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Code Issues Packages Projects Releases Wiki Activity

1、目标检测：支持自己训练的模型推理

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2、目标检测：支持yolo12模型
3、支持JDK8使用
4、引入离线依赖库，支持完全离线使用
5、优化FaceNet人脸比对速度
6、支持4通道图片检测
This commit is contained in:
dengwenjie
2025-05-17 11:19:46 +08:00
parent ab669d58e3
commit a7a118c5aa
27 changed files with 401 additions and 237 deletions
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19
README.md
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@@ -13,7 +13,7 @@
<img src="https://img.shields.io/:license-MulanPSL2-blue.svg" />
</a>
<a target="_blank" href="https://www.oracle.com/java/technologies/javase/javase-jdk11-downloads.html">
<img src="https://img.shields.io/badge/JDK-11+-green.svg" />
<img src="https://img.shields.io/badge/JDK-8+-green.svg" />
</a>
<a target="_blank" href='https://gitee.com/dengwenjie/SmartJavaAI/stargazers'>
<img src='https://gitee.com/dengwenjie/SmartJavaAI/badge/star.svg?theme=gvp' alt='star'/>
@@ -230,7 +230,7 @@ SmartJavaAI是专为JAVA 开发者打造的一个功能丰富、开箱即用的
### 1、环境要求
- Java 版本：**JDK 11或更高版本**
- Java 版本：**JDK 8或更高版本**
- 操作系统：不同模型支持的系统不一样，具体请查看文档
### 2、Maven
@@ -240,7 +240,7 @@ SmartJavaAI是专为JAVA 开发者打造的一个功能丰富、开箱即用的
<dependency>
<groupId>cn.smartjavaai</groupId>
<artifactId>smartjavaai-all</artifactId>
<version>1.0.12</version>
<version>1.0.13</version>
</dependency>
```
### 3、完整示例代码
@@ -273,7 +273,15 @@ SmartJavaAI是专为JAVA 开发者打造的一个功能丰富、开箱即用的
🚀 **如果这个项目对你有帮助，别忘了点个 Star ⭐！你的支持是我持续优化升级的动力！** ❤️
## 更新日志
## 近期更新日志
## [v1.0.13] - 2025-05-17
- 支持 JDK8 环境运行
- 引入离线依赖，支持完全离线使用
- 优化 FaceNet 人脸比对性能，提升比对速度
- 支持带 Alpha 通道的 4 通道图片检测
- 目标检测：新增 YOLOv12 官方模型支持
- 目标检测：支持加载自训练模型进行推理
## [v1.0.12] - 2025-05-09
- 新增图片与视频活体检测
@@ -297,9 +305,6 @@ SmartJavaAI是专为JAVA 开发者打造的一个功能丰富、开箱即用的
- 修复若干已知问题
- 支持自定义选择使用 GPU 或 CPU 运算
- 人脸识别模块新增多种接口，功能更加完善
## [v1.0.6] - 2025-04-01
- 修复人脸识别算法facenet-pytorch实现方式
- 优化Seetaface6算法，兼容jdk高版本

44
examples/pom.xml
View File

@@ -12,7 +12,7 @@
<maven.compiler.source>11</maven.compiler.source>
<maven.compiler.target>11</maven.compiler.target>
<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
<smartjavaai.version>1.0.12</smartjavaai.version>
<smartjavaai.version>1.0.13</smartjavaai.version>
<!--如果打包运行，需要替换成你的main-->
<exec.mainClass>smartai.examples.face.facerec.RetinaFaceDemo</exec.mainClass>
@@ -104,6 +104,14 @@
</dependency>
<dependency>
<groupId>ai.djl.pytorch</groupId>
<artifactId>pytorch-jni</artifactId>
<version>2.5.1-0.32.0</version>
<scope>runtime</scope>
</dependency>
<!-- windows平台 (保留对应平台的配置，可以减小包大小)-->
<dependency>
<groupId>org.bytedeco</groupId>
@@ -132,6 +140,15 @@
<classifier>${javacv.platform.windows-x86_64}</classifier>
</dependency>
<dependency>
<groupId>ai.djl.pytorch</groupId>
<artifactId>pytorch-native-cpu</artifactId>
<classifier>${djl.platform.windows-x86_64}</classifier>
<version>2.5.1</version>
<scope>runtime</scope>
</dependency>
<!-- linux x86 平台 (保留对应平台的配置，可以减小包大小)-->
<dependency>
@@ -161,6 +178,14 @@
<classifier>${javacv.platform.linux-x86_64}</classifier>
</dependency>
<dependency>
<groupId>ai.djl.pytorch</groupId>
<artifactId>pytorch-native-cpu</artifactId>
<classifier>${djl.platform.linux-x86_64}</classifier>
<version>2.5.1</version>
<scope>runtime</scope>
</dependency>
<!-- macOS M系列 平台 (保留对应平台的配置，可以减小包大小)-->
<dependency>
@@ -190,6 +215,14 @@
<classifier>${javacv.platform.macosx-arm64}</classifier>
</dependency>
<dependency>
<groupId>ai.djl.pytorch</groupId>
<artifactId>pytorch-native-cpu</artifactId>
<classifier>${djl.platform.osx-aarch64}</classifier>
<version>2.5.1</version>
<scope>runtime</scope>
</dependency>
<!-- linux aarch64 平台 (保留对应平台的配置，可以减小包大小)-->
<dependency>
@@ -220,8 +253,13 @@
<classifier>${javacv.platform.linux-arm64}</classifier>
</dependency>
<dependency>
<groupId>ai.djl.pytorch</groupId>
<artifactId>pytorch-native-cpu-precxx11</artifactId>
<classifier>${djl.platform.linux-aarch64}</classifier>
<version>2.5.1</version>
<scope>runtime</scope>
</dependency>

24
examples/src/main/java/smartai/examples/face/facerec/FaceNetDemo.java
View File

@@ -54,14 +54,17 @@ public class FaceNetDemo {
@Test
public void testExtractFeaturesWithCustomConfig(){
try {
//人脸特征提取模型
FaceModel faceModel = FaceModelFactory.getInstance().getModel(
new FaceModelConfig(FaceModelEnum.FACENET_FEATURE_EXTRACTION));
//人脸模型参数
FaceModelConfig config = new FaceModelConfig();
config.setModelEnum(FaceModelEnum.FACENET_FEATURE_EXTRACTION);
//人脸特征提取参数
FaceExtractConfig extractConfig = new FaceExtractConfig();
//人脸检测模型配置
extractConfig.setDetectModelConfig(new FaceModelConfig(FaceModelEnum.ULTRA_LIGHT_FAST_GENERIC_FACE));
List<float[]> faceResult = faceModel.extractFeatures("src/main/resources/kana1.jpg",extractConfig);
extractConfig.setDetectModel(FaceModelFactory.getInstance().getModel(new FaceModelConfig(FaceModelEnum.ULTRA_LIGHT_FAST_GENERIC_FACE)));
config.setExtractConfig(extractConfig);
//人脸特征提取模型
FaceModel faceModel = FaceModelFactory.getInstance().getModel(config);
List<float[]> faceResult = faceModel.extractFeatures("src/main/resources/kana1.jpg");
log.info("人脸特征提取结果：{}", JSONObject.toJSONString(faceResult));
