更多形态学变换

目录

• 目标
• 理论
  • 开运算
  • 闭运算
  • 形态学梯度
  • 顶帽
  • 黑帽
• 代码
• 解释
• 结果

上一教程： 腐蚀与膨胀
下一教程： 击中或击不中

原始作者	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::morphologyEx 应用形态学转换，例如
  • 开运算
  • 闭运算
  • 形态学梯度
  • 顶帽
  • 黑帽

理论

注意

以下解释摘自Bradski和Kaehler的著作《Learning OpenCV》。

在上一教程中，我们介绍了两个基本的形态学操作

• 腐蚀
• 膨胀。

基于这两个操作，我们可以对图像进行更复杂的转换。这里我们简要讨论OpenCV提供的5种操作

开运算

• 开运算是通过对图像进行腐蚀后，再进行膨胀得到的。

  \[dst = open( src, element) = dilate( erode( src, element ) )\]

• 用于移除小物体（假设物体在暗背景上是亮的）
• 例如，请看下面的示例。左侧图像是原始图像，右侧图像是应用开运算转换后的结果。我们可以观察到小点已经消失了。

闭运算

• 闭运算是通过对图像进行膨胀后，再进行腐蚀得到的。

  \[dst = close( src, element ) = erode( dilate( src, element ) )\]

• 用于移除小孔（暗区域）。

形态学梯度

• 它是图像膨胀与腐蚀之间的差异。

  \[dst = morph_{grad}( src, element ) = dilate( src, element ) - erode( src, element )\]

• 它有助于找到物体的轮廓，如下所示

顶帽

• 它是输入图像与其开运算之间的差异。

  \[dst = tophat( src, element ) = src - open( src, element )\]

黑帽

• 它是闭运算与其输入图像之间的差异

  \[dst = blackhat( src, element ) = close( src, element ) - src\]

代码

C++

本教程的代码如下所示。您也可以从此处下载

 
#include "opencv2/imgproc.hpp"
#include "opencv2/imgcodecs.hpp"
#include "opencv2/highgui.hpp"
#include <iostream>
 
using namespace cv;
 
Mat src, dst;
 
int morph_elem = 0;
int morph_size = 0;
int morph_operator = 0;
int const max_operator = 4;
int const max_elem = 3;
int const max_kernel_size = 21;
 
const char* window_name = "Morphology Transformations Demo";
 
 
void Morphology_Operations( int, void* );
 
int main( int argc, char** argv )
{
  CommandLineParser parser( argc, argv, "{@input | baboon.jpg | input image}" );
src = imread( samples::findFile( parser.get<String>( "@input" ) ), IMREAD_COLOR );
  if (src.empty())
  {
std::cout << "Could not open or find the image!\n" << std::endl;
std::cout << "Usage: " << argv[0] << " <Input image>" << std::endl;
    return EXIT_FAILURE;
  }
 
namedWindow( window_name, WINDOW_AUTOSIZE ); // Create window
 
createTrackbar("Operator:\n 0: Opening - 1: Closing \n 2: Gradient - 3: Top Hat \n 4: Black Hat", window_name, &morph_operator, max_operator, Morphology_Operations );
 
createTrackbar( "Element:\n 0: Rect - 1: Cross - 2: Ellipse - 3: Diamond", window_name,
&morph_elem, max_elem,
Morphology_Operations );
 
createTrackbar( "Kernel size:\n 2n +1", window_name,
&morph_size, max_kernel_size,
Morphology_Operations );
 
Morphology_Operations( 0, 0 );
 
waitKey(0);
  return 0;
}
 
 
void Morphology_Operations( int, void* )
{
  // 由于 MORPH_X : 2,3,4,5 和 6
  int operation = morph_operator + 2;
 
  Mat element = getStructuringElement( morph_elem, Size( 2*morph_size + 1, 2*morph_size+1 ), Point( morph_size, morph_size ) );
 
morphologyEx( src, dst, operation, element );
imshow( window_name, dst );
}

Java

本教程的代码如下所示。您也可以从此处下载

import java.awt.BorderLayout;
import java.awt.Container;
import java.awt.Image;
import java.awt.event.ActionEvent;
import java.awt.event.ActionListener;
 
import javax.swing.BoxLayout;
import javax.swing.ImageIcon;
import javax.swing.JComboBox;
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.Mat;
import org.opencv.core.Point;
import org.opencv.core.Size;
import org.opencv.highgui.HighGui;
import org.opencv.imgcodecs.Imgcodecs;
import org.opencv.imgproc.Imgproc;
 
