OXIESEC PANEL

Name	Size	Modified	Perms
📁 ..	-	10/28/2024 06:50:42 AM	rwxr-xr-x
📄 motempl.hpp	7.18 KB	05/12/2017 03:45:27 AM	rw-r--r--
📄 pcaflow.hpp	5.56 KB	05/12/2017 03:45:27 AM	rw-r--r--
📄 sparse_matching_gpc.hpp	12.92 KB	05/12/2017 03:45:27 AM	rw-r--r--

Editing: pcaflow.hpp

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/**
 * @file   pcaflow.hpp
 * @author Vladislav Samsonov <vvladxx@gmail.com>
 * @brief  Implementation of the PCAFlow algorithm from the following paper:
 * http://files.is.tue.mpg.de/black/papers/cvpr2015_pcaflow.pdf
 *
 * @cite Wulff:CVPR:2015
 *
 * There are some key differences which distinguish this algorithm from the original PCAFlow (see paper):
 * - Discrete Cosine Transform basis is used instead of basis extracted with PCA.
 *   Reasoning: DCT basis has comparable performance and it doesn't require additional storage space.
 *   Also, this decision helps to avoid overloading the algorithm with a lot of external input.
 * - Usage of built-in OpenCV feature tracking instead of libviso.
*/

#ifndef __OPENCV_OPTFLOW_PCAFLOW_HPP__
#define __OPENCV_OPTFLOW_PCAFLOW_HPP__

#include "opencv2/core.hpp"
#include "opencv2/video.hpp"

namespace cv
{
namespace optflow
{

//! @addtogroup optflow
//! @{

/** @brief
 * This class can be used for imposing a learned prior on the resulting optical flow.
 * Solution will be regularized according to this prior.
 * You need to generate appropriate prior file with "learn_prior.py" script beforehand.
 */
class CV_EXPORTS_W PCAPrior
{
private:
  Mat L1;
  Mat L2;
  Mat c1;
  Mat c2;

public:
  PCAPrior( const char *pathToPrior );

int getPadding() const { return L1.size().height; }

int getBasisSize() const { return L1.size().width; }

void fillConstraints( float *A1, float *A2, float *b1, float *b2 ) const;
};

/** @brief PCAFlow algorithm.
 */
class CV_EXPORTS_W OpticalFlowPCAFlow : public DenseOpticalFlow
{
protected:
  const Ptr<const PCAPrior> prior;
  const Size basisSize;
  const float sparseRate;              // (0 .. 0.1)
  const float retainedCornersFraction; // [0 .. 1]
  const float occlusionsThreshold;
  const float dampingFactor;
  const float claheClip;
  bool useOpenCL;

public:
  /** @brief Creates an instance of PCAFlow algorithm.
   * @param _prior Learned prior or no prior (default). @see cv::optflow::PCAPrior
   * @param _basisSize Number of basis vectors.
   * @param _sparseRate Controls density of sparse matches.
   * @param _retainedCornersFraction Retained corners fraction.
   * @param _occlusionsThreshold Occlusion threshold.
   * @param _dampingFactor Regularization term for solving least-squares. It is not related to the prior regularization.
   * @param _claheClip Clip parameter for CLAHE.
   */
  OpticalFlowPCAFlow( Ptr<const PCAPrior> _prior = Ptr<const PCAPrior>(), const Size _basisSize = Size( 18, 14 ),
                      float _sparseRate = 0.024, float _retainedCornersFraction = 0.2,
                      float _occlusionsThreshold = 0.0003, float _dampingFactor = 0.00002, float _claheClip = 14 );

void calc( InputArray I0, InputArray I1, InputOutputArray flow );
  void collectGarbage();

private:
  void findSparseFeatures( UMat &from, UMat &to, std::vector<Point2f> &features,
                           std::vector<Point2f> &predictedFeatures ) const;

void removeOcclusions( UMat &from, UMat &to, std::vector<Point2f> &features,
                         std::vector<Point2f> &predictedFeatures ) const;

void getSystem( OutputArray AOut, OutputArray b1Out, OutputArray b2Out, const std::vector<Point2f> &features,
                  const std::vector<Point2f> &predictedFeatures, const Size size );

void getSystem( OutputArray A1Out, OutputArray A2Out, OutputArray b1Out, OutputArray b2Out,
                  const std::vector<Point2f> &features, const std::vector<Point2f> &predictedFeatures,
                  const Size size );

OpticalFlowPCAFlow& operator=( const OpticalFlowPCAFlow& ); // make it non-assignable
};

/** @brief Creates an instance of PCAFlow
*/
CV_EXPORTS_W Ptr<DenseOpticalFlow> createOptFlow_PCAFlow();

//! @}

}
}

#endif