创建你自己的角点检测器

目录

• 目标
• 理论
• 代码
• 解释
• 结果

上一篇教程： Shi-Tomasi 角点检测器
下一篇教程： 亚像素级角点定位检测

原作者	Ana Huamán
兼容性	OpenCV >= 3.0

目标

在本教程中，你将学习如何：

• 使用 OpenCV 函数 cv::cornerEigenValsAndVecs 查找特征值和特征向量以确定像素是否为角点。
• 使用 OpenCV 函数 cv::cornerMinEigenVal 查找角点检测的最小特征值。
• 通过使用上述两个函数，实现我们自己的 Harris 检测器和 Shi-Tomasi 检测器版本。

理论

代码

C++

本教程代码如下所示。你也可以从 这里 下载。

 
#include "opencv2/highgui.hpp"
#include "opencv2/imgproc.hpp"
#include <iostream>
 
using namespace cv;
using namespace std;
 
Mat src, src_gray;
Mat myHarris_dst, myHarris_copy, Mc;
Mat myShiTomasi_dst, myShiTomasi_copy;
 
int myShiTomasi_qualityLevel = 50;
int myHarris_qualityLevel = 50;
int max_qualityLevel = 100;
 
double myHarris_minVal, myHarris_maxVal;
double myShiTomasi_minVal, myShiTomasi_maxVal;
 
RNG rng(12345);
 
const char* myHarris_window = "我的Harris角点检测器";
const char* myShiTomasi_window = "我的Shi Tomasi角点检测器";
 
void myShiTomasi_function( int, void* );
void myHarris_function( int, void* );
 
int main( int argc, char** argv )
{
    CommandLineParser parser( argc, argv, "{@input | building.jpg | 输入图像}" );
src = imread( samples::findFile( parser.get<String>( "@input" ) ) );
    if ( src.empty() )
    {
cout << "无法打开或找到图像！\n" << endl;
cout << "用法：" << argv[0] << " <输入图像>" << endl;
        return -1; -1;
    }
cvtColor( src, src_gray, COLOR_BGR2GRAY );
 
    int blockSize = 3, apertureSize = 3;
 
cornerEigenValsAndVecs( src_gray, myHarris_dst, blockSize, apertureSize );
 
    /* 计算 Mc */
Mc = Mat( src_gray.size(), CV_32FC1 );
    for( int i = 0; i < src_gray.rows; i++ )
    {
        for( int j = 0; j < src_gray.cols; j++ )
        {
            {

            float lambda_2 = myHarris_dst.at<Vec6f>(i, j)[1];
Mc.at<float>(i, j) = lambda_1*lambda_2 - 0.04f*((lambda_1 + lambda_2) * (lambda_1 + lambda_2));

        }
    }
 
minMaxLoc( Mc, &myHarris_minVal, &myHarris_maxVal );
 
    /* 创建窗口和轨迹条 */
namedWindow( myHarris_window );
createTrackbar( "质量等级:", myHarris_window, &myHarris_qualityLevel, max_qualityLevel, myHarris_function );
myHarris_function( 0, 0 );
 
cornerMinEigenVal( src_gray, myShiTomasi_dst, blockSize, apertureSize );
 
minMaxLoc( myShiTomasi_dst, &myShiTomasi_minVal, &myShiTomasi_maxVal );
 
    /* 创建窗口和轨迹条 */
namedWindow( myShiTomasi_window );
createTrackbar( "质量等级:", myShiTomasi_window, &myShiTomasi_qualityLevel, max_qualityLevel, myShiTomasi_function );
myShiTomasi_function( 0, 0 );
 
waitKey();
    return -1; 0;
}
 
void myShiTomasi_function( int, void* )
{
{

myShiTomasi_qualityLevel = MAX(myShiTomasi_qualityLevel, 1);
 
    for( int i = 0; i < src_gray.rows; i++ )
    {
        for( int j = 0; j < src_gray.cols; j++ )
        {
            for( int i = 0; i < src_gray.rows; i++ )
            {
                for( int j = 0; j < src_gray.cols; j++ )
            }
        }
    }
    if( myShiTomasi_dst.at<float>(i,j) > myShiTomasi_minVal + ( myShiTomasi_maxVal - myShiTomasi_minVal )*myShiTomasi_qualityLevel/max_qualityLevel )
}
 
