亚像素级角点检测

目录

• 目标
• 理论
• 代码
• 解释
• 结果

上一篇教程： 创建你自己的角点检测器
下一篇教程： 特征检测

原作者	Ana Huamán
兼容性	OpenCV >= 3.0

目标

在本教程中，您将学习如何

• 使用 OpenCV 函数 cv::cornerSubPix 查找更精确的角点位置（比整数像素更精确）。

理论

代码

C++

本教程代码如下所示。您也可以从 这里 下载。

 
#include "opencv2/highgui.hpp"
#include "opencv2/imgproc.hpp"
#include <iostream>
 
using namespace cv;
using namespace std;
 
Mat src, src_gray;
 
int maxCorners = 10;
int maxTrackbar = 25;
 
RNG rng(12345);
const char* source_window = "Image";
 
void goodFeaturesToTrack_Demo( int, void* );
 
int main( int argc, char** argv )
{
    CommandLineParser parser( argc, argv, "{@input | pic3.png | input image}" );
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;
    }
cvtColor( src, src_gray, COLOR_BGR2GRAY );
 
namedWindow( source_window );
 
createTrackbar( "最大角点数:", source_window, &maxCorners, maxTrackbar, goodFeaturesToTrack_Demo );
 
imshow( source_window, src );
 
goodFeaturesToTrack_Demo( 0, 0 );
 
waitKey();
    return 0;
}
 
void goodFeaturesToTrack_Demo( int, void* )
{
maxCorners = MAX(maxCorners, 1);
vector<Point2f> corners;
    double qualityLevel = 0.01;
    double minDistance = 10;
    int blockSize = 3, gradientSize = 3;
    bool useHarrisDetector = false;
    double k = 0.04;
 
    Mat copy = src.clone();
 
goodFeaturesToTrack( src_gray,
corners,
maxCorners,
qualityLevel,
minDistance,
                         Mat() ,(),
blockSize,
gradientSize,
useHarrisDetector,
k );
 
 
cout << "** 检测到的角点数：" << corners.size() << endl;
    int radius = 4;
    for( size_t i = 0; i < corners.size(); i++ )
    {
circle( copy, corners[i], radius, Scalar(rng.uniform(0,255), rng.uniform(0, 256), rng.uniform(0, 256)), FILLED );
    }
 
namedWindow( source_window );
imshow( source_window, copy );
 
    Size winSize = Size( 5, 5 );
    Size zeroZone = Size( -1, -1 );
    TermCriteria criteria = TermCriteria( TermCriteria::EPS + TermCriteria::COUNT, 40, 0.001 );
 
cornerSubPix( src_gray, corners, winSize, zeroZone, criteria );
 
    for( size_t i = 0; i < corners.size(); i++ )
    {
cout << " -- 精确角点 [" << i << "] (" << corners[i].x << "," << corners[i].y << ")" << endl;
    }
}

Java

本教程代码如下所示。您也可以从 这里 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.CvType;
import org.opencv.core.Mat;
import org.opencv.core.MatOfPoint;
import org.opencv.core.Point;
import org.opencv.core.Scalar;
import org.opencv.core.Size;
import org.opencv.core.TermCriteria;
import org.opencv.highgui.HighGui;
import org.opencv.imgcodecs.Imgcodecs;
import org.opencv.imgproc.Imgproc;
 
class CornerSubPix {
    private Mat src = new Mat();
    private Mat srcGray = new Mat();
    private JFrame frame;
    private JLabel imgLabel;
    private static final int MAX_CORNERS = 25;
    private int maxCorners = 10;
    private Random rng = new Random(12345);
 
    public CornerSubPix(String[] args) {
String filename = args.length > 0 ? args[0] : "../data/pic3.png";
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("Shi-Tomasi 角点检测演示");
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);
update();
    }
 
    private void addComponentsToPane(Container pane, Image img) {
        if (!(pane.getLayout() instanceof BorderLayout)) {
pane.add(new JLabel("容器未使用 BorderLayout!"));
            return;
        }
 
JPanel sliderPanel = new JPanel();
sliderPanel.setLayout(new BoxLayout(sliderPanel, BoxLayout.PAGE_AXIS));
 
sliderPanel.add(new JLabel("最大角点数:"));
JSlider slider = new JSlider(0, MAX_CORNERS, maxCorners);
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();
maxCorners = source.getValue();
update();
            }
        });
update();
}
 
