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Doxygen documentation: shape, superres, videostab

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modules/shape/include/opencv2/shape.hpp
View file @ 9d89f8d3
......@@ -48,6 +48,10 @@
#include "opencv2/shape/hist_cost.hpp"
#include "opencv2/shape/shape_distance.hpp"
/**
@defgroup shape Shape Distance and Matching
*/
namespace cv
{
CV_EXPORTS bool initModule_shape();
......
modules/shape/include/opencv2/shape/emdL1.hpp
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......@@ -51,8 +51,22 @@ namespace cv
* EMDL1 Function *
\****************************************************************************************/
//! @addtogroup shape
//! @{
/** @brief Computes the "minimal work" distance between two weighted point configurations base on the papers
"EMD-L1: An efficient and Robust Algorithm for comparing histogram-based descriptors", by Haibin
Ling and Kazunori Okuda; and "The Earth Mover's Distance is the Mallows Distance: Some Insights from
Statistics", by Elizaveta Levina and Peter Bickel.
@param signature1 First signature, a single column floating-point matrix. Each row is the value of
the histogram in each bin.
@param signature2 Second signature of the same format and size as signature1.
*/
CV_EXPORTS float EMDL1(InputArray signature1, InputArray signature2);
//! @}
}//namespace cv
#endif
modules/shape/include/opencv2/shape/hist_cost.hpp
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......@@ -49,8 +49,10 @@
namespace cv
{
/*!
* The base class for HistogramCostExtractor.
//! @addtogroup shape
//! @{
/** @brief Abstract base class for histogram cost algorithms.
*/
class CV_EXPORTS_W HistogramCostExtractor : public Algorithm
{
......@@ -64,7 +66,8 @@ public:
CV_WRAP virtual float getDefaultCost() const = 0;
};
/*! */
/** @brief A norm based cost extraction. :
*/
class CV_EXPORTS_W NormHistogramCostExtractor : public HistogramCostExtractor
{
public:
......@@ -75,7 +78,8 @@ public:
CV_EXPORTS_W Ptr<HistogramCostExtractor>
createNormHistogramCostExtractor(int flag=DIST_L2, int nDummies=25, float defaultCost=0.2f);
/*! */
/** @brief An EMD based cost extraction. :
*/
class CV_EXPORTS_W EMDHistogramCostExtractor : public HistogramCostExtractor
{
public:
......@@ -86,18 +90,22 @@ public:
CV_EXPORTS_W Ptr<HistogramCostExtractor>
createEMDHistogramCostExtractor(int flag=DIST_L2, int nDummies=25, float defaultCost=0.2f);
/*! */
/** @brief An Chi based cost extraction. :
*/
class CV_EXPORTS_W ChiHistogramCostExtractor : public HistogramCostExtractor
{};
CV_EXPORTS_W Ptr<HistogramCostExtractor> createChiHistogramCostExtractor(int nDummies=25, float defaultCost=0.2f);
/*! */
/** @brief An EMD-L1 based cost extraction. :
*/
class CV_EXPORTS_W EMDL1HistogramCostExtractor : public HistogramCostExtractor
{};
CV_EXPORTS_W Ptr<HistogramCostExtractor>
createEMDL1HistogramCostExtractor(int nDummies=25, float defaultCost=0.2f);
//! @}
} // cv
#endif
modules/shape/include/opencv2/shape/shape_distance.hpp
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......@@ -50,65 +50,131 @@
namespace cv
{
/*!
* The base class for ShapeDistanceExtractor.
* This is just to define the common interface for
* shape comparisson techniques.
//! @addtogroup shape
//! @{
/** @brief Abstract base class for shape distance algorithms.
*/
class CV_EXPORTS_W ShapeDistanceExtractor : public Algorithm
{
public:
/** @brief Compute the shape distance between two shapes defined by its contours.
@param contour1 Contour defining first shape.
@param contour2 Contour defining second shape.
*/
CV_WRAP virtual float computeDistance(InputArray contour1, InputArray contour2) = 0;
};
/***********************************************************************************/
/***********************************************************************************/
/***********************************************************************************/
/*!
