예제 #1
0
  public static void main(String[] args) {
    System.loadLibrary("opencv_java2410");

    Mat src = Highgui.imread("img/hx_30.jpg", 0);
    Mat dst = new Mat();

    Imgproc.equalizeHist(src, dst);

    Highgui.imwrite("out/hx_30_src.jpg", src);
    Highgui.imwrite("out/hx_30.jpg", dst);
  }
예제 #2
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 public static void main(String[] args) {
   System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
   Mat mat = Card.findCardNumber("test/1.jpg");
   String filename = "test/card/img_card_number.png";
   System.out.println(String.format("Writing %s", filename));
   Highgui.imwrite(filename, mat);
 }
예제 #3
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    @Override
    protected Void doInBackground(Void... params) {
      publishProgress(0);
      Mat hsvImg = heatmap(data, progressDialog);
      Mat finishedImage = new Mat();
      Imgproc.cvtColor(hsvImg, finishedImage, Imgproc.COLOR_HSV2BGR);

      File mediaStorageDir =
          new File(Environment.getExternalStorageDirectory().getPath(), "images/Colored_Images");

      if (!mediaStorageDir.exists()) {
        if (!mediaStorageDir.mkdirs()) {
          Log.e(TAG, "failed to create directory");
          return null;
        }
      }

      String timeStamp = new SimpleDateFormat("yyyyMMdd_HHmmss").format(new Date());
      Log.v(
          TAG,
          "SAVING: " + mediaStorageDir.getPath() + File.separator + "scan_" + timeStamp + ".jpg");
      Highgui.imwrite(
          mediaStorageDir.getPath() + File.separator + "scan_" + timeStamp + ".jpg", finishedImage);

      return null;
    }
예제 #4
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  public static void main(String[] args) {

    // 指定读出的图片路径和输出的文件
    String inputImagePath =
        identificate.class.getClassLoader().getResource("hf.jpg").getPath().substring(1);
    String outputImageFile = "identificate.png";

    String xmlPath =
        identificate
            .class
            .getClassLoader()
            .getResource("cascade_storage.xml")
            .getPath()
            .substring(1);
    System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
    CascadeClassifier faceDetector = new CascadeClassifier(xmlPath);
    Mat image = Highgui.imread(inputImagePath);
    MatOfRect faceDetections = new MatOfRect();
    faceDetector.detectMultiScale(image, faceDetections);

    // 画出脸的位置
    for (Rect rect : faceDetections.toArray()) {
      Core.rectangle(
          image,
          new Point(rect.x, rect.y),
          new Point(rect.x + rect.width, rect.y + rect.height),
          new Scalar(0, 0, 255));
    }

    // 写入到文件
    Highgui.imwrite(outputImageFile, image);

    System.out.print("\nOK!");
  }
예제 #5
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파일: Square.java 프로젝트: emre801/PokeEye
  private static Mat findLargestRectangle(Mat original_image) {
    Mat imgSource = original_image.clone();

    // convert the image to black and white
    Imgproc.cvtColor(imgSource, imgSource, Imgproc.COLOR_BGR2GRAY);

    // convert the image to black and white does (8 bit)
    Imgproc.Canny(imgSource, imgSource, 50, 50);

    // apply gaussian blur to smoothen lines of dots
    Imgproc.GaussianBlur(imgSource, imgSource, new Size(5, 5), 5);

    // find the contours
    List<MatOfPoint> contours = new ArrayList<MatOfPoint>();
    Imgproc.findContours(
        imgSource, contours, new Mat(), Imgproc.RETR_LIST, Imgproc.CHAIN_APPROX_SIMPLE);

    double maxArea = -1;
    MatOfPoint temp_contour = contours.get(0); // the largest is at the
    // index 0 for starting
    // point
    MatOfPoint2f approxCurve = new MatOfPoint2f();
    List<MatOfPoint> largest_contours = new ArrayList<MatOfPoint>();
    for (int idx = 0; idx < contours.size(); idx++) {
      temp_contour = contours.get(idx);
      double contourarea = Imgproc.contourArea(temp_contour);
      // compare this contour to the previous largest contour found
      if (contourarea > maxArea) {
        // check if this contour is a square
        MatOfPoint2f new_mat = new MatOfPoint2f(temp_contour.toArray());
        int contourSize = (int) temp_contour.total();
        Imgproc.approxPolyDP(new_mat, approxCurve, contourSize * 0.05, true);
        if (approxCurve.total() == 4) {
          maxArea = contourarea;
          largest_contours.add(temp_contour);
        }
      }
    }
    MatOfPoint temp_largest = largest_contours.get(largest_contours.size() - 1);
    largest_contours = new ArrayList<MatOfPoint>();

    largest_contours.add(temp_largest);

    // Imgproc.cvtColor(imgSource, imgSource, Imgproc.COLOR_BayerBG2RGB);
    Imgproc.drawContours(original_image, largest_contours, -1, new Scalar(0, 255, 0), 10);

