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CristianoLiraCristianoLira
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Add features.
1 parent 70330de commit 4e1f8c9

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‎src/main/java/ic/ufal/br/PDI/Main.java

+167-30
Original file line numberDiff line numberDiff line change
@@ -10,7 +10,10 @@
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import net.imagej.Dataset;
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import net.imagej.ImageJ;
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import net.imagej.ops.image.cooccurrenceMatrix.MatrixOrientation;
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import net.imagej.ops.image.cooccurrenceMatrix.MatrixOrientation2D;
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import net.imglib2.IterableInterval;
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import net.imglib2.RandomAccessibleInterval;
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import net.imglib2.type.numeric.real.DoubleType;
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import net.imagej.plugins.commands.typechange.TypeChanger;
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@@ -19,36 +22,170 @@ public class Main {
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public static void main(String[] args) throws IOException {
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ImageJ ij = new ImageJ();
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ModuleInfo module = ij.command().getCommand(TypeChanger.class);
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File file = ij.ui().chooseFile(null, "Open");
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String fileName = file.getName();
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Dataset dataset;
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Map<String, Object> input;
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//File file = ij.ui().chooseFile(null, "Open");
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File imgsPath = new File("nodulo");
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String fileName = "";
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PrintWriter pw = new PrintWriter(new File("test.csv"));
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StringBuilder sb = new StringBuilder();
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sb.append("magnitude");
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sb.append(",");
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sb.append("label");
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sb.append("\n");
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Dataset dataset = ij.scifio().datasetIO().open(file.getPath());
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ij.ui().show(dataset);
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Map<String, Object> input = new HashMap<String, Object>();
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input.put("data", dataset);
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input.put("typeName", "8-bit unsigned integer");
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input.put("combineChannels", true);
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ij.module().waitFor(ij.module().run(module, true, input));
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// ij.op().haralick, ij.op().zernike, ij.op().tamura, ij.op().stats estes são os descritores que possuem
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// os atributos
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DoubleType value = ij.op().zernike().magnitude((IterableInterval<DoubleType>)dataset.getImgPlus(), 0,0);
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sb.append(value);
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sb.append(",");
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// maligno ou benigno
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sb.append(fileName.substring(fileName.lastIndexOf(".") - 1, fileName.lastIndexOf(".")));
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sb.append("\n");
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pw.write(sb.toString());
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pw.close();
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System.out.println(value);
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pw.write("kurtosis,maximum,minimum,mean,geometricMean,standardDeviation,skewness,variance,sum,sumOfSq,haralickContrastV,haralickEntropyV,haralickDiffEntropyV,haralickSumEntropyV,haralickVarianceV,haralickDiffVarianceV,haralickIcm2V,haralickContrastH,haralickEntropyH,haralickDiffEntropyH,haralickSumEntropyH,haralickVarianceH,haralickDiffVarianceH,haralickIcm2H,tamuraCoarseness,zernikeMagnitude,label\n");
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for(File file : imgsPath.listFiles()) {
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StringBuilder sb = new StringBuilder();
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fileName = file.getName();
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dataset = ij.scifio().datasetIO().open(file.getPath());
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//ij.ui().show(dataset);
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input = new HashMap<String, Object>();
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input.put("data", dataset);
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input.put("typeName", "8-bit unsigned integer");
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input.put("combineChannels", true);
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ij.module().waitFor(ij.module().run(module, true, input));
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// ij.op().haralick, ij.op().zernike, ij.op().tamura, ij.op().stats estes são os descritores que possuem
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// os atributos
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// Kurtosis
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DoubleType value = ij.op().stats().kurtosis((IterableInterval<DoubleType>)dataset.getImgPlus());
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sb.append(value);
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sb.append(",");
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// Maximum
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value = ij.op().stats().max((IterableInterval<DoubleType>)dataset.getImgPlus());
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sb.append(value);
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sb.append(",");
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// Minimum
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value = ij.op().stats().min((IterableInterval<DoubleType>)dataset.getImgPlus());
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sb.append(value);
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sb.append(",");
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// Mean
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value = ij.op().stats().mean((IterableInterval<DoubleType>)dataset.getImgPlus());
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sb.append(value);
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sb.append(",");
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// Geometric mean
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value = ij.op().stats().geometricMean((IterableInterval<DoubleType>)dataset.getImgPlus());
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sb.append(value);
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sb.append(",");
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// Standard deviation
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value = ij.op().stats().stdDev((IterableInterval<DoubleType>)dataset.getImgPlus());
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sb.append(value);
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sb.append(",");
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// Skewness
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value = ij.op().stats().skewness((IterableInterval<DoubleType>)dataset.getImgPlus());
