Learning opencv lecture 6 (extracting lines, contours, and components)

In this chapter, we will cover:

• Detecting Image Generation s with the Canny operator
• Detecting lines in images with the Hough Transform
• Fitting a line to a set of points
• Extracting the components' expiration s
• Computing components 'shape Descriptors

 

• Detecting Image Generation s with the Canny operator

The Canny algorithm is implemented in opencv by the function CV: canny. As will be explained, this algorithm requires the specification of two thresholds. The call to the function is therefore as follows:

// Apply Canny algorithmcv::Mat contours;cv::Canny(image, // gray-level imagecontours, // output contours125, // low threshold350); // high threshold

When applied on the following image: The result is as follows:

Note that to obtain an image as shown in the preceding screenshot, we had to invert the black and white values since the normal result represents seconds s by non-zero pixels. the inverted representation, which is nicer to print on a page, is simply produced as follows:

cv::Mat contoursInv; // inverted imagecv::threshold(contours,contoursInv,128, // values below this255, // becomes thiscv::THRESH_BINARY_INV);

　　

Detecting lines in images with the Hough TransformWith the Hough transform, lines are represented using the following equation: the output of the CV: houghlinesfunction is a vector of CV: vec2felements, each of them being a pair of floating point values which represents the parameters of a detected line (∞, θ ).

cv::Mat image = cv:: imread("../road.jpg" , 0 );                 if (! image.data ) {                                 return 0 ;                 }                cv ::namedWindow( "Original Image" );                cv ::imshow( "Original Image" , image);                 // Apply Canny algorithm                cv ::Mat contours;                cv ::Canny( image, contours , 125 , 350 );                cv ::namedWindow( "Canny edges" );                cv ::imshow( "Canny edges" , contours);                cv ::Mat result( contours.rows ,contours. cols,CV_8U ,cv:: Scalar(255 ));                image .copyTo( result);                 // Hough transform for line detection                std ::vector< cv::Vec2f > lines;                cv ::HoughLines( contours, lines ,                                 1, PI / 180 ,              // step size                                 80);                                          // minimum number of votes                std ::vector< cv::Vec2f >::const_iterator it = lines.begin ();                 while ( it != lines .end()) {                                 float rho = (*it )[0];                 // first element is distance rho                                 float theta = (*it )[1]; // second element is angle theta                                 if ( theta < PI /4. || theta > 3.* PI/4. ) {     // ~vertical line                                                 // point of intersection of the line with first row                                                cv ::Point pt1( rho / cos (theta), 0);                                                 // point of intersection of the line with last row                                                cv ::Point pt2(( rho - result .rows * sin(theta )) / cos(theta ), result. rows);                                                 // draw a while line                                                cv ::line( result, pt1 , pt2, cv::Scalar (255), 1);                                 } else {    //~horizontal line                                                 // point of intersection of the line with first column                                                cv ::Point pt1( 0, rho / sin( theta));                                                 // point of intersection of the line with last column                                                cv ::Point pt2( result.cols , ( rho - result .cols * cos(theta )) / sin(theta ));                                                 // draw a white line                                                cv ::line( result, pt1 , pt2, cv::Scalar (255), 1);                                 }                                 ++it;                 }                cv ::namedWindow( "Detected lines with hough" );                cv ::imshow( "Detected lines with hough" , result);

Gets the following results:

As it can be seen, the Hough transform simply looks for an alignment of edge pixels within ss the image. this can potentially create some false detection due to an incidental pixel alignment, or multiple detections when several lines pass through the same alignment of pixels. to overcome some of these problems, and to allow line segments to be detected (that is, with end points), a variant of the transform has been proposed. this is the probabilistic Hough Transform and it is implemented in opencv as function CV: houghlinesp. we use it here to create our linefinderclass that encapsulates the function parameters: linefinder. HPP:

