EstervQrCode 2.0.0
Library for qr code manipulation
Loading...
Searching...
No Matches
tracking.hpp
1/*M///////////////////////////////////////////////////////////////////////////////////////
2//
3// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
4//
5// By downloading, copying, installing or using the software you agree to this license.
6// If you do not agree to this license, do not download, install,
7// copy or use the software.
8//
9//
10// License Agreement
11// For Open Source Computer Vision Library
12//
13// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
14// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
15// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
16// Third party copyrights are property of their respective owners.
17//
18// Redistribution and use in source and binary forms, with or without modification,
19// are permitted provided that the following conditions are met:
20//
21// * Redistribution's of source code must retain the above copyright notice,
22// this list of conditions and the following disclaimer.
23//
24// * Redistribution's in binary form must reproduce the above copyright notice,
25// this list of conditions and the following disclaimer in the documentation
26// and/or other materials provided with the distribution.
27//
28// * The name of the copyright holders may not be used to endorse or promote products
29// derived from this software without specific prior written permission.
30//
31// This software is provided by the copyright holders and contributors "as is" and
32// any express or implied warranties, including, but not limited to, the implied
33// warranties of merchantability and fitness for a particular purpose are disclaimed.
34// In no event shall the Intel Corporation or contributors be liable for any direct,
35// indirect, incidental, special, exemplary, or consequential damages
36// (including, but not limited to, procurement of substitute goods or services;
37// loss of use, data, or profits; or business interruption) however caused
38// and on any theory of liability, whether in contract, strict liability,
39// or tort (including negligence or otherwise) arising in any way out of
40// the use of this software, even if advised of the possibility of such damage.
41//
42//M*/
43
44#ifndef OPENCV_TRACKING_HPP
45#define OPENCV_TRACKING_HPP
46
47#include "opencv2/core.hpp"
48#include "opencv2/imgproc.hpp"
49
50namespace cv
51{
52
55
59 };
60
105
122 Size winSize, int maxLevel, bool withDerivatives = true,
123 int pyrBorder = BORDER_REFLECT_101,
124 int derivBorder = BORDER_CONSTANT,
125 bool tryReuseInputImage = true );
126
179 InputArray prevPts, InputOutputArray nextPts,
180 OutputArray status, OutputArray err,
181 Size winSize = Size(21,21), int maxLevel = 3,
183 int flags = 0, double minEigThreshold = 1e-4 );
184
224 double pyr_scale, int levels, int winsize,
225 int iterations, int poly_n, double poly_sigma,
226 int flags );
227
259
260enum
261{
267
279CV_EXPORTS_W double computeECC(InputArray templateImage, InputArray inputImage, InputArray inputMask = noArray());
280
336CV_EXPORTS_W double findTransformECC( InputArray templateImage, InputArray inputImage,
337 InputOutputArray warpMatrix, int motionType,
339 InputArray inputMask, int gaussFiltSize);
340
343double findTransformECC(InputArray templateImage, InputArray inputImage,
344 InputOutputArray warpMatrix, int motionType = MOTION_AFFINE,
346 InputArray inputMask = noArray());
