前言：很多时候我们需要将两个图片进行对比，确定两个图片的相似度。一般常用的就是openCv库，这里就是使用openCv进行图片相似度对比。
依赖：
org.bytedeco
javacv
1.3.3
org.bytedeco
javacv-platform
1.3.3
基本算法：
/**
* 基本算法
* 1、判断高度是否一致，如果不一致，需要截取到高度一致
* 2、截取算法
* a、因为图片有通用的顶部bar和底部bar，需要先找到底部bar。
* b、截取长图片的部分，然后和底部bar拼接，就完成了图片截取。
* c、这里设置一个默认的宽度，然后对比，找到相同部分，就是底部bar。
*/
相关代码：
package com.test.image;
import org.bytedeco.javacpp.BytePointer;
import org.bytedeco.javacpp.opencv_core;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import static org.bytedeco.javacpp.opencv_core.*;
import static org.bytedeco.javacpp.opencv_imgcodecs.imread;
import static org.bytedeco.javacpp.opencv_imgcodecs.imwrite;
import static org.bytedeco.javacpp.opencv_imgproc.*;
import static org.bytedeco.javacpp.opencv_imgproc.THRESH_BINARY;
public class ImageService {
private static Logger Log = LoggerFactory.getLogger(ImageService.class);
public static void compareImage( String targetImageUrl, String baseImageUrl ){
/**
* 读取图片到数组
*/
opencv_core.Mat targetImage = imread(targetImageUrl);
opencv_core.Mat baseImage = imread(baseImageUrl);
Log.info("read image success");
/**
* 首先对比的两个图片宽度要一致，否则不能对比
*/
if(targetImage.size().width()==baseImage.size().width()){
/**
* 基本算法
* 1、判断高度是否一致，如果不一致，需要截取到高度一致
* 2、截取算法
* a、因为图片有通用的顶部bar和底部bar，需要先找到底部bar。
* b、截取长图片的部分，然后和底部bar拼接，就完成了图片截取。
* c、这里设置一个默认的宽度，然后对比，找到相同部分，就是底部bar。
*/
if(targetImage.size().height()!=baseImage.size().height()){
if(targetImage.size().height()>baseImage.size().height()){
targetImage = dealLongImage(targetImage.clone(),baseImage.clone());
} else {
baseImage = dealLongImage(baseImage.clone(),targetImage.clone());
}
}
/**
* 进行图片差异对比
*/
Mat imageDiff = compareImage(targetImage,baseImage);
double nonZeroPercent = 100 * (double) countNonZero(imageDiff) / (imageDiff.size().height() * imageDiff.size().width());
/**
* 展示图片，将标准图，对比图，差异图，拼接成一张大图。
* 其中差异图会用绿色标出差异的部分。
*/
set3ImageTo1("", targetImage, baseImage, showDiff(imageDiff, baseImage), "xxxx.jpg" );
imageDiff.release();
baseImage.release();
targetImage.release();
} else {
}
}
/**
* 2、截取算法
* a、因为图片有通用的顶部bar和底部bar，需要先找到底部bar。
* b、截取长图片的部分，然后和底部bar拼接，就完成了图片截取。
* c、这里设置一个默认的宽度，然后对比，找到相同部分，就是底部bar。
* @return bar的高度
*/
public static int interceptBarHeight( Mat longImage, Mat shortImage ){
/**
* 设置的默认高度。
*/
int imageSearchMaxHeight = 400;
Mat subImageLong = new Mat(longImage, new Rect(0, longImage.size().height() - imageSearchMaxHeight, longImage.size().width(), imageSearchMaxHeight));
Mat subImageShort = new Mat(shortImage, new Rect(0, shortImage.size().height() - imageSearchMaxHeight, shortImage.size().width(), imageSearchMaxHeight));
opencv_core.Mat imageDiff = compareImage(subImageLong,subImageShort);
for (int row = imageDiff.size().height() - 1; row > -1; row--) {
for (int col = 0; col < imageDiff.size().width(); col++) {
BytePointer bytePointer = imageDiff.ptr(row, col);
if (bytePointer.get(0) != 0) {
imageDiff.release();
return imageSearchMaxHeight-row;
}
}
}
return imageSearchMaxHeight;
}
/**
* 这里将两张图片作为参数传入，
* 获取到共同的底部之后。对长图进行截取，
* 然后将顶部和底部拼接在一起就ok了。
* @param longImage
* @param shortImage
* @return
*/
public static opencv_core.Mat dealLongImage( Mat longImage, Mat shortImage ){