#if ! defined LINE_FINDER#define LINE_FINDER#include <opencv2/core/core.hpp>#include <opencv2/highgui/highgui.hpp>#include <opencv2/imgproc/imgproc.hpp>#include <vector>#define PI 3.1415926class LineFinder {private:                 // original image                cv ::Mat img;                                 // vector containing the end points                 // of the detected lines                std ::vector< cv::Vec4i > lines;                 // accumulator resolution parameters                 double deltaRho;                 double deltaTheta;                 // minimum number of votes that a line                 // must receive before being considered                 int minVote;                 // min length of a line                 double minLength;                 // max allowed gap along the line                 double maxGap;public:                 // Default accumulator resolution is 1 pixel by 1 degree                 // no gap, no minimum length                LineFinder () : deltaRho(1 ), deltaTheta( PI / 180),                                                                minVote (10), minLength(0. ), maxGap( 0.) {}                 // Set the resolution of the accumulator                 void setAccResolution( double dRho, double dTheta ) {                                deltaRho = dRho;                                deltaTheta = dTheta;                 }                 // Set the minimum number of votes                 void setMinVote( int minV) {                                minVote = minV;                 }                 // Set line length and gap                 void setLineLengthAndGap( double length, double gap ) {                                minLength = length;                                maxGap = gap;                 }                 // Apply probabilistic Hough Transform                std ::vector< cv::Vec4i > findLines( cv::Mat &binary) {                                lines .clear();                                cv ::HoughLinesP( binary, lines ,                                                deltaRho , deltaTheta, minVote, minLength , maxGap);                                 return lines;                 }                 // Draw the detected lines on image                 void drawDetectedLines( cv::Mat &image,                                cv ::Scalar color = cv::Scalar (255, 255, 255)) {                                 // Draw the lines                                std ::vector< cv::Vec4i >::const_iterator it2 = lines.begin ();                                 while ( it2 != lines .end()){                                                cv ::Point pt1((* it2)[0 ], (* it2)[1 ]);                                                cv ::Point pt2((* it2)[2 ], (* it2)[3 ]);                                                cv ::line( image, pt1 , pt2, color);                                                 ++ it2;                                 }                 }};#endif

Main. cpp:

// Create LineFinder instance                LineFinder finder ;                 // Set probabilistic Hough parameters                finder .setLineLengthAndGap( 100, 20);                finder .setMinVote( 80);                 // Detect lines and draw them                std ::vector< cv::Vec4i > linesP = finder.findLines (contours);                finder .drawDetectedLines( image);                cv ::namedWindow( "Detected Lines with HoughP" );                cv ::imshow( "Detected Lines with HoughP" , image);

Result:

Detecting Circles
In the case of circles, the corresponding parametric equation is:

image = cv ::imread( "../chariot.jpg" , 0 );                cv ::GaussianBlur( image, image , cv:: Size(5 , 5 ), 1.5 );                std ::vector< cv::Vec3f > circles;                cv ::HoughCircles( image, circles , CV_HOUGH_GRADIENT,                                 2,                                                              // accumulator resolution (size of the image / 2)                                 50,                                                            // minimum distance between two circles                                 200,                                          // Canny high threshold                                 100,                                          // minimum number of votes                                 25, 100);                  // min and max radius                std ::vector< cv::Vec3f >::const_iterator itc = circles.begin ();                 while ( itc != circles .end()) {                                cv ::circle( image,                                                cv ::Point((* itc)[0 ], (* itc)[1 ]),                // circle centre                                                 (*itc)[ 2],                                                  // circle radius                                                cv ::Scalar( 255),                      // color                                                 2                                                                                               // thickness                                                 );                                 ++ itc;                 }                cv ::namedWindow( "Detected Circles" );                cv ::imshow( "Detected Circles" , image);    

Result:

 

Fitting a line to a set of points

// Fitting a line to a set of points                 int n = 0;                  // we select line 0                 // black image                cv ::Mat oneline( contours.size (), CV_8U, cv::Scalar (0));                 // white line                cv ::line( oneline,                                cv ::Point( linesP[n ][0], linesP[n ][1]),                                cv ::Point( linesP[n ][2], linesP[n ][3]),                                cv ::Scalar( 255),                                 5);                 // contours And white line                cv ::bitwise_and( contours, oneline , oneline);                cv ::namedWindow( "One line" );                cv ::imshow( "One line" , oneline);

　　

std::vector <cv:: Point> points ;                 // Iterate over the pixels to obtain all point positions                 for ( int y = 0; y < oneline .rows; y++) {                                 // row y                                uchar *rowPtr = oneline.ptr <uchar>( y);                                 for ( int x = 0; x < oneline .cols; x++) {                                                 // column x                                                 // if on a contour                                                 if ( rowPtr[x ]) {                                                                points .push_back( cv::Point (x, y));                                                 }                                 }                 }                cv ::Vec4f line;                cv ::fitLine( cv::Mat (points), line,                                CV_DIST_L2 ,                        // distance type                                 0,                                                              // not used with L2 distance                                 0.01, 0.01                                 // accuracy                                 );                 int x0 = line[2 ];                       // a point on the line                 int y0 = line[3 ];                                      int x1 = x0 - 200 * line[0 ];     // add a vector of length 200                 int y1 = y0 - 200 * line[1 ];   // using the unit vector                image = cv:: imread("../road.jpg" , 0 );                cv ::line( image, cv ::Point( x0, y0 ), cv:: Point(x1 , y1), cv::Scalar (0), 3);                cv ::namedWindow( "Estimated line" );                cv ::imshow( "Estimated line" , image);

