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BoofCV is an open source Java library for real-time computer vision and robotics applications.
/*
* Copyright (c) 2011-2013, 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.factory.weights;
import boofcv.alg.weights.*;
/**
* Factory for creating sample weight functions of different types.
*
* @author Peter Abeles
*/
public class FactoryWeights {
/**
* Creates a weight function for the provided distributions.
*
* @param type Which type of distribution should be used
* @param param Distribution parameters. For uniform this is the maximum distance.
* Guassian its the standard deviation.
* @param safe If true it will then check the input distance to see if it matches.
* @return WeightDistance_F32
*/
public static WeightDistance_F32 distance( WeightType type , float param , boolean safe ) {
if( safe )
throw new IllegalArgumentException("Safe distributons not implemented yet");
switch( type ) {
case GAUSSIAN_SQ:
return new WeightDistanceSqGaussian_F32(param);
case UNIFORM:
return new WeightDistanceUniform_F32(param);
}
throw new IllegalArgumentException("Unknown type "+type);
}
/**
* Creates a weight function for the provided distributions.
*
* @param type Which type of distribution should be used
* @param safe If true it will then check the input distance to see if it matches.
* @return WeightDistance_F32
*/
public static WeightPixel_F32 pixel( WeightType type , boolean safe ) {
if( safe )
throw new IllegalArgumentException("Safe distributons not implemented yet");
switch( type ) {
case GAUSSIAN_SQ:
return new WeightPixelGaussian_F32();
case UNIFORM:
return new WeightPixelUniform_F32();
}
throw new IllegalArgumentException("Unknown type "+type);
}
}
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