private void beginConversion() {
final Serializable imageId = getImageId();
final Handler thumbnailHandler =
new Handler() {
@Override
public void handleMessage(Message result) {
final ImageConverter converter;
synchronized (ImageSource.this) {
converter = conversionJob;
conversionJob = null;
}
final Throwable status = converter.getStatus();
if (null != status) {
String msg = context.getResources().getString(R.string.image_load_error);
Log.e(LOG_TAG, msg, status);
context.alert(msg);
} else {
Bitmap thumbnail = converter.getBitmap();
synchronized (ImageSource.this) {
setThumbnailCache(thumbnail);
if (null != frame) {
frame.removeAllViews();
showThumbnail(thumbnail);
}
}
}
}
};
final ImageConverter converter = new ImageConverter(context, thumbnailHandler);
converter.setImageId(imageId);
converter.setMaxFrameDimension(getMaxThumbnailSize());
this.conversionJob = converter;
context.submitBackgroundTask(converter);
}
/* (non-Javadoc)
* @see java.awt.image.BufferedImageOp#filter(java.awt.image.BufferedImage, java.awt.image.BufferedImage)
*/
public BufferedImage filter(BufferedImage src, BufferedImage dst) {
if (src == null) {
throw new NullPointerException("src image is null");
}
if (src == dst) {
throw new IllegalArgumentException("src image cannot be the " + "same as the dst image");
}
boolean needToConvert = false;
ColorModel srcCM = src.getColorModel();
ColorModel dstCM;
BufferedImage origDst = dst;
if (srcCM instanceof IndexColorModel) {
IndexColorModel icm = (IndexColorModel) srcCM;
src = icm.convertToIntDiscrete(src.getRaster(), false);
srcCM = src.getColorModel();
}
if (dst == null) {
dst = createCompatibleDestImage(src, null);
dstCM = srcCM;
origDst = dst;
} else {
dstCM = dst.getColorModel();
if (srcCM.getColorSpace().getType() != dstCM.getColorSpace().getType()) {
needToConvert = true;
dst = createCompatibleDestImage(src, null);
dstCM = dst.getColorModel();
} else if (dstCM instanceof IndexColorModel) {
dst = createCompatibleDestImage(src, null);
dstCM = dst.getColorModel();
}
}
java.awt.image.CropImageFilter cropfilter =
new java.awt.image.CropImageFilter(region.x, region.y, region.width, region.height);
Image returnImage =
Toolkit.getDefaultToolkit()
.createImage(new java.awt.image.FilteredImageSource(src.getSource(), cropfilter));
dst = ImageConverter.convertImage(returnImage);
origDst = dst;
if (needToConvert) {
ColorConvertOp ccop = new ColorConvertOp(hints);
ccop.filter(dst, origDst);
} else if (origDst != dst) {
java.awt.Graphics2D g2 = origDst.createGraphics();
try {
g2.drawImage(dst, 0, 0, null);
} finally {
g2.dispose();
}
}
return origDst;
}
public Coin(String filename, int width, int height) {
dim = new Dim(width, height, 1000, 700);
ImageIcon ii = new ImageIcon(filename);
ImageTransparency it = new ImageTransparency();
ImageConverter ic = new ImageConverter();
BufferedImage bi = ic.imageToBufferedImage(ii.getImage());
coinImage = it.makeColorTransparent(bi, Color.white);
getNewCoordinates();
isVisible = false;
isDead = false;
appearTimer = new Timer();
disappearTimer = new Timer();
appear = new Appear();
disappear = new Disappear();
appearTimer.schedule(appear, getRandomAppearTime());
appearTimerRunning = true;
disappearTimerRunning = false;
timersPaused = false;
}
// Conversion Options
void conversions() {
double[] weights = ColorProcessor.getWeightingFactors();
boolean weighted = !(weights[0] == 1d / 3d && weights[1] == 1d / 3d && weights[2] == 1d / 3d);
// boolean weighted = !(Math.abs(weights[0]-1d/3d)<0.0001 && Math.abs(weights[1]-1d/3d)<0.0001
// && Math.abs(weights[2]-1d/3d)<0.0001);
GenericDialog gd = new GenericDialog("Conversion Options");
gd.addCheckbox("Scale when converting", ImageConverter.getDoScaling());
String prompt = "Weighted RGB conversions";
if (weighted)
prompt +=
" (" + IJ.d2s(weights[0]) + "," + IJ.d2s(weights[1]) + "," + IJ.d2s(weights[2]) + ")";
gd.addCheckbox(prompt, weighted);
gd.showDialog();
if (gd.wasCanceled()) return;
ImageConverter.setDoScaling(gd.getNextBoolean());
Prefs.weightedColor = gd.getNextBoolean();