347
361{
362public:
370 CV_WRAP KalmanFilter( int dynamParams, int measureParams, int controlParams = 0, int type = CV_32F );
371
379 void init( int dynamParams, int measureParams, int controlParams = 0, int type = CV_32F );
380
385 CV_WRAP const Mat& predict( const Mat& control = Mat() );
386
391 CV_WRAP const Mat& correct( const Mat& measurement );
392
403
404 // temporary matrices
410};
411
412
432
437{
438public:
445 CV_WRAP virtual void calc( InputArray I0, InputArray I1, InputOutputArray flow ) = 0;
448 CV_WRAP virtual void collectGarbage() = 0;
449};
450
454{
455public:
466 CV_WRAP virtual void calc(InputArray prevImg, InputArray nextImg,
467 InputArray prevPts, InputOutputArray nextPts,
468 OutputArray status,
469 OutputArray err = cv::noArray()) = 0;
470};
471
472
476{
477public:
478 CV_WRAP virtual int getNumLevels() const = 0;
479 CV_WRAP virtual void setNumLevels(int numLevels) = 0;
480
481 CV_WRAP virtual double getPyrScale() const = 0;
482 CV_WRAP virtual void setPyrScale(double pyrScale) = 0;
483
484 CV_WRAP virtual bool getFastPyramids() const = 0;
485 CV_WRAP virtual void setFastPyramids(bool fastPyramids) = 0;
486
487 CV_WRAP virtual int getWinSize() const = 0;
488 CV_WRAP virtual void setWinSize(int winSize) = 0;
489
490 CV_WRAP virtual int getNumIters() const = 0;
491 CV_WRAP virtual void setNumIters(int numIters) = 0;
492
493 CV_WRAP virtual int getPolyN() const = 0;
494 CV_WRAP virtual void setPolyN(int polyN) = 0;
495
496 CV_WRAP virtual double getPolySigma() const = 0;
497 CV_WRAP virtual void setPolySigma(double polySigma) = 0;
498
499 CV_WRAP virtual int getFlags() const = 0;
500 CV_WRAP virtual void setFlags(int flags) = 0;
501
503 int numLevels = 5,
504 double pyrScale = 0.5,
505 bool fastPyramids = false,
506 int winSize = 13,
507 int numIters = 10,
508 int polyN = 5,
509 double polySigma = 1.1,
510 int flags = 0);
511};
512
524{
525public:
528 CV_WRAP virtual void calcUV(InputArray I0, InputArray I1, InputOutputArray flow_u, InputOutputArray flow_v) = 0;
529
532 CV_WRAP virtual int getFixedPointIterations() const = 0;
534 CV_WRAP virtual void setFixedPointIterations(int val) = 0;
535
539 CV_WRAP virtual int getSorIterations() const = 0;
541 CV_WRAP virtual void setSorIterations(int val) = 0;
542
545 CV_WRAP virtual float getOmega() const = 0;
547 CV_WRAP virtual void setOmega(float val) = 0;
548
551 CV_WRAP virtual float getAlpha() const = 0;
553 CV_WRAP virtual void setAlpha(float val) = 0;
554
557 CV_WRAP virtual float getDelta() const = 0;
559 CV_WRAP virtual void setDelta(float val) = 0;
560
563 CV_WRAP virtual float getGamma() const = 0;
565 CV_WRAP virtual void setGamma(float val) = 0;
566
569 CV_WRAP virtual float getEpsilon() const = 0;
571 CV_WRAP virtual void setEpsilon(float val) = 0;
572
576};
577
591{
592public:
593 enum
594 {
595 PRESET_ULTRAFAST = 0,
596 PRESET_FAST = 1,
597 PRESET_MEDIUM = 2
598 };
599
603 CV_WRAP virtual int getFinestScale() const = 0;
605 CV_WRAP virtual void setFinestScale(int val) = 0;
606
610 CV_WRAP virtual int getPatchSize() const = 0;
612 CV_WRAP virtual void setPatchSize(int val) = 0;
613
617 CV_WRAP virtual int getPatchStride() const = 0;
619 CV_WRAP virtual void setPatchStride(int val) = 0;
620
624 CV_WRAP virtual int getGradientDescentIterations() const = 0;
626 CV_WRAP virtual void setGradientDescentIterations(int val) = 0;
627
635
638 CV_WRAP virtual float getVariationalRefinementAlpha() const = 0;
