void testConnectedComponent()
{
Mat testMat(480,640,CV_32FC1);
Mat mask(480,640,CV_32FC1);
mask.setTo(Scalar(1.0));
Mat firstGroup = testMat(Range(0,120),Range(0,200));
Mat secondGroup = testMat(Range(120,240),Range(200,400));
Mat thirdGroup = testMat(Range(240,480),Range(400,640));
firstGroup.setTo(Scalar(15));
secondGroup.setTo(Scalar(21));
thirdGroup.setTo(Scalar(15));
FileStorage fs1("testCompon.yml",FileStorage::READ);
fs1["Depth"] >> testMat;
fs1.release();
//Next we want to dilate and erode just a little
dilate(testMat,testMat,Mat(),Point(-1,-1),2);
//FileStorage fs3("FilteredDepthDilate.yml",FileStorage::WRITE);
//fs3 << "FilteredDepthDilate" << filteredDepth;
//fs3.release();
//Next we want to dilate and erode just a little
erode(testMat,testMat,Mat(),Point(-1,-1),2);
//We will need to normalize to such that it fits in 255 to 0 for the canny function so create a mat for that
Mat normalizedFilteredDepth(testMat.size(),CV_8UC1);
normalize(testMat,normalizedFilteredDepth,0,255,NORM_MINMAX,CV_32FC1);
testMat = normalizedFilteredDepth;
imshow("TestMat",testMat);
Mat testComponentMat;
struct ConnectedComponentConfig config;
config.eightWayNeighborhood = true;
config.depthDifferenceThreshold = .05f;
config.minPixelArea = 10;
vector<vector <Point> > testComponents = findConnectedComponents(config, testMat, mask, testComponentMat);
displayComponentResults(testComponentMat);
displayComponentResults(testComponents, testMat.size());
}
int main()
{
int Error(0);
Error += testVec();
Error += testMat();
return Error;
}
*outStream << "FAILURE --> ";
}
*outStream << "entry[" << i << "," << j << "]:" << " "
<< testMat(i, j) << " " << num1 << "/" << num2 << " "
<< absdiff << " " << "<?" << " " << abstol << "\n";
}
else {
std::istringstream chunkstream(chunk);
if (analyticDataType == INTREPID2_UTILS_FRACTION) {
chunkstream >> num1;
testentry = (ValueType)(num1);
}
else if (analyticDataType == INTREPID2_UTILS_SCALAR)
chunkstream >> testentry;
abstol = ( std::fabs(testentry) < reltol ?reltol : std::fabs(reltol*testentry) );
absdiff = std::fabs(testentry - testMat(i, j));
if (absdiff > abstol) {
++err;
*outStream << "FAILURE --> ";
}
*outStream << "entry[" << i << "," << j << "]:" << " "
<< testMat(i, j) << " " << testentry << " "
<< absdiff << " " << "<?" << " " << abstol << "\n";
}
++j;
}
++i;
}
// reset format state of std::cout
std::cout.copyfmt(oldFormatState);
void testVuFunctions()
{
MatrixData testMat(10,10,CV_32FC1);
MatrixData testMat2(10,10,CV_32FC1);
MatrixData buffer(10,10,CV_32FC1);
MatrixSize windowSize;
windowSize.height = 5;
windowSize.width = 5;
testMat.setTo(Scalar(1));
testMat.at<float>(5,5) = 0;
namedWindow("Before");
imshow("Before",testMat);
waitKey(0);
vuErodeFloat(&testMat,windowSize, &buffer, &testMat2);
namedWindow("After");
namedWindow("Results");
namedWindow("Buffer");
imshow("Before",testMat);
imshow("After",testMat);
imshow("Results",testMat2);
imshow("Buffer",buffer);
cout <<"TestMat:\n" << endl;
printFloatMatrixToConsole(&testMat);
cout << "\n\n" << endl;
cout <<"TestMat2\n:" << endl;
printFloatMatrixToConsole(&testMat2);
cout << "\n\n" << endl;
cout << "Matrix Results\n" << testMat2 << endl;
waitKey(0);
vuDilateFloat(&testMat2,windowSize, &buffer, &testMat);
imshow("Before",testMat2);
imshow("After",testMat2);
imshow("Results",testMat);
imshow("Buffer",buffer);
cout <<"TestMat:\n" << endl;
printFloatMatrixToConsole(&testMat2);
cout << "\n\n" << endl;
cout <<"TestMat2\n:" << endl;
printFloatMatrixToConsole(&testMat);
cout << "\n\n" << endl;
cout << "Matrix Results\n" << testMat << endl;
waitKey(0);
testMat.create(10,10,CV_16SC1);
testMat2.create(10,10,CV_16SC1);
buffer.create(10,10,CV_16SC1);
vuErodeInt(&testMat,windowSize, &buffer, &testMat2);