* Shape Context implementation.
* The SCD class implements SCD algorithm proposed by Belongie et al.in
* "Shape Matching and Object Recognition Using Shape Contexts".
* Implemented by Juan M. Perez for the GSOC 2013.
*/
/** @brief Implementation of the Shape Context descriptor and matching algorithm
proposed by Belongie et al. in "Shape Matching and Object Recognition Using Shape Contexts" (PAMI
2002). This implementation is packaged in a generic scheme, in order to allow you the
implementation of the common variations of the original pipeline.
*/
class CV_EXPORTS_W ShapeContextDistanceExtractor : public ShapeDistanceExtractor
{
public:
/** @brief Establish the number of angular bins for the Shape Context Descriptor used in the shape matching
pipeline.
@param nAngularBins The number of angular bins in the shape context descriptor.
*/
CV_WRAP virtual void setAngularBins(int nAngularBins) = 0;
CV_WRAP virtual int getAngularBins() const = 0;
/** @brief Establish the number of radial bins for the Shape Context Descriptor used in the shape matching
pipeline.
@param nRadialBins The number of radial bins in the shape context descriptor.
*/
CV_WRAP virtual void setRadialBins(int nRadialBins) = 0;
CV_WRAP virtual int getRadialBins() const = 0;
/** @brief Set the inner radius of the shape context descriptor.
@param innerRadius The value of the inner radius.
*/
CV_WRAP virtual void setInnerRadius(float innerRadius) = 0;
CV_WRAP virtual float getInnerRadius() const = 0;
/** @brief Set the outer radius of the shape context descriptor.
@param outerRadius The value of the outer radius.
*/
CV_WRAP virtual void setOuterRadius(float outerRadius) = 0;
CV_WRAP virtual float getOuterRadius() const = 0;
CV_WRAP virtual void setRotationInvariant(bool rotationInvariant) = 0;
CV_WRAP virtual bool getRotationInvariant() const = 0;
/** @brief Set the weight of the shape context distance in the final value of the shape distance. The shape
context distance between two shapes is defined as the symmetric sum of shape context matching costs
over best matching points. The final value of the shape distance is a user-defined linear
combination of the shape context distance, an image appearance distance, and a bending energy.
@param shapeContextWeight The weight of the shape context distance in the final distance value.
*/
CV_WRAP virtual void setShapeContextWeight(float shapeContextWeight) = 0;
CV_WRAP virtual float getShapeContextWeight() const = 0;
/** @brief Set the weight of the Image Appearance cost in the final value of the shape distance. The image
appearance cost is defined as the sum of squared brightness differences in Gaussian windows around
corresponding image points. The final value of the shape distance is a user-defined linear
combination of the shape context distance, an image appearance distance, and a bending energy. If
this value is set to a number different from 0, is mandatory to set the images that correspond to
each shape.
@param imageAppearanceWeight The weight of the appearance cost in the final distance value.
*/
CV_WRAP virtual void setImageAppearanceWeight(float imageAppearanceWeight) = 0;
CV_WRAP virtual float getImageAppearanceWeight() const = 0;
/** @brief Set the weight of the Bending Energy in the final value of the shape distance. The bending energy
definition depends on what transformation is being used to align the shapes. The final value of the
shape distance is a user-defined linear combination of the shape context distance, an image
appearance distance, and a bending energy.
@param bendingEnergyWeight The weight of the Bending Energy in the final distance value.
*/
CV_WRAP virtual void setBendingEnergyWeight(float bendingEnergyWeight) = 0;
CV_WRAP virtual float getBendingEnergyWeight() const = 0;
/** @brief Set the images that correspond to each shape. This images are used in the calculation of the Image
Appearance cost.
@param image1 Image corresponding to the shape defined by contours1.
@param image2 Image corresponding to the shape defined by contours2.
*/
CV_WRAP virtual void setImages(InputArray image1, InputArray image2) = 0;
CV_WRAP virtual void getImages(OutputArray image1, OutputArray image2) const = 0;
CV_WRAP virtual void setIterations(int iterations) = 0;
CV_WRAP virtual int getIterations() const = 0;
/** @brief Set the algorithm used for building the shape context descriptor cost matrix.
