void Crossline::Process(const cv::Mat& prevImg, const cv::Mat& nextImg, float** density_feature, float** x_feature, float** y_feature) {
std::vector<cv::Mat> src1_spl;
std::vector<cv::Mat> src2_spl;
split(prevImg, src1_spl);
split(nextImg, src2_spl);
std::vector<cv::Mat> channels;
channels.push_back(src1_spl[0]);
channels.push_back(src1_spl[1]);
channels.push_back(src1_spl[2]);
channels.push_back(src2_spl[0]);
channels.push_back(src2_spl[1]);
channels.push_back(src2_spl[2]);
cv::Mat input_data;
merge(channels, input_data);
std::vector<cv::Mat> dv (1, input_data);
std::vector<int> dv1 (1, 0);
md_layer_->AddMatVector(dv, dv1);
feature_extraction_net_->ForwardFromTo(0, feature_extraction_net_->layers().size() - 1);
const shared_ptr<Blob<float> > feature_blob_density = feature_extraction_net_->blob_by_name("density_out");
const shared_ptr<Blob<float> > feature_blob_X = feature_extraction_net_->blob_by_name("vx_out");
const shared_ptr<Blob<float> > feature_blob_Y = feature_extraction_net_->blob_by_name("vy_out");
*density_feature = feature_blob_density->mutable_cpu_data();
*x_feature = feature_blob_X->mutable_cpu_data();
*y_feature = feature_blob_Y->mutable_cpu_data();
}
float* Reid::CalcFeatures(const std::vector<cv::Mat>& images,
const std::string& feature_blob_name) const {
// initialize dummy labels, which is required by MemoryDataLayer
std::vector<int> dv1(images.size(), 0);
// Note: for sake of efficiency,
// we don't check the input image size, which
// should be cv::Size(56, 144) exactly, or the program will crash
md_layer_->AddMatVector(images, dv1);
// forward
feature_extraction_net_->ForwardFromTo(
0, feature_extraction_net_->layers().size() - 1);
// extract feature blob
return feature_extraction_net_->blob_by_name(
feature_blob_name)->mutable_cpu_data();
}
int main()
{
double ar[SIZE_ar] = { 0, 1, 2, 3, 4, 5 };
double ar2[SIZE_ar2] = { 8,7,6,5,4,3,2,1,0};
DoubleVector dv1(SIZE_ar, ar);
DoubleVector dv2(SIZE_ar2, ar2);
DoubleVector dv3;
dv3 = dv1;
double p = dv1[1];
DoubleVector v;
v = dv2 + dv1 + dv1 - dv2 - dv1;
v.print();
dv1 += dv3 += dv2 -= dv2;
dv1.print();
dv1 = dv3 * 2 * 4 ;
dv1 *= 2;
dv3 = dv1 / 2;
dv3 /= 2;
double scalar = dv3% dv1;
BoolVector a(2);
cout<<"number in binary ...00000010 --> in array pos[31] bit is "<<a[31]
<<endl<<"in array pos[3] - bit is "<<a[30]<<endl;
a.set(30);
cout << "Nothing changes because in pos 30 it has 1. The number is the same (2) :" << a.get_number()<<endl;
a.set(31);
cout << "Change on pos 31 from 0 to 1. The new number is 3:" << a.get_number()<<endl;
BoolVector b(55);
b.set(28);
cout << "We are working with this number 00..110111 in binary (decimal 55)."<<endl
<<"We want to make it 00.. 111111 exacly 63 in decimal : " << b.get_number() << endl;
b.get(28);
cout << "We are working with 00.. 111111 decimal 63 changing into 00..110111 decimal 55:" << b.get_number() << endl;
}
void resultCombiner(const char * name_dv1, const char * name_cm1, const int length1, const char * name_dv2, const char * name_cm2, const int length2, int ftr = 1, const char * outputNameStub = "output")
{
TVectorD dv1(length1), dv2(length2);
TMatrixT<double> cm1(length1, length1), cm2(length2, length2);
double binLimits1[length1][2], binLimits2[length2][2], ccCov1[length1], ccCov2[length1];
