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save Tmat in binary format

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This commit is contained in:
nuknal
2024-06-15 03:04:50 +08:00
parent 4a2fc805c9
commit 0c17fee9c7
6 changed files with 245 additions and 90 deletions
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8
pkg/producer/rrc.go
View File

@@ -9,7 +9,7 @@ import (
func (r *Registrator) DoRRC() error {
logrus.Println("try to do RRC...")
tablePAN, err := rrc.LoadGrayTableMatrix("data/rrc/pan_gray_table.dat")
lutPAN, err := rrc.LoadLUT("data/rrc/B0.LUT", 9344)
if err != nil {
logrus.Error("load pan gray table failed")
return err
@@ -17,13 +17,13 @@ func (r *Registrator) DoRRC() error {
for y := 0; y < r.PanImage.Rows(); y++ {
for x := 0; x < r.PanImage.Cols(); x++ {
newGray := tablePAN.At(x, int(uint16(r.PanImage.GetShortAt(y, x))))
newGray := lutPAN[x][int(uint16(r.PanImage.GetShortAt(y, x)))]
r.PanImage.SetShortAt(y, x, int16(newGray))
}
}
for i := 0; i < 4; i++ {
tableMSS, err := rrc.LoadGrayTableMatrix(fmt.Sprintf("data/rrc/mss%d_gray_table.dat", i+1))
lutMSS, err := rrc.LoadLUT(fmt.Sprintf("data/rrc/B%d.LUT", i+1), 2336)
if err != nil {
logrus.Error("load mss gray table failed")
return err
@@ -31,7 +31,7 @@ func (r *Registrator) DoRRC() error {
for y := 0; y < r.MssImages[i].Rows(); y++ {
for x := 0; x < r.MssImages[i].Cols(); x++ {
newGray := tableMSS.At(x, int(uint16(r.MssImages[i].GetShortAt(y, x))))
newGray := lutMSS[x][int(uint16(r.MssImages[i].GetShortAt(y, x)))]
r.MssImages[i].SetShortAt(y, x, int16(newGray))
}
}

26
pkg/rrc/helper.go Normal file
View File

@@ -0,0 +1,26 @@
package rrc
func searchVL(V []float64, Sik float64) int {
left, right := 0, len(V)-2
// if Sik < V[0] || Sik > V[len(V)-1] {
// return -1
// }
for left <= right {
mid := left + (right-left)/2
// Check if Sik is between V[mid] and V[mid+1]
if V[mid]-1e-6 <= Sik && Sik <= V[mid+1]+1e-6 {
return mid
} else if Sik < V[mid] {
right = mid - 1
} else if Sik == V[mid] {
return mid
} else {
left = mid + 1
}
}
// Return -1 if no such position is found
return -1
}

