/**
* @author YAO Wei, JIA Pei
* @version 2010-05-20
* @brief Find the best offset for one point
* @param ioShape Input and output - the input and output shape
* @param iImg Input - image to be fitted
* @param oImages Output - the output images
* @param iLev Input - current pyramid level
* @param PClose Input - percentage of converged points. Say, 0.9 means if 90% of the points
* are judged as converged, the iteration of this pyramid can stop
* @param epoch Input - the maximum iteration times
* @param profdim Input - dimension used during fitting. For example, the trained data could be 4D, but the user may only use 1D
* @note Refer to "AAM Revisited, page 34, figure 13", particularly, those steps.
*/
void VO_FittingASMNDProfiles::PyramidFit( VO_Shape& ioShape,
const cv::Mat& iImg,
std::vector<cv::Mat>& oImages,
unsigned int iLev,
float PClose,
unsigned int epoch,
unsigned int profdim,
bool record)
{
VO_Shape tempShape = ioShape;
int nGoodLandmarks = 0;
float PyrScale = pow(2.0f, (float) (iLev) );
const int nQualifyingDisplacements = (int)(this->m_VOASMNDProfile->m_iNbOfPoints * PClose);
for(unsigned int iter = 0; iter < epoch; iter++)
{
this->m_iIteration++;
// estimate the best ioShape by profile matching the landmarks in this->m_VOFittingShape
nGoodLandmarks = VO_FittingASMNDProfiles::UpdateShape( this->m_VOASMNDProfile,
iImg,
tempShape,
this->m_vShape2DInfo,
this->m_VOASMNDProfile->m_vvMeanNormalizedProfile[iLev],
this->m_VOASMNDProfile->m_vvvCVMInverseOfSg[iLev],
3,
profdim);
// conform ioShape to the shape model
this->m_VOASMNDProfile->VO_CalcAllParams4AnyShapeWithConstrain( tempShape,
this->m_MatModelAlignedShapeParam,
this->m_fScale,
this->m_vRotateAngles,
this->m_MatCenterOfGravity );
tempShape.ConstrainShapeInImage(iImg);
if(record)
{
// If we get better fitting result, record this fitting result
cv::Mat temp3 = cv::Mat(this->m_ImageInput.size(), this->m_ImageInput.type(), this->m_ImageInput.channels());
cv::Mat temp3ROI = temp3(cv::Range (0, (int)(this->m_ImageInput.rows/PyrScale) ), cv::Range (0, (int)(this->m_ImageInput.cols/PyrScale) ) );
cv::resize(this->m_ImageInput, temp3ROI, temp3ROI.size());
VO_Fitting2DSM::VO_DrawMesh(tempShape / this->m_fScale2, this->m_VOASMNDProfile, temp3);
oImages.push_back(temp3);
}
// the fitting result is good enough to stop the iteration
if(nGoodLandmarks > nQualifyingDisplacements)
break;
}
ioShape = tempShape;
}
/**
* @author YAO Wei, JIA Pei
* @version 2010-05-20
* @brief Find the best offset for one point
* @param iImg Input - image to be fitted
* @param ioShape Input and output - the input and output shape
* @param iShapeInfo Input - the shape information
* @param iLev Input - current pyramid level
* @param PClose Input - percentage of converged points. Say, 0.9 means if 90% of the points
* are judged as converged, the iteration of this pyramid can stop
* @param epoch Input - the maximum iteration times
* @param profdim Input - dimension used during fitting. For example, the trained data could be 4D, but the user may only use 1D
* @note Refer to "AAM Revisited, page 34, figure 13", particularly, those steps.
*/
void VO_FittingASMNDProfiles::PyramidFit( VO_Shape& ioShape,
const cv::Mat& iImg,
unsigned int iLev,
float PClose,
unsigned int epoch,
unsigned int profdim)
{
VO_Shape tempShape = ioShape;
int nGoodLandmarks = 0;
float PyrScale = pow(2.0f, (float) (iLev-1.0f) );
const int nQualifyingDisplacements = (int)(this->m_VOASMNDProfile->m_iNbOfPoints * PClose);
for(unsigned int iter = 0; iter < epoch; iter++)
{
this->m_iIteration++;
// estimate the best ioShape by profile matching the landmarks in this->m_VOFittingShape
nGoodLandmarks = VO_FittingASMNDProfiles::UpdateShape( this->m_VOASMNDProfile,
iImg,
tempShape,
this->m_vShape2DInfo,
this->m_VOASMNDProfile->m_vvMeanNormalizedProfile[iLev],
this->m_VOASMNDProfile->m_vvvCVMInverseOfSg[iLev],
3,
profdim);
// conform ioShape to the shape model
this->m_VOASMNDProfile->VO_CalcAllParams4AnyShapeWithConstrain( tempShape,
this->m_MatModelAlignedShapeParam,
this->m_fScale,
this->m_vRotateAngles,
this->m_MatCenterOfGravity );
tempShape.ConstrainShapeInImage(iImg);
// the fitting result is good enough to stop the iteration
if(nGoodLandmarks > nQualifyingDisplacements)
break;
}
ioShape = tempShape;
}
