mirror of https://bitbucket.org/ausocean/av.git
resolve sharpness/saturation terminology issue, updated package names and code cleanup
This commit is contained in:
parent
6d97486876
commit
00e1390893
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@ -6,7 +6,7 @@ AUTHORS
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Russell Stanley <russell@ausocean.org>
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LICENSE
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Copyright (C) 2020 the Australian Ocean Lab (AusOcean)
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Copyright (C) 2021-2022 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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@ -22,7 +22,7 @@ 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 main
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package turbidity
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import (
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"fmt"
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@ -63,15 +63,13 @@ func normalize(slice []float64) []float64 {
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// Return the average of a slice.
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func average(slice []float64) float64 {
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var out float64
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// Sum all elements in the slice.
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for i := range slice {
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out += slice[i]
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}
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return out / float64(len(slice))
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}
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func plotResults(x, saturation, contrast []float64) error {
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func plotResults(x, sharpness, contrast []float64) error {
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err := plotToFile(
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"Results",
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"Almond Milk (ml)",
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@ -79,7 +77,7 @@ func plotResults(x, saturation, contrast []float64) error {
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func(p *plot.Plot) error {
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return plotutil.AddLinePoints(p,
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"Contrast", plotterXY(x, contrast),
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"Saturation", plotterXY(x, saturation),
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"Sharpness", plotterXY(x, sharpness),
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)
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},
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)
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@ -9,7 +9,7 @@ AUTHORS
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Russell Stanley <russell@ausocean.org>
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LICENSE
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Copyright (C) 2020 the Australian Ocean Lab (AusOcean)
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Copyright (C) 2021-2022 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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@ -25,27 +25,26 @@ 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 main
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package turbidity
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import "fmt"
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// Results holds the results of the turbidity sensor.
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type Results struct {
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Turbidity []float64
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Saturation []float64
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Contrast []float64
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Turbidity []float64
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Sharpness []float64
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Contrast []float64
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}
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// NewResults constructs the results object.
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// NewResults returns a new Results
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func NewResults(n int) (*Results, error) {
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if n <= 0 {
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return nil, fmt.Errorf("invalid result size: %v.", n)
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return nil, fmt.Errorf("invalid result size: %v", n)
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}
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r := new(Results)
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r.Turbidity = make([]float64, n)
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r.Saturation = make([]float64, n)
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r.Sharpness = make([]float64, n)
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r.Contrast = make([]float64, n)
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return r, nil
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@ -53,7 +52,7 @@ func NewResults(n int) (*Results, error) {
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// Update adds new values to slice at specified index.
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func (r *Results) Update(newSat, newCont, newTurb float64, index int) {
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r.Saturation[index] = newSat
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r.Sharpness[index] = newSat
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r.Contrast[index] = newCont
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r.Turbidity[index] = newTurb
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}
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@ -3,14 +3,16 @@
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/*
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DESCRIPTION
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Holds the turbidity sensor struct. Can evaluate 4x4 chessboard markers
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in an image to measure the sharpness and contrast.
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Holds the turbidity sensor struct. Can evaluate 4x4 chessboard markers in an
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image to measure the sharpness and contrast. This implementation is based off
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a master thesis from Aalborg University, Turbidity measurement based on computer vision.
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The full paper is avaible at https://projekter.aau.dk/projekter/files/306657262/master.pdf
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AUTHORS
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Russell Stanley <russell@ausocean.org>
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LICENSE
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Copyright (C) 2020 the Australian Ocean Lab (AusOcean)
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Copyright (C) 2021-2022 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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@ -26,7 +28,7 @@ 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 main
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package turbidity
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import (
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"errors"
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@ -37,7 +39,7 @@ import (
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"gocv.io/x/gocv"
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)
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// TurbiditySensor is a software based turbidity sensor that uses CV to determine saturation and constrast level
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// TurbiditySensor is a software based turbidity sensor that uses CV to determine sharpness and constrast level
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// of a chessboard-like target submerged in water that can be correlated to turbidity/visibility values.
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type TurbiditySensor struct {
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template, templateCorners gocv.Mat
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@ -46,7 +48,7 @@ type TurbiditySensor struct {
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scale, alpha float64
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}
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// NewTurbiditySensor constructor for a turbidity sensor.
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// NewTurbiditySensor returns a new TurbiditySensor.
