Java Code Examples for ij.process.ImageProcessor#getMax()

/** Update type, original dimensions and min,max from the given ImagePlus. */
private void readProps(final ImagePlus imp) {
	this.type = imp.getType();
	this.false_color = imp.getProcessor().isColorLut();
	if (imp.getWidth() != (int)this.o_width || imp.getHeight() != this.o_height) {
		this.o_width = imp.getWidth();
		this.o_height = imp.getHeight();
		this.width = o_width;
		this.height = o_height;
		updateBucket();
	}
	final ImageProcessor ip = imp.getProcessor();
	this.min = ip.getMin();
	this.max = ip.getMax();
	final HashSet<String> keys = new HashSet<String>();
	keys.add("type");
	keys.add("dimensions");
	keys.add("min_and_max");
	updateInDatabase(keys);
	//updateInDatabase(new HashSet<String>(Arrays.asList(new String[]{"type", "dimensions", "min_and_max"})));
}
 

static public ImageProcessor scaleImage(final ImagePlus imp, double mag, final boolean quality) {
	if (mag > 1) mag = 1;
	ImageProcessor ip = imp.getProcessor();
	if (Math.abs(mag - 1) < 0.000001) return ip;
	// else, make a properly scaled image:
	//  - gaussian blurred for best quality when resizing with nearest neighbor
	//  - direct nearest neighbor otherwise
	final int w = ip.getWidth();
	final int h = ip.getHeight();
	// TODO releseToFit !
	if (quality) {
		// apply proper gaussian filter
		final double sigma = Math.sqrt(Math.pow(2, getMipMapLevel(mag, Math.max(imp.getWidth(), imp.getHeight()))) - 0.25); // sigma = sqrt(level^2 - 0.5^2)
		ip = new FloatProcessorT2(w, h, ImageFilter.computeGaussianFastMirror(new FloatArray2D((float[])ip.convertToFloat().getPixels(), w, h), (float)sigma).data, ip.getDefaultColorModel(), ip.getMin(), ip.getMax());
		ip = ip.resize((int)(w * mag), (int)(h * mag)); // better while float
		return Utils.convertTo(ip, imp.getType(), false);
	} else {
		return ip.resize((int)(w * mag), (int)(h * mag));
	}
}
 

static public ImageProcessor scaleImage(final ImagePlus imp, final int level, final boolean quality) {
	if (level <= 0) return imp.getProcessor();
	// else, make a properly scaled image:
	//  - gaussian blurred for best quality when resizing with nearest neighbor
	//  - direct nearest neighbor otherwise
	ImageProcessor ip = imp.getProcessor();
	final int w = ip.getWidth();
	final int h = ip.getHeight();
	final double mag = 1 / Math.pow(2, level);
	// TODO releseToFit !
	if (quality) {
		// apply proper gaussian filter
		final double sigma = Math.sqrt(Math.pow(2, level) - 0.25); // sigma = sqrt(level^2 - 0.5^2)
		ip = new FloatProcessorT2(w, h, ImageFilter.computeGaussianFastMirror(new FloatArray2D((float[])ip.convertToFloat().getPixels(), w, h), (float)sigma).data, ip.getDefaultColorModel(), ip.getMin(), ip.getMax());
		ip = ip.resize((int)(w * mag), (int)(h * mag)); // better while float
		return Utils.convertTo(ip, imp.getType(), false);
	} else {
		return ip.resize((int)(w * mag), (int)(h * mag));
	}
}
 

PatchIntensityRange( final Patch patch )
{
	this.patch = patch;
	a = patch.getMax() - patch.getMin();
	final ImageProcessor ip = patch.getImageProcessor();
	ip.resetMinAndMax();
	min = ( ip.getMin() - patch.getMin() ) / a;
	max = ( ip.getMax() - patch.getMin() ) / a;
	ip.setMinAndMax( patch.getMin(), patch.getMax() );
}
 

/**
 * Computes the distance propagated from the boundary of the white
 * particles, within the white phase.
 * 
 * @param markerPlus
 *            the binary marker image from which distances will be
 *            propagated
 * @param maskPlus
 *            the binary mask image that will constrain the propagation
 * @param newName
 *            the name of the result image
 * @param weights
 *            the set of chamfer weights for computing distances
 * @param normalize
 *            specifies whether the resulting distance map should be
 *            normalized
 * @return an array of object, containing the name of the new image, and the
 *         new ImagePlus instance
 */
public ImagePlus process(ImagePlus markerPlus, ImagePlus maskPlus,
		String newName, float[] weights, boolean normalize)
{
	// Check validity of parameters
	if (markerPlus == null)
	{
		throw new IllegalArgumentException("Marker image not specified");
	}
	if (maskPlus == null)
	{
		throw new IllegalArgumentException("Mask image not specified");
	}
	if (newName == null)
		newName = createResultImageName(maskPlus);
	if (weights == null)
	{
		throw new IllegalArgumentException("Weights not specified");
	}

