【计算机视觉】OpenCV篇(9) - 轮廓（寻找/绘制轮廓）

什么是轮廓？

轮廓是一系列相连的点组成的曲线，代表了物体的基本外形。

轮廓与边缘好像挺像的？

是的，确实挺像，那么区别是什么呢？简而言之，轮廓是连续的，而边缘并不全都连续（见下图示例）。其实边缘主要是作为图像的特征使用，比如可以用边缘特征可以区分脸和手，而轮廓主要用来分析物体的形态，比如物体的周长和面积等，可以说边缘包括轮廓。

边缘和轮廓的区别（图片来源：http://pic.ex2tron.top/cv2_understand_contours.jpg）

寻找轮廓的操作一般用于二值化图，所以通常会使用阈值分割或Canny边缘检测先得到二值图。

【注：寻找轮廓是针对白色物体的，一定要保证物体是白色，而背景是黑色，不然很多人在寻找轮廓时会找到图片最外面的一个框】

 

OpenCV4.1.0 C++ Sample Code:

/**
 * @function findContours_Demo.cpp
 * @brief Demo code to find contours in an image
 * @author OpenCV team
 */

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

using namespace cv;
using namespace std;

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

/// Function header
void thresh_callback(int, void* );

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

    /// Convert image to gray and blur it
    cvtColor( src, src_gray, COLOR_BGR2GRAY );
    blur( src_gray, src_gray, Size(3,3) );

    /// Create Window
    const char* source_window = "Source";
    namedWindow( source_window );
    imshow( source_window, src );

    const int max_thresh = 255;
    createTrackbar( "Canny thresh:", source_window, &thresh, max_thresh, thresh_callback );
    thresh_callback( 0, 0 );

    waitKey();
    return 0;
}

/**
 * @function thresh_callback
 */
void thresh_callback(int, void* )
{
    /// Detect edges using Canny
    Mat canny_output;
    Canny( src_gray, canny_output, thresh, thresh*2 );

    /// Find contours
    vector<vector<Point> > contours;
    vector<Vec4i> hierarchy;
    findContours( canny_output, contours, hierarchy, RETR_TREE, CHAIN_APPROX_SIMPLE );

    /// Draw contours
    Mat drawing = Mat::zeros( canny_output.size(), CV_8UC3 );
    for( size_t i = 0; i< contours.size(); i++ )
    {
        Scalar color = Scalar( rng.uniform(0, 256), rng.uniform(0,256), rng.uniform(0,256) );
        drawContours( drawing, contours, (int)i, color, 2, LINE_8, hierarchy, 0 );
    }

    /// Show in a window
    imshow( "Contours", drawing );
}

Result:

 

应用1：寻找正方形（squares.cpp）

// The "Square Detector" program.
// It loads several images sequentially and tries to find squares in
// each image

#include "opencv2/core.hpp"
#include "opencv2/imgproc.hpp"
#include "opencv2/imgcodecs.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/core/utils/filesystem.hpp"

#include <iostream>

using namespace cv;
using namespace std;

static void help(const char* programName)
{
    cout <<
    "\nA program using pyramid scaling, Canny, contours and contour simplification\n"
    "to find squares in a list of images (pic1-6.png)\n"
    "Returns sequence of squares detected on the image.\n"
    "Call:\n"
    "./" << programName << " [file_name (optional)]\n"
    "Using OpenCV version " << CV_VERSION << "\n" << endl;
}


int thresh = 50, N = 11;
const char* wndname = "Square Detection Demo";

// helper function:
// finds a cosine of angle between vectors
// from pt0->pt1 and from pt0->pt2
static double angle( Point pt1, Point pt2, Point pt0 )
{
    double dx1 = pt1.x - pt0.x;
    double dy1 = pt1.y - pt0.y;
    double dx2 = pt2.x - pt0.x;
    double dy2 = pt2.y - pt0.y;
    return (dx1*dx2 + dy1*dy2)/sqrt((dx1*dx1 + dy1*dy1)*(dx2*dx2 + dy2*dy2) + 1e-10);
}

