基本实现Mac环境下没有大问题

Signed-off-by: JoeAllen <2494136784@qq.com>
2022-07-28 13:34:08 +08:00
parent 7cdf89a036
commit 92166625c2
7 changed files with 737 additions and 0 deletions
@@ -0,0 +1,100 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+         xmlns="http://maven.apache.org/POM/4.0.0"
+         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
+    <modelVersion>4.0.0</modelVersion>
+
+    <groupId>org.example</groupId>
+    <artifactId>DetectAnimalsFromCameras</artifactId>
+    <version>1.0-SNAPSHOT</version>
+
+    <properties>
+        <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
+        <maven.compiler.source>8</maven.compiler.source>
+        <maven.compiler.target>8</maven.compiler.target>
+        <djl.version>0.17.0</djl.version>
+    </properties>
+    <build>
+        <plugins>
+            <plugin>
+                <groupId>org.apache.maven.plugins</groupId>
+                <artifactId>maven-compiler-plugin</artifactId>
+                <configuration>
+                    <source>8</source>
+                    <target>8</target>
+                </configuration>
+                <version>3.8.1</version>
+            </plugin>
+        </plugins>
+    </build>
+    <dependencies>
+        <dependency>
+            <groupId>com.google.code.gson</groupId>
+            <artifactId>gson</artifactId>
+            <version>2.8.6</version>
+        </dependency>
+        <dependency>
+            <groupId>org.slf4j</groupId>
+            <artifactId>slf4j-api</artifactId>
+            <version>1.7.30</version>
+        </dependency>
+        <dependency>
+            <groupId>commons-cli</groupId>
+            <artifactId>commons-cli</artifactId>
+            <version>1.4</version>
+        </dependency>
+        <dependency>
+            <groupId>org.apache.logging.log4j</groupId>
+            <artifactId>log4j-slf4j-impl</artifactId>
+            <version>2.17.2</version>
+        </dependency>
+        <dependency>
+            <groupId>ai.djl</groupId>
+            <artifactId>api</artifactId>
+            <version>${djl.version}</version>
+        </dependency>
+        <dependency>
+            <groupId>ai.djl</groupId>
+            <artifactId>basicdataset</artifactId>
+            <version>${djl.version}</version>
+        </dependency>
+        <dependency>
+            <groupId>ai.djl</groupId>
+            <artifactId>model-zoo</artifactId>
+            <version>${djl.version}</version>
+        </dependency>
+        <!-- Pytorch -->
+        <dependency>
+            <groupId>ai.djl.pytorch</groupId>
+            <artifactId>pytorch-engine</artifactId>
+            <version>${djl.version}</version>
+        </dependency>
+        <dependency>
+            <groupId>ai.djl.pytorch</groupId>
+            <artifactId>pytorch-model-zoo</artifactId>
+            <version>${djl.version}</version>
+        </dependency>
+        <!-- PaddlePaddle -->
+        <dependency>
+            <groupId>ai.djl.paddlepaddle</groupId>
+            <artifactId>paddlepaddle-model-zoo</artifactId>
+            <version>${djl.version}</version>
+        </dependency>
+        <dependency>
+            <groupId>ai.djl.paddlepaddle</groupId>
+            <artifactId>paddlepaddle-engine</artifactId>
+            <version>${djl.version}</version>
+        </dependency>
+        <dependency>
+            <groupId>org.projectlombok</groupId>
+            <artifactId>lombok</artifactId>
+            <version>1.18.18</version>
+            <scope>provided</scope>
+        </dependency>
