365 lines
16 KiB
C++
365 lines
16 KiB
C++
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// This file is part of OpenCV project.
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// It is subject to the license terms in the LICENSE file found in the top-level directory
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// of this distribution and at http://opencv.org/license.html.
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//
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// Copyright (C) 2020 Intel Corporation
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#ifndef OPENCV_GAPI_VIDEO_HPP
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#define OPENCV_GAPI_VIDEO_HPP
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#include <utility> // std::tuple
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#include <opencv2/gapi/gkernel.hpp>
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/** \defgroup gapi_video G-API Video processing functionality
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*/
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namespace cv { namespace gapi {
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/** @brief Structure for the Kalman filter's initialization parameters.*/
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struct GAPI_EXPORTS KalmanParams
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{
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// initial state
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//! corrected state (x(k)): x(k)=x'(k)+K(k)*(z(k)-H*x'(k))
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Mat state;
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//! posteriori error estimate covariance matrix (P(k)): P(k)=(I-K(k)*H)*P'(k)
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Mat errorCov;
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// dynamic system description
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//! state transition matrix (A)
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Mat transitionMatrix;
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//! measurement matrix (H)
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Mat measurementMatrix;
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//! process noise covariance matrix (Q)
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Mat processNoiseCov;
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//! measurement noise covariance matrix (R)
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Mat measurementNoiseCov;
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//! control matrix (B) (Optional: not used if there's no control)
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Mat controlMatrix;
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};
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/**
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* @brief This namespace contains G-API Operations and functions for
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* video-oriented algorithms, like optical flow and background subtraction.
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*/
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namespace video
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{
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using GBuildPyrOutput = std::tuple<GArray<GMat>, GScalar>;
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using GOptFlowLKOutput = std::tuple<cv::GArray<cv::Point2f>,
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cv::GArray<uchar>,
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cv::GArray<float>>;
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G_TYPED_KERNEL(GBuildOptFlowPyramid, <GBuildPyrOutput(GMat,Size,GScalar,bool,int,int,bool)>,
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"org.opencv.video.buildOpticalFlowPyramid")
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{
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static std::tuple<GArrayDesc,GScalarDesc>
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outMeta(GMatDesc,const Size&,GScalarDesc,bool,int,int,bool)
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{
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return std::make_tuple(empty_array_desc(), empty_scalar_desc());
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}
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};
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G_TYPED_KERNEL(GCalcOptFlowLK,
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<GOptFlowLKOutput(GMat,GMat,cv::GArray<cv::Point2f>,cv::GArray<cv::Point2f>,Size,
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GScalar,TermCriteria,int,double)>,
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"org.opencv.video.calcOpticalFlowPyrLK")
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{
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static std::tuple<GArrayDesc,GArrayDesc,GArrayDesc> outMeta(GMatDesc,GMatDesc,GArrayDesc,
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GArrayDesc,const Size&,GScalarDesc,
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const TermCriteria&,int,double)
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{
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return std::make_tuple(empty_array_desc(), empty_array_desc(), empty_array_desc());
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}
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};
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G_TYPED_KERNEL(GCalcOptFlowLKForPyr,
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<GOptFlowLKOutput(cv::GArray<cv::GMat>,cv::GArray<cv::GMat>,
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cv::GArray<cv::Point2f>,cv::GArray<cv::Point2f>,Size,GScalar,
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TermCriteria,int,double)>,
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"org.opencv.video.calcOpticalFlowPyrLKForPyr")
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{
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static std::tuple<GArrayDesc,GArrayDesc,GArrayDesc> outMeta(GArrayDesc,GArrayDesc,
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GArrayDesc,GArrayDesc,
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const Size&,GScalarDesc,
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const TermCriteria&,int,double)
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{
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return std::make_tuple(empty_array_desc(), empty_array_desc(), empty_array_desc());
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}
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};
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enum BackgroundSubtractorType
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{
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TYPE_BS_MOG2,
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TYPE_BS_KNN
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};
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/** @brief Structure for the Background Subtractor operation's initialization parameters.*/
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struct BackgroundSubtractorParams
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{
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//! Type of the Background Subtractor operation.
