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Open Source Computer Vision Library
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327 lines
12 KiB
327 lines
12 KiB
/*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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void defaultDistribs( vector<Mat>& means, vector<Mat>& covs ) |
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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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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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m0.copyTo(means[0]), c0.copyTo(covs[0]); |
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m1.copyTo(means[1]), c1.copyTo(covs[1]); |
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m2.copyTo(means[2]), c2.copyTo(covs[2]); |
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} |
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// generate points sets by normal distributions |
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void generateData( Mat& data, Mat& labels, const vector<int>& sizes, const vector<Mat>& means, const vector<Mat>& covs, 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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assert( means.size() == sizes.size() && covs.size() == sizes.size() ); |
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assert( !data.empty() && data.rows == total ); |
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assert( data.type() == CV_32FC1 ); |
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labels.create( data.rows, 1, labelType ); |
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randn( data, Scalar::all(0.0), Scalar::all(1.0) ); |
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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(1, data.cols, CV_32FC1, data.ptr<float>(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 |
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labels.at<int>(p, 0) = l; |
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} |
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} |
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} |
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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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bool getLabelsMap( const Mat& labels, const vector<int>& sizes, vector<int>& labelsMap ) |
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{ |
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int total = 0, setCount = (int)sizes.size(); |
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vector<int>::const_iterator sit = sizes.begin(); |
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for( ; sit != sizes.end(); ++sit ) |
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total += *sit; |
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assert( !labels.empty() ); |
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assert( labels.rows == total && labels.cols == 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(setCount); |
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vector<int>::iterator lmit = labelsMap.begin(); |
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vector<bool> buzy(setCount, false); |
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int bi, ei = 0; |
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for( sit = sizes.begin(); sit != sizes.end(); ++sit, ++lmit ) |
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{ |
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vector<int> count( setCount, 0 ); |
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bi = ei; |
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ei = bi + *sit; |
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if( isFlt ) |
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{ |
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for( int i = bi; i < ei; i++ ) |
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count[(int)labels.at<float>(i, 0)]++; |
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} |
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else |
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{ |
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for( int i = bi; i < ei; i++ ) |
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count[labels.at<int>(i, 0)]++; |
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} |
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*lmit = maxIdx( count ); |
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if( buzy[*lmit] ) |
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return false; |
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buzy[*lmit] = true; |
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} |
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return true; |
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} |
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float calcErr( const Mat& labels, const Mat& origLabels, const vector<int>& sizes, bool labelsEquivalent = true ) |
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{ |
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int err = 0; |
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assert( !labels.empty() && !origLabels.empty() ); |
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assert( labels.cols == 1 && origLabels.cols == 1 ); |
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assert( labels.rows == origLabels.rows ); |
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assert( labels.type() == origLabels.type() ); |
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assert( labels.type() == CV_32SC1 || labels.type() == CV_32FC1 ); |
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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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getLabelsMap( labels, sizes, labelsMap ); |
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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, 0) != labelsMap[(int)origLabels.at<float>(i, 0)]; |
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else |
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err += labels.at<int>(i, 0) != labelsMap[origLabels.at<int>(i, 0)]; |
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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, 0) != origLabels.at<float>(i, 0); |
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else |
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err += labels.at<int>(i, 0) != origLabels.at<int>(i, 0); |
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} |
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return (float)err / (float)labels.rows; |
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} |
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//-------------------------------------------------------------------------------------------- |
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class CV_KMeansTest : public cvtest::BaseTest { |
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public: |
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CV_KMeansTest() {} |
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protected: |
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virtual void run( int start_from ); |
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}; |
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void CV_KMeansTest::run( int /*start_from*/ ) |
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{ |
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const int iters = 100; |
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int sizesArr[] = { 5000, 7000, 8000 }; |
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int pointsCount = sizesArr[0]+ sizesArr[1] + sizesArr[2]; |
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Mat data( pointsCount, 2, CV_32FC1 ), labels; |
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vector<int> sizes( sizesArr, sizesArr + sizeof(sizesArr) / sizeof(sizesArr[0]) ); |
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vector<Mat> means, covs; |
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defaultDistribs( means, covs ); |
