package com.xindao.ocr.smartjavaai.model.plate;
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import ai.djl.MalformedModelException;
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import ai.djl.engine.Engine;
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import ai.djl.inference.Predictor;
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import ai.djl.modality.cv.Image;
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import ai.djl.modality.cv.ImageFactory;
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import ai.djl.modality.cv.output.DetectedObjects;
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import ai.djl.repository.zoo.Criteria;
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import ai.djl.repository.zoo.ModelNotFoundException;
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import ai.djl.repository.zoo.ModelZoo;
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import ai.djl.repository.zoo.ZooModel;
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import cn.smartjavaai.common.entity.R;
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import cn.smartjavaai.common.pool.PredictorFactory;
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import cn.smartjavaai.common.utils.Base64ImageUtils;
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import cn.smartjavaai.common.utils.FileUtils;
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import cn.smartjavaai.common.utils.ImageUtils;
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import cn.smartjavaai.common.utils.OpenCVUtils;
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import com.xindao.ocr.smartjavaai.config.PlateDetModelConfig;
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import com.xindao.ocr.smartjavaai.entity.PlateInfo;
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import com.xindao.ocr.smartjavaai.exception.OcrException;
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import com.xindao.ocr.smartjavaai.model.plate.criteria.PlateDetCriterialFactory;
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import com.xindao.ocr.smartjavaai.utils.OcrUtils;
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import lombok.extern.slf4j.Slf4j;
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import org.apache.commons.lang3.StringUtils;
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import org.apache.commons.pool2.impl.GenericObjectPool;
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import org.opencv.core.Mat;
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import javax.imageio.ImageIO;
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import java.awt.image.BufferedImage;
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import java.io.ByteArrayInputStream;
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import java.io.ByteArrayOutputStream;
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import java.io.IOException;
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import java.io.InputStream;
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import java.nio.file.Files;
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import java.nio.file.Path;
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import java.nio.file.Paths;
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import java.util.List;
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import java.util.Objects;
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/**
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* Yolov5 车牌检测模型
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* @author dwj
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* @date 2025/7/23
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*/
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@Slf4j
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public class Yolov5PlateDetModel implements PlateDetModel{
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private GenericObjectPool<Predictor<Image, DetectedObjects>> detPredictorPool;
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private ZooModel<Image, DetectedObjects> detectionModel;
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private PlateDetModelConfig config;
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@Override
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public void loadModel(PlateDetModelConfig config) {
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if(StringUtils.isBlank(config.getModelPath())){
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throw new OcrException("modelPath is null");
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}
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this.config = config;
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//初始化 检测Criteria
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Criteria<Image, DetectedObjects> detCriteria = PlateDetCriterialFactory.createCriteria(config);
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try{
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detectionModel = ModelZoo.loadModel(detCriteria);
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// 创建池子：每个线程独享 Predictor
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this.detPredictorPool = new GenericObjectPool<>(new PredictorFactory<>(detectionModel));
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int predictorPoolSize = config.getPredictorPoolSize();
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if(config.getPredictorPoolSize() <= 0){
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predictorPoolSize = Runtime.getRuntime().availableProcessors(); // 默认等于CPU核心数
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}
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detPredictorPool.setMaxTotal(predictorPoolSize);
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log.debug("当前设备: " + detectionModel.getNDManager().getDevice());
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log.debug("当前引擎: " + Engine.getInstance().getEngineName());
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log.debug("模型推理器线程池最大数量: " + predictorPoolSize);
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} catch (IOException | ModelNotFoundException | MalformedModelException e) {
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throw new OcrException("检测模型加载失败", e);
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}
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}
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@Override
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public R<List<PlateInfo>> detect(String imagePath) {
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if(!FileUtils.isFileExists(imagePath)){
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return R.fail(R.Status.FILE_NOT_FOUND);
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}
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Image img = null;
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try {
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img = ImageFactory.getInstance().fromFile(Paths.get(imagePath));
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} catch (IOException e) {
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throw new OcrException("无效的图片", e);
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}
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DetectedObjects detectedObjects = detect(img);
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if (Objects.isNull(detectedObjects) || detectedObjects.getNumberOfObjects() == 0){
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return R.fail(R.Status.NO_OBJECT_DETECTED);
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}
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List<PlateInfo> plateInfoList = OcrUtils.convertToPlateInfo(detectedObjects, img);
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((Mat)img.getWrappedImage()).release();
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return R.ok(plateInfoList);
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}
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@Override
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public R<List<PlateInfo>> detectBase64(String base64Image) {
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if(StringUtils.isBlank(base64Image)){
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return R.fail(R.Status.INVALID_IMAGE);
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}
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byte[] imageData = Base64ImageUtils.base64ToImage(base64Image);
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return detect(imageData);
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}
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@Override
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public R<List<PlateInfo>> detect(BufferedImage image) {
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if(!ImageUtils.isImageValid(image)){
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return R.fail(R.Status.INVALID_IMAGE);
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}
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Image img = ImageFactory.getInstance().fromImage(OpenCVUtils.image2Mat(image));
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DetectedObjects detectedObjects = detect(img);