if (!Prefs.weightedColor) ColorProcessor.setWeightingFactors(1d / 3d, 1d / 3d, 1d / 3d);
else if (Prefs.weightedColor && !weighted)
ColorProcessor.setWeightingFactors(0.299, 0.587, 0.114);
return;
}
void Mean(ImagePlus imp, int radius, double par1, double par2, boolean doIwhite) {
// See: Image Processing Learning Resourches HIPR2
// http://homepages.inf.ed.ac.uk/rbf/HIPR2/adpthrsh.htm
ImagePlus Meanimp;
ImageProcessor ip = imp.getProcessor(), ipMean;
int c_value = 0;
byte object;
byte backg;
if (par1 != 0) {
IJ.log("Mean: changed c_value from :" + c_value + " to:" + par1);
c_value = (int) par1;
}
if (doIwhite) {
object = (byte) 0xff;
backg = (byte) 0;
} else {
object = (byte) 0;
backg = (byte) 0xff;
}
Meanimp = duplicateImage(ip);
ImageConverter ic = new ImageConverter(Meanimp);
ic.convertToGray32();
ipMean = Meanimp.getProcessor();
RankFilters rf = new RankFilters();
rf.rank(ipMean, radius, rf.MEAN); // Mean
// Meanimp.show();
byte[] pixels = (byte[]) ip.getPixels();
float[] mean = (float[]) ipMean.getPixels();
for (int i = 0; i < pixels.length; i++)
pixels[i] = ((int) (pixels[i] & 0xff) > (int) (mean[i] - c_value)) ? object : backg;
// imp.updateAndDraw();
return;
}
@Override
public void onFragmentInteraction(String up, String down) {
mMemeFrag.setMyTextArgs(up, down);
Bitmap imageForShare = mMemeFrag.getUpdatedImage();
Intent share = new Intent(Intent.ACTION_SEND);
share.setType("image/jpeg");
mImageConverter.saveBitmapTo(imageForShare, getString(R.string.nameOfStoredImageFile));
Uri myUri =
Uri.parse(
"file://"
+ Environment.getExternalStorageDirectory().getPath()
+ "/"
+ getString(R.string.nameOfStoredImageFile)
+ ".jpg");
share.putExtra(Intent.EXTRA_STREAM, myUri);
shareMenu.setEnabled(true);
setShareIntent(share);
}
private void createCommodityView() throws Exception {
commodityView =
new CommodityView(imageLoader.getBytes("htc.jpg", ImageType.jpg), ImageType.bmp, false);
}
void Sauvola(ImagePlus imp, int radius, double par1, double par2, boolean doIwhite) {
// Sauvola recommends K_VALUE = 0.5 and R_VALUE = 128.
// This is a modification of Niblack's thresholding method.
// Sauvola J. and Pietaksinen M. (2000) "Adaptive Document Image Binarization"
// Pattern Recognition, 33(2): 225-236
// http://www.ee.oulu.fi/mvg/publications/show_pdf.php?ID=24
// Ported to ImageJ plugin from E Celebi's fourier_0.8 routines
// This version uses a circular local window, instead of a rectagular one
ImagePlus Meanimp, Varimp;
ImageProcessor ip = imp.getProcessor(), ipMean, ipVar;
double k_value = 0.5;
double r_value = 128;
byte object;
byte backg;
if (par1 != 0) {
IJ.log("Sauvola: changed k_value from :" + k_value + " to:" + par1);
k_value = par1;
}
if (par2 != 0) {
IJ.log("Sauvola: changed r_value from :" + r_value + " to:" + par2);
r_value = par2;
}
if (doIwhite) {
object = (byte) 0xff;
backg = (byte) 0;
} else {
object = (byte) 0;
backg = (byte) 0xff;
}
Meanimp = duplicateImage(ip);
ImageConverter ic = new ImageConverter(Meanimp);
ic.convertToGray32();
ipMean = Meanimp.getProcessor();
RankFilters rf = new RankFilters();
rf.rank(ipMean, radius, rf.MEAN); // Mean
// Meanimp.show();
Varimp = duplicateImage(ip);
ic = new ImageConverter(Varimp);
ic.convertToGray32();
ipVar = Varimp.getProcessor();
rf.rank(ipVar, radius, rf.VARIANCE); // Variance
// Varimp.show();
byte[] pixels = (byte[]) ip.getPixels();
float[] mean = (float[]) ipMean.getPixels();
float[] var = (float[]) ipVar.getPixels();
for (int i = 0; i < pixels.length; i++)
pixels[i] =
((int) (pixels[i] & 0xff)
> (int) (mean[i] * (1.0 + k_value * ((Math.sqrt(var[i]) / r_value) - 1.0))))
? object
: backg;
// imp.updateAndDraw();
return;
}
void Phansalkar(ImagePlus imp, int radius, double par1, double par2, boolean doIwhite) {
// This is a modification of Sauvola's thresholding method to deal with low contrast images.
// Phansalskar N. et al. Adaptive local thresholding for detection of nuclei in diversity
// stained
// cytology images.International Conference on Communications and Signal Processing (ICCSP),
// 2011,
// 218 - 220.