    // Mat perspectiveTransform = new Mat(3, 3, CvType.CV_32FC1);
    // Imgproc.warpPerspective(original_image, imgSource,
    // perspectiveTransform, new Size(300,300));

    Highgui.imwrite(output, original_image);

    // create the new image here using the largest detected square

    // Toast.makeText(getApplicationContext(), "Largest Contour: ",
    // Toast.LENGTH_LONG).show();

    return imgSource;
  }
  public String writePhotoFileToSDCard(
      String filePhotoName, Mat imageMat, File sdCardAbsolutePathFile) {
    File file = new File(sdCardAbsolutePathFile, filePhotoName);

    filePhotoName = file.toString();
    Highgui.imwrite(filePhotoName, imageMat);

    return (filePhotoName);
  }
예제 #7
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  /** Capture images and run color processing through here */
  public void capture() {
    VideoCapture camera = new VideoCapture();

    camera.set(12, -20); // change contrast, might not be necessary

    // CaptureImage image = new CaptureImage();

    camera.open(0); // Useless
    if (!camera.isOpened()) {
      System.out.println("Camera Error");

      // Determine whether to use System.exit(0) or return

    } else {
      System.out.println("Camera OK");
    }

    boolean success = camera.read(capturedFrame);
    if (success) {
      try {
        processWithContours(capturedFrame, processedFrame);
      } catch (Exception e) {
        System.out.println(e);
      }
      // image.processFrame(capturedFrame, processedFrame);
      // processedFrame should be CV_8UC3

      // image.findCaptured(processedFrame);

      // image.determineKings(capturedFrame);

      int bufferSize = processedFrame.channels() * processedFrame.cols() * processedFrame.rows();
      byte[] b = new byte[bufferSize];

      processedFrame.get(0, 0, b); // get all the pixels
      // This might need to be BufferedImage.TYPE_INT_ARGB
      img =
          new BufferedImage(
              processedFrame.cols(), processedFrame.rows(), BufferedImage.TYPE_INT_RGB);
      int width = (int) camera.get(Highgui.CV_CAP_PROP_FRAME_WIDTH);
      int height = (int) camera.get(Highgui.CV_CAP_PROP_FRAME_HEIGHT);
      // img.getRaster().setDataElements(0, 0, width, height, b);
      byte[] a = new byte[bufferSize];
      System.arraycopy(b, 0, a, 0, bufferSize);

      Highgui.imwrite("camera.jpg", processedFrame);
      System.out.println("Success");
    } else System.out.println("Unable to capture image");

    camera.release();
  }
예제 #8
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  // OpenCV code
  private void modifyImage(String fileName) {
    // Create a face detector from the cascade file
    CascadeClassifier faceDetector = new CascadeClassifier("haarcascade_frontalface_alt.xml");
    Mat image = Highgui.imread(fileName);

    // Detect faces in the image.
    // MatOfRect is a special container class for Rect.
    MatOfRect faceDetections = new MatOfRect();
    faceDetector.detectMultiScale(image, faceDetections);

    // Blur each face
    for (Rect rect : faceDetections.toArray()) {
      Mat faceArea = image.submat(rect);
      Imgproc.blur(faceArea, faceArea, new Size(30, 30));
    }
    // Save the modified image
    Highgui.imwrite("edited_" + fileName, image);
  }
  public void templateMatching() {
    System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
    int match_method = 5;
    int max_Trackbar = 5;
    Mat data = Highgui.imread("images/training_data/1" + "/data (" + 1 + ").jpg");
    Mat temp = Highgui.imread("images/template.jpg");
    Mat img = data.clone();

    int result_cols = img.cols() - temp.cols() + 1;
    int result_rows = img.rows() - temp.rows() + 1;
    Mat result = new Mat(result_rows, result_cols, CvType.CV_32FC1);

    Imgproc.matchTemplate(img, temp, result, match_method);
    Core.normalize(result, result, 0, 1, Core.NORM_MINMAX, -1, new Mat());

    double minVal;
    double maxVal;
    Point minLoc;
    Point maxLoc;
    Point matchLoc;
    // minMaxLoc( result, &minVal, &maxVal, &minLoc, &maxLoc, Mat() );
    Core.MinMaxLocResult res = Core.minMaxLoc(result);

    if (match_method == Imgproc.TM_SQDIFF || match_method == Imgproc.TM_SQDIFF_NORMED) {
      matchLoc = res.minLoc;
    } else {
      matchLoc = res.maxLoc;
    }

    // / Show me what you got
    Core.rectangle(
        img,
        matchLoc,
        new Point(matchLoc.x + temp.cols(), matchLoc.y + temp.rows()),
        new Scalar(0, 255, 0));

    // Save the visualized detection.
    Highgui.imwrite("images/samp.jpg", img);
  }
예제 #10
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  public void run() {
    System.out.println("\nRunning DetectFaceDemo");

