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451 lines
16 KiB
C++
451 lines
16 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// Intel License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000, Intel Corporation, all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of Intel Corporation may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#include "test_precomp.hpp"
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using namespace std;
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using namespace cv;
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static
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void defaultDistribs( Mat& means, vector<Mat>& covs, int type=CV_32FC1 )
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{
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float mp0[] = {0.0f, 0.0f}, cp0[] = {0.67f, 0.0f, 0.0f, 0.67f};
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float mp1[] = {5.0f, 0.0f}, cp1[] = {1.0f, 0.0f, 0.0f, 1.0f};
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float mp2[] = {1.0f, 5.0f}, cp2[] = {1.0f, 0.0f, 0.0f, 1.0f};
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means.create(3, 2, type);
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Mat m0( 1, 2, CV_32FC1, mp0 ), c0( 2, 2, CV_32FC1, cp0 );
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Mat m1( 1, 2, CV_32FC1, mp1 ), c1( 2, 2, CV_32FC1, cp1 );
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Mat m2( 1, 2, CV_32FC1, mp2 ), c2( 2, 2, CV_32FC1, cp2 );
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means.resize(3), covs.resize(3);
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Mat mr0 = means.row(0);
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m0.convertTo(mr0, type);
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c0.convertTo(covs[0], type);
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Mat mr1 = means.row(1);
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m1.convertTo(mr1, type);
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c1.convertTo(covs[1], type);
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Mat mr2 = means.row(2);
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m2.convertTo(mr2, type);
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c2.convertTo(covs[2], type);
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}
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// generate points sets by normal distributions
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static
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void generateData( Mat& data, Mat& labels, const vector<int>& sizes, const Mat& _means, const vector<Mat>& covs, int dataType, int labelType )
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{
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vector<int>::const_iterator sit = sizes.begin();
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int total = 0;
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for( ; sit != sizes.end(); ++sit )
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total += *sit;
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CV_Assert( _means.rows == (int)sizes.size() && covs.size() == sizes.size() );
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CV_Assert( !data.empty() && data.rows == total );
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CV_Assert( data.type() == dataType );
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labels.create( data.rows, 1, labelType );
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randn( data, Scalar::all(-1.0), Scalar::all(1.0) );
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vector<Mat> means(sizes.size());
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for(int i = 0; i < _means.rows; i++)
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means[i] = _means.row(i);
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vector<Mat>::const_iterator mit = means.begin(), cit = covs.begin();
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int bi, ei = 0;
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sit = sizes.begin();
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for( int p = 0, l = 0; sit != sizes.end(); ++sit, ++mit, ++cit, l++ )
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{
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bi = ei;
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ei = bi + *sit;
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assert( mit->rows == 1 && mit->cols == data.cols );
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assert( cit->rows == data.cols && cit->cols == data.cols );
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for( int i = bi; i < ei; i++, p++ )
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{
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Mat r = data.row(i);
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r = r * (*cit) + *mit;
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if( labelType == CV_32FC1 )
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labels.at<float>(p, 0) = (float)l;
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else if( labelType == CV_32SC1 )
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labels.at<int>(p, 0) = l;
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else
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{
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CV_DbgAssert(0);
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}
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}
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}
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}
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static
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int maxIdx( const vector<int>& count )
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{
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int idx = -1;
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int maxVal = -1;
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vector<int>::const_iterator it = count.begin();
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for( int i = 0; it != count.end(); ++it, i++ )
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{
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if( *it > maxVal)
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{
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maxVal = *it;
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idx = i;
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}
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}
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assert( idx >= 0);
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return idx;
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}
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static
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bool getLabelsMap( const Mat& labels, const vector<int>& sizes, vector<int>& labelsMap, bool checkClusterUniq=true )
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{
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size_t total = 0, nclusters = sizes.size();
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for(size_t i = 0; i < sizes.size(); i++)
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total += sizes[i];
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assert( !labels.empty() );
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assert( labels.total() == total && (labels.cols == 1 || labels.rows == 1));