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BackgroundSubtractorType operation = TYPE_BS_MOG2;
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//! Length of the history.
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int history = 500;
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//! For MOG2: Threshold on the squared Mahalanobis distance between the pixel
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//! and the model to decide whether a pixel is well described by
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//! the background model.
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//! For KNN: Threshold on the squared distance between the pixel and the sample
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//! to decide whether a pixel is close to that sample.
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double threshold = 16;
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//! If true, the algorithm will detect shadows and mark them.
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bool detectShadows = true;
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//! The value between 0 and 1 that indicates how fast
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//! the background model is learnt.
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//! Negative parameter value makes the algorithm use some automatically
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//! chosen learning rate.
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double learningRate = -1;
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//! default constructor
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BackgroundSubtractorParams() {}
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/** Full constructor
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@param op MOG2/KNN Background Subtractor type.
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@param histLength Length of the history.
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@param thrshld For MOG2: Threshold on the squared Mahalanobis distance between
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the pixel and the model to decide whether a pixel is well described by the background model.
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For KNN: Threshold on the squared distance between the pixel and the sample to decide
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whether a pixel is close to that sample.
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@param detect If true, the algorithm will detect shadows and mark them. It decreases the
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speed a bit, so if you do not need this feature, set the parameter to false.
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@param lRate The value between 0 and 1 that indicates how fast the background model is learnt.
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Negative parameter value makes the algorithm to use some automatically chosen learning rate.
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*/
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BackgroundSubtractorParams(BackgroundSubtractorType op, int histLength,
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double thrshld, bool detect, double lRate) : operation(op),
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history(histLength),
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threshold(thrshld),
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detectShadows(detect),
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learningRate(lRate){}
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};
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G_TYPED_KERNEL(GBackgroundSubtractor, <GMat(GMat, BackgroundSubtractorParams)>,
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"org.opencv.video.BackgroundSubtractor")
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{
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static GMatDesc outMeta(const GMatDesc& in, const BackgroundSubtractorParams& bsParams)
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{
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GAPI_Assert(bsParams.history >= 0);
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GAPI_Assert(bsParams.learningRate <= 1);
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return in.withType(CV_8U, 1);
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}
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};
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void checkParams(const cv::gapi::KalmanParams& kfParams,
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const cv::GMatDesc& measurement, const cv::GMatDesc& control = {});
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G_TYPED_KERNEL(GKalmanFilter, <GMat(GMat, GOpaque<bool>, GMat, KalmanParams)>,
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"org.opencv.video.KalmanFilter")
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{
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static GMatDesc outMeta(const GMatDesc& measurement, const GOpaqueDesc&,
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const GMatDesc& control, const KalmanParams& kfParams)
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{
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checkParams(kfParams, measurement, control);
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return measurement.withSize(Size(1, kfParams.transitionMatrix.rows));
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}
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};
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G_TYPED_KERNEL(GKalmanFilterNoControl, <GMat(GMat, GOpaque<bool>, KalmanParams)>, "org.opencv.video.KalmanFilterNoControl")
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{
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static GMatDesc outMeta(const GMatDesc& measurement, const GOpaqueDesc&, const KalmanParams& kfParams)
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{
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checkParams(kfParams, measurement);
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return measurement.withSize(Size(1, kfParams.transitionMatrix.rows));
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}
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};
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} //namespace video
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//! @addtogroup gapi_video
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//! @{
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/** @brief Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK.
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@note Function textual ID is "org.opencv.video.buildOpticalFlowPyramid"
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@param img 8-bit input image.
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@param winSize window size of optical flow algorithm. Must be not less than winSize
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argument of calcOpticalFlowPyrLK. It is needed to calculate required
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padding for pyramid levels.
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@param maxLevel 0-based maximal pyramid level number.
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@param withDerivatives set to precompute gradients for the every pyramid level. If pyramid is
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constructed without the gradients then calcOpticalFlowPyrLK will calculate
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them internally.
