// // This file is auto-generated. Please don't modify it! // #pragma once #ifdef __cplusplus //#import "opencv.hpp" #import "opencv2/video.hpp" #else #define CV_EXPORTS #endif #import @class BackgroundSubtractorKNN; @class BackgroundSubtractorMOG2; @class Mat; @class Rect2i; @class RotatedRect; @class Size2i; @class TermCriteria; NS_ASSUME_NONNULL_BEGIN // C++: class Video /** * The Video module * * Member classes: `KalmanFilter`, `DenseOpticalFlow`, `SparseOpticalFlow`, `FarnebackOpticalFlow`, `VariationalRefinement`, `DISOpticalFlow`, `SparsePyrLKOpticalFlow`, `Tracker`, `TrackerMIL`, `TrackerMILParams`, `TrackerGOTURN`, `TrackerGOTURNParams`, `TrackerDaSiamRPN`, `TrackerDaSiamRPNParams`, `BackgroundSubtractor`, `BackgroundSubtractorMOG2`, `BackgroundSubtractorKNN` * */ CV_EXPORTS @interface Video : NSObject #pragma mark - Class Constants @property (class, readonly) int OPTFLOW_USE_INITIAL_FLOW NS_SWIFT_NAME(OPTFLOW_USE_INITIAL_FLOW); @property (class, readonly) int OPTFLOW_LK_GET_MIN_EIGENVALS NS_SWIFT_NAME(OPTFLOW_LK_GET_MIN_EIGENVALS); @property (class, readonly) int OPTFLOW_FARNEBACK_GAUSSIAN NS_SWIFT_NAME(OPTFLOW_FARNEBACK_GAUSSIAN); @property (class, readonly) int MOTION_TRANSLATION NS_SWIFT_NAME(MOTION_TRANSLATION); @property (class, readonly) int MOTION_EUCLIDEAN NS_SWIFT_NAME(MOTION_EUCLIDEAN); @property (class, readonly) int MOTION_AFFINE NS_SWIFT_NAME(MOTION_AFFINE); @property (class, readonly) int MOTION_HOMOGRAPHY NS_SWIFT_NAME(MOTION_HOMOGRAPHY); #pragma mark - Methods // // RotatedRect cv::CamShift(Mat probImage, Rect& window, TermCriteria criteria) // /** * 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 */ + (RotatedRect*)CamShift:(Mat*)probImage window:(Rect2i*)window criteria:(TermCriteria*)criteria NS_SWIFT_NAME(CamShift(probImage:window:criteria:)); // // int cv::meanShift(Mat probImage, Rect& window, TermCriteria criteria) // /** * 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. */ + (int)meanShift:(Mat*)probImage window:(Rect2i*)window criteria:(TermCriteria*)criteria NS_SWIFT_NAME(meanShift(probImage:window:criteria:)); // // int cv::buildOpticalFlowPyramid(Mat img, vector_Mat& pyramid, Size winSize, int maxLevel, bool withDerivatives = true, int pyrBorder = BORDER_REFLECT_101, int derivBorder = BORDER_CONSTANT, bool tryReuseInputImage = true) // /** * Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK. * * @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. */ + (int)buildOpticalFlowPyramid:(Mat*)img pyramid:(NSMutableArray*)pyramid winSize:(Size2i*)winSize maxLevel:(int)maxLevel withDerivatives:(BOOL)withDerivatives pyrBorder:(int)pyrBorder derivBorder:(int)derivBorder tryReuseInputImage:(BOOL)tryReuseInputImage NS_SWIFT_NAME(buildOpticalFlowPyramid(img:pyramid:winSize:maxLevel:withDerivatives:pyrBorder:derivBorder:tryReuseInputImage:)); /** * 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. * to force data copying. * @return number of levels in constructed pyramid. Can be less than maxLevel. */ + (int)buildOpticalFlowPyramid:(Mat*)img pyramid:(NSMutableArray*)pyramid winSize:(Size2i*)winSize maxLevel:(int)maxLevel withDerivatives:(BOOL)withDerivatives pyrBorder:(int)pyrBorder derivBorder:(int)derivBorder NS_SWIFT_NAME(buildOpticalFlowPyramid(img:pyramid:winSize:maxLevel:withDerivatives:pyrBorder:derivBorder:)); /** * 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. * to force data copying. * @return number of levels in constructed pyramid. Can be less than maxLevel. */ + (int)buildOpticalFlowPyramid:(Mat*)img pyramid:(NSMutableArray*)pyramid winSize:(Size2i*)winSize maxLevel:(int)maxLevel withDerivatives:(BOOL)withDerivatives pyrBorder:(int)pyrBorder NS_SWIFT_NAME(buildOpticalFlowPyramid(img:pyramid:winSize:maxLevel:withDerivatives:pyrBorder:)); /** * 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. * to force data copying. * @return number of levels in constructed pyramid. Can be less than maxLevel. */ + (int)buildOpticalFlowPyramid:(Mat*)img pyramid:(NSMutableArray*)pyramid winSize:(Size2i*)winSize maxLevel:(int)maxLevel withDerivatives:(BOOL)withDerivatives NS_SWIFT_NAME(buildOpticalFlowPyramid(img:pyramid:winSize:maxLevel:withDerivatives:)); /** * 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. * constructed without the gradients then calcOpticalFlowPyrLK will calculate them internally. * to force data copying. * @return number of levels in constructed pyramid. Can be less than maxLevel. */ + (int)buildOpticalFlowPyramid:(Mat*)img pyramid:(NSMutableArray*)pyramid winSize:(Size2i*)winSize maxLevel:(int)maxLevel NS_SWIFT_NAME(buildOpticalFlowPyramid(img:pyramid:winSize:maxLevel:)); // // void cv::calcOpticalFlowPyrLK(Mat prevImg, Mat nextImg, Mat prevPts, Mat& nextPts, Mat& status, Mat& err, Size winSize = Size(21,21), int maxLevel = 3, TermCriteria criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, 0.01), int flags = 0, double minEigThreshold = 1e-4) // /** * Calculates an optical flow for a sparse feature set using the iterative Lucas-Kanade method with * 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 */ + (void)calcOpticalFlowPyrLK:(Mat*)prevImg nextImg:(Mat*)nextImg prevPts:(Mat*)prevPts nextPts:(Mat*)nextPts status:(Mat*)status err:(Mat*)err winSize:(Size2i*)winSize maxLevel:(int)maxLevel criteria:(TermCriteria*)criteria flags:(int)flags minEigThreshold:(double)minEigThreshold NS_SWIFT_NAME(calcOpticalFlowPyrLK(prevImg:nextImg:prevPts:nextPts:status:err:winSize:maxLevel:criteria:flags:minEigThreshold:)); /** * 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. * 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 */ + (void)calcOpticalFlowPyrLK:(Mat*)prevImg nextImg:(Mat*)nextImg prevPts:(Mat*)prevPts nextPts:(Mat*)nextPts status:(Mat*)status err:(Mat*)err winSize:(Size2i*)winSize maxLevel:(int)maxLevel criteria:(TermCriteria*)criteria flags:(int)flags NS_SWIFT_NAME(calcOpticalFlowPyrLK(prevImg:nextImg:prevPts:nextPts:status:err:winSize:maxLevel:criteria:flags:)); /** * 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. * - **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. * 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 */ + (void)calcOpticalFlowPyrLK:(Mat*)prevImg nextImg:(Mat*)nextImg prevPts:(Mat*)prevPts nextPts:(Mat*)nextPts status:(Mat*)status err:(Mat*)err winSize:(Size2i*)winSize maxLevel:(int)maxLevel criteria:(TermCriteria*)criteria NS_SWIFT_NAME(calcOpticalFlowPyrLK(prevImg:nextImg:prevPts:nextPts:status:err:winSize:maxLevel:criteria:)); /** * 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. * (after the specified maximum number of iterations criteria.maxCount or when the search window * moves by less than criteria.epsilon. * - **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. * 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 */ + (void)calcOpticalFlowPyrLK:(Mat*)prevImg nextImg:(Mat*)nextImg prevPts:(Mat*)prevPts nextPts:(Mat*)nextPts status:(Mat*)status err:(Mat*)err winSize:(Size2i*)winSize maxLevel:(int)maxLevel