JavaJDK1.8适配完成从摄像头中检测动物

Signed-off-by: JoeAllen <2494136784@qq.com>
2022-07-29 09:13:20 +08:00
parent 41179c52b2
commit 2c8c05dedf
5 changed files with 36 additions and 126 deletions
@@ -1,36 +1,20 @@
 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
@@ -38,97 +22,22 @@ import static org.bytedeco.opencv.global.opencv_imgproc.*;
@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);
-      }
+    // 获取图像帧
+    while ((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);
+      Classifications classifications = AnimalsClassification.predict(image);
+      Classifications.Classification bestItem = classifications.best();
+      log.info(bestItem.getClassName() + " : " + bestItem.getProbability());
+      log.info(classifications.toJson());
    }
-
-    canvas.dispose();
-    grabber.close();
  }
 }
@@ -59,25 +59,27 @@ public final class AnimalsClassification {
    return new Classifications(names, probs);
  }

+  static Criteria<Image, Classifications> criteria;
+  static ZooModel<Image, Classifications> rotateModel;
+  static Predictor<Image, Classifications> classifier;
+
  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;
-      }
-    }
+    criteria =
+        criteria == null
+            ? 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()
+            : criteria;
+    rotateModel = rotateModel == null ? ModelZoo.loadModel(criteria) : rotateModel;
+    classifier = classifier == null ? rotateModel.newPredictor() : classifier;
+    return classifier.predict(img);
  }
 }
@@ -37,7 +37,9 @@ public final class LightAnimalsClassification {
      Classifications.Classification item = items.get(i);
      double prob = item.getProbability();
      probArr[i] = prob;
-      if (prob > max) max = prob;
+      if (prob > max) {
+        max = prob;
+      }
    }

    for (int i = 0; i < items.size(); i++) {
@@ -69,7 +71,7 @@ public final class LightAnimalsClassification {
            .optProgress(new ProgressBar())
            .build();

-    try (ZooModel rotateModel = ModelZoo.loadModel(criteria)) {
+    try (ZooModel<Image, Classifications> rotateModel = ModelZoo.loadModel(criteria)) {
      try (Predictor<Image, Classifications> classifier = rotateModel.newPredictor()) {
        Classifications classifications = classifier.predict(img);
        return classifications;