125
pkg/rrc/histogram.go
View File

@@ -1,14 +1,18 @@
package rrc
import (
"bytes"
"encoding/binary"
"fmt"
"math"
"os"
"github.com/sirupsen/logrus"
log "github.com/sirupsen/logrus"
"gocv.io/x/gocv"
"gonum.org/v1/gonum/mat"
"gonum.org/v1/plot"
"gonum.org/v1/plot/plotter"
"gonum.org/v1/plot/plotutil"
"gonum.org/v1/plot/vg"
)
type ProbeHistogram struct {
@@ -21,7 +25,7 @@ type ProbeHistogram struct {
P_l []float64 // P_l = m_l/M 所有探元的期望直方图灰度等级为l的概率密度
S_ik [][]float64 // S_ik = sum(p_ij),j=0..k 第i个探元直方图灰度等级k的累积概率密度
V_l []float64 // V_l = sum(P_j),j=0..l // 期望直方图灰度级l对应的累积概率密度
Tmat *mat.Dense // 第i个像元的j灰度等级对应的新的灰度值，用于修正图像
Tmat [][]uint16 // 第i个像元的j灰度等级对应的新的灰度值，用于修正图像
}
func (hist *ProbeHistogram) init(width int) {
@@ -34,19 +38,30 @@ func (hist *ProbeHistogram) init(width int) {
hist.P_l = make([]float64, MaxGrayLevel)
hist.S_ik = make([][]float64, width)
hist.V_l = make([]float64, MaxGrayLevel)
hist.Tmat = make([][]uint16, width)
for i := 0; i < width; i++ {
hist.n_ik[i] = make([]int64, MaxGrayLevel)
hist.p_ik[i] = make([]float64, MaxGrayLevel)
hist.S_ik[i] = make([]float64, MaxGrayLevel)
hist.Tmat[i] = make([]uint16, MaxGrayLevel)
}
}
hist.Tmat = mat.NewDense(width, MaxGrayLevel, nil)
func (hist *ProbeHistogram) init0(width int) {
hist.probes = width
hist.M = 0
hist.N_i = make([]int64, width)
hist.n_ik = make([][]int64, width)
hist.m_l = make([]int64, MaxGrayLevel)
for i := 0; i < width; i++ {
hist.n_ik[i] = make([]int64, MaxGrayLevel)
}
}
func (hist *ProbeHistogram) statistical(img gocv.Mat) error {
hist.M += int64(img.Rows() * img.Cols())
fmt.Println("Hist.M:", hist.M)
// 探元i像素总数
for i := 0; i < hist.probes; i++ {
@@ -72,87 +87,118 @@ func (hist *ProbeHistogram) statistical(img gocv.Mat) error {
}
func (hist *ProbeHistogram) compute() {
// 探元i灰度概率密度
// 探元i灰度k概率密度
log.Info("computing p_ik")
for i := 0; i < hist.probes; i++ {
for k := 0; k < MaxGrayLevel; k++ {
hist.p_ik[i][k] = float64(hist.n_ik[i][k]) / float64(hist.N_i[i])
}
}
points := plotter.XYs{}
var maxY int64
// 所有探元的期望直方图灰度等级为l的概率密度
log.Info("computing p_l")
for gray := 0; gray < MaxGrayLevel; gray++ {
hist.P_l[gray] = float64(hist.m_l[gray]) / float64(hist.M)
points = append(points, plotter.XY{X: float64(gray), Y: float64(hist.m_l[gray])})
if hist.m_l[gray] > maxY {
maxY = hist.m_l[gray]
}
}
plt := plot.New()
plt.Title.Text = "Gray Level Histogram"
plt.X.Label.Text = "Gray Level"
plt.Y.Label.Text = "Pixels"
plt.Y.Min, plt.X.Min, plt.Y.Max, plt.X.Max = 0, 0, float64(maxY), 65536
plotutil.AddLines(plt, points)
plt.Save(5*vg.Inch, 5*vg.Inch, "data/rrc/01-gray-level-histogram.png")
// 第i个探元直方图灰度等级k的累积概率密度
log.Info("computing s_ik")
for i := 0; i < hist.probes; i++ {
for k := 0; k < MaxGrayLevel; k++ {
hist.S_ik[i][k] = 0
for j := 0; j <= k; j++ {
hist.S_ik[i][k] += hist.p_ik[i][j]
if k == 0 {
hist.S_ik[i][k] = hist.p_ik[i][0]