    // Create a face detector from the cascade file in the resources
    // directory.
    // String facefilterpath =
    // getClass().getResource("../resources/haarcascade_mcs_eyepair_big.xml").getPath();
    String facefilterpath = getClass().getResource("../resources/haarcascade_eye.xml").getPath();
    facefilterpath = facefilterpath.substring(1, facefilterpath.length());
    CascadeClassifier faceDetector = new CascadeClassifier(facefilterpath);
    String pngpath = getClass().getResource("../resources/brown_eyes.jpg").getPath();
    pngpath = pngpath.substring(1, pngpath.length());
    Mat image = Highgui.imread(pngpath);

    // Detect faces in the ismage.
    // MatOfRect is a special container class for Rect.
    MatOfRect faceDetections = new MatOfRect();
    faceDetector.detectMultiScale(image, faceDetections);

    Mat image2 = image;

    Imgproc.cvtColor(image2, image, 6); // 6 = CV_BGR2GRAY not working
    Imgproc.GaussianBlur(image, image, new Size(7, 7), 4, 4);
    // Imgproc.medianBlur(image,image, 2);
    MatOfPoint3f circles = new MatOfPoint3f();
    MatOfPoint3f circles2 = new MatOfPoint3f();

    Imgproc.HoughCircles(
        image, circles, Imgproc.CV_HOUGH_GRADIENT, 5, image.rows() / 5, 100, 100, 10, 50);

    Imgproc.HoughCircles(
        image, circles2, Imgproc.CV_HOUGH_GRADIENT, 5, image.rows() / 5, 100, 100, 50, 400);

    Imgproc.cvtColor(image, image, 8); // 6 = CV_BGR2GRAY not working

    System.out.println(String.format("Detected %s faces", faceDetections));
    // Draw a bounding box around each face.
    for (Rect rect : faceDetections.toArray()) {
      // Core.rectangle(image, new Point(rect.x, rect.y), new Point(rect.x + rect.width, rect.y +
      // rect.height), new Scalar(0, 255, 0),100);
    }

    System.out.println(String.format("Detected %s circles", circles.total()));

    for (Point3 circle : circles.toArray()) {
      Point center = new Point(circle.x, circle.y);
      int radius = (int) Math.round(circle.z);
      Core.circle(image, center, 3, new Scalar(0, 255, 0), -1, 8, 0);
      Core.circle(image, center, radius, new Scalar(0, 0, 255), 3, 8, 0);
      // Core.circle(image, center, radius, new Scalar(0,255,0), 10,8, 0);
    }
    for (Point3 circle : circles2.toArray()) {
      Point center = new Point(circle.x, circle.y);
      int radius = (int) Math.round(circle.z);
      Core.circle(image, center, 3, new Scalar(0, 255, 0), -1, 8, 0);
      Core.circle(image, center, radius, new Scalar(0, 0, 255), 3, 8, 0);
      // Core.circle(image, center, radius, new Scalar(0,255,0), 10,8, 0);
    }

    // Core.circle(image, new Point(100,100), 10, new Scalar(0,255,0), 10, 8, 0);
    // Save the visualized detection.

    String filename = "faceDetection.png";
    System.out.println(String.format("Writing %s", filename));
    Highgui.imwrite(filename, image);
  }
예제 #11
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  /**
   * Determines which pieces are kings
   *
   * @param in Mat image of board
   */
  public void determineKings(Mat in) {
    int playSquares = 32;

    Mat dst = new Mat(in.rows(), in.cols(), in.type());
    in.copyTo(dst);

    Imgproc.cvtColor(dst, dst, Imgproc.COLOR_BGR2GRAY); // change to single color

    Mat canny = new Mat();
    Imgproc.Canny(dst, canny, 100, 200); // make image a canny image that is only edges; 2,4
    // lower threshold values find more edges
    List<MatOfPoint> contours = new ArrayList<MatOfPoint>();
    Mat hierarchy = new Mat(); // holds nested contour information
    Imgproc.findContours(
        canny,
        contours,
        hierarchy,
        Imgproc.RETR_LIST,
        Imgproc.CHAIN_APPROX_SIMPLE); // Imgproc.RETR_LIST, TREE

    // draw contour image
    Mat mask = new Mat();
    mask = Mat.zeros(dst.size(), dst.type());
    Imgproc.drawContours(
        mask, contours, -1, new Scalar(255, 255, 255), 1, 8, hierarchy, 2, new Point());
    Highgui.imwrite("contours.jpg", mask);

    ArrayList occupied = new ArrayList<Integer>();
    for (int i = 0; i < playSquares; i++) {
      if (board[i] != 0) occupied.add(i);
    }

    for (int i = 0; i < contours.size(); i++) // assuming only contours are checker pieces
    {
      // determine if it should be a king
      // use Rect r = Imgproc.boundingRect then find height of it by r.height