640 CV_WRAP virtual void setVariationalRefinementAlpha(float val) = 0;
641
644 CV_WRAP virtual float getVariationalRefinementDelta() const = 0;
646 CV_WRAP virtual void setVariationalRefinementDelta(float val) = 0;
647
650 CV_WRAP virtual float getVariationalRefinementGamma() const = 0;
652 CV_WRAP virtual void setVariationalRefinementGamma(float val) = 0;
653
656 CV_WRAP virtual float getVariationalRefinementEpsilon() const = 0;
658 CV_WRAP virtual void setVariationalRefinementEpsilon(float val) = 0;
659
660
666 CV_WRAP virtual bool getUseMeanNormalization() const = 0;
668 CV_WRAP virtual void setUseMeanNormalization(bool val) = 0;
669
675 CV_WRAP virtual bool getUseSpatialPropagation() const = 0;
677 CV_WRAP virtual void setUseSpatialPropagation(bool val) = 0;
678
683 CV_WRAP static Ptr<DISOpticalFlow> create(int preset = DISOpticalFlow::PRESET_FAST);
684};
685
695{
696public:
697 CV_WRAP virtual Size getWinSize() const = 0;
698 CV_WRAP virtual void setWinSize(Size winSize) = 0;
699
700 CV_WRAP virtual int getMaxLevel() const = 0;
701 CV_WRAP virtual void setMaxLevel(int maxLevel) = 0;
702
704 CV_WRAP virtual void setTermCriteria(TermCriteria& crit) = 0;
705
706 CV_WRAP virtual int getFlags() const = 0;
707 CV_WRAP virtual void setFlags(int flags) = 0;
708
709 CV_WRAP virtual double getMinEigThreshold() const = 0;
710 CV_WRAP virtual void setMinEigThreshold(double minEigThreshold) = 0;
711
713 Size winSize = Size(21, 21),
714 int maxLevel = 3, TermCriteria crit =
715 TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, 0.01),
716 int flags = 0,
717 double minEigThreshold = 1e-4);
718};
719
720
721
722
726{
727protected:
729public:
730 virtual ~Tracker();
731
736 CV_WRAP virtual
737 void init(InputArray image, const Rect& boundingBox) = 0;
738
748 CV_WRAP virtual
749 bool update(InputArray image, CV_OUT Rect& boundingBox) = 0;
750};
751
752
753
763{
764protected:
765 TrackerMIL(); // use ::create()
766public:
768
781
785 static CV_WRAP
787
788 //void init(InputArray image, const Rect& boundingBox) CV_OVERRIDE;
789 //bool update(InputArray image, CV_OUT Rect& boundingBox) CV_OVERRIDE;
790};
791
792
793
810{
811protected:
812 TrackerGOTURN(); // use ::create()
813public:
815
822
826 static CV_WRAP
828
829 //void init(InputArray image, const Rect& boundingBox) CV_OVERRIDE;
830 //bool update(InputArray image, CV_OUT Rect& boundingBox) CV_OVERRIDE;
831};
832
834{
835protected:
836 TrackerDaSiamRPN(); // use ::create()
837public:
839
849
853 static CV_WRAP
855
858 CV_WRAP virtual float getTrackingScore() = 0;
859
860 //void init(InputArray image, const Rect& boundingBox) CV_OVERRIDE;
861 //bool update(InputArray image, CV_OUT Rect& boundingBox) CV_OVERRIDE;
862};
863
873{
874protected:
875 TrackerNano(); // use ::create()
876public:
878
887
891 static CV_WRAP
893
896 CV_WRAP virtual float getTrackingScore() = 0;
897
898 //void init(InputArray image, const Rect& boundingBox) CV_OVERRIDE;
899 //bool update(InputArray image, CV_OUT Rect& boundingBox) CV_OVERRIDE;
900};
901
909{
910protected:
911 TrackerVit(); // use ::create()
912public:
914
924
928 static CV_WRAP
930
933 CV_WRAP virtual float getTrackingScore() = 0;
934
935 // void init(InputArray image, const Rect& boundingBox) CV_OVERRIDE;
936 // bool update(InputArray image, CV_OUT Rect& boundingBox) CV_OVERRIDE;
937};
938
940
941} // cv
942
943#endif
This is a base class for all more or less complex algorithms in OpenCV.
Definition core.hpp:3197
DIS optical flow algorithm.
