Java OpenCV FileStorageとMat.push_back

Question

私はthisプロジェクト、つまりGenData.cpp（C++で書かれています）をJavaのKNNクラシファイアに実装しようとしています。
私はこれらのコード行に到達し、立ち往生しました：C言語で

matClassificationInts.push_back(intChar); 
cv::FileStorage fsClassifications("classifications.xml", cv::FileStorage::WRITE); 
fsClassifications << "classifications" << matClassificationInts; 
fsClassifications.release(); 

++我々は（）を一backする整数を渡すことができますが、Javaでは、私はエラーを取得しています：「intはマットに変換できません」。
最初の質問は、someMat.push_back（）にintを渡すにはどうすればいいですか？
2番目の方法：JavaでFileStorageを実装するにはどうすればよいですか？Matrix *形式に書き込むことができますか？

これまでのところ、私のコード：事前に

import java.io.IOException; 
    import java.util.ArrayList; 
    import java.util.Arrays; 
    import java.util.Scanner; 
    import org.opencv.core.Core; 
    import static org.opencv.core.CvType.CV_32FC1; 
    import org.opencv.core.Mat; 
    import org.opencv.core.MatOfInt4; 
    import org.opencv.core.MatOfPoint; 
    import org.opencv.core.Rect; 
    import org.opencv.core.Scalar; 
    import org.opencv.core.Size; 
    import org.opencv.imgcodecs.Imgcodecs; 
    import org.opencv.imgproc.Imgproc; 
    import static org.opencv.imgproc.Imgproc.ADAPTIVE_THRESH_GAUSSIAN_C; 
    import static org.opencv.imgproc.Imgproc.CHAIN_APPROX_SIMPLE; 
    import static org.opencv.imgproc.Imgproc.RETR_EXTERNAL; 
    import static org.opencv.imgproc.Imgproc.THRESH_BINARY_INV; 

    public class genData { 

    private static final int 
      MIN_CONTOUR_AREA = 100, 
      RESIZED_IMAGE_WIDTH = 20, 
      RESIZED_IMAGE_HEIGHT = 30; 

    public static void main(String[] args) throws IOException { 
     System.loadLibrary(Core.NATIVE_LIBRARY_NAME); 

     Scanner keyboard = new Scanner(System.in); 
     boolean exit = false; 

     Mat imgTrainingNumbers; 
     Mat imgGrayscale = new Mat(); 
     Mat imgBlurred = new Mat(); 
     Mat imgThresh = new Mat(); 
     Mat imgThreshCopy = new Mat(); 

     ArrayList<MatOfPoint> ptContours = new ArrayList<MatOfPoint>(); 
     MatOfInt4 v4iHierarchy; 
     Mat matClassificationInts = new Mat(); 
     Mat matTrainingImagesAsFlattenedFloats = new Mat(); 

     int[] intValidChars = { '0', '1', '2', 
     'A', 'B', 'C'}; //Here I did not make List<Integer>, because I can't pass char to Integer. 
     Arrays.sort(intValidChars); //for binary search 

     imgTrainingNumbers = Imgcodecs.imread("test.png"); //here Text on white paper. 

     if (imgTrainingNumbers.empty()) { 
      System.out.println("err"); 
      return; 
     } 

     Imgproc.cvtColor(imgTrainingNumbers, imgGrayscale, Imgproc.COLOR_BGR2GRAY); 
     Imgproc.GaussianBlur(imgGrayscale, imgBlurred, new Size(5, 5), 0); 
     Imgproc.adaptiveThreshold(imgBlurred, imgThresh, 255, ADAPTIVE_THRESH_GAUSSIAN_C, THRESH_BINARY_INV, 11, 2); 

     /* 
     //imshow class implementation (found via google, works properly, but this block is commented for now) 
     Imshow im = new Imshow("imgThresh"); 
     im.showImage(imgThresh); 
     imgThreshCopy = imgThresh.clone(); 
     */ 

