static void *handleTemperaturePressure (void *data)
{
double temperature;
double pressure;
time_t sec;
double sample = 1200; //trigger time (second) for trend
time_t initial = time (NULL) ;
double delta ;
for (;;){
if(bmp085_Calibration(fd) > 0 ){
temperature = bmp085_GetTemperature(bmp085_ReadUT(fd));
sleep(1);
pressure = bmp085_GetPressure(bmp085_ReadUP(fd));
int press = round(pressure/100);
int temp = round(temperature/10);
updateTemp (temp) ;
updatePressure (press) ;
sleep(4) ; //Wait 4s to avoid concurrent access
sec = time (NULL);
delta = difftime(sec, initial);
if (delta > sample ){
trend (press);
initial = time(NULL);
sleep (1);
}
}
sleep (2);
}
return 0;
}
void Logic::drawPage0() {
_display->setCursor(0, 0);
_display->print("Z:");
_display->print(_zasilanie);
_display->print(" ");
_display->print(trend(_zasilanie, _prevZasilanie));
_display->setCursor(0, 10);
_display->print("G:");
_display->print(_gora);
_display->print(" ");
_display->print(trend(_gora, _prevGora));
_display->setCursor(0, 20);
_display->print("D:");
_display->print(_dol);
_display->print(" ");
_display->print(trend(_dol, _prevDol));
_display->setCursor(0, 30);
_display->print("*");
if (_load) {
_display->print("lad");
}
}
int TrackThread::process(Mat &frame,Mat &output)
{
int re=0;
cvtColor (frame,gray,CV_BGR2GRAY);
frame.copyTo (output);
//特征点太少了，重新检测特征点
//if(points[0].size ()<=10)
{
goodFeaturesToTrack (gray,//图片
features,//输出特征点
max_count,//特征点最大数目
qlevel,//质量指标
minDist);//最小容忍距离
//插入检测到的特征点
// points[0].insert (points[0].end (),features.begin (),features.end ());
// initial.insert (initial.end (),features.begin (),features.end ());
points[0]=features;
initial=features;
}
//第一帧
if(gray_prev.empty ()){
gray.copyTo (gray_prev);
}
//根据前后两帧灰度图估计前一帧特征点在当前帧的位置
//默认窗口是15*15
calcOpticalFlowPyrLK (
gray_prev,//前一帧灰度图
gray,//当前帧灰度图
points[0],//前一帧特征点位置
points[1],//当前帧特征点位置
status,//特征点被成功跟踪的标志
err);//前一帧特征点点小区域和当前特征点小区域间的差，根据差的大小可删除那些运动变化剧烈的点
int k = 0;
//去除那些未移动的特征点
for(int i=0;i<points[1].size();i++){
if(acceptTrackedPoint (i))
{
initial[k]=initial[i];
points[1][k++] = points[1][i];
}
}
points[1].resize (k);
initial.resize (k);
//标记被跟踪的特征点
for(int i=0;i<points[1].size ();i++)
{
//当前特征点到初始位置用直线表示
line(output,initial[i],points[1][i],Scalar(20,150,210));
//当前位置用圈标出
circle(output,points[1][i],3,Scalar(120,250,10));
}
if(points[1].size()!=0)
trend(output);
//向心速度
curx*=0.9;
cury*=0.9;
lens=min(output.rows,output.cols)/8;
edge=min(output.rows,output.cols)/5;
//控制域
rectangle(output,
Point(output.cols/2-lens,output.rows/2-lens),
Point(output.cols/2+lens,output.rows/2+lens),
Scalar(1,100,200),3);
//追踪域
rectangle(output,
Point(edge,edge),
Point(output.cols-edge,output.rows-edge),
Scalar(10,210,1),3);
if(first){
if(dirx==1){
re=4;
line(output,
Point(output.cols/2+lens,output.rows/2-lens),
Point(output.cols/2+lens,output.rows/2+lens),
Scalar(20,250,110),5);
}
else if(dirx==-1){
re=3;
line(output,
Point(output.cols/2-lens,output.rows/2-lens),
Point(output.cols/2-lens,output.rows/2+lens),
Scalar(20,250,110),5);
}
if(diry==1){
re=2;
line(output,
Point(output.cols/2-lens,output.rows/2+lens),
Point(output.cols/2+lens,output.rows/2+lens),
Scalar(20,250,110),5);
}
else if(diry==-1){
re=1;
line(output,
Point(output.cols/2-lens,output.rows/2-lens),
Point(output.cols/2+lens,output.rows/2-lens),
Scalar(20,250,110),5);
}
}
//为下一帧跟踪初始化特征点集和灰度图像
circle(output,Point(output.cols/2+curx,output.rows/2+cury),10,Scalar(2,1,250),3);
std::swap(points[1],points[0]);
cv::swap(gray_prev,gray);
return re;
}
bool RtDesignMatrix::buildDesignMatrix() {
numAddedColumns = 0;
// TODO this will need to get called by unserializeXML as well
// fill the rest of the measurements as periodic stim
if (loadDesignMatrixFromFile) {
if (padConditionWithZeros) {
// TODO
}
else {
unsigned int i = blockLen - 1;
