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https://github.com/opencv/opencv_contrib.git
synced 2025-10-17 07:04:18 +08:00
minor update of ar_hmdb code
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@@ -10,7 +10,7 @@ _`"HMDB: A Large Human Motion Database"`: http://serre-lab.clps.brown.edu/resour
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1. From link above download dataset files: hmdb51_org.rar & test_train_splits.rar.
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2. Unpack them.
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2. Unpack them. Unpack all archives from directory: hmdb51_org/ and remove them.
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3. To load data run: ./opencv/build/bin/example_datasets_ar_hmdb -p=/home/user/path_to_unpacked_folders/
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@@ -25,7 +25,7 @@ To run this benchmark execute:
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./opencv/build/bin/example_datasets_ar_hmdb_benchmark -p=/home/user/path_to_unpacked_folders/
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(precomputed features should be unpacked in the same folder: /home/user/path_to_unpacked_folders/hmdb51_org_stips/)
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(precomputed features should be unpacked in the same folder: /home/user/path_to_unpacked_folders/hmdb51_org_stips/. Also unpack all archives from directory: hmdb51_org_stips/ and remove them.)
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**References:**
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@@ -56,8 +56,9 @@ namespace datasets
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struct AR_hmdbObj : public Object
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{
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int id;
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std::string name;
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std::vector<std::string> videoNames;
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std::string videoName;
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};
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class CV_EXPORTS AR_hmdb : public Dataset
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@@ -74,15 +74,14 @@ int main(int argc, char *argv[])
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// And its size.
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int numSplits = dataset->getNumSplits();
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printf("splits number: %u\n", numSplits);
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printf("train 1 size: %u\n", (unsigned int)dataset->getTrain(1).size());
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printf("test 1 size: %u\n", (unsigned int)dataset->getTest(1).size());
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AR_hmdbObj *example = static_cast<AR_hmdbObj *>(dataset->getTrain(1)[0].get());
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printf("name: %s\n", example->name.c_str());
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vector<string> &videoNames = example->videoNames;
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printf("size: %u\n", (unsigned int)videoNames.size());
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for (vector<string>::iterator it=videoNames.begin(); it!=videoNames.end(); ++it)
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{
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printf("%s\n", (*it).c_str());
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}
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printf("first image:\n");
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printf("action id: %u\n", example->id);
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printf("action: %s\n", example->name.c_str());
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printf("file: %s\n", example->videoName.c_str());
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return 0;
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}
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@@ -58,23 +58,6 @@ using namespace cv::datasets;
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using namespace cv::flann;
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using namespace cv::ml;
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unsigned int getNumFiles(vector< Ptr<Object> > &curr);
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unsigned int getNumFiles(vector< Ptr<Object> > &curr)
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{
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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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vector<string> &videoNames = example->videoNames;
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for (vector<string>::iterator it=videoNames.begin(); it!=videoNames.end(); ++it)
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{
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numFiles++;
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}
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}
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return numFiles;
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}
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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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@@ -87,36 +70,31 @@ void fillData(const string &path, vector< Ptr<Object> > &curr, Index &flann_inde
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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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vector<string> &videoNames = example->videoNames;
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for (vector<string>::iterator it=videoNames.begin(); it!=videoNames.end(); ++it)
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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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string featuresFile = *it + ".txt";
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string featuresFullPath = path + "hmdb51_org_stips/" + example->name + "/" + featuresFile;
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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) = i;
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numFiles++;
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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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@@ -148,43 +126,35 @@ int main(int argc, char *argv[])
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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 numTrainFiles = getNumFiles(curr);
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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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vector<string> &videoNames = example->videoNames;
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for (vector<string>::iterator it=videoNames.begin(); it!=videoNames.end(); ++it)
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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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string featuresFile = *it + ".txt";
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string featuresFullPath = path + "hmdb51_org_stips/" + example->name + "/" + featuresFile;
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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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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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for (unsigned int j=0; j<descriptorNum; ++j)
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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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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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@@ -202,6 +172,7 @@ int main(int argc, char *argv[])
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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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@@ -232,7 +203,7 @@ int main(int argc, char *argv[])
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// prepare to predict
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curr = dataset->getTest(currSplit);
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unsigned int numTestFiles = getNumFiles(curr);
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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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@@ -262,7 +233,6 @@ int main(int argc, char *argv[])
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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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@@ -42,6 +42,8 @@
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#include "opencv2/datasets/ar_hmdb.hpp"
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#include "opencv2/datasets/util.hpp"
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#include <map>
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namespace cv
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{
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namespace datasets
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@@ -63,26 +65,9 @@ private:
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void loadDataset(const string &path);
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void loadAction(const string &fileName, vector<string> &train_, vector<string> &test_);
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map<string, int> actionsId;
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};
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void AR_hmdbImp::loadAction(const string &fileName, vector<string> &train_, vector<string> &test_)
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{
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ifstream infile(fileName.c_str());
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string video, label;
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while (infile >> video >> label)
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{
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if ("1"==label)
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{
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train_.push_back(video);
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} else
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if ("2"==label)
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{
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test_.push_back(video);
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}
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}
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}
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/*AR_hmdbImp::AR_hmdbImp(const string &path, int number)
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{
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loadDataset(path, number);
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@@ -120,18 +105,40 @@ void AR_hmdbImp::loadDatasetSplit(const string &path, int number)
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getDirList(pathDataset, fileNames);
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for (vector<string>::iterator it=fileNames.begin(); it!=fileNames.end(); ++it)
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{
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Ptr<AR_hmdbObj> currTrain(new AR_hmdbObj);
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Ptr<AR_hmdbObj> currTest(new AR_hmdbObj);
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currTrain->name = *it;
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currTest->name = *it;
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train.back().push_back(currTrain);
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test.back().push_back(currTest);
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string &action = *it;
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map<string, int>::iterator itId = actionsId.find(action);
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int id;
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if (itId == actionsId.end())
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{
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actionsId.insert(make_pair(action, actionsId.size()));
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id = actionsId.size();
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} else
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{
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id = (*itId).second;
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}
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char tmp[2];
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sprintf(tmp, "%u", number+1);
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string fileName(pathSplit + currTrain->name + "_test_split" + tmp + ".txt");
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loadAction(fileName, currTrain->videoNames, currTest->videoNames);
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string fileName(pathSplit + action + "_test_split" + tmp + ".txt");
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ifstream infile(fileName.c_str());
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string video, label;
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while (infile >> video >> label)
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{
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Ptr<AR_hmdbObj> curr(new AR_hmdbObj);
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curr->id = id;
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curr->name = action;
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curr->videoName = video;
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if ("1"==label)
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{
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train.back().push_back(curr);
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} else
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if ("2"==label)
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{
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test.back().push_back(curr);
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}
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}
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}
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}
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