samples/cpp/tutorial_code/ml/introduction_to_pca/introduction_to_pca.cpp


Check the corresponding tutorial 了解更多细节

#include " opencv2/core.hpp "
#include <iostream>
using namespace std ;
using namespace cv ;
// Function declarations
void drawAxis ( Mat &, Point , Point , Scalar , const float );
double getOrientation( const vector<Point> &, Mat &);
void drawAxis ( Mat & img, Point p, Point q, Scalar colour, const float scale = 0.2)
{
double angle = atan2 ( ( double ) p. y - q. y , ( double ) p. x - q. x ); // angle in radians
double hypotenuse = sqrt ( ( double ) (p. y - q. y ) * (p. y - q. y ) + (p. x - q. x ) * (p. x - q. x ));
// Here we lengthen the arrow by a factor of scale
q. x = (int) (p. x - scale * hypotenuse * cos (angle));
q. y = (int) (p. y - scale * hypotenuse * sin (angle));
line (img, p, q, colour, 1, LINE_AA );
// create the arrow hooks
p. x = (int) (q. x + 9 * cos (angle + CV_PI / 4));
p. y = (int) (q. y + 9 * sin (angle + CV_PI / 4));
line (img, p, q, colour, 1, LINE_AA );
p. x = (int) (q. x + 9 * cos (angle - CV_PI / 4));
p. y = (int) (q. y + 9 * sin (angle - CV_PI / 4));
line (img, p, q, colour, 1, LINE_AA );
}
double getOrientation( const vector<Point> &pts, Mat &img)
{
//Construct a buffer used by the pca analysis
int sz = static_cast< int > (pts.size());
Mat data_pts = Mat (sz, 2, CV_64F );
for ( int i = 0; i < data_pts. rows ; i++)
{
data_pts. at < double >(i, 0) = pts[i].x;
data_pts. at < double >(i, 1) = pts[i].y;
}
//Perform PCA analysis
PCA pca_analysis(data_pts, Mat (), PCA::DATA_AS_ROW);
//Store the center of the object
Point cntr = Point (static_cast<int>(pca_analysis.mean.at< double >(0, 0)),
static_cast<int>(pca_analysis.mean.at< double >(0, 1)));
//Store the eigenvalues and eigenvectors
vector<Point2d> eigen_vecs(2);
vector<double> eigen_val(2);
for ( int i = 0; i < 2; i++)
{
eigen_vecs[i] = Point2d (pca_analysis.eigenvectors.at< double >(i, 0),
pca_analysis.eigenvectors.at< double >(i, 1));
eigen_val[i] = pca_analysis.eigenvalues.at< double >(i);
}
// Draw the principal components
circle (img, cntr, 3, Scalar (255, 0, 255), 2);
Point p1 = cntr + 0.02 * Point (static_cast<int>(eigen_vecs[0].x * eigen_val[0]), static_cast<int>(eigen_vecs[0].y * eigen_val[0]));
Point p2 = cntr - 0.02 * Point (static_cast<int>(eigen_vecs[1].x * eigen_val[1]), static_cast<int>(eigen_vecs[1].y * eigen_val[1]));
drawAxis (img, cntr, p1, Scalar (0, 255, 0), 1);
drawAxis (img, cntr, p2, Scalar (255, 255, 0), 5);
double angle = atan2 (eigen_vecs[0].y, eigen_vecs[0].x); // orientation in radians
return angle;
}
int main( int argc, char ** argv)
{
// Load image
CommandLineParser parser(argc, argv, "{@input | pca_test1.jpg | input image}" );
parser.about( "This program demonstrates how to use OpenCV PCA to extract the orientation of an object.\n" );
parser.printMessage();
Mat src = imread ( samples::findFile ( parser.get< 字符串 >( "@input" ) ) );
// Check if image is loaded successfully
if (src. empty ())
{
cout << "Problem loading image!!!" << endl;
return EXIT_FAILURE;
}
imshow ( "src" , src);
// Convert image to grayscale
Mat gray;
// Convert image to binary
Mat bw;
// Find all the contours in the thresholded image
vector<vector<Point> > contours;
for ( size_t i = 0; i < contours.size(); i++)
{
// Calculate the area of each contour
double area = contourArea (contours[i]);
// Ignore contours that are too small or too large
if (area < 1e2 || 1e5 < area) continue ;
// Draw each contour only for visualisation purposes
drawContours (src, contours, static_cast<int>(i), Scalar (0, 0, 255), 2);
// Find the orientation of each shape
getOrientation(contours[i], src);
}
imshow ( "output" , src);
return EXIT_SUCCESS;
}