使用java+OpenCV进行图片对比并标记差异部分（支持中文图片路径）
1、设计方法为：
- 首先将两个图片转化为灰度图；
- 进行灰度图比对，1为完全相同，此处可以插入阀值；
- 计算两个灰度图的绝对差值并放入一个新的Mat对象；
- 将新的mat对象进行绝对差值化；
- 寻找轮廓图并用红色进行标记；
- 输出到新图片中（带有标记的图片）；
2、难点说明：
- 读取图片时，OpenCV默认不支持中文图片路径，故需要用其他方法来进行，参考https://blog.csdn.net/wangyulj/article/details/79058390里描述的方法来解决该问题，具体代码见代码块中的方法readMat;
- 由于为中文路径，故使用OpenCV自带的写出图片方法会导致代码运行无异常，但是无法输出图片，故更换使用ImageIO中的方法来写出图片，代码见方法writeImage；
3、代码块如下所示
package com.dff.test;
import java.awt.HeadlessException;
import java.awt.image.BufferedImage;
import java.io.ByteArrayInputStream;
import java.io.File;
import java.io.FileInputStream;
import java.io.IOException;
import java.io.InputStream;
import java.io.UnsupportedEncodingException;
import java.util.ArrayList;
import java.util.List;
import javax.imageio.ImageIO;
import org.opencv.core.Core;
import org.opencv.core.CvType;
import org.opencv.core.Mat;
import org.opencv.core.MatOfByte;
import org.opencv.core.MatOfPoint;
import org.opencv.core.Rect;
import org.opencv.core.Scalar;
import org.opencv.imgcodecs.Imgcodecs;
import org.opencv.imgproc.Imgproc;
import org.opencv.utils.Converters;
public class ImageCompare {
private boolean compareResult = false;
private String mark = "_compareResult";
/**
* 比较两张图片，如不同则将不同处标记并输出到新的图片中
* @param imagePath1 图片1的路径
* @param imagePath2 图片2的路径
*/
public void CompareAndMarkDiff(String imagePath1, String imagePath2)
{
Mat mat1 = readMat(imagePath1);
Mat mat2 = readMat(imagePath2);
mat1 = Imgcodecs.imdecode(mat1, Imgcodecs.IMREAD_UNCHANGED);
mat2 = Imgcodecs.imdecode(mat2, Imgcodecs.IMREAD_UNCHANGED);
/*Mat mat1 = Imgcodecs.imread(imagePath1, Imgcodecs.IMREAD_UNCHANGED);
Mat mat2 = Imgcodecs.imread(imagePath2, Imgcodecs.IMREAD_UNCHANGED);*/
if(mat1.cols() == 0 || mat2.cols() == 0 || mat1.rows() == 0 || mat2.rows() == 0)
{
System.out.println("图片文件路径异常，获取的图片大小为0，无法读取");
return;
}
if(mat1.cols() != mat2.cols() || mat1.rows() != mat2.rows())
{
System.out.println("两张图片大小不同，无法比较");
return;
}
mat1.convertTo(mat1, CvType.CV_8UC1);
mat2.convertTo(mat2, CvType.CV_8UC1);
Mat mat1_gray = new Mat();
Imgproc.cvtColor(mat1, mat1_gray, Imgproc.COLOR_BGR2GRAY);
Mat mat2_gray = new Mat();
Imgproc.cvtColor(mat2, mat2_gray, Imgproc.COLOR_BGR2GRAY);
mat1_gray.convertTo(mat1_gray, CvType.CV_32F);
mat2_gray.convertTo(mat2_gray, CvType.CV_32F);
double result = Imgproc.compareHist(mat1_gray, mat2_gray, Imgproc.CV_COMP_CORREL);
if(result == 1)
{
compareResult = true;//此处结果为1则为完全相同
return;
}
System.out.println("相似度数值为:"+result);
Mat mat_result = new Mat();
//计算两个灰度图的绝对差值，并输出到一个Mat对象中
Core.absdiff(mat1_gray, mat2_gray, mat_result);
//将灰度图按照阈值进行绝对值化
mat_result.convertTo(mat_result, CvType.CV_8UC1);
List mat2_list = new ArrayList();
Mat mat2_hi = new Mat();
//寻找轮廓图