	// size of image
	int width = maskPlus.getWidth();
	int height = maskPlus.getHeight();

	// check input and mask have the same size
	if (markerPlus.getWidth() != width || markerPlus.getHeight() != height)
	{
		IJ.showMessage("Error",
				"Input and marker images\nshould have the same size");
		return null;
	}

	// Initialize calculator
	GeodesicDistanceTransform algo = new GeodesicDistanceTransformFloat5x5(weights, normalize);
	DefaultAlgoListener.monitor(algo);
   	

	// Compute distance on specified images
	ImageProcessor marker = markerPlus.getProcessor();
	ImageProcessor mask = maskPlus.getProcessor();
	ImageProcessor result = algo.geodesicDistanceMap(marker, mask);
	ImagePlus resultPlus = new ImagePlus(newName, result);
	
	// setup display options
	double maxVal = result.getMax();
	resultPlus.setLut(createFireLUT(maxVal));
	resultPlus.setDisplayRange(0, maxVal);
	
	// return result image
	return resultPlus;
}
 

/**
 * Computes the distance propagated from the boundary of the white
 * particles, within the black phase.
 * 
 * @param marker
 *            the binary marker image from which distances will be
 *            propagated
 * @param mask
 *            the binary mask image that will constrain the propagation
 * @param newName
 *            the name of the result image
 * @param weights
 *            the set of chamfer weights for computing distances
 * @param normalize
 *            specifies whether the resulting distance map should be
 *            normalized
 * @return an array of object, containing the name of the new image, and the
 *         new ImagePlus instance
 */
public ImagePlus process(ImagePlus marker, ImagePlus mask, String newName,
		short[] weights, boolean normalize)
{
	// Check validity of parameters
	if (marker == null)
	{
		throw new IllegalArgumentException("Marker image not specified");
	}
	if (mask == null)
	{
		throw new IllegalArgumentException("Mask image not specified");
	}
	if (newName == null)
		newName = createResultImageName(mask);
	if (weights == null)
	{
		throw new IllegalArgumentException("Weights not specified");
	}

	// size of image
	int width = mask.getWidth();
	int height = mask.getHeight();

	// check input and mask have the same size
	if (marker.getWidth() != width || marker.getHeight() != height)
	{
		IJ.showMessage("Error",
				"Input and marker images\nshould have the same size");
		return null;
	}

	// Initialize calculator
	GeodesicDistanceTransform algo = new GeodesicDistanceTransformShort5x5(weights, normalize);

	DefaultAlgoListener.monitor(algo);

	// Compute distance on specified images
	ImageProcessor result = algo.geodesicDistanceMap(marker.getProcessor(),
			mask.getProcessor());
	ImagePlus resultPlus = new ImagePlus(newName, result);

	// setup display options
	double maxVal = result.getMax();
	resultPlus.setLut(createFireLUT(maxVal));
	resultPlus.setDisplayRange(0, maxVal);
	
	// create result array
	return resultPlus;
}
 

/**
 * Computes the distance propagated from the boundary of the white
 * particles, within the white phase.
 *
 * @param mask
 *            the binary mask image that will constrain the propagation
 * @param roi
 * 			  the roi to define the marker image
 * @param weights
 *            the set of chamfer weights for computing distances
 * @param normalize
 *            specifies whether the resulting distance map should be
 *            normalized
 * @return geodesic distance map image
 */
public ImageProcessor process( ImageProcessor mask, Roi roi,
		float[] weights, boolean normalize)
{
	if( mask == null || imagePlus == null || baseImage == null)
	{
		IJ.showMessage( "Please run the plugin with an image open." );
		return null;
	}

	if( weights == null )
	{
		IJ.showMessage( "Weights not specified" );
		return null;
	}

	if( roi == null )
	{
		IJ.showMessage( "Please define the markers using for example "
				+ "the point selection tool." );
		return null;
	}
	// Create marker image from ROI
	ByteProcessor marker = new ByteProcessor( mask.getWidth(),
			mask.getHeight() );
	marker.setColor( java.awt.Color.WHITE );
	marker.draw( roi );

	// Initialize calculator
	GeodesicDistanceTransform algo;
	if( weights.length == 2 )
		algo = new GeodesicDistanceTransformFloat( weights, normalize );
	else
		algo = new GeodesicDistanceTransformFloat5x5( weights, normalize );

	DefaultAlgoListener.monitor( algo );

	// Compute distance on specified images
	ImageProcessor result = algo.geodesicDistanceMap( marker, mask );

	// setup display options
	double maxVal = result.getMax();
	result.setLut( createFireLUT( maxVal ) );

