package gocv

/*
#include <stdlib.h>
#include "video.h"
*/
import "C"
import (
	"image"
	"unsafe"
)

/**
  cv::OPTFLOW_USE_INITIAL_FLOW = 4,
  cv::OPTFLOW_LK_GET_MIN_EIGENVALS = 8,
  cv::OPTFLOW_FARNEBACK_GAUSSIAN = 256
  For further details, please see: https://docs.opencv.org/master/dc/d6b/group__video__track.html#gga2c6cc144c9eee043575d5b311ac8af08a9d4430ac75199af0cf6fcdefba30eafe
*/
const (
	OptflowUseInitialFlow    = 4
	OptflowLkGetMinEigenvals = 8
	OptflowFarnebackGaussian = 256
)

/**
  cv::MOTION_TRANSLATION = 0,
  cv::MOTION_EUCLIDEAN = 1,
  cv::MOTION_AFFINE = 2,
  cv::MOTION_HOMOGRAPHY = 3
  For further details, please see: https://docs.opencv.org/4.x/dc/d6b/group__video__track.html#ggaaedb1f94e6b143cef163622c531afd88a01106d6d20122b782ff25eaeffe9a5be
*/
const (
	MotionTranslation = 0
	MotionEuclidean   = 1
	MotionAffine      = 2
	MotionHomography  = 3
)

// BackgroundSubtractorMOG2 is a wrapper around the cv::BackgroundSubtractorMOG2.
type BackgroundSubtractorMOG2 struct {
	// C.BackgroundSubtractorMOG2
	p unsafe.Pointer
}

// NewBackgroundSubtractorMOG2 returns a new BackgroundSubtractor algorithm
// of type MOG2. MOG2 is a Gaussian Mixture-based Background/Foreground
// Segmentation Algorithm.
//
// For further details, please see:
// https://docs.opencv.org/master/de/de1/group__video__motion.html#ga2beb2dee7a073809ccec60f145b6b29c
// https://docs.opencv.org/master/d7/d7b/classcv_1_1BackgroundSubtractorMOG2.html
//
func NewBackgroundSubtractorMOG2() BackgroundSubtractorMOG2 {
	return BackgroundSubtractorMOG2{p: unsafe.Pointer(C.BackgroundSubtractorMOG2_Create())}
}

// NewBackgroundSubtractorMOG2WithParams returns a new BackgroundSubtractor algorithm
// of type MOG2 with customized parameters. MOG2 is a Gaussian Mixture-based Background/Foreground
// Segmentation Algorithm.
//
// For further details, please see:
// https://docs.opencv.org/master/de/de1/group__video__motion.html#ga2beb2dee7a073809ccec60f145b6b29c
// https://docs.opencv.org/master/d7/d7b/classcv_1_1BackgroundSubtractorMOG2.html
//
func NewBackgroundSubtractorMOG2WithParams(history int, varThreshold float64, detectShadows bool) BackgroundSubtractorMOG2 {
	return BackgroundSubtractorMOG2{p: unsafe.Pointer(C.BackgroundSubtractorMOG2_CreateWithParams(C.int(history), C.double(varThreshold), C.bool(detectShadows)))}
}

// Close BackgroundSubtractorMOG2.
func (b *BackgroundSubtractorMOG2) Close() error {
	C.BackgroundSubtractorMOG2_Close((C.BackgroundSubtractorMOG2)(b.p))
	b.p = nil
	return nil
}

// Apply computes a foreground mask using the current BackgroundSubtractorMOG2.
//
// For further details, please see:
// https://docs.opencv.org/master/d7/df6/classcv_1_1BackgroundSubtractor.html#aa735e76f7069b3fa9c3f32395f9ccd21
//
func (b *BackgroundSubtractorMOG2) Apply(src Mat, dst *Mat) {
	C.BackgroundSubtractorMOG2_Apply((C.BackgroundSubtractorMOG2)(b.p), src.p, dst.p)
	return
}

// BackgroundSubtractorKNN is a wrapper around the cv::BackgroundSubtractorKNN.
type BackgroundSubtractorKNN struct {
	// C.BackgroundSubtractorKNN
	p unsafe.Pointer
}