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@param pyrBorder the border mode for pyramid layers.
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@param derivBorder the border mode for gradients.
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@param tryReuseInputImage put ROI of input image into the pyramid if possible. You can pass false
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to force data copying.
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@return
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- output pyramid.
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- number of levels in constructed pyramid. Can be less than maxLevel.
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*/
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GAPI_EXPORTS std::tuple<GArray<GMat>, GScalar>
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buildOpticalFlowPyramid(const GMat &img,
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const Size &winSize,
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const GScalar &maxLevel,
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bool withDerivatives = true,
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int pyrBorder = BORDER_REFLECT_101,
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int derivBorder = BORDER_CONSTANT,
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bool tryReuseInputImage = true);
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/** @brief Calculates an optical flow for a sparse feature set using the iterative Lucas-Kanade
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method with pyramids.
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See @cite Bouguet00 .
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@note Function textual ID is "org.opencv.video.calcOpticalFlowPyrLK"
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@param prevImg first 8-bit input image (GMat) or pyramid (GArray<GMat>) constructed by
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buildOpticalFlowPyramid.
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@param nextImg second input image (GMat) or pyramid (GArray<GMat>) of the same size and the same
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type as prevImg.
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@param prevPts GArray of 2D points for which the flow needs to be found; point coordinates must be
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single-precision floating-point numbers.
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@param predPts GArray of 2D points initial for the flow search; make sense only when
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OPTFLOW_USE_INITIAL_FLOW flag is passed; in that case the vector must have the same size as in
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the input.
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@param winSize size of the search window at each pyramid level.
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@param maxLevel 0-based maximal pyramid level number; if set to 0, pyramids are not used (single
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level), if set to 1, two levels are used, and so on; if pyramids are passed to input then
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algorithm will use as many levels as pyramids have but no more than maxLevel.
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@param criteria parameter, specifying the termination criteria of the iterative search algorithm
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(after the specified maximum number of iterations criteria.maxCount or when the search window
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moves by less than criteria.epsilon).
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@param flags operation flags:
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- **OPTFLOW_USE_INITIAL_FLOW** uses initial estimations, stored in nextPts; if the flag is
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not set, then prevPts is copied to nextPts and is considered the initial estimate.
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- **OPTFLOW_LK_GET_MIN_EIGENVALS** use minimum eigen values as an error measure (see
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minEigThreshold description); if the flag is not set, then L1 distance between patches
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around the original and a moved point, divided by number of pixels in a window, is used as a
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error measure.
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@param minEigThresh the algorithm calculates the minimum eigen value of a 2x2 normal matrix of
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optical flow equations (this matrix is called a spatial gradient matrix in @cite Bouguet00), divided
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by number of pixels in a window; if this value is less than minEigThreshold, then a corresponding
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feature is filtered out and its flow is not processed, so it allows to remove bad points and get a
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performance boost.
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@return
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- GArray of 2D points (with single-precision floating-point coordinates)
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containing the calculated new positions of input features in the second image.
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- status GArray (of unsigned chars); each element of the vector is set to 1 if
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the flow for the corresponding features has been found, otherwise, it is set to 0.
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- GArray of errors (doubles); each element of the vector is set to an error for the
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corresponding feature, type of the error measure can be set in flags parameter; if the flow wasn't
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found then the error is not defined (use the status parameter to find such cases).
