259 lines
9.5 KiB
Go
259 lines
9.5 KiB
Go
package gocv
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/*
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#include <stdlib.h>
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#include "video.h"
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*/
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import "C"
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import (
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"image"
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"unsafe"
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)
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/**
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cv::OPTFLOW_USE_INITIAL_FLOW = 4,
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cv::OPTFLOW_LK_GET_MIN_EIGENVALS = 8,
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cv::OPTFLOW_FARNEBACK_GAUSSIAN = 256
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For further details, please see: https://docs.opencv.org/master/dc/d6b/group__video__track.html#gga2c6cc144c9eee043575d5b311ac8af08a9d4430ac75199af0cf6fcdefba30eafe
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*/
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const (
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OptflowUseInitialFlow = 4
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OptflowLkGetMinEigenvals = 8
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OptflowFarnebackGaussian = 256
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)
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/**
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cv::MOTION_TRANSLATION = 0,
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cv::MOTION_EUCLIDEAN = 1,
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cv::MOTION_AFFINE = 2,
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cv::MOTION_HOMOGRAPHY = 3
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For further details, please see: https://docs.opencv.org/4.x/dc/d6b/group__video__track.html#ggaaedb1f94e6b143cef163622c531afd88a01106d6d20122b782ff25eaeffe9a5be
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*/
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const (
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MotionTranslation = 0
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MotionEuclidean = 1
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MotionAffine = 2
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MotionHomography = 3
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)
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// BackgroundSubtractorMOG2 is a wrapper around the cv::BackgroundSubtractorMOG2.
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type BackgroundSubtractorMOG2 struct {
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// C.BackgroundSubtractorMOG2
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p unsafe.Pointer
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}
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// NewBackgroundSubtractorMOG2 returns a new BackgroundSubtractor algorithm
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// of type MOG2. MOG2 is a Gaussian Mixture-based Background/Foreground
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// Segmentation Algorithm.
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//
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// For further details, please see:
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// https://docs.opencv.org/master/de/de1/group__video__motion.html#ga2beb2dee7a073809ccec60f145b6b29c
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// https://docs.opencv.org/master/d7/d7b/classcv_1_1BackgroundSubtractorMOG2.html
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//
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func NewBackgroundSubtractorMOG2() BackgroundSubtractorMOG2 {
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return BackgroundSubtractorMOG2{p: unsafe.Pointer(C.BackgroundSubtractorMOG2_Create())}
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}
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// NewBackgroundSubtractorMOG2WithParams returns a new BackgroundSubtractor algorithm
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// of type MOG2 with customized parameters. MOG2 is a Gaussian Mixture-based Background/Foreground
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// Segmentation Algorithm.
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//
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// For further details, please see:
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// https://docs.opencv.org/master/de/de1/group__video__motion.html#ga2beb2dee7a073809ccec60f145b6b29c
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// https://docs.opencv.org/master/d7/d7b/classcv_1_1BackgroundSubtractorMOG2.html
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//
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func NewBackgroundSubtractorMOG2WithParams(history int, varThreshold float64, detectShadows bool) BackgroundSubtractorMOG2 {
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return BackgroundSubtractorMOG2{p: unsafe.Pointer(C.BackgroundSubtractorMOG2_CreateWithParams(C.int(history), C.double(varThreshold), C.bool(detectShadows)))}
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}
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// Close BackgroundSubtractorMOG2.
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func (b *BackgroundSubtractorMOG2) Close() error {
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C.BackgroundSubtractorMOG2_Close((C.BackgroundSubtractorMOG2)(b.p))
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b.p = nil
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return nil
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}
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// Apply computes a foreground mask using the current BackgroundSubtractorMOG2.
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//
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// For further details, please see:
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// https://docs.opencv.org/master/d7/df6/classcv_1_1BackgroundSubtractor.html#aa735e76f7069b3fa9c3f32395f9ccd21
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//
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func (b *BackgroundSubtractorMOG2) Apply(src Mat, dst *Mat) {
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C.BackgroundSubtractorMOG2_Apply((C.BackgroundSubtractorMOG2)(b.p), src.p, dst.p)
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return
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}
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// BackgroundSubtractorKNN is a wrapper around the cv::BackgroundSubtractorKNN.