     Imgproc.findContours(imgThreshCopy, ptContours, new Mat(), RETR_EXTERNAL, CHAIN_APPROX_SIMPLE); 


     for (int i = 0; i < ptContours.size(); i++) { 
      if (Imgproc.contourArea(ptContours.get(i)) > MIN_CONTOUR_AREA) { 
       Rect boundingRect = Imgproc.boundingRect(ptContours.get(i)); 
       Imgproc.rectangle(imgTrainingNumbers, boundingRect.tl(), boundingRect.br(), new Scalar(0, 0, 255), 2); 
       Mat matROI = imgThresh.submat(boundingRect.y, boundingRect.y + boundingRect.height, boundingRect.x, boundingRect.x + boundingRect.width); 
       Mat matROIResized = new Mat(); 
       Imgproc.resize(matROI, matROIResized, new Size(RESIZED_IMAGE_WIDTH, RESIZED_IMAGE_HEIGHT)); 
       /* 
       im.showImage(matROI); 
       im.showImage(matROIResized); 
       im.showImage(imgTrainingNumbers); 
       */ 
       String input = keyboard.nextLine(); 
       int intChar = (int)input.charAt(0); 
       if (Arrays.binarySearch(intValidChars, intChar) >=0) { 
        /* 
        matClassificationInts.push_back(intChar); 
        //Here I'm getting an error. 
        */ 
        Mat matImageFloat = new Mat(); 
        matROIResized.convertTo(matImageFloat, CV_32FC1); 
        Mat matImageFlattenedFloat = matImageFloat.reshape(1, 1); 
        matTrainingImagesAsFlattenedFloats.push_back(matImageFlattenedFloat); 
       } 
      } 
     } 
     //Here should go FileStorage stuff. 
    } 
} 

感謝。
P. OpenCV_310 + Java（JavaCVではなく）を使用

Answer 1

私は自分で解決策を作りました。 かなり汚い、そうだと思いますが、それはそれです。コードを改善する方法を知っていれば、私はあなたのコメントを読んでうれしく思います。
1）私の最初の質問は、intにMatを入れることでした（* .xmlをさらに作成するため）。私はそのアプローチを避け、int（実際はInteger）をListに入れることに決めました。

Scanner keyboard = new Scanner(System.in); 
String input = keyboard.nextLine(); 
int intChar = (int)input.charAt(0); 
List<Integer> matClassificationInts = new ArrayList<Integer>(); 
if (Arrays.binarySearch(intValidChars, intChar) >=0) { 
    matClassificationInts.add(new Integer(intChar)); 
    ...... 
} 
String dataImages = ""; 
for (Integer i : matClassificationInts) { 
    dataImages += i + " "; 
} 

そして、私は文字列を作ることができる（ "49 48" の文字<のような - 例えば> "1 0" int型、）* .xmlをして、それを保存する（次の段落を参照）。
2）2番目の質問は、Matの抽出データを* .xmlに保存することでした。まあ、C経由++私はがFileStorageことによってそれを行うことができます。

cv::FileStorage fsClassifications("classifications.xml", cv::FileStorage::WRITE); 
fsClassifications << "classifications" << matClassificationInts; 
fsClassifications.release(); 

しかし、JavaのOpenCVのは、そのような機能を持っていないので、私の2Dアレイをループ（Mat.rows（）とMat.cols（））とexctract必要なデータを経由get（）メソッド（Mat.get（行、列）が - 配列の二重、長さの配列を与える= 1）：今すぐ

String dataClassifications = ""; 
for (int i = 0; i < matTrainingImagesAsFlattenedFloats.rows(); i++) { 
    for (int j = 0; j < matTrainingImagesAsFlattenedFloats.cols(); j++) { 
     double[] temp = matTrainingImagesAsFlattenedFloats.get(i, j); 
     dataClassifications += temp[0] + " "; 
    } 
    dataClassifications += "\n"; 
} 