// returns sequence of squares detected on the image.
static void findSquares( const Mat& image, vector<vector<Point> >& squares )
{
    squares.clear();

    Mat pyr, timg, gray0(image.size(), CV_8U), gray;

    // down-scale and upscale the image to filter out the noise
    pyrDown(image, pyr, Size(image.cols/2, image.rows/2));
    pyrUp(pyr, timg, image.size());
    vector<vector<Point> > contours;

    // find squares in every color plane of the image
    for( int c = 0; c < 3; c++ )
    {
        int ch[] = {c, 0};
        mixChannels(&timg, 1, &gray0, 1, ch, 1);

        // try several threshold levels
        for( int l = 0; l < N; l++ )
        {
            // hack: use Canny instead of zero threshold level.
            // Canny helps to catch squares with gradient shading
            if( l == 0 )
            {
                // apply Canny. Take the upper threshold from slider
                // and set the lower to 0 (which forces edges merging)
                Canny(gray0, gray, 0, thresh, 5);
                // dilate canny output to remove potential
                // holes between edge segments
                dilate(gray, gray, Mat(), Point(-1,-1));
            }
            else
            {
                // apply threshold if l!=0:
                //     tgray(x,y) = gray(x,y) < (l+1)*255/N ? 255 : 0
                gray = gray0 >= (l+1)*255/N;
            }

            // find contours and store them all as a list
            findContours(gray, contours, RETR_LIST, CHAIN_APPROX_SIMPLE);

            vector<Point> approx;

            // test each contour
            for( size_t i = 0; i < contours.size(); i++ )
            {
                // approximate contour with accuracy proportional
                // to the contour perimeter
                approxPolyDP(contours[i], approx, arcLength(contours[i], true)*0.02, true);

                // square contours should have 4 vertices after approximation
                // relatively large area (to filter out noisy contours)
                // and be convex.
                // Note: absolute value of an area is used because
                // area may be positive or negative - in accordance with the
                // contour orientation
                if( approx.size() == 4 &&
                    fabs(contourArea(approx)) > 1000 &&
                    isContourConvex(approx) )
                {
                    double maxCosine = 0;

                    for( int j = 2; j < 5; j++ )
                    {
                        // find the maximum cosine of the angle between joint edges
                        double cosine = fabs(angle(approx[j%4], approx[j-2], approx[j-1]));
                        maxCosine = MAX(maxCosine, cosine);
                    }

                    // if cosines of all angles are small
                    // (all angles are ~90 degree) then write quandrange
                    // vertices to resultant sequence
                    if( maxCosine < 0.3 )
                        squares.push_back(approx);
                }
            }
        }
    }
}


// the function draws all the squares in the image
static void drawSquares( Mat& image, const vector<vector<Point> >& squares )
{
    for( size_t i = 0; i < squares.size(); i++ )
    {
        const Point* p = &squares[i][0];
        int n = (int)squares[i].size();
        polylines(image, &p, &n, 1, true, Scalar(0,255,0), 3, LINE_AA);
    }

    imshow(wndname, image);
}


String absoluteFilePath(const String& relative_path) {
    String root_path = "F:/opencv/build/bin/sample-data/";
    String path = utils::fs::join(root_path, relative_path);
    return path;
}

int main(int argc, char** argv)
{
    static const char* names[] = { "pic1.png", "pic2.png", "pic3.png",
        "pic4.png", "pic5.png", "pic6.png", 0 };
    help(names[0]);

    vector<vector<Point> > squares;

    for( int i = 0; names[i] != 0; i++ )
    {
        string filename = absoluteFilePath(names[i]);
        Mat image = imread(filename, IMREAD_COLOR);
        if( image.empty() )
        {
            cout << "Couldn't load " << filename << endl;
            continue;
        }

        findSquares(image, squares);
        drawSquares(image, squares);

        int c = waitKey();
        if( c == 27 )
            break;
    }

    return 0;
}

 

结果：

 

 

 

 

　　

　　

 

推荐：

OpenCV 对轮廓的绘图与筛选操作总结

基于OpenCV的形状检测