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ijp-biop-run-macro/src/main/java/algorithms/InputCheck.java
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InputCheck.java
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package algorithms;
import gadgets.DataContainer;
import gadgets.DataContainer.MaskType;
import ij.IJ;
import net.imglib2.RandomAccessibleInterval;
import net.imglib2.TwinCursor;
import net.imglib2.type.logic.BitType;
import net.imglib2.type.numeric.RealType;
import net.imglib2.view.Views;
import results.ResultHandler;
/**
* This class implements some basic checks for the input image data. For
* instance: Is the percentage of zero-zero or saturated pixels too high? Also,
* we get basic image properties / stats from imglib2, and also the
* colocalization job name from the DataContainer and allow implementations of
* ResultHandler to report them.
*/
public class InputCheck<T extends RealType< T >> extends Algorithm<T> {
/* the maximum allowed ratio between zero-zero and
* normal pixels
*/
protected final double maxZeroZeroRatio = 0.1f;
/* the maximum allowed ratio between saturated and
* normal pixels within a channel
*/
protected final double maxSaturatedRatio = 0.1f;
// the zero-zero pixel ratio
double zeroZeroPixelRatio;
// the saturated pixel ratio of channel 1
double saturatedRatioCh1;
// the saturated pixel ratio of channel 2
double saturatedRatioCh2;
// the coloc job name
String colocJobName;
// general image stats/parameters/values
double ch1Max;
double ch2Max;
double ch1Min;
double ch2Min;
double ch1Mean;
double ch2Mean;
double ch1Integral;
double ch2Integral;
// Mask infos
MaskType maskType;
double maskID;
public InputCheck() {
super("input data check");
}
@Override
public void execute(DataContainer<T> container)
throws MissingPreconditionException {
// get the 2 images and the mask
final RandomAccessibleInterval<T> img1 = container.getSourceImage1();
final RandomAccessibleInterval<T> img2 = container.getSourceImage2();
final RandomAccessibleInterval<BitType> mask = container.getMask();
// get the cursors for iterating through pixels in images
TwinCursor<T> cursor = new TwinCursor<T>(img1.randomAccess(),
img2.randomAccess(), Views.iterable(mask).cursor());
// get various general image properties/stats/values from the DataContainer
ch1Max = container.getMaxCh1();
ch2Max = container.getMaxCh2();
ch1Min = container.getMinCh1();
ch2Min = container.getMinCh2();
ch1Mean = container.getMeanCh1();
ch2Mean = container.getMeanCh2();
ch1Integral = container.getIntegralCh1();
ch2Integral = container.getIntegralCh2();
// get the info about the mask/ROI being used or not.
maskType = container.getMaskType();
maskID = (double)container.getMaskID();
// the total amount of pixels that have been taken into consideration
long N = 0;
// the number of pixels that are zero in both channels
long Nzero = 0;
// the number of ch1 pixels with the maximum ch1 value;
long NsaturatedCh1 = 0;
// the number of ch2 pixels with the maximum ch2 value;
long NsaturatedCh2 = 0;
while (cursor.hasNext()) {
cursor.fwd();
double ch1 = cursor.getFirst().getRealDouble();
double ch2 = cursor.getSecond().getRealDouble();
// is the current pixels combination a zero-zero pixel?
if (Math.abs(ch1 + ch2) < 0.00001)
Nzero++;
// is the current pixel of channel one saturated?
if (Math.abs(ch1Max - ch1) < 0.00001)
NsaturatedCh1++;
// is the current pixel of channel one saturated?
if (Math.abs(ch2Max - ch2) < 0.00001)
NsaturatedCh2++;
N++;
}
// calculate results
double zeroZeroRatio = (double)Nzero / (double)N;
// for channel wise ratios we have to use half of the total pixel amount
double ch1SaturatedRatio = (double)NsaturatedCh1 / ( (double)N *0.5);
double ch2SaturatedRatio = (double)NsaturatedCh2 / ( (double)N * 0.5);
/* save results
* Percentage results need to be multiplied by 100
*/
zeroZeroPixelRatio = zeroZeroRatio * 100.0;
saturatedRatioCh1 = ch1SaturatedRatio * 100.0;
saturatedRatioCh2 = ch2SaturatedRatio * 100.0;
// get job name so the ResultsHandler implementation can have it.
colocJobName = container.getJobName();
// add warnings if images contain negative values
if (ch1Min < 0 || ch2Min < 0) {
addWarning("negative minimum pixel value found",
"The minimum pixel value in at least one of the is channels is negative. Negative values might break the logic of some analysis methods. The reason is a basic assumption: The pixel value is assumed to be proportional to the photon count detected in a pixel. Negative photon counts make no physical sense.");
}
// add warnings if values are not in tolerance range
if ( Math.abs(zeroZeroRatio) > maxZeroZeroRatio ) {
addWarning("zero-zero ratio too high",
"The ratio between zero-zero pixels and other pixels is large: "
+ IJ.d2s(zeroZeroRatio, 2) + ". Maybe you should use a ROI.");
}
if ( Math.abs(ch1SaturatedRatio) > maxSaturatedRatio ) {
addWarning("saturated ch1 ratio too high",
"The ratio between saturated pixels and other pixels in channel one is large: "
+ IJ.d2s(maxSaturatedRatio, 2) + ". Maybe you should use a ROI.");
}
if ( Math.abs(ch1SaturatedRatio) > maxSaturatedRatio ) {
addWarning("saturated ch2 ratio too high",
"The ratio between saturated pixels and other pixels in channel two is large: "
+ IJ.d2s(maxSaturatedRatio, 2) + ". Maybe you should use a ROI.");
}
}
@Override
public void processResults(ResultHandler<T> handler) {
super.processResults(handler);
// Let us have a ValueResult for the colocalisation analysis job name:
// A ValueResult can be two Strings (or a string and a numerical value)
// We want to keep the jobName close to all the value results
// so they get shown together by whatever implementation of ResultsHandler.
handler.handleValue("Coloc_Job_Name", colocJobName);
// Make the ResultsHander implementation deal with the input check results.
handler.handleValue("% zero-zero pixels", zeroZeroPixelRatio, 2);
handler.handleValue("% saturated ch1 pixels", saturatedRatioCh1, 2);
handler.handleValue("% saturated ch2 pixels", saturatedRatioCh2, 2);
// Make the ResultsHander implementation deal with the images'
// stats/parameters/values
handler.handleValue("Channel 1 Max", ch1Max, 3);
handler.handleValue("Channel 2 Max", ch2Max, 3);
handler.handleValue("Channel 1 Min", ch1Min, 3);
handler.handleValue("Channel 2 Min", ch2Min, 3);
handler.handleValue("Channel 1 Mean", ch1Mean, 3);
handler.handleValue("Channel 2 Mean", ch2Mean, 3);
handler.handleValue("Channel 1 Integrated (Sum) Intensity", ch1Integral, 3);
handler.handleValue("Channel 2 Integrated (Sum) Intensity", ch2Integral, 3);
// Make the ResultsHandler implementation deal with the images'
// ROI or mask or lack thereof, so the user knows what they used.
handler.handleValue("Mask Type Used", maskType.name());
handler.handleValue("Mask ID Used", maskID, 0);
}
}
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