SpringBoot使用OpenCV的超详细步骤
作者：Lovme_du
最近有个项⽬需要对图⽚图像进⾏处理，使⽤到了开源框架OpenCV,所以下面这篇文章主要给大家介绍了关于SpringBoot使用OpenCV的相关资料,文中通过代码介绍的非常详细,需要的朋友可以参考下
Spring boot 整合 OpenCV 4.5
本文展示Windows下Spring Boot 整合Opencv 4.5 进行对图片中的人脸提取，开发工具IDEA。
环境安装
1、下载opencv安装包【下载地址】 或者点击这里下载最新版
2、下载后运行exe、安装。
配置spring boot项目
1、创建空白spring boot项目，jar放入如下图，pom添加依赖。
<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
<modelVersion>4.0.0</modelVersion>
<groupId>org.example</groupId>
<artifactId>OpenCVStudy</artifactId>
<version>1.0-SNAPSHOT</version>
<packaging>pom</packaging>
<name>OpenCVStudy</name>
<description>项目骨架</description>
<parent>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-parent</artifactId>
<version>2.0.6.RELEASE</version>
<relativePath/>
</parent>
<properties>
<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
<project.reporting.outputEncoding>UTF-8</project.reporting.outputEncoding>
<java.version>1.8</java.version>
<spring-cloud.version>Finchley.SR1</spring-cloud.version>
</properties>
<dependencies>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-test</artifactId>
<scope>test</scope>
</dependency>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-web</artifactId>
</dependency>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-test</artifactId>
<scope>test</scope>
</dependency>
<!--openCV 依赖包-->
<dependency>
<groupId>org.opencv</groupId>
<artifactId>opencv</artifactId>
<version>4.5.1</version>
<scope>system</scope>
<systemPath>${project.basedir}/src/main/resources/lib/opencv-451.jar</systemPath>
</dependency>
</dependencies>
<build>
<plugins>
<plugin>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-maven-plugin</artifactId>
</plugin>
</plugins>
</build>
<repositories>
<repository>
<id>gfs-maven-snapshot-repository</id>
<name>gfs-maven-snapshot-repository</name>
<url>https://raw.githubusercontent.com/gefangshuai/maven/master/</url>
</repository>
</repositories>
</project>
2、opencv\build\java目录的dll，opencv\sources\data\haarcascades数据集，按图存放。
3、测试代码
创建类 StreamUtils.java
import javax.imageio.ImageIO;
import java.awt.*;
import java.awt.image.BufferedImage;
import java.awt.image.DataBufferByte;
import java.io.ByteArrayInputStream;
import java.io.IOException;
public class StreamUtils {
/**
* 装换回编码
*
* @param correctMat
* @return
*/
public static String catToBase64(Mat correctMat) {
return bufferToBase64(toByteArray(correctMat));
}
/**
* 转换成base64编码
*
* @param buffer
* @return
*/
public static String bufferToBase64(byte[] buffer) {
return Base64Utils.encodeToString(buffer);
}
/**
* base64编码转换成字节数组
*
* @param base64Str
* @return
*/
public static byte[] base64ToByteArray(String base64Str) {
return Base64Utils.decodeFromString(base64Str);
}
/**
* base64 编码转换为 BufferedImage
*
* @param base64
* @return
*/
public static BufferedImage base64ToBufferedImage(String base64) {
BASE64Decoder Base64 = new BASE64Decoder();
try {
byte[] bytes1 = Base64.decodeBuffer(base64);
ByteArrayInputStream bais = new ByteArrayInputStream(bytes1);
return ImageIO.read(bais);
} catch (IOException e) {
e.printStackTrace();
}
return null;
}
/**
* mat转换成bufferedImage
*
* @param matrix
* @return
*/
public static byte[] toByteArray(Mat matrix) {
MatOfByte mob = new MatOfByte();
Imgcodecs.imencode(".jpg", matrix, mob);
return mob.toArray();