int diffHeight = longImage.size().height()-shortImage.size().height();
int barHeight = interceptBarHeight(longImage,shortImage);
opencv_core.Mat dealedLongImage = new Mat(longImage,new Rect(0,0,longImage.size().width(),shortImage.size().height()-barHeight) );
opencv_core.Mat imageBar = new Mat(longImage,new Rect(0,longImage.size().height()-barHeight,longImage.size().width(),barHeight) );
opencv_core.Mat dealedLongImageNew = dealedLongImage.clone();
/**
* 将头部和底部bar拼接在一起。
*/
vconcat(dealedLongImage, imageBar, dealedLongImageNew);
imageBar.release();
dealedLongImage.release();
return dealedLongImageNew;
}
public static opencv_core.Mat compareImage( opencv_core.Mat targetImage, opencv_core.Mat baseImage ){
opencv_core.Mat targetImageClone = targetImage.clone();
opencv_core.Mat baseImageColne = baseImage.clone();
opencv_core.Mat imgDiff1 = targetImage.clone();
opencv_core.Mat imgDiff = targetImage.clone();
/**
* 首先将图片转成灰度图，
*/
cvtColor(targetImage, targetImageClone, COLOR_BGR2GRAY);
cvtColor(baseImage, baseImageColne, COLOR_BGR2GRAY);
/**
* 两个矩阵相减，获得差异图。
*/
subtract(targetImageClone, baseImageColne, imgDiff1);
subtract(baseImageColne, targetImageClone, imgDiff);
/**
* 按比重进行叠加。
*/
addWeighted(imgDiff, 1, imgDiff1, 1, 0, imgDiff);
/**
* 图片二值化，大于24的为1，小于24的为0
*/
threshold(imgDiff, imgDiff, 24, 255, THRESH_BINARY);
erode(imgDiff, imgDiff, new opencv_core.Mat());
dilate(imgDiff, imgDiff, new opencv_core.Mat());
return imgDiff;
}
private static void set3ImageTo1(String logTag, Mat imageSrc, Mat imageBaseSrc, Mat imageDest, String mergePicResult ) {
if (imageSrc.size().width() == imageDest.size().width() && imageBaseSrc.size().height() == imageDest.size().height()) {
Mat img = imageSrc.clone();
Mat imgBase = imageBaseSrc.clone();
Mat imgDest = imageDest.clone();
Mat imgLine = new Mat(imgBase.size().height(), 1, CV_8UC3, new Scalar(0, 0, 0, 255));
Mat largeImg2 = new Mat();
Mat largeImg3 = new Mat();
Mat largeImg4 = new Mat();
Mat largeImg5 = new Mat();
/**
* 横向拼接。
*/
hconcat(img, imgLine, largeImg2);
hconcat(largeImg2, imgBase, largeImg3);
hconcat(largeImg3, imgLine, largeImg4);
hconcat(largeImg4, imgDest, largeImg5);
imwrite( mergePicResult, largeImg5);
img.release();
imgBase.release();
imgDest.release();
imgLine.release();
largeImg2.release();
largeImg3.release();
largeImg4.release();
largeImg5.release();
} else {
Log.info(logTag+" pictures merge failed");
imwrite( mergePicResult, imageDest);
}
}
private static Mat showDiff(Mat imgDiff, Mat imgBase) {
MatVector rgbFrame = new MatVector();
Mat imgDest = imgBase.clone();
split(imgBase, rgbFrame);
subtract(rgbFrame.get(2), imgDiff, rgbFrame.get(2));
subtract(rgbFrame.get(0), imgDiff, rgbFrame.get(0));
addWeighted(rgbFrame.get(1), 1, imgDiff, 1, 0, rgbFrame.get(1));
merge(rgbFrame, imgDest);
return imgDest;
}
public static void main( String[] args ){
String targetImageUrl = "2022-03-15-11-37-35-2ouA9yi9gjsGWHDAoaZTaNe4awr0xSlohFq0gF0m.png";
String baseImageUrl = "2022-03-15-11-37-38-njH2kVzd3boX1i8q8bLCfnnIj8xTLyHhHufgs9rp.png";
compareImage(targetImageUrl,baseImageUrl);
}
}