	// create result image
	return result;
}
 

/**
 * Computes the distance propagated from the boundary of the white
 * particles, within the white phase.
 *
 * @param mask
 *            the binary mask image that will constrain the propagation
 * @param roi
 * 			  the roi to define the marker image
 * @param weights
 *            the set of chamfer weights for computing distances
 * @param normalize
 *            specifies whether the resulting distance map should be
 *            normalized
 * @return geodesic distance map image
 */
public ImageProcessor process( ImageProcessor mask, Roi roi,
		short[] weights, boolean normalize)
{
	if( mask == null || imagePlus == null || baseImage == null)
	{
		IJ.showMessage( "Please run the plugin with an image open." );
		return null;
	}

	if( weights == null )
	{
		IJ.showMessage( "Weights not specified" );
		return null;
	}

	if( roi == null )
	{
		IJ.showMessage( "Please define the markers using for example "
				+ "the point selection tool." );
		return null;
	}
	// Create marker image from ROI
	ByteProcessor marker = new ByteProcessor( mask.getWidth(),
			mask.getHeight() );
	marker.setColor( java.awt.Color.WHITE );
	marker.draw( roi );

	// Initialize calculator
	GeodesicDistanceTransform algo;
	if( weights.length == 2 )
		algo = new GeodesicDistanceTransformShort( weights, normalize );
	else
		algo = new GeodesicDistanceTransformShort5x5( weights, normalize );

	DefaultAlgoListener.monitor( algo );

	// Compute distance on specified images
	ImageProcessor result = algo.geodesicDistanceMap( marker, mask );

	// setup display options
	double maxVal = result.getMax();
	result.setLut( createFireLUT( maxVal ) );

	// create result image
	return result;
}
 

/** Returns a string containing information about the specified  image. */
static public final String getDescriptionString(final ImagePlus imp, final FileInfo fi) {
	final Calibration cal = imp.getCalibration();
	final StringBuilder sb = new StringBuilder(100);
	sb.append("ImageJ="+ImageJ.VERSION+"\n");
	if (fi.nImages>1 && fi.fileType!=FileInfo.RGB48)
		sb.append("images="+fi.nImages+"\n");
	int channels = imp.getNChannels();
	if (channels>1)
		sb.append("channels="+channels+"\n");
	int slices = imp.getNSlices();
	if (slices>1)
		sb.append("slices="+slices+"\n");
	int frames = imp.getNFrames();
	if (frames>1)
		sb.append("frames="+frames+"\n");
	if (fi.unit!=null)
		sb.append("unit="+fi.unit+"\n");
	if (fi.valueUnit!=null && fi.calibrationFunction!=Calibration.CUSTOM) {
		sb.append("cf="+fi.calibrationFunction+"\n");
		if (fi.coefficients!=null) {
			for (int i=0; i<fi.coefficients.length; i++)
				sb.append("c"+i+"="+fi.coefficients[i]+"\n");
		}
		sb.append("vunit="+fi.valueUnit+"\n");
		if (cal.zeroClip()) sb.append("zeroclip=true\n");
	}
	
	// get stack z-spacing and fps
	if (fi.nImages>1) {
		if (fi.pixelDepth!=0.0 && fi.pixelDepth!=1.0)
			sb.append("spacing="+fi.pixelDepth+"\n");
		if (cal.fps!=0.0) {
			if ((int)cal.fps==cal.fps)
				sb.append("fps="+(int)cal.fps+"\n");
			else
				sb.append("fps="+cal.fps+"\n");
		}
		sb.append("loop="+(cal.loop?"true":"false")+"\n");
		if (cal.frameInterval!=0.0) {
			if ((int)cal.frameInterval==cal.frameInterval)
				sb.append("finterval="+(int)cal.frameInterval+"\n");
			else
				sb.append("finterval="+cal.frameInterval+"\n");
		}
		if (!cal.getTimeUnit().equals("sec"))
			sb.append("tunit="+cal.getTimeUnit()+"\n");
	}
	
	// get min and max display values
	final ImageProcessor ip = imp.getProcessor();
	final double min = ip.getMin();
	final double max = ip.getMax();
	final int type = imp.getType();
	final boolean enhancedLut = (type==ImagePlus.GRAY8 || type==ImagePlus.COLOR_256) && (min!=0.0 || max !=255.0);
	if (enhancedLut || type==ImagePlus.GRAY16 || type==ImagePlus.GRAY32) {
		sb.append("min="+min+"\n");
		sb.append("max="+max+"\n");
	}
	
	// get non-zero origins
	if (cal.xOrigin!=0.0)
		sb.append("xorigin="+cal.xOrigin+"\n");
	if (cal.yOrigin!=0.0)
		sb.append("yorigin="+cal.yOrigin+"\n");
	if (cal.zOrigin!=0.0)
		sb.append("zorigin="+cal.zOrigin+"\n");
	if (cal.info!=null && cal.info.length()<=64 && cal.info.indexOf('=')==-1 && cal.info.indexOf('\n')==-1)
		sb.append("info="+cal.info+"\n");			
	sb.append((char)0);
	return new String(sb);
}