}catch (Exception e){
e.printStackTrace();
@@ -94,13 +97,16 @@ public class FaceNetDemo {
@Test
public void testExtractTopFaceFeatureWithCustomConfig(){
try {
//人脸特征提取模型
FaceModel faceModel = FaceModelFactory.getInstance().getModel(
new FaceModelConfig(FaceModelEnum.FACENET_FEATURE_EXTRACTION));
//人脸模型参数
FaceModelConfig config = new FaceModelConfig();
config.setModelEnum(FaceModelEnum.FACENET_FEATURE_EXTRACTION);
//人脸特征提取参数
FaceExtractConfig extractConfig = new FaceExtractConfig();
//人脸检测模型配置
extractConfig.setDetectModelConfig(new FaceModelConfig(FaceModelEnum.ULTRA_LIGHT_FAST_GENERIC_FACE));
extractConfig.setDetectModel(FaceModelFactory.getInstance().getModel(new FaceModelConfig(FaceModelEnum.ULTRA_LIGHT_FAST_GENERIC_FACE)));
config.setExtractConfig(extractConfig);
//人脸特征提取模型
FaceModel faceModel = FaceModelFactory.getInstance().getModel(config);
float[] faceResult = faceModel.extractTopFaceFeature("src/main/resources/kana1.jpg");
log.info("人脸特征提取结果：{}", JSONObject.toJSONString(faceResult));
}catch (Exception e){

36
examples/src/main/java/smartai/examples/objectdetection/ObjectDetection.java
View File

@@ -13,8 +13,8 @@ import ai.djl.repository.zoo.ZooModel;
import ai.djl.training.util.ProgressBar;
import cn.smartjavaai.common.entity.DetectionResponse;
import cn.smartjavaai.common.enums.DeviceEnum;
import cn.smartjavaai.objectdetection.DetectorModelConfig;
import cn.smartjavaai.objectdetection.DetectorModelEnum;
import cn.smartjavaai.objectdetection.config.DetectorModelConfig;
import cn.smartjavaai.objectdetection.enums.DetectorModelEnum;
import cn.smartjavaai.objectdetection.exception.DetectionException;
import cn.smartjavaai.objectdetection.model.DetectorModel;
import cn.smartjavaai.objectdetection.model.ObjectDetectionModelFactory;
@@ -62,7 +62,7 @@ public class ObjectDetection {
@Test
public void objectDetection2(){
DetectorModelConfig config = new DetectorModelConfig();
config.setModelEnum(DetectorModelEnum.SSD_300_RESNET50);//检测模型，目前支持19种模型
config.setModelEnum(DetectorModelEnum.SSD_300_RESNET50);//检测模型，目前支持19种预置模型
DetectorModel detectorModel = ObjectDetectionModelFactory.getInstance().getModel(config);
DetectionResponse detectionResponse = detectorModel.detect("src/main/resources/dog_bike_car.jpg");
log.info("目标检测结果：{}", JSONObject.toJSONString(detectionResponse));
@@ -110,5 +110,35 @@ public class ObjectDetection {
}
/**
* 使用yolo官方模型检测
*/
@Test
public void objectDetectionWithOfficialModel(){
DetectorModelConfig config = new DetectorModelConfig();
//也支持YoloV8：YOLOV8_OFFICIAL 模型可以从文档中提供的地址下载
config.setModelEnum(DetectorModelEnum.YOLOV12_OFFICIAL);//检测模型，目前支持19种模型
// 指定模型路径，需要更改为自己的模型路径
config.setModelPath("/Users/xxx/Documents/develop/face_model/yolov12n.onnx");
DetectorModel detectorModel = ObjectDetectionModelFactory.getInstance().getModel(config);
//一定要将yolo官方的类别文件：synset.txt（文档中下载）放在模型同目录下，否则报错
detectorModel.detectAndDraw("src/main/resources/object_detection.jpg","output/object_detection_detected.png");
}
/**
* 使用自己训练的模型检测
*/
@Test
public void objectDetectionWithCustomModel(){
DetectorModelConfig config = new DetectorModelConfig();
//也支持YoloV8：YOLOV8_CUSTOM 模型需要自己训练，训练教程可以查看文档
config.setModelEnum(DetectorModelEnum.YOLOV12_CUSTOM);//自定义YOLOV12模型
// 指定模型路径，需要更改为自己的模型路径
config.setModelPath("/Users/xxx/Documents/develop/fire_model/best.onnx");
DetectorModel detectorModel = ObjectDetectionModelFactory.getInstance().getModel(config);
//一定要将类别文件：synset.txt 放在模型同目录下，否则报错(具体请参看文档)
detectorModel.detectAndDraw("/Users/xxx/Downloads/test.jpg","output/test_detected.jpg");
}
}

14
pom.xml
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@@ -6,7 +6,7 @@
<groupId>cn.smartjavaai</groupId>
<artifactId>smartjavaai-parent</artifactId>
<version>1.0.12</version>
<version>1.0.13</version>
<packaging>pom</packaging>
<description>SmartJavaAI</description>
<modules>
@@ -19,8 +19,8 @@
</modules>
<properties>
<maven.compiler.source>11</maven.compiler.source>
<maven.compiler.target>11</maven.compiler.target>
<maven.compiler.source>8</maven.compiler.source>
<maven.compiler.target>8</maven.compiler.target>
<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
<djl.version>0.32.0</djl.version>
@@ -39,13 +39,13 @@
<dependency>
<groupId>cn.smartjavaai</groupId>
<artifactId>smartjavaai-common</artifactId>
<version>1.0.12</version>
<version>1.0.13</version>
</dependency>
<dependency>
<groupId>cn.smartjavaai</groupId>
<artifactId>smartjavaai-face</artifactId>
<version>1.0.12</version>
<version>1.0.13</version>
</dependency>
</dependencies>
@@ -95,7 +95,7 @@
<dependency>
<groupId>org.testng</groupId>
<artifactId>testng</artifactId>
<version>7.10.2</version>
<version>7.4.0</version>
<scope>test</scope>
</dependency>
@@ -260,7 +260,7 @@
<artifactId>maven-javadoc-plugin</artifactId>
<version>3.1.0</version>
<configuration>
<javadocExecutable>${java.home}/bin/javadoc</javadocExecutable>
<!-- <javadocExecutable>${java.home}/bin/javadoc</javadocExecutable>-->
<doclint>none</doclint>
<additionalJOptions>
<additionalJOption>-Xdoclint:none</additionalJOption>

10
smartjavaai-all/pom.xml
