マッチングに使用した特徴点を後で確認できるようにするために元画像と白黒画像、特徴点データもデータとして保持するように修正した。 see #24

webapps/Recognition-Tester/src/main/java/imgrecognition/Feature.java

@@ -0,0 +1,17 @@
+package imgrecognition;
+import org.opencv.core.Mat;
+import org.opencv.core.MatOfKeyPoint;
+
+public class Feature {
+	public final Mat srcImage;
+	public final Mat grayImage;
+	public final Mat descriptors;
+	MatOfKeyPoint keyPoints = new MatOfKeyPoint();
+
+	public Feature(Mat srcImage, Mat grayImage, MatOfKeyPoint keyPoints, Mat descriptors) {
+		this.srcImage = srcImage;
+		this.grayImage = grayImage;
+		this.keyPoints = keyPoints;
+		this.descriptors = descriptors;
+	}
+}

webapps/Recognition-Tester/src/main/java/imgrecognition/FeatureExtractor.java

@@ -24,7 +24,7 @@ public FeatureExtractor() {
 //		descriptorExtractor.read(configPath);//configは後で検討
 	}
 
-	public Mat extract(Path imagePath) {
+	public Feature extract(Path imagePath) {
 		// 画像データの読み込み
 		Mat image = Imgcodecs.imread(imagePath.toString());
 
@@ -39,6 +39,6 @@ public Mat extract(Path imagePath) {
 		// 特徴量記述
 		Mat descriptors = new Mat();
 		descriptorExtractor.compute(grayImage, keyPoints, descriptors);
-		return descriptors;
+		return new Feature(image,grayImage,keyPoints,descriptors);
 	}
 }

webapps/Recognition-Tester/src/main/java/imgrecognition/ImageRecognizer.java

@@ -7,7 +7,6 @@
 import java.util.List;
 
 import org.opencv.core.Core;
-import org.opencv.core.Mat;
 
 public class ImageRecognizer {
 	private final FeatureExtractor featureExtractor = new FeatureExtractor();
@@ -23,8 +22,8 @@ public ImageRecognizer(Path trainImageRoot) throws IOException {
 	}
 
 	public List<Result> recognize(Path queryImagePath) {
-		Mat queryFeatures = featureExtractor.extract(queryImagePath);
-		return voter.vote(queryFeatures, trainData);
+		Feature queryFeatures = featureExtractor.extract(queryImagePath);
+		return voter.vote(queryFeatures.descriptors, trainData);
 	}
 
 	// 登録された訓練画像を読み込み、特徴量を抽出する
@@ -37,8 +36,8 @@ private List<TrainData> loadTrainData(Path trainImageRoot) throws IOException {
 			String label = labelDir.getFileName().toString();
 			for (Path imagePath : Files.newDirectoryStream(labelDir)) {
 				try {
-					Mat features = featureExtractor.extract(imagePath);
-					trainData.add(new TrainData(label, features));
+					Feature features = featureExtractor.extract(imagePath);
+					trainData.add(new TrainData(label, features.descriptors));
 				} catch(org.opencv.core.CvException e) {
 					System.out.println(e);
 					continue;