void ExtractFeaturesFromEachFrame(const std::string & video_source,
std::vector<Features> features_collection,
const cv::Size & window_size,
bool scale) {
std::srand(std::time(0));
cv::VideoCapture video(video_source);
if(!video.isOpened()) return;
cv::HOGDescriptor hog;
hog.winSize=window_size;
cv::Mat frame;
cv::Mat extracted_frame;
Features features;
while(video.read(frame)) {
if(scale) cv::resize(frame, extracted_frame, window_size);
else {
// Extract frame
cv::Rect patch;
patch.width=window_size.width;
patch.height=window_size.height;
patch.x=std::rand()%(frame.cols-window_size.width);
patch.y=std::rand()%(frame.rows-window_size.height);
extracted_frame=((frame)(patch)).clone();
features_collection.push_back(Features((features_collection.size()==0?0:features_collection.front().size())));
ComputeFeatures(extracted_frame, features_collection.back(), window_size);
}
features.clear();
}
}
Example(int a = 0) : ans(a) {
feat.clear();
feat.resize(MAX, 0);
}
/**
* Extracts the features from the buffers for the given sensor code.
* If useSpecSize is set to true only the last specSize frames in the buffer will be taken into account.
*/
Features GestureManagement::extractFeatures(int sensor, int specSize, bool useSpecSize) {
if (config->getInt("verbosity") > 2) {
cout << "* FEATURE EXTRACTION *************" << endl;
cout << "Sensor: " << sensor << endl;
}
Features f;
f.clear();
// for every measurement (accX, accY, ...)
vector<int> indices = config->getInts("sensor data indices");
int fullsize = buffers.at(sensor)->size();
int size = fullsize;
if (useSpecSize && fullsize > specSize) {
size = specSize;
}
int offset = 0;
if (useSpecSize) {
offset = fullsize - size;
}
vector<int> meanPositions;
meanPositions.clear();
if (config->getInt("use mean") == 1) {
int chunks = config->getInt("number of mean window chunks");
for (int i=0; i<chunks; ++i) {
meanPositions.push_back(offset+i*size/chunks);
}
meanPositions.push_back(fullsize-1);
}
vector<int> sdPositions;
sdPositions.clear();
if (config->getInt("use standard deviation") == 1) {
int chunks = config->getInt("number of sd window chunks");
for (int i=0; i<chunks; ++i) {
sdPositions.push_back(offset+i*size/chunks);
}
sdPositions.push_back(fullsize-1);
}
vector<int> mcrPositions;
mcrPositions.clear();
if (config->getInt("use mean crossing rate") == 1) {
int chunks = config->getInt("number of mcr window chunks");
for (int i=0; i<chunks; ++i) {
mcrPositions.push_back(offset+i*size/chunks);
}
mcrPositions.push_back(fullsize-1);
}
for (vector<int>::iterator it=indices.begin(); it!=indices.end(); ++it) {
if (config->getInt("verbosity") > 2) {
cout << "Index: " << *it << endl;
}
if (config->getInt("use mean") == 1) {
for (int i=1; i<meanPositions.size(); ++i) {
float mean = extr.at(sensor)->getMean(*it,meanPositions.at(i-1),meanPositions.at(i));
if (config->getInt("verbosity") > 2) {
cout << "Mean: " << mean << endl;
}
f.push_back(mean);
}
}
if (config->getInt("use standard deviation") == 1) {
for (int i=1; i<sdPositions.size(); ++i) {
float sd = extr.at(sensor)->getStandardDeviation(*it,sdPositions.at(i-1),sdPositions.at(i));
if (config->getInt("verbosity") > 2) {
cout << "SD: " << sd << endl;
}
f.push_back(sd);
}
}
if (config->getInt("use mean crossing rate") == 1) {
for (int i=1; i<mcrPositions.size(); ++i) {
float mcr = extr.at(sensor)->getMeanCrossingRate(*it,mcrPositions.at(i-1),mcrPositions.at(i));
if (config->getInt("verbosity") > 2) {
cout << "MCR: " << mcr << endl;
}
f.push_back(mcr);
}
}
} // for every measurement index (accX, ...)
if (config->getInt("verbosity") > 2) {
cout << "**********************************" << endl;
}
return f;
}
int Alpha(TPoint *ray){
/* 0. Subinitialization - clear auxiliary variables and empty and nonsignificant input axes */
properties.resize(d); for (unsigned int i = 0; i < d; i++){properties[i] = i;} // initialize properties: all available
features.clear();
outMatrix(x);
/* 1. Null-cycle */
if (numStartFeatures == 2){ // start with two features?
Feature optFeatureX;
Feature optFeatureY;
for (unsigned int i = 0; i < properties.size() - 1; i++){
for (unsigned int j = i + 1; j < properties.size(); j++){
/* Calculating minimal error on the plane of the i-th and the j-th properties */
Feature tmpFeature;
curFeature = x[properties[i]];
unsigned int error = DGetMinError(properties[j], &tmpFeature);
#ifdef DEF_OUT_ALPHA
if (OUT_ALPHA){
Rcout << properties[i] << ", " << properties[j] << ", " << tmpFeature.angle << ", " << error << ", " << endl;
}
#endif
if (error < optFeatureY.error){optFeatureX.number = properties[i]; optFeatureY = tmpFeature;}
}
}
features.push_back(optFeatureX);
features.push_back(optFeatureY);
for (unsigned int i = 0; i < properties.size(); i++){ // delete recently found X and Y properties
if (properties[i] == optFeatureX.number){properties.erase(properties.begin() + i);}
if (properties[i] == optFeatureY.number){properties.erase(properties.begin() + i);}
}
curFeature = x[features[0].number];
UpdateCurFeature();
outString("Feature 1:");
outVector(curFeature);
}
/* 2. Main cycle */
/* Searching an optimal feature space while empirical error rate decreases */
while(features[features.size() - 1].error > 0 && properties.size() > 0){
Feature optFeature;
for (unsigned int i = 0; i < properties.size(); i++){
/* Calculating minimal error on the plane of the curFeature and the j-th properties */
Feature tmpFeature;
unsigned int error = DGetMinError(properties[i], &tmpFeature);
#ifdef DEF_OUT_ALPHA
if (OUT_ALPHA){
Rcout << properties[i] << ", " << tmpFeature.angle << ", " << error << ", " << endl;
}
#endif
if (error < optFeature.error){optFeature = tmpFeature;}
}
if (optFeature.error < features[features.size() - 1].error){
features.push_back(optFeature);
for (unsigned int i = 0; i < properties.size(); i++){ // delete recently found property
if (properties[i] == optFeature.number){properties.erase(properties.begin() + i);}
}
UpdateCurFeature();
outString("Feature :");
outVector(curFeature);
}else{break;}
}
outString("Features:");
outFeatures(features);
/* Restoring the projection vector */
GetRay(ray);
return features[features.size() - 1].error;
}