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@@ -6,11 +6,11 @@
<parent>
<groupId>cn.smartjavaai</groupId>
<artifactId>smartjavaai-parent</artifactId>
<version>1.0.12</version>
<version>1.0.13</version>
</parent>
<artifactId>smartjavaai-all</artifactId>
<version>1.0.12</version>
<version>1.0.13</version>
<name>${project.artifactId}</name>
<description>SmartJavaAI</description>
<url>https://github.com/geekwenjie/SmartJavaAI</url>
@@ -23,8 +23,8 @@
<properties>
<maven.compiler.source>11</maven.compiler.source>
<maven.compiler.target>11</maven.compiler.target>
<!-- <maven.compiler.source>11</maven.compiler.source>-->
<!-- <maven.compiler.target>11</maven.compiler.target>-->
<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
<maven.test.skip>true</maven.test.skip>
</properties>
@@ -111,7 +111,7 @@
<artifactId>maven-javadoc-plugin</artifactId>
<version>3.1.0</version>
<configuration>
<javadocExecutable>${java.home}/bin/javadoc</javadocExecutable>
<!-- <javadocExecutable>${java.home}/bin/javadoc</javadocExecutable>-->
<doclint>none</doclint>
<additionalJOptions>
<additionalJOption>-Xdoclint:none</additionalJOption>

10
smartjavaai-bom/pom.xml
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@@ -6,17 +6,17 @@
<parent>
<groupId>cn.smartjavaai</groupId>
<artifactId>smartjavaai-parent</artifactId>
<version>1.0.12</version>
<version>1.0.13</version>
</parent>
<version>1.0.12</version>
<version>1.0.13</version>
<artifactId>smartjavaai-bom</artifactId>
<name>smartjavaai-bom</name>
<description>统一版本管理的 BOM 包，同时支持 import 和全量依赖</description>
<properties>
<maven.compiler.source>11</maven.compiler.source>
<maven.compiler.target>11</maven.compiler.target>
<!-- <maven.compiler.source>11</maven.compiler.source>-->
<!-- <maven.compiler.target>11</maven.compiler.target>-->
<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
</properties>
@@ -77,7 +77,7 @@
<artifactId>maven-javadoc-plugin</artifactId>
<version>3.1.0</version>
<configuration>
<javadocExecutable>${java.home}/bin/javadoc</javadocExecutable>
<!-- <javadocExecutable>${java.home}/bin/javadoc</javadocExecutable>-->
<doclint>none</doclint>
<additionalJOptions>
<additionalJOption>-Xdoclint:none</additionalJOption>

4
smartjavaai-common/pom.xml
View File

@@ -6,7 +6,7 @@
<parent>
<groupId>cn.smartjavaai</groupId>
<artifactId>smartjavaai-parent</artifactId>
<version>1.0.12</version>
<version>1.0.13</version>
</parent>
<artifactId>smartjavaai-common</artifactId>
@@ -58,7 +58,7 @@
<artifactId>maven-javadoc-plugin</artifactId>
<version>3.1.0</version>
<configuration>
<javadocExecutable>${java.home}/bin/javadoc</javadocExecutable>
<!-- <javadocExecutable>${java.home}/bin/javadoc</javadocExecutable>-->
<doclint>none</doclint>
<additionalJOptions>
<additionalJOption>-Xdoclint:none</additionalJOption>

10
smartjavaai-face/pom.xml
View File

@@ -6,11 +6,11 @@
<parent>
<groupId>cn.smartjavaai</groupId>
<artifactId>smartjavaai-parent</artifactId>
<version>1.0.12</version>
<version>1.0.13</version>
</parent>
<artifactId>smartjavaai-face</artifactId>
<version>1.0.12</version>
<version>1.0.13</version>
<name>smartjavaai-face</name>
<description>SmartJavaAI</description>
<url>https://github.com/geekwenjie/SmartJavaAI</url>
@@ -22,8 +22,8 @@
</licenses>
<properties>
<maven.compiler.source>11</maven.compiler.source>
<maven.compiler.target>11</maven.compiler.target>
<!-- <maven.compiler.source>11</maven.compiler.source>-->
<!-- <maven.compiler.target>11</maven.compiler.target>-->
<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
<maven.test.skip>true</maven.test.skip>
<javacv.version>1.5.8</javacv.version>
@@ -87,7 +87,7 @@
<artifactId>maven-javadoc-plugin</artifactId>
<version>3.1.0</version>
<configuration>
<javadocExecutable>${java.home}/bin/javadoc</javadocExecutable>
<!-- <javadocExecutable>${java.home}/bin/javadoc</javadocExecutable>-->
<doclint>none</doclint>
<additionalJOptions>
<additionalJOption>-Xdoclint:none</additionalJOption>

9
smartjavaai-face/src/main/java/cn/smartjavaai/face/config/FaceExtractConfig.java
View File

@@ -1,5 +1,6 @@
package cn.smartjavaai.face.config;
import cn.smartjavaai.face.model.facerec.FaceModel;
import lombok.Data;
/**
@@ -21,17 +22,17 @@ public class FaceExtractConfig {
private boolean align = true;
/**
* 人脸检测模型配置
* 人脸检测模型
*/
private FaceModelConfig detectModelConfig;
private FaceModel detectModel;
public FaceExtractConfig() {
}
public FaceExtractConfig(boolean cropFace, boolean align, FaceModelConfig detectModelConfig) {
public FaceExtractConfig(boolean cropFace, boolean align, FaceModel detectModel) {
this.cropFace = cropFace;
this.align = align;
this.detectModelConfig = detectModelConfig;
this.detectModel = detectModel;
}

5
smartjavaai-face/src/main/java/cn/smartjavaai/face/config/FaceModelConfig.java
View File

@@ -52,6 +52,11 @@ public class FaceModelConfig {
*/
private int gpuId = 0;
/**
* 人脸特征提取配置
*/
private FaceExtractConfig extractConfig;
public FaceModelConfig() {
}

28
smartjavaai-face/src/main/java/cn/smartjavaai/face/model/facerec/AbstractFaceModel.java
View File

@@ -140,21 +140,6 @@ public abstract class AbstractFaceModel implements FaceModel {
