/**
   * This method is required if the AlgorithmPerformed interface is implemented. It is called by the
   * algorithms when it has completed or failed to to complete, so that the dialog can be display
   * the result image and/or clean up.
   *
   * @param algorithm Algorithm that caused the event.
   */
  public void algorithmPerformed(AlgorithmBase algorithm) {

    if (algorithm instanceof AlgorithmNonlocalMeansFilter) {
      image.clearMask();

      if ((nlMeansFilterAlgo.isCompleted() == true) && (resultImage != null)) {

        updateFileInfo(image, resultImage);
        resultImage.clearMask();

        // The algorithm has completed and produced a new image to be displayed.
        try {
          new ViewJFrameImage(resultImage, null, new Dimension(610, 200));
        } catch (OutOfMemoryError error) {
          MipavUtil.displayError("Out of memory: unable to open new frame");
        }
      } else if (resultImage == null) {

        // These next lines set the titles in all frames where the source image is displayed to
        // image name so as to indicate that the image is now unlocked!
        // The image frames are enabled and then registed to the userinterface.
        Vector<ViewImageUpdateInterface> imageFrames = image.getImageFrameVector();

        for (int i = 0; i < imageFrames.size(); i++) {
          ((Frame) (imageFrames.elementAt(i))).setTitle(titles[i]);
          ((Frame) (imageFrames.elementAt(i))).setEnabled(true);

          if (((Frame) (imageFrames.elementAt(i))) != parentFrame) {
            userInterface.registerFrame((Frame) (imageFrames.elementAt(i)));
          }
        }

        if (parentFrame != null) {
          userInterface.registerFrame(parentFrame);
        }

        image.notifyImageDisplayListeners(null, true);
      } else if (resultImage != null) {

        // algorithm failed but result image still has garbage
        resultImage.disposeLocal(); // clean up memory
        resultImage = null;
      }
    }

    if (algorithm.isCompleted()) {
      insertScriptLine();
    }
    // save the completion status for later
    setComplete(algorithm.isCompleted());

    nlMeansFilterAlgo.finalize();
    nlMeansFilterAlgo = null;
    dispose();
  }
  /**
   * Returns the name of an image output by this algorithm, the image returned depends on the
   * parameter label given (which can be used to retrieve the image object from the image registry).
   *
   * @param imageParamName The output image parameter label for which to get the image name.
   * @return The image name of the requested output image parameter label.
   */
  public String getOutputImageName(final String imageParamName) {
    if (imageParamName.equals(AlgorithmParameters.RESULT_IMAGE)) {
      if (getResultImage() != null) {
        // algo produced a new result image
        return getResultImage().getImageName();
      } else {
        // algo was done in place
        return image.getImageName();
      }
    }

    Preferences.debug(
        "Unrecognized output image parameter: " + imageParamName + "\n",
        Preferences.DEBUG_SCRIPTING);

    return null;
  }
  /** {@inheritDoc} */
  protected void setGUIFromParams() {
    image = scriptParameters.retrieveInputImage();
    userInterface = ViewUserInterface.getReference();
    parentFrame = image.getParentFrame();

    if (scriptParameters.doOutputNewImage()) {
      setDisplayLocNew();
    } else {
      setDisplayLocReplace();
    }

    searchWindowSide = scriptParameters.getParams().getInt("search_window_side");
    similarityWindowSide = scriptParameters.getParams().getInt("similarity_window_side");
    noiseStandardDeviation = scriptParameters.getParams().getFloat("noise_standard_deviation");
    degreeOfFiltering = scriptParameters.getParams().getFloat("degree_of_filtering");
    doRician = scriptParameters.getParams().getBoolean("do_rician");
    image25D = scriptParameters.doProcess3DAs25D();
  }
  /**
   * Use the GUI results to set up the variables needed to run the algorithm.
   *
   * @return <code>true</code> if parameters set successfully, <code>false</code> otherwise.
   */
  private boolean setVariables() {
    String tmpStr;