Definition tracking.hpp:591
virtual CV_WRAP void setVariationalRefinementIterations(int val)=0
Maximum number of gradient descent iterations in the patch inverse search stage. Higher values may im...
virtual CV_WRAP float getVariationalRefinementDelta() const =0
Weight of the color constancy term.
virtual CV_WRAP void setGradientDescentIterations(int val)=0
Maximum number of gradient descent iterations in the patch inverse search stage. Higher values may im...
virtual CV_WRAP void setVariationalRefinementEpsilon(float val)=0
Norm value shift for robust penalizer.
virtual CV_WRAP int getPatchSize() const =0
Size of an image patch for matching (in pixels). Normally, default 8x8 patches work well enough in mo...
virtual CV_WRAP void setVariationalRefinementDelta(float val)=0
Weight of the color constancy term.
virtual CV_WRAP void setVariationalRefinementAlpha(float val)=0
Weight of the smoothness term.
virtual CV_WRAP float getVariationalRefinementAlpha() const =0
Weight of the smoothness term.
virtual CV_WRAP void setFinestScale(int val)=0
Finest level of the Gaussian pyramid on which the flow is computed (zero level corresponds to the ori...
virtual CV_WRAP void setVariationalRefinementGamma(float val)=0
Weight of the gradient constancy term.
virtual CV_WRAP void setPatchStride(int val)=0
Stride between neighbor patches. Must be less than patch size. Lower values correspond to higher flow...
virtual CV_WRAP int getPatchStride() const =0
Stride between neighbor patches. Must be less than patch size. Lower values correspond to higher flow...
virtual CV_WRAP float getVariationalRefinementEpsilon() const =0
Norm value shift for robust penalizer.
virtual CV_WRAP int getVariationalRefinementIterations() const =0
Number of fixed point iterations of variational refinement per scale. Set to zero to disable variatio...
virtual CV_WRAP void setPatchSize(int val)=0
Size of an image patch for matching (in pixels). Normally, default 8x8 patches work well enough in mo...
static CV_WRAP Ptr< DISOpticalFlow > create(int preset=DISOpticalFlow::PRESET_FAST)
Creates an instance of DISOpticalFlow.
virtual CV_WRAP bool getUseSpatialPropagation() const =0
Whether to use spatial propagation of good optical flow vectors. This option is turned on by default,...
virtual CV_WRAP bool getUseMeanNormalization() const =0
Whether to use mean-normalization of patches when computing patch distance. It is turned on by defaul...
virtual CV_WRAP int getGradientDescentIterations() const =0
Maximum number of gradient descent iterations in the patch inverse search stage. Higher values may im...
virtual CV_WRAP int getFinestScale() const =0
Finest level of the Gaussian pyramid on which the flow is computed (zero level corresponds to the ori...
virtual CV_WRAP float getVariationalRefinementGamma() const =0
Weight of the gradient constancy term.
virtual CV_WRAP void setUseSpatialPropagation(bool val)=0
Whether to use spatial propagation of good optical flow vectors. This option is turned on by default,...
virtual CV_WRAP void setUseMeanNormalization(bool val)=0
Whether to use mean-normalization of patches when computing patch distance. It is turned on by defaul...
Definition tracking.hpp:437
virtual CV_WRAP void calc(InputArray I0, InputArray I1, InputOutputArray flow)=0
Calculates an optical flow.
virtual CV_WRAP void collectGarbage()=0
Releases all inner buffers.
Class computing a dense optical flow using the Gunnar Farneback's algorithm.