circle( myShiTomasi_copy, Point(j,i), 4, Scalar( rng.uniform(0,256), rng.uniform(0,256), rng.uniform(0,256) ), FILLED );
{
imshow( myShiTomasi_window, myShiTomasi_copy );

void myHarris_function( int, void* )
 
    for( int i = 0; i < src_gray.rows; i++ )
    {
        for( int j = 0; j < src_gray.cols; j++ )
        {
            {

            {
                myHarris_qualityLevel = MAX(myHarris_qualityLevel, 1);
            }
        }
    }
    for( int i = 0; i < src_gray.rows; i++ )
}

Java

本教程代码的代码行如下所示。您也可以从此处下载。

import java.awt.BorderLayout;
import java.awt.Container;
import java.awt.Image;
import java.util.Random;
 
import javax.swing.BoxLayout;
import javax.swing.ImageIcon;
import javax.swing.JFrame;
import javax.swing.JLabel;
import javax.swing.JPanel;
import javax.swing.JSlider;
import javax.swing.event.ChangeEvent;
import javax.swing.event.ChangeListener;
 
import org.opencv.core.Core;
import org.opencv.core.Core.MinMaxLocResult;
import org.opencv.core.CvType;
import org.opencv.core.Mat;
import org.opencv.core.Point;
import org.opencv.core.Scalar;
import org.opencv.highgui.HighGui;
import org.opencv.imgcodecs.Imgcodecs;
import org.opencv.imgproc.Imgproc;
 
class CornerDetector {
    private Mat src = new Mat();
    private Mat srcGray = new Mat();
    private Mat harrisDst = new Mat();
    private Mat shiTomasiDst = new Mat();
    private Mat harrisCopy = new Mat();
    private Mat shiTomasiCopy = new Mat();
    private Mat Mc = new Mat();
    private JFrame frame;
    private JLabel harrisImgLabel;
    private JLabel shiTomasiImgLabel;
    private static final int MAX_QUALITY_LEVEL = 100;
    private int qualityLevel = 50;
    private double harrisMinVal;
    private double harrisMaxVal;
    private double shiTomasiMinVal;
    private double shiTomasiMaxVal;
    private Random rng = new Random(12345);
 
    public CornerDetector(String[] args) {
String filename = args.length > 0 ? args[0] : "../data/building.jpg";
src = Imgcodecs.imread(filename);
        if (src.empty()) {
System.err.println("无法读取图像: " + filename);
System.exit(0);
        }
 
Imgproc.cvtColor(src, srcGray, Imgproc.COLOR_BGR2GRAY);
 
        // 创建并设置窗口。
frame = new JFrame("创建您自己的角点检测器演示");
frame.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE);
        // 设置内容窗格。
Image img = HighGui.toBufferedImage(src);
addComponentsToPane(frame.getContentPane(), img);
        // 使用内容窗格的默认 BorderLayout。无需
        // setLayout(new BorderLayout());
        // 显示窗口。
frame.pack();
frame.setVisible(true);
 
        int blockSize = 3, apertureSize = 3;
 
Imgproc.cornerEigenValsAndVecs(srcGray, harrisDst, blockSize, apertureSize);
 
        /* 计算 Mc */
Mc = Mat.zeros(srcGray.size(), CvType.CV_32F);
 
        float[] harrisData = new float[(int) (harrisDst.total() * harrisDst.channels())];
harrisDst.get(0, 0, harrisData);
        float[] McData = new float[(int) (Mc.total() * Mc.channels())];
Mc.get(0, 0, McData);
 
        for( int i = 0; i < srcGray.rows(); i++ ) {
            for( int j = 0; j < srcGray.cols(); j++ ) {
                float lambda1 = harrisData[(i*srcGray.cols() + j) * 6];
                float lambda2 = harrisData[(i*srcGray.cols() + j) * 6 + 1];
McData[i*srcGray.cols()+j] = (float) (lambda1*lambda2 - 0.04f*Math.pow( ( lambda1 + lambda2 ), 2 ));
            }
        }
Mc.put(0, 0, McData);
 