　　

 

Extracting the components' expiration s

cv::Mat image = cv:: imread("../binaryGroup.bmp" , 0 );                 if (! image.data ) {                                 return 0 ;                 }                cv ::namedWindow( "Binary Group" );                cv ::imshow( "Binary Group" , image);                std ::vector< std::vector <cv:: Point>> contours ;                cv ::findContours( image,                                contours ,                                                // a vector of contours                                CV_RETR_EXTERNAL ,     // retrieve the external contours                                CV_CHAIN_APPROX_NONE           // all pixels of each contours                 );                 // Draw black contours on a white image                cv ::Mat result( image.size (), CV_8U, cv::Scalar (255));                cv ::drawContours( result, contours ,                                 -1,                                                              // draw all contours                                cv ::Scalar( 0),          // in black                                 2                                                               // with a thickness of 2                                 );                cv ::namedWindow( "Contours" );                cv ::imshow( "Contours" , result);                 //Eliminate too short or too long contours                 int cmin = 100;        // minimum contour length                 int cmax = 1000;     //maximum contour length                std ::vector< std::vector <cv:: Point>>::const_iterator itc = contours. begin();                 while ( itc != contours .end()) {                                 if ( itc->size () < cmin || itc ->size() > cmax ) {                                                itc = contours. erase(itc );                                 }                                 else                                                 ++itc;                 }                 // draw contours on the original image                cv ::Mat original = cv::imread ("../group.jpg");                cv ::drawContours( original, contours , - 1, cv ::Scalar( 255), 2);                cv ::namedWindow( "Contours on Animals" );                cv ::imshow( "Contours on Animals" , original);

　　

 

Computing components 'shape Descriptors

// draw contours on the white image                result .setTo( cv::Scalar (255));                cv ::drawContours( result, contours ,                                 -1,                                                              // draw all contours                                cv ::Scalar( 0),          // in black                                 2                                                               // with a thickness of 2                                 );                cv ::namedWindow( "Contours on Animals" );                cv ::imshow( "Contours on Animals" , result);                 // Computing components‘ shape descriptor---------------------------                 // testing the bounding box                cv ::Rect r0 = cv::boundingRect (cv:: Mat(contours [0]));                cv ::rectangle( result, r0 , cv:: Scalar(0 ), 2 );                 // testing the enclosing circle                 float radius;                cv ::Point2f center;                cv ::minEnclosingCircle( cv::Mat (contours[ 1]), center , radius);                cv ::circle( result, cv ::Point( center), static_cast<int >(radius), cv::Scalar (0), 2);                 // testing the approximate polygon                std ::vector< cv::Point > poly;                cv ::approxPolyDP( cv::Mat (contours[ 2]), poly , 5 , true );                 // Iterate over each segment and draw it                std ::vector< cv::Point >::const_iterator itp = poly.begin ();                 while ( itp != (poly. end() - 1 )) {                                cv ::line( result, *itp, *(itp + 1 ), cv:: Scalar(0 ), 2 );                                 ++ itp;                 }                 // last point linked to first point                cv ::line( result, *(poly. begin()), *(poly. end() - 1 ), cv:: Scalar(20 ), 2 );                 // testing the convex hull                std ::vector< cv::Point > hull;                cv ::convexHull( cv::Mat (contours[ 3]), hull );                 // testing the moments iterate over all contours                itc = contours. begin();                 while ( itc != contours .end()) {                                 // compute all moments                                cv ::Moments mom = cv::moments (cv:: Mat(*itc ++));                                 // draw mass center                                cv ::circle( result,                                                 // position of mass center converted to integer                                                cv ::Point( mom.m10 / mom. m00, mom .m01 / mom.m00 ),                                                 2, cv ::Scalar( 0), 2  // draw black dot                                                 );                 }                cv ::namedWindow( "Some shape descriptors" );                cv ::imshow( "Some shape descriptors" , result);