+        <dependency>
+            <groupId>org.bytedeco</groupId>
+            <artifactId>javacv-platform</artifactId>
+            <version>1.5.7</version>
+        </dependency>
+    </dependencies>
+</project>
@@ -0,0 +1,134 @@
+package cn.heavenlybook;
+
+import ai.djl.ModelException;
+import ai.djl.inference.Predictor;
+import ai.djl.modality.Classifications;
+import ai.djl.modality.cv.Image;
+import ai.djl.modality.cv.ImageFactory;
+import ai.djl.modality.cv.output.BoundingBox;
+import ai.djl.modality.cv.output.DetectedObjects;
+import ai.djl.modality.cv.output.Rectangle;
+import ai.djl.repository.zoo.Criteria;
+import ai.djl.repository.zoo.ModelZoo;
+import ai.djl.repository.zoo.ZooModel;
+import ai.djl.translate.TranslateException;
+import cn.heavenlybook.utils.AnimalsClassification;
+import cn.heavenlybook.utils.FaceDetection;
+import cn.heavenlybook.utils.OpenCVImageUtil;
+import lombok.extern.java.Log;
+import org.bytedeco.javacv.CanvasFrame;
+import org.bytedeco.javacv.Frame;
+import org.bytedeco.javacv.OpenCVFrameConverter;
+import org.bytedeco.javacv.OpenCVFrameGrabber;
+import org.bytedeco.opencv.opencv_core.Mat;
+import org.bytedeco.opencv.opencv_core.Point;
+import org.bytedeco.opencv.opencv_core.Rect;
+import org.bytedeco.opencv.opencv_core.Scalar;
+
+import javax.swing.*;
+import java.awt.image.BufferedImage;
+import java.io.IOException;
+import java.util.List;
+
+import static org.bytedeco.opencv.global.opencv_imgproc.*;
+
+/**
+ * @author ht
+ */
+@Log
+public class Main {
+  public static void main(String[] args) throws IOException, ModelException, TranslateException {
+    float shrink = 0.5f;
+    float threshold = 0.7f;
+
+    Criteria<Image, DetectedObjects> criteria = new FaceDetection().criteria(shrink, threshold);
+
+    // 开启摄像头，获取图像（得到的图像为frame类型，需要转换为mat类型进行检测和识别）
+    OpenCVFrameGrabber grabber = new OpenCVFrameGrabber(0);
+
+    grabber.start();
+
+    // Frame与Mat转换
+    OpenCVFrameConverter.ToMat converter = new OpenCVFrameConverter.ToMat();
+    // 新建一个预览窗口
+    CanvasFrame canvas = new CanvasFrame("人脸检测");
+    canvas.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE);
+    canvas.setVisible(true);
+    canvas.setFocusable(true);
+    // 窗口置顶
+    if (canvas.isAlwaysOnTopSupported()) {
+      canvas.setAlwaysOnTop(true);
+    }
+    Frame frame;
+
+    try (ZooModel<Image, DetectedObjects> model = ModelZoo.loadModel(criteria);
+        Predictor<Image, DetectedObjects> predictor = model.newPredictor()) {
+      // 获取图像帧
+      while (canvas.isVisible() && (frame = grabber.grab()) != null) {
+
+        // 将获取的frame转化成mat数据类型
+        Mat img = converter.convert(frame);
+        BufferedImage buffImg = OpenCVImageUtil.mat2BufferedImage(img);
+
+        Image image = ImageFactory.getInstance().fromImage(buffImg);
+
+        // region 动物识别
+        Classifications classifications = AnimalsClassification.predict(image);
+
+        Classifications.Classification bestItem = classifications.best();
+        System.out.println(bestItem.getClassName() + " : " + bestItem.getProbability());
+        //    List<Classifications.Classification> items = classifications.items();
+        //    List<String> names = new ArrayList<>();
+        //    List<Double> probs = new ArrayList<>();
+        //    for (int i = 0; i < items.size(); i++) {
+        //      Classifications.Classification item = items.get(i);
+        //      names.add(item.getClassName());
+        //      probs.add(item.getProbability());
+        //    }
+
+        log.info(classifications.toJson());