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assert( labels.type() == CV_32SC1 || labels.type() == CV_32FC1 );
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bool isFlt = labels.type() == CV_32FC1;
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labelsMap.resize(nclusters);
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vector<bool> buzy(nclusters, false);
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int startIndex = 0;
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for( size_t clusterIndex = 0; clusterIndex < sizes.size(); clusterIndex++ )
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{
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vector<int> count( nclusters, 0 );
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for( int i = startIndex; i < startIndex + sizes[clusterIndex]; i++)
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{
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int lbl = isFlt ? (int)labels.at<float>(i) : labels.at<int>(i);
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CV_Assert(lbl < (int)nclusters);
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count[lbl]++;
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CV_Assert(count[lbl] < (int)total);
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}
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startIndex += sizes[clusterIndex];
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int cls = maxIdx( count );
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CV_Assert( !checkClusterUniq || !buzy[cls] );
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labelsMap[clusterIndex] = cls;
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buzy[cls] = true;
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}
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if(checkClusterUniq)
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{
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for(size_t i = 0; i < buzy.size(); i++)
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if(!buzy[i])
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return false;
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}
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return true;
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}
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static
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bool calcErr( const Mat& labels, const Mat& origLabels, const vector<int>& sizes, float& err, bool labelsEquivalent, bool checkClusterUniq )
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{
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err = 0;
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CV_Assert( !labels.empty() && !origLabels.empty() );
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CV_Assert( labels.rows == 1 || labels.cols == 1 );
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CV_Assert( origLabels.rows == 1 || origLabels.cols == 1 );
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CV_Assert( labels.total() == origLabels.total() );
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CV_Assert( labels.type() == CV_32SC1 || labels.type() == CV_32FC1 );
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CV_Assert( origLabels.type() == labels.type() );
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vector<int> labelsMap;
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bool isFlt = labels.type() == CV_32FC1;
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if( !labelsEquivalent )
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{
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if( !getLabelsMap( labels, sizes, labelsMap, checkClusterUniq ) )
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return false;
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for( int i = 0; i < labels.rows; i++ )
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if( isFlt )
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err += labels.at<float>(i) != labelsMap[(int)origLabels.at<float>(i)] ? 1.f : 0.f;
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else
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err += labels.at<int>(i) != labelsMap[origLabels.at<int>(i)] ? 1.f : 0.f;
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}
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else
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{
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for( int i = 0; i < labels.rows; i++ )
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if( isFlt )
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err += labels.at<float>(i) != origLabels.at<float>(i) ? 1.f : 0.f;
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else
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err += labels.at<int>(i) != origLabels.at<int>(i) ? 1.f : 0.f;
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}
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err /= (float)labels.rows;
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return true;
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}
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///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
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class CV_CvEMTest : public cvtest::BaseTest
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{
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public:
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CV_CvEMTest() {}
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protected:
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virtual void run( int start_from );
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int runCase( int caseIndex, const CvEMParams& params,
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const cv::Mat& trainData, const cv::Mat& trainLabels,
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const cv::Mat& testData, const cv::Mat& testLabels,
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const vector<int>& sizes);
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};
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int CV_CvEMTest::runCase( int caseIndex, const CvEMParams& params,
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const cv::Mat& trainData, const cv::Mat& trainLabels,
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const cv::Mat& testData, const cv::Mat& testLabels,
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const vector<int>& sizes )
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{
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int code = cvtest::TS::OK;
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cv::Mat labels;
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float err;
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CvEM em;
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em.train( trainData, Mat(), params, &labels );
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// check train error
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if( !calcErr( labels, trainLabels, sizes, err , false, false ) )
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{
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ts->printf( cvtest::TS::LOG, "Case index %i : Bad output labels.\n", caseIndex );
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code = cvtest::TS::FAIL_INVALID_OUTPUT;
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}
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else if( err > 0.006f )