      // Get bounding rect of contour
      Rect bound = Imgproc.boundingRect(contours.get(i));

      if (bound.height > in.rows() / 8) {
        // board[(int) occupied.get(0)]++; // make it a king
        // occupied.remove(0);
      }
    }

    // or apply to each region of interest

    /*
    // keep track of starting row square
    int parity = 0; // 0 is even, 1 is odd, tied to row number
    int count = 0; // row square
    int rowNum = 0; // row number, starting at 0

    int vsegment = in.rows() / 8; // only accounts 8 playable
    int hsegment = in.cols() / 12; // 8 playable, 2 capture, 2 extra
    int offset = hsegment * 2; // offset for playable board

    // For angle of camera
    int dx = 48;
    hsegment -= 8;


    // Go through all playable squares
    for (int i = 0; i < playSquares; i++)
    {
    	// change offset depending on the row
    	if (parity == 0) // playable squares start on immediate left
    		offset = hsegment * 3 + dx;
    	else // playable squares start on 2nd square from left
    		offset = hsegment * 2 + dx;

    	// find where roi should be
    	Point p1 = new Point(offset + count * hsegment, rowNum * vsegment); // top left point of rectangle (x,y)
    	Point p2 = new Point(offset + (count + 1) * hsegment, (rowNum + 1) * vsegment); // bottom right point of rectangle (x,y)

    	// create rectangle that is board square
    	Rect bound = new Rect(p1, p2);

    	// frame only includes rectangle
    	Mat roi = new Mat(in, bound);

           Imgproc.cvtColor(roi, roi, Imgproc.COLOR_BGR2GRAY); // change to single color

           Mat canny = new Mat();
           Imgproc.Canny(roi, canny, 2, 4); // make image a canny image that is only edges; 2,4
           // lower threshold values find more edges
           List<MatOfPoint> contours = new ArrayList<MatOfPoint>();
           Mat hierarchy = new Mat(); // holds nested contour information
           Imgproc.findContours(canny, contours, hierarchy, Imgproc.RETR_EXTERNAL, Imgproc.CHAIN_APPROX_SIMPLE); // Imgproc.RETR_LIST, TREE

           // Get bounding rect of contour
              Rect rect = Imgproc.boundingRect(contours.get(0));

              if (rect.height > in.rows() / 8)
    	{
    		board[i]++; // make it a king
    	}

    	count += 2;
    	if (count == 8)
    	{
    		parity = ++parity % 2; // change odd or even
    		count = 0;
    		rowNum++;
    		hsegment += 1;
    		dx -= 6;
    	}
    }*/
  }
예제 #12
0
  public void btn_camera_ok(View view) {
    Log.i(TAG, "btn_camera_ok");
    if (!bPictaken) {
      ToastUtil.showShortToast(getApplicationContext(), "亲要先进行拍照哟!");
      return; // do not forget!
    }
    // async do -> runtime exception: method called after release()
    // maybe the activity releases before the thread returns!
    final Mat image = Highgui.imread(filePath);
    int width = image.width();
    int height = image.height();
    if (width > height) { // portrait should be rotated! direction? yes!
      Core.flip(image.t(), image, 0); // counter-clock wise 90
    }
    Imgproc.cvtColor(image, image, Imgproc.COLOR_BGR2GRAY); // gray
    Imgproc.resize(image, image, new Size(CommonUtil.IMAGE_WIDTH, CommonUtil.IMAGE_HEIGHT)); //
    int total = 0;
    String stotal = CommonUtil.userProps.getProperty("total");
    if (null != stotal) { // have some users!
      total = Integer.parseInt(stotal);
    }
    if (userid <= 0) { // not have this one!
      userid = total + 1;
      try { // save new data!
        CommonUtil.userProps.setProperty("total", String.valueOf(userid));
        CommonUtil.userProps.setProperty(String.valueOf(userid), name);
        CommonUtil.saveUserProperties(CommonUtil.userProps);
      } catch (Exception e) {
        e.printStackTrace();
      }
      // creat folder for this user!
      File userfolder =
          new File(
              CommonUtil.USERFOLDER.getAbsolutePath() + File.separator + String.valueOf(userid));
      if (!userfolder.exists()) {
        userfolder.mkdir();
      }
    }
    filePath =
        CommonUtil.USERFOLDER.getAbsolutePath()
            + File.separator
            + String.valueOf(userid)
            + File.separator
            + System.currentTimeMillis()
            + ".jpg"; // folder (user / userid)
    Highgui.imwrite(filePath, image);
    // save data to facedata.txt
    String data = filePath + ";" + userid + "\n"; // user image file path;user id
    try {
      RandomAccessFile facedataFile =
          new RandomAccessFile(
              CommonUtil.SDFOLDER + File.separator + CommonUtil.FACEDATA_FILENAME, "rw");
      facedataFile.seek(facedataFile.length());
      facedataFile.write(data.getBytes());
      facedataFile.close();
    } catch (FileNotFoundException e) {
      e.printStackTrace();
    } catch (IOException e) {
      e.printStackTrace();
    }
    Log.i(TAG, "image process ok");

