opencv 我想给分割出来的数字二值化图片去噪。请问可以用啥方式?

Posted 2023-05-06

如图，我想把除了K以外的白色小块，和小点，变成黑色，增加字符匹配的成功率
请问可以用什么方式？

整个项目的结构图：

编写DetectFaceDemo.java,代码如下：

[java] view
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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 参考技术A 最简单的方法， 形态学的方法。 opencv 有自带的。本回答被提问者采纳

OpenCV之图像二值化与去噪

python代码：

 

import cv2 as cv
import numpy as np


def method_1(image):
    gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
    t, binary = cv.threshold(gray, 0, 255, cv.THRESH_BINARY | cv.THRESH_OTSU)
    return binary


def method_2(image):
    blurred = cv.GaussianBlur(image, (3, 3), 0)
    gray = cv.cvtColor(blurred, cv.COLOR_BGR2GRAY)
    t, binary = cv.threshold(gray, 0, 255, cv.THRESH_BINARY | cv.THRESH_OTSU)
    return binary


def method_3(image):
    blurred = cv.pyrMeanShiftFiltering(image, 10, 100)
    gray = cv.cvtColor(blurred, cv.COLOR_BGR2GRAY)
    t, binary = cv.threshold(gray, 0, 255, cv.THRESH_BINARY | cv.THRESH_OTSU)
    return binary


src = cv.imread("./test.png")
h, w = src.shape[:2]
ret = method_3(src)

result = np.zeros([h, w*2, 3], dtype=src.dtype)
result[0:h,0:w,:] = src
result[0:h,w:2*w,:] = cv.cvtColor(ret, cv.COLOR_GRAY2BGR)
cv.putText(result, "input",