MinMaxLocResult res = Core.minMaxLoc(Mc);
harrisMinVal = res.minVal;
harrisMaxVal = res.maxVal;
 
Imgproc.cornerMinEigenVal(srcGray, shiTomasiDst, blockSize, apertureSize);
res = Core.minMaxLoc(shiTomasiDst);
shiTomasiMinVal = res.minVal;
shiTomasiMaxVal = res.maxVal;
 
update();
    }
 
    private void addComponentsToPane(Container pane, Image img) {
        if (!(pane.getLayout() instanceof BorderLayout)) {
pane.add(new JLabel("容器不使用 BorderLayout!"));
            return -1;;
        }
 
JPanel sliderPanel = new JPanel();
sliderPanel.setLayout(new BoxLayout(sliderPanel, BoxLayout.PAGE_AXIS));
 
sliderPanel.add(new JLabel("最大角点:"));
JSlider slider = new JSlider(0, MAX_QUALITY_LEVEL, qualityLevel);
slider.setMajorTickSpacing(20);
slider.setMinorTickSpacing(10);
slider.setPaintTicks(true);
slider.setPaintLabels(true);
slider.addChangeListener(new ChangeListener() {
@Override
            public void stateChanged(ChangeEvent e) {
JSlider source = (JSlider) e.getSource();
qualityLevel = source.getValue();
update();
            }
        });
sliderPanel.add(slider);
pane.add(sliderPanel, BorderLayout.PAGE_START);
 
JPanel imgPanel = new JPanel();
harrisImgLabel = new JLabel(new ImageIcon(img));
shiTomasiImgLabel = new JLabel(new ImageIcon(img));
imgPanel.add(harrisImgLabel);
imgPanel.add(shiTomasiImgLabel);
pane.add(imgPanel, BorderLayout.CENTER);
    }
 
    private void update() {
        int qualityLevelVal = Math.max(qualityLevel, 1);
 
        //Harris
harrisCopy = src.clone();
 
        float[] McData = new float[(int) (Mc.total() * Mc.channels())];
Mc.get(0, 0, McData);
        for (int i = 0; i < srcGray.rows(); i++) {
            for (int j = 0; j < srcGray.cols(); j++) {
                如果 (McData[i * srcGray.cols() + j] > harrisMinVal
+ (harrisMaxVal - harrisMinVal) * qualityLevelVal / MAX_QUALITY_LEVEL) {
Imgproc.circle(harrisCopy, new Point(j, i), 4,
                            new Scalar(rng.nextInt(256), rng.nextInt(256), rng.nextInt(256)), Imgproc.FILLED);
                }
            }
        }
 
        //Shi-Tomasi 角点检测
shiTomasiCopy = src.clone();
 
        float[] shiTomasiData = new float[(int) (shiTomasiDst.total() * shiTomasiDst.channels())];
shiTomasiDst.get(0, 0, shiTomasiData);
        for (int i = 0; i < srcGray.rows(); i++) {
            for (int j = 0; j < srcGray.cols(); j++) {
                如果 (shiTomasiData[i * srcGray.cols() + j] > shiTomasiMinVal
+ (shiTomasiMaxVal - shiTomasiMinVal) * qualityLevelVal / MAX_QUALITY_LEVEL) {
Imgproc.circle(shiTomasiCopy, new Point(j, i), 4,
                            new Scalar(rng.nextInt(256), rng.nextInt(256), rng.nextInt(256)), Imgproc.FILLED);
                }
            }
        }
 
harrisImgLabel.setIcon(new ImageIcon(HighGui.toBufferedImage(harrisCopy)));
shiTomasiImgLabel.setIcon(new ImageIcon(HighGui.toBufferedImage(shiTomasiCopy)));
frame.repaint();
    }
}
 
public class CornerDetectorDemo {
    public static void main(String[] args) {
        // 加载 OpenCV 原生库
System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
 
