diff --git a/visioncw3/src/main/java/uk/ac/soton/ecs/ik1g19/App.java b/visioncw3/src/main/java/uk/ac/soton/ecs/ik1g19/App.java
index 41e666879dfcb66d805b6bef3d3cf6b5bd212954..e061f9b06d1f389c7a131882c819e1259d4f365b 100644
--- a/visioncw3/src/main/java/uk/ac/soton/ecs/ik1g19/App.java
+++ b/visioncw3/src/main/java/uk/ac/soton/ecs/ik1g19/App.java
@@ -17,11 +17,18 @@ import org.openimaj.feature.FloatFV;
import org.openimaj.feature.SparseIntFV;
import org.openimaj.feature.local.LocalFeatureImpl;
import org.openimaj.feature.local.Location;
+import org.openimaj.feature.local.SpatialLocation;
import org.openimaj.feature.local.data.LocalFeatureListDataSource;
import org.openimaj.feature.local.list.LocalFeatureList;
+import org.openimaj.feature.local.list.MemoryLocalFeatureList;
import org.openimaj.image.FImage;
+import org.openimaj.image.Image;
import org.openimaj.image.ImageUtilities;
+import org.openimaj.image.analyser.ImageAnalyser;
import org.openimaj.image.feature.local.aggregate.BagOfVisualWords;
+import org.openimaj.image.pixel.sampling.RectangleSampler;
+import org.openimaj.image.processor.SinglebandImageProcessor;
+import org.openimaj.math.geometry.shape.Rectangle;
import org.openimaj.ml.annotation.linear.LiblinearAnnotator;
import org.openimaj.ml.clustering.FloatCentroidsResult;
import org.openimaj.ml.clustering.assignment.HardAssigner;
@@ -29,6 +36,7 @@ import org.openimaj.ml.clustering.kmeans.FloatKMeans;
import org.openimaj.util.pair.IntFloatPair;
import java.util.ArrayList;
+import java.util.Iterator;
import java.util.List;
import java.util.Map;
@@ -45,7 +53,7 @@ public class App {
VFSListDataset<FImage> testingData;
try {
trainingData = new VFSGroupDataset<FImage>(
- "zip:C:/Users/isaac/Documents/GitHub Repos/visioncw3/visioncw3/data/training.zip", ImageUtilities.FIMAGE_READER);
+ "zip:http://comp3204.ecs.soton.ac.uk/cw/training.zip", ImageUtilities.FIMAGE_READER);
testingData = new VFSListDataset<FImage>(
"zip:C:/Users/isaac/Documents/GitHub Repos/visioncw3/visioncw3/data/testing.zip", ImageUtilities.FIMAGE_READER);
} catch (Exception e) {
@@ -62,7 +70,7 @@ public class App {
//analyse patches of 8x8 sizes
- PatchFeature patch = new PatchFeature(8);
+ PatchFeature patch = new PatchFeature(8f,4f, 4f);
//train quantiser on a sample of the data set
@@ -74,11 +82,13 @@ public class App {
FeatureExtractor<SparseIntFV, FImage> extractor = new PatchExtractor(patch, assigner);
+ //train classification
LiblinearAnnotator<FImage, String> ann = new LiblinearAnnotator<FImage, String>(
extractor, LiblinearAnnotator.Mode.MULTICLASS, SolverType.L2R_L2LOSS_SVC, 1.0, 0.00001);
ann.train(splits.getTrainingDataset());
+ //evaluation
ClassificationEvaluator<CMResult<String>, String, FImage> eval =
new ClassificationEvaluator<CMResult<String>, String, FImage>(
ann, splits.getTestDataset(), new CMAnalyser<FImage, String>(CMAnalyser.Strategy.SINGLE));
@@ -90,39 +100,75 @@ public class App {
}
+
+ /**
+ * @desc trains the quantiser using a sample of data and densely sampled patches
+ * @param sample sample of image data
+ * @param patch patch sampler
+ * @return trained assigner
+ */
static HardAssigner<float[], float[], IntFloatPair> trainQuantiser(
Dataset<FImage> sample, PatchFeature<FImage> patch)
{
List<LocalFeatureList<LocalFeatureImpl<Location, FloatFV>>> allkeys = new ArrayList<>();
