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OpenCV
4.4.0
Open Source Computer Vision
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Functions | |
| std::tuple< GArray< GMat >, GScalar > | cv::gapi::buildOpticalFlowPyramid (const GMat &img, const Size &winSize, const GScalar &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. More... | |
| std::tuple< GArray< Point2f >, GArray< uchar >, GArray< float > > | cv::gapi::calcOpticalFlowPyrLK (const GMat &prevImg, const GMat &nextImg, const GArray< Point2f > &prevPts, const GArray< Point2f > &predPts, const Size &winSize=Size(21, 21), const GScalar &maxLevel=3, const TermCriteria &criteria=TermCriteria(TermCriteria::COUNT|TermCriteria::EPS, 30, 0.01), int flags=0, double minEigThresh=1e-4) |
| Calculates an optical flow for a sparse feature set using the iterative Lucas-Kanade method with pyramids. More... | |
| std::tuple< GArray< Point2f >, GArray< uchar >, GArray< float > > | cv::gapi::calcOpticalFlowPyrLK (const GArray< GMat > &prevPyr, const GArray< GMat > &nextPyr, const GArray< Point2f > &prevPts, const GArray< Point2f > &predPts, const Size &winSize=Size(21, 21), const GScalar &maxLevel=3, const TermCriteria &criteria=TermCriteria(TermCriteria::COUNT|TermCriteria::EPS, 30, 0.01), int flags=0, double minEigThresh=1e-4) |
| std::tuple<GArray<GMat>, GScalar> cv::gapi::buildOpticalFlowPyramid | ( | const GMat & | img, |
| const Size & | winSize, | ||
| const GScalar & | maxLevel, | ||
| 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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| ) |
#include <opencv2/gapi/video.hpp>
Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK.
| img | 8-bit input image. |
| 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. |
| maxLevel | 0-based maximal pyramid level number. |
| withDerivatives | set to precompute gradients for the every pyramid level. If pyramid is constructed without the gradients then calcOpticalFlowPyrLK will calculate them internally. |
| pyrBorder | the border mode for pyramid layers. |
| derivBorder | the border mode for gradients. |
| tryReuseInputImage | put ROI of input image into the pyramid if possible. You can pass false to force data copying. |
| std::tuple<GArray<Point2f>, GArray<uchar>, GArray<float> > cv::gapi::calcOpticalFlowPyrLK | ( | const GMat & | prevImg, |
| const GMat & | nextImg, | ||
| const GArray< Point2f > & | prevPts, | ||
| const GArray< Point2f > & | predPts, | ||
| const Size & | winSize = Size(21, 21), |
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| const GScalar & | maxLevel = 3, |
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| const TermCriteria & | criteria = TermCriteria(TermCriteria::COUNT|TermCriteria::EPS, 30, 0.01), |
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| int | flags = 0, |
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| double | minEigThresh = 1e-4 |
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| ) |
#include <opencv2/gapi/video.hpp>
Calculates an optical flow for a sparse feature set using the iterative Lucas-Kanade method with pyramids.
See [25] .
| prevImg | first 8-bit input image (GMat) or pyramid (GArray<GMat>) constructed by buildOpticalFlowPyramid. |
| nextImg | second input image (GMat) or pyramid (GArray<GMat>) of the same size and the same type as prevImg. |
| prevPts | GArray of 2D points for which the flow needs to be found; point coordinates must be single-precision floating-point numbers. |
| predPts | GArray of 2D points initial for the flow search; make sense only when OPTFLOW_USE_INITIAL_FLOW flag is passed; in that case the vector must have the same size as in the input. |
| winSize | size of the search window at each pyramid level. |
| 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. |
| 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). |
| flags | operation flags:
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| minEigThresh | 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 [25]), 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. |
| std::tuple<GArray<Point2f>, GArray<uchar>, GArray<float> > cv::gapi::calcOpticalFlowPyrLK | ( | const GArray< GMat > & | prevPyr, |
| const GArray< GMat > & | nextPyr, | ||
| const GArray< Point2f > & | prevPts, | ||
| const GArray< Point2f > & | predPts, | ||
| const Size & | winSize = Size(21, 21), |
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| const GScalar & | maxLevel = 3, |
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| const TermCriteria & | criteria = TermCriteria(TermCriteria::COUNT|TermCriteria::EPS, 30, 0.01), |
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| int | flags = 0, |
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| double | minEigThresh = 1e-4 |
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| ) |
#include <opencv2/gapi/video.hpp>
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
1.8.13