、* .xmlのためにデータを保存する方法について：
私はちょうどを使用javafx.xmlおよびorg.wc3.dom libs。 DOM-ノードをreturingため
作られた2つの機能：

private static Node getMatXML(Document doc, String option_id, String type_id, String rows, String cols, String dt, String data) { 
    Element elem = doc.createElement(option_id); 
    elem.setAttribute("type_id", type_id); 
    elem.appendChild(getMatXMLElement(doc,"rows", rows)); 
    elem.appendChild(getMatXMLElement(doc, "cols", cols)); 
    elem.appendChild(getMatXMLElement(doc, "dt", dt)); 
    elem.appendChild(getMatXMLElement(doc, "data", data)); 
    return elem; 
} 

private static Node getMatXMLElement(Document doc, String name, String value) { 
    Element node = doc.createElement(name); 
    node.appendChild(doc.createTextNode(value)); 
    return node; 
} 

と* .xmlファイルを作成するため、これらの機能を使用：
のClassifications.xml：

DocumentBuilderFactory icFactory_images = DocumentBuilderFactory.newInstance(); 
    DocumentBuilder icBuilder_images; 
    try { 
     icBuilder_images = icFactory_images.newDocumentBuilder(); 
     Document doc = icBuilder_images.newDocument(); 
     Element mainRootElement = doc.createElement("opencv_storage"); 
     doc.appendChild(mainRootElement); 
     mainRootElement.appendChild(getMatXML(doc, "classifications", "opencv-matrix", rowsImages, colsImages, "i", dataImages)); 
     Transformer transformer = TransformerFactory.newInstance().newTransformer(); 
     transformer.setOutputProperty(OutputKeys.INDENT, "yes"); 
     DOMSource source = new DOMSource(doc); 
     String filename = "classifications.xml"; 
     File file = new File(filename); 
     StreamResult console = new StreamResult(file); //(System.out) 
     transformer.transform(source, console); 
    } catch (Exception e) { 
     e.printStackTrace(); 
    } 

Images.xml：

DocumentBuilderFactory icFactory_classifications = DocumentBuilderFactory.newInstance(); 
    DocumentBuilder icBuilder_classifications; 
    try { 
     icBuilder_classifications = icFactory_classifications.newDocumentBuilder(); 
     Document doc = icBuilder_classifications.newDocument(); 
     Element mainRootElement = doc.createElement("opencv_storage"); 
     doc.appendChild(mainRootElement); 
     mainRootElement.appendChild(getMatXML(doc, "images", "opencv-matrix", rowsClassifications, colsClassifications, "f", dataClassifications)); 
     Transformer transformer = TransformerFactory.newInstance().newTransformer(); 
     transformer.setOutputProperty(OutputKeys.INDENT, "yes"); 
     DOMSource source = new DOMSource(doc); 
     String filename = "images.xml"; 
     File file = new File(filename); 
     StreamResult console = new StreamResult(file); //(System.out) 
     transformer.transform(source, console); 
    } catch (Exception e) { 
     e.printStackTrace(); 
    } 

したがって、生成された分類ファイルは次のとおりです。

<?xml version="1.0" encoding="UTF-8" standalone="no"?> 
<opencv_storage> 
<classifications type_id="opencv-matrix"> 
<rows>2</rows> 
<cols>1</cols> 
<dt>i</dt> 
<data>49 48 </data> 
</classifications> 
</opencv_storage> 

私はこの写真のためのテストでした：GenData.cpp経由

（問題のリンクを参照してください - 1行目）を、私のJavaコードを（完全なコードは、以下を参照してください）。どちらのプログラムでも同じ結果が得られました。
Java OpenCV Imshowの実装では、this link（私ではありません）を見ることができます。

import java.io.File; 
import java.io.FileNotFoundException; 
import java.io.FileOutputStream; 
import java.io.FileReader; 
import java.io.IOException; 
import java.util.ArrayList; 
import java.util.Arrays; 
import java.util.List; 
import java.util.Map; 
import java.util.Scanner; 
import org.opencv.core.Core; 
import static org.opencv.core.CvType.CV_32FC1; 
import org.opencv.core.Mat; 
import org.opencv.core.MatOfInt4; 
import org.opencv.core.MatOfPoint; 
import org.opencv.core.Rect; 
import org.opencv.core.Scalar; 
import org.opencv.core.Size; 
import org.opencv.imgcodecs.Imgcodecs; 
import org.opencv.imgproc.Imgproc; 
import static org.opencv.imgproc.Imgproc.ADAPTIVE_THRESH_GAUSSIAN_C; 
import static org.opencv.imgproc.Imgproc.CHAIN_APPROX_SIMPLE; 
import static org.opencv.imgproc.Imgproc.RETR_EXTERNAL; 
import static org.opencv.imgproc.Imgproc.THRESH_BINARY_INV; 