NS_SWIFT_NAME(calcOpticalFlowPyrLK(prevImg:nextImg:prevPts:nextPts:status:err:winSize:maxLevel:)); /** * 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. * 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. * (after the specified maximum number of iterations criteria.maxCount or when the search window * moves by less than criteria.epsilon. * - **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. * 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 */ + (void)calcOpticalFlowPyrLK:(Mat*)prevImg nextImg:(Mat*)nextImg prevPts:(Mat*)prevPts nextPts:(Mat*)nextPts status:(Mat*)status err:(Mat*)err winSize:(Size2i*)winSize NS_SWIFT_NAME(calcOpticalFlowPyrLK(prevImg:nextImg:prevPts:nextPts:status:err:winSize:)); /** * 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). * 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. * (after the specified maximum number of iterations criteria.maxCount or when the search window * moves by less than criteria.epsilon. * - **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. * 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 */ + (void)calcOpticalFlowPyrLK:(Mat*)prevImg nextImg:(Mat*)nextImg prevPts:(Mat*)prevPts nextPts:(Mat*)nextPts status:(Mat*)status err:(Mat*)err NS_SWIFT_NAME(calcOpticalFlowPyrLK(prevImg:nextImg:prevPts:nextPts:status:err:)); // // void cv::calcOpticalFlowFarneback(Mat prev, Mat next, Mat& flow, double pyr_scale, int levels, int winsize, int iterations, int poly_n, double poly_sigma, int flags) // /** * Computes a dense optical flow using the Gunnar Farneback's algorithm. * * @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 `$$\texttt{winsize}\times\texttt{winsize}$$` * 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 * * `$$\texttt{prev} (y,x) \sim \texttt{next} ( y + \texttt{flow} (y,x)[1], x + \texttt{flow} (y,x)[0])$$` * * 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 */ + (void)calcOpticalFlowFarneback:(Mat*)prev next:(Mat*)next flow:(Mat*)flow pyr_scale:(double)pyr_scale levels:(int)levels winsize:(int)winsize iterations:(int)iterations poly_n:(int)poly_n poly_sigma:(double)poly_sigma flags:(int)flags NS_SWIFT_NAME(calcOpticalFlowFarneback(prev:next:flow:pyr_scale:levels:winsize:iterations:poly_n:poly_sigma:flags:)); // // double cv::computeECC(Mat templateImage, Mat inputImage, Mat inputMask = Mat()) // /** * Computes the Enhanced Correlation Coefficient value between two images CITE: EP08 . * * @param templateImage single-channel template image; CV_8U or CV_32F array. * @param inputImage single-channel input image to be warped to provide an image similar to * templateImage, same type as templateImage. * @param inputMask An optional mask to indicate valid values of inputImage. * * @sa * findTransformECC */ + (double)computeECC:(Mat*)templateImage inputImage:(Mat*)inputImage inputMask:(Mat*)inputMask NS_SWIFT_NAME(computeECC(templateImage:inputImage:inputMask:)); /** * Computes the Enhanced Correlation Coefficient value between two images CITE: EP08 . * * @param templateImage single-channel template image; CV_8U or CV_32F array. * @param inputImage single-channel input image to be warped to provide an image similar to * templateImage, same type as templateImage. * * @sa * findTransformECC */ + (double)computeECC:(Mat*)templateImage inputImage:(Mat*)inputImage NS_SWIFT_NAME(computeECC(templateImage:inputImage:)); // // double cv::findTransformECC(Mat templateImage, Mat inputImage, Mat& warpMatrix, int motionType, TermCriteria criteria, Mat inputMask, int