@param comparer Smart pointer to a HistogramCostExtractor, an algorithm that defines the cost
matrix between descriptors.
*/
CV_WRAP virtual void setCostExtractor(Ptr<HistogramCostExtractor> comparer) = 0;
CV_WRAP virtual Ptr<HistogramCostExtractor> getCostExtractor() const = 0;
/** @brief Set the value of the standard deviation for the Gaussian window for the image appearance cost.
@param sigma Standard Deviation.
*/
CV_WRAP virtual void setStdDev(float sigma) = 0;
CV_WRAP virtual float getStdDev() const = 0;
/** @brief Set the algorithm used for aligning the shapes.
@param transformer Smart pointer to a ShapeTransformer, an algorithm that defines the aligning
transformation.
*/
CV_WRAP virtual void setTransformAlgorithm(Ptr<ShapeTransformer> transformer) = 0;
CV_WRAP virtual Ptr<ShapeTransformer> getTransformAlgorithm() const = 0;
};
......@@ -123,15 +189,28 @@ CV_EXPORTS_W Ptr<ShapeContextDistanceExtractor>
/***********************************************************************************/
/***********************************************************************************/
/***********************************************************************************/
/*!
* Hausdorff distace implementation based on
/** @brief A simple Hausdorff distance measure between shapes defined by contours
according to the paper "Comparing Images using the Hausdorff distance." by D.P. Huttenlocher, G.A.
Klanderman, and W.J. Rucklidge. (PAMI 1993). :
*/
class CV_EXPORTS_W HausdorffDistanceExtractor : public ShapeDistanceExtractor
{
public:
/** @brief Set the norm used to compute the Hausdorff value between two shapes. It can be L1 or L2 norm.
@param distanceFlag Flag indicating which norm is used to compute the Hausdorff distance
(NORM\_L1, NORM\_L2).
*/
CV_WRAP virtual void setDistanceFlag(int distanceFlag) = 0;
CV_WRAP virtual int getDistanceFlag() const = 0;
/** @brief This method sets the rank proportion (or fractional value) that establish the Kth ranked value of
the partial Hausdorff distance. Experimentally had been shown that 0.6 is a good value to compare
shapes.
@param rankProportion fractional value (between 0 and 1).
*/
CV_WRAP virtual void setRankProportion(float rankProportion) = 0;
CV_WRAP virtual float getRankProportion() const = 0;
};
......@@ -139,5 +218,7 @@ public:
/* Constructor */
CV_EXPORTS_W Ptr<HausdorffDistanceExtractor> createHausdorffDistanceExtractor(int distanceFlag=cv::NORM_L2, float rankProp=0.6f);
//! @}
} // cv
#endif
modules/shape/include/opencv2/shape/shape_transformer.hpp
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......@@ -50,20 +50,38 @@
namespace cv
{
/*!
* The base class for ShapeTransformer.
* This is just to define the common interface for
* shape transformation techniques.
//! @addtogroup shape
//! @{
/** @brief Abstract base class for shape transformation algorithms.
*/
class CV_EXPORTS_W ShapeTransformer : public Algorithm
{
public:
/* Estimate, Apply Transformation and return Transforming cost*/
/** @brief Estimate the transformation parameters of the current transformer algorithm, based on point matches.
@param transformingShape Contour defining first shape.
@param targetShape Contour defining second shape (Target).
@param matches Standard vector of Matches between points.
*/
CV_WRAP virtual void estimateTransformation(InputArray transformingShape, InputArray targetShape,
std::vector<DMatch>& matches) = 0;
/** @brief Apply a transformation, given a pre-estimated transformation parameters.
@param input Contour (set of points) to apply the transformation.
@param output Output contour.
*/
CV_WRAP virtual float applyTransformation(InputArray input, OutputArray output=noArray()) = 0;
/** @brief Apply a transformation, given a pre-estimated transformation parameters, to an Image.
@param transformingImage Input image.
@param output Output image.