// In this method, the threshold t = mean*(1+p*exp(-q*mean)+k*((stdev/r)-1))
// Phansalkar recommends k = 0.25, r = 0.5, p = 2 and q = 10. In this plugin, k and r are the
// parameters 1 and 2 respectively, but the values of p and q are fixed.
//
// Implemented from Phansalkar's paper description by G. Landini
// This version uses a circular local window, instead of a rectagular one
ImagePlus Meanimp, Varimp, Orimp;
ImageProcessor ip = imp.getProcessor(), ipMean, ipVar, ipOri;
double k_value = 0.25;
double r_value = 0.5;
double p_value = 2.0;
double q_value = 10.0;
byte object;
byte backg;
if (par1 != 0) {
IJ.log("Phansalkar: changed k_value from :" + k_value + " to:" + par1);
k_value = par1;
}
if (par2 != 0) {
IJ.log("Phansalkar: changed r_value from :" + r_value + " to:" + par2);
r_value = par2;
}
if (doIwhite) {
object = (byte) 0xff;
backg = (byte) 0;
} else {
object = (byte) 0;
backg = (byte) 0xff;
}
Meanimp = duplicateImage(ip);
ContrastEnhancer ce = new ContrastEnhancer();
ce.stretchHistogram(Meanimp, 0.0);
ImageConverter ic = new ImageConverter(Meanimp);
ic.convertToGray32();
ipMean = Meanimp.getProcessor();
ipMean.multiply(1.0 / 255);
Orimp = duplicateImage(ip);
ce.stretchHistogram(Orimp, 0.0);
ic = new ImageConverter(Orimp);
ic.convertToGray32();
ipOri = Orimp.getProcessor();
ipOri.multiply(1.0 / 255); // original to compare
// Orimp.show();
RankFilters rf = new RankFilters();
rf.rank(ipMean, radius, rf.MEAN); // Mean
// Meanimp.show();
Varimp = duplicateImage(ip);
ce.stretchHistogram(Varimp, 0.0);
ic = new ImageConverter(Varimp);
ic.convertToGray32();
ipVar = Varimp.getProcessor();
ipVar.multiply(1.0 / 255);
rf.rank(ipVar, radius, rf.VARIANCE); // Variance
ipVar.sqr(); // SD
// Varimp.show();
byte[] pixels = (byte[]) ip.getPixels();
float[] ori = (float[]) ipOri.getPixels();
float[] mean = (float[]) ipMean.getPixels();
float[] sd = (float[]) ipVar.getPixels();
for (int i = 0; i < pixels.length; i++)
pixels[i] =
((ori[i])
> (mean[i]
* (1.0
+ p_value * Math.exp(-q_value * mean[i])
+ k_value * ((sd[i] / r_value) - 1.0))))
? object
: backg;
// imp.updateAndDraw();
return;
}
void Niblack(ImagePlus imp, int radius, double par1, double par2, boolean doIwhite) {
// Niblack recommends K_VALUE = -0.2 for images with black foreground
// objects, and K_VALUE = +0.2 for images with white foreground objects.
// Niblack W. (1986) "An introduction to Digital Image Processing" Prentice-Hall.
// Ported to ImageJ plugin from E Celebi's fourier_0.8 routines
// This version uses a circular local window, instead of a rectagular one
ImagePlus Meanimp, Varimp;
ImageProcessor ip = imp.getProcessor(), ipMean, ipVar;
double k_value;
int c_value = 0;
byte object;
byte backg;
if (doIwhite) {
k_value = 0.2;
object = (byte) 0xff;
backg = (byte) 0;
} else {
k_value = -0.2;
object = (byte) 0;
backg = (byte) 0xff;
}
if (par1 != 0) {
IJ.log("Niblack: changed k_value from :" + k_value + " to:" + par1);
k_value = par1;
}
if (par2 != 0) {
IJ.log(
"Niblack: changed c_value from :"
+ c_value
+ " to:"
+ par2); // requested feature, not in original
c_value = (int) par2;
}
Meanimp = duplicateImage(ip);
ImageConverter ic = new ImageConverter(Meanimp);
ic.convertToGray32();
ipMean = Meanimp.getProcessor();
RankFilters rf = new RankFilters();
rf.rank(ipMean, radius, rf.MEAN); // Mean
// Meanimp.show();
Varimp = duplicateImage(ip);
ic = new ImageConverter(Varimp);
ic.convertToGray32();
ipVar = Varimp.getProcessor();
rf.rank(ipVar, radius, rf.VARIANCE); // Variance
// Varimp.show();
byte[] pixels = (byte[]) ip.getPixels();
float[] mean = (float[]) ipMean.getPixels();
float[] var = (float[]) ipVar.getPixels();
for (int i = 0; i < pixels.length; i++)
pixels[i] =
((int) (pixels[i] & 0xff) > (int) (mean[i] + k_value * Math.sqrt(var[i]) - c_value))
? object
: backg;
// imp.updateAndDraw();
return;
}
public Image convert(Image image, ImageDesc desc) {
for (ImageConverter f : this) {
image = f.convert(image, desc);
}
return image;
}
