目标
在本教程中，您将学习如何：
- 使用OpenCV类cv :: PCA来计算对象的方向。
PCA
主成分分析（PCA）是提取数据集最重要特征的统计程序。是一种重要的降纬技术。
PCA算法步骤，设有m条n维数据。
1）将原始数据按列组成n行m列矩阵X
2）将X的每一行（代表一个属性字段）进行零均值化，即减去这一行的均值
3）求出协方差矩阵
4）求出协方差矩阵的特征值及对应的特征向量
5）将特征向量按对应特征值大小从上到下按行排列成矩阵，取前k行组成矩阵P
6）PX即为降维到k维后的数据
程序
#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;
double hypotenuse;
angle = atan2( (double) p.y - q.y, (double) p.x - q.x ); // angle in radians
hypotenuse = sqrt( (double) (p.y - q.y) * (p.y - q.y) + (p.x - q.x) * (p.x - q.x));
// double degrees = angle * 180 / CV_PI; // convert radians to degrees (0-180 range)
// cout << "Degrees: " << abs(degrees - 180) << endl; // angle in 0-360 degrees range
// 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箭头
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_64FC1);
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 矩阵行
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 | lena.jpg | input image}");
parser.about( "This program demonstrates how to use OpenCV PCA to extract the orienation 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, 50, 255, THRESH_BINARY | THRESH_OTSU);
// Find all the contours in the thresholded image
vector<vector<Point> > contours;
findContours(bw, contours, RETR_LIST, 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, LINE_8);
// Find the orientation of each shape
getOrientation(contours[i], src);
}
imshow("output", src);
waitKey(0);
return 0;
}
说明
结果