@param flags Image interpolation method.
@param borderMode border style.
@param borderValue border value.
*/
CV_WRAP virtual void warpImage(InputArray transformingImage, OutputArray output,
int flags=INTER_LINEAR, int borderMode=BORDER_CONSTANT,
const Scalar& borderValue=Scalar()) const = 0;
......@@ -71,30 +89,33 @@ public:
/***********************************************************************************/
/***********************************************************************************/
/*!
* Thin Plate Spline Transformation
* Implementation of the TPS transformation
* according to "Principal Warps: Thin-Plate Splines and the
* Decomposition of Deformations" by Juan Manuel Perez for the GSOC 2013
*/
/** @brief Definition of the transformation
ocupied in the paper "Principal Warps: Thin-Plate Splines and Decomposition of Deformations", by
F.L. Bookstein (PAMI 1989). :
*/
class CV_EXPORTS_W ThinPlateSplineShapeTransformer : public ShapeTransformer
{
public:
/** @brief Set the regularization parameter for relaxing the exact interpolation requirements of the TPS
algorithm.
@param beta value of the regularization parameter.
*/
CV_WRAP virtual void setRegularizationParameter(double beta) = 0;
CV_WRAP virtual double getRegularizationParameter() const = 0;
};
/* Complete constructor */
/** Complete constructor */
CV_EXPORTS_W Ptr<ThinPlateSplineShapeTransformer>
createThinPlateSplineShapeTransformer(double regularizationParameter=0);
/***********************************************************************************/
/***********************************************************************************/
/*!
* Affine Transformation as a derivated from ShapeTransformer
*/
/** @brief Wrapper class for the OpenCV Affine Transformation algorithm. :
*/
class CV_EXPORTS_W AffineTransformer : public ShapeTransformer
{
public:
......@@ -102,8 +123,10 @@ public:
CV_WRAP virtual bool getFullAffine() const = 0;
};
/* Complete constructor */
/** Complete constructor */
CV_EXPORTS_W Ptr<AffineTransformer> createAffineTransformer(bool fullAffine);
//! @}
} // cv
#endif
modules/superres/include/opencv2/superres.hpp
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......@@ -45,10 +45,23 @@
#include "opencv2/core.hpp"
/**
@defgroup superres Super Resolution
The Super Resolution module contains a set of functions and classes that can be used to solve the
problem of resolution enhancement. There are a few methods implemented, most of them are descibed in
the papers @cite Farsiu03 and @cite Mitzel09.
*/
namespace cv
{
namespace superres
{
//! @addtogroup superres
//! @{
CV_EXPORTS bool initModule_superres();
class CV_EXPORTS FrameSource
......@@ -67,14 +80,29 @@ namespace cv
CV_EXPORTS Ptr<FrameSource> createFrameSource_Camera(int deviceId = 0);
/** @brief Base class for Super Resolution algorithms.
The class is only used to define the common interface for the whole family of Super Resolution
algorithms.
*/
class CV_EXPORTS SuperResolution : public cv::Algorithm, public FrameSource
{
public:
/** @brief Set input frame source for Super Resolution algorithm.
@param frameSource Input frame source
*/
void setInput(const Ptr<FrameSource>& frameSource);
/** @brief Process next frame from input and return output result.
@param frame Output result
*/
void nextFrame(OutputArray frame);
void reset();
/** @brief Clear all inner buffers.
*/
virtual void collectGarbage();
protected:
......@@ -90,11 +118,31 @@ namespace cv
bool firstCall_;
};
// S. Farsiu , D. Robinson, M. Elad, P. Milanfar. Fast and robust multiframe super resolution.
// Dennis Mitzel, Thomas Pock, Thomas Schoenemann, Daniel Cremers. Video Super Resolution using Duality Based TV-L1 Optical Flow.
/** @brief Create Bilateral TV-L1 Super Resolution.
This class implements Super Resolution algorithm described in the papers @cite Farsiu03 and
@cite Mitzel09 .
Here are important members of the class that control the algorithm, which you can set after
constructing the class instance:
- **int scale** Scale factor.
