package rrc

import (
	"os"

	log "github.com/sirupsen/logrus"
	"gocv.io/x/gocv"
)

// Relative Radiation Correction
// 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。

const (
	PANCameraProbeNum = 9344 // 全色探元数
	MSSCameraProbeNum = 2336 // 多光谱探元数
	MaxGrayLevel      = 65536
)

type RRC struct {
	PANDataSet []string
	MSSDataSet []string

	Histograms [5]BandHistogram
}

type BandHistogram struct {
	width int         // 探元数
	N_i   []int       // N_i 探元像素总数 PAN 0-9343 MSS 0-2335
	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
	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
	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
}

func (hist *BandHistogram) init(width int) {
	hist.width = width
	hist.M = 0
	hist.N_i = make([]int, width)
	hist.n_ik = make([][]int, 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)
	hist.Tij = make([][]float64, width)

	for i := 0; i < width; i++ {
		hist.n_ik[i] = make([]int, MaxGrayLevel)
		hist.p_ik[i] = make([]float64, MaxGrayLevel)
		hist.S_ik[i] = make([]float64, MaxGrayLevel)
		hist.Tij[i] = make([]float64, MaxGrayLevel)
	}
}

// 统计探元灰度的累积概率密度
func (rrc *RRC) StatisticalPAN(l0 string) error {
	data, err := os.ReadFile(l0)
	if err != nil {
		return err
	}

	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
	}

	hist := BandHistogram{}
	hist.init(PANCameraProbeNum)

	rrc.statistical(img, &hist)
	rrc.compute(&hist)
	rrc.output(&hist, "pan_gray_table.tif")

	return nil
}

func (rrc *RRC) statistical(img gocv.Mat, hist *BandHistogram) error {
	hist.M += int64(img.Rows() * img.Cols())

	for i := 0; i < hist.width; i++ {
		hist.N_i[i] += img.Rows()
	}

	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]++
		}
	}

	for gray := 0; gray < MaxGrayLevel; gray++ {
		for i := 0; i < hist.width; i++ {
			hist.m_l[gray] += int64(hist.n_ik[i][gray])
		}
	}

	return nil
}

func (rrc *RRC) compute(hist *BandHistogram) error {
	width := len(hist.N_i)

	for i := 0; i < width; i++ {
		for k := 0; k < MaxGrayLevel; k++ {
			hist.p_ik[i][k] = float64(hist.n_ik[i][k]) / float64(hist.N_i[i])
		}
	}

	for gray := 0; gray < MaxGrayLevel; gray++ {
		hist.P_l[gray] = float64(hist.m_l[gray]) / float64(hist.M)
	}

	for i := 0; i < width; 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]
			}
		}
	}

	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]
		}
	}

	return nil
}

func (rrc *RRC) output(hist *BandHistogram, referenceTIF string) error {
	return nil
}