如何使用opencv获取已经灰度化二值化的一张黑色图片中的一个亮点的具体像素坐标

Posted 2023-04-05

如何使用vs编译平台来调用opencv获取已经灰度化二值化的一张黑色图片中的一个亮点的具体像素坐标？有什么方法？

参考技术A OpenCV

整个项目的结构图：

编写DetectFaceDemo.java,代码如下：

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package com.njupt.zhb.test;

import org.opencv.core.Core;

import org.opencv.core.Mat;

import org.opencv.core.MatOfRect;

import org.opencv.core.Point;

import org.opencv.core.Rect;

import org.opencv.core.Scalar;

import org.opencv.highgui.Highgui;

import org.opencv.objdetect.CascadeClassifier;

//

// Detects faces in an image, draws boxes around them, and writes the results

// to "faceDetection.png".

//

public class DetectFaceDemo

public void run()

System.out.println("\nRunning DetectFaceDemo");

System.out.println(getClass().getResource("lbpcascade_frontalface.xml").getPath());

// Create a face detector from the cascade file in the resources

// directory.

//CascadeClassifier faceDetector = new CascadeClassifier(getClass().getResource("lbpcascade_frontalface.xml").getPath());

//Mat image = Highgui.imread(getClass().getResource("lena.png").getPath());

//注意：源程序的路径会多打印一个‘/’，因此总是出现如下错误

/*

* Detected 0 faces Writing faceDetection.png libpng warning: Image

* width is zero in IHDR libpng warning: Image height is zero in IHDR

* libpng error: Invalid IHDR data

*/

//因此，我们将第一个字符去掉

String xmlfilePath=getClass().getResource("lbpcascade_frontalface.xml").getPath().substring(1);

CascadeClassifier faceDetector = new CascadeClassifier(xmlfilePath);

Mat image = Highgui.imread(getClass().getResource("we.jpg").getPath().substring(1));

// Detect faces in the image.

// MatOfRect is a special container class for Rect.

MatOfRect faceDetections = new MatOfRect();

faceDetector.detectMultiScale(image, faceDetections);

System.out.println(String.format("Detected %s faces", faceDetections.toArray().length));

// Draw a bounding box around each face.

for (Rect rect : faceDetections.toArray())

Core.rectangle(image, new Point(rect.x, rect.y), new Point(rect.x + rect.width, rect.y + rect.height), new Scalar(0, 255, 0));



// Save the visualized detection.

String filename = "faceDetection.png";

System.out.println(String.format("Writing %s", filename));

Highgui.imwrite(filename, image);




package com.njupt.zhb.test;
import org.opencv.core.Core;
import org.opencv.core.Mat;
import org.opencv.core.MatOfRect;
import org.opencv.core.Point;
import org.opencv.core.Rect;
import org.opencv.core.Scalar;
import org.opencv.highgui.Highgui;
import org.opencv.objdetect.CascadeClassifier;

//
// Detects faces in an image, draws boxes around them, and writes the results
// to "faceDetection.png".
//
public class DetectFaceDemo
public void run()
System.out.println("\nRunning DetectFaceDemo");
System.out.println(getClass().getResource("lbpcascade_frontalface.xml").getPath());
// Create a face detector from the cascade file in the resources
// directory.
//CascadeClassifier faceDetector = new CascadeClassifier(getClass().getResource("lbpcascade_frontalface.xml").getPath());
//Mat image = Highgui.imread(getClass().getResource("lena.png").getPath());
//注意：源程序的路径会多打印一个‘/’，因此总是出现如下错误
/*
* Detected 0 faces Writing faceDetection.png libpng warning: Image
* width is zero in IHDR libpng warning: Image height is zero in IHDR
* libpng error: Invalid IHDR data
*/
//因此，我们将第一个字符去掉
String xmlfilePath=getClass().getResource("lbpcascade_frontalface.xml").getPath().substring(1);
CascadeClassifier faceDetector = new CascadeClassifier(xmlfilePath);
Mat image = Highgui.imread(getClass().getResource("we.jpg").getPath().substring(1));
// Detect faces in the image.
// MatOfRect is a special container class for Rect.
MatOfRect faceDetections = new MatOfRect();
faceDetector.detectMultiScale(image, faceDetections);

System.out.println(String.format("Detected %s faces", faceDetections.toArray().length));

// Draw a bounding box around each face.
for (Rect rect : faceDetections.toArray())
Core.rectangle(image, new Point(rect.x, rect.y), new Point(rect.x + rect.width, rect.y + rect.height), new Scalar(0, 255, 0));


// Save the visualized detection.
String filename = "faceDetection.png";
System.out.println(String.format("Writing %s", filename));
Highgui.imwrite(filename, image);



3.编写测试类：

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package com.njupt.zhb.test;

public class TestMain

public static void main(String[] args)

System.out.println("Hello, OpenCV");

// Load the native library.

System.loadLibrary("opencv_java246");

new DetectFaceDemo().run();





//运行结果:

//Hello, OpenCV

//

//Running DetectFaceDemo

///E:/eclipse_Jee/workspace/JavaOpenCV246/bin/com/njupt/zhb/test/lbpcascade_frontalface.xml

//Detected 8 faces

//Writing faceDetection.png
package com.njupt.zhb.test;
public class TestMain
public static void main(String[] args)
System.out.println("Hello, OpenCV");
// Load the native library.
System.loadLibrary("opencv_java246");
new DetectFaceDemo().run();


//运行结果:
//Hello, OpenCV
//
//Running DetectFaceDemo
///E:/eclipse_Jee/workspace/JavaOpenCV246/bin/com/njupt/zhb/test/lbpcascade_frontalface.xml
//Detected 8 faces
//Writing faceDetection.png本回答被提问者和网友采纳

Python使用openCV把原始彩色图像转化为灰度图使用OpenCV把图像二值化（仅仅包含黑色和白色的简化版本）基于自适应阈值预处理（adaptive thresholding）方法

Python使用openCV把原始彩色图像转化为灰度图、使用OpenCV把图像二值化（仅仅包含黑色和白色的简化版本）、基于自适应阈值预处理（adaptive thresholding）方法

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