// NewBackgroundSubtractorKNN returns a new BackgroundSubtractor algorithm
// of type KNN. K-Nearest Neighbors (KNN) uses a Background/Foreground
// Segmentation Algorithm
//
// For further details, please see:
// https://docs.opencv.org/master/de/de1/group__video__motion.html#gac9be925771f805b6fdb614ec2292006d
// https://docs.opencv.org/master/db/d88/classcv_1_1BackgroundSubtractorKNN.html
//
func NewBackgroundSubtractorKNN() BackgroundSubtractorKNN {
	return BackgroundSubtractorKNN{p: unsafe.Pointer(C.BackgroundSubtractorKNN_Create())}
}

// NewBackgroundSubtractorKNNWithParams returns a new BackgroundSubtractor algorithm
// of type KNN with customized parameters. K-Nearest Neighbors (KNN) uses a Background/Foreground
// Segmentation Algorithm
//
// For further details, please see:
// https://docs.opencv.org/master/de/de1/group__video__motion.html#gac9be925771f805b6fdb614ec2292006d
// https://docs.opencv.org/master/db/d88/classcv_1_1BackgroundSubtractorKNN.html
//
func NewBackgroundSubtractorKNNWithParams(history int, dist2Threshold float64, detectShadows bool) BackgroundSubtractorKNN {
	return BackgroundSubtractorKNN{p: unsafe.Pointer(C.BackgroundSubtractorKNN_CreateWithParams(C.int(history), C.double(dist2Threshold), C.bool(detectShadows)))}
}

// Close BackgroundSubtractorKNN.
func (k *BackgroundSubtractorKNN) Close() error {
	C.BackgroundSubtractorKNN_Close((C.BackgroundSubtractorKNN)(k.p))
	k.p = nil
	return nil
}

// Apply computes a foreground mask using the current BackgroundSubtractorKNN.
//
// For further details, please see:
// https://docs.opencv.org/master/d7/df6/classcv_1_1BackgroundSubtractor.html#aa735e76f7069b3fa9c3f32395f9ccd21
//
func (k *BackgroundSubtractorKNN) Apply(src Mat, dst *Mat) {
	C.BackgroundSubtractorKNN_Apply((C.BackgroundSubtractorKNN)(k.p), src.p, dst.p)
	return
}

// CalcOpticalFlowFarneback computes a dense optical flow using
// Gunnar Farneback's algorithm.
//
// For further details, please see:
// https://docs.opencv.org/master/dc/d6b/group__video__track.html#ga5d10ebbd59fe09c5f650289ec0ece5af
//
func CalcOpticalFlowFarneback(prevImg Mat, nextImg Mat, flow *Mat, pyrScale float64, levels int, winsize int,
	iterations int, polyN int, polySigma float64, flags int) {
	C.CalcOpticalFlowFarneback(prevImg.p, nextImg.p, flow.p, C.double(pyrScale), C.int(levels), C.int(winsize),
		C.int(iterations), C.int(polyN), C.double(polySigma), C.int(flags))
	return
}

// CalcOpticalFlowPyrLK calculates an optical flow for a sparse feature set using
// the iterative Lucas-Kanade method with pyramids.
//
// For further details, please see:
// https://docs.opencv.org/master/dc/d6b/group__video__track.html#ga473e4b886d0bcc6b65831eb88ed93323
//
func CalcOpticalFlowPyrLK(prevImg Mat, nextImg Mat, prevPts Mat, nextPts Mat, status *Mat, err *Mat) {
	C.CalcOpticalFlowPyrLK(prevImg.p, nextImg.p, prevPts.p, nextPts.p, status.p, err.p)
	return
}