} else {
hist.S_ik[i][k] = hist.S_ik[i][k-1] + hist.p_ik[i][k]
}
}
}
// 期望直方图灰度级l对应的累积概率密度
log.Info("computing v_l")
for gray := 0; gray < MaxGrayLevel; gray++ {
hist.V_l[gray] = 0
for j := 0; j <= gray; j++ {
hist.V_l[gray] += hist.P_l[j]
if gray == 0 {
hist.V_l[gray] = hist.P_l[0]
} else {
hist.V_l[gray] = hist.V_l[gray-1] + hist.P_l[gray]
}
}
// 生成查找表
nT := 0
log.Info("computing Tij table")
var nT int64
for i := 0; i < hist.probes; i++ {
for k := 0; k < MaxGrayLevel; k++ {
for l := 0; l < MaxGrayLevel-1; l++ {
if hist.S_ik[i][k] >= hist.V_l[l] && hist.S_ik[i][k] <= hist.V_l[l+1] {
nT += 1
if math.Abs(hist.S_ik[i][k]-hist.V_l[l]) <= math.Abs(hist.S_ik[i][k]-hist.V_l[l+1]) {
hist.Tmat.Set(i, k, float64(l))
} else {
hist.Tmat.Set(i, k, float64(l+1))
}
}
l := searchVL(hist.V_l, hist.S_ik[i][k])
if l == -1 {
continue
}
nT++
if math.Abs(hist.S_ik[i][k]-hist.V_l[l]) <= math.Abs(hist.S_ik[i][k]-hist.V_l[l+1]) {
hist.Tmat[i][k] = uint16(l)
} else {
hist.Tmat[i][k] = uint16(l + 1)
}
}
}
if nT != hist.probes*MaxGrayLevel {
logrus.Error("error in computing Tij table, some values are not satisfied")
log.Info("total Tij table entries:", nT)
if nT != int64(hist.probes*MaxGrayLevel) {
log.Warn("error in computing Tij table, some values are not satisfied")
}
}
func (hist *ProbeHistogram) save(matrixFile string) error {
log.Printf("total pixels: %d", hist.M)
f, err := os.OpenFile(matrixFile, os.O_TRUNC|os.O_WRONLY|os.O_CREATE, 0644)
func (hist *ProbeHistogram) saveLUT(fLUT string) error {
file, err := os.Create(fLUT)
if err != nil {
return err
}
defer f.Close()
_, err = hist.Tmat.MarshalBinaryTo(f)
if err != nil {
return err
defer file.Close()
for i := 0; i < hist.probes; i++ {
binary.Write(file, binary.LittleEndian, hist.Tmat[i])
}
return nil
}
func LoadGrayTableMatrix(matrixFile string) (*mat.Dense, error) {
f, err := os.OpenFile(matrixFile, os.O_RDONLY, 0644)
const (
CheckPointProbe = 1000
CheckPointGray = 15000
)
func LoadLUT(fLUT string, probes int) ([][]uint16, error) {
data, err := os.ReadFile(fLUT)
if err != nil {
return nil, err
}
defer f.Close()
matrix := mat.Dense{}
if _, err := matrix.UnmarshalBinaryFrom(f); err != nil {
return nil, err
Tmat := make([][]uint16, probes)
for i := 0; i < probes; i++ {
Tmat[i] = make([]uint16, MaxGrayLevel)
binary.Read(bytes.NewReader(data[i*MaxGrayLevel*2:i*MaxGrayLevel*2+MaxGrayLevel*2]), binary.LittleEndian, &Tmat[i])
if i == CheckPointProbe {
log.Infof("Probes[%d], LUT check point [%d][%d]: %d", probes, i, CheckPointGray, Tmat[i][CheckPointGray])
}
}
return &matrix, nil
return Tmat, nil
}
func (hist *ProbeHistogram) sum(hists []*ProbeHistogram) {
@@ -168,4 +214,7 @@ func (hist *ProbeHistogram) sum(hists []*ProbeHistogram) {
hist.m_l[gray] += h.m_l[gray]
}
}
fmt.Println("Hist.M:", hist.M)
fmt.Println("Hist.probes:", hist.probes)
}

