mirror of https://bitbucket.org/ausocean/av.git
133 lines
3.7 KiB
Go
133 lines
3.7 KiB
Go
//go:build withcv
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// +build withcv
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/*
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DESCRIPTION
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A filter that detects motion and discards frames without motion. The
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filter uses a K-Nearest Neighbours (KNN) to determine what is
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background and what is foreground.
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AUTHORS
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Ella Pietraroia <ella@ausocean.org>
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LICENSE
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KNN.go is Copyright (C) 2019 the Australian Ocean Lab (AusOcean)
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It is free software: you can redistribute it and/or modify them
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under the terms of the GNU General Public License as published by the
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Free Software Foundation, either version 3 of the License, or (at your
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option) any later version.
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It is distributed in the hope that it will be useful, but WITHOUT
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ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
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FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License
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for more details.
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You should have received a copy of the GNU General Public License
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in gpl.txt. If not, see http://www.gnu.org/licenses.
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*/
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package filter
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import (
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"image"
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"io"
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"gocv.io/x/gocv"
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"bitbucket.org/ausocean/av/revid/config"
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)
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const (
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defaultKNNMinArea = 25.0
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defaultKNNThreshold = 300
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defaultKNNHistory = 300
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defaultKNNKernel = 4
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)
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// NewKNN returns a pointer to a new KNN motion filter.
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func NewKNN(dst io.WriteCloser, c config.Config) *Motion {
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// Validate parameters.
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if c.MotionMinArea <= 0 {
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c.LogInvalidField("MotionMinArea", defaultKNNMinArea)
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c.MotionMinArea = defaultKNNMinArea
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}
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if c.MotionThreshold <= 0 {
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c.LogInvalidField("MotionThreshold", defaultKNNThreshold)
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c.MotionThreshold = defaultKNNThreshold
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}
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if c.MotionHistory == 0 {
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c.LogInvalidField("MotionHistory", defaultKNNHistory)
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c.MotionHistory = defaultKNNHistory
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}
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if c.MotionKernel <= 0 {
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c.LogInvalidField("MotionKernel", defaultKNNKernel)
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c.MotionKernel = defaultKNNKernel
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}
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bs := gocv.NewBackgroundSubtractorKNNWithParams(int(c.MotionHistory), c.MotionThreshold, false)
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alg := &KNN{
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area: c.MotionMinArea,
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bs: &bs,
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knl: gocv.GetStructuringElement(gocv.MorphRect, image.Pt(int(c.MotionKernel), int(c.MotionKernel))),
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debugging: newWindows("KNN"),
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}
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return NewMotion(dst, alg, c)
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}
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// KNN is motion detection algorithm. KNN is short for
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// K-Nearest Neighbours method.
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type KNN struct {
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debugging debugWindows
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area float64 // The minimum area that a contour can be found in.
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bs *gocv.BackgroundSubtractorKNN // Uses the KNN algorithm to find the difference between the current and background frame.
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knl gocv.Mat // Matrix that is used for calculations.
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}
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// Close frees resources used by gocv. It has to be done manually,
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// due to gocv using c-go.
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func (m *KNN) Close() error {
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m.bs.Close()
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m.knl.Close()
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m.debugging.close()
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return nil
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}
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// Detect performs the motion detection on a frame. It returns true
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// if motion is detected.
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func (m *KNN) Detect(img *gocv.Mat) bool {
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imgDelta := gocv.NewMat()
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defer imgDelta.Close()
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// Seperate foreground and background.
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m.bs.Apply(*img, &imgDelta)
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// Threshold imgDelta.
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gocv.Threshold(imgDelta, &imgDelta, 25, 255, gocv.ThresholdBinary)
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// Remove noise.
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gocv.Erode(imgDelta, &imgDelta, m.knl)
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gocv.Dilate(imgDelta, &imgDelta, m.knl)
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// Fill small holes.
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gocv.Dilate(imgDelta, &imgDelta, m.knl)
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gocv.Erode(imgDelta, &imgDelta, m.knl)
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// Find contours and reject ones with a small area.
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var contours [][]image.Point
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allContours := gocv.FindContours(imgDelta, gocv.RetrievalExternal, gocv.ChainApproxSimple)
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for i := 0; i < allContours.Size(); i++ {
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if gocv.ContourArea(allContours.At(i)) > m.area {
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contours = append(contours, allContours.At(i).ToPoints())
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}
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}
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// Draw debug information.
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m.debugging.show(*img, imgDelta, len(contours) > 0, &contours)
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// Return if there is motion.
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return len(contours) > 0
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}
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