315 lines
9.9 KiB
C++
315 lines
9.9 KiB
C++
/***********************************************************************
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* Software License Agreement (BSD License)
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*
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* Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
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* Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
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*
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* THE BSD LICENSE
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions
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* are met:
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*
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* 1. Redistributions of source code must retain the above copyright
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* notice, this list of conditions and the following disclaimer.
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* 2. Redistributions in binary form must reproduce the above copyright
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* notice, this list of conditions and the following disclaimer in the
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* documentation and/or other materials provided with the distribution.
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*
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* THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
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* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
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* OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
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* IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
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* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
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* NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
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* DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
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* THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
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* THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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*************************************************************************/
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#include "index_testing.h"
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#include "result_set.h"
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#include "timer.h"
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#include "logger.h"
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#include "dist.h"
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#include "common.h"
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#include <algorithm>
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#include <math.h>
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#include <string.h>
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#include <stdlib.h>
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namespace cvflann
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{
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const float SEARCH_EPS = 0.001f;
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int countCorrectMatches(int* neighbors, int* groundTruth, int n)
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{
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int count = 0;
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for (int i=0;i<n;++i) {
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for (int k=0;k<n;++k) {
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if (neighbors[i]==groundTruth[k]) {
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count++;
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break;
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}
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}
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}
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return count;
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}
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float computeDistanceRaport(const Matrix<float>& inputData, float* target, int* neighbors, int* groundTruth, int veclen, int n)
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{
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float* target_end = target + veclen;
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float ret = 0;
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for (int i=0;i<n;++i) {
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float den = (float)flann_dist(target,target_end, inputData[groundTruth[i]]);
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float num = (float)flann_dist(target,target_end, inputData[neighbors[i]]);
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// printf("den=%g,num=%g\n",den,num);
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if (den==0 && num==0) {
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ret += 1;
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} else {
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ret += num/den;
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}
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}
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return ret;
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}
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float search_with_ground_truth(NNIndex& index, const Matrix<float>& inputData, const Matrix<float>& testData, const Matrix<int>& matches, int nn, int checks, float& time, float& dist, int skipMatches)
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{
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if (matches.cols<nn) {
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logger.info("matches.cols=%d, nn=%d\n",matches.cols,nn);
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throw FLANNException("Ground truth is not computed for as many neighbors as requested");
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}
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KNNResultSet resultSet(nn+skipMatches);
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SearchParams searchParams(checks);
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int correct = 0;
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float distR = 0;
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StartStopTimer t;
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int repeats = 0;
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while (t.value<0.2) {
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repeats++;
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t.start();
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correct = 0;
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distR = 0;
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for (int i = 0; i < testData.rows; i++) {
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float* target = testData[i];
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resultSet.init(target, testData.cols);
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index.findNeighbors(resultSet,target, searchParams);
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int* neighbors = resultSet.getNeighbors();
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neighbors = neighbors+skipMatches;
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correct += countCorrectMatches(neighbors,matches[i], nn);
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distR += computeDistanceRaport(inputData, target,neighbors,matches[i], testData.cols, nn);
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}
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t.stop();
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}
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time = (float)(t.value/repeats);
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float precicion = (float)correct/(nn*testData.rows);
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dist = distR/(testData.rows*nn);
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logger.info("%8d %10.4g %10.5g %10.5g %10.5g\n",
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checks, precicion, time, 1000.0 * time / testData.rows, dist);
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return precicion;
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}
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void search_for_neighbors(NNIndex& index, const Matrix<float>& testset, Matrix<int>& result, Matrix<float>& dists, const SearchParams& searchParams, int skip)
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{
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assert(testset.rows == result.rows);
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int nn = result.cols;
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KNNResultSet resultSet(nn+skip);
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for (int i = 0; i < testset.rows; i++) {
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float* target = testset[i];
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resultSet.init(target, testset.cols);
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index.findNeighbors(resultSet,target, searchParams);
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int* neighbors = resultSet.getNeighbors();
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float* distances = resultSet.getDistances();
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memcpy(result[i], neighbors+skip, nn*sizeof(int));
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memcpy(dists[i], distances+skip, nn*sizeof(float));
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}
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}
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float test_index_checks(NNIndex& index, const Matrix<float>& inputData, const Matrix<float>& testData, const Matrix<int>& matches, int checks, float& precision, int nn, int skipMatches)
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{
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logger.info(" Nodes Precision(%) Time(s) Time/vec(ms) Mean dist\n");