+        // endregion
+
+        int imageWidth = image.getWidth();
+        int imageHeight = image.getHeight();
+
+        DetectedObjects detections = predictor.predict(image);
+        List<DetectedObjects.DetectedObject> items = detections.items();
+
+        // 遍历人脸
+        for (DetectedObjects.DetectedObject item : items) {
+          BoundingBox box = item.getBoundingBox();
+          Rectangle rectangle = box.getBounds();
+          int x = (int) (rectangle.getX() * imageWidth);
+          int y = (int) (rectangle.getY() * imageHeight);
+          Rect face =
+              new Rect(
+                  x,
+                  y,
+                  (int) (rectangle.getWidth() * imageWidth),
+                  (int) (rectangle.getHeight() * imageHeight));
+
+          // 绘制人脸矩形区域，scalar色彩顺序：BGR(蓝绿红)
+          rectangle(img, face, new Scalar(0, 0, 255, 1));
+
+          int posX = Math.max(face.tl().x() - 10, 0);
+          int posY = Math.max(face.tl().y() - 10, 0);
+          // 在人脸矩形上面绘制文字
+          putText(
+              img,
+              "Face",
+              new Point(posX, posY),
+              FONT_HERSHEY_COMPLEX,
+              1.0,
+              new Scalar(0, 0, 255, 2.0));
+        }
+
+        // 显示视频图像
+        canvas.showImage(frame);
+      }
+    }
+
+    canvas.dispose();
+    grabber.close();
+  }
+}
@@ -0,0 +1,80 @@
+package cn.heavenlybook.utils;
+
+import ai.djl.Model;
+import ai.djl.modality.Classifications;
+import ai.djl.modality.cv.Image;
+import ai.djl.modality.cv.util.NDImageUtils;
+import ai.djl.ndarray.NDArray;
+import ai.djl.ndarray.NDList;
+import ai.djl.ndarray.types.DataType;
+import ai.djl.ndarray.types.Shape;
+import ai.djl.translate.Batchifier;
+import ai.djl.translate.Translator;
+import ai.djl.translate.TranslatorContext;
+import ai.djl.util.Utils;
+
+import java.io.IOException;
+import java.io.InputStream;
+import java.util.List;
+
+/**
+ * @author ht
+ */
+public class AnimalTranslator implements Translator<Image, Classifications> {
+  List<String> classes;
+
+  public AnimalTranslator() {}
+
+  @Override
+  public void prepare(TranslatorContext ctx) throws IOException {
+    Model model = ctx.getModel();
+    try (InputStream is = model.getArtifact("label_list.txt").openStream()) {
+      classes = Utils.readLines(is, true);
+      //            classes.add(0, "blank");
+      //            classes.add("");
+    }
+  }
+
+  @Override
+  public Classifications processOutput(TranslatorContext ctx, NDList list) {
+    NDArray prob = list.singletonOrThrow();
+    return new Classifications(this.classes, prob);
+  }
+
+  @Override
+  public NDList processInput(TranslatorContext ctx, Image input) throws Exception {
+    NDArray array = input.toNDArray(ctx.getNDManager(), Image.Flag.COLOR);
+
+    float percent = 256f / Math.min(input.getWidth(), input.getHeight());
+    int resizedWidth = Math.round(input.getWidth() * percent);
+    int resizedHeight = Math.round(input.getHeight() * percent);
+    //        img = img.resize((resizedWidth, resizedHeight), Image.LANCZOS)
+
+    array = NDImageUtils.resize(array, resizedWidth, resizedHeight);
+    array = NDImageUtils.centerCrop(array, 224, 224);
+
+    // The network by default takes float32
+    if (!array.getDataType().equals(DataType.FLOAT32)) {
+      array = array.toType(DataType.FLOAT32, false);
+    }
+
+    array = array.transpose(2, 0, 1).div(255f); // HWC -> CHW RGB
+
+    NDArray mean =
+        ctx.getNDManager().create(new float[] {0.485f, 0.456f, 0.406f}, new Shape(3, 1, 1));