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type BackgroundSubtractorKNN struct {
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// C.BackgroundSubtractorKNN
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p unsafe.Pointer
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}
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// NewBackgroundSubtractorKNN returns a new BackgroundSubtractor algorithm
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// of type KNN. K-Nearest Neighbors (KNN) uses a Background/Foreground
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// Segmentation Algorithm
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//
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// For further details, please see:
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// https://docs.opencv.org/master/de/de1/group__video__motion.html#gac9be925771f805b6fdb614ec2292006d
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// https://docs.opencv.org/master/db/d88/classcv_1_1BackgroundSubtractorKNN.html
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//
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func NewBackgroundSubtractorKNN() BackgroundSubtractorKNN {
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return BackgroundSubtractorKNN{p: unsafe.Pointer(C.BackgroundSubtractorKNN_Create())}
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}
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// NewBackgroundSubtractorKNNWithParams returns a new BackgroundSubtractor algorithm
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// of type KNN with customized parameters. K-Nearest Neighbors (KNN) uses a Background/Foreground
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// Segmentation Algorithm
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//
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// For further details, please see:
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// https://docs.opencv.org/master/de/de1/group__video__motion.html#gac9be925771f805b6fdb614ec2292006d
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// https://docs.opencv.org/master/db/d88/classcv_1_1BackgroundSubtractorKNN.html
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//
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func NewBackgroundSubtractorKNNWithParams(history int, dist2Threshold float64, detectShadows bool) BackgroundSubtractorKNN {
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return BackgroundSubtractorKNN{p: unsafe.Pointer(C.BackgroundSubtractorKNN_CreateWithParams(C.int(history), C.double(dist2Threshold), C.bool(detectShadows)))}
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}
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// Close BackgroundSubtractorKNN.
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func (k *BackgroundSubtractorKNN) Close() error {
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C.BackgroundSubtractorKNN_Close((C.BackgroundSubtractorKNN)(k.p))
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k.p = nil
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return nil
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}
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// Apply computes a foreground mask using the current BackgroundSubtractorKNN.
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//
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// For further details, please see:
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// https://docs.opencv.org/master/d7/df6/classcv_1_1BackgroundSubtractor.html#aa735e76f7069b3fa9c3f32395f9ccd21
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//
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func (k *BackgroundSubtractorKNN) Apply(src Mat, dst *Mat) {
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C.BackgroundSubtractorKNN_Apply((C.BackgroundSubtractorKNN)(k.p), src.p, dst.p)
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return
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}
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// CalcOpticalFlowFarneback computes a dense optical flow using
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// Gunnar Farneback's algorithm.
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//
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// For further details, please see:
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// https://docs.opencv.org/master/dc/d6b/group__video__track.html#ga5d10ebbd59fe09c5f650289ec0ece5af
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//
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func CalcOpticalFlowFarneback(prevImg Mat, nextImg Mat, flow *Mat, pyrScale float64, levels int, winsize int,
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iterations int, polyN int, polySigma float64, flags int) {
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C.CalcOpticalFlowFarneback(prevImg.p, nextImg.p, flow.p, C.double(pyrScale), C.int(levels), C.int(winsize),
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C.int(iterations), C.int(polyN), C.double(polySigma), C.int(flags))
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return
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}
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// CalcOpticalFlowPyrLK calculates an optical flow for a sparse feature set using
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// the iterative Lucas-Kanade method with pyramids.
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//
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// For further details, please see:
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// https://docs.opencv.org/master/dc/d6b/group__video__track.html#ga473e4b886d0bcc6b65831eb88ed93323
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//
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func CalcOpticalFlowPyrLK(prevImg Mat, nextImg Mat, prevPts Mat, nextPts Mat, status *Mat, err *Mat) {
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C.CalcOpticalFlowPyrLK(prevImg.p, nextImg.p, prevPts.p, nextPts.p, status.p, err.p)
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return
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}
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// CalcOpticalFlowPyrLKWithParams calculates an optical flow for a sparse feature set using
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// the iterative Lucas-Kanade method with pyramids.
