av/filter/knn.go

144 lines
4.3 KiB
Go

// +build !circleci
/*
DESCRIPTION
A filter that detects motion and discards frames without motion. The
filter uses a K-Nearest Neighbours (KNN) to determine what is
background and what is foreground.
AUTHORS
Ella Pietraroia <ella@ausocean.org>
LICENSE
KNN.go is Copyright (C) 2019 the Australian Ocean Lab (AusOcean)
It is free software: you can redistribute it and/or modify them
under the terms of the GNU General Public License as published by the
Free Software Foundation, either version 3 of the License, or (at your
option) any later version.
It is distributed in the hope that it will be useful, but WITHOUT
ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License
for more details.
You should have received a copy of the GNU General Public License
in gpl.txt. If not, see http://www.gnu.org/licenses.
*/
package filter
import (
"fmt"
"image"
"io"
"gocv.io/x/gocv"
)
// KNN is a filter that provides basic motion detection. KNN is short for
// K-Nearest Neighbours method.
type KNN struct {
debugWindows
dst io.WriteCloser // Destination to which motion containing frames go.
area float64 // The minimum area that a contour can be found in.
bs *gocv.BackgroundSubtractorKNN // Uses the KNN algorithm to find the difference between the current and background frame.
knl gocv.Mat // Matrix that is used for calculations.
hold [][]byte // Will hold all frames up to hf (so only every hf frame is motion detected).
hf int // The number of frames to be held.
hfCount int // Counter for the hold array.
scale float64 // The factor that frames will be downscaled by for motion detection.
}
// NewKNN returns a pointer to a new KNN filter struct.
func NewKNN(dst io.WriteCloser, area, threshold float64, history, kernelSize int, hf int, scaleFactor int) *KNN {
bs := gocv.NewBackgroundSubtractorKNNWithParams(history, threshold, false)
k := gocv.GetStructuringElement(gocv.MorphRect, image.Pt(kernelSize, kernelSize))
m := &KNN{
dst: dst,
area: area,
bs: &bs,
knl: k,
hold: make([][]byte, hf-1),
hf: hf,
scale: 1 / float64(scaleFactor),
}
m.newWindows("KNN")
return m
}
// Implements io.Closer.
// Close frees resources used by gocv, because it has to be done manually, due to
// it using c-go.
func (m *KNN) Close() error {
m.bs.Close()
m.knl.Close()
m.closeWindows()
return nil
}
// Implements io.Writer.
// Write applies the motion filter to the video stream. Only frames with motion
// are written to the destination encoder, frames without are discarded.
func (m *KNN) Write(f []byte) (int, error) {
if m.hfCount < (m.hf - 1) {
m.hold[m.hfCount] = f
m.hfCount++
return len(f), nil
}
m.hfCount = 0
img, err := gocv.IMDecode(f, gocv.IMReadColor)
if err != nil {
return 0, fmt.Errorf("can't decode image: %w", err)
}
defer img.Close()
imgDelta := gocv.NewMat()
defer imgDelta.Close()
// Downsize image to speed up calculations.
gocv.Resize(img, &img, image.Point{}, m.scale, m.scale, gocv.InterpolationNearestNeighbor)
// Seperate foreground and background.
m.bs.Apply(img, &imgDelta)
// Threshold imgDelta.
gocv.Threshold(imgDelta, &imgDelta, 25, 255, gocv.ThresholdBinary)
// Remove noise.
gocv.Erode(imgDelta, &imgDelta, m.knl)
gocv.Dilate(imgDelta, &imgDelta, m.knl)
// Fill small holes.
gocv.Dilate(imgDelta, &imgDelta, m.knl)
gocv.Erode(imgDelta, &imgDelta, m.knl)
// Find contours and reject ones with a small area.
var contours [][]image.Point
allContours := gocv.FindContours(imgDelta, gocv.RetrievalExternal, gocv.ChainApproxSimple)
for _, c := range allContours {
if gocv.ContourArea(c) > m.area {
contours = append(contours, c)
}
}
// Draw debug information.
m.showDebug(img, imgDelta, len(contours) > 0, contours)
// Don't write to destination if there is no motion.
if len(contours) == 0 {
return len(f), nil
}
// Write to destination, past 4 frames then current frame.
for i, h := range m.hold {
_, err := m.dst.Write(h)
m.hold[i] = nil
if err != nil {
return len(f), fmt.Errorf("could not write previous frames: %w", err)
}
}
return m.dst.Write(f)
}