/*M/////////////////////////////////////////////////////////////////////////////////////// // // IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. // // By downloading, copying, installing or using the software you agree to this license. // If you do not agree to this license, do not download, install, // copy or use the software. // // // License Agreement // For Open Source Computer Vision Library // // Copyright (C) 2014, Itseez Inc, all rights reserved. // Third party copyrights are property of their respective owners. // // Redistribution and use in source and binary forms, with or without modification, // are permitted provided that the following conditions are met: // // * Redistribution's of source code must retain the above copyright notice, // this list of conditions and the following disclaimer. // // * Redistribution's in binary form must reproduce the above copyright notice, // this list of conditions and the following disclaimer in the documentation // and/or other materials provided with the distribution. // // * The name of the copyright holders may not be used to endorse or promote products // derived from this software without specific prior written permission. // // This software is provided by the copyright holders and contributors "as is" and // any express or implied warranties, including, but not limited to, the implied // warranties of merchantability and fitness for a particular purpose are disclaimed. // In no event shall the Itseez Inc or contributors be liable for any direct, // indirect, incidental, special, exemplary, or consequential damages // (including, but not limited to, procurement of substitute goods or services; // loss of use, data, or profits; or business interruption) however caused // and on any theory of liability, whether in contract, strict liability, // or tort (including negligence or otherwise) arising in any way out of // the use of this software, even if advised of the possibility of such damage. // //M*/ #include "opencv2/datasets/ar_hmdb.hpp" #include "opencv2/datasets/util.hpp" #include <opencv2/core.hpp> #include <opencv2/flann.hpp> #include <opencv2/ml.hpp> #include <cstdio> #include <string> #include <vector> #include <fstream> using namespace std; using namespace cv; using namespace cv::datasets; using namespace cv::flann; using namespace cv::ml; void fillData(const string &path, vector< Ptr<Object> > &curr, Index &flann_index, Mat1f &data, Mat1i &labels); void fillData(const string &path, vector< Ptr<Object> > &curr, Index &flann_index, Mat1f &data, Mat1i &labels) { const unsigned int descriptorNum = 162; Mat1f sample(1, descriptorNum); Mat1i nresps(1, 1); Mat1f dists(1, 1); unsigned int numFiles = 0; for (unsigned int i=0; i<curr.size(); ++i) { AR_hmdbObj *example = static_cast<AR_hmdbObj *>(curr[i].get()); string featuresFullPath = path + "hmdb51_org_stips/" + example->name + "/" + example->videoName + ".txt"; ifstream infile(featuresFullPath.c_str()); string line; // skip header for (unsigned int j=0; j<3; ++j) { getline(infile, line); } while (getline(infile, line)) { // 7 skip, hog+hof: 72+90 read vector<string> elems; split(line, elems, '\t'); for (unsigned int j=0; j<descriptorNum; ++j) { sample(0, j) = (float)atof(elems[j+7].c_str()); } flann_index.knnSearch(sample, nresps, dists, 1, SearchParams()); data(numFiles, nresps(0, 0)) ++; } labels(numFiles, 0) = example->id; numFiles++; } } int main(int argc, char *argv[]) { const char *keys = "{ help h usage ? | | show this message }" "{ path p |true| path to dataset }"; CommandLineParser parser(argc, argv, keys); string path(parser.get<string>("path")); if (parser.has("help") || path=="true") { parser.printMessage(); return -1; } // loading dataset Ptr<AR_hmdb> dataset = AR_hmdb::create(); dataset->load(path); int numSplits = dataset->getNumSplits(); printf("splits number: %u\n", numSplits); const unsigned int descriptorNum = 162; const unsigned int clusterNum = 4000; const unsigned int sampleNum = 5613856; // max for all 3 splits vector<double> res; for (int currSplit=0; currSplit<numSplits; ++currSplit) { Mat1f samples(sampleNum, descriptorNum); unsigned int currSample = 0; vector< Ptr<Object> > &curr = dataset->getTrain(currSplit); unsigned int numFeatures = 0; for (unsigned int i=0; i<curr.size(); ++i) { AR_hmdbObj *example = static_cast<AR_hmdbObj *>(curr[i].get()); string featuresFullPath = path + "hmdb51_org_stips/" + example->name + "/" + example->videoName + ".txt"; ifstream infile(featuresFullPath.c_str()); string line; // skip header for (unsigned int j=0; j<3; ++j) { getline(infile, line); } while (getline(infile, line)) { numFeatures++; if (currSample < sampleNum) { // 7 skip, hog+hof: 72+90 read vector<string> elems; split(line, elems, '\t'); for (unsigned int j=0; j<descriptorNum; ++j) { samples(currSample, j) = (float)atof(elems[j+7].c_str()); } currSample++; } } } printf("split %u, train features number: %u, samples number: %u\n", currSplit, numFeatures, currSample); // clustering Mat1f centers(clusterNum, descriptorNum); ::cvflann::KMeansIndexParams kmean_params; unsigned int resultClusters = hierarchicalClustering< L2<float> >(samples, centers, kmean_params); if (resultClusters < clusterNum) { centers = centers.rowRange(Range(0, resultClusters)); } Index flann_index(centers, KDTreeIndexParams()); printf("resulted clusters number: %u\n", resultClusters); unsigned int numTrainFiles = curr.size(); Mat1f trainData(numTrainFiles, resultClusters); Mat1i trainLabels(numTrainFiles, 1); for (unsigned int i=0; i<numTrainFiles; ++i) { for (unsigned int j=0; j<resultClusters; ++j) { trainData(i, j) = 0; } } printf("calculating train histograms\n"); fillData(path, curr, flann_index, trainData, trainLabels); printf("train svm\n"); Ptr<SVM> svm = SVM::create(); svm->setType(SVM::C_SVC); svm->setKernel(SVM::POLY); //SVM::RBF; svm->setDegree(0.5); svm->setGamma(1); svm->setCoef0(1); svm->setC(1); svm->setNu(0.5); svm->setP(0); svm->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS, 1000, 0.01)); svm->train(trainData, ROW_SAMPLE, trainLabels); // prepare to predict curr = dataset->getTest(currSplit); unsigned int numTestFiles = curr.size(); Mat1f testData(numTestFiles, resultClusters); Mat1i testLabels(numTestFiles, 1); // ground true for (unsigned int i=0; i<numTestFiles; ++i) { for (unsigned int j=0; j<resultClusters; ++j) { testData(i, j) = 0; } } printf("calculating test histograms\n"); fillData(path, curr, flann_index, testData, testLabels); printf("predicting\n"); Mat1f testPredicted(numTestFiles, 1); svm->predict(testData, testPredicted); unsigned int correct = 0; for (unsigned int i=0; i<numTestFiles; ++i) { if ((int)testPredicted(i, 0) == testLabels(i, 0)) { correct++; } } double accuracy = 1.0*correct/numTestFiles; printf("correctly recognized actions: %f\n", accuracy); res.push_back(accuracy); } double accuracy = 0.0; for (unsigned int i=0; i<res.size(); ++i) { accuracy += res[i]; } printf("average: %f\n", accuracy/res.size()); return 0; }