gaussFiltSize) // /** * 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 templateImage. * @param warpMatrix floating-point `$$2\times 3$$` or `$$3\times 3$$` mapping matrix (warp). * @param motionType parameter, specifying the type of motion: * - **MOTION_TRANSLATION** sets a translational motion model; warpMatrix is `$$2\times 3$$` with * the first `$$2\times 2$$` 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 `$$2\times 3$$`. * - **MOTION_AFFINE** sets an affine motion model (DEFAULT); six parameters are estimated; * warpMatrix is `$$2\times 3$$`. * - **MOTION_HOMOGRAPHY** sets a homography as a motion model; eight parameters are * estimated;\`warpMatrix\` is `$$3\times 3$$`. * @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. * @param gaussFiltSize An optional value indicating size of gaussian blur filter; (DEFAULT: 5) * * The function estimates the optimum transformation (warpMatrix) with respect to ECC criterion * (CITE: EP08), that is * * `$$\texttt{warpMatrix} = \arg\max_{W} \texttt{ECC}(\texttt{templateImage}(x,y),\texttt{inputImage}(x',y'))$$` * * where * * `$$\begin{bmatrix} x' \\ y' \end{bmatrix} = W \cdot \begin{bmatrix} x \\ y \\ 1 \end{bmatrix}$$` * * (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 `$$3\times 3$$` 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) is used as an initialization. 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 * computeECC, estimateAffine2D, estimateAffinePartial2D, findHomography */ + (double)findTransformECC:(Mat*)templateImage inputImage:(Mat*)inputImage warpMatrix:(Mat*)warpMatrix motionType:(int)motionType criteria:(TermCriteria*)criteria inputMask:(Mat*)inputMask gaussFiltSize:(int)gaussFiltSize NS_SWIFT_NAME(findTransformECC(templateImage:inputImage:warpMatrix:motionType:criteria:inputMask:gaussFiltSize:)); // // double cv::findTransformECC(Mat templateImage, Mat inputImage, Mat& warpMatrix, int motionType = MOTION_AFFINE, TermCriteria criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 50, 0.001), Mat inputMask = Mat()) // + (double)findTransformECC:(Mat*)templateImage inputImage:(Mat*)inputImage warpMatrix:(Mat*)warpMatrix motionType:(int)motionType criteria:(TermCriteria*)criteria inputMask:(Mat*)inputMask NS_SWIFT_NAME(findTransformECC(templateImage:inputImage:warpMatrix:motionType:criteria:inputMask:)); + (double)findTransformECC:(Mat*)templateImage inputImage:(Mat*)inputImage warpMatrix:(Mat*)warpMatrix motionType:(int)motionType criteria:(TermCriteria*)criteria NS_SWIFT_NAME(findTransformECC(templateImage:inputImage:warpMatrix:motionType:criteria:)); + (double)findTransformECC:(Mat*)templateImage inputImage:(Mat*)inputImage warpMatrix:(Mat*)warpMatrix motionType:(int)motionType NS_SWIFT_NAME(findTransformECC(templateImage:inputImage:warpMatrix:motionType:)); + (double)findTransformECC:(Mat*)templateImage inputImage:(Mat*)inputImage warpMatrix:(Mat*)warpMatrix NS_SWIFT_NAME(findTransformECC(templateImage:inputImage:warpMatrix:)); // // Mat cv::readOpticalFlow(String path) // /** * Read a .flo file * * @param path Path to the file to be loaded * * The function readOpticalFlow loads a flow field from a file and returns it as a single matrix. * Resulting Mat has a type CV_32FC2 - floating-point, 2-channel. First channel corresponds to the * flow in the horizontal direction (u), second - vertical (v). */ + (Mat*)readOpticalFlow:(NSString*)path NS_SWIFT_NAME(readOpticalFlow(path:)); // // bool cv::writeOpticalFlow(String path, Mat flow) // /** * Write