Definition tracking.hpp:476
virtual CV_WRAP void setFastPyramids(bool fastPyramids)=0
virtual CV_WRAP int getNumLevels() const =0
virtual CV_WRAP void setNumIters(int numIters)=0
virtual CV_WRAP int getPolyN() const =0
virtual CV_WRAP void setNumLevels(int numLevels)=0
virtual CV_WRAP void setFlags(int flags)=0
virtual CV_WRAP void setPolySigma(double polySigma)=0
virtual CV_WRAP void setWinSize(int winSize)=0
virtual CV_WRAP bool getFastPyramids() const =0
virtual CV_WRAP double getPyrScale() const =0
virtual CV_WRAP int getWinSize() const =0
virtual CV_WRAP double getPolySigma() const =0
virtual CV_WRAP int getNumIters() const =0
static CV_WRAP Ptr< FarnebackOpticalFlow > create(int numLevels=5, double pyrScale=0.5, bool fastPyramids=false, int winSize=13, int numIters=10, int polyN=5, double polySigma=1.1, int flags=0)
virtual CV_WRAP void setPyrScale(double pyrScale)=0
virtual CV_WRAP void setPolyN(int polyN)=0
virtual CV_WRAP int getFlags() const =0
Kalman filter class.
Definition tracking.hpp:361
CV_WRAP KalmanFilter(int dynamParams, int measureParams, int controlParams=0, int type=CV_32F)
CV_PROP_RW Mat errorCovPre
priori error estimate covariance matrix (P'(k)): P'(k)=A*P(k-1)*At + Q)*‍/
Definition tracking.hpp:400
CV_PROP_RW Mat processNoiseCov
process noise covariance matrix (Q)
Definition tracking.hpp:398
Mat temp2
Definition tracking.hpp:406
CV_PROP_RW Mat errorCovPost
posteriori error estimate covariance matrix (P(k)): P(k)=(I-K(k)*H)*P'(k)
Definition tracking.hpp:402
CV_PROP_RW Mat statePost
corrected state (x(k)): x(k)=x'(k)+K(k)*(z(k)-H*x'(k))
Definition tracking.hpp:394
CV_PROP_RW Mat statePre
predicted state (x'(k)): x(k)=A*x(k-1)+B*u(k)
Definition tracking.hpp:393
CV_PROP_RW Mat measurementMatrix
measurement matrix (H)
Definition tracking.hpp:397
void init(int dynamParams, int measureParams, int controlParams=0, int type=CV_32F)
Re-initializes Kalman filter. The previous content is destroyed.
Mat temp4
Definition tracking.hpp:408
Mat temp5
Definition tracking.hpp:409
CV_PROP_RW Mat gain
Kalman gain matrix (K(k)): K(k)=P'(k)*Ht*inv(H*P'(k)*Ht+R)
Definition tracking.hpp:401
CV_WRAP const Mat & correct(const Mat &measurement)
Updates the predicted state from the measurement.
Mat temp1
Definition tracking.hpp:405
CV_WRAP const Mat & predict(const Mat &control=Mat())
Computes a predicted state.
CV_WRAP KalmanFilter()
CV_PROP_RW Mat measurementNoiseCov
measurement noise covariance matrix (R)
Definition tracking.hpp:399
CV_PROP_RW Mat transitionMatrix
state transition matrix (A)
Definition tracking.hpp:395
CV_PROP_RW Mat controlMatrix
control matrix (B) (not used if there is no control)
Definition tracking.hpp:396
Mat temp3
Definition tracking.hpp:407
n-dimensional dense array class
Definition mat.hpp:812
Template class for 2D rectangles.
Definition types.hpp:444
The class represents rotated (i.e. not up-right) rectangles on a plane.
Definition types.hpp:531
Template class for specifying the size of an image or rectangle.
Definition types.hpp:335
Base interface for sparse optical flow algorithms.
Definition tracking.hpp:454
virtual CV_WRAP void calc(InputArray prevImg, InputArray nextImg, InputArray prevPts, InputOutputArray nextPts, OutputArray status, OutputArray err=cv::noArray())=0
Calculates a sparse optical flow.
Class used for calculating a sparse optical flow.