    // add this pic to the model data
    new AsyncTask<Void, Void, Boolean>() {

      @Override
      protected Boolean doInBackground(Void... params) {
        xface.addImage(image, userid); // how to determinate the result of adding image?!TODO!
        return true;
      }

      @Override
      protected void onPostExecute(Boolean result) {
        if (result) {
          ToastUtil.showShortToast(getApplicationContext(), "照片保存成功,模型建立好咯!");
        } else {
          ToastUtil.showShortToast(getApplicationContext(), "照片保存成功,模型建立失败啦!");
        }
        btn_camera_ok.setEnabled(true);
      }

      @Override
      protected void onPreExecute() {
        ToastUtil.showShortToast(getApplicationContext(), "照片保存中...");
        btn_camera_ok.setEnabled(false); // can not let user save two images at the same time!
      }
    }.execute();
  }
예제 #13
0
파일: Square.java 프로젝트: emre801/PokeEye
  public static void getSquare(Mat imgSource) {
    Mat sourceImage = imgSource.clone();
    Imgproc.cvtColor(imgSource, imgSource, Imgproc.COLOR_BGR2GRAY);
    // convert the image to black and white does (8 bit)
    Imgproc.Canny(imgSource, imgSource, 50, 50);

    // apply gaussian blur to smoothen lines of dots
    Imgproc.GaussianBlur(imgSource, imgSource, new org.opencv.core.Size(5, 5), 5);

    // find the contours
    List<MatOfPoint> contours = new ArrayList<MatOfPoint>();
    Imgproc.findContours(
        imgSource, contours, new Mat(), Imgproc.RETR_LIST, Imgproc.CHAIN_APPROX_SIMPLE);

    double maxArea = -1;
    int maxAreaIdx = -1;
    // Log.d("size",Integer.toString(contours.size()));
    MatOfPoint temp_contour = contours.get(0); // the largest is at the
    // index 0 for starting
    // point
    MatOfPoint2f approxCurve = new MatOfPoint2f();
    MatOfPoint largest_contour = contours.get(0);
    // largest_contour.ge
    List<MatOfPoint> largest_contours = new ArrayList<MatOfPoint>();
    // Imgproc.drawContours(imgSource,contours, -1, new Scalar(0, 255, 0),
    // 1);

    for (int idx = 0; idx < contours.size(); idx++) {
      temp_contour = contours.get(idx);
      double contourarea = Imgproc.contourArea(temp_contour);
      // compare this contour to the previous largest contour found
      if (contourarea > maxArea) {
        // check if this contour is a square
        MatOfPoint2f new_mat = new MatOfPoint2f(temp_contour.toArray());
        int contourSize = (int) temp_contour.total();
        MatOfPoint2f approxCurve_temp = new MatOfPoint2f();
        Imgproc.approxPolyDP(new_mat, approxCurve_temp, contourSize * 0.05, true);
        if (approxCurve_temp.total() == 4) {
          maxArea = contourarea;
          maxAreaIdx = idx;
          approxCurve = approxCurve_temp;
          largest_contour = temp_contour;
        }
      }
    }

    Imgproc.cvtColor(imgSource, imgSource, Imgproc.COLOR_BayerBG2RGB);

    double[] temp_double;
    temp_double = approxCurve.get(0, 0);
    Point p1 = new Point(temp_double[0], temp_double[1]);
    // Core.circle(imgSource,p1,55,new Scalar(0,0,255));
    // Imgproc.warpAffine(sourceImage, dummy, rotImage,sourceImage.size());
    temp_double = approxCurve.get(1, 0);
    Point p2 = new Point(temp_double[0], temp_double[1]);
    // Core.circle(imgSource,p2,150,new Scalar(255,255,255));
    temp_double = approxCurve.get(2, 0);
    Point p3 = new Point(temp_double[0], temp_double[1]);
    // Core.circle(imgSource,p3,200,new Scalar(255,0,0));
    temp_double = approxCurve.get(3, 0);
    Point p4 = new Point(temp_double[0], temp_double[1]);
    // Core.circle(imgSource,p4,100,new Scalar(0,0,255));
    List<Point> source = getCorners(p1, p2, p3, p4);
    for (Point p : source) {
      // System.out.println(p);
    }
    Mat startM = Converters.vector_Point2f_to_Mat(source);
    // Imgproc.cvtColor(sourceImage, sourceImage, Imgproc.COLOR_BGR2GRAY);
    Mat result = warp(sourceImage, startM, 5);
    // result = warp(result,result,1);
    // Imgproc.cvtColor(result, result, Imgproc.COLOR_BGR2GRAY);
    Highgui.imwrite(output, result);
    // System.out.println("Done");
    // return result;
  }
예제 #14
0
  public Mat onCameraFrame(CvCameraViewFrame inputFrame) {
    img = inputFrame.rgba();
    endTime = System.currentTimeMillis();

    switch (viewMode) {
      case VIEW_MODE_RGBA:
      /** Detects the circles in the RGB format */
        frameCount++;
        drawCircles = false;
        /** Setting thrust */
        twist.dx(thrust);
        /** Flip image when using on boat */
        // Core.flip(img, img, 0);
        /** Convert it to hue, convert to range color, and blur to remove false circles */
        Imgproc.cvtColor(img, img_hue, Imgproc.COLOR_RGB2HSV);
        img_hue = InRangeCircles(img_hue);
        Imgproc.GaussianBlur(img_hue, img_hue, new Size(9, 9), 10, 10);