a .flo to disk * * @param path Path to the file to be written * @param flow Flow field to be stored * * The function stores a flow field in a file, returns true on success, false otherwise. * The flow field must be a 2-channel, floating-point matrix (CV_32FC2). First channel corresponds * to the flow in the horizontal direction (u), second - vertical (v). */ + (BOOL)writeOpticalFlow:(NSString*)path flow:(Mat*)flow NS_SWIFT_NAME(writeOpticalFlow(path:flow:)); // // Ptr_BackgroundSubtractorMOG2 cv::createBackgroundSubtractorMOG2(int history = 500, double varThreshold = 16, bool detectShadows = true) // /** * 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. */ + (BackgroundSubtractorMOG2*)createBackgroundSubtractorMOG2:(int)history varThreshold:(double)varThreshold detectShadows:(BOOL)detectShadows NS_SWIFT_NAME(createBackgroundSubtractorMOG2(history:varThreshold:detectShadows:)); /** * 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. * speed a bit, so if you do not need this feature, set the parameter to false. */ + (BackgroundSubtractorMOG2*)createBackgroundSubtractorMOG2:(int)history varThreshold:(double)varThreshold NS_SWIFT_NAME(createBackgroundSubtractorMOG2(history:varThreshold:)); /** * Creates MOG2 Background Subtractor * * @param history Length of the history. * to decide whether a pixel is well described by the background model. This parameter does not * affect the background update. * speed a bit, so if you do not need this feature, set the parameter to false. */ + (BackgroundSubtractorMOG2*)createBackgroundSubtractorMOG2:(int)history NS_SWIFT_NAME(createBackgroundSubtractorMOG2(history:)); /** * Creates MOG2 Background Subtractor * * to decide whether a pixel is well described by the background model. This parameter does not * affect the background update. * speed a bit, so if you do not need this feature, set the parameter to false. */ + (BackgroundSubtractorMOG2*)createBackgroundSubtractorMOG2 NS_SWIFT_NAME(createBackgroundSubtractorMOG2()); // // Ptr_BackgroundSubtractorKNN cv::createBackgroundSubtractorKNN(int history = 500, double dist2Threshold = 400.0, bool detectShadows = true) // /** * 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. */ + (BackgroundSubtractorKNN*)createBackgroundSubtractorKNN:(int)history dist2Threshold:(double)dist2Threshold detectShadows:(BOOL)detectShadows NS_SWIFT_NAME(createBackgroundSubtractorKNN(history:dist2Threshold:detectShadows:)); /** * 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. * speed a bit, so if you do not need this feature, set the parameter to false. */ + (BackgroundSubtractorKNN*)createBackgroundSubtractorKNN:(int)history dist2Threshold:(double)dist2Threshold NS_SWIFT_NAME(createBackgroundSubtractorKNN(history:dist2Threshold:)); /** * Creates KNN Background Subtractor * * @param history Length of the history. * whether a pixel is close to that sample. This parameter does not affect the background update. * speed a bit, so if you do not need this feature, set the parameter to false. */ + (BackgroundSubtractorKNN*)createBackgroundSubtractorKNN:(int)history NS_SWIFT_NAME(createBackgroundSubtractorKNN(history:)); /** * Creates KNN Background Subtractor * * whether a pixel is close to that sample. This parameter does not affect the background update. * speed a bit, so if you do not need this feature, set the parameter to false. */ + (BackgroundSubtractorKNN*)createBackgroundSubtractorKNN NS_SWIFT_NAME(createBackgroundSubtractorKNN()); @end NS_ASSUME_NONNULL_END