+    NDArray std =
+        ctx.getNDManager().create(new float[] {0.229f, 0.224f, 0.225f}, new Shape(3, 1, 1));
+
+    array = array.sub(mean);
+    array = array.div(std);
+
+    array = array.expandDims(0);
+
+    return new NDList(array);
+  }
+
+  @Override
+  public Batchifier getBatchifier() {
+    return null;
+  }
+}
@@ -0,0 +1,83 @@
+package cn.heavenlybook.utils;
+
+import ai.djl.ModelException;
+import ai.djl.inference.Predictor;
+import ai.djl.modality.Classifications;
+import ai.djl.modality.cv.Image;
+import ai.djl.repository.zoo.Criteria;
+import ai.djl.repository.zoo.ModelZoo;
+import ai.djl.repository.zoo.ZooModel;
+import ai.djl.training.util.ProgressBar;
+import ai.djl.translate.TranslateException;
+
+import java.io.IOException;
+import java.util.ArrayList;
+import java.util.List;
+
+// https://www.paddlepaddle.org.cn/hubdetail?name=resnet50_vd_animals&en_category=ImageClassification
+//
+// Users/calvin/Desktop/Download/browser/PaddleHub-release-v2.1/modules/image/classification/resnet50_vd_animals
+
+/**
+ * @author ht
+ */
+public final class AnimalsClassification {
+
+  private AnimalsClassification() {}
+
+  public static Classifications predict(Image img)
+      throws IOException, ModelException, TranslateException {
+    Classifications classifications = AnimalsClassification.classfier(img);
+    List<Classifications.Classification> items = classifications.items();
+    double sum = 0;
+    double max = 0;
+    double[] probArr = new double[items.size()];
+
+    List<String> names = new ArrayList<>();
+    List<Double> probs = new ArrayList<>();
+
+    for (int i = 0; i < items.size(); i++) {
+      Classifications.Classification item = items.get(i);
+      double prob = item.getProbability();
+      probArr[i] = prob;
+      if (prob > max) {
+        max = prob;
+      }
+    }
+
+    for (int i = 0; i < items.size(); i++) {
+      probArr[i] = Math.exp(probArr[i] - max);
+      sum = sum + probArr[i];
+    }
+
+    for (int i = 0; i < items.size(); i++) {
+      Classifications.Classification item = items.get(i);
+      names.add(item.getClassName());
+      probs.add(probArr[i]);
+    }
+
+    return new Classifications(names, probs);
+  }
+
+  public static Classifications classfier(Image img)
+      throws IOException, ModelException, TranslateException {
+
+    Criteria<Image, Classifications> criteria =
+        Criteria.builder()
+            .optEngine("PaddlePaddle")
+            .setTypes(Image.class, Classifications.class)
+            .optModelUrls("https://aias-home.oss-cn-beijing.aliyuncs.com/models/animals.zip")
+            //            .optModelUrls("/Users/calvin/model/animals/")
+            .optModelName("inference")
+            .optTranslator(new AnimalTranslator())
+            .optProgress(new ProgressBar())
+            .build();
+
+    try (ZooModel<Image, Classifications> rotateModel = ModelZoo.loadModel(criteria)) {
+      try (Predictor<Image, Classifications> classifier = rotateModel.newPredictor()) {
+        Classifications classifications = classifier.predict(img);
+        return classifications;
+      }
+    }
+  }
+}
@@ -0,0 +1,103 @@
+package cn.heavenlybook.utils;
+
+import ai.djl.modality.cv.Image;
+import ai.djl.modality.cv.output.BoundingBox;
+import ai.djl.modality.cv.output.DetectedObjects;
+import ai.djl.modality.cv.output.Rectangle;
+import ai.djl.modality.cv.util.NDImageUtils;
+import ai.djl.ndarray.NDArray;
+import ai.djl.ndarray.NDList;
+import ai.djl.ndarray.NDManager;
+import ai.djl.ndarray.types.Shape;
