/*
* Copyright (c) 2011-2016, 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.descriptor;
import boofcv.struct.feature.*;
/**
* Series of simple functions for computing difference distance measures between two descriptors.
*
* @author Peter Abeles
*/
public class DescriptorDistance {
/**
* Returns the Euclidean distance (L2-norm) between the two descriptors.
*
* @param a First descriptor
* @param b Second descriptor
* @return Euclidean distance
*/
public static double euclidean(TupleDesc_F64 a, TupleDesc_F64 b) {
final int N = a.value.length;
double total = 0;
for( int i = 0; i < N; i++ ) {
double d = a.value[i]-b.value[i];
total += d*d;
}
return Math.sqrt(total);
}
/**
* Returns the Euclidean distance squared between the two descriptors.
*
* @param a First descriptor
* @param b Second descriptor
* @return Euclidean distance squared
*/
public static double euclideanSq(TupleDesc_F64 a, TupleDesc_F64 b) {
final int N = a.value.length;
double total = 0;
for( int i = 0; i < N; i++ ) {
double d = a.value[i]-b.value[i];
total += d*d;
}
return total;
}
/**
* Returns the Euclidean distance squared between the two descriptors.
*
* @param a First descriptor
* @param b Second descriptor
* @return Euclidean distance squared
*/
public static double euclideanSq(TupleDesc_F32 a, TupleDesc_F32 b) {
final int N = a.value.length;
float total = 0;
for( int i = 0; i < N; i++ ) {
double d = a.value[i]-b.value[i];
total += d*d;
}
return total;
}
/**
* Correlation score
*
* @param a First descriptor
* @param b Second descriptor
* @return Correlation score
*/
public static double correlation( TupleDesc_F64 a, TupleDesc_F64 b) {
final int N = a.value.length;
double total = 0;
for( int i = 0; i < N; i++ ) {
total += a.value[i]*b.value[i];
}
return total;
}
/**
* <p>
* Normalized cross correlation (NCC) computed using a faster technique.<br>
* <br>
* NCC = sum(a[i]*b[i]) / (N*sigma_a * sigma_b)<br>
* where a[i] = I[i]-mean(a), I[i] is the image pixel intensity around the feature, and N is the number of
* elements.
* </p>
*
* @param a First descriptor
* @param b Second descriptor
* @return NCC score
*/
public static double ncc(NccFeature a, NccFeature b) {
double top = 0;
final int N = a.value.length;
for( int i = 0; i < N; i++ ) {
top += a.value[i]*b.value[i];
}
return top/(N*a.sigma * b.sigma);
}
/**
* Sum of absolute difference (SAD) score
*
* @param a First descriptor
* @param b Second descriptor
* @return SAD score
*/
public static int sad(TupleDesc_U8 a, TupleDesc_U8 b) {
int total = 0;
for( int i = 0; i < a.value.length; i++ ) {
total += Math.abs( (a.value[i] & 0xFF) - (b.value[i] & 0xFF));
}
return total;
}
/**
* Sum of absolute difference (SAD) score
*
* @param a First descriptor
* @param b Second descriptor
* @return SAD score
*/
public static int sad(TupleDesc_S8 a, TupleDesc_S8 b) {
int total = 0;
for( int i = 0; i < a.value.length; i++ ) {
total += Math.abs( a.value[i] - b.value[i]);
}
return total;
}
/**
* Sum of absolute difference (SAD) score
*
* @param a First descriptor
* @param b Second descriptor
* @return SAD score
*/
public static float sad(TupleDesc_F32 a, TupleDesc_F32 b) {
float total = 0;
for( int i = 0; i < a.value.length; i++ ) {
total += Math.abs( a.value[i] - b.value[i]);
}
return total;
}
/**
* Sum of absolute difference (SAD) score
*
* @param a First descriptor
* @param b Second descriptor
* @return SAD score
*/
public static double sad(TupleDesc_F64 a, TupleDesc_F64 b) {
double total = 0;
for( int i = 0; i < a.value.length; i++ ) {
total += Math.abs( a.value[i] - b.value[i]);
}
return total;
}
/**
* Computes the hamming distance between two binary feature descriptors
*
* @param a First variable
* @param b Second variable
* @return The hamming distance
*/
public static int hamming( TupleDesc_B a, TupleDesc_B b ) {
int score = 0;
final int N = a.data.length;
for( int i = 0; i < N; i++ ) {
score += hamming(a.data[i] ^ b.data[i]);
}
return score;
}
/**
* <p>Computes the hamming distance. A bit = 0 is a match and 1 is not match<p>
*
* Based on code snippet from <a href="http://graphics.stanford.edu/~seander/bithacks.html">Sean Eron Anderson Bit Twiddling Hacks</a>.
*
* @param val Hamming encoding
* @return The hamming distance
*/
public static int hamming( int val ) {
int c;
int v = val;
v = v - ((v >> 1) & 0x55555555);
v = (v & 0x33333333) + ((v >> 2) & 0x33333333);
c = ((v + (v >> 4) & 0xF0F0F0F) * 0x1010101) >> 24;
return c;
}
}