- **int iterations** Iteration count.
- **double tau** Asymptotic value of steepest descent method.
- **double lambda** Weight parameter to balance data term and smoothness term.
- **double alpha** Parameter of spacial distribution in Bilateral-TV.
- **int btvKernelSize** Kernel size of Bilateral-TV filter.
- **int blurKernelSize** Gaussian blur kernel size.
- **double blurSigma** Gaussian blur sigma.
- **int temporalAreaRadius** Radius of the temporal search area.
- **Ptr\<DenseOpticalFlowExt\> opticalFlow** Dense optical flow algorithm.
*/
CV_EXPORTS Ptr<SuperResolution> createSuperResolution_BTVL1();
CV_EXPORTS Ptr<SuperResolution> createSuperResolution_BTVL1_CUDA();
CV_EXPORTS Ptr<SuperResolution> createSuperResolution_BTVL1_OCL();
//! @} superres
}
}
......
modules/superres/include/opencv2/superres/optical_flow.hpp
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......@@ -49,6 +49,10 @@ namespace cv
{
namespace superres
{
//! @addtogroup superres
//! @{
class CV_EXPORTS DenseOpticalFlowExt : public cv::Algorithm
{
public:
......@@ -70,6 +74,9 @@ namespace cv
CV_EXPORTS Ptr<DenseOpticalFlowExt> createOptFlow_PyrLK_CUDA();
CV_EXPORTS Ptr<DenseOpticalFlowExt> createOptFlow_PyrLK_OCL();
//! @}
}
}
......
modules/videostab/include/opencv2/videostab.hpp
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......@@ -40,15 +40,41 @@
//
//M*/
// REFERENCES
// 1. "Full-Frame Video Stabilization with Motion Inpainting"
// Yasuyuki Matsushita, Eyal Ofek, Weina Ge, Xiaoou Tang, Senior Member, and Heung-Yeung Shum
// 2. "Auto-Directed Video Stabilization with Robust L1 Optimal Camera Paths"
// Matthias Grundmann, Vivek Kwatra, Irfan Essa
#ifndef __OPENCV_VIDEOSTAB_HPP__
#define __OPENCV_VIDEOSTAB_HPP__
/**
@defgroup videostab Video Stabilization
The video stabilization module contains a set of functions and classes that can be used to solve the
problem of video stabilization. There are a few methods implemented, most of them are descibed in
the papers @cite OF06 and @cite G11. However, there are some extensions and deviations from the orginal
paper methods.
### References
1. "Full-Frame Video Stabilization with Motion Inpainting"
Yasuyuki Matsushita, Eyal Ofek, Weina Ge, Xiaoou Tang, Senior Member, and Heung-Yeung Shum
2. "Auto-Directed Video Stabilization with Robust L1 Optimal Camera Paths"
Matthias Grundmann, Vivek Kwatra, Irfan Essa
@{
@defgroup videostab_motion Global Motion Estimation
The video stabilization module contains a set of functions and classes for global motion estimation
between point clouds or between images. In the last case features are extracted and matched
internally. For the sake of convenience the motion estimation functions are wrapped into classes.
Both the functions and the classes are available.
@defgroup videostab_marching Fast Marching Method
The Fast Marching Method @cite T04 is used in of the video stabilization routines to do motion and
color inpainting. The method is implemented is a flexible way and it's made public for other users.
@}
*/
#include "opencv2/videostab/stabilizer.hpp"
#include "opencv2/videostab/ring_buffer.hpp"
......
modules/videostab/include/opencv2/videostab/deblurring.hpp
View file @ 9d89f8d3
......@@ -51,6 +51,9 @@ namespace cv
namespace videostab
{
//! @addtogroup videostab
//! @{
CV_EXPORTS float calcBlurriness(const Mat &frame);
class CV_EXPORTS DeblurerBase
......@@ -105,6 +108,8 @@ private:
Mat_<float> bSum_, gSum_, rSum_, wSum_;
};
//! @}
} // namespace videostab
} // namespace cv
......
modules/videostab/include/opencv2/videostab/fast_marching.hpp
View file @ 9d89f8d3
......@@ -53,15 +53,31 @@ namespace cv
namespace videostab
{
// See http://iwi.eldoc.ub.rug.nl/FILES/root/2004/JGraphToolsTelea/2004JGraphToolsTelea.pdf
//! @addtogroup videostab_marching
//! @{
/** @brief Describes the Fast Marching Method implementation.