throw new UnsupportedOperationException("默认不支持该功能");
}
@Override
public List<float[]> extractFeatures(BufferedImage image, FaceExtractConfig config) {
throw new UnsupportedOperationException("默认不支持该功能");
}
@Override
public List<float[]> extractFeatures(String imagePath, FaceExtractConfig config) {
throw new UnsupportedOperationException("默认不支持该功能");
}
@Override
public List<float[]> extractFeatures(byte[] imageData, FaceExtractConfig config) {
throw new UnsupportedOperationException("默认不支持该功能");
}
@Override
public float[] extractTopFaceFeature(BufferedImage image) {
throw new UnsupportedOperationException("默认不支持该功能");
@@ -170,18 +155,5 @@ public abstract class AbstractFaceModel implements FaceModel {
throw new UnsupportedOperationException("默认不支持该功能");
}
@Override
public float[] extractTopFaceFeature(BufferedImage image, FaceExtractConfig config) {
throw new UnsupportedOperationException("默认不支持该功能");
}
@Override
public float[] extractTopFaceFeature(String imagePath, FaceExtractConfig config) {
throw new UnsupportedOperationException("默认不支持该功能");
}
@Override
public float[] extractTopFaceFeature(byte[] imageData, FaceExtractConfig config) {
throw new UnsupportedOperationException("默认不支持该功能");
}
}

45
smartjavaai-face/src/main/java/cn/smartjavaai/face/model/facerec/FaceModel.java
View File

@@ -199,30 +199,6 @@ public interface FaceModel {
*/
List<float[]> extractFeatures(BufferedImage image);
/**
* 特征提取（使用自定义配置）
* 强制裁剪操作
* @param image BufferedImage
* @param config
* @return
*/
List<float[]> extractFeatures(BufferedImage image, FaceExtractConfig config);
/**
* 特征提取（使用自定义配置）
* @param imagePath 图片路径
* @param config
* @return
*/
List<float[]> extractFeatures(String imagePath, FaceExtractConfig config);
/**
* 特征提取（使用自定义配置）
* @param imageData 图片字节流
* @param config
* @return
*/
List<float[]> extractFeatures(byte[] imageData, FaceExtractConfig config);
/**
* 提取分数最高人脸特征（使用默认配置）
@@ -245,27 +221,6 @@ public interface FaceModel {
*/
float[] extractTopFaceFeature(byte[] imageData);
/**
* 提取分数最高人脸特征（使用自定义配置）
* @param image BufferedImage
* @return
*/
float[] extractTopFaceFeature(BufferedImage image, FaceExtractConfig config);
/**
* 提取分数最高人脸特征（使用自定义配置）
* @param imagePath 图片路径
* @param config
* @return
*/
float[] extractTopFaceFeature(String imagePath, FaceExtractConfig config);
/**
* 提取分数最高人脸特征（使用自定义配置）
* @param imageData 图片字节流
* @param config
* @return
*/
float[] extractTopFaceFeature(byte[] imageData, FaceExtractConfig config);
}

112
smartjavaai-face/src/main/java/cn/smartjavaai/face/model/facerec/FeatureExtractionModel.java
View File

@@ -59,6 +59,8 @@ public class FeatureExtractionModel extends AbstractFaceModel implements AutoClo
private ZooModel<Image, float[]> model;
private FaceModelConfig config;
public static final List<Float> mean =
Arrays.asList(
127.5f / 255.0f,
@@ -75,10 +77,21 @@ public class FeatureExtractionModel extends AbstractFaceModel implements AutoClo
*/
@Override
public void loadModel(FaceModelConfig config) {
if(Objects.isNull(config)){
throw new FaceException("config为null");
}
if(Objects.isNull(config.getExtractConfig())){
config.setExtractConfig(getDefaultConfig());
}else{
if(Objects.isNull(config.getExtractConfig().getDetectModel())){
throw new FaceException("请设置人脸检测模型");
}
}
Device device = null;
if(!Objects.isNull(config.getDevice())){
device = config.getDevice() == DeviceEnum.CPU ? Device.cpu() : Device.gpu();
}
this.config = config;
String normalize = mean.stream().map(Object::toString).collect(Collectors.joining(","));
Criteria<Image, float[]> faceFeatureCriteria =
Criteria.builder()
@@ -162,7 +175,8 @@ public class FeatureExtractionModel extends AbstractFaceModel implements AutoClo
}
float[] feature1 = extractTopFaceFeature(imagePath1);
float[] feature2 = extractTopFaceFeature(imagePath2);
return calculSimilar(feature1, feature2);
float ret = calculSimilar(feature1, feature2);
return ret;
}
@@ -194,37 +208,18 @@ public class FeatureExtractionModel extends AbstractFaceModel implements AutoClo
FaceExtractConfig config = new FaceExtractConfig();
FaceModelConfig detectModelConfig = new FaceModelConfig();
detectModelConfig.setModelEnum(FaceModelEnum.ULTRA_LIGHT_FAST_GENERIC_FACE);
config.setDetectModelConfig(detectModelConfig);
log.debug("创建默认检测模型：ULTRA_LIGHT_FAST_GENERIC_FACE");
FaceModel detectModel = FaceModelFactory.getInstance().getModel(detectModelConfig);
log.debug("创建检测模型完毕");
config.setDetectModel(detectModel);
return config;
}
@Override
public List<float[]> extractFeatures(String imagePath) {
return extractFeatures(imagePath, getDefaultConfig());
}
@Override
public List<float[]> extractFeatures(byte[] imageData) {
return extractFeatures(imageData, getDefaultConfig());
}
@Override
public List<float[]> extractFeatures(BufferedImage image) {
return extractFeatures(image, getDefaultConfig());
}
@Override
public List<float[]> extractFeatures(BufferedImage image, FaceExtractConfig config) {
if(Objects.isNull(config)){
throw new FaceException("config为null");
}
List<float[]> featureList = new ArrayList<float[]>();
if(Objects.isNull(config.getDetectModelConfig())){
