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159 lines
4.9 KiB
C++
159 lines
4.9 KiB
C++
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// The "Square Detector" program.
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// It loads several images sequentially and tries to find squares in
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// each image
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#include "opencv2/core.hpp"
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#include "opencv2/imgproc.hpp"
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#include "opencv2/imgcodecs.hpp"
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#include "opencv2/highgui.hpp"
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#include <iostream>
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using namespace cv;
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using namespace std;
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static void help(const char* programName)
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{
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cout <<
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"\nA program using pyramid scaling, Canny, contours and contour simplification\n"
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"to find squares in a list of images (pic1-6.png)\n"
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"Returns sequence of squares detected on the image.\n"
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"Call:\n"
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"./" << programName << " [file_name (optional)]\n"
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"Using OpenCV version " << CV_VERSION << "\n" << endl;
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}
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int thresh = 50, N = 11;
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const char* wndname = "Square Detection Demo";
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// helper function:
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// finds a cosine of angle between vectors
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// from pt0->pt1 and from pt0->pt2
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static double angle( Point pt1, Point pt2, Point pt0 )
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{
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double dx1 = pt1.x - pt0.x;
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double dy1 = pt1.y - pt0.y;
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double dx2 = pt2.x - pt0.x;
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double dy2 = pt2.y - pt0.y;
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return (dx1*dx2 + dy1*dy2)/sqrt((dx1*dx1 + dy1*dy1)*(dx2*dx2 + dy2*dy2) + 1e-10);
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}
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// returns sequence of squares detected on the image.
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static void findSquares( const Mat& image, vector<vector<Point> >& squares )
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{
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squares.clear();
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Mat pyr, timg, gray0(image.size(), CV_8U), gray;
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// down-scale and upscale the image to filter out the noise
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pyrDown(image, pyr, Size(image.cols/2, image.rows/2));
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pyrUp(pyr, timg, image.size());
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vector<vector<Point> > contours;
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// find squares in every color plane of the image
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for( int c = 0; c < 3; c++ )
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{
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int ch[] = {c, 0};
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mixChannels(&timg, 1, &gray0, 1, ch, 1);
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// try several threshold levels
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for( int l = 0; l < N; l++ )
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{
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// hack: use Canny instead of zero threshold level.
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// Canny helps to catch squares with gradient shading
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if( l == 0 )
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{
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// apply Canny. Take the upper threshold from slider
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// and set the lower to 0 (which forces edges merging)
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Canny(gray0, gray, 0, thresh, 5);
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// dilate canny output to remove potential
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// holes between edge segments
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dilate(gray, gray, Mat(), Point(-1,-1));
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}
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else
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{
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// apply threshold if l!=0:
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// tgray(x,y) = gray(x,y) < (l+1)*255/N ? 255 : 0
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gray = gray0 >= (l+1)*255/N;
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}
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// find contours and store them all as a list
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findContours(gray, contours, RETR_LIST, CHAIN_APPROX_SIMPLE);
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vector<Point> approx;
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// test each contour
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for( size_t i = 0; i < contours.size(); i++ )
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{
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// approximate contour with accuracy proportional
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// to the contour perimeter
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approxPolyDP(contours[i], approx, arcLength(contours[i], true)*0.02, true);
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// square contours should have 4 vertices after approximation
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// relatively large area (to filter out noisy contours)
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// and be convex.
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// Note: absolute value of an area is used because
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// area may be positive or negative - in accordance with the
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// contour orientation
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if( approx.size() == 4 &&
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fabs(contourArea(approx)) > 1000 &&
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isContourConvex(approx) )
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{
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double maxCosine = 0;
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for( int j = 2; j < 5; j++ )
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{
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// find the maximum cosine of the angle between joint edges
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double cosine = fabs(angle(approx[j%4], approx[j-2], approx[j-1]));
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maxCosine = MAX(maxCosine, cosine);
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}
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// if cosines of all angles are small
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// (all angles are ~90 degree) then write quandrange
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// vertices to resultant sequence
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if( maxCosine < 0.3 )
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squares.push_back(approx);
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}
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}
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}
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}
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}
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int main(int argc, char** argv)
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{
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static const char* names[] = { "pic1.png", "pic2.png", "pic3.png",
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"pic4.png", "pic5.png", "pic6.png", 0 };
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help(argv[0]);
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if( argc > 1)
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{
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names[0] = argv[1];
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names[1] = "0";
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}
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for( int i = 0; names[i] != 0; i++ )
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{
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string filename = samples::findFile(names[i]);
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Mat image = imread(filename, IMREAD_COLOR);
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if( image.empty() )
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{
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cout << "Couldn't load " << filename << endl;
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continue;
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}
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vector<vector<Point> > squares;
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findSquares(image, squares);
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polylines(image, squares, true, Scalar(0, 255, 0), 3, LINE_AA);
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imshow(wndname, image);
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int c = waitKey();
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if( c == 27 )
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break;
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}
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return 0;
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}
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