Definition tracking.hpp:695
virtual CV_WRAP int getMaxLevel() const =0
virtual CV_WRAP Size getWinSize() const =0
virtual CV_WRAP double getMinEigThreshold() const =0
virtual CV_WRAP void setWinSize(Size winSize)=0
virtual CV_WRAP void setFlags(int flags)=0
virtual CV_WRAP void setMinEigThreshold(double minEigThreshold)=0
virtual CV_WRAP void setTermCriteria(TermCriteria &crit)=0
virtual CV_WRAP void setMaxLevel(int maxLevel)=0
virtual CV_WRAP TermCriteria getTermCriteria() const =0
virtual CV_WRAP int getFlags() const =0
static CV_WRAP Ptr< SparsePyrLKOpticalFlow > create(Size winSize=Size(21, 21), int maxLevel=3, TermCriteria crit=TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, 0.01), int flags=0, double minEigThreshold=1e-4)
The class defining termination criteria for iterative algorithms.
Definition types.hpp:886
@ EPS
the desired accuracy or change in parameters at which the iterative algorithm stops
Definition types.hpp:895
@ COUNT
the maximum number of iterations or elements to compute
Definition types.hpp:893
Definition tracking.hpp:834
static CV_WRAP Ptr< TrackerDaSiamRPN > create(const TrackerDaSiamRPN::Params &parameters=TrackerDaSiamRPN::Params())
Constructor.
virtual CV_WRAP float getTrackingScore()=0
Return tracking score.
virtual ~TrackerDaSiamRPN() CV_OVERRIDE
the GOTURN (Generic Object Tracking Using Regression Networks) tracker
Definition tracking.hpp:810
virtual ~TrackerGOTURN() CV_OVERRIDE
static CV_WRAP Ptr< TrackerGOTURN > create(const TrackerGOTURN::Params &parameters=TrackerGOTURN::Params())
Constructor.
The MIL algorithm trains a classifier in an online manner to separate the object from the background.
Definition tracking.hpp:763
static CV_WRAP Ptr< TrackerMIL > create(const TrackerMIL::Params &parameters=TrackerMIL::Params())
Create MIL tracker instance.
virtual ~TrackerMIL() CV_OVERRIDE
the Nano tracker is a super lightweight dnn-based general object tracking.
Definition tracking.hpp:873
virtual CV_WRAP float getTrackingScore()=0
Return tracking score.
static CV_WRAP Ptr< TrackerNano > create(const TrackerNano::Params &parameters=TrackerNano::Params())
Constructor.
virtual ~TrackerNano() CV_OVERRIDE
the VIT tracker is a super lightweight dnn-based general object tracking.
Definition tracking.hpp:909
static CV_WRAP Ptr< TrackerVit > create(const TrackerVit::Params &parameters=TrackerVit::Params())
Constructor.
virtual ~TrackerVit() CV_OVERRIDE
virtual CV_WRAP float getTrackingScore()=0
Return tracking score.
Base abstract class for the long-term tracker.
Definition tracking.hpp:726
virtual ~Tracker()
virtual CV_WRAP bool update(InputArray image, CV_OUT Rect &boundingBox)=0
Update the tracker, find the new most likely bounding box for the target.
virtual CV_WRAP void init(InputArray image, const Rect &boundingBox)=0
Initialize the tracker with a known bounding box that surrounded the target.
Variational optical flow refinement.
Definition tracking.hpp:524
virtual CV_WRAP void setEpsilon(float val)=0
Norm value shift for robust penalizer.
virtual CV_WRAP float getGamma() const =0
Weight of the gradient constancy term.
virtual CV_WRAP int getSorIterations() const =0
Number of inner successive over-relaxation (SOR) iterations in the minimization procedure to solve th...
virtual CV_WRAP int getFixedPointIterations() const =0
Number of outer (fixed-point) iterations in the minimization procedure.
virtual CV_WRAP float getDelta() const =0
Weight of the color constancy term.
virtual CV_WRAP void setFixedPointIterations(int val)=0
Number of outer (fixed-point) iterations in the minimization procedure.
virtual CV_WRAP void setGamma(float val)=0
Weight of the gradient constancy term.
static CV_WRAP Ptr< VariationalRefinement > create()
Creates an instance of VariationalRefinement.
virtual CV_WRAP void setOmega(float val)=0
Relaxation factor in SOR.
virtual CV_WRAP void setSorIterations(int val)=0
Number of inner successive over-relaxation (SOR) iterations in the minimization procedure to solve th...
virtual CV_WRAP float getEpsilon() const =0
Norm value shift for robust penalizer.
virtual CV_WRAP void calcUV(InputArray I0, InputArray I1, InputOutputArray flow_u, InputOutputArray flow_v)=0
calc function overload to handle separate horizontal (u) and vertical (v) flow components (to avoid e...
virtual CV_WRAP void setAlpha(float val)=0
Weight of the smoothness term.
virtual CV_WRAP float getOmega() const =0
Relaxation factor in SOR.
virtual CV_WRAP void setDelta(float val)=0
Weight of the color constancy term.
virtual CV_WRAP float getAlpha() const =0
Weight of the smoothness term.
Definition mat.hpp:387
This type is very similar to InputArray except that it is used for input/output and output function p...
Definition mat.hpp:296
InputArrayOfArrays Size InputOutputArray InputOutputArray OutputArrayOfArrays OutputArrayOfArrays OutputArray OutputArray OutputArray int flags
Definition calib3d.hpp:1617
InputArrayOfArrays Size InputOutputArray InputOutputArray OutputArrayOfArrays OutputArrayOfArrays OutputArray OutputArray OutputArray int TermCriteria criteria
Definition calib3d.hpp:1617
@ BORDER_REFLECT_101
gfedcb|abcdefgh|gfedcba
Definition base.hpp:273
@ BORDER_CONSTANT
iiiiii|abcdefgh|iiiiiii with some specified i
Definition base.hpp:269
Size2i Size
Definition types.hpp:370
CV_EXPORTS InputOutputArray noArray()
CV__DEBUG_NS_END typedef const _InputArray & InputArray
Definition mat.hpp:442
const CvArr const CvArr const CvArr CvArr int flags
Definition core_c.h:1342
int int type
Definition core_c.h:221
#define CV_32F
Definition interface.h:78
#define CV_EXPORTS_W_SIMPLE
Definition cvdef.h:473
#define CV_EXPORTS
Definition cvdef.h:435
#define CV_DEPRECATED
Definition cvdef.h:450
#define CV_IN_OUT
Definition cvdef.h:477
#define CV_OVERRIDE
Definition cvdef.h:792
#define CV_OUT
Definition cvdef.h:478
#define CV_EXPORTS_W
Definition cvdef.h:472
#define CV_PROP_RW
Definition cvdef.h:480
#define CV_WRAP
Definition cvdef.h:481
InputArray int InputArray CV_IN_OUT Ptr< float > OutputArray flow
Definition imgproc.hpp:3388
OutputArray dst
Definition imgproc.hpp:3564
CV_EXPORTS_W RotatedRect CamShift(InputArray probImage, CV_IN_OUT Rect &window, TermCriteria criteria)
Finds an object center, size, and orientation.
CV_EXPORTS_W void calcOpticalFlowFarneback(InputArray prev, InputArray next, InputOutputArray flow, double pyr_scale, int levels, int winsize, int iterations, int poly_n, double poly_sigma, int flags)
Computes a dense optical flow using the Gunnar Farneback's algorithm.
CV_EXPORTS_W int meanShift(InputArray probImage, CV_IN_OUT Rect &window, TermCriteria criteria)
Finds an object on a back projection image.
CV_EXPORTS_W int buildOpticalFlowPyramid(InputArray img, OutputArrayOfArrays pyramid, Size winSize, int maxLevel, bool withDerivatives=true, int pyrBorder=BORDER_REFLECT_101, int derivBorder=BORDER_CONSTANT, bool tryReuseInputImage=true)
Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK.
CV_EXPORTS_W double findTransformECC(InputArray templateImage, InputArray inputImage, InputOutputArray warpMatrix, int motionType, TermCriteria criteria, InputArray inputMask, int gaussFiltSize)
Finds the geometric transform (warp) between two images in terms of the ECC criterion .