throw new FaceException("config.detectModelConfig为null");
}
FaceModel faceModel = FaceModelFactory.getInstance().getModel(config.getDetectModelConfig());
DetectionResponse detectedResult = faceModel.detect(image);
DetectionResponse detectedResult = config.getExtractConfig().getDetectModel().detect(image);
if(Objects.isNull(detectedResult) || Objects.isNull(detectedResult.getDetectionInfoList()) || detectedResult.getDetectionInfoList().isEmpty()){
throw new FaceException("未检测到人脸");
}
@@ -237,7 +232,7 @@ public class FeatureExtractionModel extends AbstractFaceModel implements AutoClo
//裁剪人脸
Image subImage = djlImage.getSubImage(rectangle.getX(), rectangle.getY() , rectangle.getWidth() , rectangle.getHeight());
//人脸对齐
if(config.isAlign()){
if(config.getExtractConfig().isAlign()){
//获取子图中人脸关键点坐标
double[][] pointsArray = FaceUtils.facePoints(detectionInfo.getFaceInfo().getKeyPoints());
NDArray srcPoints = manager.create(pointsArray);
@@ -263,8 +258,21 @@ public class FeatureExtractionModel extends AbstractFaceModel implements AutoClo
return featureList;
}
@Override
public List<float[]> extractFeatures(String imagePath, FaceExtractConfig config) {
public List<float[]> extractFeatures(byte[] imageData) {
if(Objects.isNull(imageData)){
throw new FaceException("图像无效");
}
try {
return extractFeatures(ImageIO.read(new ByteArrayInputStream(imageData)));
} catch (IOException e) {
throw new FaceException("错误的图像", e);
}
}
@Override
public List<float[]> extractFeatures(String imagePath) {
if(!FileUtils.isFileExists(imagePath)){
throw new FaceException("图像文件不存在");
}
@@ -275,49 +283,15 @@ public class FeatureExtractionModel extends AbstractFaceModel implements AutoClo
} catch (IOException e) {
throw new FaceException("无效图片路径", e);
}
return extractFeatures(image, config);
}
@Override
public List<float[]> extractFeatures(byte[] imageData, FaceExtractConfig config) {
if(Objects.isNull(imageData)){
throw new FaceException("图像无效");
}
try {
return extractFeatures(ImageIO.read(new ByteArrayInputStream(imageData)), config);
} catch (IOException e) {
throw new FaceException("错误的图像", e);
}
return extractFeatures(image);
}
@Override
public float[] extractTopFaceFeature(BufferedImage image) {
return extractTopFaceFeature(image, getDefaultConfig());
}
@Override
public float[] extractTopFaceFeature(String imagePath) {
return extractTopFaceFeature(imagePath, getDefaultConfig());
}
@Override
public float[] extractTopFaceFeature(byte[] imageData) {
return extractTopFaceFeature(imageData, getDefaultConfig());
}
@Override
public float[] extractTopFaceFeature(BufferedImage image, FaceExtractConfig config) {
if(Objects.isNull(config)){
throw new FaceException("config为null");
}
if(Objects.isNull(config.getDetectModelConfig())){
throw new FaceException("config.detectModelConfig为null");
}
Image djlImage = ImageFactory.getInstance().fromImage(OpenCVUtils.image2Mat(image));
float[] features = null;
if(config.isCropFace()){
FaceModel faceModel = FaceModelFactory.getInstance().getModel(config.getDetectModelConfig());
DetectionResponse detectedResult = faceModel.detect(image);
if(config.getExtractConfig().isCropFace()){
DetectionResponse detectedResult = config.getExtractConfig().getDetectModel().detect(image);
if(Objects.isNull(detectedResult) || Objects.isNull(detectedResult.getDetectionInfoList()) || detectedResult.getDetectionInfoList().isEmpty()){
throw new FaceException("未检测到人脸");
}
@@ -327,7 +301,7 @@ public class FeatureExtractionModel extends AbstractFaceModel implements AutoClo
//裁剪人脸
Image subImage = djlImage.getSubImage(rectangle.getX(), rectangle.getY() , rectangle.getWidth() , rectangle.getHeight());
//人脸对齐
if(config.isAlign()){
if(config.getExtractConfig().isAlign()){
NDManager manager = NDManager.newBaseManager();
//获取子图中人脸关键点坐标
double[][] pointsArray = FaceUtils.facePoints(detectionInfo.getFaceInfo().getKeyPoints());
@@ -355,7 +329,7 @@ public class FeatureExtractionModel extends AbstractFaceModel implements AutoClo
}
@Override
public float[] extractTopFaceFeature(String imagePath, FaceExtractConfig config) {
public float[] extractTopFaceFeature(String imagePath) {
if(!FileUtils.isFileExists(imagePath)){
throw new FaceException("图像文件不存在");
}
@@ -366,16 +340,16 @@ public class FeatureExtractionModel extends AbstractFaceModel implements AutoClo
} catch (IOException e) {
throw new FaceException("无效图片路径", e);
}
return extractTopFaceFeature(image, config);
return extractTopFaceFeature(image);
}
@Override
public float[] extractTopFaceFeature(byte[] imageData, FaceExtractConfig config) {
public float[] extractTopFaceFeature(byte[] imageData) {
if(Objects.isNull(imageData)){
throw new FaceException("图像无效");
}
try {
return extractTopFaceFeature(ImageIO.read(new ByteArrayInputStream(imageData)), config);
return extractTopFaceFeature(ImageIO.read(new ByteArrayInputStream(imageData)));
} catch (IOException e) {
throw new FaceException("错误的图像", e);
}

8
smartjavaai-face/src/main/java/cn/smartjavaai/face/model/facerec/SeetaFace6Model.java
View File

@@ -629,6 +629,7 @@ public class SeetaFace6Model extends AbstractFaceModel {
@Override
public FaceResult search(BufferedImage image) {