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func NewTurbiditySensor(template, standard gocv.Mat, k1, k2, sobelFilterSize int, scale, alpha float64) (*TurbiditySensor, error) {
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ts := new(TurbiditySensor)
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templateCorners := gocv.NewMat()
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@ -54,7 +56,7 @@ func NewTurbiditySensor(template, standard gocv.Mat, k1, k2, sobelFilterSize int
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// Validate template image is not empty and has valid corners.
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if template.Empty() {
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return nil, errors.New("template image is empty.")
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return nil, errors.New("template image is empty")
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}
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if !gocv.FindChessboardCorners(template, image.Pt(3, 3), &templateCorners, gocv.CalibCBNormalizeImage) {
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return nil, errors.New("could not find corners in template image")
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@ -64,7 +66,7 @@ func NewTurbiditySensor(template, standard gocv.Mat, k1, k2, sobelFilterSize int
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// Validate standard image is not empty and has valid corners.
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if standard.Empty() {
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return nil, errors.New("standard image is empty.")
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return nil, errors.New("standard image is empty")
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}
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if !gocv.FindChessboardCorners(standard, image.Pt(3, 3), &standardCorners, gocv.CalibCBNormalizeImage) {
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return nil, errors.New("could not find corners in standard image")
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@ -74,7 +76,6 @@ func NewTurbiditySensor(template, standard gocv.Mat, k1, k2, sobelFilterSize int
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ts.k1, ts.k2, ts.sobelFilterSize = k1, k2, sobelFilterSize
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ts.alpha, ts.scale = alpha, scale
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return ts, nil
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}
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@ -82,28 +83,22 @@ func NewTurbiditySensor(template, standard gocv.Mat, k1, k2, sobelFilterSize int
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func (ts TurbiditySensor) Evaluate(imgs []gocv.Mat) (*Results, error) {
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result, err := NewResults(len(imgs))
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if err != nil {
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return result, err
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return nil, fmt.Errorf("could not create results: %w", err)
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}
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for i := range imgs {
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// Transform image.
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marker, err := ts.transform(imgs[i])
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if err != nil {
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return result, fmt.Errorf("image %v: %w", i, err)
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return nil, fmt.Errorf("could not transform image: %d: %w", i, err)
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}
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// Apply sobel filter.
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edge := ts.sobel(marker)
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// Evaluate image.
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sharpScore, contScore, err := ts.EvaluateImage(marker, edge)
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if err != nil {
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return result, err
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}
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result.Update(sharpScore, contScore, float64(i), i)
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}
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return result, nil
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}
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@ -115,19 +110,16 @@ func (ts TurbiditySensor) EvaluateImage(img, edge gocv.Mat) (float64, float64, e
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if img.Rows()%ts.k1 != 0 || img.Cols()%ts.k2 != 0 {
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return math.NaN(), math.NaN(), fmt.Errorf("dimensions not compatible (%v, %v)", ts.k1, ts.k2)
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}
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lStep := img.Rows() / ts.k1
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kStep := img.Cols() / ts.k2
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for l := 0; l < img.Rows(); l += lStep {
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for k := 0; k < img.Cols(); k += kStep {
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// Enhancement Measure Estimation (EME), provides a measure of the sharpness.
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sharpValue := ts.evaluateBlockEME(edge, l, k, l+lStep, k+kStep)
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sharpness += sharpValue
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sharpness += ts.evaluateBlockEME(edge, l, k, l+lStep, k+kStep)
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// AMEE, provides a measure of the contrast.
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contValue := ts.evaluateBlockAMEE(img, l, k, l+lStep, k+kStep)
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contrast += contValue
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contrast += ts.evaluateBlockAMEE(img, l, k, l+lStep, k+kStep)
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}
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}
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@ -165,7 +157,7 @@ func (ts TurbiditySensor) minMax(img gocv.Mat, xStart, yStart, xEnd, yEnd int) (
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func (ts TurbiditySensor) evaluateBlockEME(img gocv.Mat, xStart, yStart, xEnd, yEnd int) float64 {
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max, min := ts.minMax(img, xStart, yStart, xEnd, yEnd)
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// Blocks which have no information are ignored.
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// Blocks where all pixel values are equal are ignored to avoid division by 0.
