histogram RRC
This commit is contained in:
nuknal
41
pkg/producer/rrc.go
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41
pkg/producer/rrc.go
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@@ -0,0 +1,41 @@
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package producer
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import (
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"fmt"
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"github.com/sirupsen/logrus"
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"starwiz.cn/sjy01/image-proc/pkg/rrc"
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)
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func (r *Registrator) DoRRC() error {
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logrus.Println("try to do RRC...")
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tablePAN, err := rrc.LoadGrayTableMatrix("data/rrc/pan_gray_table.dat")
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if err != nil {
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logrus.Error("load pan gray table failed")
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return err
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}
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for y := 0; y < r.PanImage.Rows(); y++ {
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for x := 0; x < r.PanImage.Cols(); x++ {
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newGray := tablePAN.At(x, int(uint16(r.PanImage.GetShortAt(y, x))))
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r.PanImage.SetShortAt(y, x, int16(newGray))
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}
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}
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for i := 0; i < 4; i++ {
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tableMSS, err := rrc.LoadGrayTableMatrix(fmt.Sprintf("data/rrc/mss%d_gray_table.dat", i+1))
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if err != nil {
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logrus.Error("load mss gray table failed")
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return err
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}
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for y := 0; y < r.MssImages[i].Rows(); y++ {
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for x := 0; x < r.MssImages[i].Cols(); x++ {
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newGray := tableMSS.At(x, int(uint16(r.MssImages[i].GetShortAt(y, x))))
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r.MssImages[i].SetShortAt(y, x, int16(newGray))
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}
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}
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}
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return nil
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}
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171
pkg/rrc/histogram.go
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171
pkg/rrc/histogram.go
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@@ -0,0 +1,171 @@
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package rrc
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import (
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"fmt"
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"math"
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"os"
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"github.com/sirupsen/logrus"
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log "github.com/sirupsen/logrus"
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"gocv.io/x/gocv"
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"gonum.org/v1/gonum/mat"
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)
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type ProbeHistogram struct {
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probes int // 探元数
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N_i []int64 // N_i 探元像素总数 PAN 0-9343 MSS 0-2335
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n_ik [][]int64 // n_ik 第i探元灰度等级为k的像素数统计 PAN 9343 x 65536 MSS 2335 x 65536
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p_ik [][]float64 // p_ik = n_ik / N_i 探元灰度概率密度 PAN 9343 x 65536 MSS 2335 x 65536
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m_l []int64 // 具有灰度等级l的像素总数 l = 0-65535
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M int64 // 参与直方图统计的总像素数
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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
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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
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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
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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
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}
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func (hist *ProbeHistogram) init(width int) {
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hist.probes = width
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hist.M = 0
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hist.N_i = make([]int64, width)
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hist.n_ik = make([][]int64, width)
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hist.p_ik = make([][]float64, width)
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hist.m_l = make([]int64, MaxGrayLevel)
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hist.P_l = make([]float64, MaxGrayLevel)
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hist.S_ik = make([][]float64, width)
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hist.V_l = make([]float64, MaxGrayLevel)
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for i := 0; i < width; i++ {
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hist.n_ik[i] = make([]int64, MaxGrayLevel)
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hist.p_ik[i] = make([]float64, MaxGrayLevel)
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hist.S_ik[i] = make([]float64, MaxGrayLevel)
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}
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hist.Tmat = mat.NewDense(width, MaxGrayLevel, nil)
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}
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func (hist *ProbeHistogram) statistical(img gocv.Mat) error {
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hist.M += int64(img.Rows() * img.Cols())
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fmt.Println("Hist.M:", hist.M)
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// 探元i像素总数
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for i := 0; i < hist.probes; i++ {
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hist.N_i[i] += int64(img.Rows())
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}
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// 探元i灰度等级k的像素数统计
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for y := 0; y < img.Rows(); y++ {
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for x := 0; x < img.Cols(); x++ {
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gray := uint16(img.GetShortAt(x, y))
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hist.n_ik[x][gray]++
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}
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}
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// 灰度等级l的像素总数
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for gray := 0; gray < MaxGrayLevel; gray++ {
