OpenCV_4.2.0/opencv_contrib-4.2.0/modules/datasets/samples/ar_hmdb_benchmark.cpp

246 lines
8.3 KiB
C++

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#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;
}