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//
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// For further details, please see:
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// https://docs.opencv.org/master/dc/d6b/group__video__track.html#ga473e4b886d0bcc6b65831eb88ed93323
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//
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func CalcOpticalFlowPyrLKWithParams(prevImg Mat, nextImg Mat, prevPts Mat, nextPts Mat, status *Mat, err *Mat,
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winSize image.Point, maxLevel int, criteria TermCriteria, flags int, minEigThreshold float64) {
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winSz := C.struct_Size{
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width: C.int(winSize.X),
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height: C.int(winSize.Y),
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}
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C.CalcOpticalFlowPyrLKWithParams(prevImg.p, nextImg.p, prevPts.p, nextPts.p, status.p, err.p, winSz, C.int(maxLevel), criteria.p, C.int(flags), C.double(minEigThreshold))
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return
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}
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// FindTransformECC finds the geometric transform (warp) between two images in terms of the ECC criterion.
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//
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// For futther details, please see:
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// https://docs.opencv.org/4.x/dc/d6b/group__video__track.html#ga1aa357007eaec11e9ed03500ecbcbe47
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//
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func FindTransformECC(templateImage Mat, inputImage Mat, warpMatrix *Mat, motionType int, criteria TermCriteria, inputMask Mat, gaussFiltSize int) float64 {
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return float64(C.FindTransformECC(templateImage.p, inputImage.p, warpMatrix.p, C.int(motionType), criteria.p, inputMask.p, C.int(gaussFiltSize)))
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}
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// Tracker is the base interface for object tracking.
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//
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// see: https://docs.opencv.org/master/d0/d0a/classcv_1_1Tracker.html
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//
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type Tracker interface {
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// Close closes, as Trackers need to be Closed manually.
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//
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Close() error
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// Init initializes the tracker with a known bounding box that surrounded the target.
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// Note: this can only be called once. If you lose the object, you have to Close() the instance,
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// create a new one, and call Init() on it again.
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//
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// see: https://docs.opencv.org/master/d0/d0a/classcv_1_1Tracker.html#a4d285747589b1bdd16d2e4f00c3255dc
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//
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Init(image Mat, boundingBox image.Rectangle) bool
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// Update updates the tracker, returns a new bounding box and a boolean determining whether the tracker lost the target.
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//
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// see: https://docs.opencv.org/master/d0/d0a/classcv_1_1Tracker.html#a549159bd0553e6a8de356f3866df1f18
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//
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Update(image Mat) (image.Rectangle, bool)
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}
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func trackerInit(trk C.Tracker, img Mat, boundingBox image.Rectangle) bool {
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cBox := C.struct_Rect{
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x: C.int(boundingBox.Min.X),
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y: C.int(boundingBox.Min.Y),
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width: C.int(boundingBox.Size().X),
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height: C.int(boundingBox.Size().Y),
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}
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ret := C.Tracker_Init(trk, C.Mat(img.Ptr()), cBox)
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return bool(ret)
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}
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func trackerUpdate(trk C.Tracker, img Mat) (image.Rectangle, bool) {
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cBox := C.struct_Rect{}
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ret := C.Tracker_Update(trk, C.Mat(img.Ptr()), &cBox)
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rect := image.Rect(int(cBox.x), int(cBox.y), int(cBox.x+cBox.width), int(cBox.y+cBox.height))
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return rect, bool(ret)
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}
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// TrackerMIL is a Tracker that uses the MIL algorithm. MIL trains a classifier in an online manner
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// to separate the object from the background.
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// Multiple Instance Learning avoids the drift problem for a robust tracking.
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//
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// For further details, please see:
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// https://docs.opencv.org/master/d0/d26/classcv_1_1TrackerMIL.html
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//
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type TrackerMIL struct {
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p C.TrackerMIL
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}
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// NewTrackerMIL returns a new TrackerMIL.
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func NewTrackerMIL() Tracker {
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return TrackerMIL{p: C.TrackerMIL_Create()}
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}
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// Close closes the TrackerMIL.
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func (trk TrackerMIL) Close() error {
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C.TrackerMIL_Close(trk.p)
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trk.p = nil
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return nil
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}
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// Init initializes the TrackerMIL.
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func (trk TrackerMIL) Init(img Mat, boundingBox image.Rectangle) bool {
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return trackerInit(C.Tracker(trk.p), img, boundingBox)
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}
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// Update updates the TrackerMIL.
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func (trk TrackerMIL) Update(img Mat) (image.Rectangle, bool) {
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return trackerUpdate(C.Tracker(trk.p), img)
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}
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