See http://iwi.eldoc.ub.rug.nl/FILES/root/2004/JGraphToolsTelea/2004JGraphToolsTelea.pdf
*/
class CV_EXPORTS FastMarchingMethod
{
public:
FastMarchingMethod() : inf_(1e6f) {}
/** @brief Template method that runs the Fast Marching Method.
@param mask Image mask. 0 value indicates that the pixel value must be inpainted, 255 indicates
that the pixel value is known, other values aren't acceptable.
@param inpaint Inpainting functor that overloads void operator ()(int x, int y).
@return Inpainting functor.
*/
template <typename Inpaint>
Inpaint run(const Mat &mask, Inpaint inpaint);
/**
@return Distance map that's created during working of the method.
*/
Mat distanceMap() const { return dist_; }
private:
......@@ -95,6 +111,8 @@ private:
int size_; // narrow band size
};
//! @}
} // namespace videostab
} // namespace cv
......
modules/videostab/include/opencv2/videostab/frame_source.hpp
View file @ 9d89f8d3
......@@ -51,6 +51,9 @@ namespace cv
namespace videostab
{
//! @addtogroup videostab
//! @{
class CV_EXPORTS IFrameSource
{
public:
......@@ -83,6 +86,8 @@ private:
Ptr<IFrameSource> impl;
};
//! @}
} // namespace videostab
} // namespace cv
......
modules/videostab/include/opencv2/videostab/global_motion.hpp
View file @ 9d89f8d3
......@@ -61,23 +61,62 @@ namespace cv
namespace videostab
{
//! @addtogroup videostab_motion
//! @{
/** @brief Estimates best global motion between two 2D point clouds in the least-squares sense.
@note Works in-place and changes input point arrays.
@param points0 Source set of 2D points (32F).
@param points1 Destination set of 2D points (32F).
@param model Motion model (up to MM\_AFFINE).
@param rmse Final root-mean-square error.
@return 3x3 2D transformation matrix (32F).
*/
CV_EXPORTS Mat estimateGlobalMotionLeastSquares(
InputOutputArray points0, InputOutputArray points1, int model = MM_AFFINE,
float *rmse = 0);
/** @brief Estimates best global motion between two 2D point clouds robustly (using RANSAC method).
@param points0 Source set of 2D points (32F).
@param points1 Destination set of 2D points (32F).
@param model Motion model. See cv::videostab::MotionModel.
@param params RANSAC method parameters. See videostab::RansacParams.
@param rmse Final root-mean-square error.
@param ninliers Final number of inliers.
*/
CV_EXPORTS Mat estimateGlobalMotionRansac(
InputArray points0, InputArray points1, int model = MM_AFFINE,
const RansacParams &params = RansacParams::default2dMotion(MM_AFFINE),
float *rmse = 0, int *ninliers = 0);
/** @brief Base class for all global motion estimation methods.
*/
class CV_EXPORTS MotionEstimatorBase
{
public:
virtual ~MotionEstimatorBase() {}
/** @brief Sets motion model.
@param val Motion model. See cv::videostab::MotionModel.
*/
virtual void setMotionModel(MotionModel val) { motionModel_ = val; }
/**
@return Motion model. See cv::videostab::MotionModel.
*/
virtual MotionModel motionModel() const { return motionModel_; }
/** @brief Estimates global motion between two 2D point clouds.
@param points0 Source set of 2D points (32F).
@param points1 Destination set of 2D points (32F).
@param ok Indicates whether motion was estimated successfully.
@return 3x3 2D transformation matrix (32F).
*/
virtual Mat estimate(InputArray points0, InputArray points1, bool *ok = 0) = 0;
protected:
......@@ -87,6 +126,8 @@ private:
MotionModel motionModel_;
};
/** @brief Describes a robust RANSAC-based global 2D motion estimation method which minimizes L2 error.