// CalcOpticalFlowPyrLKWithParams calculates an optical flow for a sparse feature set using
// the iterative Lucas-Kanade method with pyramids.
//
// For further details, please see:
// https://docs.opencv.org/master/dc/d6b/group__video__track.html#ga473e4b886d0bcc6b65831eb88ed93323
//
func CalcOpticalFlowPyrLKWithParams(prevImg Mat, nextImg Mat, prevPts Mat, nextPts Mat, status *Mat, err *Mat,
	winSize image.Point, maxLevel int, criteria TermCriteria, flags int, minEigThreshold float64) {
	winSz := C.struct_Size{
		width:  C.int(winSize.X),
		height: C.int(winSize.Y),
	}
	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))
	return
}

// FindTransformECC finds the geometric transform (warp) between two images in terms of the ECC criterion.
//
// For futther details, please see:
// https://docs.opencv.org/4.x/dc/d6b/group__video__track.html#ga1aa357007eaec11e9ed03500ecbcbe47
//
func FindTransformECC(templateImage Mat, inputImage Mat, warpMatrix *Mat, motionType int, criteria TermCriteria, inputMask Mat, gaussFiltSize int) float64 {
	return float64(C.FindTransformECC(templateImage.p, inputImage.p, warpMatrix.p, C.int(motionType), criteria.p, inputMask.p, C.int(gaussFiltSize)))
}

// Tracker is the base interface for object tracking.
//
// see: https://docs.opencv.org/master/d0/d0a/classcv_1_1Tracker.html
//
type Tracker interface {
	// Close closes, as Trackers need to be Closed manually.
	//
	Close() error

	// Init initializes the tracker with a known bounding box that surrounded the target.
	// Note: this can only be called once. If you lose the object, you have to Close() the instance,
	// create a new one, and call Init() on it again.
	//
	// see: https://docs.opencv.org/master/d0/d0a/classcv_1_1Tracker.html#a4d285747589b1bdd16d2e4f00c3255dc
	//
	Init(image Mat, boundingBox image.Rectangle) bool

	// Update updates the tracker, returns a new bounding box and a boolean determining whether the tracker lost the target.
	//
	// see: https://docs.opencv.org/master/d0/d0a/classcv_1_1Tracker.html#a549159bd0553e6a8de356f3866df1f18
	//
	Update(image Mat) (image.Rectangle, bool)
}

func trackerInit(trk C.Tracker, img Mat, boundingBox image.Rectangle) bool {
	cBox := C.struct_Rect{
		x:      C.int(boundingBox.Min.X),
		y:      C.int(boundingBox.Min.Y),
		width:  C.int(boundingBox.Size().X),
		height: C.int(boundingBox.Size().Y),
	}

	ret := C.Tracker_Init(trk, C.Mat(img.Ptr()), cBox)
	return bool(ret)
}

func trackerUpdate(trk C.Tracker, img Mat) (image.Rectangle, bool) {
	cBox := C.struct_Rect{}

	ret := C.Tracker_Update(trk, C.Mat(img.Ptr()), &cBox)

	rect := image.Rect(int(cBox.x), int(cBox.y), int(cBox.x+cBox.width), int(cBox.y+cBox.height))
	return rect, bool(ret)
}

// TrackerMIL is a Tracker that uses the MIL algorithm. MIL trains a classifier in an online manner
// to separate the object from the background.
// Multiple Instance Learning avoids the drift problem for a robust tracking.
//
// For further details, please see:
// https://docs.opencv.org/master/d0/d26/classcv_1_1TrackerMIL.html
//
type TrackerMIL struct {
	p C.TrackerMIL
}

// NewTrackerMIL returns a new TrackerMIL.
func NewTrackerMIL() Tracker {
	return TrackerMIL{p: C.TrackerMIL_Create()}
}

// Close closes the TrackerMIL.
func (trk TrackerMIL) Close() error {
	C.TrackerMIL_Close(trk.p)
	trk.p = nil
	return nil
}

// Init initializes the TrackerMIL.
func (trk TrackerMIL) Init(img Mat, boundingBox image.Rectangle) bool {
	return trackerInit(C.Tracker(trk.p), img, boundingBox)
}

// Update updates the TrackerMIL.
func (trk TrackerMIL) Update(img Mat) (image.Rectangle, bool) {
	return trackerUpdate(C.Tracker(trk.p), img)
}