246 lines
8.3 KiB
C++
246 lines
8.3 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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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2014, Itseez Inc, 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 the copyright holders 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 Itseez Inc 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 "opencv2/datasets/ar_hmdb.hpp"
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#include "opencv2/datasets/util.hpp"
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#include <opencv2/core.hpp>
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#include <opencv2/flann.hpp>
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#include <opencv2/ml.hpp>
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#include <cstdio>
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#include <string>
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#include <vector>
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#include <fstream>
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using namespace std;
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using namespace cv;
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using namespace cv::datasets;
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using namespace cv::flann;
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using namespace cv::ml;
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void fillData(const string &path, vector< Ptr<Object> > &curr, Index &flann_index, Mat1f &data, Mat1i &labels);
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void fillData(const string &path, vector< Ptr<Object> > &curr, Index &flann_index, Mat1f &data, Mat1i &labels)
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{
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const unsigned int descriptorNum = 162;
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Mat1f sample(1, descriptorNum);
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Mat1i nresps(1, 1);
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Mat1f dists(1, 1);
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unsigned int numFiles = 0;
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for (unsigned int i=0; i<curr.size(); ++i)
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{
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AR_hmdbObj *example = static_cast<AR_hmdbObj *>(curr[i].get());
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string featuresFullPath = path + "hmdb51_org_stips/" + example->name + "/" + example->videoName + ".txt";
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ifstream infile(featuresFullPath.c_str());
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string line;
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// skip header
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for (unsigned int j=0; j<3; ++j)
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{
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getline(infile, line);
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}
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while (getline(infile, line))
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{
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// 7 skip, hog+hof: 72+90 read
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vector<string> elems;
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split(line, elems, '\t');
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for (unsigned int j=0; j<descriptorNum; ++j)
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{
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sample(0, j) = (float)atof(elems[j+7].c_str());
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}
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flann_index.knnSearch(sample, nresps, dists, 1, SearchParams());
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data(numFiles, nresps(0, 0)) ++;
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}
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labels(numFiles, 0) = example->id;
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numFiles++;
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}
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}
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int main(int argc, char *argv[])
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{
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const char *keys =
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"{ help h usage ? | | show this message }"
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"{ path p |true| path to dataset }";
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CommandLineParser parser(argc, argv, keys);
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string path(parser.get<string>("path"));
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if (parser.has("help") || path=="true")
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{
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parser.printMessage();
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return -1;
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}
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// loading dataset
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Ptr<AR_hmdb> dataset = AR_hmdb::create();
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dataset->load(path);
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int numSplits = dataset->getNumSplits();
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printf("splits number: %u\n", numSplits);
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const unsigned int descriptorNum = 162;
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const unsigned int clusterNum = 4000;
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const unsigned int sampleNum = 5613856; // max for all 3 splits
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vector<double> res;
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for (int currSplit=0; currSplit<numSplits; ++currSplit)
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{
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Mat1f samples(sampleNum, descriptorNum);
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unsigned int currSample = 0;
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vector< Ptr<Object> > &curr = dataset->getTrain(currSplit);
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unsigned int numFeatures = 0;
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for (unsigned int i=0; i<curr.size(); ++i)
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{
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AR_hmdbObj *example = static_cast<AR_hmdbObj *>(curr[i].get());
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string featuresFullPath = path + "hmdb51_org_stips/" + example->name + "/" + example->videoName + ".txt";
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ifstream infile(featuresFullPath.c_str());
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string line;
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// skip header
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for (unsigned int j=0; j<3; ++j)
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{
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getline(infile, line);
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}
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while (getline(infile, line))
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{
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numFeatures++;
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if (currSample < sampleNum)
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{
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// 7 skip, hog+hof: 72+90 read
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vector<string> elems;
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split(line, elems, '\t');
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for (unsigned int j=0; j<descriptorNum; ++j)
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{
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samples(currSample, j) = (float)atof(elems[j+7].c_str());
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}
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currSample++;
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}
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}
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}
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printf("split %u, train features number: %u, samples number: %u\n", currSplit, numFeatures, currSample);
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// clustering
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Mat1f centers(clusterNum, descriptorNum);
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::cvflann::KMeansIndexParams kmean_params;
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unsigned int resultClusters = hierarchicalClustering< L2<float> >(samples, centers, kmean_params);
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if (resultClusters < clusterNum)
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{
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centers = centers.rowRange(Range(0, resultClusters));
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}
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Index flann_index(centers, KDTreeIndexParams());
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printf("resulted clusters number: %u\n", resultClusters);
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unsigned int numTrainFiles = curr.size();
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Mat1f trainData(numTrainFiles, resultClusters);
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Mat1i trainLabels(numTrainFiles, 1);
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for (unsigned int i=0; i<numTrainFiles; ++i)
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{
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for (unsigned int j=0; j<resultClusters; ++j)
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{
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trainData(i, j) = 0;
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}
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}
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printf("calculating train histograms\n");
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fillData(path, curr, flann_index, trainData, trainLabels);
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printf("train svm\n");
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Ptr<SVM> svm = SVM::create();
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svm->setType(SVM::C_SVC);
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svm->setKernel(SVM::POLY); //SVM::RBF;
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svm->setDegree(0.5);
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svm->setGamma(1);
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svm->setCoef0(1);
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svm->setC(1);
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svm->setNu(0.5);
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svm->setP(0);
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svm->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS, 1000, 0.01));
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svm->train(trainData, ROW_SAMPLE, trainLabels);
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// prepare to predict
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curr = dataset->getTest(currSplit);
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unsigned int numTestFiles = curr.size();
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Mat1f testData(numTestFiles, resultClusters);
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Mat1i testLabels(numTestFiles, 1); // ground true
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for (unsigned int i=0; i<numTestFiles; ++i)
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{
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for (unsigned int j=0; j<resultClusters; ++j)
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{
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testData(i, j) = 0;
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}
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}
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printf("calculating test histograms\n");
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fillData(path, curr, flann_index, testData, testLabels);
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printf("predicting\n");
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Mat1f testPredicted(numTestFiles, 1);
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svm->predict(testData, testPredicted);
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unsigned int correct = 0;
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for (unsigned int i=0; i<numTestFiles; ++i)
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{
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if ((int)testPredicted(i, 0) == testLabels(i, 0))
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{
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correct++;
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}
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}
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double accuracy = 1.0*correct/numTestFiles;
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printf("correctly recognized actions: %f\n", accuracy);
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res.push_back(accuracy);
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}
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double accuracy = 0.0;
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for (unsigned int i=0; i<res.size(); ++i)
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{
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accuracy += res[i];
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}
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printf("average: %f\n", accuracy/res.size());
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return 0;
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}
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