Vector AgentDetector::getSkeletonPattern(Player p)
{
//Create the skeleton pattern
Vector pattern(5);
pattern[0] = distanceVector(p.skeleton[EFAA_OPC_BODY_PART_TYPE_ELBOW_L], p.skeleton[EFAA_OPC_BODY_PART_TYPE_SHOULDER_L]);
pattern[1] = distanceVector(p.skeleton[EFAA_OPC_BODY_PART_TYPE_SHOULDER_L], p.skeleton[EFAA_OPC_BODY_PART_TYPE_SHOULDER_C]);
pattern[2] = distanceVector(p.skeleton[EFAA_OPC_BODY_PART_TYPE_SHOULDER_C], p.skeleton[EFAA_OPC_BODY_PART_TYPE_SHOULDER_R]);
pattern[3] = distanceVector(p.skeleton[EFAA_OPC_BODY_PART_TYPE_SHOULDER_C], p.skeleton[EFAA_OPC_BODY_PART_TYPE_SPINE]);
pattern[4] = distanceVector(p.skeleton[EFAA_OPC_BODY_PART_TYPE_SHOULDER_C], p.skeleton[EFAA_OPC_BODY_PART_TYPE_HEAD]);
//Arms
//pattern[0] = distanceVector(p.skeleton[EFAA_OPC_BODY_PART_TYPE_HAND_L], p.skeleton[EFAA_OPC_BODY_PART_TYPE_WRIST_L]);
//pattern[1] = distanceVector(p.skeleton[EFAA_OPC_BODY_PART_TYPE_WRIST_L], p.skeleton[EFAA_OPC_BODY_PART_TYPE_ELBOW_L]);
//pattern[2] = distanceVector(p.skeleton[EFAA_OPC_BODY_PART_TYPE_ELBOW_L], p.skeleton[EFAA_OPC_BODY_PART_TYPE_SHOULDER_L]);
//pattern[3] = distanceVector(p.skeleton[EFAA_OPC_BODY_PART_TYPE_SHOULDER_L], p.skeleton[EFAA_OPC_BODY_PART_TYPE_SHOULDER_C]);
//pattern[4] = distanceVector(p.skeleton[EFAA_OPC_BODY_PART_TYPE_SHOULDER_C], p.skeleton[EFAA_OPC_BODY_PART_TYPE_SHOULDER_R]);
//pattern[5] = distanceVector(p.skeleton[EFAA_OPC_BODY_PART_TYPE_SHOULDER_R], p.skeleton[EFAA_OPC_BODY_PART_TYPE_ELBOW_R]);
//pattern[6] = distanceVector(p.skeleton[EFAA_OPC_BODY_PART_TYPE_ELBOW_R], p.skeleton[EFAA_OPC_BODY_PART_TYPE_WRIST_R]);
//pattern[7] = distanceVector(p.skeleton[EFAA_OPC_BODY_PART_TYPE_WRIST_R], p.skeleton[EFAA_OPC_BODY_PART_TYPE_HAND_R]);
//Legs
//pattern[8] = distanceVector(p.skeleton[EFAA_OPC_BODY_PART_TYPE_FOOT_L], p.skeleton[EFAA_OPC_BODY_PART_TYPE_ANKLE_L]);
//pattern[9] = distanceVector(p.skeleton[EFAA_OPC_BODY_PART_TYPE_ANKLE_L], p.skeleton[EFAA_OPC_BODY_PART_TYPE_KNEE_L]);
//pattern[10] = distanceVector(p.skeleton[EFAA_OPC_BODY_PART_TYPE_KNEE_L], p.skeleton[EFAA_OPC_BODY_PART_TYPE_HIP_L]);
//pattern[11] = distanceVector(p.skeleton[EFAA_OPC_BODY_PART_TYPE_HIP_L], p.skeleton[EFAA_OPC_BODY_PART_TYPE_HIP_C]);
//pattern[12] = distanceVector(p.skeleton[EFAA_OPC_BODY_PART_TYPE_HIP_C], p.skeleton[EFAA_OPC_BODY_PART_TYPE_HIP_R]);
//pattern[13] = distanceVector(p.skeleton[EFAA_OPC_BODY_PART_TYPE_HIP_R], p.skeleton[EFAA_OPC_BODY_PART_TYPE_KNEE_R]);
//pattern[14] = distanceVector(p.skeleton[EFAA_OPC_BODY_PART_TYPE_KNEE_R], p.skeleton[EFAA_OPC_BODY_PART_TYPE_ANKLE_R]);
//pattern[15] = distanceVector(p.skeleton[EFAA_OPC_BODY_PART_TYPE_ANKLE_R], p.skeleton[EFAA_OPC_BODY_PART_TYPE_FOOT_R]);
return pattern;
}
Object* getSelectedObject(std::list<Object*>* allObjects, Player* player)
{
if(!allObjects || allObjects->empty())
return NULL;
float a,b,c,d,e,f;
float minDistance = 999999999999;
Object* closestObject;
cv::Vec3f objectPosition;
cv::Vec4f distanceVector;
cv::Vec4f playerPosition(player->position[0], player->position[1], player->position[2], 1);
cv::Vec4f lookAt(player->lookAtVector[0], player->lookAtVector[1], player->lookAtVector[2], 1);
cv::Matx<float,4,4> plueckerCoords = playerPosition*lookAt.t() - lookAt*playerPosition.t();