        /** Create mat for circles and apply the Hough Transform to find the circles */
        Mat circles = new Mat();
        Imgproc.HoughCircles(
            img_hue,
            circles,
            Imgproc.CV_HOUGH_GRADIENT,
            2,
            minDistance,
            70,
            20,
            minRadius,
            maxRadius);

        /** Draws the circles and angle */
        drawCircles(img, circles);
        break;

      case VIEW_MODE_BW:
      /** This mode displays image in black/white to show what the algorithm sees */
        Imgproc.cvtColor(img, img_hue, Imgproc.COLOR_RGB2HSV);
        img_hue = InRangeCircles(img_hue);
        Imgproc.GaussianBlur(img_hue, img, new Size(9, 9), 10, 10);
        break;

      case VIEW_MODE_PIC:
      /** Takes pictures every 20 frames */
        frameCount++;
        /// Need for normally saving raw photos
        Imgproc.cvtColor(img, img_hue, Imgproc.COLOR_RGB2BGR);
        Core.flip(img_hue, img_hue, 0);
        if (frameCount % 10 == 0) {
          // Imgproc.cvtColor(img, img_hue, Imgproc.COLOR_RGBA2BGR);
          Highgui.imwrite("/sdcard/TestPics/test" + frameCount / 10 + ".jpg", img_hue);
        }
        break;

      case VIEW_MODE_TEST:
      /** Testing mode for new code using the pictures as a simulation */
        frameCount++;
        fileNum++;
        if (fileNum > 175) {
          fileNum = 1;
        }
        Mat temp = Highgui.imread("/sdcard/TestPics/test" + fileNum + ".jpg");
        // Mat temp = Highgui.imread("/sdcard/TestPics/test17.jpg"); //120
        Imgproc.cvtColor(temp, img_hue, Imgproc.COLOR_BGR2HSV);
        // Imgproc.cvtColor(temp, temp, Imgproc.COLOR_BGR2RGB);
        img_hue = InRangeCircles(img_hue);
        Imgproc.GaussianBlur(img_hue, img_hue, new Size(9, 9), 10, 10);
        /** Create mat for circles and apply the Hough Transform to find the circles */
        Mat circles2 = new Mat();
        Imgproc.HoughCircles(
            img_hue,
            circles2,
            Imgproc.CV_HOUGH_GRADIENT,
            2,
            minDistance,
            70,
            20,
            minRadius,
            maxRadius);
        /** Draws the circles and angle */
        drawCircles(temp, circles2);
        // Highgui.imwrite("/sdcard/TestPics/test"+(fileNum+2)+".jpg", temp);
        // drawCircles(img_hue,circles2);
        startTime = System.currentTimeMillis();
        return temp;

      default:
        break;
    }

    startTime = System.currentTimeMillis();
    return img;
  }
예제 #15
0
  /**
   * Analyze video frames using computer vision approach and generate a ArrayList<AttitudeRec>
   *
   * @param recs output ArrayList of AttitudeRec
   * @return total number of frame of the video
   */
  private int analyzeVideo(ArrayList<AttitudeRec> recs) {
    VideoMetaInfo meta = new VideoMetaInfo(new File(mPath, "videometa.json"));

    int decimation = 1;

    if (meta.fps > DECIMATION_FPS_TARGET) {
      decimation = (int) (meta.fps / DECIMATION_FPS_TARGET);
      meta.fps /= decimation;
    }

    VideoDecoderForOpenCV videoDecoder =
        new VideoDecoderForOpenCV(
            new File(mPath, "video.mp4"), decimation); // every 3 frame process 1 frame

    Mat frame;
    Mat gray = new Mat();
    int i = -1;

    Size frameSize = videoDecoder.getSize();

    if (frameSize.width != meta.frameWidth || frameSize.height != meta.frameHeight) {
      // this is very unlikely
      return -1;
    }

    if (TRACE_VIDEO_ANALYSIS) {
      Debug.startMethodTracing("cvprocess");
    }

    Size patternSize = new Size(4, 11);

    float fc = (float) (meta.frameWidth / 2.0 / Math.tan(meta.fovWidth / 2.0));
    Mat camMat = cameraMatrix(fc, new Size(frameSize.width / 2, frameSize.height / 2));
    MatOfDouble coeff = new MatOfDouble(); // dummy