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for i := 0; i < hist.probes; i++ {
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hist.m_l[gray] += int64(hist.n_ik[i][gray])
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}
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}
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return nil
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}
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func (hist *ProbeHistogram) compute() {
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// 探元i灰度概率密度
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for i := 0; i < hist.probes; i++ {
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for k := 0; k < MaxGrayLevel; k++ {
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hist.p_ik[i][k] = float64(hist.n_ik[i][k]) / float64(hist.N_i[i])
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}
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}
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// 所有探元的期望直方图灰度等级为l的概率密度
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for gray := 0; gray < MaxGrayLevel; gray++ {
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hist.P_l[gray] = float64(hist.m_l[gray]) / float64(hist.M)
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}
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// 第i个探元直方图灰度等级k的累积概率密度
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for i := 0; i < hist.probes; i++ {
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for k := 0; k < MaxGrayLevel; k++ {
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hist.S_ik[i][k] = 0
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for j := 0; j <= k; j++ {
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hist.S_ik[i][k] += hist.p_ik[i][j]
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}
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}
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}
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// 期望直方图灰度级l对应的累积概率密度
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for gray := 0; gray < MaxGrayLevel; gray++ {
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hist.V_l[gray] = 0
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for j := 0; j <= gray; j++ {
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hist.V_l[gray] += hist.P_l[j]
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}
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}
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// 生成查找表
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nT := 0
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for i := 0; i < hist.probes; i++ {
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for k := 0; k < MaxGrayLevel; k++ {
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for l := 0; l < MaxGrayLevel-1; l++ {
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if hist.S_ik[i][k] >= hist.V_l[l] && hist.S_ik[i][k] <= hist.V_l[l+1] {
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nT += 1
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if math.Abs(hist.S_ik[i][k]-hist.V_l[l]) <= math.Abs(hist.S_ik[i][k]-hist.V_l[l+1]) {
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hist.Tmat.Set(i, k, float64(l))
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} else {
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hist.Tmat.Set(i, k, float64(l+1))
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}
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}
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}
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}
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}
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if nT != hist.probes*MaxGrayLevel {
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logrus.Error("error in computing Tij table, some values are not satisfied")
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}
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}
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func (hist *ProbeHistogram) save(matrixFile string) error {
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log.Printf("total pixels: %d", hist.M)
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f, err := os.OpenFile(matrixFile, os.O_TRUNC|os.O_WRONLY|os.O_CREATE, 0644)
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if err != nil {
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return err
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}
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defer f.Close()
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_, err = hist.Tmat.MarshalBinaryTo(f)
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if err != nil {
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return err
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}
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return nil
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}
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func LoadGrayTableMatrix(matrixFile string) (*mat.Dense, error) {
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f, err := os.OpenFile(matrixFile, os.O_RDONLY, 0644)
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if err != nil {
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return nil, err
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}
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defer f.Close()
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matrix := mat.Dense{}
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if _, err := matrix.UnmarshalBinaryFrom(f); err != nil {
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return nil, err
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}
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return &matrix, nil
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}
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func (hist *ProbeHistogram) sum(hists []*ProbeHistogram) {
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for _, h := range hists {
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hist.M += h.M
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for i := 0; i < hist.probes; i++ {
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hist.N_i[i] += h.N_i[i]
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for k := 0; k < MaxGrayLevel; k++ {
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hist.n_ik[i][k] += h.n_ik[i][k]
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}
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}
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for gray := 0; gray < MaxGrayLevel; gray++ {
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hist.m_l[gray] += h.m_l[gray]
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}
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}
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}
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181
pkg/rrc/rrc.go
181
pkg/rrc/rrc.go
@@ -1,6 +1,8 @@
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package rrc
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import (
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"bufio"
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"fmt"
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"os"
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log "github.com/sirupsen/logrus"
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@@ -11,130 +13,119 @@ import (
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// 采用在轨统计定标方法。利用卫星在轨后获取的常规影像数据，统计每个探元出现的灰度频次。
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const (