long time1 = System.currentTimeMillis();
if(!ImageUtils.isImageValid(image)){
throw new FaceException("图像无效");
}
@@ -648,7 +649,12 @@ public class SeetaFace6Model extends AbstractFaceModel {
if(similarity[0] < config.getSimilarityThreshold()){
return null;
}
return searchFaceDb(index[0], similarity[0]);
long time2 = System.currentTimeMillis();
System.out.println("总耗时1：" + (time2 - time1) + " ms");
FaceResult faceResult = searchFaceDb(index[0], similarity[0]);
long time3 = System.currentTimeMillis();
System.out.println("总耗时2：" + (time3 - time2) + " ms");
return faceResult;
} catch (FaceException e) {
throw e;
} catch (Exception e) {

20
smartjavaai-face/src/main/java/cn/smartjavaai/face/utils/OpenCVUtils.java
View File

@@ -111,8 +111,26 @@ public class OpenCVUtils {
public static Mat image2Mat(BufferedImage img) {
int width = img.getWidth();
int height = img.getHeight();
int channels;
// 获取图像类型
int imageType = img.getType();
// 判断是3通道还是4通道
if (imageType == BufferedImage.TYPE_3BYTE_BGR) {
channels = 3;
} else if (imageType == BufferedImage.TYPE_4BYTE_ABGR || imageType == BufferedImage.TYPE_4BYTE_ABGR_PRE) {
channels = 4;
} else {
// 如果不是已知格式，强制转换为 3 通道 BGR
BufferedImage convertedImg = new BufferedImage(width, height, BufferedImage.TYPE_3BYTE_BGR);
convertedImg.getGraphics().drawImage(img, 0, 0, null);
img = convertedImg;
channels = 3;
}
byte[] data = ((DataBufferByte) img.getRaster().getDataBuffer()).getData();
Mat mat = new Mat(height, width, CvType.CV_8UC3);
Mat mat = new Mat(height, width, CvType.CV_8UC(channels));
mat.put(0, 0, data);
return mat;
}

10
smartjavaai-objectdetection/pom.xml
View File

@@ -6,11 +6,11 @@
<parent>
<groupId>cn.smartjavaai</groupId>
<artifactId>smartjavaai-parent</artifactId>
<version>1.0.12</version>
<version>1.0.13</version>
</parent>
<artifactId>smartjavaai-objectdetection</artifactId>
<version>1.0.12</version>
<version>1.0.13</version>
<name>smartjavaai-objectdetection</name>
<description>SmartJavaAI</description>
<url>https://github.com/geekwenjie/SmartJavaAI</url>
@@ -23,8 +23,8 @@
<properties>
<maven.compiler.source>11</maven.compiler.source>
<maven.compiler.target>11</maven.compiler.target>
<!-- <maven.compiler.source>11</maven.compiler.source>-->
<!-- <maven.compiler.target>11</maven.compiler.target>-->
<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
<maven.test.skip>true</maven.test.skip>
</properties>
@@ -72,7 +72,7 @@
<artifactId>maven-javadoc-plugin</artifactId>
<version>3.1.0</version>
<configuration>
<javadocExecutable>${java.home}/bin/javadoc</javadocExecutable>
<!-- <javadocExecutable>${java.home}/bin/javadoc</javadocExecutable>-->
<doclint>none</doclint>
<additionalJOptions>
<additionalJOption>-Xdoclint:none</additionalJOption>

18
smartjavaai-objectdetection/src/main/java/cn/smartjavaai/objectdetection/DetectorModelConfig.java → smartjavaai-objectdetection/src/main/java/cn/smartjavaai/objectdetection/config/DetectorModelConfig.java
View File

@@ -1,6 +1,8 @@
package cn.smartjavaai.objectdetection;
package cn.smartjavaai.objectdetection.config;
import cn.smartjavaai.common.enums.DeviceEnum;
import cn.smartjavaai.objectdetection.constant.DetectorConstant;
import cn.smartjavaai.objectdetection.enums.DetectorModelEnum;
import lombok.Data;
/**
@@ -20,13 +22,25 @@ public class DetectorModelConfig {
/**
* 置信度阈值
*/
private float threshold = DetectorConfig.DEFAULT_THRESHOLD;
private float threshold = DetectorConstant.DEFAULT_THRESHOLD;
/**
* 设备类型
*/
private DeviceEnum device;
/**
* 模型路径
*/
private String modelPath;
/**
* 候选框数量：默认为8400. 应设置0到8400之间的整数
* 用于性能优化的关键参数，它通过限制模型后处理阶段需要处理的候选框（bounding boxes）数量来提高推理速度
* 建议不低于1000
*/
private int maxBox;
public DetectorModelConfig() {
}

7
smartjavaai-objectdetection/src/main/java/cn/smartjavaai/objectdetection/DetectorConfig.java → smartjavaai-objectdetection/src/main/java/cn/smartjavaai/objectdetection/constant/DetectorConstant.java
View File

@@ -1,14 +1,17 @@
package cn.smartjavaai.objectdetection;
package cn.smartjavaai.objectdetection.constant;
/**
* @author dwj
* @date 2025/4/7
*/
public class DetectorConfig {
public class DetectorConstant {
/**
* 置信度阈值
*/
public static final float DEFAULT_THRESHOLD = 0.5F;
}

43
smartjavaai-objectdetection/src/main/java/cn/smartjavaai/objectdetection/criteria/CriteriaBuilderFactory.java Normal file
View File

@@ -0,0 +1,43 @@
package cn.smartjavaai.objectdetection.criteria;
import ai.djl.modality.cv.Image;
import ai.djl.modality.cv.output.DetectedObjects;
import ai.djl.repository.zoo.Criteria;
import cn.smartjavaai.objectdetection.config.DetectorModelConfig;
import cn.smartjavaai.objectdetection.enums.DetectorModelEnum;
import cn.smartjavaai.objectdetection.exception.DetectionException;
import org.apache.commons.lang3.StringUtils;
/**
* Criteria构建工厂
* @author dwj
* @date 2025/5/14
*/
public class CriteriaBuilderFactory {
public static Criteria<Image, DetectedObjects> createCriteria(DetectorModelConfig config) {
//以下模型modelPath不允许为空
if(config.getModelEnum() == DetectorModelEnum.YOLOV8_OFFICIAL ||
config.getModelEnum() == DetectorModelEnum.YOLOV12_OFFICIAL ||
