皮皮猪

Signed-off-by: JoeAllen <2494136784@qq.com>

src/main/java/cn/heavenlybook/Main.java

@@ -6,7 +6,7 @@ import ai.djl.modality.cv.Image;
 import ai.djl.modality.cv.ImageFactory;
 import ai.djl.translate.TranslateException;
 import cn.heavenlybook.utils.AnimalsClassification;
-import cn.heavenlybook.utils.OpenCVImageUtil;
+import cn.heavenlybook.utils.OpenCvImageUtil;
 import lombok.extern.java.Log;
 import org.bytedeco.javacv.CanvasFrame;
 import org.bytedeco.javacv.Frame;
@@ -48,7 +48,7 @@ public class Main {
     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);
+      BufferedImage buffImg = OpenCvImageUtil.mat2BufferedImage(img);
       Image image = ImageFactory.getInstance().fromImage(buffImg);
       Classifications classifications = AnimalsClassification.predict(image);
       Classifications.Classification bestItem = classifications.best();
@@ -56,7 +56,7 @@ public class Main {
       log.info(classifications.toJson());
       // 显示视频图像
       canvas.showImage(frame);
-      if (bestItem.getProbability() == 1.0 && !"非动物".equals(bestItem.getClassName())) {
+      if (bestItem.getProbability() < 0.00001 && !"非动物".equals(bestItem.getClassName())) {
         File outPutFile = Paths.get("src/test/resources/coffee" + i++ + ".png").toFile();
         if (buffImg != null) {
           if (!outPutFile.exists()) {

src/main/java/cn/heavenlybook/utils/AnimalTranslator.java

@@ -42,34 +42,28 @@ public class AnimalTranslator implements Translator<Image, Classifications> {
   }
 
   @Override
-  public NDList processInput(TranslatorContext ctx, Image input) throws Exception {
+  public NDList processInput(TranslatorContext ctx, Image input) {
     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
+    // HWC -> CHW RGB
-
+    array = array.transpose(2, 0, 1).div(255f);
     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);
   }
 

src/main/java/cn/heavenlybook/utils/FaceDetection.java

@@ -1,103 +0,0 @@
-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);
-    }
-  }
-}

src/main/java/cn/heavenlybook/utils/OpenCvImageUtil.java

@@ -14,7 +14,7 @@ import java.nio.IntBuffer;
  *
  * @author Calvin
  */
-public class OpenCVImageUtil {
+public class OpenCvImageUtil {
 
   /**
    * 将 BufferedImage 转 Mat