+import ai.djl.repository.zoo.Criteria;
+import ai.djl.training.util.ProgressBar;
+import ai.djl.translate.Batchifier;
+import ai.djl.translate.Translator;
+import ai.djl.translate.TranslatorContext;
+import lombok.extern.java.Log;
+
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.List;
+
+/**
+ * @author ht
+ */
+@Log
+public final class FaceDetection {
+
+  public FaceDetection() {}
+
+  public Criteria<Image, DetectedObjects> criteria(float shrink, float threshold) {
+    Criteria<Image, DetectedObjects> criteria =
+        Criteria.builder()
+            .optEngine("PaddlePaddle")
+            .setTypes(Image.class, DetectedObjects.class)
+            .optModelUrls(
+                "https://aias-home.oss-cn-beijing.aliyuncs.com/models/face_mask/face_detection.zip")
+            // .optModelUrls("/Users/calvin/model/face_mask/pyramidbox_lite/")
+            // .optModelName("inference")
+            .optProgress(new ProgressBar())
+            .optTranslator(new FaceTranslator(shrink, threshold))
+            .build();
+
+    return criteria;
+  }
+
+  private static final class FaceTranslator implements Translator<Image, DetectedObjects> {
+
+    private final float shrink;
+    private final float threshold;
+    private final List<String> className;
+
+    FaceTranslator(float shrink, float threshold) {
+      this.shrink = shrink;
+      this.threshold = threshold;
+      className = Arrays.asList("Not Face", "Face");
+    }
+
+    @Override
+    public DetectedObjects processOutput(TranslatorContext ctx, NDList list) {
+      return processImageOutput(list, className, threshold);
+    }
+
+    @Override
+    public NDList processInput(TranslatorContext ctx, Image input) {
+      return processImageInput(ctx.getNDManager(), input, shrink);
+    }
+
+    @Override
+    public Batchifier getBatchifier() {
+      return null;
+    }
+
+    NDList processImageInput(NDManager manager, Image input, float shrink) {
+      NDArray array = input.toNDArray(manager);
+      Shape shape = array.getShape();
+      array =
+          NDImageUtils.resize(array, (int) (shape.get(1) * shrink), (int) (shape.get(0) * shrink));
+      array = array.transpose(2, 0, 1).flip(0); // HWC -> CHW BGR -> RGB
+      NDArray mean = manager.create(new float[] {104f, 117f, 123f}, new Shape(3, 1, 1));
+      array = array.sub(mean).mul(0.007843f); // normalization
+      array = array.expandDims(0); // make batch dimension
+      return new NDList(array);
+    }
+
+    DetectedObjects processImageOutput(NDList list, List<String> className, float threshold) {
+      NDArray result = list.singletonOrThrow();
+      float[] probabilities = result.get(":,1").toFloatArray();
+      List<String> names = new ArrayList<>();
+      List<Double> prob = new ArrayList<>();
+      List<BoundingBox> boxes = new ArrayList<>();
+      for (int i = 0; i < probabilities.length; i++) {
+        if (probabilities[i] >= threshold) {
+          float[] array = result.get(i).toFloatArray();
+          names.add(className.get((int) array[0]));
+          prob.add((double) probabilities[i]);
+          boxes.add(new Rectangle(array[2], array[3], array[4] - array[2], array[5] - array[3]));
+        }
+      }
+      return new DetectedObjects(names, prob, boxes);
+    }
+  }
+}
@@ -0,0 +1,79 @@
+package cn.heavenlybook.utils;
+
+import ai.djl.ModelException;
+import ai.djl.inference.Predictor;
+import ai.djl.modality.Classifications;
+import ai.djl.modality.cv.Image;
+import ai.djl.repository.zoo.Criteria;
+import ai.djl.repository.zoo.ModelZoo;