    System.gc();

    if (replaceImage.isSelected()) {
      displayLoc = REPLACE;
    } else if (newImage.isSelected()) {
      displayLoc = NEW;
    }

    tmpStr = textSearchWindowSide.getText();

    if (testParameter(tmpStr, 5, 101)) {
      searchWindowSide = Integer.valueOf(tmpStr).intValue();
    } else {
      MipavUtil.displayError("Search window side must be between 5 and 101");
      textSearchWindowSide.requestFocus();
      textSearchWindowSide.selectAll();

      return false;
    }

    if ((searchWindowSide % 2) == 0) {
      MipavUtil.displayError("Search window side must be an odd number");
      textSearchWindowSide.requestFocus();
      textSearchWindowSide.selectAll();
      return false;
    }

    tmpStr = textSimilarityWindowSide.getText();

    if (testParameter(tmpStr, 3, 99)) {
      similarityWindowSide = Integer.valueOf(tmpStr).intValue();
    } else {
      MipavUtil.displayError("Similarity window side must be between 3 and 99");
      textSimilarityWindowSide.requestFocus();
      textSimilarityWindowSide.selectAll();

      return false;
    }

    if ((similarityWindowSide % 2) == 0) {
      MipavUtil.displayError("Similarity window side must be an odd number");
      textSimilarityWindowSide.requestFocus();
      textSimilarityWindowSide.selectAll();
      return false;
    }

    if (similarityWindowSide >= searchWindowSide) {
      MipavUtil.displayError("Similarity window side must be less than search window side");
      textSimilarityWindowSide.requestFocus();
      textSimilarityWindowSide.selectAll();
      return false;
    }

    tmpStr = textNoiseStandardDeviation.getText();

    if (testParameter(tmpStr, 0.001, 1000.0)) {
      noiseStandardDeviation = Float.valueOf(tmpStr).floatValue();
    } else {
      MipavUtil.displayError("Radius must be between 0.001 and 1000.0");
      textNoiseStandardDeviation.requestFocus();
      textNoiseStandardDeviation.selectAll();

      return false;
    }

    doRician = doRicianCheckBox.isSelected();

    if (doRician) {
      tmpStr = textDegree.getText();
      if (testParameter(tmpStr, 1.0, 10.0)) {
        degreeOfFiltering = Float.valueOf(tmpStr).floatValue();
      } else {
        MipavUtil.displayError("Degree of filtering must be between 1.0 and 10.0");
        textDegree.requestFocus();
        textDegree.selectAll();
      }
    }

    if (image.getNDims() > 2) {
      image25D = image25DCheckBox.isSelected();
    }

    return true;
  }
  /**
   * Initializes the GUI by creating the components, placing them in the dialog, and displaying
   * them.
   */
  private void init() {
    setForeground(Color.black);

    setTitle("Nonlocal Means Filter");

    JPanel mainPanel;
    mainPanel = new JPanel();
    mainPanel.setBorder(BorderFactory.createEmptyBorder(3, 3, 3, 3));
    mainPanel.setLayout(new GridBagLayout());

    GridBagConstraints gbc = new GridBagConstraints();
    gbc.gridwidth = 1;
    gbc.gridheight = 1;
    gbc.anchor = GridBagConstraints.WEST;
    gbc.weightx = 1;
    gbc.insets = new Insets(3, 3, 3, 3);
    gbc.gridx = 0;
    gbc.gridy = 0;
    gbc.fill = GridBagConstraints.HORIZONTAL;

    paramPanel = new JPanel(new GridBagLayout());
    paramPanel.setForeground(Color.black);
    paramPanel.setBorder(buildTitledBorder("Parameters"));
    mainPanel.add(paramPanel, gbc);