//XML - write. 
import javax.xml.parsers.DocumentBuilder; 
import javax.xml.parsers.DocumentBuilderFactory; 
import javax.xml.transform.OutputKeys; 
import javax.xml.transform.Transformer; 
import javax.xml.transform.TransformerFactory; 
import javax.xml.transform.dom.DOMSource; 
import javax.xml.transform.stream.StreamResult; 
import org.w3c.dom.Document; 
import org.w3c.dom.Element; 
import org.w3c.dom.Node; 

public class genData { 


    private static final int 
      MIN_CONTOUR_AREA = 100, 
      RESIZED_IMAGE_WIDTH = 20, 
      RESIZED_IMAGE_HEIGHT = 30; 

    public static void main(String[] args) throws IOException { 
     System.loadLibrary(Core.NATIVE_LIBRARY_NAME); 
     Scanner keyboard = new Scanner(System.in); 

     Mat imgTrainingNumbers; 
     Mat imgGrayscale = new Mat(); 
     Mat imgBlurred = new Mat(); 
     Mat imgThresh = new Mat(); 
     Mat imgThreshCopy = new Mat(); 

     ArrayList<MatOfPoint> ptContours = new ArrayList<MatOfPoint>(); 
     MatOfInt4 v4iHierarchy = new MatOfInt4(); 

     List<Integer> matClassificationInts = new ArrayList<Integer>(); 

     Mat matTrainingImagesAsFlattenedFloats = new Mat(); 

     int[] intValidChars = { '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 
     'A', 'B', 'C', 'E', 'H', 
     'K', 'M', 'O', 'P', 'T', 
     'X', 'Y'}; 
     Arrays.sort(intValidChars); 

     imgTrainingNumbers = Imgcodecs.imread("01.png"); 

     if (imgTrainingNumbers.empty()) { 
      System.out.println("Error: file is not found"); 
      return; 
     } 

     Imgproc.cvtColor(imgTrainingNumbers, imgGrayscale, Imgproc.COLOR_BGR2GRAY); 
     Imgproc.GaussianBlur(imgGrayscale, imgBlurred, new Size(5, 5), 0); 
     Imgproc.adaptiveThreshold(imgBlurred, imgThresh, 255, ADAPTIVE_THRESH_GAUSSIAN_C, THRESH_BINARY_INV, 11, 2); 

     Imshow im = new Imshow("imgThresh"); 
     im.showImage(imgThresh); 
     imgThreshCopy = imgThresh.clone(); 

     Imgproc.findContours(imgThreshCopy, ptContours, v4iHierarchy, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE); 


     for (int i = 0; i < ptContours.size(); i++) { 
      if (Imgproc.contourArea(ptContours.get(i)) > MIN_CONTOUR_AREA) { 
       Rect boundingRect = Imgproc.boundingRect(ptContours.get(i)); 
       Imgproc.rectangle(imgTrainingNumbers, boundingRect.tl(), boundingRect.br(), new Scalar(0, 0, 255), 2); 
       Mat matROI = imgThresh.submat(boundingRect.y, boundingRect.y + boundingRect.height, boundingRect.x, boundingRect.x + boundingRect.width); 
       Mat matROIResized = new Mat(); 
       Imgproc.resize(matROI, matROIResized, new Size(RESIZED_IMAGE_WIDTH, RESIZED_IMAGE_HEIGHT)); 
       im.showImage(matROI); 
       im.showImage(matROIResized); 
       im.showImage(imgTrainingNumbers); 
       String input = keyboard.nextLine(); 
       int intChar = (int)input.charAt(0); 
       if (Arrays.binarySearch(intValidChars, intChar) >=0) { 
        matClassificationInts.add(new Integer(intChar)); 
        Mat matImageFloat = new Mat(); 
        matROIResized.convertTo(matImageFloat, CV_32FC1); 
        Mat matImageFlattenedFloat = matImageFloat.reshape(1, 1); 
        matTrainingImagesAsFlattenedFloats.push_back(matImageFlattenedFloat); 
       } 
      } 
     } 

