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/*
* Copyright (c) 2021, Peter Abeles. All Rights Reserved.
*
* This file is part of BoofCV (http://boofcv.org).
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package boofcv.alg.bow;
import boofcv.struct.feature.TupleDesc_F64;
import org.ddogleg.clustering.AssignCluster;
import org.ddogleg.clustering.ComputeClusters;
import org.ddogleg.clustering.misc.ListAccessor;
import org.ddogleg.struct.LArrayAccessor;
import java.util.ArrayList;
import java.util.List;
/**
* Finds clusters of {@link TupleDesc_F64} which can be used to identify frequent features, a.k.a words.
* Internally it uses {@link org.ddogleg.clustering.ComputeClusters} and simply extracts the inner array
* from the tuple.
*
* @author Peter Abeles
*/
// TODO make completely generic? Not F64 specific?
public class ClusterVisualWords {
// cluster finding algorithm
ComputeClusters computeClusters;
// inner arrays extracted from the input features
List tuples = new ArrayList<>();
LArrayAccessor accessor = new ListAccessor<>(tuples,
( a, b ) -> System.arraycopy(a, 0, b, 0, a.length), double[].class);
/**
* Constructor which configures the cluster finder.
*
* @param computeClusters Cluster finding algorithm
* @param randomSeed Seed for random number generator
*/
public ClusterVisualWords( ComputeClusters computeClusters, long randomSeed ) {
this.computeClusters = computeClusters;
computeClusters.initialize(randomSeed);
}
/**
* Add a feature to the list.
*
* @param feature image feature. Reference to inner array is saved.
*/
public void addReference( TupleDesc_F64 feature ) {
tuples.add(feature.getData());
}
/**
* Clusters the list of features into the specified number of words
*
* @param numberOfWords Number of words/clusters it should find
*/
public void process( int numberOfWords ) {
computeClusters.process(accessor, numberOfWords);
}
/**
* Returns a transform from point to cluster.
*/
public AssignCluster getAssignment() {
return computeClusters.getAssignment();
}
}
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