c++ - 提取角度和轴的信息

Question

在这里我得到了提取图像中物体方向的代码。我是 OpenCV 和 C++ 的新手。但我需要完成这项工作。我的问题是，如何提取、写出这段代码中的角度和轴的信息？

#include "pch.h"
#include "opencv2/core.hpp"
#include "opencv2/imgproc.hpp"
#include "opencv2/highgui.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 | joint2.bmp | 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(parser.get<String>("@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;
    cvtColor(src, gray, COLOR_BGR2GRAY);
    // Convert image to binary
    Mat bw;
    threshold(gray, bw, 200, 255, THRESH_BINARY | THRESH_OTSU);
    // Find all the contours in the thresholded image
    vector<vector<Point> > contours;
    findContours(bw, contours, RETR_EXTERNAL, CHAIN_APPROX_NONE);
    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);
    waitKey();
    return 0;
}

这是对象的图像:

结果如下:

如您所见，它可以正确找到方向，但我需要有关角度以及要写入哪个轴的信息。如果有人知道怎么做，将不胜感激!

编辑:我已经弄清楚如何找到有关中心、面积和角度的信息。

    /// Get the moments
    vector<Moments> mu(contours.size());
    for (size_t i = 0; i < contours.size(); i++)
    {
        mu[i] = moments(contours[i]);
    }
    ///  Get the mass centers
    vector<Point2f> mc(contours.size());
    for (size_t i = 0; i < contours.size(); i++)
    {
        //add 1e-5 to avoid division by zero
        mc[i] = Point2f(static_cast<float>(mu[i].m10 / (mu[i].m00 + 1e-5)),
            static_cast<float>(mu[i].m01 / (mu[i].m00 + 1e-5)));
    }


    imshow("output", src);

    cout << "\t Info: Area and angle \n";
    for (size_t i = 0; i < contours.size(); i++)
    {
        cout << " * Contour[" << i << "]  - Center: "<< mc[i] 
            << " - Area: " << contourArea(contours[i]) << " - Angle: " << getOrientation(contours[i],src)*180/CV_PI << endl;
    }

但仍然不知道如何表示图像中哪个箭头是哪个轴。

Answer 1

所以我已经弄清楚了我需要的一切(几乎)。这是最终代码:

#include "pch.h"
#include "opencv2/core.hpp"
#include "opencv2/imgproc.hpp"
#include "opencv2/highgui.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&);
string s = "";
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 + 0.01 * Point(static_cast<int>(eigen_vecs[0].x * eigen_val[0]), static_cast<int>(eigen_vecs[0].y * eigen_val[0]));
    Point p2 = cntr - 0.005 * 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);
    putText(img, s = "Y-axis",  p1, cv::FONT_HERSHEY_COMPLEX_SMALL, 1, cv::Scalar(255, 0, 100));
    drawAxis(img, cntr, p2, Scalar(255, 255, 0), 5);
    putText(img, s = "X-axis", p2/1.1 , cv::FONT_HERSHEY_COMPLEX_SMALL, 1, cv::Scalar(255, 0, 255));
    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 | circle3.bmp | 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(parser.get<String>("@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;
    cvtColor(src, gray, COLOR_BGR2GRAY);
    // Convert image to binary
    Mat bw;
    threshold(gray, bw, 70, 255, THRESH_BINARY | THRESH_OTSU);
    // Find all the contours in the thresholded image
    vector<vector<Point> > contours;
    findContours(bw, contours, RETR_EXTERNAL, CHAIN_APPROX_NONE);
    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);

    }

    /// Get the moments
    vector<Moments> mu(contours.size());
    for (size_t i = 0; i < contours.size(); i++)
    {
        mu[i] = moments(contours[i]);
    }
    ///  Get the mass centers
    vector<Point2f> mc(contours.size());
    for (size_t i = 0; i < contours.size(); i++)
    {
        //add 1e-5 to avoid division by zero
        mc[i] = Point2f(static_cast<float>(mu[i].m10 / (mu[i].m00 + 1e-5)),
            static_cast<float>(mu[i].m01 / (mu[i].m00 + 1e-5)));
        for (int i = 0; i < contours.size(); i++) {
            std::stringstream ss; ss << i;
            putText(src, ss.str(), mc[i] + Point2f(10,-10), cv::FONT_HERSHEY_COMPLEX_SMALL, 1, cv::Scalar(255, 0, 255));
        }
    }



    imshow("output", src);



    cout << "\t Info: Area and angle \n";
    for (size_t i = 0; i < contours.size(); i++)
    {

        cout << " * Contour[" << i << "]  - Center: "<< mc[i] 
            << " - Area: " << contourArea(contours[i]) << " - Angle X: " << getOrientation(contours[i],src)*180/CV_PI << endl;

    }

    waitKey();
    return 0;
}

我唯一想知道的是如何在图像的角点绘制坐标系。因为我不明白角度结果。

最终代码的结果:https://imgur.com/l7t9bns

及相关资料:https://imgur.com/OuE79rR