config.getModelEnum() == DetectorModelEnum.YOLOV8_CUSTOM ||
config.getModelEnum() == DetectorModelEnum.YOLOV12_CUSTOM){
if(StringUtils.isBlank(config.getModelPath())){
throw new DetectionException("modelPath is null");
}
}
switch (config.getModelEnum()) {
case YOLOV8_OFFICIAL:
return new YoloCriteriaBuilder().buildCriteria(config);
case YOLOV12_OFFICIAL:
return new YoloCriteriaBuilder().buildCriteria(config);
case YOLOV8_CUSTOM:
return new YoloCriteriaBuilder().buildCriteria(config);
case YOLOV12_CUSTOM:
return new YoloCriteriaBuilder().buildCriteria(config);
// 其他类型
default:
return new DJLModelCriteriaBuilder().buildCriteria(config);
}
}
}

22
smartjavaai-objectdetection/src/main/java/cn/smartjavaai/objectdetection/criteria/CriteriaBuilderStrategy.java Normal file
View File

@@ -0,0 +1,22 @@
package cn.smartjavaai.objectdetection.criteria;
import ai.djl.modality.cv.Image;
import ai.djl.modality.cv.output.DetectedObjects;
import ai.djl.repository.zoo.Criteria;
import cn.smartjavaai.objectdetection.config.DetectorModelConfig;
/**
* 模型加载策略接口，用于根据不同模型类型构建对应的 DJL Criteria 实例
* @author dwj
* @date 2025/5/14
*/
public interface CriteriaBuilderStrategy {
/**
* 根据模型类型构建对应的 DJL Criteria 实例
* @param config
* @return
*/
Criteria<Image, DetectedObjects> buildCriteria(DetectorModelConfig config);
}

40
smartjavaai-objectdetection/src/main/java/cn/smartjavaai/objectdetection/criteria/DJLModelCriteriaBuilder.java Normal file
View File

@@ -0,0 +1,40 @@
package cn.smartjavaai.objectdetection.criteria;
import ai.djl.Application;
import ai.djl.Device;
import ai.djl.modality.cv.Image;
import ai.djl.modality.cv.output.DetectedObjects;
import ai.djl.repository.zoo.Criteria;
import ai.djl.training.util.ProgressBar;
import cn.smartjavaai.common.enums.DeviceEnum;
import cn.smartjavaai.objectdetection.config.DetectorModelConfig;
import cn.smartjavaai.objectdetection.constant.DetectorConstant;
import java.util.Objects;
/**
* DJL提供的Criteria 构建器
* @author dwj
* @date 2025/5/14
*/
public class DJLModelCriteriaBuilder implements CriteriaBuilderStrategy {
private static final String DJL_MODEL_PREFIX = "djl://";
@Override
public Criteria<Image, DetectedObjects> buildCriteria(DetectorModelConfig config) {
Device device = null;
if(!Objects.isNull(config.getDevice())){
device = config.getDevice() == DeviceEnum.CPU ? Device.cpu() : Device.gpu();
}
Criteria<Image, DetectedObjects> criteria = Criteria.builder()
.optApplication(Application.CV.OBJECT_DETECTION)
.setTypes(Image.class, DetectedObjects.class)
.optArgument("threshold", config.getThreshold() > 0 ? config.getThreshold() : DetectorConstant.DEFAULT_THRESHOLD)
.optModelUrls(DJL_MODEL_PREFIX + config.getModelEnum().getModelUri())
.optDevice(device)
.optProgress(new ProgressBar())
.build();
return criteria;
}
}

40
smartjavaai-objectdetection/src/main/java/cn/smartjavaai/objectdetection/criteria/YoloCriteriaBuilder.java Normal file
View File

@@ -0,0 +1,40 @@
package cn.smartjavaai.objectdetection.criteria;
import ai.djl.modality.cv.Image;
import ai.djl.modality.cv.output.DetectedObjects;
import ai.djl.modality.cv.translator.YoloV8TranslatorFactory;
import ai.djl.repository.zoo.Criteria;
import ai.djl.training.util.ProgressBar;
import cn.smartjavaai.objectdetection.constant.DetectorConstant;
import cn.smartjavaai.objectdetection.config.DetectorModelConfig;
import java.nio.file.Paths;
/**
* YOLO模型Criteria 构建器
* @author dwj
* @date 2025/5/14
*/
public class YoloCriteriaBuilder implements CriteriaBuilderStrategy {
@Override
public Criteria<Image, DetectedObjects> buildCriteria(DetectorModelConfig config) {
Criteria.Builder criteriaBuilder = Criteria.builder()
.setTypes(Image.class, DetectedObjects.class)
//.optModelUrls("/Users/wenjie/Documents/develop/face_model/yolo")
.optModelPath(Paths.get(config.getModelPath()))
.optEngine("OnnxRuntime")
.optArgument("width", 640) //将输入图像的宽度缩放为 640 像素
.optArgument("height", 640)
.optArgument("resize", true)
.optArgument("toTensor", true)
.optArgument("applyRatio", true)
.optTranslatorFactory(new YoloV8TranslatorFactory())
.optProgress(new ProgressBar())
.optArgument("threshold", config.getThreshold() > 0 ? config.getThreshold() : DetectorConstant.DEFAULT_THRESHOLD);
if(config.getMaxBox() > 0){
criteriaBuilder.optArgument("maxBox", config.getMaxBox());
}
Criteria<Image, DetectedObjects> criteria = criteriaBuilder.build();
return criteria;
}
}

11
smartjavaai-objectdetection/src/main/java/cn/smartjavaai/objectdetection/DetectorModelEnum.java → smartjavaai-objectdetection/src/main/java/cn/smartjavaai/objectdetection/enums/DetectorModelEnum.java
View File

@@ -1,4 +1,4 @@
package cn.smartjavaai.objectdetection;
package cn.smartjavaai.objectdetection.enums;
/**
* 目标检测模型枚举
@@ -34,7 +34,14 @@ public enum DetectorModelEnum {
YOLO3_DARKNET_COCO_608("ai.djl.mxnet/yolo/0.0.1/yolo3_darknet_coco_608"),
YOLO3_MOBILENET_COCO_320("ai.djl.mxnet/yolo/0.0.1/yolo3_mobilenet_coco_320"),
YOLO3_MOBILENET_COCO_416("ai.djl.mxnet/yolo/0.0.1/yolo3_mobilenet_coco_416"),
YOLO3_MOBILENET_COCO_608("ai.djl.mxnet/yolo/0.0.1/yolo3_mobilenet_coco_608");
YOLO3_MOBILENET_COCO_608("ai.djl.mxnet/yolo/0.0.1/yolo3_mobilenet_coco_608"),