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{
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ts->printf( cvtest::TS::LOG, "Case index %i : Bad accuracy (%f) on train data.\n", caseIndex, err );
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code = cvtest::TS::FAIL_BAD_ACCURACY;
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}
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// check test error
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labels.create( testData.rows, 1, CV_32SC1 );
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for( int i = 0; i < testData.rows; i++ )
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{
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Mat sample = testData.row(i);
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labels.at<int>(i,0) = (int)em.predict( sample, 0 );
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}
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if( !calcErr( labels, testLabels, sizes, err, false, false ) )
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{
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ts->printf( cvtest::TS::LOG, "Case index %i : Bad output labels.\n", caseIndex );
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code = cvtest::TS::FAIL_INVALID_OUTPUT;
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}
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else if( err > 0.006f )
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{
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ts->printf( cvtest::TS::LOG, "Case index %i : Bad accuracy (%f) on test data.\n", caseIndex, err );
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code = cvtest::TS::FAIL_BAD_ACCURACY;
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}
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return code;
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}
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void CV_CvEMTest::run( int /*start_from*/ )
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{
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int sizesArr[] = { 500, 700, 800 };
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int pointsCount = sizesArr[0]+ sizesArr[1] + sizesArr[2];
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// Points distribution
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Mat means;
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vector<Mat> covs;
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defaultDistribs( means, covs );
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// train data
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Mat trainData( pointsCount, 2, CV_32FC1 ), trainLabels;
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vector<int> sizes( sizesArr, sizesArr + sizeof(sizesArr) / sizeof(sizesArr[0]) );
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generateData( trainData, trainLabels, sizes, means, covs, CV_32FC1, CV_32SC1 );
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// test data
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Mat testData( pointsCount, 2, CV_32FC1 ), testLabels;
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generateData( testData, testLabels, sizes, means, covs, CV_32FC1, CV_32SC1 );
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CvEMParams params;
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params.nclusters = 3;
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Mat probs(trainData.rows, params.nclusters, CV_32FC1, cv::Scalar(1));
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CvMat probsHdr = probs;
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params.probs = &probsHdr;
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Mat weights(1, params.nclusters, CV_32FC1, cv::Scalar(1));
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CvMat weightsHdr = weights;
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params.weights = &weightsHdr;
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CvMat meansHdr = means;
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params.means = &meansHdr;
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std::vector<CvMat> covsHdrs(params.nclusters);
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std::vector<const CvMat*> covsPtrs(params.nclusters);
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for(int i = 0; i < params.nclusters; i++)
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{
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covsHdrs[i] = covs[i];
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covsPtrs[i] = &covsHdrs[i];
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}
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params.covs = &covsPtrs[0];
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int code = cvtest::TS::OK;
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int caseIndex = 0;
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{
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params.start_step = cv::EM::START_AUTO_STEP;
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params.cov_mat_type = cv::EM::COV_MAT_GENERIC;
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int currCode = runCase(caseIndex++, params, trainData, trainLabels, testData, testLabels, sizes);
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code = currCode == cvtest::TS::OK ? code : currCode;
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}
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{
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params.start_step = cv::EM::START_AUTO_STEP;
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params.cov_mat_type = cv::EM::COV_MAT_DIAGONAL;
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int currCode = runCase(caseIndex++, params, trainData, trainLabels, testData, testLabels, sizes);
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code = currCode == cvtest::TS::OK ? code : currCode;
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}
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{
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params.start_step = cv::EM::START_AUTO_STEP;
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params.cov_mat_type = cv::EM::COV_MAT_SPHERICAL;
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int currCode = runCase(caseIndex++, params, trainData, trainLabels, testData, testLabels, sizes);
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code = currCode == cvtest::TS::OK ? code : currCode;
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}
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{
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params.start_step = cv::EM::START_M_STEP;
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params.cov_mat_type = cv::EM::COV_MAT_GENERIC;
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int currCode = runCase(caseIndex++, params, trainData, trainLabels, testData, testLabels, sizes);
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code = currCode == cvtest::TS::OK ? code : currCode;
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}
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{
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params.start_step = cv::EM::START_M_STEP;
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params.cov_mat_type = cv::EM::COV_MAT_DIAGONAL;
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int currCode = runCase(caseIndex++, params, trainData, trainLabels, testData, testLabels, sizes);