});
sliderPanel.add(slider);
    }
 
    pane.add(sliderPanel, BorderLayout.PAGE_START);
imgLabel = new JLabel(new ImageIcon(img));
pane.add(imgLabel, BorderLayout.CENTER);
        double qualityLevel = 0.01;
        double minDistance = 10;
        int blockSize = 3, gradientSize = 3;
        }
        double k = 0.04;
 
private void update() {
 
maxCorners = Math.max(maxCorners, 1);
MatOfPoint corners = new MatOfPoint();
 
boolean useHarrisDetector = false;
        Mat copy = src.clone();
Imgproc.goodFeaturesToTrack(srcGray, corners, maxCorners, qualityLevel, minDistance, new Mat(),
        int radius = 4;
blockSize, gradientSize, useHarrisDetector, k);
        System.out.println("** 检测到的角点数: " + corners.rows());
int[] cornersData = new int[(int) (corners.total() * corners.channels())];
        corners.get(0, 0, cornersData);
Mat matCorners = new Mat(corners.rows(), 2, CvType.CV_32F);
                    float[] matCornersData = new float[(int) (matCorners.total() * matCorners.channels())];
matCorners.get(0, 0, matCornersData);
for (int i = 0; i < corners.rows(); i++) {
        }
Imgproc.circle(copy, new Point(cornersData[i * 2], cornersData[i * 2 + 1]), radius,
 
new Scalar(rng.nextInt(256), rng.nextInt(256), rng.nextInt(256)), Imgproc.FILLED);
matCornersData[i * 2] = cornersData[i * 2];
 
        matCornersData[i * 2 + 1] = cornersData[i * 2 + 1];
        }
matCorners.put(0, 0, matCornersData);
 
imgLabel.setIcon(new ImageIcon(HighGui.toBufferedImage(copy)));
 
int[] cornersData = new int[(int) (corners.total() * corners.channels())];
        corners.get(0, 0, cornersData);
frame.repaint();
                    Size winSize = new Size(5, 5);
        }
    }
}
 
Size zeroZone = new Size(-1, -1);
    TermCriteria criteria = new TermCriteria(TermCriteria.EPS + TermCriteria.COUNT, 40, 0.001);
        Imgproc.cornerSubPix(srcGray, matCorners, winSize, zeroZone, criteria);
System.out.println(
 
        " -- 精确后的角点 [" + i + "] (" + matCornersData[i * 2] + "," + matCornersData[i * 2 + 1] + ")");
        }
public class CornerSubPixDemo {
@Override
            public static void main(String[] args) {
                // 加载本地 OpenCV 库
            }
        });
    }
}

Point2i Point

定义 types.hpp:209

cv::Size
Size2i Size
定义 types.hpp:370
cv::Scalar
Scalar_< double > Scalar
 
定义 types.hpp:709
cv::gapi::oak::copy
GFrame copy(const GFrame &in)
 
Python
本教程代码如下所示。您也可以从此处下载。
 
    from __future__ import print_function
import cv2 as cv
import numpy as np
import argparse
import random as rng
source_window = 'Image'
maxTrackbar = 25
 
    rng.seed(12345)
def goodFeaturesToTrack_Demo(val):
 
    maxCorners = max(val, 1)
# Shi-Tomasi 算法参数
qualityLevel = 0.01
 
    minDistance = 10
blockSize = 3
gradientSize = 3
    useHarrisDetector = False
        k = 0.04
 
    # 复制源图像
    copy = np.copy(src)
    # 应用角点检测
 
    corners = cv.goodFeaturesToTrack(src_gray, maxCorners, qualityLevel, minDistance, None, \
blockSize=blockSize, gradientSize=gradientSize, useHarrisDetector=useHarrisDetector, k=k)
# 绘制检测到的角点
print('** 检测到的角点数:', corners.shape[0])
 
    radius = 4
for i in range(corners.shape[0]):
 
    cv.circle(copy, (int(corners[i,0,0]), int(corners[i,0,1])), radius, (rng.randint(0,256), rng.randint(0,256), rng.randint(0,256)), cv.FILLED)
    useHarrisDetector = False
# 显示结果
 
# 加载源图像并将其转换为灰度
parser = argparse.ArgumentParser(description='Shi-Tomasi 角点检测教程代码。')
parser.add_argument('--input', help='输入图像路径。', default='pic3.png')
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)
 
# 创建窗口和轨迹条
copy = np.copy(src)
maxCorners = 10 # 初始阈值
cv.createTrackbar('阈值：', source_window, maxCorners, maxTrackbar, goodFeaturesToTrack_Demo)
cv.imshow(source_window, src)
goodFeaturesToTrack_Demo(maxCorners)
 
cv.waitKey()

说明

结果

以下是结果