a = plueckerCoords(0,1);
b = plueckerCoords(0,2);
c = plueckerCoords(0,3);
d = plueckerCoords(1,2);
e = plueckerCoords(1,3);
f = plueckerCoords(2,3);
cv::Matx<float,4,4> dualPlueckerCoords(0, f, -e, d,
-f, 0, c, -b,
e, -c, 0, a,
-d, b, -a, 0);
cv::Matx<float,3,3> A(0, f, -e,
-f, 0, c,
e, -c, 0);
A = A.t()*A;
dualPlueckerCoords = sqrt(2)/(A(0,0)*A(1,1)*A(2,2))*dualPlueckerCoords;
for(std::list<Object*>::iterator it = allObjects->begin(); it != allObjects->end(); ++it)
{
objectPosition = (*it)->position;
distanceVector = dualPlueckerCoords*cv::Vec4f(objectPosition(0), objectPosition(1), objectPosition(2), 1);
std::cout << "vectorNorm" << cv::norm(cv::Vec3f(distanceVector(0),distanceVector(1),distanceVector(2))) << std::endl;
if (minDistance > cv::norm(cv::Vec3f(distanceVector(0),distanceVector(1),distanceVector(2))))
{
minDistance = cv::norm(cv::Vec3f(distanceVector(0),distanceVector(1),distanceVector(2)));
closestObject = (*it);
}
}
std::cout << "plueckerCoords: " << plueckerCoords << std::endl;
std::cout << "dualPlueckerCoords: " << dualPlueckerCoords << std::endl;
std::cout << "plueckerCoords: " << a << " " << b << " " << c << " " << d << " " << e << " " << f << std::endl;
std::cout << "Minimum distance " << minDistance << std::endl;
return closestObject;
return NULL;
}
vector<float> ReconnaissanceHandler::recognise(vector<float>& caracteristicVector, vector<vector<float>>& classes)
{
vector<float> dist;
for (int i = 0; i < classes.size(); ++i)
{
dist.push_back(distanceVector(caracteristicVector, classes.at(i)));
}
return dist;
}
// По координатам курсора получает уникальный номер вершины, которая
// попадает под него. В противном случае возвращает номер вешины,
// следующей за последней в списке вершин (то есть verticesList.size()).
VertexIndex MaxFlowVisualizer::getVertexUnderCursor(QPoint cursorPosition) {
QVector2D cursor(cursorPosition);
for (VertexIndex vertex = 0; vertex < verteciesList.size(); ++vertex) {
QVector2D currentVertexCenter(verteciesList[vertex].getCenterCoordX(), verteciesList[vertex].getCenterCoordY());
QVector2D distanceVector(currentVertexCenter - cursor);
if (distanceVector.length() < verteciesList[vertex].getRadius()) {
return vertex;
}
}
return verteciesList.size();
}
bool Enemy::isPlayerInRadius(int radius)
{
sf::Vector2f playerPos = Game::getPlayersPosition();
sf::Vector2f enemyPos = this->GetCenterPosition();
sf::Vector2f distanceVector(playerPos.x - enemyPos.x,
playerPos.y - enemyPos.y);
if(sqrt(pow(distanceVector.x,2) + pow(distanceVector.y,2)) <= radius)
{
return true;
}
return false;
}
int getValue(GradientInfo *gradInfo, ImageInfo *image, int x, int y)
{
int x_pos_in_cell = x % gradInfo->grid_cell_width;
int y_pos_in_cell = y % gradInfo->grid_cell_height;
double x_weight = (double)x_pos_in_cell / (double)gradInfo->grid_cell_width;
double y_weight = (double)y_pos_in_cell / (double)gradInfo->grid_cell_height;
int x_grid_cell = x / gradInfo->grid_cell_width;
int y_grid_cell = y / gradInfo->grid_cell_height;
int x0 = x_grid_cell;
int x1 = x0 + 1;
int y0 = y_grid_cell;
int y1 = y0 + 1;
Vector2 p;
p.x = x_weight + x_grid_cell;
p.y = y_weight + y_grid_cell;
// ERROR!!!!!! Redo these distance vector calculations!