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code = currCode == cvtest::TS::OK ? code : currCode;
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}
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{
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params.start_step = cv::EM::START_M_STEP;
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params.cov_mat_type = cv::EM::COV_MAT_SPHERICAL;
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int currCode = runCase(caseIndex++, params, trainData, trainLabels, testData, testLabels, sizes);
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code = currCode == cvtest::TS::OK ? code : currCode;
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}
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{
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params.start_step = cv::EM::START_E_STEP;
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params.cov_mat_type = cv::EM::COV_MAT_GENERIC;
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int currCode = runCase(caseIndex++, params, trainData, trainLabels, testData, testLabels, sizes);
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code = currCode == cvtest::TS::OK ? code : currCode;
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}
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{
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params.start_step = cv::EM::START_E_STEP;
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params.cov_mat_type = cv::EM::COV_MAT_DIAGONAL;
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int currCode = runCase(caseIndex++, params, trainData, trainLabels, testData, testLabels, sizes);
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code = currCode == cvtest::TS::OK ? code : currCode;
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}
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{
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params.start_step = cv::EM::START_E_STEP;
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params.cov_mat_type = cv::EM::COV_MAT_SPHERICAL;
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int currCode = runCase(caseIndex++, params, trainData, trainLabels, testData, testLabels, sizes);
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code = currCode == cvtest::TS::OK ? code : currCode;
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}
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ts->set_failed_test_info( code );
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}
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class CV_CvEMTest_SaveLoad : public cvtest::BaseTest {
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public:
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CV_CvEMTest_SaveLoad() {}
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protected:
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virtual void run( int /*start_from*/ )
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{
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int code = cvtest::TS::OK;
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Mat samples = Mat(3,1,CV_32F);
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samples.at<float>(0,0) = 1;
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samples.at<float>(1,0) = 2;
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samples.at<float>(2,0) = 3;
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Mat labels(samples.rows, 1, CV_32S);
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CvEMParams params;
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params.nclusters = 2;
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CvMat samples_c = samples, labels_c = labels;
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CvEM em(&samples_c, 0, params, &labels_c);
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Mat firstResult(samples.rows, 1, CV_32FC1);
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for( int i = 0; i < samples.rows; i++)
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firstResult.at<float>(i) = em.predict( samples.row(i) );
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// Write out
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string filename = tempfile() + ".xml";
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{
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FileStorage fs = FileStorage(filename, FileStorage::WRITE);
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try
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{
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em.write(fs.fs, "em");
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}
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catch(...)
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{
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ts->printf( cvtest::TS::LOG, "Crash in write method.\n" );
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ts->set_failed_test_info( cvtest::TS::FAIL_EXCEPTION );
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}
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}
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em.clear();
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// Read in
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{
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FileStorage fs = FileStorage(filename, FileStorage::READ);
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CV_Assert(fs.isOpened());
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FileNode fn = fs["em"];
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try
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{
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em.read(fs.fs, (CvFileNode*)fn.node);
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}
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catch(...)
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{
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ts->printf( cvtest::TS::LOG, "Crash in read method.\n" );
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ts->set_failed_test_info( cvtest::TS::FAIL_EXCEPTION );
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}
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}
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remove( filename.c_str() );
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int errCaseCount = 0;
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for( int i = 0; i < samples.rows; i++)
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errCaseCount = std::abs(em.predict(samples.row(i)) - firstResult.at<float>(i)) < FLT_EPSILON ? 0 : 1;
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if( errCaseCount > 0 )
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{
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ts->printf( cvtest::TS::LOG, "Different prediction results before writeing and after reading (errCaseCount=%d).\n", errCaseCount );
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code = cvtest::TS::FAIL_BAD_ACCURACY;
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}
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ts->set_failed_test_info( code );
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}
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};
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TEST(Legacy_CvEM, accuracy) { CV_CvEMTest test; test.safe_run(); }
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TEST(Legacy_CvEM, save_load) { CV_CvEMTest_SaveLoad test; test.safe_run(); }
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