for (; i < numMeas; i++) {
inputConditions.put(
i, numInputConditions - 1, inputConditions.get(
i % blockLen, numInputConditions - 1));
}
}
}
// the rest was originally in build()
// setup the size
unsigned int numConditions = getNumConditionBases();
unsigned int numNuisance = getNumNuisanceBases();
columnNames.clear();
columnNames.reserve(numConditions + numNuisance);
set_size(numMeas, numConditions + numNuisance);
// set neural condition regressors
buildHrf();
// store the new condition names as we go along
vector<string> inputConditionNames = conditionNames;
// build shifted delta for potential condition shifting
vnl_vector<double> shiftdelta(numMeas, 0.0);
if (conditionShift > 0 && conditionShift < numMeas) {
shiftdelta.put(conditionShift, 1);
}
else {
shiftdelta.put(0, 1);
}
// consider each input condition individually
for (unsigned int cond = 0; cond < numConditions; cond++) {
vnl_vector<double> basis = inputConditions.get_column(cond);
// incorporate condition shift via convolution with shifted delta
if (conditionShift > 0 && conditionShift < numMeas) {
vnl_vector<double> shiftcol = vnl_convolve(basis, shiftdelta);
basis.update(shiftcol.extract(basis.size()));
}
// build block-wise condition vectors if required
if (modelEachBlock) {
unsigned int numBlocks =
ceil((double) ((numMeas - conditionShift) / blockLen));
// create a design matrix column per block
for (unsigned int block = 0; block <= numBlocks; block++) {
vnl_vector<double> blockCol(numMeas, 0);
// copy the entire block from the input condition vector
for (unsigned int meas = conditionShift + block * blockLen;
meas < conditionShift + (block + 1) * blockLen && meas < numMeas;
meas++) {
blockCol.put(meas, basis[meas]);
}
string blockStr;
RtConfigVal::convertToString<unsigned int>(blockStr, block);
addColumn(convolveVecWithHrf(blockCol),
inputConditionNames[cond] + "_block" + blockStr,
conditionInterestIndicator[cond]);
// add columns for temporal derivatives if required
if (modelTemporalDerivatives) {
addColumn(convolveVecWithTemporalDerivative(blockCol),
inputConditionNames[cond] + "_block" +
blockStr + "_deriv",
conditionInterestIndicator[cond]);
}
}
}
else { // no block splitting necessary
addColumn(convolveVecWithHrf(basis),
inputConditionNames[cond],
conditionInterestIndicator[cond]);
// add columns for temporal derivatives if required
if (modelTemporalDerivatives) {
addColumn(convolveVecWithTemporalDerivative(basis),
inputConditionNames[cond] + "_deriv",
conditionInterestIndicator[cond]);
}
}
}
// build the nuisance columns
// fill the trend colums
for (unsigned int i = 0; i <= maxTrendOrder; i++) {
vnl_vector<double> trend(numMeas, 0);
for (unsigned int j = 0; j < numMeas; j++) {
switch (i) {
case 0: // mean
trend.put(j, 1.0);
break;
case 1: // linear
trend.put(j, j + 1);
break;
default: // higher
trend.put(j, pow((double) j - numMeas / 2, (int) i));
break;
}
}
string order;
RtConfigVal::convertToString(order, i);
addColumn(trend, string("trend_order_") + order, false);
}
// name the motion columns
vnl_vector<double> motStub(numMeas, 0);
if (modelMotionParameters) {
addColumn(motStub, "motion_tx", false);
addColumn(motStub, "motion_ty", false);
addColumn(motStub, "motion_tz", false);
addColumn(motStub, "motion_rx", false);
addColumn(motStub, "motion_ry", false);
addColumn(motStub, "motion_rz", false);
}
// event and artifact columns will be left blank until they are received
// check number of conditions against filled columns
if (numAddedColumns != numNuisance + numConditions) {
cerr << "WARNING: number of columns generated ("
<< numAddedColumns << ") is different from that expected ("
<< numNuisance + numConditions
<< "). further calculations are suspect."
<< endl;
}
if (DEBUG_LEVEL & MODERATE) {
print();
}
built = true;
return true;
}