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PANCameraProbeNum = 9344 // 全色探元数
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MSSCameraProbeNum = 2336 // 多光谱探元数
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MaxGrayLevel = 65536
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PANCameraProbeNum = 9344 // 全色探元数
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MSSCameraProbeNum = 2336 // 多光谱探元数
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MaxGrayLevel = 65536 // 16bit像素值
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)
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type RRC struct {
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PANDataSet []string
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MSSDataSet []string
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Histograms [5]BandHistogram
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Histograms [5]ProbeHistogram
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}
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type BandHistogram struct {
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width int // 探元数
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N_i []int // N_i 探元像素总数 PAN 0-9343 MSS 0-2335
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n_ik [][]int // n_ik 第i探元灰度等级为k的像素数统计 PAN 9343 x 65536 MSS 2335 x 65536
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p_ik [][]float64 // p_ik = n_ik / N_i 探元灰度概率密度 PAN 9343 x 65536 MSS 2335 x 65536
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m_l []int64 // 具有灰度等级l的像素总数 l = 0-65535
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M int64 // 参与直方图统计的总像素数
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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
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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
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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
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Tij [][]float64 // 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
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}
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func (hist *BandHistogram) init(width int) {
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hist.width = width
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hist.M = 0
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hist.N_i = make([]int, width)
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hist.n_ik = make([][]int, width)
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hist.p_ik = make([][]float64, width)
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hist.m_l = make([]int64, MaxGrayLevel)
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hist.P_l = make([]float64, MaxGrayLevel)
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hist.S_ik = make([][]float64, width)
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hist.V_l = make([]float64, MaxGrayLevel)
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hist.Tij = make([][]float64, width)
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for i := 0; i < width; i++ {
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hist.n_ik[i] = make([]int, MaxGrayLevel)
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hist.p_ik[i] = make([]float64, MaxGrayLevel)
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hist.S_ik[i] = make([]float64, MaxGrayLevel)
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hist.Tij[i] = make([]float64, MaxGrayLevel)
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func NewRRC() *RRC {
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r := RRC{}
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r.Histograms[0].init(PANCameraProbeNum)
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for i := 1; i < 5; i++ {
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r.Histograms[i].init(MSSCameraProbeNum)
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}
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os.MkdirAll("data/rrc", 0755)
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return &r
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}
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func (rrc *RRC) Statistical(dsPAN, dsMSS string) error {
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rrc.StatisticalPAN(dsPAN)
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rrc.StatisticalMSS(dsMSS)
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return nil
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}
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func (rrc *RRC) Close() {}
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// 统计探元灰度的累积概率密度
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func (rrc *RRC) StatisticalPAN(l0 string) error {
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data, err := os.ReadFile(l0)
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func (rrc *RRC) StatisticalPAN(dsfile string) error {
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f, err := os.Open(dsfile)
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if err != nil {
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return err
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}
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defer f.Close()
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height := len(data) / (PANCameraProbeNum * 2)
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img, err := gocv.NewMatFromBytes(height, PANCameraProbeNum, gocv.MatTypeCV16U, data)
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if err != nil {
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log.Error("Error creating Mat from bytes: ", err)
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return err
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scanner := bufio.NewScanner(f)
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for scanner.Scan() {
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l0 := scanner.Text()
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log.Println("statistical PAN RAW: ", l0)
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data, err := os.ReadFile(l0)
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if err != nil {
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log.Error("Error reading file: ", err)
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continue
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}
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height := len(data) / (PANCameraProbeNum * 2)
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img, err := gocv.NewMatFromBytes(height, PANCameraProbeNum, gocv.MatTypeCV16U, data)
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if err != nil {
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log.Error("Error creating Mat from bytes: ", err)
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return err
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}
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rrc.Histograms[0].statistical(img)
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img.Close()
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}
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hist := BandHistogram{}
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hist.init(PANCameraProbeNum)
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rrc.statistical(img, &hist)
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rrc.compute(&hist)
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rrc.output(&hist, "pan_gray_table.tif")
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log.Println("compute PAN histogram...")