}
/**
* mat转换成bufferedImage
*
* @param matrix
* @return
*/
public static BufferedImage toBufferedImage(Mat matrix) throws IOException {
byte[] buffer = toByteArray(matrix);
ByteArrayInputStream bais = new ByteArrayInputStream(buffer);
return ImageIO.read(bais);
}
/**
* base64转Mat
*
* @param base64
* @return
* @throws IOException
*/
public static Mat base642Mat(String base64) {
return bufImg2Mat(base64ToBufferedImage(base64), BufferedImage.TYPE_3BYTE_BGR, CvType.CV_8UC3);
}
/**
* BufferedImage转换成Mat
*
* @param original 要转换的BufferedImage
* @param imgType bufferedImage的类型 如 BufferedImage.TYPE_3BYTE_BGR
* @param matType 转换成mat的type 如 CvType.CV_8UC3
*/
public static Mat bufImg2Mat(BufferedImage original, int imgType, int matType) {
if (original == null) {
throw new IllegalArgumentException("original == null");
}
// Don't convert if it already has correct type
if (original.getType() != imgType) {
// Create a buffered image
BufferedImage image = new BufferedImage(original.getWidth(), original.getHeight(), imgType);
// Draw the image onto the new buffer
Graphics2D g = image.createGraphics();
try {
g.setComposite(AlphaComposite.Src);
g.drawImage(original, 0, 0, null);
original = image;
} catch (Exception e) {
e.printStackTrace();
} finally {
g.dispose();
}
}
byte[] pixels = ((DataBufferByte) original.getRaster().getDataBuffer()).getData();
Mat mat = Mat.eye(original.getHeight(), original.getWidth(), matType);
mat.put(0, 0, pixels);
return mat;
}
}
测试代码
public static String markFace(String base64Images) {
String path = System.getProperty("user.dir").concat("/haarcascades/haarcascade_frontalface_alt.xml");
CascadeClassifier faceDetector = new CascadeClassifier(path);
MatOfRect faceDetections = new MatOfRect();
Mat mat = StreamUtils.base642Mat(base64Images);
faceDetector.detectMultiScale(mat, faceDetections);
if (faceDetections.toArray().length > 0) {
for (Rect rect : faceDetections.toList()) {
Imgproc.rectangle(mat, new Point(rect.x, rect.y), new Point(rect.x + rect.width, rect.y + rect.height), new Scalar(0, 255, 0), 3);
}
}
return StreamUtils.catToBase64(mat);
}
public static void main(String[] args) {
String base64Img = "";
String base64Back = markFace(base64Img);
}OpenCV 训练自己的模型，实现特定物体的识别
opencv 3.4版本才能有训练器文件，4.5版本去掉了；但是训练出的数据集能通用。本人喜欢用新版，前面介绍使用的是高版本，训练自己的模型必须用3.4.X版本的。
环境安装
1、下载opencv安装包【下载地址】
2、下载后选择目录安装，提取文件到本地，检查是否存在目录。
前期准备
1、正样本数据图片5张（image\positive\img）；创建文件info.dat（image\positive）并编辑如下内容。
img/1.jpg 1 0 0 55 55 img/2.jpg 1 0 0 55 55 img/3.jpg 1 0 0 55 55 img/4.jpg 1 0 0 55 55 img/5.jpg 1 0 0 55 55
2、负样本数据图片5张（image\negitive\img）；创建bg.txt文件并编辑如下内容。
D:\tools\OpenCV\xl\image\negitive\img\1.jpg D:\tools\OpenCV\xl\image\negitive\img\2.jpg D:\tools\OpenCV\xl\image\negitive\img\3.jpg D:\tools\OpenCV\xl\image\negitive\img\4.jpg D:\tools\OpenCV\xl\image\negitive\img\5.jpg
3、cmd执行，生成sample.vec文件；
> D:\tools\OpenCV\opencv3.4\opencv\build\x64\vc15\bin\opencv_createsamples.exe -info D:\tools\OpenCV\xl\image\positive\info.dat -vec D:\tools\OpenCV\xl\image\sample.vec -num 5 -bgcolor 0 -bgthresh 0 -w 24 -h 24
4、生成的sample.vec和bg.txt拷贝到opencv_traincascade.exe同级目录（opencv有这个bug，不能指定目录，不然会产生报错），cmd执行；
注意：numPos 不能为正样本数量，只能小于实际数量。numNeg为负样本数量，可以大于实际数量