    MatOfPoint2f centers = new MatOfPoint2f();
    MatOfPoint3f grid = asymmetricalCircleGrid(patternSize);
    Mat rvec = new MatOfFloat();
    Mat tvec = new MatOfFloat();

    MatOfPoint2f reprojCenters = new MatOfPoint2f();

    if (LOCAL_LOGV) {
      Log.v(TAG, "Camera Mat = \n" + camMat.dump());
    }

    long startTime = System.nanoTime();

    while ((frame = videoDecoder.getFrame()) != null) {
      if (LOCAL_LOGV) {
        Log.v(TAG, "got a frame " + i);
      }

      // has to be in front, as there are cases where execution
      // will skip the later part of this while
      i++;

      // convert to gray manually as by default findCirclesGridDefault uses COLOR_BGR2GRAY
      Imgproc.cvtColor(frame, gray, Imgproc.COLOR_RGB2GRAY);

      boolean foundPattern =
          Calib3d.findCirclesGridDefault(
              gray, patternSize, centers, Calib3d.CALIB_CB_ASYMMETRIC_GRID);

      if (!foundPattern) {
        // skip to next frame
        continue;
      }

      if (OUTPUT_DEBUG_IMAGE) {
        Calib3d.drawChessboardCorners(frame, patternSize, centers, true);
      }

      // figure out the extrinsic parameters using real ground truth 3D points and the pixel
      // position of blobs found in findCircleGrid, an estimated camera matrix and
      // no-distortion are assumed.
      boolean foundSolution =
          Calib3d.solvePnP(grid, centers, camMat, coeff, rvec, tvec, false, Calib3d.CV_ITERATIVE);

      if (!foundSolution) {
        // skip to next frame
        if (LOCAL_LOGV) {
          Log.v(TAG, "cannot find pnp solution in frame " + i + ", skipped.");
        }
        continue;
      }

      // reproject points to for evaluation of result accuracy of solvePnP
      Calib3d.projectPoints(grid, rvec, tvec, camMat, coeff, reprojCenters);

      // error is evaluated in norm2, which is real error in pixel distance / sqrt(2)
      double error = Core.norm(centers, reprojCenters, Core.NORM_L2);

      if (LOCAL_LOGV) {
        Log.v(TAG, "Found attitude, re-projection error = " + error);
      }

      // if error is reasonable, add it into the results
      if (error < REPROJECTION_THREASHOLD) {
        double[] rv = new double[3];
        rvec.get(0, 0, rv);
        recs.add(new AttitudeRec((double) i / meta.fps, rodr2rpy(rv)));
      }

      if (OUTPUT_DEBUG_IMAGE) {
        Calib3d.drawChessboardCorners(frame, patternSize, reprojCenters, true);
        Highgui.imwrite(
            Environment.getExternalStorageDirectory().getPath()
                + "/RVCVRecData/DebugCV/img"
                + i
                + ".png",
            frame);
      }
    }