+
for (FImage rec : sample) {
FImage img = rec;
+ //densely sample patches and store
patch.analyseImage(img);
allkeys.add(patch.getPatchKeypoints());
}
+
+ //take first 1000 samples
if (allkeys.size() > 10000)
allkeys = allkeys.subList(0, 10000);
+
+ //k-means clustering
FloatKMeans km = FloatKMeans.createExact(500);
- DataSource<float[]> datasource = new LocalFeatureListDataSource<LocalFeatureImpl<Location, FloatFV>, float[]>(allkeys);
+ DataSource<float[]> datasource =
+ new LocalFeatureListDataSource<LocalFeatureImpl<Location, FloatFV>, float[]>(allkeys);
FloatCentroidsResult result = km.cluster(datasource);
+
return result.defaultHardAssigner();
}
}
+
+
+
+
+/**
+ * @desc feature extractor to sample patches as visual words
+ */
class PatchExtractor implements FeatureExtractor<SparseIntFV, FImage> {
PatchFeature<FImage> patch;
HardAssigner<float[], float[], IntFloatPair> assigner;
+
+
+ /**
+ * @desc construct an extractor using a patch sampler and assigner
+ * @param patch a dense patch sampler
+ * @param assigner the trained quantiser
+ */
public PatchExtractor(PatchFeature<FImage> patch, HardAssigner<float[], float[], IntFloatPair> assigner)
{
this.patch = patch;
this.assigner = assigner;
}
+
+
+ /**
+ * @desc extract visual words from image
+ * @param object FImage to extract from
+ * @return extracted visual words as a SpareIntFV
+ */
public SparseIntFV extractFeature(FImage object) {
FImage image = object.getImage();
patch.analyseImage(image);
@@ -131,4 +177,72 @@ class PatchExtractor implements FeatureExtractor<SparseIntFV, FImage> {
return bovw.aggregate(patch.getPatchKeypoints());
}
+}
+
+
+
+
+
+
+/**
+ * @desc image analyser for densely sampling patches across an image
+ */
+class PatchFeature <IMAGE extends Image<Float, IMAGE> & SinglebandImageProcessor.Processable<Float, FImage, IMAGE>> implements ImageAnalyser<IMAGE> {
+ float size;
+ float stepx;
+ float stepy;
+ private LocalFeatureList<LocalFeatureImpl<Location, FloatFV>> patchList;
+
+
+
+ /**
+ * @desc construct a patch sampler with given size and step
+ * @param size the size of the patch (a x a)
+ * @param stepx size of horizontal step
+ * @param stepy size of vertical step
+ */
+ public PatchFeature(float size, float stepx, float stepy) {
+ this.size = size;
+ this.stepx = stepx;
+ this.stepy = stepy;
+ patchList = new MemoryLocalFeatureList<>();
+ }
+
+
+
+ public void analyseImage(IMAGE image) {
+ //clear patches of previous analysis
+ patchList.clear();
+
+
+ //sample patches using sliding window provided by RectangleSampler
+ RectangleSampler sampler = new RectangleSampler(image, stepx, stepy, size, size);
+
+
+ Iterator<Rectangle> rects = sampler.allRectangles().iterator();
+ while (rects.hasNext()) {
+ Rectangle rect = rects.next();
+
+ IMAGE rImg = image.extractROI(rect).normalise();
+
+ Float[] pvF = new Float[rImg.getWidth()* rImg.getHeight()];
+ rImg.getPixelVector(pvF);
+
+ float[] pvf = new float[rImg.getWidth()* rImg.getHeight()];
+ for (int i = 0; i < pvF.length; i++) { pvf[i] = pvF[i].floatValue(); }
+
+ FloatFV fv = new FloatFV(pvf);
+
+ patchList.add(new LocalFeatureImpl<Location, FloatFV>(new SpatialLocation(rect.x, rect.y),fv));
+ }
+ }
+
+
+ /**
+ * @desc returns densely sampled patches of analysed image
+ * @return densely sampled patches as LocalFeatureList
+ */
