package org.jeecg.modules.dry.util;
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import ai.djl.Device;
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import ai.djl.Model;
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import ai.djl.inference.Predictor;
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import ai.djl.modality.Classifications;
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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.transform.*;
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import ai.djl.modality.cv.translator.ImageClassificationTranslator;
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import ai.djl.translate.Translator;
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import org.springframework.stereotype.Component;
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import javax.imageio.ImageIO;
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import javax.imageio.stream.ImageOutputStream;
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import java.awt.*;
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import java.awt.image.BufferedImage;
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import java.io.*;
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import java.util.ArrayList;
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import java.util.List;
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@Component
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public class HerbUtil {
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//规定输入尺寸
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private static final int INPUT_SIZE = 224;
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private static final int TARGET_SIZE = 256;
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//标签文件 一种类别名字占一行
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private List<String> herbNames;
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//用于识别
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Predictor<Image, Classifications> predictor;
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//模型
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private Model model;
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public HerbUtil() {
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//加载标签到herbNames中
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this.loadHerbNames();
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//初始化模型工作
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this.init();
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}
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public List<Classifications.Classification> predict(InputStream inputStream) {
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List<Classifications.Classification> result = new ArrayList<>();
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Image input = this.resizeImage(inputStream);
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try {
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Classifications output = predictor.predict(input);
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System.out.println("推测为：" + output.best().getClassName()
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+ ", 概率：" + output.best().getProbability());
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System.out.println(output);
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result = output.topK();
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} catch (Exception e) {
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e.printStackTrace();
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}
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return result;
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}
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private void loadHerbNames() {
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BufferedReader reader = null;
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herbNames = new ArrayList<>();
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try {
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InputStream in = HerbUtil.class.getClassLoader().getResourceAsStream("class.txt");
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reader = new BufferedReader(new InputStreamReader(in));
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String name = null;
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while ((name = reader.readLine()) != null) {
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herbNames.add(name);
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}
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System.out.println(herbNames);
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} catch (Exception e) {
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e.printStackTrace();
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} finally {
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if (reader != null) {
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try {
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reader.close();
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} catch (IOException e) {
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e.printStackTrace();
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}
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}
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}
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}
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private void init() {
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Translator<Image, Classifications> translator = ImageClassificationTranslator.builder()
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//下面的transform根据自己的改
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.addTransform(new CenterCrop(INPUT_SIZE,INPUT_SIZE))
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.addTransform(new ToTensor())
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.addTransform(new Normalize(
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new float[] {0.485f, 0.456f, 0.406f},
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new float[] {0.229f, 0.224f, 0.225f}))
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//载入所有标签进去
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.optSynset(herbNames)
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//最终显示概率最高的5个
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.optTopK(5)
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.build();
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//随便起名
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Model model = Model.newInstance("model", Device.cpu());
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try {
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InputStream inputStream = HerbUtil.class.getClassLoader().getResourceAsStream("model34.pt");
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if (inputStream == null) {
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throw new RuntimeException("找不到模型文件");
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}
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model.load(inputStream);
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predictor = model.newPredictor(translator);
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} catch (Exception e) {
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e.printStackTrace();
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}
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}
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private Image resizeImage(InputStream inputStream) {
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BufferedImage input = null;
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try {
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input = ImageIO.read(inputStream);
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} catch (IOException e) {
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e.printStackTrace();
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}
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int iw = input.getWidth(), ih = input.getHeight();
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int w = 256, h = 256;
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double scale = Math.max(1. * w / iw, 1. * h / ih);
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int nw = (int) (iw * scale), nh = (int) (ih * scale);
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java.awt.Image img;
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//只有太长或太宽才会保留横纵比，填充颜色
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// boolean needResize = 1. * iw / ih > 1.4 || 1. * ih / iw > 1.4;
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// if (needResize) {
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img = input.getScaledInstance(nw, nh, BufferedImage.SCALE_SMOOTH);
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// } else {
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// img = input.getScaledInstance(INPUT_SIZE, INPUT_SIZE, BufferedImage.SCALE_SMOOTH);
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// }
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BufferedImage out = new BufferedImage(nw, nh, BufferedImage.TYPE_INT_RGB);
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Graphics g = out.getGraphics();
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//先将整个224*224区域填充128 128 128颜色
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g.setColor(new Color(255, 255, 255));
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g.fillRect(0, 0, nw, nh);
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out.getGraphics().drawImage(img, 0, 0, null);
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ByteArrayOutputStream outputStream = new ByteArrayOutputStream();
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try {
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ImageOutputStream imageOutputStream = ImageIO.createImageOutputStream(outputStream);
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ImageIO.write(out, "jpg", imageOutputStream);
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//去D盘看效果
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ImageIO.write(out, "jpg", new File("E:\\out.jpg"));
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InputStream is = new ByteArrayInputStream(outputStream.toByteArray());
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return ImageFactory.getInstance().fromInputStream(is);
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} catch (IOException e) {
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e.printStackTrace();
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throw new RuntimeException("图片转换失败");
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}
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}
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}
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