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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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43
smartjavaai-objectdetection/src/main/java/cn/smartjavaai/objectdetection/criteria/CriteriaBuilderFactory.java Normal file
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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);
}
}
}

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smartjavaai-objectdetection/src/main/java/cn/smartjavaai/objectdetection/criteria/CriteriaBuilderStrategy.java Normal file
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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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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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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;
}
}