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/**
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* @file introduction_to_pca.cpp
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* @brief This program demonstrates how to use OpenCV PCA to extract the orientation of an object
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* @author OpenCV team
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*/
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#include "opencv2/core.hpp"
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#include "opencv2/imgproc.hpp"
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#include "opencv2/highgui.hpp"
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#include <iostream>
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using namespace std;
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using namespace cv;
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// Function declarations
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void drawAxis(Mat&, Point, Point, Scalar, const float);
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double getOrientation(const vector<Point> &, Mat&);
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/**
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* @function drawAxis
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*/
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void drawAxis(Mat& img, Point p, Point q, Scalar colour, const float scale = 0.2)
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{
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//! [visualization1]
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double angle = atan2( (double) p.y - q.y, (double) p.x - q.x ); // angle in radians
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double hypotenuse = sqrt( (double) (p.y - q.y) * (p.y - q.y) + (p.x - q.x) * (p.x - q.x));
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// Here we lengthen the arrow by a factor of scale
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q.x = (int) (p.x - scale * hypotenuse * cos(angle));
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q.y = (int) (p.y - scale * hypotenuse * sin(angle));
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line(img, p, q, colour, 1, LINE_AA);
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// create the arrow hooks
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p.x = (int) (q.x + 9 * cos(angle + CV_PI / 4));
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p.y = (int) (q.y + 9 * sin(angle + CV_PI / 4));
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line(img, p, q, colour, 1, LINE_AA);
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p.x = (int) (q.x + 9 * cos(angle - CV_PI / 4));
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p.y = (int) (q.y + 9 * sin(angle - CV_PI / 4));
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line(img, p, q, colour, 1, LINE_AA);
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//! [visualization1]
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}
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/**
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* @function getOrientation
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*/
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double getOrientation(const vector<Point> &pts, Mat &img)
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{
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//! [pca]
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//Construct a buffer used by the pca analysis
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int sz = static_cast<int>(pts.size());
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Mat data_pts = Mat(sz, 2, CV_64F);
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for (int i = 0; i < data_pts.rows; i++)
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{
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data_pts.at<double>(i, 0) = pts[i].x;
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data_pts.at<double>(i, 1) = pts[i].y;
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}
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//Perform PCA analysis
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PCA pca_analysis(data_pts, Mat(), PCA::DATA_AS_ROW);
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//Store the center of the object
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Point cntr = Point(static_cast<int>(pca_analysis.mean.at<double>(0, 0)),
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static_cast<int>(pca_analysis.mean.at<double>(0, 1)));
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//Store the eigenvalues and eigenvectors
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vector<Point2d> eigen_vecs(2);
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vector<double> eigen_val(2);
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for (int i = 0; i < 2; i++)
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{
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eigen_vecs[i] = Point2d(pca_analysis.eigenvectors.at<double>(i, 0),
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pca_analysis.eigenvectors.at<double>(i, 1));
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eigen_val[i] = pca_analysis.eigenvalues.at<double>(i);
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}
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//! [pca]
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//! [visualization]
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// Draw the principal components
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circle(img, cntr, 3, Scalar(255, 0, 255), 2);
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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]));
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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]));
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drawAxis(img, cntr, p1, Scalar(0, 255, 0), 1);
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drawAxis(img, cntr, p2, Scalar(255, 255, 0), 5);
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double angle = atan2(eigen_vecs[0].y, eigen_vecs[0].x); // orientation in radians
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//! [visualization]
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return angle;
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}
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/**
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* @function main
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*/
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int main(int argc, char** argv)
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{
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//! [pre-process]
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// Load image
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CommandLineParser parser(argc, argv, "{@input | pca_test1.jpg | input image}");
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parser.about( "This program demonstrates how to use OpenCV PCA to extract the orientation of an object.\n" );
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parser.printMessage();
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Mat src = imread( samples::findFile( parser.get<String>("@input") ) );
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// Check if image is loaded successfully
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if(src.empty())
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{
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cout << "Problem loading image!!!" << endl;
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return EXIT_FAILURE;
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}
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imshow("src", src);
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// Convert image to grayscale
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Mat gray;
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cvtColor(src, gray, COLOR_BGR2GRAY);
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// Convert image to binary
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Mat bw;
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threshold(gray, bw, 50, 255, THRESH_BINARY | THRESH_OTSU);
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//! [pre-process]
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//! [contours]
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// Find all the contours in the thresholded image
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vector<vector<Point> > contours;
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findContours(bw, contours, RETR_LIST, CHAIN_APPROX_NONE);
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for (size_t i = 0; i < contours.size(); i++)
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{
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// Calculate the area of each contour
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double area = contourArea(contours[i]);
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// Ignore contours that are too small or too large
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if (area < 1e2 || 1e5 < area) continue;
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// Draw each contour only for visualisation purposes
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drawContours(src, contours, static_cast<int>(i), Scalar(0, 0, 255), 2);
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// Find the orientation of each shape
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getOrientation(contours[i], src);
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}
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//! [contours]
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imshow("output", src);
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waitKey();
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return EXIT_SUCCESS;
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}
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