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generateData( data, labels, sizes, means, covs, CV_32SC1 ); |
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int code = cvtest::TS::OK; |
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Mat bestLabels; |
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// 1. flag==KMEANS_PP_CENTERS |
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kmeans( data, 3, bestLabels, TermCriteria( TermCriteria::COUNT, iters, 0.0), 0, KMEANS_PP_CENTERS, None() ); |
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if( calcErr( bestLabels, labels, sizes, false ) > 0.01f ) |
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{ |
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ts->printf( cvtest::TS::LOG, "bad accuracy if flag==KMEANS_PP_CENTERS" ); |
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code = cvtest::TS::FAIL_BAD_ACCURACY; |
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} |
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// 2. flag==KMEANS_RANDOM_CENTERS |
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kmeans( data, 3, bestLabels, TermCriteria( TermCriteria::COUNT, iters, 0.0), 0, KMEANS_RANDOM_CENTERS, None() ); |
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if( calcErr( bestLabels, labels, sizes, false ) > 0.01f ) |
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{ |
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ts->printf( cvtest::TS::LOG, "bad accuracy if flag==KMEANS_PP_CENTERS" ); |
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code = cvtest::TS::FAIL_BAD_ACCURACY; |
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} |
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// 3. flag==KMEANS_USE_INITIAL_LABELS |
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labels.copyTo( bestLabels ); |
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RNG rng; |
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for( int i = 0; i < 0.5f * pointsCount; i++ ) |
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bestLabels.at<int>( rng.next() % pointsCount, 0 ) = rng.next() % 3; |
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kmeans( data, 3, bestLabels, TermCriteria( TermCriteria::COUNT, iters, 0.0), 0, KMEANS_USE_INITIAL_LABELS, None() ); |
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if( calcErr( bestLabels, labels, sizes, false ) > 0.01f ) |
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{ |
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ts->printf( cvtest::TS::LOG, "bad accuracy if flag==KMEANS_PP_CENTERS" ); |
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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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class CV_KNearestTest : public cvtest::BaseTest { |
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public: |
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CV_KNearestTest() {} |
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protected: |
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virtual void run( int start_from ); |
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}; |
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void CV_KNearestTest::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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// 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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vector<Mat> means, covs; |
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defaultDistribs( means, covs ); |
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generateData( trainData, trainLabels, sizes, means, covs, CV_32FC1 ); |
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// test data |
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Mat testData( pointsCount, 2, CV_32FC1 ), testLabels, bestLabels; |
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generateData( testData, testLabels, sizes, means, covs, CV_32FC1 ); |
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int code = cvtest::TS::OK; |
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KNearest knearest; |
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knearest.train( trainData, trainLabels ); |
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knearest.find_nearest( testData, 4, &bestLabels ); |
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if( calcErr( bestLabels, testLabels, sizes, true ) > 0.01f ) |
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{ |
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ts->printf( cvtest::TS::LOG, "bad accuracy on test data" ); |
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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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class CV_EMTest : public cvtest::BaseTest { |
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public: |
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CV_EMTest() {} |
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protected: |
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virtual void run( int start_from ); |
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}; |
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void CV_EMTest::run( int /*start_from*/ ) |
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{ |
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int sizesArr[] = { 5000, 7000, 8000 }; |
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int pointsCount = sizesArr[0]+ sizesArr[1] + sizesArr[2]; |
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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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vector<Mat> means, covs; |
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defaultDistribs( means, covs ); |
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generateData( trainData, trainLabels, sizes, means, covs, CV_32SC1 ); |
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// test data |
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Mat testData( pointsCount, 2, CV_32FC1 ), testLabels, bestLabels; |
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generateData( testData, testLabels, sizes, means, covs, CV_32SC1 ); |
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int code = cvtest::TS::OK; |
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ExpectationMaximization em; |
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CvEMParams params; |
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params.nclusters = 3; |
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em.train( trainData, Mat(), params, &bestLabels ); |
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// check train error |
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if( calcErr( bestLabels, trainLabels, sizes, true ) > 0.002f ) |
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{ |
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ts->printf( cvtest::TS::LOG, "bad accuracy on train data" ); |
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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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bestLabels.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( 1, testData.cols, CV_32FC1, testData.ptr<float>(i)); |
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bestLabels.at<int>(i,0) = (int)em.predict( sample, 0 ); |
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} |
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if( calcErr( bestLabels, testLabels, sizes, true ) > 0.005f ) |
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{ |
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ts->printf( cvtest::TS::LOG, "bad accuracy on test data" ); |
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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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TEST(ML_KMeans, accuracy) { CV_KMeansTest test; test.safe_run(); } |
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TEST(ML_KNearest, accuracy) { CV_KNearestTest test; test.safe_run(); } |
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TEST(ML_EMTest, accuracy) { CV_EMTest test; test.safe_run(); }
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