lankun/sjy01-image-proc
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

save Tmat in binary format

Browse Source
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
Download Patch File Download Diff File Expand all files Collapse all files

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)
}