drawing = Mat::zeros( threshold_output.size(), CV_8UC3 );

  for( size_t i = 0; i< contours.size(); i++ )
  {
    Scalar color = Scalar( rng.uniform(0, 255), rng.uniform(0,255), rng.uniform(0,255) );
    drawContours( drawing, contours_poly, (int)i, color, 1, 8, vector<Vec4i>(), 0, Point() );
    rectangle( drawing, boundRect[i].tl(), boundRect[i].br(), color, 2, 8, 0 );
    circle( drawing, center[i], (int)radius[i], color, 2, 8, 0 );
  }

  namedWindow( "Contours", WINDOW_AUTOSIZE );
  imshow( "Contours", drawing );
}

Explanation

The main function is rather simple, as follows from the comments we do the following:

1. Open the image, convert it into grayscale and blur it to get rid of the noise.

     CommandLineParser parser( argc, argv, "{@input | ../data/stuff.jpg | input image}" );
     src = imread( parser.get<String>( "@input" ), IMREAD_COLOR );
     if( src.empty() )
       {
         cout << "Could not open or find the image!\n" << endl;
         cout << "usage: " << argv[0] << " <Input image>" << endl;
         return -1;
       }
   
     cvtColor( src, src_gray, COLOR_BGR2GRAY );
     blur( src_gray, src_gray, Size(3,3) );

2. Create a window with header "Source" and display the source file in it.

     const char* source_window = "Source";
     namedWindow( source_window, WINDOW_AUTOSIZE );
     imshow( source_window, src );

3. Create a trackbar on the source_window and assign a callback function to it In general callback functions are used to react to some kind of signal, in our case it's trackbar's state change.

     createTrackbar( " Threshold:", "Source", &thresh, max_thresh, thresh_callback );

4. Explicit one-time call of thresh_callback is necessary to display the "Contours" window simultaniously with the "Source" window.

     thresh_callback( 0, 0 );

5. Wait for user to close the windows.

     waitKey(0);

The callback function thresh_callback does all the interesting job.

1. Writes to threshold_output the threshold of the grayscale picture (you can check out about thresholding here).

     threshold( src_gray, threshold_output, thresh, 255, THRESH_BINARY );

2. Finds contours and saves them to the vectors contour and hierarchy.

     findContours( threshold_output, contours, hierarchy, RETR_TREE, CHAIN_APPROX_SIMPLE, Point(0, 0) );

3. For every found contour we now apply approximation to polygons with accuracy +-3 and stating that the curve must me closed.

   After that we find a bounding rect for every polygon and save it to boundRect.

   At last we find a minimum enclosing circle for every polygon and save it to center and radius vectors.

     for( size_t i = 0; i < contours.size(); i++ )
     {
       approxPolyDP( contours[i], contours_poly[i], 3, true );
       boundRect[i] = boundingRect( contours_poly[i] );
       minEnclosingCircle( contours_poly[i], center[i], radius[i] );
     }

   We found everything we need, all we have to do is to draw.

4. Create new Mat of unsigned 8-bit chars, filled with zeros. It will contain all the drawings we are going to make (rects and circles).

     Mat drawing = Mat::zeros( threshold_output.size(), CV_8UC3 );

5. For every contour: pick a random color, draw the contour, the bounding rectangle and the minimal enclosing circle with it,

     for( size_t i = 0; i< contours.size(); i++ )
     {
       Scalar color = Scalar( rng.uniform(0, 255), rng.uniform(0,255), rng.uniform(0,255) );
       drawContours( drawing, contours_poly, (int)i, color, 1, 8, vector<Vec4i>(), 0, Point() );
       rectangle( drawing, boundRect[i].tl(), boundRect[i].br(), color, 2, 8, 0 );
       circle( drawing, center[i], (int)radius[i], color, 2, 8, 0 );
     }

6. Display the results: create a new window "Contours" and show everything we added to drawings on it.

     namedWindow( "Contours", WINDOW_AUTOSIZE );
     imshow( "Contours", drawing );

   Result

Here it is: