diff options
Diffstat (limited to 'thirdparty1/linux/include/opencv2/video')
| -rw-r--r-- | thirdparty1/linux/include/opencv2/video/background_segm.hpp | 306 | ||||
| -rw-r--r-- | thirdparty1/linux/include/opencv2/video/tracking.hpp | 626 | ||||
| -rw-r--r-- | thirdparty1/linux/include/opencv2/video/tracking_c.h | 232 | ||||
| -rw-r--r-- | thirdparty1/linux/include/opencv2/video/video.hpp | 48 |
4 files changed, 1212 insertions, 0 deletions
diff --git a/thirdparty1/linux/include/opencv2/video/background_segm.hpp b/thirdparty1/linux/include/opencv2/video/background_segm.hpp new file mode 100644 index 0000000..2952d57 --- /dev/null +++ b/thirdparty1/linux/include/opencv2/video/background_segm.hpp @@ -0,0 +1,306 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_BACKGROUND_SEGM_HPP +#define OPENCV_BACKGROUND_SEGM_HPP + +#include "opencv2/core.hpp" + +namespace cv +{ + +//! @addtogroup video_motion +//! @{ + +/** @brief Base class for background/foreground segmentation. : + +The class is only used to define the common interface for the whole family of background/foreground +segmentation algorithms. + */ +class CV_EXPORTS_W BackgroundSubtractor : public Algorithm +{ +public: + /** @brief Computes a foreground mask. + + @param image Next video frame. + @param fgmask The output foreground mask as an 8-bit binary image. + @param learningRate The value between 0 and 1 that indicates how fast the background model is + learnt. Negative parameter value makes the algorithm to use some automatically chosen learning + rate. 0 means that the background model is not updated at all, 1 means that the background model + is completely reinitialized from the last frame. + */ + CV_WRAP virtual void apply(InputArray image, OutputArray fgmask, double learningRate=-1) = 0; + + /** @brief Computes a background image. + + @param backgroundImage The output background image. + + @note Sometimes the background image can be very blurry, as it contain the average background + statistics. + */ + CV_WRAP virtual void getBackgroundImage(OutputArray backgroundImage) const = 0; +}; + + +/** @brief Gaussian Mixture-based Background/Foreground Segmentation Algorithm. + +The class implements the Gaussian mixture model background subtraction described in @cite Zivkovic2004 +and @cite Zivkovic2006 . + */ +class CV_EXPORTS_W BackgroundSubtractorMOG2 : public BackgroundSubtractor +{ +public: + /** @brief Returns the number of last frames that affect the background model + */ + CV_WRAP virtual int getHistory() const = 0; + /** @brief Sets the number of last frames that affect the background model + */ + CV_WRAP virtual void setHistory(int history) = 0; + + /** @brief Returns the number of gaussian components in the background model + */ + CV_WRAP virtual int getNMixtures() const = 0; + /** @brief Sets the number of gaussian components in the background model. + + The model needs to be reinitalized to reserve memory. + */ + CV_WRAP virtual void setNMixtures(int nmixtures) = 0;//needs reinitialization! + + /** @brief Returns the "background ratio" parameter of the algorithm + + If a foreground pixel keeps semi-constant value for about backgroundRatio\*history frames, it's + considered background and added to the model as a center of a new component. It corresponds to TB + parameter in the paper. + */ + CV_WRAP virtual double getBackgroundRatio() const = 0; + /** @brief Sets the "background ratio" parameter of the algorithm + */ + CV_WRAP virtual void setBackgroundRatio(double ratio) = 0; + + /** @brief Returns the variance threshold for the pixel-model match + + The main threshold on the squared Mahalanobis distance to decide if the sample is well described by + the background model or not. Related to Cthr from the paper. + */ + CV_WRAP virtual double getVarThreshold() const = 0; + /** @brief Sets the variance threshold for the pixel-model match + */ + CV_WRAP virtual void setVarThreshold(double varThreshold) = 0; + + /** @brief Returns the variance threshold for the pixel-model match used for new mixture component generation + + Threshold for the squared Mahalanobis distance that helps decide when a sample is close to the + existing components (corresponds to Tg in the paper). If a pixel is not close to any component, it + is considered foreground or added as a new component. 3 sigma =\> Tg=3\*3=9 is default. A smaller Tg + value generates more components. A higher Tg value may result in a small number of components but + they can grow too large. + */ + CV_WRAP virtual double getVarThresholdGen() const = 0; + /** @brief Sets the variance threshold for the pixel-model match used for new mixture component generation + */ + CV_WRAP virtual void setVarThresholdGen(double varThresholdGen) = 0; + + /** @brief Returns the initial variance of each gaussian component + */ + CV_WRAP virtual double getVarInit() const = 0; + /** @brief Sets the initial variance of each gaussian component + */ + CV_WRAP virtual void setVarInit(double varInit) = 0; + + CV_WRAP virtual double getVarMin() const = 0; + CV_WRAP virtual void setVarMin(double varMin) = 0; + + CV_WRAP virtual double getVarMax() const = 0; + CV_WRAP virtual void setVarMax(double varMax) = 0; + + /** @brief Returns the complexity reduction threshold + + This parameter defines the number of samples needed to accept to prove the component exists. CT=0.05 + is a default value for all the samples. By setting CT=0 you get an algorithm very similar to the + standard Stauffer&Grimson algorithm. + */ + CV_WRAP virtual double getComplexityReductionThreshold() const = 0; + /** @brief Sets the complexity reduction threshold + */ + CV_WRAP virtual void setComplexityReductionThreshold(double ct) = 0; + + /** @brief Returns the shadow detection flag + + If true, the algorithm detects shadows and marks them. See createBackgroundSubtractorMOG2 for + details. + */ + CV_WRAP virtual bool getDetectShadows() const = 0; + /** @brief Enables or disables shadow detection + */ + CV_WRAP virtual void setDetectShadows(bool detectShadows) = 0; + + /** @brief Returns the shadow value + + Shadow value is the value used to mark shadows in the foreground mask. Default value is 127. Value 0 + in the mask always means background, 255 means foreground. + */ + CV_WRAP virtual int getShadowValue() const = 0; + /** @brief Sets the shadow value + */ + CV_WRAP virtual void setShadowValue(int value) = 0; + + /** @brief Returns the shadow threshold + + A shadow is detected if pixel is a darker version of the background. The shadow threshold (Tau in + the paper) is a threshold defining how much darker the shadow can be. Tau= 0.5 means that if a pixel + is more than twice darker then it is not shadow. See Prati, Mikic, Trivedi and Cucchiarra, + *Detecting Moving Shadows...*, IEEE PAMI,2003. + */ + CV_WRAP virtual double getShadowThreshold() const = 0; + /** @brief Sets the shadow threshold + */ + CV_WRAP virtual void setShadowThreshold(double threshold) = 0; +}; + +/** @brief Creates MOG2 Background Subtractor + +@param history Length of the history. +@param varThreshold Threshold on the squared Mahalanobis distance between the pixel and the model +to decide whether a pixel is well described by the background model. This parameter does not +affect the background update. +@param detectShadows If true, the algorithm will detect shadows and mark them. It decreases the +speed a bit, so if you do not need this feature, set the parameter to false. + */ +CV_EXPORTS_W Ptr<BackgroundSubtractorMOG2> + createBackgroundSubtractorMOG2(int history=500, double varThreshold=16, + bool detectShadows=true); + +/** @brief K-nearest neigbours - based Background/Foreground Segmentation Algorithm. + +The class implements the K-nearest neigbours background subtraction described in @cite Zivkovic2006 . +Very efficient if number of foreground pixels is low. + */ +class CV_EXPORTS_W BackgroundSubtractorKNN : public BackgroundSubtractor +{ +public: + /** @brief Returns the number of last frames that affect the background model + */ + CV_WRAP virtual int getHistory() const = 0; + /** @brief Sets the number of last frames that affect the background model + */ + CV_WRAP virtual void setHistory(int history) = 0; + + /** @brief Returns the number of data samples in the background model + */ + CV_WRAP virtual int getNSamples() const = 0; + /** @brief Sets the number of data samples in the background model. + + The model needs to be reinitalized to reserve memory. + */ + CV_WRAP virtual void setNSamples(int _nN) = 0;//needs reinitialization! + + /** @brief Returns the threshold on the squared distance between the pixel and the sample + + The threshold on the squared distance between the pixel and the sample to decide whether a pixel is + close to a data sample. + */ + CV_WRAP virtual double getDist2Threshold() const = 0; + /** @brief Sets the threshold on the squared distance + */ + CV_WRAP virtual void setDist2Threshold(double _dist2Threshold) = 0; + + /** @brief Returns the number of neighbours, the k in the kNN. + + K is the number of samples that need to be within dist2Threshold in order to decide that that + pixel is matching the kNN background model. + */ + CV_WRAP virtual int getkNNSamples() const = 0; + /** @brief Sets the k in the kNN. How many nearest neigbours need to match. + */ + CV_WRAP virtual void setkNNSamples(int _nkNN) = 0; + + /** @brief Returns the shadow detection flag + + If true, the algorithm detects shadows and marks them. See createBackgroundSubtractorKNN for + details. + */ + CV_WRAP virtual bool getDetectShadows() const = 0; + /** @brief Enables or disables shadow detection + */ + CV_WRAP virtual void setDetectShadows(bool detectShadows) = 0; + + /** @brief Returns the shadow value + + Shadow value is the value used to mark shadows in the foreground mask. Default value is 127. Value 0 + in the mask always means background, 255 means foreground. + */ + CV_WRAP virtual int getShadowValue() const = 0; + /** @brief Sets the shadow value + */ + CV_WRAP virtual void setShadowValue(int value) = 0; + + /** @brief Returns the shadow threshold + + A shadow is detected if pixel is a darker version of the background. The shadow threshold (Tau in + the paper) is a threshold defining how much darker the shadow can be. Tau= 0.5 means that if a pixel + is more than twice darker then it is not shadow. See Prati, Mikic, Trivedi and Cucchiarra, + *Detecting Moving Shadows...*, IEEE PAMI,2003. + */ + CV_WRAP virtual double getShadowThreshold() const = 0; + /** @brief Sets the shadow threshold + */ + CV_WRAP virtual void setShadowThreshold(double threshold) = 0; +}; + +/** @brief Creates KNN Background Subtractor + +@param history Length of the history. +@param dist2Threshold Threshold on the squared distance between the pixel and the sample to decide +whether a pixel is close to that sample. This parameter does not affect the background update. +@param detectShadows If true, the algorithm will detect shadows and mark them. It decreases the +speed a bit, so if you do not need this feature, set the parameter to false. + */ +CV_EXPORTS_W Ptr<BackgroundSubtractorKNN> + createBackgroundSubtractorKNN(int history=500, double dist2Threshold=400.0, + bool detectShadows=true); + +//! @} video_motion + +} // cv + +#endif diff --git a/thirdparty1/linux/include/opencv2/video/tracking.hpp b/thirdparty1/linux/include/opencv2/video/tracking.hpp new file mode 100644 index 0000000..c6ead3a --- /dev/null +++ b/thirdparty1/linux/include/opencv2/video/tracking.hpp @@ -0,0 +1,626 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_TRACKING_HPP +#define OPENCV_TRACKING_HPP + +#include "opencv2/core.hpp" +#include "opencv2/imgproc.hpp" + +namespace cv +{ + +//! @addtogroup video_track +//! @{ + +enum { OPTFLOW_USE_INITIAL_FLOW = 4, + OPTFLOW_LK_GET_MIN_EIGENVALS = 8, + OPTFLOW_FARNEBACK_GAUSSIAN = 256 + }; + +/** @brief Finds an object center, size, and orientation. + +@param probImage Back projection of the object histogram. See calcBackProject. +@param window Initial search window. +@param criteria Stop criteria for the underlying meanShift. +returns +(in old interfaces) Number of iterations CAMSHIFT took to converge +The function implements the CAMSHIFT object tracking algorithm @cite Bradski98 . First, it finds an +object center using meanShift and then adjusts the window size and finds the optimal rotation. The +function returns the rotated rectangle structure that includes the object position, size, and +orientation. The next position of the search window can be obtained with RotatedRect::boundingRect() + +See the OpenCV sample camshiftdemo.c that tracks colored objects. + +@note +- (Python) A sample explaining the camshift tracking algorithm can be found at + opencv_source_code/samples/python/camshift.py + */ +CV_EXPORTS_W RotatedRect CamShift( InputArray probImage, CV_IN_OUT Rect& window, + TermCriteria criteria ); + +/** @brief Finds an object on a back projection image. + +@param probImage Back projection of the object histogram. See calcBackProject for details. +@param window Initial search window. +@param criteria Stop criteria for the iterative search algorithm. +returns +: Number of iterations CAMSHIFT took to converge. +The function implements the iterative object search algorithm. It takes the input back projection of +an object and the initial position. The mass center in window of the back projection image is +computed and the search window center shifts to the mass center. The procedure is repeated until the +specified number of iterations criteria.maxCount is done or until the window center shifts by less +than criteria.epsilon. The algorithm is used inside CamShift and, unlike CamShift , the search +window size or orientation do not change during the search. You can simply pass the output of +calcBackProject to this function. But better results can be obtained if you pre-filter the back +projection and remove the noise. For example, you can do this by retrieving connected components +with findContours , throwing away contours with small area ( contourArea ), and rendering the +remaining contours with drawContours. + +@note +- A mean-shift tracking sample can be found at opencv_source_code/samples/cpp/camshiftdemo.cpp + */ +CV_EXPORTS_W int meanShift( InputArray probImage, CV_IN_OUT Rect& window, TermCriteria criteria ); + +/** @brief Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK. + +@param img 8-bit input image. +@param pyramid output pyramid. +@param winSize window size of optical flow algorithm. Must be not less than winSize argument of +calcOpticalFlowPyrLK. It is needed to calculate required padding for pyramid levels. +@param maxLevel 0-based maximal pyramid level number. +@param withDerivatives set to precompute gradients for the every pyramid level. If pyramid is +constructed without the gradients then calcOpticalFlowPyrLK will calculate them internally. +@param pyrBorder the border mode for pyramid layers. +@param derivBorder the border mode for gradients. +@param tryReuseInputImage put ROI of input image into the pyramid if possible. You can pass false +to force data copying. +@return number of levels in constructed pyramid. Can be less than maxLevel. + */ +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 ); + +/** @brief Calculates an optical flow for a sparse feature set using the iterative Lucas-Kanade method with +pyramids. + +@param prevImg first 8-bit input image or pyramid constructed by buildOpticalFlowPyramid. +@param nextImg second input image or pyramid of the same size and the same type as prevImg. +@param prevPts vector of 2D points for which the flow needs to be found; point coordinates must be +single-precision floating-point numbers. +@param nextPts output vector of 2D points (with single-precision floating-point coordinates) +containing the calculated new positions of input features in the second image; when +OPTFLOW_USE_INITIAL_FLOW flag is passed, the vector must have the same size as in the input. +@param status output status vector (of unsigned chars); each element of the vector is set to 1 if +the flow for the corresponding features has been found, otherwise, it is set to 0. +@param err output vector of errors; each element of the vector is set to an error for the +corresponding feature, type of the error measure can be set in flags parameter; if the flow wasn't +found then the error is not defined (use the status parameter to find such cases). +@param winSize size of the search window at each pyramid level. +@param maxLevel 0-based maximal pyramid level number; if set to 0, pyramids are not used (single +level), if set to 1, two levels are used, and so on; if pyramids are passed to input then +algorithm will use as many levels as pyramids have but no more than maxLevel. +@param criteria parameter, specifying the termination criteria of the iterative search algorithm +(after the specified maximum number of iterations criteria.maxCount or when the search window +moves by less than criteria.epsilon. +@param flags operation flags: + - **OPTFLOW_USE_INITIAL_FLOW** uses initial estimations, stored in nextPts; if the flag is + not set, then prevPts is copied to nextPts and is considered the initial estimate. + - **OPTFLOW_LK_GET_MIN_EIGENVALS** use minimum eigen values as an error measure (see + minEigThreshold description); if the flag is not set, then L1 distance between patches + around the original and a moved point, divided by number of pixels in a window, is used as a + error measure. +@param minEigThreshold the algorithm calculates the minimum eigen value of a 2x2 normal matrix of +optical flow equations (this matrix is called a spatial gradient matrix in @cite Bouguet00), divided +by number of pixels in a window; if this value is less than minEigThreshold, then a corresponding +feature is filtered out and its flow is not processed, so it allows to remove bad points and get a +performance boost. + +The function implements a sparse iterative version of the Lucas-Kanade optical flow in pyramids. See +@cite Bouguet00 . The function is parallelized with the TBB library. + +@note + +- An example using the Lucas-Kanade optical flow algorithm can be found at + opencv_source_code/samples/cpp/lkdemo.cpp +- (Python) An example using the Lucas-Kanade optical flow algorithm can be found at + opencv_source_code/samples/python/lk_track.py +- (Python) An example using the Lucas-Kanade tracker for homography matching can be found at + opencv_source_code/samples/python/lk_homography.py + */ +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 ); + +/** @brief Computes a dense optical flow using the Gunnar Farneback's algorithm. + +@param prev first 8-bit single-channel input image. +@param next second input image of the same size and the same type as prev. +@param flow computed flow image that has the same size as prev and type CV_32FC2. +@param pyr_scale parameter, specifying the image scale (\<1) to build pyramids for each image; +pyr_scale=0.5 means a classical pyramid, where each next layer is twice smaller than the previous +one. +@param levels number of pyramid layers including the initial image; levels=1 means that no extra +layers are created and only the original images are used. +@param winsize averaging window size; larger values increase the algorithm robustness to image +noise and give more chances for fast motion detection, but yield more blurred motion field. +@param iterations number of iterations the algorithm does at each pyramid level. +@param poly_n size of the pixel neighborhood used to find polynomial expansion in each pixel; +larger values mean that the image will be approximated with smoother surfaces, yielding more +robust algorithm and more blurred motion field, typically poly_n =5 or 7. +@param poly_sigma standard deviation of the Gaussian that is used to smooth derivatives used as a +basis for the polynomial expansion; for poly_n=5, you can set poly_sigma=1.1, for poly_n=7, a +good value would be poly_sigma=1.5. +@param flags operation flags that can be a combination of the following: + - **OPTFLOW_USE_INITIAL_FLOW** uses the input flow as an initial flow approximation. + - **OPTFLOW_FARNEBACK_GAUSSIAN** uses the Gaussian \f$\texttt{winsize}\times\texttt{winsize}\f$ + filter instead of a box filter of the same size for optical flow estimation; usually, this + option gives z more accurate flow than with a box filter, at the cost of lower speed; + normally, winsize for a Gaussian window should be set to a larger value to achieve the same + level of robustness. + +The function finds an optical flow for each prev pixel using the @cite Farneback2003 algorithm so that + +\f[\texttt{prev} (y,x) \sim \texttt{next} ( y + \texttt{flow} (y,x)[1], x + \texttt{flow} (y,x)[0])\f] + +@note + +- An example using the optical flow algorithm described by Gunnar Farneback can be found at + opencv_source_code/samples/cpp/fback.cpp +- (Python) An example using the optical flow algorithm described by Gunnar Farneback can be + found at opencv_source_code/samples/python/opt_flow.py + */ +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 ); + +/** @brief Computes an optimal affine transformation between two 2D point sets. + +@param src First input 2D point set stored in std::vector or Mat, or an image stored in Mat. +@param dst Second input 2D point set of the same size and the same type as A, or another image. +@param fullAffine If true, the function finds an optimal affine transformation with no additional +restrictions (6 degrees of freedom). Otherwise, the class of transformations to choose from is +limited to combinations of translation, rotation, and uniform scaling (4 degrees of freedom). + +The function finds an optimal affine transform *[A|b]* (a 2 x 3 floating-point matrix) that +approximates best the affine transformation between: + +* Two point sets +* Two raster images. In this case, the function first finds some features in the src image and + finds the corresponding features in dst image. After that, the problem is reduced to the first + case. +In case of point sets, the problem is formulated as follows: you need to find a 2x2 matrix *A* and +2x1 vector *b* so that: + +\f[[A^*|b^*] = arg \min _{[A|b]} \sum _i \| \texttt{dst}[i] - A { \texttt{src}[i]}^T - b \| ^2\f] +where src[i] and dst[i] are the i-th points in src and dst, respectively +\f$[A|b]\f$ can be either arbitrary (when fullAffine=true ) or have a form of +\f[\begin{bmatrix} a_{11} & a_{12} & b_1 \\ -a_{12} & a_{11} & b_2 \end{bmatrix}\f] +when fullAffine=false. + +@sa +estimateAffine2D, estimateAffinePartial2D, getAffineTransform, getPerspectiveTransform, findHomography + */ +CV_EXPORTS_W Mat estimateRigidTransform( InputArray src, InputArray dst, bool fullAffine ); + + +enum +{ + MOTION_TRANSLATION = 0, + MOTION_EUCLIDEAN = 1, + MOTION_AFFINE = 2, + MOTION_HOMOGRAPHY = 3 +}; + +/** @brief Finds the geometric transform (warp) between two images in terms of the ECC criterion @cite EP08 . + +@param templateImage single-channel template image; CV_8U or CV_32F array. +@param inputImage single-channel input image which should be warped with the final warpMatrix in +order to provide an image similar to templateImage, same type as temlateImage. +@param warpMatrix floating-point \f$2\times 3\f$ or \f$3\times 3\f$ mapping matrix (warp). +@param motionType parameter, specifying the type of motion: + - **MOTION_TRANSLATION** sets a translational motion model; warpMatrix is \f$2\times 3\f$ with + the first \f$2\times 2\f$ part being the unity matrix and the rest two parameters being + estimated. + - **MOTION_EUCLIDEAN** sets a Euclidean (rigid) transformation as motion model; three + parameters are estimated; warpMatrix is \f$2\times 3\f$. + - **MOTION_AFFINE** sets an affine motion model (DEFAULT); six parameters are estimated; + warpMatrix is \f$2\times 3\f$. + - **MOTION_HOMOGRAPHY** sets a homography as a motion model; eight parameters are + estimated;\`warpMatrix\` is \f$3\times 3\f$. +@param criteria parameter, specifying the termination criteria of the ECC algorithm; +criteria.epsilon defines the threshold of the increment in the correlation coefficient between two +iterations (a negative criteria.epsilon makes criteria.maxcount the only termination criterion). +Default values are shown in the declaration above. +@param inputMask An optional mask to indicate valid values of inputImage. + +The function estimates the optimum transformation (warpMatrix) with respect to ECC criterion +(@cite EP08), that is + +\f[\texttt{warpMatrix} = \texttt{warpMatrix} = \arg\max_{W} \texttt{ECC}(\texttt{templateImage}(x,y),\texttt{inputImage}(x',y'))\f] + +where + +\f[\begin{bmatrix} x' \\ y' \end{bmatrix} = W \cdot \begin{bmatrix} x \\ y \\ 1 \end{bmatrix}\f] + +(the equation holds with homogeneous coordinates for homography). It returns the final enhanced +correlation coefficient, that is the correlation coefficient between the template image and the +final warped input image. When a \f$3\times 3\f$ matrix is given with motionType =0, 1 or 2, the third +row is ignored. + +Unlike findHomography and estimateRigidTransform, the function findTransformECC implements an +area-based alignment that builds on intensity similarities. In essence, the function updates the +initial transformation that roughly aligns the images. If this information is missing, the identity +warp (unity matrix) should be given as input. Note that if images undergo strong +displacements/rotations, an initial transformation that roughly aligns the images is necessary +(e.g., a simple euclidean/similarity transform that allows for the images showing the same image +content approximately). Use inverse warping in the second image to take an image close to the first +one, i.e. use the flag WARP_INVERSE_MAP with warpAffine or warpPerspective. See also the OpenCV +sample image_alignment.cpp that demonstrates the use of the function. Note that the function throws +an exception if algorithm does not converges. + +@sa +estimateAffine2D, estimateAffinePartial2D, findHomography + */ +CV_EXPORTS_W double findTransformECC( InputArray templateImage, InputArray inputImage, + InputOutputArray warpMatrix, int motionType = MOTION_AFFINE, + TermCriteria criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 50, 0.001), + InputArray inputMask = noArray()); + +/** @brief Kalman filter class. + +The class implements a standard Kalman filter <http://en.wikipedia.org/wiki/Kalman_filter>, +@cite Welch95 . However, you can modify transitionMatrix, controlMatrix, and measurementMatrix to get +an extended Kalman filter functionality. See the OpenCV sample kalman.cpp. + +@note + +- An example using the standard Kalman filter can be found at + opencv_source_code/samples/cpp/kalman.cpp + */ +class CV_EXPORTS_W KalmanFilter +{ +public: + /** @brief The constructors. + + @note In C API when CvKalman\* kalmanFilter structure is not needed anymore, it should be released + with cvReleaseKalman(&kalmanFilter) + */ + CV_WRAP KalmanFilter(); + /** @overload + @param dynamParams Dimensionality of the state. + @param measureParams Dimensionality of the measurement. + @param controlParams Dimensionality of the control vector. + @param type Type of the created matrices that should be CV_32F or CV_64F. + */ + CV_WRAP KalmanFilter( int dynamParams, int measureParams, int controlParams = 0, int type = CV_32F ); + + /** @brief Re-initializes Kalman filter. The previous content is destroyed. + + @param dynamParams Dimensionality of the state. + @param measureParams Dimensionality of the measurement. + @param controlParams Dimensionality of the control vector. + @param type Type of the created matrices that should be CV_32F or CV_64F. + */ + void init( int dynamParams, int measureParams, int controlParams = 0, int type = CV_32F ); + + /** @brief Computes a predicted state. + + @param control The optional input control + */ + CV_WRAP const Mat& predict( const Mat& control = Mat() ); + + /** @brief Updates the predicted state from the measurement. + + @param measurement The measured system parameters + */ + CV_WRAP const Mat& correct( const Mat& measurement ); + + CV_PROP_RW Mat statePre; //!< predicted state (x'(k)): x(k)=A*x(k-1)+B*u(k) + CV_PROP_RW Mat statePost; //!< corrected state (x(k)): x(k)=x'(k)+K(k)*(z(k)-H*x'(k)) + CV_PROP_RW Mat transitionMatrix; //!< state transition matrix (A) + CV_PROP_RW Mat controlMatrix; //!< control matrix (B) (not used if there is no control) + CV_PROP_RW Mat measurementMatrix; //!< measurement matrix (H) + CV_PROP_RW Mat processNoiseCov; //!< process noise covariance matrix (Q) + CV_PROP_RW Mat measurementNoiseCov;//!< measurement noise covariance matrix (R) + CV_PROP_RW Mat errorCovPre; //!< priori error estimate covariance matrix (P'(k)): P'(k)=A*P(k-1)*At + Q)*/ + CV_PROP_RW Mat gain; //!< Kalman gain matrix (K(k)): K(k)=P'(k)*Ht*inv(H*P'(k)*Ht+R) + CV_PROP_RW Mat errorCovPost; //!< posteriori error estimate covariance matrix (P(k)): P(k)=(I-K(k)*H)*P'(k) + + // temporary matrices + Mat temp1; + Mat temp2; + Mat temp3; + Mat temp4; + Mat temp5; +}; + + +class CV_EXPORTS_W DenseOpticalFlow : public Algorithm +{ +public: + /** @brief Calculates an optical flow. + + @param I0 first 8-bit single-channel input image. + @param I1 second input image of the same size and the same type as prev. + @param flow computed flow image that has the same size as prev and type CV_32FC2. + */ + CV_WRAP virtual void calc( InputArray I0, InputArray I1, InputOutputArray flow ) = 0; + /** @brief Releases all inner buffers. + */ + CV_WRAP virtual void collectGarbage() = 0; +}; + +/** @brief Base interface for sparse optical flow algorithms. + */ +class CV_EXPORTS_W SparseOpticalFlow : public Algorithm +{ +public: + /** @brief Calculates a sparse optical flow. + + @param prevImg First input image. + @param nextImg Second input image of the same size and the same type as prevImg. + @param prevPts Vector of 2D points for which the flow needs to be found. + @param nextPts Output vector of 2D points containing the calculated new positions of input features in the second image. + @param status Output status vector. Each element of the vector is set to 1 if the + flow for the corresponding features has been found. Otherwise, it is set to 0. + @param err Optional output vector that contains error response for each point (inverse confidence). + */ + CV_WRAP virtual void calc(InputArray prevImg, InputArray nextImg, + InputArray prevPts, InputOutputArray nextPts, + OutputArray status, + OutputArray err = cv::noArray()) = 0; +}; + +/** @brief "Dual TV L1" Optical Flow Algorithm. + +The class implements the "Dual TV L1" optical flow algorithm described in @cite Zach2007 and +@cite Javier2012 . +Here are important members of the class that control the algorithm, which you can set after +constructing the class instance: + +- member double tau + Time step of the numerical scheme. + +- member double lambda + Weight parameter for the data term, attachment parameter. This is the most relevant + parameter, which determines the smoothness of the output. The smaller this parameter is, + the smoother the solutions we obtain. It depends on the range of motions of the images, so + its value should be adapted to each image sequence. + +- member double theta + Weight parameter for (u - v)\^2, tightness parameter. It serves as a link between the + attachment and the regularization terms. In theory, it should have a small value in order + to maintain both parts in correspondence. The method is stable for a large range of values + of this parameter. + +- member int nscales + Number of scales used to create the pyramid of images. + +- member int warps + Number of warpings per scale. Represents the number of times that I1(x+u0) and grad( + I1(x+u0) ) are computed per scale. This is a parameter that assures the stability of the + method. It also affects the running time, so it is a compromise between speed and + accuracy. + +- member double epsilon + Stopping criterion threshold used in the numerical scheme, which is a trade-off between + precision and running time. A small value will yield more accurate solutions at the + expense of a slower convergence. + +- member int iterations + Stopping criterion iterations number used in the numerical scheme. + +C. Zach, T. Pock and H. Bischof, "A Duality Based Approach for Realtime TV-L1 Optical Flow". +Javier Sanchez, Enric Meinhardt-Llopis and Gabriele Facciolo. "TV-L1 Optical Flow Estimation". +*/ +class CV_EXPORTS_W DualTVL1OpticalFlow : public DenseOpticalFlow +{ +public: + //! @brief Time step of the numerical scheme + /** @see setTau */ + CV_WRAP virtual double getTau() const = 0; + /** @copybrief getTau @see getTau */ + CV_WRAP virtual void setTau(double val) = 0; + //! @brief Weight parameter for the data term, attachment parameter + /** @see setLambda */ + CV_WRAP virtual double getLambda() const = 0; + /** @copybrief getLambda @see getLambda */ + CV_WRAP virtual void setLambda(double val) = 0; + //! @brief Weight parameter for (u - v)^2, tightness parameter + /** @see setTheta */ + CV_WRAP virtual double getTheta() const = 0; + /** @copybrief getTheta @see getTheta */ + CV_WRAP virtual void setTheta(double val) = 0; + //! @brief coefficient for additional illumination variation term + /** @see setGamma */ + CV_WRAP virtual double getGamma() const = 0; + /** @copybrief getGamma @see getGamma */ + CV_WRAP virtual void setGamma(double val) = 0; + //! @brief Number of scales used to create the pyramid of images + /** @see setScalesNumber */ + CV_WRAP virtual int getScalesNumber() const = 0; + /** @copybrief getScalesNumber @see getScalesNumber */ + CV_WRAP virtual void setScalesNumber(int val) = 0; + //! @brief Number of warpings per scale + /** @see setWarpingsNumber */ + CV_WRAP virtual int getWarpingsNumber() const = 0; + /** @copybrief getWarpingsNumber @see getWarpingsNumber */ + CV_WRAP virtual void setWarpingsNumber(int val) = 0; + //! @brief Stopping criterion threshold used in the numerical scheme, which is a trade-off between precision and running time + /** @see setEpsilon */ + CV_WRAP virtual double getEpsilon() const = 0; + /** @copybrief getEpsilon @see getEpsilon */ + CV_WRAP virtual void setEpsilon(double val) = 0; + //! @brief Inner iterations (between outlier filtering) used in the numerical scheme + /** @see setInnerIterations */ + CV_WRAP virtual int getInnerIterations() const = 0; + /** @copybrief getInnerIterations @see getInnerIterations */ + CV_WRAP virtual void setInnerIterations(int val) = 0; + //! @brief Outer iterations (number of inner loops) used in the numerical scheme + /** @see setOuterIterations */ + CV_WRAP virtual int getOuterIterations() const = 0; + /** @copybrief getOuterIterations @see getOuterIterations */ + CV_WRAP virtual void setOuterIterations(int val) = 0; + //! @brief Use initial flow + /** @see setUseInitialFlow */ + CV_WRAP virtual bool getUseInitialFlow() const = 0; + /** @copybrief getUseInitialFlow @see getUseInitialFlow */ + CV_WRAP virtual void setUseInitialFlow(bool val) = 0; + //! @brief Step between scales (<1) + /** @see setScaleStep */ + CV_WRAP virtual double getScaleStep() const = 0; + /** @copybrief getScaleStep @see getScaleStep */ + CV_WRAP virtual void setScaleStep(double val) = 0; + //! @brief Median filter kernel size (1 = no filter) (3 or 5) + /** @see setMedianFiltering */ + CV_WRAP virtual int getMedianFiltering() const = 0; + /** @copybrief getMedianFiltering @see getMedianFiltering */ + CV_WRAP virtual void setMedianFiltering(int val) = 0; + + /** @brief Creates instance of cv::DualTVL1OpticalFlow*/ + CV_WRAP static Ptr<DualTVL1OpticalFlow> create( + double tau = 0.25, + double lambda = 0.15, + double theta = 0.3, + int nscales = 5, + int warps = 5, + double epsilon = 0.01, + int innnerIterations = 30, + int outerIterations = 10, + double scaleStep = 0.8, + double gamma = 0.0, + int medianFiltering = 5, + bool useInitialFlow = false); +}; + +/** @brief Creates instance of cv::DenseOpticalFlow +*/ +CV_EXPORTS_W Ptr<DualTVL1OpticalFlow> createOptFlow_DualTVL1(); + +/** @brief Class computing a dense optical flow using the Gunnar Farneback’s algorithm. + */ +class CV_EXPORTS_W FarnebackOpticalFlow : public DenseOpticalFlow +{ +public: + CV_WRAP virtual int getNumLevels() const = 0; + CV_WRAP virtual void setNumLevels(int numLevels) = 0; + + CV_WRAP virtual double getPyrScale() const = 0; + CV_WRAP virtual void setPyrScale(double pyrScale) = 0; + + CV_WRAP virtual bool getFastPyramids() const = 0; + CV_WRAP virtual void setFastPyramids(bool fastPyramids) = 0; + + CV_WRAP virtual int getWinSize() const = 0; + CV_WRAP virtual void setWinSize(int winSize) = 0; + + CV_WRAP virtual int getNumIters() const = 0; + CV_WRAP virtual void setNumIters(int numIters) = 0; + + CV_WRAP virtual int getPolyN() const = 0; + CV_WRAP virtual void setPolyN(int polyN) = 0; + + CV_WRAP virtual double getPolySigma() const = 0; + CV_WRAP virtual void setPolySigma(double polySigma) = 0; + + CV_WRAP virtual int getFlags() const = 0; + CV_WRAP virtual void setFlags(int flags) = 0; + + CV_WRAP static 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); +}; + + +/** @brief Class used for calculating a sparse optical flow. + +The class can calculate an optical flow for a sparse feature set using the +iterative Lucas-Kanade method with pyramids. + +@sa calcOpticalFlowPyrLK + +*/ +class CV_EXPORTS_W SparsePyrLKOpticalFlow : public SparseOpticalFlow +{ +public: + CV_WRAP virtual Size getWinSize() const = 0; + CV_WRAP virtual void setWinSize(Size winSize) = 0; + + CV_WRAP virtual int getMaxLevel() const = 0; + CV_WRAP virtual void setMaxLevel(int maxLevel) = 0; + + CV_WRAP virtual TermCriteria getTermCriteria() const = 0; + CV_WRAP virtual void setTermCriteria(TermCriteria& crit) = 0; + + CV_WRAP virtual int getFlags() const = 0; + CV_WRAP virtual void setFlags(int flags) = 0; + + CV_WRAP virtual double getMinEigThreshold() const = 0; + CV_WRAP virtual void setMinEigThreshold(double minEigThreshold) = 0; + + CV_WRAP static 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); +}; + +//! @} video_track + +} // cv + +#endif diff --git a/thirdparty1/linux/include/opencv2/video/tracking_c.h b/thirdparty1/linux/include/opencv2/video/tracking_c.h new file mode 100644 index 0000000..3e32fbd --- /dev/null +++ b/thirdparty1/linux/include/opencv2/video/tracking_c.h @@ -0,0 +1,232 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_TRACKING_C_H +#define OPENCV_TRACKING_C_H + +#include "opencv2/imgproc/types_c.h" + +#ifdef __cplusplus +extern "C" { +#endif + +/** @addtogroup video_c + @{ +*/ + +/****************************************************************************************\ +* Motion Analysis * +\****************************************************************************************/ + +/************************************ optical flow ***************************************/ + +#define CV_LKFLOW_PYR_A_READY 1 +#define CV_LKFLOW_PYR_B_READY 2 +#define CV_LKFLOW_INITIAL_GUESSES 4 +#define CV_LKFLOW_GET_MIN_EIGENVALS 8 + +/* It is Lucas & Kanade method, modified to use pyramids. + Also it does several iterations to get optical flow for + every point at every pyramid level. + Calculates optical flow between two images for certain set of points (i.e. + it is a "sparse" optical flow, which is opposite to the previous 3 methods) */ +CVAPI(void) cvCalcOpticalFlowPyrLK( const CvArr* prev, const CvArr* curr, + CvArr* prev_pyr, CvArr* curr_pyr, + const CvPoint2D32f* prev_features, + CvPoint2D32f* curr_features, + int count, + CvSize win_size, + int level, + char* status, + float* track_error, + CvTermCriteria criteria, + int flags ); + + +/* Modification of a previous sparse optical flow algorithm to calculate + affine flow */ +CVAPI(void) cvCalcAffineFlowPyrLK( const CvArr* prev, const CvArr* curr, + CvArr* prev_pyr, CvArr* curr_pyr, + const CvPoint2D32f* prev_features, + CvPoint2D32f* curr_features, + float* matrices, int count, + CvSize win_size, int level, + char* status, float* track_error, + CvTermCriteria criteria, int flags ); + +/* Estimate rigid transformation between 2 images or 2 point sets */ +CVAPI(int) cvEstimateRigidTransform( const CvArr* A, const CvArr* B, + CvMat* M, int full_affine ); + +/* Estimate optical flow for each pixel using the two-frame G. Farneback algorithm */ +CVAPI(void) cvCalcOpticalFlowFarneback( const CvArr* prev, const