        // 为事件分发线程安排一个任务
        // 创建并显示该应用程序的 GUI。
javax.swing.SwingUtilities.invokeLater(new Runnable() {
@Override
            public void run() {
                new CornerDetector(args);
            }
        });
    }
}

Python 代码

本教程代码如下所示。您也可以从此处下载：此处

from __future__ import print_function
import cv2 as cv
import numpy as np
import argparse
import random as rng
 
myHarris_window = "我的 Harris 角点检测器"
myShiTomasi_window = "我的 Shi Tomasi 角点检测器"
myHarris_qualityLevel = 50
myShiTomasi_qualityLevel = 50
max_qualityLevel = 100
rng.seed(12345)
 
def myHarris_function(val):
myHarris_copy = np.copy(src)
myHarris_qualityLevel = max(val, 1)
 
    for i in range(src_gray.shape[0]):
        for j in range(src_gray.shape[1]):
            if Mc[i,j] > myHarris_minVal + ( myHarris_maxVal - myHarris_minVal )*myHarris_qualityLevel/max_qualityLevel:
                cv.circle(myHarris_copy, (j,i), 4, (rng.randint(0,256), rng.randint(0,256), rng.randint(0,256)), cv.FILLED)
 
    cv.imshow(myHarris_window, myHarris_copy)
 
def myShiTomasi_function(val):
myShiTomasi_copy = np.copy(src)
myShiTomasi_qualityLevel = max(val, 1)
 
    for i in range(src_gray.shape[0]):
        for j in range(src_gray.shape[1]):
            if myShiTomasi_dst[i,j] > myShiTomasi_minVal + ( myShiTomasi_maxVal - myShiTomasi_minVal )*myShiTomasi_qualityLevel/max_qualityLevel:
                cv.circle(myShiTomasi_copy, (j,i), 4, (rng.randint(0,256), rng.randint(0,256), rng.randint(0,256)), cv.FILLED)
 
    cv.imshow(myShiTomasi_window, myShiTomasi_copy)
 
# 加载源图像并将其转换为灰度图像
parser = argparse.ArgumentParser(description='创建自己的角点检测器教程的代码。')
parser.add_argument('--input', help='输入图像路径。', default='building.jpg')
args = parser.parse_args()
 
src = cv.imread(cv.samples.findFile(args.input))
if src is None:
print('无法打开或找到图像：', args.input)
exit(0)
 
src_gray = cv.cvtColor(src, cv.COLOR_BGR2GRAY)
 
# 设置一些参数
blockSize = 3
apertureSize = 3
 
# 我的 Harris 矩阵 -- 使用 cornerEigenValsAndVecs
myHarris_dst = cv.cornerEigenValsAndVecs(src_gray, blockSize, apertureSize)
 
# 计算 Mc
Mc = np.empty(src_gray.shape, dtype=np.float32)
for i in range(src_gray.shape[0]):
    for j in range(src_gray.shape[1]):
lambda_1 = myHarris_dst[i,j,0]
lambda_2 = myHarris_dst[i,j,1]
Mc[i,j] = lambda_1*lambda_2 - 0.04*pow( ( lambda_1 + lambda_2 ), 2 )
 
myHarris_minVal, myHarris_maxVal, _, _ = cv.minMaxLoc(Mc)
 
# 创建窗口和轨迹条
cv.namedWindow(myHarris_window)
cv.createTrackbar('质量等级：', myHarris_window, myHarris_qualityLevel, max_qualityLevel, myHarris_function)
myHarris_function(myHarris_qualityLevel)
 
# 我的 Shi-Tomasi -- 使用 cornerMinEigenVal
myShiTomasi_dst = cv.cornerMinEigenVal(src_gray, blockSize, apertureSize)
 
myShiTomasi_minVal, myShiTomasi_maxVal, _, _ = cv.minMaxLoc(myShiTomasi_dst)
 
# 创建窗口和轨迹条
cv.namedWindow(myShiTomasi_window)
cv.createTrackbar('质量等级：', myShiTomasi_window, myShiTomasi_qualityLevel, max_qualityLevel, myShiTomasi_function)
myShiTomasi_function(myShiTomasi_qualityLevel)
 
cv.waitKey()()

说明

结果