public class MorphologyDemo2 {
    private static final String[] MORPH_OP = { "Opening", "Closing", "Gradient", "Top Hat", "Black Hat" };
    private static final int[] MORPH_OP_TYPE = { Imgproc.MORPH_OPEN, Imgproc.MORPH_CLOSE,
Imgproc.MORPH_GRADIENT, Imgproc.MORPH_TOPHAT, Imgproc.MORPH_BLACKHAT };
    private static final String[] ELEMENT_TYPE = { "Rectangle", "Cross", "Ellipse" };
    private static final int MAX_KERNEL_SIZE = 21;
    private Mat matImgSrc;
    private Mat matImgDst = new Mat();
    private int morphOpType = Imgproc.MORPH_OPEN;
    private int elementType = Imgproc.CV_SHAPE_RECT;
    private int kernelSize = 0;
    private JFrame frame;
    private JLabel imgLabel;
 
    public MorphologyDemo2(String[] args) {
String imagePath = args.length > 0 ? args[0] : "../data/LinuxLogo.jpg";
matImgSrc = Imgcodecs.imread(imagePath);
        if (matImgSrc.empty()) {
System.out.println("Empty image: " + imagePath);
System.exit(0);
        }
 
        // Create and set up the window.
frame = new JFrame("Morphology Transformations demo");
frame.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE);
        // Set up the content pane.
Image img = HighGui.toBufferedImage(matImgSrc);
addComponentsToPane(frame.getContentPane(), img);
        // Use the content pane's default BorderLayout. No need for
        // setLayout(new BorderLayout());
        // Display the window.
frame.pack();
frame.setVisible(true);
    }
 
    private void addComponentsToPane(Container pane, Image img) {
        if (!(pane.getLayout() instanceof BorderLayout)) {
pane.add(new JLabel("Container doesn't use BorderLayout!"));
            return;
        }
 
JPanel sliderPanel = new JPanel();
sliderPanel.setLayout(new BoxLayout(sliderPanel, BoxLayout.PAGE_AXIS));
 
JComboBox<String> morphOpBox = new JComboBox<>(MORPH_OP);
morphOpBox.addActionListener(new ActionListener() {
@Override
            public void actionPerformed(ActionEvent e) {
@SuppressWarnings("unchecked")
JComboBox<String> cb = (JComboBox<String>)e.getSource();
morphOpType = MORPH_OP_TYPE[cb.getSelectedIndex()];
update();
            }
        });
sliderPanel.add(morphOpBox);
 
JComboBox<String> elementTypeBox = new JComboBox<>(ELEMENT_TYPE);
elementTypeBox.addActionListener(new ActionListener() {
@Override
            public void actionPerformed(ActionEvent e) {
@SuppressWarnings("unchecked")
JComboBox<String> cb = (JComboBox<String>)e.getSource();
if (cb.getSelectedIndex() == 0) {
elementType = Imgproc.CV_SHAPE_RECT;
} else if (cb.getSelectedIndex() == 1) {
elementType = Imgproc.CV_SHAPE_CROSS;
} else if (cb.getSelectedIndex() == 2) {
elementType = Imgproc.CV_SHAPE_ELLIPSE;
                }
update();
            }
        });
sliderPanel.add(elementTypeBox);
 
sliderPanel.add(new JLabel("Kernel size: 2n + 1"));
JSlider slider = new JSlider(0, MAX_KERNEL_SIZE, 0);
slider.setMajorTickSpacing(5);
slider.setMinorTickSpacing(5);
slider.setPaintTicks(true);
slider.setPaintLabels(true);
slider.addChangeListener(new ChangeListener() {
@Override
            public void stateChanged(ChangeEvent e) {
JSlider source = (JSlider) e.getSource();
kernelSize = source.getValue();
update();
            }
        });
sliderPanel.add(slider);
 
pane.add(sliderPanel, BorderLayout.PAGE_START);
imgLabel = new JLabel(new ImageIcon(img));
pane.add(imgLabel, BorderLayout.CENTER);
    }
 
    private void update() {
Mat element = Imgproc.getStructuringElement(elementType, new Size(2 * kernelSize + 1, 2 * kernelSize + 1),
                new Point(kernelSize, kernelSize));
 
Imgproc.morphologyEx(matImgSrc, matImgDst, morphOpType, element);
Image img = HighGui.toBufferedImage(matImgDst);
imgLabel.setIcon(new ImageIcon(img));
frame.repaint();
    }
 
    public static void main(String[] args) {
        // 加载本地 OpenCV 库
System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
 
        // Schedule a job for the event dispatch thread
        // creating and showing this application's GUI.
javax.swing.SwingUtilities.invokeLater(new Runnable() {
@Override
            public void run() {
                new MorphologyDemo2(args);
            }
        });
    }
}