CvArr* next, + CvArr* flow, double pyr_scale, int levels, + int winsize, int iterations, int poly_n, + double poly_sigma, int flags ); + +/********************************* motion templates *************************************/ + +/****************************************************************************************\ +* All the motion template functions work only with single channel images. * +* Silhouette image must have depth IPL_DEPTH_8U or IPL_DEPTH_8S * +* Motion history image must have depth IPL_DEPTH_32F, * +* Gradient mask - IPL_DEPTH_8U or IPL_DEPTH_8S, * +* Motion orientation image - IPL_DEPTH_32F * +* Segmentation mask - IPL_DEPTH_32F * +* All the angles are in degrees, all the times are in milliseconds * +\****************************************************************************************/ + +/* Updates motion history image given motion silhouette */ +CVAPI(void) cvUpdateMotionHistory( const CvArr* silhouette, CvArr* mhi, + double timestamp, double duration ); + +/* Calculates gradient of the motion history image and fills + a mask indicating where the gradient is valid */ +CVAPI(void) cvCalcMotionGradient( const CvArr* mhi, CvArr* mask, CvArr* orientation, + double delta1, double delta2, + int aperture_size CV_DEFAULT(3)); + +/* Calculates average motion direction within a selected motion region + (region can be selected by setting ROIs and/or by composing a valid gradient mask + with the region mask) */ +CVAPI(double) cvCalcGlobalOrientation( const CvArr* orientation, const CvArr* mask, + const CvArr* mhi, double timestamp, + double duration ); + +/* Splits a motion history image into a few parts corresponding to separate independent motions + (e.g. left hand, right hand) */ +CVAPI(CvSeq*) cvSegmentMotion( const CvArr* mhi, CvArr* seg_mask, + CvMemStorage* storage, + double timestamp, double seg_thresh ); + +/****************************************************************************************\ +* Tracking * +\****************************************************************************************/ + +/* Implements CAMSHIFT algorithm - determines object position, size and orientation + from the object histogram back project (extension of meanshift) */ +CVAPI(int) cvCamShift( const CvArr* prob_image, CvRect window, + CvTermCriteria criteria, CvConnectedComp* comp, + CvBox2D* box CV_DEFAULT(NULL) ); + +/* Implements MeanShift algorithm - determines object position + from the object histogram back project */ +CVAPI(int) cvMeanShift( const CvArr* prob_image, CvRect window, + CvTermCriteria criteria, CvConnectedComp* comp ); + +/* +standard Kalman filter (in G. Welch' and G. Bishop's notation): + + x(k)=A*x(k-1)+B*u(k)+w(k) p(w)~N(0,Q) + z(k)=H*x(k)+v(k), p(v)~N(0,R) +*/ +typedef struct CvKalman +{ + int MP; /* number of measurement vector dimensions */ + int DP; /* number of state vector dimensions */ + int CP; /* number of control vector dimensions */ + + /* backward compatibility fields */ +#if 1 + float* PosterState; /* =state_pre->data.fl */ + float* PriorState; /* =state_post->data.fl */ + float* DynamMatr; /* =transition_matrix->data.fl */ + float* MeasurementMatr; /* =measurement_matrix->data.fl */ + float* MNCovariance; /* =measurement_noise_cov->data.fl */ + float* PNCovariance; /* =process_noise_cov->data.fl */ + float* KalmGainMatr; /* =gain->data.fl */ + float* PriorErrorCovariance;/* =error_cov_pre->data.fl */ + float* PosterErrorCovariance;/* =error_cov_post->data.fl */ + float* Temp1; /* temp1->data.fl */ + float* Temp2; /* temp2->data.fl */ +#endif + + CvMat* state_pre; /* predicted state (x'(k)): + x(k)=A*x(k-1)+B*u(k) */ + CvMat* state_post; /* corrected state (x(k)): + x(k)=x'(k)+K(k)*(z(k)-H*x'(k)) */ + CvMat* transition_matrix; /* state transition matrix (A) */ + CvMat* control_matrix; /* control matrix (B) + (it is not used if there is no control)*/ + CvMat* measurement_matrix; /* measurement matrix (H) */ + CvMat* process_noise_cov; /* process noise covariance matrix (Q) */ + CvMat* measurement_noise_cov; /* measurement noise covariance matrix (R) */ + CvMat* error_cov_pre; /* priori error estimate covariance matrix (P'(k)): + P'(k)=A*P(k-1)*At + Q)*/ + CvMat* gain; /* Kalman gain matrix (K(k)): + K(k)=P'(k)*Ht*inv(H*P'(k)*Ht+R)*/ + CvMat* error_cov_post; /* posteriori error estimate covariance matrix (P(k)): + P(k)=(I-K(k)*H)*P'(k) */ + CvMat* temp1; /* temporary matrices */ + CvMat* temp2; + CvMat* temp3; + CvMat* temp4; + CvMat* temp5; +} CvKalman; + +/* Creates Kalman filter and sets A, B, Q, R and state to some initial values */ +CVAPI(CvKalman*) cvCreateKalman( int dynam_params, int measure_params, + int control_params CV_DEFAULT(0)); + +/* Releases Kalman filter state */ +CVAPI(void) cvReleaseKalman( CvKalman** kalman); + +/* Updates Kalman filter by time (predicts future state of the system) */ +CVAPI(const CvMat*) cvKalmanPredict( CvKalman* kalman, + const CvMat* control CV_DEFAULT(NULL)); + +/* Updates Kalman filter by measurement + (corrects state of the system and internal matrices) */ +CVAPI(const CvMat*) cvKalmanCorrect( CvKalman* kalman, const CvMat* measurement ); + +#define cvKalmanUpdateByTime cvKalmanPredict +#define cvKalmanUpdateByMeasurement cvKalmanCorrect + +/** @} video_c */ + +#ifdef __cplusplus +} // extern "C" +#endif + + +#endif // OPENCV_TRACKING_C_H diff --git a/thirdparty1/linux/include/opencv2/video/video.hpp b/thirdparty1/linux/include/opencv2/video/video.hpp new file mode 100644 index 0000000..8267b85 --- /dev/null +++ b/thirdparty1/linux/include/opencv2/video/video.hpp @@ -0,0 +1,48 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifdef __OPENCV_BUILD +#error this is a compatibility header which should not be used inside the OpenCV library +#endif + +#include "opencv2/video.hpp" |