histogram RRC

pkg/rrc/histogram.go

@@ -0,0 +1,171 @@
+package rrc
+
+import (
+	"fmt"
+	"math"
+	"os"
+
+	"github.com/sirupsen/logrus"
+	log "github.com/sirupsen/logrus"
+	"gocv.io/x/gocv"
+	"gonum.org/v1/gonum/mat"
+)
+
+type ProbeHistogram struct {
+	probes int         // 探元数
+	N_i    []int64     // N_i 探元像素总数 PAN 0-9343 MSS 0-2335
+	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
+	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
+	m_l    []int64     // 具有灰度等级l的像素总数 l = 0-65535
+	M      int64       // 参与直方图统计的总像素数
+	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
+}
+
+func (hist *ProbeHistogram) init(width int) {
+	hist.probes = width
+	hist.M = 0
+	hist.N_i = make([]int64, width)
+	hist.n_ik = make([][]int64, width)
+	hist.p_ik = make([][]float64, width)
+	hist.m_l = make([]int64, MaxGrayLevel)
+	hist.P_l = make([]float64, MaxGrayLevel)
+	hist.S_ik = make([][]float64, width)
+	hist.V_l = make([]float64, MaxGrayLevel)
+
+	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 = mat.NewDense(width, MaxGrayLevel, nil)
+}
+
+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++ {
+		hist.N_i[i] += int64(img.Rows())
+	}
+
+	// 探元i灰度等级k的像素数统计
+	for y := 0; y < img.Rows(); y++ {
+		for x := 0; x < img.Cols(); x++ {
+			gray := uint16(img.GetShortAt(x, y))
+			hist.n_ik[x][gray]++
+		}
+	}
+
+	// 灰度等级l的像素总数
+	for gray := 0; gray < MaxGrayLevel; gray++ {
+		for i := 0; i < hist.probes; i++ {
+			hist.m_l[gray] += int64(hist.n_ik[i][gray])
+		}
+	}
+
+	return nil
+}
+
+func (hist *ProbeHistogram) compute() {
+	// 探元i灰度概率密度
+	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])
+		}
+	}
+
+	// 所有探元的期望直方图灰度等级为l的概率密度
+	for gray := 0; gray < MaxGrayLevel; gray++ {
+		hist.P_l[gray] = float64(hist.m_l[gray]) / float64(hist.M)
+	}
+
+	// 第i个探元直方图灰度等级k的累积概率密度
+	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]
+			}
+		}
+	}
+
+	// 期望直方图灰度级l对应的累积概率密度
+	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]
+		}
+	}
+
+	// 生成查找表
+	nT := 0
+	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))
+					}
+				}
+			}
+		}
+	}
+
+	if nT != hist.probes*MaxGrayLevel {
+		logrus.Error("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)
+	if err != nil {
+		return err
+	}
+	defer f.Close()
+
+	_, err = hist.Tmat.MarshalBinaryTo(f)
+	if err != nil {
+		return err
+	}
+
+	return nil
+}
+
+func LoadGrayTableMatrix(matrixFile string) (*mat.Dense, error) {
+	f, err := os.OpenFile(matrixFile, os.O_RDONLY, 0644)
+	if err != nil {
+		return nil, err
+	}
+	defer f.Close()
+
+	matrix := mat.Dense{}
+	if _, err := matrix.UnmarshalBinaryFrom(f); err != nil {
+		return nil, err
+	}
+
+	return &matrix, nil
+}
+
+func (hist *ProbeHistogram) sum(hists []*ProbeHistogram) {
+	for _, h := range hists {
+		hist.M += h.M
+		for i := 0; i < hist.probes; i++ {
+			hist.N_i[i] += h.N_i[i]
+			for k := 0; k < MaxGrayLevel; k++ {
+				hist.n_ik[i][k] += h.n_ik[i][k]
+			}
+		}
+		for gray := 0; gray < MaxGrayLevel; gray++ {
+			hist.m_l[gray] += h.m_l[gray]
+		}
+	}
+}