Display camera frame

vendor/gocv.io/x/gocv/video.go

@@ -0,0 +1,157 @@
+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
+)
+
+// 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
+}