MatrixData queryDataIn(5,3,CV_32FC1);
MatrixData trainDataIn(10,3,CV_32FC1);
MatrixData rows;
vector<FeatureMatch> matchResults;
int numberOfMatches = 15;
matchResults.resize(queryDataIn.rows * numberOfMatches);
for(int i = 0; i< trainDataIn.rows;i++)
{
rows= trainDataIn.row(i);
rows.setTo(Scalar(i));
}
for(int i = 0; i< queryDataIn.rows;i++)
{
rows= queryDataIn.row(i);
rows.setTo(Scalar(i));
}
rows = trainDataIn.row(9);
rows.setTo(Scalar(1));
vuTopNMatchesFloat(&queryDataIn, &trainDataIn, numberOfMatches,&matchResults[0]);
for(int i = 0; i < queryDataIn.rows; i ++)
{
cout << "Query Feature vector " << i << ":"<<endl;
vuPrintFeatureVectorToConsole(&queryDataIn, i);
for(int j = 0; j< numberOfMatches; j++)
{
vuPrintFeatureMatchToConsole(&matchResults[i*numberOfMatches + j], &queryDataIn, &trainDataIn);
}
}
vector< vector<DMatch > > testMatches;
converFeatureMatchToDMatch(&matchResults[0], queryDataIn.rows, numberOfMatches,testMatches);
for(int i = 0; i < (int)testMatches.size(); i ++)
{
cout << "DMatch Query Feature vector " << i << ":"<<endl;
for(int j = 0; j< (int)testMatches[i].size(); j++)
{
printf("\t Dist: %g, queryId %d -> train Id %d\n",testMatches[i][j].distance,testMatches[i][j].queryIdx,testMatches[i][j].trainIdx);
}
}
waitKey(0);
}
void
VectorGaussianRandomField<P_V,P_M,Q_V,Q_M>::sampleFunction(const std::vector<P_V*>& fieldPositions, Q_V& sampleValues)
{
if ((m_env.subDisplayFile()) && (m_env.displayVerbosity() >= 3)) {
*m_env.subDisplayFile() << "Entering VectorGaussianRandomField<P_V,P_M,Q_V,Q_M>::sampleFunction()"
<< std::endl;
}
queso_require_equal_to_msg(( sampleValues.sizeLocal() % fieldPositions.size() ), 0, "input data is not multiple of each other");
unsigned int numberOfImageValuesPerIndex = sampleValues.sizeLocal()/fieldPositions.size();
queso_require_equal_to_msg(numberOfImageValuesPerIndex, m_imageSetPerIndex.vectorSpace().dimLocal(), "invalid input data dimension");
if ((m_savedPositions.size() == 0 ) &&
(m_savedRvImageSpace == NULL) &&
(m_savedRvLawExpVector == NULL) &&
(m_savedRvLawCovMatrix == NULL) &&
(m_savedRv == NULL)) {
// Ok
}
else if ((m_savedPositions.size() != 0 ) &&
(m_savedRvImageSpace != NULL) &&
(m_savedRvLawExpVector != NULL) &&
(m_savedRvLawCovMatrix != NULL) &&
(m_savedRv != NULL)) {
// Ok
}
else {
queso_error_msg("invalid combination of pointer values");
}
unsigned int numberOfPositions = fieldPositions.size();
bool instantiate = true;
if (m_savedPositions.size() == numberOfPositions) {
bool allPositionsAreEqual = true;
for (unsigned int i = 0; i < numberOfPositions; ++i) {
queso_require_msg(m_savedPositions[i], "m_savedPositions[i] should not be NULL");
if ((m_savedPositions[i]->sizeLocal() == fieldPositions[i]->sizeLocal()) &&
(*(m_savedPositions[i]) == *(fieldPositions[i]) )) {
// Ok
}
else {
allPositionsAreEqual = false;
break;
}
} // for i
instantiate = !allPositionsAreEqual;
}
if ((m_env.subDisplayFile()) && (m_env.displayVerbosity() >= 3)) {
*m_env.subDisplayFile() << "In VectorGaussianRandomField<P_V,P_M,Q_V,Q_M>::sampleFunction()"
<< ": numberOfPositions = " << numberOfPositions
<< ", instantiate = " << instantiate
<< std::endl;
}
if (instantiate) {
delete m_savedRv;
delete m_savedRvLawCovMatrix;
delete m_savedRvLawExpVector;
delete m_savedRvImageSpace;
for (unsigned int i = 0; i < m_savedPositions.size(); ++i) {
delete m_savedPositions[i];
}
m_savedPositions.clear();
// Set m_savedPositions
m_savedPositions.resize(numberOfPositions,NULL);
for (unsigned int i = 0; i < m_savedPositions.size(); ++i) {
m_savedPositions[i] = new P_V(*(fieldPositions[i]));
}
// Set m_savedRvImageSpace
m_savedRvImageSpace = new VectorSpace<Q_V,Q_M>(m_env, "grf_", numberOfPositions*numberOfImageValuesPerIndex, NULL);
// Set m_savedRvLawExpVector