149
pkg/rrc/rrc.go
View File

@@ -4,6 +4,7 @@ import (
"bufio"
"fmt"
"os"
"sync"
log "github.com/sirupsen/logrus"
"gocv.io/x/gocv"
@@ -52,30 +53,57 @@ func (rrc *RRC) StatisticalPAN(dsfile string) error {
defer f.Close()
scanner := bufio.NewScanner(f)
var files []string
for scanner.Scan() {
l0 := scanner.Text()
log.Println("statistical PAN RAW: ", l0)
data, err := os.ReadFile(l0)
if err != nil {
log.Error("Error reading file: ", err)
continue
}
height := len(data) / (PANCameraProbeNum * 2)
img, err := gocv.NewMatFromBytes(height, PANCameraProbeNum, gocv.MatTypeCV16U, data)
if err != nil {
log.Error("Error creating Mat from bytes: ", err)
return err
}
rrc.Histograms[0].statistical(img)
img.Close()
files = append(files, scanner.Text())
}
// 并发处理
var wg sync.WaitGroup
jobs := make(chan struct{}, 5)
var hists []*ProbeHistogram
var mutex sync.Mutex
for _, file := range files {
wg.Add(1)
go func(l0 string) {
defer wg.Done()
jobs <- struct{}{}
defer func() { <-jobs }()
log.Println("statistical PAN RAW: ", l0)
data, err := os.ReadFile(l0)
if err != nil {
log.Error("Error reading file: ", err)
return
}
height := len(data) / (PANCameraProbeNum * 2)
img, err := gocv.NewMatFromBytes(height, PANCameraProbeNum, gocv.MatTypeCV16U, data)
if err != nil {
log.Error("Error creating Mat from bytes: ", err)
return
}
var hist ProbeHistogram
hist.init(PANCameraProbeNum)
hist.statistical(img)
img.Close()
mutex.Lock()
hists = append(hists, &hist)
mutex.Unlock()
}(file)
}
wg.Wait()
log.Println("sum PAN histogram...")
rrc.Histograms[0].sum(hists)
log.Println("compute PAN histogram...")
rrc.Histograms[0].compute()
log.Println("save PAN gray table matrix.")
rrc.Histograms[0].save("data/rrc/pan_gray_table.dat")
rrc.Histograms[0].saveLUT("data/rrc/B0.LUT")
return nil
}
@@ -88,43 +116,68 @@ func (rrc *RRC) StatisticalMSS(dsfile string) error {
defer f.Close()
scanner := bufio.NewScanner(f)
var files []string
for scanner.Scan() {
l0 := scanner.Text()
log.Println("statistical MSS RAW: ", l0)
data, err := os.ReadFile(l0)
if err != nil {
log.Error("Error reading file: ", err)
continue
}
width := MSSCameraProbeNum
height := len(data) / (width * 2)
mssData := make([][]byte, 4)
for h := 0; h < height; h++ {
row := data[h*width*4*2 : (h+1)*width*4*2]
for i := 0; i < 4; i++ {
mssData[i] = append(mssData[i], row[i*width*2:(i+1)*width*2]...)
}
}
var mssImages [4]gocv.Mat
for i := 0; i < 4; i++ {
mssImages[i], err = gocv.NewMatFromBytes(height, width, gocv.MatTypeCV16U, mssData[i])
if err != nil {
log.Error("Error creating Mat from bytes: ", err)
return err
}
rrc.Histograms[i+1].statistical(mssImages[i])
mssImages[i].Close()
}
files = append(files, scanner.Text())
}
var wg sync.WaitGroup
jobs := make(chan struct{}, 5)
var hists [4][]*ProbeHistogram
var mutex sync.Mutex
for _, file := range files {
wg.Add(1)
go func(l0 string) {
defer wg.Done()
jobs <- struct{}{}
defer func() { <-jobs }()
log.Println("statistical MSS RAW: ", l0)
data, err := os.ReadFile(l0)
if err != nil {
log.Error("Error reading file: ", err)
return
}
width := MSSCameraProbeNum
height := len(data) / (width * 2)
mssData := make([][]byte, 4)
for h := 0; h < height; h++ {
row := data[h*width*4*2 : (h+1)*width*4*2]
for i := 0; i < 4; i++ {
mssData[i] = append(mssData[i], row[i*width*2:(i+1)*width*2]...)
}
}
var mssImages [4]gocv.Mat
for i := 0; i < 4; i++ {
mssImages[i], err = gocv.NewMatFromBytes(height, width, gocv.MatTypeCV16U, mssData[i])
if err != nil {
log.Error("Error creating Mat from bytes: ", err)
return
}
var hist ProbeHistogram
hist.init(PANCameraProbeNum)
hist.statistical(mssImages[i])
mssImages[i].Close()
mutex.Lock()
hists[i] = append(hists[i], &hist)
mutex.Unlock()
}
}(file)
}
wg.Wait()
for i := 1; i < 5; i++ {
log.Println("sum MSS histogram...")
rrc.Histograms[i].sum(hists[i-1])
log.Println("compute MSS histogram...")
rrc.Histograms[i].compute()
log.Println("save MSS gray table matrix.")
rrc.Histograms[i].save(fmt.Sprintf("data/rrc/mss%d_gray_table.dat", i))
rrc.Histograms[i].saveLUT(fmt.Sprintf("data/rrc/B%d.LUT", i))
}
return nil