YOLOV12_OFFICIAL(""),
YOLOV8_OFFICIAL(""),
YOLOV8_CUSTOM(""),
YOLOV12_CUSTOM("");
/**
* 根据名称获取枚举 (忽略大小写和下划线变体)

34
smartjavaai-objectdetection/src/main/java/cn/smartjavaai/objectdetection/model/DetectorModel.java
View File

@@ -1,41 +1,34 @@
package cn.smartjavaai.objectdetection.model;
import ai.djl.Application;
import ai.djl.Device;
import ai.djl.MalformedModelException;
import ai.djl.inference.Predictor;
import ai.djl.modality.cv.Image;
import ai.djl.modality.cv.ImageFactory;
import ai.djl.modality.cv.output.DetectedObjects;
import ai.djl.opencv.OpenCVImageFactory;
import ai.djl.modality.cv.translator.YoloV8TranslatorFactory;
import ai.djl.repository.zoo.Criteria;
import ai.djl.repository.zoo.ModelNotFoundException;
import ai.djl.repository.zoo.ZooModel;
import ai.djl.training.util.ProgressBar;
import ai.djl.translate.TranslateException;
import cn.smartjavaai.common.entity.DetectionResponse;
import cn.smartjavaai.common.enums.DeviceEnum;
import cn.smartjavaai.common.pool.ModelPredictorPoolManager;
import cn.smartjavaai.common.pool.PredictorFactory;
import cn.smartjavaai.common.utils.FileUtils;
import cn.smartjavaai.common.utils.ImageUtils;
import cn.smartjavaai.common.utils.OpenCVUtils;
import cn.smartjavaai.objectdetection.DetectorConfig;
import cn.smartjavaai.objectdetection.DetectorModelConfig;
import cn.smartjavaai.objectdetection.config.DetectorModelConfig;
import cn.smartjavaai.objectdetection.constant.DetectorConstant;
import cn.smartjavaai.objectdetection.criteria.CriteriaBuilderFactory;
import cn.smartjavaai.objectdetection.exception.DetectionException;
import cn.smartjavaai.objectdetection.utils.DetectorUtils;
import lombok.extern.slf4j.Slf4j;
import org.apache.commons.lang3.Validate;
import org.apache.commons.lang3.StringUtils;
import org.apache.commons.pool2.ObjectPool;
import org.apache.commons.pool2.impl.GenericObjectPool;
import org.apache.commons.pool2.impl.GenericObjectPoolConfig;
import javax.imageio.ImageIO;
import java.awt.image.BufferedImage;
import java.io.*;
import java.nio.file.Path;
import java.nio.file.Paths;
import java.time.Duration;
import java.util.Objects;
/**
@@ -50,23 +43,16 @@ public class DetectorModel implements AutoCloseable{
//private Predictor<Image, DetectedObjects> predictor;
private static final String DJL_MODEL_PREFIX = "djl://";
private ObjectPool<Predictor<Image, DetectedObjects>> predictorPool;
public void loadModel(DetectorModelConfig config){
Device device = null;
if(!Objects.isNull(config.getDevice())){
device = config.getDevice() == DeviceEnum.CPU ? Device.cpu() : Device.gpu();
if(Objects.isNull(config.getModelEnum())){
throw new DetectionException("未配置模型枚举");
}
Criteria<Image, DetectedObjects> criteria = Criteria.builder()
.optApplication(Application.CV.OBJECT_DETECTION)
.setTypes(Image.class, DetectedObjects.class)
.optArgument("threshold", config.getThreshold() > 0 ? config.getThreshold() : DetectorConfig.DEFAULT_THRESHOLD)
.optModelUrls(DJL_MODEL_PREFIX + config.getModelEnum().getModelUri())
.optDevice(device)
.optProgress(new ProgressBar())
.build();
Criteria<Image, DetectedObjects> criteria = CriteriaBuilderFactory.createCriteria(config);
try {
model = criteria.loadModel();
// 创建池子：每个线程独享 Predictor

5
smartjavaai-objectdetection/src/main/java/cn/smartjavaai/objectdetection/model/ObjectDetectionModelFactory.java
View File

@@ -1,12 +1,11 @@
package cn.smartjavaai.objectdetection.model;
import cn.smartjavaai.common.config.Config;
import cn.smartjavaai.objectdetection.DetectorModelConfig;
import cn.smartjavaai.objectdetection.DetectorModelEnum;
import cn.smartjavaai.objectdetection.config.DetectorModelConfig;
import cn.smartjavaai.objectdetection.enums.DetectorModelEnum;
import cn.smartjavaai.objectdetection.exception.DetectionException;
import lombok.extern.slf4j.Slf4j;
import java.util.Map;
import java.util.Objects;
import java.util.concurrent.ConcurrentHashMap;

10
smartjavaai-ocr/pom.xml
View File

@@ -6,14 +6,14 @@
<parent>
<groupId>cn.smartjavaai</groupId>
<artifactId>smartjavaai-parent</artifactId>
<version>1.0.12</version>
<version>1.0.13</version>
</parent>
<artifactId>smartjavaai-ocr</artifactId>
<properties>
<maven.compiler.source>11</maven.compiler.source>
<maven.compiler.target>11</maven.compiler.target>
<!-- <maven.compiler.source>11</maven.compiler.source>-->
<!-- <maven.compiler.target>11</maven.compiler.target>-->
<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
<javacv.version>1.5.8</javacv.version>
<javacv.ffmpeg.version>5.1.2-1.5.8</javacv.ffmpeg.version>
@@ -41,7 +41,7 @@
</dependency>
</dependencies>
<version>1.0.12</version>
<version>1.0.13</version>
<name>smartjavaai-ocr</name>
<description>SmartJavaAI</description>
<url>https://github.com/geekwenjie/SmartJavaAI</url>
@@ -84,7 +84,7 @@
<artifactId>maven-javadoc-plugin</artifactId>
<version>3.1.0</version>
<configuration>
<javadocExecutable>${java.home}/bin/javadoc</javadocExecutable>
<!-- <javadocExecutable>${java.home}/bin/javadoc</javadocExecutable>-->
<doclint>none</doclint>
<additionalJOptions>
<additionalJOption>-Xdoclint:none</additionalJOption>