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added type selection in the Kalman filter (thanks to Nghia Ho for the patch; see ticket #693)

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This commit is contained in:
Vadim Pisarevsky 2010-11-22 21:05:22 +00:00
parent e406dfee44
commit 92fb499ba9
2 changed files with 21 additions and 20 deletions
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4
modules/video/include/opencv2/video/tracking.hpp
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@ -280,9 +280,9 @@ public:
//! the default constructor
CV_WRAP KalmanFilter();
//! the full constructor taking the dimensionality of the state, of the measurement and of the control vector
CV_WRAP KalmanFilter(int dynamParams, int measureParams, int controlParams=0);
CV_WRAP KalmanFilter(int dynamParams, int measureParams, int controlParams=0, int type=CV_32F);
//! re-initializes Kalman filter. The previous content is destroyed.
void init(int dynamParams, int measureParams, int controlParams=0);
void init(int dynamParams, int measureParams, int controlParams=0, int type=CV_32F);
//! computes predicted state
CV_WRAP const Mat& predict(const Mat& control=Mat());

37
modules/video/src/kalman.cpp
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@ -211,38 +211,39 @@ namespace cv
{
KalmanFilter::KalmanFilter() {}
KalmanFilter::KalmanFilter(int dynamParams, int measureParams, int controlParams)
KalmanFilter::KalmanFilter(int dynamParams, int measureParams, int controlParams, int type)
{
init(dynamParams, measureParams, controlParams);
init(dynamParams, measureParams, controlParams, type);
}
void KalmanFilter::init(int DP, int MP, int CP)
void KalmanFilter::init(int DP, int MP, int CP, int type)
{
CV_Assert( DP > 0 && MP > 0 );
CV_Assert( type == CV_32F || type == CV_64F );
CP = std::max(CP, 0);
statePre = Mat::zeros(DP, 1, CV_32F);
statePost = Mat::zeros(DP, 1, CV_32F);
transitionMatrix = Mat::eye(DP, DP, CV_32F);
statePre = Mat::zeros(DP, 1, type);
statePost = Mat::zeros(DP, 1, type);
transitionMatrix = Mat::eye(DP, DP, type);
processNoiseCov = Mat::eye(DP, DP, CV_32F);
measurementMatrix = Mat::zeros(MP, DP, CV_32F);
measurementNoiseCov = Mat::eye(MP, MP, CV_32F);
processNoiseCov = Mat::eye(DP, DP, type);
measurementMatrix = Mat::zeros(MP, DP, type);
measurementNoiseCov = Mat::eye(MP, MP, type);
errorCovPre = Mat::zeros(DP, DP, CV_32F);
errorCovPost = Mat::zeros(DP, DP, CV_32F);
gain = Mat::zeros(DP, MP, CV_32F);
errorCovPre = Mat::zeros(DP, DP, type);
errorCovPost = Mat::zeros(DP, DP, type);
gain = Mat::zeros(DP, MP, type);
if( CP > 0 )
controlMatrix = Mat::zeros(DP, CP, CV_32F);
controlMatrix = Mat::zeros(DP, CP, type);
else
controlMatrix.release();
temp1.create(DP, DP, CV_32F);
temp2.create(MP, DP, CV_32F);
temp3.create(MP, MP, CV_32F);
temp4.create(MP, DP, CV_32F);
temp5.create(MP, 1, CV_32F);
temp1.create(DP, DP, type);
temp2.create(MP, DP, type);
temp3.create(MP, MP, type);
temp4.create(MP, DP, type);
temp5.create(MP, 1, type);
}
const Mat& KalmanFilter::predict(const Mat& control)