*/
class CV_EXPORTS MotionEstimatorRansacL2 : public MotionEstimatorBase
{
public:
......@@ -105,6 +146,10 @@ private:
float minInlierRatio_;
};
/** @brief Describes a global 2D motion estimation method which minimizes L1 error.
@note To be able to use this method you must build OpenCV with CLP library support. :
*/
class CV_EXPORTS MotionEstimatorL1 : public MotionEstimatorBase
{
public:
......@@ -125,6 +170,8 @@ private:
}
};
/** @brief Base class for global 2D motion estimation methods which take frames as input.
*/
class CV_EXPORTS ImageMotionEstimatorBase
{
public:
......@@ -168,6 +215,9 @@ private:
Ptr<ImageMotionEstimatorBase> motionEstimator_;
};
/** @brief Describes a global 2D motion estimation method which uses keypoints detection and optical flow for
matching.
*/
class CV_EXPORTS KeypointBasedMotionEstimator : public ImageMotionEstimatorBase
{
public:
......@@ -232,8 +282,17 @@ private:
#endif // defined(HAVE_OPENCV_CUDAIMGPROC) && defined(HAVE_OPENCV_CUDA) && defined(HAVE_OPENCV_CUDAOPTFLOW)
/** @brief Computes motion between two frames assuming that all the intermediate motions are known.
@param from Source frame index.
@param to Destination frame index.
@param motions Pair-wise motions. motions[i] denotes motion from the frame i to the frame i+1
@return Motion from the frame from to the frame to.
*/
CV_EXPORTS Mat getMotion(int from, int to, const std::vector<Mat> &motions);
//! @}
} // namespace videostab
} // namespace cv
......
modules/videostab/include/opencv2/videostab/inpainting.hpp
View file @ 9d89f8d3
......@@ -55,6 +55,9 @@ namespace cv
namespace videostab
{
//! @addtogroup videostab
//! @{
class CV_EXPORTS InpainterBase
{
public:
......@@ -201,6 +204,8 @@ CV_EXPORTS void completeFrameAccordingToFlow(
const Mat &flowMask, const Mat &flowX, const Mat &flowY, const Mat &frame1, const Mat &mask1,
float distThresh, Mat& frame0, Mat &mask0);
//! @}
} // namespace videostab
} // namespace cv
......
modules/videostab/include/opencv2/videostab/log.hpp
View file @ 9d89f8d3
......@@ -50,6 +50,9 @@ namespace cv
namespace videostab
{
//! @addtogroup videostab
//! @{
class CV_EXPORTS ILog
{
public:
......@@ -69,6 +72,8 @@ public:
virtual void print(const char *format, ...);
};
//! @}
} // namespace videostab
} // namespace cv
......
modules/videostab/include/opencv2/videostab/motion_core.hpp
View file @ 9d89f8d3
......@@ -51,6 +51,11 @@ namespace cv
namespace videostab
{
//! @addtogroup videostab_motion
//! @{
/** @brief Describes motion model between two point clouds.
*/
enum MotionModel
{
MM_TRANSLATION = 0,
......@@ -63,22 +68,37 @@ enum MotionModel
MM_UNKNOWN = 7
};
/** @brief Describes RANSAC method parameters.
*/
struct CV_EXPORTS RansacParams
{
int size; // subset size
float thresh; // max error to classify as inlier
float eps; // max outliers ratio
float prob; // probability of success
int size; //!< subset size
float thresh; //!< max error to classify as inlier
float eps; //!< max outliers ratio
float prob; //!< probability of success
RansacParams() : size(0), thresh(0), eps(0), prob(0) {}
/** @brief Constructor
@param size Subset size.
@param thresh Maximum re-projection error value to classify as inlier.
@param eps Maximum ratio of incorrect correspondences.
@param prob Required success probability.
*/
RansacParams(int size, float thresh, float eps, float prob);
/**
@return Number of iterations that'll be performed by RANSAC method.