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sb.append(value);
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sb.append(",");
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// Variance
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value = ij.op().stats().variance((IterableInterval<DoubleType>)dataset.getImgPlus());
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sb.append(value);
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sb.append(",");
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// Sum
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value = ij.op().stats().sum((IterableInterval<DoubleType>)dataset.getImgPlus());
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sb.append(value);
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sb.append(",");
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// Sum of squares
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value = ij.op().stats().sumOfSquares((IterableInterval<DoubleType>)dataset.getImgPlus());
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sb.append(value);
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sb.append(",");
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// Vertical contrast:
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value = ij.op().haralick().contrast((IterableInterval<DoubleType>)dataset.getImgPlus(), 256, 1, MatrixOrientation2D.VERTICAL);
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sb.append(value);
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sb.append(",");
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// Vertical entropy:
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value = ij.op().haralick().entropy((IterableInterval<DoubleType>)dataset.getImgPlus(), 256, 1, MatrixOrientation2D.VERTICAL);
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sb.append(value);
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sb.append(",");
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// Vertical difference Entropy:
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value = ij.op().haralick().differenceEntropy((IterableInterval<DoubleType>)dataset.getImgPlus(), 256, 1, MatrixOrientation2D.VERTICAL);
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sb.append(value);
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sb.append(",");
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// Vertical sum entropy:
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value = ij.op().haralick().sumEntropy((IterableInterval<DoubleType>)dataset.getImgPlus(), 256, 1, MatrixOrientation2D.VERTICAL);
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sb.append(value);
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sb.append(",");
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// Vertical variance:
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value = ij.op().haralick().variance((IterableInterval<DoubleType>)dataset.getImgPlus(), 256, 1, MatrixOrientation2D.VERTICAL);
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sb.append(value);
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sb.append(",");
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// Vertical difference variance:
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value = ij.op().haralick().differenceVariance((IterableInterval<DoubleType>)dataset.getImgPlus(), 256, 1, MatrixOrientation2D.VERTICAL);
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sb.append(value);
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sb.append(",");
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// Vertical icm2:
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value = ij.op().haralick().icm2((IterableInterval<DoubleType>)dataset.getImgPlus(), 256, 1, MatrixOrientation2D.VERTICAL);
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sb.append(value);
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sb.append(",");
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// Horizontal contrast:
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value = ij.op().haralick().contrast((IterableInterval<DoubleType>)dataset.getImgPlus(), 256, 1, MatrixOrientation2D.HORIZONTAL);
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sb.append(value);
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sb.append(",");
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// Horizontal entropy:
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value = ij.op().haralick().entropy((IterableInterval<DoubleType>)dataset.getImgPlus(), 256, 1, MatrixOrientation2D.HORIZONTAL);
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sb.append(value);
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sb.append(",");
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// Horizontal difference Entropy:
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value = ij.op().haralick().differenceEntropy((IterableInterval<DoubleType>)dataset.getImgPlus(), 256, 1, MatrixOrientation2D.HORIZONTAL);
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sb.append(value);
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sb.append(",");
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// Horizontal sum entropy:
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value = ij.op().haralick().sumEntropy((IterableInterval<DoubleType>)dataset.getImgPlus(), 256, 1, MatrixOrientation2D.HORIZONTAL);
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sb.append(value);
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sb.append(",");
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// Horizontal variance:
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value = ij.op().haralick().variance((IterableInterval<DoubleType>)dataset.getImgPlus(), 256, 1, MatrixOrientation2D.HORIZONTAL);
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sb.append(value);
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sb.append(",");
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// Horizontal difference variance:
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value = ij.op().haralick().differenceVariance((IterableInterval<DoubleType>)dataset.getImgPlus(), 256, 1, MatrixOrientation2D.HORIZONTAL);
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sb.append(value);
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sb.append(",");
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// Horizontal icm2:
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value = ij.op().haralick().icm2((IterableInterval<DoubleType>)dataset.getImgPlus(), 256, 1, MatrixOrientation2D.HORIZONTAL);
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sb.append(value);
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sb.append(",");
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// Coarseness:
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value = ij.op().tamura().coarseness((RandomAccessibleInterval<DoubleType>)dataset.getImgPlus());
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sb.append(value);
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sb.append(",");
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// Magnitude:
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value = ij.op().zernike().magnitude((IterableInterval<DoubleType>)dataset.getImgPlus(), 0, 0);
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sb.append(value);
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sb.append(",");
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// Maligno ou benigno:
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sb.append(fileName.substring(fileName.lastIndexOf(".") - 1, fileName.lastIndexOf(".")));
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sb.append("\n");
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pw.write(sb.toString());
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System.out.println(fileName);
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}
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pw.close();
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}
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}

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