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logger.info("---------------------------------------------------------\n");
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float time = 0;
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float dist = 0;
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precision = search_with_ground_truth(index, inputData, testData, matches, nn, checks, time, dist, skipMatches);
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return time;
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}
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float test_index_precision(NNIndex& index, const Matrix<float>& inputData, const Matrix<float>& testData, const Matrix<int>& matches,
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float precision, int& checks, int nn, int skipMatches)
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{
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logger.info(" Nodes Precision(%) Time(s) Time/vec(ms) Mean dist\n");
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logger.info("---------------------------------------------------------\n");
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int c2 = 1;
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float p2;
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int c1 = 1;
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float p1;
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float time;
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float dist;
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p2 = search_with_ground_truth(index, inputData, testData, matches, nn, c2, time, dist, skipMatches);
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if (p2>precision) {
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logger.info("Got as close as I can\n");
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checks = c2;
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return time;
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}
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while (p2<precision) {
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c1 = c2;
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p1 = p2;
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c2 *=2;
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p2 = search_with_ground_truth(index, inputData, testData, matches, nn, c2, time, dist, skipMatches);
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}
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int cx;
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float realPrecision;
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if (fabs(p2-precision)>SEARCH_EPS) {
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logger.info("Start linear estimation\n");
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// after we got to values in the vecinity of the desired precision
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// use linear approximation get a better estimation
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cx = (c1+c2)/2;
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realPrecision = search_with_ground_truth(index, inputData, testData, matches, nn, cx, time, dist, skipMatches);
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while (fabs(realPrecision-precision)>SEARCH_EPS) {
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if (realPrecision<precision) {
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c1 = cx;
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}
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else {
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c2 = cx;
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}
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cx = (c1+c2)/2;
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if (cx==c1) {
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logger.info("Got as close as I can\n");
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break;
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}
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realPrecision = search_with_ground_truth(index, inputData, testData, matches, nn, cx, time, dist, skipMatches);
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}
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c2 = cx;
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p2 = realPrecision;
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} else {
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logger.info("No need for linear estimation\n");
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cx = c2;
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realPrecision = p2;
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}
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checks = cx;
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return time;
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}
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float test_index_precisions(NNIndex& index, const Matrix<float>& inputData, const Matrix<float>& testData, const Matrix<int>& matches,
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float* precisions, int precisions_length, int nn, int skipMatches, float maxTime)
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{
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// make sure precisions array is sorted
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sort(precisions, precisions+precisions_length);
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int pindex = 0;
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float precision = precisions[pindex];
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logger.info(" Nodes Precision(%) Time(s) Time/vec(ms) Mean dist");
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logger.info("---------------------------------------------------------");
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int c2 = 1;
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float p2;
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int c1 = 1;
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float p1;
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float time;
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float dist;
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p2 = search_with_ground_truth(index, inputData, testData, matches, nn, c2, time, dist, skipMatches);
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// if precision for 1 run down the tree is already
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// better then some of the requested precisions, then
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// skip those
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while (precisions[pindex]<p2 && pindex<precisions_length) {
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pindex++;
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}
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if (pindex==precisions_length) {
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logger.info("Got as close as I can\n");
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return time;
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}
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for (int i=pindex;i<precisions_length;++i) {
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precision = precisions[i];
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while (p2<precision) {
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c1 = c2;
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p1 = p2;
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c2 *=2;
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p2 = search_with_ground_truth(index, inputData, testData, matches, nn, c2, time, dist, skipMatches);
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if (maxTime> 0 && time > maxTime && p2<precision) return time;
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}
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int cx;
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float realPrecision;
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if (fabs(p2-precision)>SEARCH_EPS) {
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logger.info("Start linear estimation\n");
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// after we got to values in the vecinity of the desired precision
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// use linear approximation get a better estimation
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cx = (c1+c2)/2;
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realPrecision = search_with_ground_truth(index, inputData, testData, matches, nn, cx, time, dist, skipMatches);
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while (fabs(realPrecision-precision)>SEARCH_EPS) {
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if (realPrecision<precision) {
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c1 = cx;
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}
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else {
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c2 = cx;
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}
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cx = (c1+c2)/2;
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if (cx==c1) {
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logger.info("Got as close as I can\n");
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break;
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}
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realPrecision = search_with_ground_truth(index, inputData, testData, matches, nn, cx, time, dist, skipMatches);
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}
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c2 = cx;
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p2 = realPrecision;
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} else {
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logger.info("No need for linear estimation\n");
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cx = c2;
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realPrecision = p2;
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}
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}
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return time;
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}
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}
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