CV_EXPORTS_W double computeECC(InputArray templateImage, InputArray inputImage, InputArray inputMask=noArray())
Computes the Enhanced Correlation Coefficient value between two images .
CV_EXPORTS_W Mat readOpticalFlow(const String &path)
Read a .flo file.
CV_EXPORTS_W void calcOpticalFlowPyrLK(InputArray prevImg, InputArray nextImg, InputArray prevPts, InputOutputArray nextPts, OutputArray status, OutputArray err, Size winSize=Size(21, 21), int maxLevel=3, TermCriteria criteria=TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, 0.01), int flags=0, double minEigThreshold=1e-4)
Calculates an optical flow for a sparse feature set using the iterative Lucas-Kanade method with pyra...
CV_EXPORTS_W bool writeOpticalFlow(const String &path, InputArray flow)
Write a .flo to disk.
CV_DEPRECATED CV_EXPORTS Mat estimateRigidTransform(InputArray src, InputArray dst, bool fullAffine)
Computes an optimal affine transformation between two 2D point sets.
@ MOTION_TRANSLATION
Definition tracking.hpp:262
@ MOTION_EUCLIDEAN
Definition tracking.hpp:263
@ MOTION_HOMOGRAPHY
Definition tracking.hpp:265
@ MOTION_AFFINE
Definition tracking.hpp:264
@ OPTFLOW_FARNEBACK_GAUSSIAN
Definition tracking.hpp:58
@ OPTFLOW_USE_INITIAL_FLOW
Definition tracking.hpp:56
@ OPTFLOW_LK_GET_MIN_EIGENVALS
Definition tracking.hpp:57
const IplImage * image
Definition videoio_c.h:131
"black box" representation of the file storage associated with a file on disk.
Definition calib3d.hpp:441
Definition cvstd_wrapper.hpp:74
Definition tracking.hpp:841
CV_PROP_RW std::string kernel_r1
Definition tracking.hpp:845
CV_PROP_RW std::string model
Definition tracking.hpp:843
CV_PROP_RW int backend
Definition tracking.hpp:846
CV_PROP_RW int target
Definition tracking.hpp:847
CV_PROP_RW std::string kernel_cls1
Definition tracking.hpp:844
Definition tracking.hpp:817
CV_PROP_RW std::string modelTxt
Definition tracking.hpp:819
CV_PROP_RW std::string modelBin
Definition tracking.hpp:820
Definition tracking.hpp:770
CV_PROP_RW float samplerTrackInRadius
radius for gathering positive instances during tracking
Definition tracking.hpp:776
CV_PROP_RW int samplerTrackMaxNegNum
Definition tracking.hpp:778
CV_PROP_RW int samplerInitMaxNegNum
Definition tracking.hpp:774
CV_PROP_RW int samplerTrackMaxPosNum
Definition tracking.hpp:777
CV_PROP_RW float samplerInitInRadius
radius for gathering positive instances during init
Definition tracking.hpp:773
CV_PROP_RW float samplerSearchWinSize
size of search window
Definition tracking.hpp:775
CV_PROP_RW int featureSetNumFeatures
Definition tracking.hpp:779
Definition tracking.hpp:880
CV_PROP_RW std::string backbone
Definition tracking.hpp:882
CV_PROP_RW std::string neckhead
Definition tracking.hpp:883
CV_PROP_RW int backend
Definition tracking.hpp:884
CV_PROP_RW int target
Definition tracking.hpp:885
Definition tracking.hpp:916
CV_PROP_RW Scalar meanvalue
Definition tracking.hpp:921
CV_PROP_RW int target
Definition tracking.hpp:920
CV_PROP_RW int backend
Definition tracking.hpp:919
CV_PROP_RW Scalar stdvalue
Definition tracking.hpp:922
CV_PROP_RW std::string net
Definition tracking.hpp:918