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rrc.Histograms[0].compute()
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log.Println("save PAN gray table matrix.")
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rrc.Histograms[0].save("data/rrc/pan_gray_table.dat")
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return nil
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}
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func (rrc *RRC) statistical(img gocv.Mat, hist *BandHistogram) error {
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hist.M += int64(img.Rows() * img.Cols())
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for i := 0; i < hist.width; i++ {
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hist.N_i[i] += img.Rows()
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func (rrc *RRC) StatisticalMSS(dsfile string) error {
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f, err := os.Open(dsfile)
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if err != nil {
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return err
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}
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defer f.Close()
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for y := 0; y < img.Rows(); y++ {
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for x := 0; x < img.Cols(); x++ {
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gray := uint16(img.GetShortAt(x, y))
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hist.n_ik[x][gray]++
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scanner := bufio.NewScanner(f)
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for scanner.Scan() {
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l0 := scanner.Text()
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log.Println("statistical MSS RAW: ", l0)
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data, err := os.ReadFile(l0)
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if err != nil {
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log.Error("Error reading file: ", err)
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continue
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}
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}
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for gray := 0; gray < MaxGrayLevel; gray++ {
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for i := 0; i < hist.width; i++ {
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hist.m_l[gray] += int64(hist.n_ik[i][gray])
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}
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}
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return nil
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}
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func (rrc *RRC) compute(hist *BandHistogram) error {
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width := len(hist.N_i)
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for i := 0; i < width; i++ {
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for k := 0; k < MaxGrayLevel; k++ {
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hist.p_ik[i][k] = float64(hist.n_ik[i][k]) / float64(hist.N_i[i])
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}
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}
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for gray := 0; gray < MaxGrayLevel; gray++ {
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hist.P_l[gray] = float64(hist.m_l[gray]) / float64(hist.M)
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}
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for i := 0; i < width; i++ {
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for k := 0; k < MaxGrayLevel; k++ {
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hist.S_ik[i][k] = 0
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for j := 0; j <= k; j++ {
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hist.S_ik[i][k] += hist.p_ik[i][j]
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width := MSSCameraProbeNum
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height := len(data) / (width * 2)
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mssData := make([][]byte, 4)
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for h := 0; h < height; h++ {
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row := data[h*width*4*2 : (h+1)*width*4*2]
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for i := 0; i < 4; i++ {
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mssData[i] = append(mssData[i], row[i*width*2:(i+1)*width*2]...)
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}
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}
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}
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for gray := 0; gray < MaxGrayLevel; gray++ {
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hist.V_l[gray] = 0
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for j := 0; j <= gray; j++ {
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hist.V_l[gray] += hist.P_l[j]
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var mssImages [4]gocv.Mat
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for i := 0; i < 4; i++ {
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mssImages[i], err = gocv.NewMatFromBytes(height, width, gocv.MatTypeCV16U, mssData[i])
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if err != nil {
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log.Error("Error creating Mat from bytes: ", err)
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return err
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}
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rrc.Histograms[i+1].statistical(mssImages[i])
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mssImages[i].Close()
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||||
}
|
||||
}
|
||||
|
||||
return nil
|
||||
}
|
||||
for i := 1; i < 5; i++ {
|
||||
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))
|
||||
}
|
||||
|
||||
func (rrc *RRC) output(hist *BandHistogram, referenceTIF string) error {
|
||||
return nil
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user