    GridBagConstraints gbc2 = new GridBagConstraints();
    gbc2.gridwidth = 1;
    gbc2.gridheight = 1;
    gbc2.anchor = GridBagConstraints.WEST;
    gbc2.weightx = 1;
    gbc2.insets = new Insets(3, 3, 3, 3);
    gbc2.gridx = 0;
    gbc2.gridy = 0;
    gbc2.fill = GridBagConstraints.HORIZONTAL;

    labelSearchWindowSide = createLabel("Search window side (odd)");

    paramPanel.add(labelSearchWindowSide, gbc2);

    gbc2.gridx = 1;
    textSearchWindowSide = createTextField("15");
    paramPanel.add(textSearchWindowSide, gbc2);

    gbc2.gridx = 0;
    gbc2.gridy = 1;
    labelSimilarityWindowSide = createLabel("Similarity window side (odd) ");
    paramPanel.add(labelSimilarityWindowSide, gbc2);

    gbc2.gridx = 1;
    textSimilarityWindowSide = createTextField("7");
    paramPanel.add(textSimilarityWindowSide, gbc2);

    gbc2.gridx = 0;
    gbc2.gridy = 2;
    labelNoiseStandardDeviation = createLabel("Noise standard deviation ");
    paramPanel.add(labelNoiseStandardDeviation, gbc2);

    gbc2.gridx = 1;
    textNoiseStandardDeviation = createTextField("10.0");
    paramPanel.add(textNoiseStandardDeviation, gbc2);

    gbc2.gridx = 0;
    gbc2.gridy = 3;
    labelDegree = createLabel("Degree of filtering ");
    labelDegree.setEnabled(doRician);
    paramPanel.add(labelDegree, gbc2);

    gbc2.gridx = 1;
    textDegree = createTextField("1.414");
    textDegree.setEnabled(doRician);
    paramPanel.add(textDegree, gbc2);

    gbc2.gridx = 0;
    gbc2.gridy = 4;
    doRicianCheckBox = new JCheckBox("Deal with Rician noise in MRI");
    doRicianCheckBox.setFont(serif12);
    doRicianCheckBox.setSelected(false);
    doRicianCheckBox.addActionListener(this);
    paramPanel.add(doRicianCheckBox, gbc2);

    if (image.getNDims() > 2) {
      gbc2.gridx = 0;
      gbc2.gridy = 5;
      gbc2.gridwidth = 2;

      image25DCheckBox = new JCheckBox("Process each slice independently (2.5D)");
      image25DCheckBox.setFont(serif12);
      paramPanel.add(image25DCheckBox, gbc2);
      image25DCheckBox.setSelected(false);
    } // if (image.getNDims > 2)

    JPanel outputOptPanel = new JPanel(new GridLayout(1, 2));
    destinationPanel = new JPanel(new BorderLayout());
    destinationPanel.setForeground(Color.black);
    destinationPanel.setBorder(buildTitledBorder("Destination"));
    outputOptPanel.add(destinationPanel);

    destinationGroup = new ButtonGroup();
    newImage = new JRadioButton("New image", true);
    newImage.setBounds(10, 16, 120, 25);
    newImage.setFont(serif12);
    destinationGroup.add(newImage);
    destinationPanel.add(newImage, BorderLayout.NORTH);

    replaceImage = new JRadioButton("Replace image", false);
    replaceImage.setFont(serif12);
    destinationGroup.add(replaceImage);
    destinationPanel.add(replaceImage, BorderLayout.CENTER);

    // Only if the image is unlocked can it be replaced.
    if (image.getLockStatus() == ModelStorageBase.UNLOCKED) {
      replaceImage.setEnabled(true);
    } else {
      replaceImage.setEnabled(false);
    }

    gbc.gridx = 0;
    gbc.gridy = 1;
    mainPanel.add(outputOptPanel, gbc);

    mainDialogPanel.add(mainPanel, BorderLayout.CENTER);
    mainDialogPanel.add(buildButtons(), BorderLayout.SOUTH);

    getContentPane().add(mainDialogPanel);

    pack();
    setResizable(true);
    // setVisible(true);