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if (Objects.isNull(detectedObjects) || detectedObjects.getNumberOfObjects() == 0){
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return R.fail(R.Status.NO_OBJECT_DETECTED);
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}
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List<PlateInfo> plateInfoList = OcrUtils.convertToPlateInfo(detectedObjects, img);
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((Mat)img.getWrappedImage()).release();
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return R.ok(plateInfoList);
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}
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@Override
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public R<List<PlateInfo>> detect(byte[] imageData) {
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if(Objects.isNull(imageData)){
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return R.fail(R.Status.INVALID_IMAGE);
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}
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return detect(new ByteArrayInputStream(imageData));
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}
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@Override
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public DetectedObjects detect(Image image) {
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Predictor<Image, DetectedObjects> predictor = null;
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try {
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predictor = detPredictorPool.borrowObject();
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return predictor.predict(image);
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} catch (Exception e) {
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throw new OcrException("车牌检测错误", e);
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}finally {
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if (predictor != null) {
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try {
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detPredictorPool.returnObject(predictor); //归还
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} catch (Exception e) {
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log.warn("归还Predictor失败", e);
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try {
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predictor.close(); // 归还失败才销毁
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} catch (Exception ex) {
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log.error("关闭Predictor失败", ex);
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}
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}
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}
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}
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}
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@Override
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public R<List<PlateInfo>> detect(InputStream inputStream) {
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if(Objects.isNull(inputStream)){
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return R.fail(R.Status.INVALID_IMAGE);
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}
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try {
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Image img = ImageFactory.getInstance().fromInputStream(inputStream);
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DetectedObjects detection = detect(img);
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List<PlateInfo> plateInfoList = OcrUtils.convertToPlateInfo(detection, img);
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((Mat)img.getWrappedImage()).release();
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return R.ok(plateInfoList);
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} catch (IOException e) {
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throw new OcrException("无效图片输入流", e);
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}
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}
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@Override
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public R<Void> detectAndDraw(String imagePath, String outputPath) {
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if(!FileUtils.isFileExists(imagePath)){
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return R.fail(R.Status.FILE_NOT_FOUND);
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}
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try {
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Image img = ImageFactory.getInstance().fromFile(Paths.get(imagePath));
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DetectedObjects detectedObjects = detect(img);
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if(Objects.isNull(detectedObjects) || detectedObjects.getNumberOfObjects() == 0){
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return R.fail(R.Status.NO_FACE_DETECTED);
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}
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img.drawBoundingBoxes(detectedObjects);
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Path output = Paths.get(outputPath);
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log.debug("Saving to {}", output.toAbsolutePath().toString());
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img.save(Files.newOutputStream(output), "png");
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return R.ok();
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} catch (IOException e) {
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throw new OcrException(e);
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}
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}
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@Override
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public R<BufferedImage> detectAndDraw(BufferedImage sourceImage) {
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if(!ImageUtils.isImageValid(sourceImage)){
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return R.fail(R.Status.INVALID_IMAGE);
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}
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Image img = ImageFactory.getInstance().fromImage(OpenCVUtils.image2Mat(sourceImage));
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DetectedObjects detectedObjects = detect(img);
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if(Objects.isNull(detectedObjects) || detectedObjects.getNumberOfObjects() == 0){
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return R.fail(R.Status.NO_FACE_DETECTED);
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}
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img.drawBoundingBoxes(detectedObjects);
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try {
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ByteArrayOutputStream outputStream = new ByteArrayOutputStream();
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// 调用 save 方法将 Image 写入字节流
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img.save(outputStream, "png");
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// 将字节流转换为 BufferedImage
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byte[] imageBytes = outputStream.toByteArray();
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return R.ok(ImageIO.read(new ByteArrayInputStream(imageBytes)));
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} catch (IOException e) {
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throw new OcrException("导出图片失败", e);
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}
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}
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@Override
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public GenericObjectPool<Predictor<Image, DetectedObjects>> getPool() {
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return detPredictorPool;
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}
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@Override
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public void close() throws Exception {
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try {
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if (detPredictorPool != null) {
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detPredictorPool.close();
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}
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} catch (Exception e) {
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log.warn("关闭 predictorPool 失败", e);
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}
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try {
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if (detectionModel != null) {
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detectionModel.close();
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}
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} catch (Exception e) {
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log.warn("关闭 model 失败", e);
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}
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}
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}
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