From 92166625c2357dd67d4ec813e18571c44e31a3e6 Mon Sep 17 00:00:00 2001 From: JoeAllen <2494136784@qq.com> Date: Thu, 28 Jul 2022 13:34:08 +0800 Subject: [PATCH] =?UTF-8?q?=E5=9F=BA=E6=9C=AC=E5=AE=9E=E7=8E=B0Mac?= =?UTF-8?q?=E7=8E=AF=E5=A2=83=E4=B8=8B=E6=B2=A1=E6=9C=89=E5=A4=A7=E9=97=AE?= =?UTF-8?q?=E9=A2=98?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: JoeAllen <2494136784@qq.com> --- pom.xml | 100 +++++++++++ src/main/java/cn/heavenlybook/Main.java | 134 +++++++++++++++ .../heavenlybook/utils/AnimalTranslator.java | 80 +++++++++ .../utils/AnimalsClassification.java | 83 +++++++++ .../cn/heavenlybook/utils/FaceDetection.java | 103 ++++++++++++ .../utils/LightAnimalsClassification.java | 79 +++++++++ .../heavenlybook/utils/OpenCVImageUtil.java | 158 ++++++++++++++++++ 7 files changed, 737 insertions(+) create mode 100644 pom.xml create mode 100644 src/main/java/cn/heavenlybook/Main.java create mode 100644 src/main/java/cn/heavenlybook/utils/AnimalTranslator.java create mode 100644 src/main/java/cn/heavenlybook/utils/AnimalsClassification.java create mode 100644 src/main/java/cn/heavenlybook/utils/FaceDetection.java create mode 100644 src/main/java/cn/heavenlybook/utils/LightAnimalsClassification.java create mode 100644 src/main/java/cn/heavenlybook/utils/OpenCVImageUtil.java diff --git a/pom.xml b/pom.xml new file mode 100644 index 0000000..d9d55a1 --- /dev/null +++ b/pom.xml @@ -0,0 +1,100 @@ + + + 4.0.0 + + org.example + DetectAnimalsFromCameras + 1.0-SNAPSHOT + + + UTF-8 + 8 + 8 + 0.17.0 + + + + + org.apache.maven.plugins + maven-compiler-plugin + + 8 + 8 + + 3.8.1 + + + + + + com.google.code.gson + gson + 2.8.6 + + + org.slf4j + slf4j-api + 1.7.30 + + + commons-cli + commons-cli + 1.4 + + + org.apache.logging.log4j + log4j-slf4j-impl + 2.17.2 + + + ai.djl + api + ${djl.version} + + + ai.djl + basicdataset + ${djl.version} + + + ai.djl + model-zoo + ${djl.version} + + + + ai.djl.pytorch + pytorch-engine + ${djl.version} + + + ai.djl.pytorch + pytorch-model-zoo + ${djl.version} + + + + ai.djl.paddlepaddle + paddlepaddle-model-zoo + ${djl.version} + + + ai.djl.paddlepaddle + paddlepaddle-engine + ${djl.version} + + + org.projectlombok + lombok + 1.18.18 + provided + + + org.bytedeco + javacv-platform + 1.5.7 + + + \ No newline at end of file diff --git a/src/main/java/cn/heavenlybook/Main.java b/src/main/java/cn/heavenlybook/Main.java new file mode 100644 index 0000000..169c29a --- /dev/null +++ b/src/main/java/cn/heavenlybook/Main.java @@ -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 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 model = ModelZoo.loadModel(criteria); + Predictor 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 items = classifications.items(); + // List names = new ArrayList<>(); + // List 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 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(); + } +} diff --git a/src/main/java/cn/heavenlybook/utils/AnimalTranslator.java b/src/main/java/cn/heavenlybook/utils/AnimalTranslator.java new file mode 100644 index 0000000..00247c3 --- /dev/null +++ b/src/main/java/cn/heavenlybook/utils/AnimalTranslator.java @@ -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 { + List 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; + } +} diff --git a/src/main/java/cn/heavenlybook/utils/AnimalsClassification.java b/src/main/java/cn/heavenlybook/utils/AnimalsClassification.java new file mode 100644 index 0000000..37d2740 --- /dev/null +++ b/src/main/java/cn/heavenlybook/utils/AnimalsClassification.java @@ -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 items = classifications.items(); + double sum = 0; + double max = 0; + double[] probArr = new double[items.size()]; + + List names = new ArrayList<>(); + List 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 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 rotateModel = ModelZoo.loadModel(criteria)) { + try (Predictor classifier = rotateModel.newPredictor()) { + Classifications classifications = classifier.predict(img); + return classifications; + } + } + } +} diff --git a/src/main/java/cn/heavenlybook/utils/FaceDetection.java b/src/main/java/cn/heavenlybook/utils/FaceDetection.java new file mode 100644 index 0000000..b28e0f0 --- /dev/null +++ b/src/main/java/cn/heavenlybook/utils/FaceDetection.java @@ -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 criteria(float shrink, float threshold) { + Criteria 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 { + + private final float shrink; + private final float threshold; + private final List 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 className, float threshold) { + NDArray result = list.singletonOrThrow(); + float[] probabilities = result.get(":,1").toFloatArray(); + List names = new ArrayList<>(); + List prob = new ArrayList<>(); + List 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); + } + } +} diff --git a/src/main/java/cn/heavenlybook/utils/LightAnimalsClassification.java b/src/main/java/cn/heavenlybook/utils/LightAnimalsClassification.java new file mode 100644 index 0000000..aa6e7a4 --- /dev/null +++ b/src/main/java/cn/heavenlybook/utils/LightAnimalsClassification.java @@ -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 items = classifications.items(); + double sum = 0; + double max = 0; + double[] probArr = new double[items.size()]; + + List names = new ArrayList<>(); + List 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 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 classifier = rotateModel.newPredictor()) { + Classifications classifications = classifier.predict(img); + return classifications; + } + } + } +} diff --git a/src/main/java/cn/heavenlybook/utils/OpenCVImageUtil.java b/src/main/java/cn/heavenlybook/utils/OpenCVImageUtil.java new file mode 100644 index 0000000..c619d03 --- /dev/null +++ b/src/main/java/cn/heavenlybook/utils/OpenCVImageUtil.java @@ -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; + } +}