Q_V tmpVec(m_imageSetPerIndex.vectorSpace().zeroVector());
m_savedRvLawExpVector = new Q_V(m_savedRvImageSpace->zeroVector());
for (unsigned int i = 0; i < numberOfPositions; ++i) {
m_meanFunction.compute(*(fieldPositions[i]),NULL,tmpVec,NULL,NULL,NULL);
for (unsigned int j = 0; j < numberOfImageValuesPerIndex; ++j) {
(*m_savedRvLawExpVector)[i*numberOfImageValuesPerIndex + j] = tmpVec[j];
}
}
// Set m_savedRvLawCovMatrix
Q_M tmpMat(m_imageSetPerIndex.vectorSpace().zeroVector());
m_savedRvLawCovMatrix = new Q_M(m_savedRvImageSpace->zeroVector());
if ((m_env.subDisplayFile()) && (m_env.displayVerbosity() >= 3)) {
*m_env.subDisplayFile() << "In VectorGaussianRandomField<P_V,P_M,Q_V,Q_M>::sampleFunction()"
<< ": m_savedRvLawCovMatrix order = " << m_savedRvLawCovMatrix->numCols()
<< ", numberOfPositions = " << numberOfPositions
<< ", tmpMat order = " << tmpMat.numCols()
<< ", numberOfImageValuesPerIndex = " << numberOfImageValuesPerIndex
<< std::endl;
}
for (unsigned int i = 0; i < numberOfPositions; ++i) {
for (unsigned int j = 0; j < numberOfPositions; ++j) {
m_covarianceFunction.covMatrix(*(fieldPositions[i]),*(fieldPositions[j]),tmpMat);
#if 1
Q_M testMat(tmpMat);
if (testMat.chol() != 0) {
*m_env.subDisplayFile() << "In VectorGaussianRandomField<P_V,P_M,Q_V,Q_M>::sampleFunction()"
<< ": i = " << i
<< ", j = " << j
<< ", *(fieldPositions[i]) = " << *(fieldPositions[i])
<< ", *(fieldPositions[j]) = " << *(fieldPositions[j])
<< ", tmpMat = " << tmpMat
<< ", testMat = " << testMat
<< ", tmpMat is not positive definite"
<< std::endl;
queso_error_msg("tmpMat is not positive definite");
}
#endif
for (unsigned int k1 = 0; k1 < numberOfImageValuesPerIndex; ++k1) {
for (unsigned int k2 = 0; k2 < numberOfImageValuesPerIndex; ++k2) {
unsigned int tmpI = i*numberOfImageValuesPerIndex + k1;
unsigned int tmpJ = j*numberOfImageValuesPerIndex + k2;
(*m_savedRvLawCovMatrix)(tmpI,tmpJ) = tmpMat(k1,k2);
if ((m_env.subDisplayFile()) && (m_env.displayVerbosity() >= 3)) {
*m_env.subDisplayFile() << "In VectorGaussianRandomField<P_V,P_M,Q_V,Q_M>::sampleFunction()"
<< ": i = " << i
<< ", j = " << j
<< ", k1 = " << k1
<< ", k2 = " << k2
<< ", tmpI = " << tmpI
<< ", tmpJ = " << tmpJ
<< ", *(fieldPositions[i]) = " << *(fieldPositions[i])
<< ", *(fieldPositions[j]) = " << *(fieldPositions[j])
<< ", (*m_savedRvLawCovMatrix)(tmpI,tmpJ) = " << (*m_savedRvLawCovMatrix)(tmpI,tmpJ)
<< std::endl;
}
}
}
}
}
// Set m_savedRv
m_savedRv = new GaussianVectorRV<Q_V,Q_M>("grf_",
*m_savedRvImageSpace,
*m_savedRvLawExpVector,
*m_savedRvLawCovMatrix);
if ((m_env.subDisplayFile()) && (m_env.displayVerbosity() >= 3)) {
*m_env.subDisplayFile() << "In VectorGaussianRandomField<P_V,P_M,Q_V,Q_M>::sampleFunction()"
<< ": just instantiated Gaussian RV"
<< "\n *m_savedRvLawExpVector = " << *m_savedRvLawExpVector
<< "\n *m_savedRvLawCovMatrix = " << *m_savedRvLawCovMatrix
<< std::endl;
for (unsigned int i = 0; i < numberOfPositions; ++i) {
*m_env.subDisplayFile() << " *(m_savedPositions[" << i
<< "]) = " << *(m_savedPositions[i])
<< std::endl;
}
}
} // if (instantiate)
// Generate sample function
if ((m_env.subDisplayFile()) && (m_env.displayVerbosity() >= 3)) {
*m_env.subDisplayFile() << "In VectorGaussianRandomField<P_V,P_M,Q_V,Q_M>::sampleFunction()"
<< ": about to realize sample values"
<< std::endl;
}
m_savedRv->realizer().realization(sampleValues);
if ((m_env.subDisplayFile()) && (m_env.displayVerbosity() >= 3)) {
*m_env.subDisplayFile() << "In VectorGaussianRandomField<P_V,P_M,Q_V,Q_M>::sampleFunction()"
<< ": just realized sample values"
<< std::endl;
}
if ((m_env.subDisplayFile()) && (m_env.displayVerbosity() >= 3)) {
*m_env.subDisplayFile() << "Leaving VectorGaussianRandomField<P_V,P_M,Q_V,Q_M>::sampleFunction()"
<< std::endl;
}
return;
}