Imgproc.findContours(mat_result, mat2_list, mat2_hi, Imgproc.RETR_EXTERNAL, Imgproc.CHAIN_APPROX_SIMPLE);
Mat mat_result1 = mat1;
Mat mat_result2 = mat2;
//使用红色标记不同点
System.out.println(mat2_list.size());
for (MatOfPoint matOfPoint : mat2_list)
{
Rect rect = Imgproc.boundingRect(matOfPoint);
Imgproc.rectangle(mat_result1, rect.tl(), rect.br(), new Scalar(0, 0, 255),2);
Imgproc.rectangle(mat_result2, rect.tl(), rect.br(), new Scalar(0, 0, 255),2);
}
String fileName1 = getFileName(imagePath1);
String targetPath1 = getParentDir(imagePath2)+File.separator+fileName1.replace(".", mark+".");
String fileName2 = getFileName(imagePath2);
String targetPath2 = getParentDir(imagePath2)+File.separator+fileName2.replace(".", mark+".");
System.out.println(targetPath1);
System.out.println(targetPath2);
//图片一的带标记的输出文件；
// Imgcodecs.imwrite(targetPath1, mat_result1);
//图片二的带标记的输出文件；
// Imgcodecs.imwrite(targetPath2, mat_result2);
writeImage(mat_result1, targetPath1);
writeImage(mat_result2, targetPath2);
}
private void writeImage(Mat mat, String outPutFile)
{
MatOfByte matOfByte = new MatOfByte();
Imgcodecs.imencode(".png", mat, matOfByte);
byte[] byteArray = matOfByte.toArray();
BufferedImage bufImage = null;
try {
InputStream in = new ByteArrayInputStream(byteArray);
bufImage = ImageIO.read(in);
ImageIO.write(bufImage, "png", new File(outPutFile));
} catch (IOException | HeadlessException e)
{
e.printStackTrace();
}
}
private String getFileName(String filePath)
{
File f = new File(filePath);
return f.getName();
}
private String getParentDir(String filePath)
{
File f = new File(filePath);
return f.getParent();
}
private Mat readMat(String filePath)
{
try {
File file = new File(filePath);
FileInputStream inputStream = new FileInputStream(filePath);
byte[] byt = new byte[(int) file.length()];
int read = inputStream.read(byt);
List bs = convert(byt);
Mat mat1 = Converters.vector_char_to_Mat(bs);
return mat1;
} catch (UnsupportedEncodingException e) {
e.printStackTrace();
} catch (IOException e) {
e.printStackTrace();
}
return new Mat();
}
private List convert(byte[] byt)
{
List bs = new ArrayList();
for (int i = 0; i < byt.length; i++)
{
bs.add(i, byt[i]);
}
return bs;
}
}
主函数：
package com.dff.test.entry;
import org.opencv.core.Core;
import com.dff.test.ImageCompare;
public class TestMain {
public static void main(String[] args)
{
//ISO-8859-1
System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
String imagePath1 = "D:\\test\\中文路径\\2018-07-07_172702.PNG";
String imagePath2 = "D:\\test\\中文路径\\2018-07-07_172702 - 副本.PNG";
ImageCompare imageCompare = new ImageCompare();
imageCompare.CompareAndMarkDiff(imagePath1, imagePath2);
}
}运行结果：
相似度数值为:0.9031714333320275
1
D:\test\中文路径\2018-07-07_172702_compare.PNG
D:\test\中文路径\2018-07-07_172702 - 副本_compare.PNG比较结果图片正常输出