    if (LOCAL_LOGV) {
      Log.v(TAG, "Finished decoding");
    }

    if (TRACE_VIDEO_ANALYSIS) {
      Debug.stopMethodTracing();
    }

    if (LOCAL_LOGV) {
      // time analysis
      double totalTime = (System.nanoTime() - startTime) / 1e9;
      Log.i(TAG, "Total time: " + totalTime + "s, Per frame time: " + totalTime / i);
    }
    return i;
  }
예제 #16
0
  /**
   * Appelée à chaque nouvelle prise de vue par la caméra.
   *
   * <p>Son comportement sera différent suivant ce que l'on cherche à faire :
   *
   * <ul>
   *   <li>Si la porte n'est pas stable, on cherche alors à détecter l'événement porte stable pour
   *       pouvoir prendre une photo.
   *   <li>Si la porte est stable mais pas fermée, cela signifie que l'on a déjà pris une photo du
   *       contenu du frigo et on attend que la porte soit fermée pour revenir dans l'état initial.
   * </ul>
   *
   * @param inputFrame Image captée par la caméra
   */
  public Mat onCameraFrame(CvCameraViewFrame inputFrame) {
    Mat current = inputFrame.rgba();
    if (stable && !fermee) {
      // Une photo a été prise
      // On va rechercher l'événement : le flux vidéo représente des images noires
      Scalar scalaireN = new Scalar(0x00, 0x00, 0x00, 0xFF);
      Mat noir = new Mat(current.size(), current.type(), scalaireN);
      // noir est une matrice noire
      // Comparaison avec une image noire, résultat stocké dans une matrice diffNoir
      Mat diffNoir = new Mat(current.size(), current.type());
      Core.absdiff(current, noir, diffNoir);
      Double normeDiffNoir =
          new Double(Core.norm(diffNoir)); // Calclule de la norme de cette matrice
      n.add(normeDiffNoir); // Ajout de cette norme dans un conteneur
      compteur++; // Compteur du nombre d'images prises
      if (compteur > 11) {
        // S'il y a suffisamment d'images déjà prises, on vérifie que la porte est fermée
        fermee = true;
        int i = 0;
        while (fermee && i < 10) {
          // La porte est fermee si sur les dix dernières photos prises, la différence
          // entre une image noire et l'image current n'est pas trop grande.
          if (n.get(compteur - 1 - i) > 4500) {
            fermee =
                false; // Si cette différence est trop grande, on considère que la porte n'est pas
            // fermée
          }
          i++;
        } // Si elle n'a jamais été trop grande, la porte est effectivement fermée
        if (fermee) {
          // Remise à 0 du compteur s'il doit être réutilisé pour une nouvelle photo
          // De même pour le tableau n
          compteur = 0;
          n.clear();
          finish(); // Retour sur l'activité principale qui attend une ouverture du frigo.
        }
      }
    } else if (!stable) {
      // Aucune photo n'a encore été prise
      // On va rechercher l'événement : l'image est stable
      if (buffer == null) { // Première image reçue, il faut créer une matrice buffer qui contiendra
        // l'image précédente
        buffer = new Mat(current.size(), current.type());
        buffer = current.clone();
      } else { // C'est au moins la deuxième image reçue
        // Comparaison entre l'image précédente et l'image courante, résultat stocké dans une
        // matrice diffBuffer
        Mat diffBuffer = new Mat(current.size(), current.type());
        Core.absdiff(current, buffer, diffBuffer);
        Double normeDiffBuffer =
            new Double(Core.norm(diffBuffer)); // Calcul de la norme de cette matrice
        n.add(normeDiffBuffer); // Ajout de cette norme dans un conteneur
        compteur++; // Compteur du nombre d'images prises
        if (compteur > 11) {
          // S'il y a suffisamment d'images déjà prises, on vérifie que la porte est stable
          stable = true;
          int i = 0;
          while (stable && i < 10) {
            // On est stable si sur les dix dernières prises, la différence entre
            // l'image current est l'image stockée n'est pas trop grande
            if (n.get(compteur - 1 - i) > 4500) {
              stable = false;
            }
            i++;
          }
          if (stable) {
            Log.i(TAG, "Prise de la photo");
            // Si l'image est stable, il faut vérifier tout d'abord que la porte n'est pas fermée.
            // (on effectue ici le même traîtement que pour une détection de porte fermée)
            Scalar scalaireN = new Scalar(0x00, 0x00, 0x00, 0xFF);
            Mat noir = new Mat(current.size(), current.type(), scalaireN);
            Mat diffNoir = new Mat(current.size(), current.type());
            Core.absdiff(current, noir, diffNoir);
            Double normeDiffNoir = new Double(Core.norm(diffNoir));
            if (normeDiffNoir > 4500) {
              // Si la porte n'est pas fermée, on va sauvegarder l'image avant de l'envoyer
              File pictureFileDir = getDir();
              SimpleDateFormat dateFormat = new SimpleDateFormat("dd-MM-yyyy-HH.mm.ss");
              String date = dateFormat.format(new Date());
              String photoFile = "PictureCV_" + date + ".jpg"; // Nom du fichier
              String filename = pictureFileDir.getPath() + File.separator + photoFile;
              // On doit convertir les couleurs avant de sauvegarder l'image.
              // La description de la fonction cvtColor explique pourquoi
              Imgproc.cvtColor(current, current, Imgproc.COLOR_BGR2RGB);
              Highgui.imwrite(filename, current); // Sauvegarde
              Log.i(TAG, "Photo sauvegardée");
              // Remise à 0 du compteur s'il doit être réutilisé pour une nouvelle photo
              // De même pour le tableau n
              compteur = 0;
              n.clear();

              /*
              //Tentative de reconnaissance d'image
              //On va essayer de détecter la présence d'une banane pour chaque nouvelle image
                	//captée par le téléphone
                	Mat Grey = inputFrame.gray(); //Image prise par la caméra
                	MatOfRect bananas = new MatOfRect();
                	Size minSize = new Size(30,20);
                	Size maxSize = new Size(150,100);
                	Log.i(TAG, "Tentative de détection de banane");
                	mCascadeClassifier.detectMultiScale(Grey, bananas, 1.1, 0, 10,minSize,maxSize);
                	if (bananas.rows()>0){
                		Log.i(TAG, "Nombre de bananes détectées : " + bananas.rows());
                	}
              envoiPhoto(filename, bananas.rows()); //Envoi de la photo avec les données de reconnaissance
              //Fin de la reconnaissance de l'image
              */

              envoiPhoto(filename); // Envoi de la photo sans les données de reconnaissance

            } else {
              // Cas où a porte est fermée
              // Remise à 0 du compteur s'il doit être réutilisé pour une nouvelle photo
              // De même pour le tableau n
              compteur = 0;
              n.clear();
              finish();
            }
          }
        }
        buffer = current.clone();
      }
    }
    return inputFrame.rgba();
  }