     String dataImages = ""; 
     for (Integer i : matClassificationInts) { 
      dataImages += i + " "; 
     } 

     String dataClassifications = ""; 
     for (int i = 0; i < matTrainingImagesAsFlattenedFloats.rows(); i++) { 
      for (int j = 0; j < matTrainingImagesAsFlattenedFloats.cols(); j++) { 
       double[] temp = matTrainingImagesAsFlattenedFloats.get(i, j); 
       dataClassifications += temp[0] + " "; 
      } 
      dataClassifications += "\n"; 
     } 

     String rowsImages = String.valueOf(matClassificationInts.size()); 
     String colsImages = "1"; 
     String rowsClassifications = String.valueOf(matTrainingImagesAsFlattenedFloats.rows()); 
     String colsClassifications = String.valueOf(matTrainingImagesAsFlattenedFloats.cols()); 

     DocumentBuilderFactory icFactory_images = DocumentBuilderFactory.newInstance(); 
     DocumentBuilder icBuilder_images; 
     try { 
      icBuilder_images = icFactory_images.newDocumentBuilder(); 
      Document doc = icBuilder_images.newDocument(); 
      Element mainRootElement = doc.createElement("opencv_storage"); 
      doc.appendChild(mainRootElement); 
      mainRootElement.appendChild(getMatXML(doc, "classifications", "opencv-matrix", rowsImages, colsImages, "i", dataImages)); 
      Transformer transformer = TransformerFactory.newInstance().newTransformer(); 
      transformer.setOutputProperty(OutputKeys.INDENT, "yes"); 
      DOMSource source = new DOMSource(doc); 
      String filename = "classifications.xml"; 
      File file = new File(filename); 
      StreamResult console = new StreamResult(file); //(System.out) 
      transformer.transform(source, console); 
     } catch (Exception e) { 
      e.printStackTrace(); 
     } 

     DocumentBuilderFactory icFactory_classifications = DocumentBuilderFactory.newInstance(); 
     DocumentBuilder icBuilder_classifications; 
     try { 
      icBuilder_classifications = icFactory_classifications.newDocumentBuilder(); 
      Document doc = icBuilder_classifications.newDocument(); 
      Element mainRootElement = doc.createElement("opencv_storage"); 
      doc.appendChild(mainRootElement); 
      mainRootElement.appendChild(getMatXML(doc, "images", "opencv-matrix", rowsClassifications, colsClassifications, "f", dataClassifications)); 
      Transformer transformer = TransformerFactory.newInstance().newTransformer(); 
      transformer.setOutputProperty(OutputKeys.INDENT, "yes"); 
      DOMSource source = new DOMSource(doc); 
      String filename = "images.xml"; 
      File file = new File(filename); 
      StreamResult console = new StreamResult(file); //(System.out) 
      transformer.transform(source, console); 
     } catch (Exception e) { 
      e.printStackTrace(); 
     } 
     System.out.println("Finished."); 
     System.exit(0); 
    } 

    private static Node getMatXML(Document doc, String option_id, String type_id, String rows, String cols, String dt, String data) { 
     Element elem = doc.createElement(option_id); 
     elem.setAttribute("type_id", type_id); 
     elem.appendChild(getMatXMLElement(doc,"rows", rows)); 
     elem.appendChild(getMatXMLElement(doc, "cols", cols)); 
     elem.appendChild(getMatXMLElement(doc, "dt", dt)); 
     elem.appendChild(getMatXMLElement(doc, "data", data)); 
     return elem; 
    } 

    private static Node getMatXMLElement(Document doc, String name, String value) { 
     Element node = doc.createElement(name); 
     node.appendChild(doc.createTextNode(value)); 
     return node; 
    } 
}