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*/
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GAPI_EXPORTS std::tuple<GArray<Point2f>, GArray<uchar>, GArray<float>>
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calcOpticalFlowPyrLK(const GMat &prevImg,
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const GMat &nextImg,
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const GArray<Point2f> &prevPts,
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const GArray<Point2f> &predPts,
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const Size &winSize = Size(21, 21),
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const GScalar &maxLevel = 3,
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const TermCriteria &criteria = TermCriteria(TermCriteria::COUNT |
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TermCriteria::EPS,
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30, 0.01),
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int flags = 0,
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double minEigThresh = 1e-4);
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/**
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@overload
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@note Function textual ID is "org.opencv.video.calcOpticalFlowPyrLKForPyr"
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*/
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GAPI_EXPORTS std::tuple<GArray<Point2f>, GArray<uchar>, GArray<float>>
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calcOpticalFlowPyrLK(const GArray<GMat> &prevPyr,
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const GArray<GMat> &nextPyr,
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const GArray<Point2f> &prevPts,
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const GArray<Point2f> &predPts,
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const Size &winSize = Size(21, 21),
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const GScalar &maxLevel = 3,
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const TermCriteria &criteria = TermCriteria(TermCriteria::COUNT |
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TermCriteria::EPS,
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30, 0.01),
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int flags = 0,
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double minEigThresh = 1e-4);
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/** @brief Gaussian Mixture-based or K-nearest neighbours-based Background/Foreground Segmentation Algorithm.
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The operation generates a foreground mask.
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@return Output image is foreground mask, i.e. 8-bit unsigned 1-channel (binary) matrix @ref CV_8UC1.
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@note Functional textual ID is "org.opencv.video.BackgroundSubtractor"
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@param src input image: Floating point frame is used without scaling and should be in range [0,255].
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@param bsParams Set of initialization parameters for Background Subtractor kernel.
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*/
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GAPI_EXPORTS GMat BackgroundSubtractor(const GMat& src, const cv::gapi::video::BackgroundSubtractorParams& bsParams);
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/** @brief Standard Kalman filter algorithm <http://en.wikipedia.org/wiki/Kalman_filter>.
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@note Functional textual ID is "org.opencv.video.KalmanFilter"
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@param measurement input matrix: 32-bit or 64-bit float 1-channel matrix containing measurements.
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@param haveMeasurement dynamic input flag that indicates whether we get measurements
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at a particular iteration .
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@param control input matrix: 32-bit or 64-bit float 1-channel matrix contains control data
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for changing dynamic system.
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@param kfParams Set of initialization parameters for Kalman filter kernel.
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@return Output matrix is predicted or corrected state. They can be 32-bit or 64-bit float
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1-channel matrix @ref CV_32FC1 or @ref CV_64FC1.
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@details If measurement matrix is given (haveMeasurements == true), corrected state will
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be returned which corresponds to the pipeline
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cv::KalmanFilter::predict(control) -> cv::KalmanFilter::correct(measurement).
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Otherwise, predicted state will be returned which corresponds to the call of
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cv::KalmanFilter::predict(control).
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@sa cv::KalmanFilter
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*/
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GAPI_EXPORTS GMat KalmanFilter(const GMat& measurement, const GOpaque<bool>& haveMeasurement,
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const GMat& control, const cv::gapi::KalmanParams& kfParams);
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/** @overload
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The case of Standard Kalman filter algorithm when there is no control in a dynamic system.
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In this case the controlMatrix is empty and control vector is absent.
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@note Function textual ID is "org.opencv.video.KalmanFilterNoControl"
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@param measurement input matrix: 32-bit or 64-bit float 1-channel matrix containing measurements.
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@param haveMeasurement dynamic input flag that indicates whether we get measurements
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at a particular iteration.
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@param kfParams Set of initialization parameters for Kalman filter kernel.
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@return Output matrix is predicted or corrected state. They can be 32-bit or 64-bit float
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1-channel matrix @ref CV_32FC1 or @ref CV_64FC1.
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@sa cv::KalmanFilter
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*/
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GAPI_EXPORTS GMat KalmanFilter(const GMat& measurement, const GOpaque<bool>& haveMeasurement,
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const cv::gapi::KalmanParams& kfParams);
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//! @} gapi_video
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} //namespace gapi
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} //namespace cv
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namespace cv { namespace detail {
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template<> struct CompileArgTag<cv::gapi::video::BackgroundSubtractorParams>
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{
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static const char* tag()
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{
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return "org.opencv.video.background_substractor_params";
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}
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};
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} // namespace detail
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} // namespace cv
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#endif // OPENCV_GAPI_VIDEO_HPP
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