    System.gc();
  }
  /**
   * Once all the necessary variables are set, call the Nonlocal Means filter algorithm based on
   * what type of image this is and whether or not there is a separate destination image.
   */
  protected void callAlgorithm() {
    String name = makeImageName(image.getImageName(), "_NonlocalMeans");
    int[] destExtents;

    if (image.getNDims() == 2) { // source image is 2D
      destExtents = new int[2];
      destExtents[0] = image.getExtents()[0]; // X dim
      destExtents[1] = image.getExtents()[1]; // Y dim
    } else {
      destExtents = new int[3];
      destExtents[0] = image.getExtents()[0];
      destExtents[1] = image.getExtents()[1];
      destExtents[2] = image.getExtents()[2];
    }

    if (displayLoc == NEW) {

      try {

        // Make result image of float type
        if (image.isColorImage()) {
          resultImage = new ModelImage(ModelImage.ARGB, destExtents, name);
        } else {
          resultImage = new ModelImage(ModelImage.FLOAT, destExtents, name);
        }

        // resultImage = (ModelImage)image.clone();
        // resultImage.setImageName(name);
        // Make algorithm
        nlMeansFilterAlgo =
            new AlgorithmNonlocalMeansFilter(
                resultImage,
                image,
                searchWindowSide,
                similarityWindowSide,
                noiseStandardDeviation,
                degreeOfFiltering,
                doRician,
                image25D);

        // This is very important. Adding this object as a listener allows the algorithm to
        // notify this object when it has completed of failed. See algorithm performed event.
        // This is made possible by implementing AlgorithmedPerformed interface
        nlMeansFilterAlgo.addListener(this);
        createProgressBar(image.getImageName(), nlMeansFilterAlgo);

        // Hide dialog
        setVisible(false);

        if (isRunInSeparateThread()) {

          // Start the thread as a low priority because we wish to still have user interface work
          // fast
          if (nlMeansFilterAlgo.startMethod(Thread.MIN_PRIORITY) == false) {
            MipavUtil.displayError("A thread is already running on this object");
          }
        } else {
          nlMeansFilterAlgo.run();
        }
      } catch (OutOfMemoryError x) {
        MipavUtil.displayError("Dialog Nonlocal Means Filter: unable to allocate enough memory");

        if (resultImage != null) {
          resultImage.disposeLocal(); // Clean up memory of result image
          resultImage = null;
        }

        return;
      }
    } else {

      try {

        // No need to make new image space because the user has choosen to replace the source image
        // Make the algorithm class
        nlMeansFilterAlgo =
            new AlgorithmNonlocalMeansFilter(
                null,
                image,
                searchWindowSide,
                similarityWindowSide,
                noiseStandardDeviation,
                degreeOfFiltering,
                doRician,
                image25D);

        // This is very important. Adding this object as a listener allows the algorithm to
        // notify this object when it has completed of failed. See algorithm performed event.
        // This is made possible by implementing AlgorithmedPerformed interface
        nlMeansFilterAlgo.addListener(this);
        createProgressBar(image.getImageName(), nlMeansFilterAlgo);

        // Hide the dialog since the algorithm is about to run.
        setVisible(false);

        // These next lines set the titles in all frames where the source image is displayed to
        // "locked - " image name so as to indicate that the image is now read/write locked!
        // The image frames are disabled and then unregisted from the userinterface until the
        // algorithm has completed.
        Vector<ViewImageUpdateInterface> imageFrames = image.getImageFrameVector();
        titles = new String[imageFrames.size()];

        for (int i = 0; i < imageFrames.size(); i++) {
          titles[i] = ((Frame) (imageFrames.elementAt(i))).getTitle();
          ((Frame) (imageFrames.elementAt(i))).setTitle("Locked: " + titles[i]);
          ((Frame) (imageFrames.elementAt(i))).setEnabled(false);
          userInterface.unregisterFrame((Frame) (imageFrames.elementAt(i)));
        }

        if (isRunInSeparateThread()) {

          // Start the thread as a low priority because we wish to still have user interface work
          // fast
          if (nlMeansFilterAlgo.startMethod(Thread.MIN_PRIORITY) == false) {
            MipavUtil.displayError("A thread is already running on this object");
          }
        } else {
          nlMeansFilterAlgo.run();
        }
      } catch (OutOfMemoryError x) {
        MipavUtil.displayError("Dialog Nonlocal Means Filter: unable to allocate enough memory");

        return;
      }
    }
  }