Vector2 d0 = distanceVector(Vector2((double)x0, (double)y0), p);
Vector2 d1 = distanceVector(Vector2((double)x1, (double)y0), p);
Vector2 d2 = distanceVector(Vector2((double)x0, (double)y1), p);
Vector2 d3 = distanceVector(Vector2((double)x1, (double)y1), p);
double dp0 = dotProduct(d0, gradInfo->Gradient[x0][y0]);
double dp1 = dotProduct(d1, gradInfo->Gradient[x1][y0]);
double dp2 = dotProduct(d2, gradInfo->Gradient[x0][y1]);
double dp3 = dotProduct(d3, gradInfo->Gradient[x1][y1]);
double m = lerp(dp0, dp1, x_weight);
double n = lerp(dp2, dp3, x_weight);
double r = lerp(m, n, y_weight);
return abs((int)(500 * r));
}
bool calculatePaths(struct distance_table * dt, struct link_costs * ln,
struct route_table * rt, struct neighbors * nb, int node_id)
{
bool b_update = false;
// Now calculate paths
for (int i = 0; i < 4; i++)
{
// Obviously don't calculate the path to itself
if (node_id == i) continue;
// Determine the best path using the distance vector algo
bool visited[4] = {(node_id==0), (node_id==1), (node_id==2), (node_id==3)};
struct path_value pv = distanceVector(dt, ln, node_id, i, 0, &visited);
// If the path changes, notify - this could be a different value, or different route
if (pv.value != dt->costs[pv.node_id][node_id] ||
rt->route[i][0] != pv.route[0] || rt->route[i][1] != pv.route[1] ||
rt->route[i][2] != pv.route[2] || rt->route[i][3] != pv.route[3])
{
// Set the new path value
b_update = true;
setNewPathValue(dt, rt, node_id, pv);
// Console Notifications
printf("rtupdate%d: cost to node #%d updated to %d\r\n", node_id, pv.node_id, pv.value);
}
}
if (b_update)
{
printf("rtupdate%d: sending out packets to neighbors with new values: [%d,%d,%d,%d]\r\n",
node_id, dt->costs[0][node_id], dt->costs[1][node_id], dt->costs[2][node_id], dt->costs[3][node_id]);
// Deliver packets to other nodes
sendPackets(dt, rt, nb, node_id);
}
// Report results
return b_update;
}
vector<float> ReconnaissanceHandler::recognisePCA(vector<float>& caracteristicVector, PCA pca, vector<vector<float>>& classes)
{
Mat v(caracteristicVector.size(),1, CV_32F);
int i = 0;
for (float f : caracteristicVector) {
v.at<float>(i, 0) = f;
++i;
}
Mat reduceVector = pca.project(v);
vector<float> caractReduced;
for (i = 0; i < reduceVector.rows; ++i) {
caractReduced.push_back(reduceVector.at<float>(i, 0));
}
vector<float> dist;
for (int i = 0; i < classes.size(); ++i)
{
dist.push_back(distanceVector(caractReduced, classes.at(i)));
}
return dist;
}
bool dunnIndex::apply(const std::vector<dmatrix>& clusteredData,
double& index, const dmatrix& centroids) const {
int nbClusters=clusteredData.size();
double denominator=0.0;
int i,j;
l2Distance<double> dist;
dvector diameters(nbClusters);
parameters param;
param=getParameters();
// pointer to the function which implements the measure according to the
// parameters
double (lti::clusteringValidity::*diamFunc)(const dmatrix&) const;
#ifdef _LTI_MSC_DOT_NET_2003
// nasty bug in this version of the .NET compiler
#define QUALIFIER
#else
#define QUALIFIER <i::dunnIndex::
#endif
switch (param.diameterMeasure) {
case parameters::Standard:
diamFunc=QUALIFIER getStandardDiameter;
break;
case parameters::Average:
diamFunc=QUALIFIER getAverageDiameter;
break;
case parameters::Centroid:
diamFunc=QUALIFIER getAverageToCentroidDiameter;
break;
default:
diamFunc=QUALIFIER getStandardDiameter;
setStatusString("Unknown diameterMeasure in clusteringValidity\n");
return false;
}
// compute all diameters of all clusters
for (i=0; i<nbClusters; i++) {
diameters[i]=(this->*diamFunc)(clusteredData[i]);
}
denominator=diameters.maximum();
// pointer to the function which calculates the distance of the functions
// a pointer to a function is used, because the function will be called
// many times later
double (lti::clusteringValidity::*distFunc)
(const dmatrix&,const dmatrix&) const ;
// set pointer to function which is set by the parameter distanceMeasure
switch (param.distanceMeasure) {
case parameters::Minimum:
distFunc=QUALIFIER getMinimumDistance;
break;
case parameters::Maximum:
distFunc=QUALIFIER getMaximumDistance;
break;
case parameters::Mean:
distFunc=QUALIFIER getAverageDistance;
break;
case parameters::Centroids:
distFunc=QUALIFIER getCentroidDistance;
break;
case parameters::Interpoint:
distFunc=QUALIFIER getAverageInterpointDistance;
break;
default:
distFunc=QUALIFIER getAverageDistance;
setStatusString("Unknown distanceMeasure in clusteringValidity\n");
return false;
}
// compute the distances of all clusters to each other
int counter=0;
dvector distanceVector(static_cast<int>(.5*(nbClusters*(nbClusters-1))));
for (i=0; i<nbClusters; i++) {
for (j=i+1; j<nbClusters; j++) {
if (distFunc==QUALIFIER getCentroidDistance) {
distanceVector[counter]=dist.apply(centroids.getRow(i),
centroids.getRow(j));
} else {
distanceVector[counter]=(this->*distFunc)(clusteredData[i],
clusteredData[j]);
}
counter++;
}
}
distanceVector.divide(denominator);
index=distanceVector.minimum();
return true;
}