Python

本教程的代码如下所示。您也可以从此处下载

from __future__ import print_function
import cv2 as cv
import numpy as np
import argparse
 
morph_size = 0
max_operator = 4
max_elem = 3
max_kernel_size = 21
title_trackbar_operator_type = 'Operator:\n 0: Opening - 1: Closing \n 2: Gradient - 3: Top Hat \n 4: Black Hat'
title_trackbar_element_type = 'Element:\n 0: Rect - 1: Cross - 2: Ellipse - 3: Diamond'
title_trackbar_kernel_size = 'Kernel size:\n 2n + 1'
title_window = 'Morphology Transformations Demo'
morph_op_dic = {0: cv.MORPH_OPEN, 1: cv.MORPH_CLOSE, 2: cv.MORPH_GRADIENT, 3: cv.MORPH_TOPHAT, 4: cv.MORPH_BLACKHAT}
 
def morphology_operations(val)
morph_operator = cv.getTrackbarPos(title_trackbar_operator_type, title_window)
morph_size = cv.getTrackbarPos(title_trackbar_kernel_size, title_window)
morph_elem = 0
val_type = cv.getTrackbarPos(title_trackbar_element_type, title_window)
    if val_type == 0
morph_elem = cv.MORPH_RECT
    elif val_type == 1
morph_elem = cv.MORPH_CROSS
    elif val_type == 2
morph_elem = cv.MORPH_ELLIPSE
    elif val_type == 3
morph_elem = cv.MORPH_DIAMOND
 
element = cv.getStructuringElement(morph_elem, (2*morph_size + 1, 2*morph_size+1), (morph_size, morph_size))
operation = morph_op_dic[morph_operator]
dst = cv.morphologyEx(src, operation, element)
    cv.imshow(title_window, dst)
 
parser = argparse.ArgumentParser(description='Code for More Morphology Transformations tutorial.')
parser.add_argument('--input', help='Path to input image.', default='LinuxLogo.jpg')
args = parser.parse_args()
 
src = cv.imread(cv.samples.findFile(args.input))
if src is None
print('Could not open or find the image: ', args.input)
exit(0)
 
cv.namedWindow(title_window)
cv.createTrackbar(title_trackbar_operator_type, title_window , 0, max_operator, morphology_operations)
cv.createTrackbar(title_trackbar_element_type, title_window , 0, max_elem, morphology_operations)
cv.createTrackbar(title_trackbar_kernel_size, title_window , 0, max_kernel_size, morphology_operations)
 
morphology_operations(0)
cv.waitKey()

解释

1. 让我们看看C++程序的总体结构
   • 加载图像
   • 创建一个窗口来显示形态学操作的结果
   • 创建三个滑动条供用户输入参数
     • 第一个滑动条Operator返回要使用的形态学操作类型（morph_operator）。

         createTrackbar("操作符:\n 0: 开运算 - 1: 闭运算 \n 2: 梯度 - 3: 顶帽 \n 4: 黑帽", window_name, &morph_operator, max_operator, Morphology_Operations );

     • 第二个滑动条Element返回morph_elem，它指示我们的核是什么类型的结构

         createTrackbar( "元素:\n 0: 矩形 - 1: 十字形 - 2: 椭圆形 - 3: 菱形", window_name,
       &morph_elem, max_elem,
       Morphology_Operations );

     • 最后一个滑动条Kernel Size返回要使用的核的大小（morph_size）

         createTrackbar( "核大小:\n 2n +1", window_name,
       &morph_size, max_kernel_size,
       Morphology_Operations );

   • 每次我们移动任何滑动条时，都会调用用户函数Morphology_Operations来执行新的形态学操作，并根据当前的滑动条值更新输出图像。

      
     void Morphology_Operations( int, void* )
     {
       // 由于 MORPH_X : 2,3,4,5 和 6
       int operation = morph_operator + 2;
      
       Mat element = getStructuringElement( morph_elem, Size( 2*morph_size + 1, 2*morph_size+1 ), Point( morph_size, morph_size ) );
      
       morphologyEx( src, dst, operation, element );
       imshow( window_name, dst );
     }

     我们可以观察到执行形态学转换的关键函数是 cv::morphologyEx 。在这个例子中，我们使用了四个参数（其余使用默认值）

     • src : 源（输入）图像
     • dst: 输出图像

     • operation: 要执行的形态学转换类型。请注意，我们有5种选择

       • 开运算: MORPH_OPEN : 2
       • 闭运算: MORPH_CLOSE: 3
       • 梯度: MORPH_GRADIENT: 4
       • 顶帽: MORPH_TOPHAT: 5
       • 黑帽: MORPH_BLACKHAT: 6

       如您所见，这些值的范围是<2-6>，这就是为什么我们将滑动条输入的值加(+2)。

         int operation = morph_operator + 2;

     • element: 要使用的核。我们使用函数 cv::getStructuringElement 来定义我们自己的结构。

结果

• 编译上述代码后，我们可以通过提供图像路径作为参数来执行它。使用图像 baboon.png 的结果：

• 这里是显示窗口的两个截图。第一张图片显示了使用开运算操作符和十字形核的结果。第二张图片（右侧）显示了使用黑帽操作符和椭圆形核的结果。