+ public LocalFeatureList<LocalFeatureImpl<Location, FloatFV>> getPatchKeypoints() {
+ return patchList;
+ }
}
\ No newline at end of file
diff --git a/visioncw3/src/main/java/uk/ac/soton/ecs/ik1g19/PatchFeature.java b/visioncw3/src/main/java/uk/ac/soton/ecs/ik1g19/PatchFeature.java
index c5dba4ec28d285b1d9c50e38b44cbd45f96df1e5..deba08aa537b87fa6b8d42a409d1c147acb9da2d 100644
--- a/visioncw3/src/main/java/uk/ac/soton/ecs/ik1g19/PatchFeature.java
+++ b/visioncw3/src/main/java/uk/ac/soton/ecs/ik1g19/PatchFeature.java
@@ -23,40 +23,54 @@ import org.openimaj.math.geometry.shape.Rectangle;
import org.openimaj.util.array.ArrayUtils;
import org.openimaj.util.pair.IntObjectPair;
-public class PatchFeature <IMAGE extends Image<Float, IMAGE> & Processable<Float, FImage, IMAGE>> implements ImageAnalyser<IMAGE> {
- float size;
- private LocalFeatureList<LocalFeatureImpl<Location, FloatFV>> patchList;
-
- public PatchFeature(float size) {
- this.size = size;
- patchList = new MemoryLocalFeatureList<>();
- }
-
- public void analyseImage(IMAGE image) {
- patchList.clear();
-
- RectangleSampler sampler = new RectangleSampler(image, 4f, 4f, size, size);
-
- Iterator<Rectangle> rects = sampler.allRectangles().iterator();
-
- while (rects.hasNext()) {
- Rectangle rect = rects.next();
-
- IMAGE rImg = image.extractROI(rect).normalise();
-
- Float[] pvF = new Float[rImg.getWidth()* rImg.getHeight()];
- rImg.getPixelVector(pvF);
-
- float[] pvf = new float[rImg.getWidth()* rImg.getHeight()];
- for (int i = 0; i < pvF.length; i++) { pvf[i] = pvF[i].floatValue(); }
-
- FloatFV fv = new FloatFV(pvf);
-
- patchList.add(new LocalFeatureImpl<Location, FloatFV>(new SpatialLocation(rect.x, rect.y),fv));
- }
- }
-
- public LocalFeatureList<LocalFeatureImpl<Location, FloatFV>> getPatchKeypoints() {
- return patchList;
- }
-}
+///**
+// * @desc image analyser for densely sampling patches across an image
+// */
+//public class PatchFeature <IMAGE extends Image<Float, IMAGE> & Processable<Float, FImage, IMAGE>> implements ImageAnalyser<IMAGE> {
+// float size;
+// float stepx;
+// float stepy;
+// private LocalFeatureList<LocalFeatureImpl<Location, FloatFV>> patchList;
+//
+// /**
+// *
+// * @param size
+// * @param stepx
+// * @param stepy
+// */
+// public PatchFeature(float size, float stepx, float stepy) {
+// this.size = size;
+// this.stepx = stepx;
+// this.stepy = stepy;
+// patchList = new MemoryLocalFeatureList<>();
+// }
+//
+//
+// public void analyseImage(IMAGE image) {
+// patchList.clear();
+//
+// RectangleSampler sampler = new RectangleSampler(image, stepx, stepy, size, size);
+//
+// Iterator<Rectangle> rects = sampler.allRectangles().iterator();
+//
+// while (rects.hasNext()) {
+// Rectangle rect = rects.next();
+//
+// IMAGE rImg = image.extractROI(rect).normalise();
+//
+// Float[] pvF = new Float[rImg.getWidth()* rImg.getHeight()];
+// rImg.getPixelVector(pvF);
+//
+// float[] pvf = new float[rImg.getWidth()* rImg.getHeight()];
+// for (int i = 0; i < pvF.length; i++) { pvf[i] = pvF[i].floatValue(); }
+//
+// FloatFV fv = new FloatFV(pvf);
+//
+// patchList.add(new LocalFeatureImpl<Location, FloatFV>(new SpatialLocation(rect.x, rect.y),fv));
+// }
+// }
+//
+// public LocalFeatureList<LocalFeatureImpl<Location, FloatFV>> getPatchKeypoints() {
+// return patchList;
+// }
+//}