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if max != -math.MaxFloat64 && min != math.MaxFloat64 && max != min {
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return math.Log(max / min)
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}
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@ -176,7 +168,7 @@ func (ts TurbiditySensor) evaluateBlockEME(img gocv.Mat, xStart, yStart, xEnd, y
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func (ts TurbiditySensor) evaluateBlockAMEE(img gocv.Mat, xStart, yStart, xEnd, yEnd int) float64 {
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max, min := ts.minMax(img, xStart, yStart, xEnd, yEnd)
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// Blocks which have no information are ignored.
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// Blocks where all pixel values are equal are ignored to avoid division by 0.
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if max != -math.MaxFloat64 && min != math.MaxFloat64 && max != min {
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contrast := (max + min) / (max - min)
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return math.Pow(ts.alpha*(contrast), ts.alpha) * math.Log(contrast)
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out := gocv.NewMat()
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mask := gocv.NewMat()
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imgCorners := gocv.NewMat()
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const (
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ransacThreshold = 3.0 // Maximum allowed reprojection error to treat a point pair as an inlier.
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maxIter = 2000 // The maximum number of RANSAC iterations.
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confidence = 0.995 // Confidence level, between 0 and 1.
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)
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// Check image is valid.
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if img.Empty() {
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return out, errors.New("image is empty, cannot transform")
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}
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@ -200,7 +196,7 @@ func (ts TurbiditySensor) transform(img gocv.Mat) (gocv.Mat, error) {
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}
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// Find and apply transformation.
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H := gocv.FindHomography(imgCorners, &ts.templateCorners, gocv.HomograpyMethodRANSAC, 3.0, &mask, 2000, 0.995)
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H := gocv.FindHomography(imgCorners, &ts.templateCorners, gocv.HomograpyMethodRANSAC, ransacThreshold, &mask, maxIter, confidence)
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gocv.WarpPerspective(img, &out, H, image.Pt(ts.template.Rows(), ts.template.Cols()))
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return out, nil
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@ -7,7 +7,7 @@ AUTHORS
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Russell Stanley <russell@ausocean.org>
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LICENSE
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Copyright (C) 2020 the Australian Ocean Lab (AusOcean)
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Copyright (C) 2021-2022 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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@ -23,7 +23,7 @@ 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 main
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package turbidity
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import (
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"fmt"
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)
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const (
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nImages = 13 // Number of images to test. (Max 13)
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nSamples = 10 // Number of samples for each image. (Max 10)
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increment = 2.5
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nImages = 13 // Number of images to test. (Max 13)
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nSamples = 10 // Number of samples for each image. (Max 10)
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increment = 2.5 // Increment of the turbidity level
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)
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func TestImages(t *testing.T) {
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const (
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k1, k2 = 8, 8
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filterSize = 3
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scale, alpha = 1.0, 1.0
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)
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// Load template and standard image.
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template := gocv.IMRead("images/template.jpg", gocv.IMReadGrayScale)
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standard := gocv.IMRead("images/default.jpg", gocv.IMReadGrayScale)
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}
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// Create turbidity sensor.
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ts, err := NewTurbiditySensor(template, standard, 8, 8, 3, 1.0, 1.0)
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ts, err := NewTurbiditySensor(template, standard, k1, k2, filterSize, scale, alpha)
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if err != nil {
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t.Fatal(err)
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t.Fatal("could not create turbidity sensor: %w", err)
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}
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// Create results.
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results, err := NewResults(nImages)
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if err != nil {
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t.Fatal(err)
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t.Fatal("could not create results: %w", err)
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}
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// Score each image by calculating the average score from camera burst.
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// Evaluate camera burst.
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sample_result, err := ts.Evaluate(imgs[i])
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if err != nil {
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t.Fatalf("Evaluation Failed: %v", err)
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t.Fatalf("evaluation Failed: %w", err)
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}
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// Add the average result from camera burst.
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results.Update(average(sample_result.Saturation), average(sample_result.Contrast), float64(i)*increment, i)
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results.Update(average(sample_result.Sharpness), average(sample_result.Contrast), float64(i)*increment, i)
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}
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// Plot the final results.
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err = plotResults(results.Turbidity, normalize(results.Saturation), normalize(results.Contrast))
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err = plotResults(results.Turbidity, normalize(results.Sharpness), normalize(results.Contrast))
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if err != nil {
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t.Fatalf("Plotting Failed: %v", err)
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t.Fatalf("plotting Failed: %w", err)
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}
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t.Logf("Saturation: %v", results.Saturation)
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t.Logf("Sharpness: %v", results.Sharpness)
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t.Logf("Contrast: %v", results.Contrast)
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}
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