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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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10
smartjavaai-objectdetection/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-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
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@@ -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
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@@ -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
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@@ -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
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@@ -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
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@@ -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
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@@ -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
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@@ -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
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@@ -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
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@@ -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;