D:\tools\OpenCV\opencv3.4\opencv\build\x64\vc15\bin\opencv_traincascade.exe -data D:\tools\OpenCV\xl\image -vec sample.vec -bg bg.txt -numPos 3 -numNeg 7 -numStages 12 -feattureType HAAR -w 24 -h 24 -minHitRate 0.995 -maxFalseAlarmRate 0.5
执行结果：
PARAMETERS: cascadeDirName: D:\tools\OpenCV\xl\image vecFileName: sample.vec bgFileName: bg.txt numPos: 4 numNeg: 7 numStages: 12 precalcValBufSize[Mb] : 1024 precalcIdxBufSize[Mb] : 1024 acceptanceRatioBreakValue : -1 stageType: BOOST featureType: HAAR sampleWidth: 24 sampleHeight: 24 boostType: GAB minHitRate: 0.995 maxFalseAlarmRate: 0.5 weightTrimRate: 0.95 maxDepth: 1 maxWeakCount: 100 mode: BASIC Number of unique features given windowSize [24,24] : 162336 ===== TRAINING 0-stage ===== <BEGIN POS count : consumed 4 : 4 NEG count : acceptanceRatio 7 : 1 Precalculation time: 0.008 +----+---------+---------+ | N | HR | FA | +----+---------+---------+ | 1| 1| 0| +----+---------+---------+ END> Training until now has taken 0 days 0 hours 0 minutes 0 seconds. ===== TRAINING 1-stage ===== <BEGIN POS count : consumed 4 : 4 NEG count : acceptanceRatio 7 : 0.875 Precalculation time: 0.008 +----+---------+---------+ | N | HR | FA | +----+---------+---------+ | 1| 1| 0| +----+---------+---------+ END> Training until now has taken 0 days 0 hours 0 minutes 0 seconds. ===== TRAINING 2-stage ===== <BEGIN POS count : consumed 4 : 4 NEG count : acceptanceRatio 7 : 0.636364 Precalculation time: 0.008 +----+---------+---------+ | N | HR | FA | +----+---------+---------+ | 1| 1| 0| +----+---------+---------+ END> Training until now has taken 0 days 0 hours 0 minutes 0 seconds. ===== TRAINING 3-stage ===== <BEGIN POS count : consumed 4 : 4 NEG count : acceptanceRatio 7 : 0.01983 Precalculation time: 0.008 +----+---------+---------+ | N | HR | FA | +----+---------+---------+ | 1| 1| 0| +----+---------+---------+ END> Training until now has taken 0 days 0 hours 0 minutes 0 seconds. ===== TRAINING 4-stage ===== <BEGIN POS count : consumed 4 : 4 NEG count : acceptanceRatio 7 : 0.00266565 Precalculation time: 0.007 +----+---------+---------+ | N | HR | FA | +----+---------+---------+ | 1| 1| 0| +----+---------+---------+ END> Training until now has taken 0 days 0 hours 0 minutes 0 seconds. ===== TRAINING 5-stage ===== <BEGIN POS count : consumed 4 : 4 NEG count : acceptanceRatio 0 : 0 Required leaf false alarm rate achieved. Branch training terminated.
5、执行完生成 cascade.xml
6、创建测试代码使用，可行。
public static String cascade(String base64Images) {
String path = System.getProperty("user.dir").concat("/haarcascades/cascade.xml");
CascadeClassifier faceDetector = new CascadeClassifier(path);
MatOfRect faceDetections = new MatOfRect();
Mat mat = StreamUtils.base642Mat(base64Images);
faceDetector.detectMultiScale(mat, faceDetections);
if (faceDetections.toArray().length > 0) {
for (Rect rect : faceDetections.toList()) {
Imgproc.rectangle(mat, new Point(rect.x, rect.y), new Point(rect.x + rect.width, rect.y + rect.height), new Scalar(0, 255, 0), 3);
}
}
return StreamUtils.catToBase64(mat);
}
public static void main(String[] args) {
String base64Img = "";
String base64Back = cascade(base64Img);
}
总结
本文只是学习如何训练自己模型，选用正本和反面数据较小，实际项目中需要选用大量得样本数据图片。
到此这篇关于SpringBoot使用OpenCV的文章就介绍到这了,更多相关SpringBoot使用OpenCV内容请搜索脚本之家以前的文章或继续浏览下面的相关文章希望大家以后多多支持脚本之家！