readDataVector(name_dv1, dv1, binLimits1, ftr, ccCov1);
printf("Read data vector 1 (%d)\n",length1);
readDataVector(name_dv2, dv2, binLimits2, ftr, ccCov2);
printf("Read data vector 2 (%d)\n",length2);
readCovMatrix(name_cm1, cm1);
printf("Read covariance matrix 1\n");
readCovMatrix(name_cm2, cm2);
printf("Read covariance matrix 2\n");
std::vector<double*> binLimits;
std::vector<std::vector<int > > preU;
int i1 = 0, i2 = 0;
while(i1 < length1 || i2 < length2)
{
if(i1 < length1 && i2 < length2)
{
if((binLimits1[i1][1] + binLimits1[i1][0])/2 > binLimits2[i2][0] && (binLimits1[i1][1] + binLimits1[i1][0])/2 < binLimits2[i2][1])
{
binLimits.push_back(binLimits1[i1]);
std::vector<int> tmp;
tmp.push_back(i1);
tmp.push_back(i2);
preU.push_back(tmp);
i1++;
i2++;
}
else if((binLimits1[i1][1] + binLimits1[i1][0])/2 <= binLimits2[i2][0])
{
binLimits.push_back(binLimits1[i1]);
std::vector<int> tmp;
tmp.push_back(i1);
tmp.push_back(-1);
preU.push_back(tmp);
i1++;
}
else
{
binLimits.push_back(binLimits2[i2]);
std::vector<int> tmp;
tmp.push_back(-1);
tmp.push_back(i2);
preU.push_back(tmp);
i2++;
}
}
else if(i1 < length1 && i2 >= length2)
{
binLimits.push_back(binLimits1[i1]);
std::vector<int> tmp;
tmp.push_back(i1);
tmp.push_back(-1);
preU.push_back(tmp);
i1++;
}
else
{
binLimits.push_back(binLimits2[i2]);
std::vector<int> tmp;
tmp.push_back(-1);
tmp.push_back(i2);
preU.push_back(tmp);
i2++;
}
}
TVectorD dv(length1 + length2);
for(int i = 0; i < length1 + length2; i++)
{
dv[i] = (i < length1) ? dv1[i] : dv2[i - length1];
}
TMatrixT<double> cm(length1 + length2, length1 + length2), U(length1 + length2, preU.size());
for(int i = 0; i < length1; i++)
{
for(int j = 0; j < length1; j++)
{
cm[i][j] = cm1[i][j];
}
}
for(int i = length1; i < length1 + length2; i++)
{
for(int j = length1; j < length1 + length2; j++)
{
cm[i][j] = cm2[i - length1][j - length1];
}
}
for(unsigned int i = 0; i < preU.size(); i++)
{
if(preU[i][0] >= 0) U[preU[i][0]][i] = 1;
if(preU[i][1] >= 0) U[preU[i][1] + length1][i] = 1;
if(ftr > 1 && preU[i][0] >= 0 && preU[i][1] >= 0) cm[preU[i][0]][preU[i][1] + length1] = cm[preU[i][1] + length1][preU[i][0]] = ccCov1[preU[i][0]]*ccCov2[preU[i][1]];
}
// cm.Print();
TMatrixT<double> Ut(U);
Ut.T();
TMatrixT<double> cmInv(cm);
cmInv.Invert();
TMatrixT<double> step1 = Ut * cmInv * U;
TMatrixT<double> step2 = Ut * cmInv;
TMatrixT<double> lambda = step1.Invert() * step2;
TVectorD bV = lambda*dv;
TMatrixT<double> bcm = (Ut * cmInv * U).Invert();
printf("Done with combination.\n");
//write output
FILE *file;
char bVoutName[128], CMoutName[128];
sprintf(bVoutName, "%s_data.txt", outputNameStub);
file = fopen(bVoutName, "w");
if(file)
{
fprintf(file, "#\n#%9s %9s %9s %15s %15s\n", "Bin", "Y_min", "Y_max", "Value", "Uncertainty");
for(int i = 0; i < bV.GetNoElements(); i++)
{
fprintf(file, " %9i %9.2f %9.2f %15e %15e\n", i + 1, binLimits[i][0], binLimits[i][1], bV[i], sqrt(bcm[i][i]));
}
fclose(file);
}
sprintf(CMoutName, "%s_covMat.txt", outputNameStub);
file = fopen(CMoutName, "w");
if(file)
{
fprintf(file, "#\n#%9s %9s %15s\n", "Bin i", "Bin j", "Value");
for(int i = 0; i < bcm.GetNrows(); i++)
{
for(int j = 0; j < bcm.GetNcols(); j++)
{
fprintf(file, " %9i %9i %15e\n", i + 1, j + 1, bcm[i][j]);
}
}
fclose(file);
}
printf("Output complete.\n");
}