+import ai.djl.repository.zoo.ZooModel;
+import ai.djl.training.util.ProgressBar;
+import ai.djl.translate.TranslateException;
+
+import java.io.IOException;
+import java.util.ArrayList;
+import java.util.List;
+
+// https://www.paddlepaddle.org.cn/hubdetail?name=mobilenet_v2_animals&en_category=ImageClassification
+//
+// /Users/calvin/Desktop/Download/browser/PaddleHub-release-v2.1/modules/image/classification/mobilenet_v2_animals
+
+public final class LightAnimalsClassification {
+
+  private LightAnimalsClassification() {}
+
+  public static Classifications predict(Image img)
+      throws IOException, ModelException, TranslateException {
+    Classifications classifications = LightAnimalsClassification.classfier(img);
+    List<Classifications.Classification> items = classifications.items();
+    double sum = 0;
+    double max = 0;
+    double[] probArr = new double[items.size()];
+
+    List<String> names = new ArrayList<>();
+    List<Double> probs = new ArrayList<>();
+
+    for (int i = 0; i < items.size(); i++) {
+      Classifications.Classification item = items.get(i);
+      double prob = item.getProbability();
+      probArr[i] = prob;
+      if (prob > max) max = prob;
+    }
+
+    for (int i = 0; i < items.size(); i++) {
+      probArr[i] = Math.exp(probArr[i] - max);
+      sum = sum + probArr[i];
+    }
+
+    for (int i = 0; i < items.size(); i++) {
+      Classifications.Classification item = items.get(i);
+      names.add(item.getClassName());
+      probs.add(probArr[i]);
+    }
+
+    return new Classifications(names, probs);
+  }
+
+  public static Classifications classfier(Image img)
+      throws IOException, ModelException, TranslateException {
+
+    Criteria<Image, Classifications> criteria =
+        Criteria.builder()
+            .optEngine("PaddlePaddle")
+            .setTypes(Image.class, Classifications.class)
+            .optModelUrls(
+                "https://aias-home.oss-cn-beijing.aliyuncs.com/models/mobilenet_animals.zip")
+            // .optModelUrls("/Users/calvin/model/mobilenet_animals/")
+            .optModelName("inference")
+            .optTranslator(new AnimalTranslator())
+            .optProgress(new ProgressBar())
+            .build();
+
+    try (ZooModel rotateModel = ModelZoo.loadModel(criteria)) {
+      try (Predictor<Image, Classifications> classifier = rotateModel.newPredictor()) {
+        Classifications classifications = classifier.predict(img);
+        return classifications;
+      }
+    }
+  }
+}
@@ -0,0 +1,158 @@
+package cn.heavenlybook.utils;
+
+import org.bytedeco.javacv.Java2DFrameConverter;
+import org.bytedeco.javacv.OpenCVFrameConverter;
+import org.bytedeco.opencv.opencv_core.Mat;
+
+import java.awt.*;
+import java.awt.image.*;
+import java.nio.ByteBuffer;
+import java.nio.IntBuffer;
+
+/**
+ * 图片类型转换
+ *
+ * @author Calvin
+ */
+public class OpenCVImageUtil {
+
+  /**
+   * 将 BufferedImage 转 Mat
+   *
+   * @param original
+   */
+  public static Mat bufferedImage2Mat(BufferedImage original) {
+    OpenCVFrameConverter.ToMat cv = new OpenCVFrameConverter.ToMat();
+    return cv.convertToMat(new Java2DFrameConverter().convert(original));
+  }
+
+  /**
+   * 将mat转BufferedImage
+   *
+   * @param matrix
+   */
+  public static BufferedImage mat2BufferedImage(Mat matrix) {
+    int cols = matrix.cols();
+    int rows = matrix.rows();
+    int elemSize = (int) matrix.elemSize();
+    byte[] data = new byte[cols * rows * elemSize];
+
+    matrix.data().get(data);
+
+    int type = 0;
+    switch (matrix.channels()) {