*/
int niters() const
{
return static_cast<int>(
std::ceil(std::log(1 - prob) / std::log(1 - std::pow(1 - eps, size))));
}
/**
@param model Motion model. See cv::videostab::MotionModel.
@return Default RANSAC method parameters for the given motion model.
*/
static RansacParams default2dMotion(MotionModel model)
{
CV_Assert(model < MM_UNKNOWN);
......@@ -101,6 +121,7 @@ struct CV_EXPORTS RansacParams
inline RansacParams::RansacParams(int _size, float _thresh, float _eps, float _prob)
: size(_size), thresh(_thresh), eps(_eps), prob(_prob) {}
//! @}
} // namespace videostab
} // namespace cv
......
modules/videostab/include/opencv2/videostab/motion_stabilizing.hpp
View file @ 9d89f8d3
......@@ -53,12 +53,15 @@ namespace cv
namespace videostab
{
//! @addtogroup videostab_motion
//! @{
class CV_EXPORTS IMotionStabilizer
{
public:
virtual ~IMotionStabilizer() {}
// assumes that [0, size-1) is in or equals to [range.first, range.second)
//! assumes that [0, size-1) is in or equals to [range.first, range.second)
virtual void stabilize(
int size, const std::vector<Mat> &motions, std::pair<int,int> range,
Mat *stabilizationMotions) = 0;
......@@ -163,6 +166,8 @@ CV_EXPORTS Mat ensureInclusionConstraint(const Mat &M, Size size, float trimRati
CV_EXPORTS float estimateOptimalTrimRatio(const Mat &M, Size size);
//! @}
} // namespace videostab
} // namespace
......
modules/videostab/include/opencv2/videostab/optical_flow.hpp
View file @ 9d89f8d3
......@@ -55,6 +55,9 @@ namespace cv
namespace videostab
{
//! @addtogroup vieostab
//! @{
class CV_EXPORTS ISparseOptFlowEstimator
{
public:
......@@ -139,6 +142,8 @@ private:
#endif
//! @}
} // namespace videostab
} // namespace cv
......
modules/videostab/include/opencv2/videostab/outlier_rejection.hpp
View file @ 9d89f8d3
......@@ -52,6 +52,9 @@ namespace cv
namespace videostab
{
//! @addtogroup vieostab
//! @{
class CV_EXPORTS IOutlierRejector
{
public:
......@@ -90,6 +93,8 @@ private:
std::vector<Cell> grid_;
};
//! @}
} // namespace videostab
} // namespace cv
......
modules/videostab/include/opencv2/videostab/ring_buffer.hpp
View file @ 9d89f8d3
......@@ -51,6 +51,9 @@ namespace cv
namespace videostab
{
//! @addtogroup vieostab
//! @{
template <typename T> inline T& at(int idx, std::vector<T> &items)
{
return items[cv::borderInterpolate(idx, static_cast<int>(items.size()), cv::BORDER_WRAP)];
......@@ -61,6 +64,8 @@ template <typename T> inline const T& at(int idx, const std::vector<T> &items)
return items[cv::borderInterpolate(idx, static_cast<int>(items.size()), cv::BORDER_WRAP)];
}
//! @}
} // namespace videostab
} // namespace cv
......
modules/videostab/include/opencv2/videostab/stabilizer.hpp
View file @ 9d89f8d3
......@@ -60,6 +60,9 @@ namespace cv
namespace videostab
{
//! @addtogroup vieostab
//! @{
class CV_EXPORTS StabilizerBase
{
public:
......@@ -189,6 +192,8 @@ protected:
Mat suppressedFrame_;
};
//! @}
} // namespace videostab
} // namespace cv
......
modules/videostab/include/opencv2/videostab/wobble_suppression.hpp
View file @ 9d89f8d3
......@@ -54,6 +54,9 @@ namespace cv
namespace videostab
{
//! @addtogroup vieostab
//! @{
class CV_EXPORTS WobbleSuppressorBase
{
public:
......@@ -129,6 +132,8 @@ private:
};
#endif
//! @}
} // namespace videostab
} // namespace cv
......
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