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

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