+      case 1:
+        type = BufferedImage.TYPE_BYTE_GRAY;
+        break;
+      case 3:
+        type = BufferedImage.TYPE_3BYTE_BGR;
+        byte b;
+        for (int i = 0; i < data.length; i = i + 3) {
+          b = data[i];
+          data[i] = data[i + 2];
+          data[i + 2] = b;
+        }
+        break;
+      default:
+        return null;
+    }
+    BufferedImage image = new BufferedImage(cols, rows, type);
+    //    BufferedImage对象中最重要的两个组件为Raster和ColorModel，分别用于存储图像的像素数据与颜色数据。
+    //    表示像素矩形数组的类。Raster 封装存储样本值的 DataBuffer，以及描述如何在 DataBuffer 中定位给定样本值的 SampleModel。
+    //    由于Raster对象是BufferedImage对象中的像素数据存储对象，因此，BufferedImage支持从Raster对象中获取任意位置（x，y）点的像素值p（x，y）。
+    image.getRaster().setDataElements(0, 0, cols, rows, data);
+    return image;
+  }
+
+  /**
+   * 将bufferImage转Mat
+   *
+   * @param original
+   * @param matType
+   * @param msg
+   * @param x
+   * @param y
+   */
+  public static Mat bufferedImage2Mat(
+      BufferedImage original, int matType, String msg, int x, int y) {
+    Graphics2D g = original.createGraphics();
+    try {
+      g.setComposite(AlphaComposite.Src);
+      g.drawImage(original, 0, 0, null);
+      g.drawString(msg, x, y);
+    } finally {
+      g.dispose();
+    }
+    Mat mat = new Mat(original.getHeight(), original.getWidth(), matType);
+    mat.data().put(((DataBufferByte) original.getRaster().getDataBuffer()).getData());
+    return mat;
+  }
+
+  /**
+   * 24位BGR数组转BufferedImage
+   *
+   * @param src -bgr排列的24位图像像素数据数组
+   * @param width -宽度
+   * @param height-高度
+   * @return
+   */
+  public static BufferedImage BGR2BufferedImage(byte[] src, int width, int height) {
+    BufferedImage image = new BufferedImage(width, height, BufferedImage.TYPE_3BYTE_BGR);
+    Raster ra = image.getRaster();
+    DataBuffer out = ra.getDataBuffer();
+    DataBufferByte db = (DataBufferByte) out;
+    ByteBuffer.wrap(db.getData()).put(src, 0, src.length);
+    return image;
+  }
+
+  /**
+   * 24位BGR字节缓冲转BufferedImage
+   *
+   * @param src -bgr排列的24位图像像素数据字节缓冲
+   * @param width -宽度
+   * @param height-高度
+   * @return
+   */
+  public static BufferedImage BGR2BufferedImage(ByteBuffer src, int width, int height) {
+    BufferedImage image = new BufferedImage(width, height, BufferedImage.TYPE_3BYTE_BGR);
+    Raster ra = image.getRaster();
+    DataBuffer out = ra.getDataBuffer();
+    DataBufferByte db = (DataBufferByte) out;
+    ByteBuffer.wrap(db.getData()).put(src);
+    return image;
+  }
+
+  /**
+   * 24位整型BGR字节缓冲转BufferedImage
+   *
+   * @param src -bgr排列的24位图像像素整型缓冲（int由3个byte组成）
+   * @param width -宽度
+   * @param height-高度
+   * @return
+   */
+  public static BufferedImage BGR2BufferedImage(IntBuffer src, int width, int height) {
+    BufferedImage image = new BufferedImage(width, height, BufferedImage.TYPE_INT_BGR);
+    Raster ra = image.getRaster();
+    DataBuffer out = ra.getDataBuffer();
+    DataBufferInt db = (DataBufferInt) out;
+    IntBuffer.wrap(db.getData()).put(src);
+    return image;
+  }
+
+  /**
+   * 24位整型RGB字节缓冲转BufferedImage
+   *
+   * @param src --rgb排列的24位图像像素整型缓冲（int由3个byte组成）
+   * @param width -宽度
+   * @param height-高度
+   * @return
+   */
+  public static BufferedImage RGB2BufferedImage(IntBuffer src, int width, int height) {
+    BufferedImage image = new BufferedImage(width, height, BufferedImage.TYPE_INT_RGB);
+    Raster ra = image.getRaster();
+    DataBuffer out = ra.getDataBuffer();
+    DataBufferInt db = (DataBufferInt) out;
+    IntBuffer.wrap(db.getData()).put(src);
+    return image;
+  }
+}