http://imagej.273.s1.nabble.com/classifying-multi-spectral-images-using-Advanced-Weka-Segmaentation-tp5002444p5002446.html
the same for testing. There is an "applyClassifier" method in
> Hello everyone,
>
> I am trying to classify/segment multi-spectral images. The original images
> are in multi-layer tiff format. I am computing a set of features for every
> slice/band and then adding it to a custom featureStack, so that the final
> feature vector is a collection of varying features coming from each of the
> spectral bands. I can successfully build the featurestack and train the
> classifier. However, when the classifier is applied to a test image, it
> seems that it classifies each band/slice separately. It is intended to
> treat each multi-sepctral image as one entity and thus produce a single
> slice classified image. As I am a newbe on Fiji's API and the javadoc site
> is currently down, I would greatly appreciate your input on this. I am also
> wandering how it would be possible to just apply such a classifier to other
> unseen images. How do I recompute the feature vector and feed it straight
> to the classifier?
>
> thanks a bunch,
> Dionysios
>
> I am sorry to clutter my post with a long script:
>
> import ij.*;
> import ij.process.*;
> import trainableSegmentation.*;
> import hr.irb.fastRandomForest.*;
>
> // training input image (it could be a stack or a single 2D image)
> opener = new Opener();
> image =
> opener.openImage("/home/dionysis/fermi/media/data/CNH/cnh_multispectral/Denmark_field_1/original
> images/15/multi-spectral.tif");
> //IJ.run(image, "8-bit", "");
> IJ.run(image, "32-bit", "");
>
>
>
> //*** Custom features***
>
> -----------------------------------
>
>
>
> // the FeatureStackArray contains a FeatureStack for every slice in our
> original image
> featuresArray = new FeatureStackArray(1, 1, 16, false, 1, 19, null);
>
> customstack = new ImageStack(image.getWidth(), image.getHeight());
> for (slice = 1; slice <= image.getStackSize(); slice++) {
> image.setSlice(slice);
>
> features = new FeatureStack(image.getProcessor().duplicate());
> features.addGaussianBlur(20);
> features.addLaplacian(5);
> features.addHessian(2);
> //features.show();
>
>
> customstack.addSlice("original "+slice, features.getProcessor(1));
> customstack.addSlice("gaussianstd "+slice,
> features.getProcessor(2));
> customstack.addSlice("Laplacianstd "+slice,
> features.getProcessor(3));
>
> }
>
> //customFeaturesImage = new ImagePlus("features_std&cust", customstack);
> //customFeaturesImage.show();
>
>
>
> // create new feature stack with the custom features
> stack2featstack = new FeatureStack( image );
> stack2featstack.setStack( customstack );
> stack2featstack.show();
> //print(stack2featstack.getSize());
>
>
>
> // create Weka segmentator
> seg = new WekaSegmentation(image);
>
> // number of samples to use per slice
> nSamplesToUse = 1000;
>
>
> // Classifier
> // In this case we use a Fast Random Forest
> rf = new FastRandomForest();
> // Number of trees in the forest
> rf.setNumTrees(100);
> // Number of features per tree
> rf.setNumFeatures(0);
> // Seed
> rf.setSeed( (new java.util.Random()).nextInt() );
>
> // set classifier
> seg.setClassifier(rf);
>
> //Set feature stack array to the weka segmentation object
> featuresArray.set(stack2featstack, 0);
> seg.setFeatureStackArray(featuresArray);
>
>
> // corresponding binary labels
> labels =
> IJ.openImage("/home/dionysis/fermi/media/data/CNH/cnh_multispectral/Denmark_field_1/original
> images/15/MOG.bmp");
> labelstack = new ImageStack(labels.getWidth(), labels.getHeight());
> for (slice = 1; slice <= image.getStackSize(); slice++) {
> labelstack.addSlice("MOG "+slice,
> labels.getProcessor().duplicate());
> }
> labelstackimage = new ImagePlus("Labels_std&cust", labelstack);
> labelstackimage.show();
>
>
>
> // Add (balanced) labels
> seg.addRandomBalancedBinaryData(image, labelstackimage, "class 2", "class
> 1", nSamplesToUse);
> //seg.addBinaryData(image, labels, "class 2", "class 1");
>
>
> // Train classifier
> seg.trainClassifier();
>
> //save Classifier
> seg.saveClassifier("/home/dionysis/Desktop/testclassifier.model");
>
>
> // Open test image
> image =
> IJ.openImage("/home/dionysis/fermi/media/data/CNH/cnh_multispectral/Denmark_field_1/original
> images/15/multi-spectral.tif");
> IJ.run(image, "32-bit", "");
> IJ.log("Test image loaded");
> // Apply trained classifier to test image and get probabilities
> prob = seg.applyClassifier(image, 0, true );
> IJ.log("Classifier applied");
>
> // Display results
> prob.setTitle( "Probability maps of test image" );
> IJ.log("Showing results");
> prob.show();
>
> --
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