使用gamma校正提升jpg亮度
This commit is contained in:
nuknal
373
pkg/producer/image_registration.go
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373
pkg/producer/image_registration.go
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package imageproc
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import (
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"fmt"
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"image"
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"image/color"
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"math"
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"os"
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"sync"
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"github.com/airbusgeo/godal"
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log "github.com/sirupsen/logrus"
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"gocv.io/x/gocv"
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)
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type Registrate interface{}
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const (
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MssBands = 4
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PixelBytes = 2
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PanWidth = 9344 // 像素宽度
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MssWidth = 2336
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BlockNH = 4
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BlockNW = 8
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OverlappedBlockLines = 3000 // 重叠块的行数
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DownSampled ResampleMethod = "down_sample_pan"
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UpSampled ResampleMethod = "up_sample_mss"
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)
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type ResampleMethod string
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type Registrator struct {
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Params Params
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PanImage gocv.Mat
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PanHeight int
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PanWidth int
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MssImages [4]gocv.Mat
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MssHeight int
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MssWidth int
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shiftMutex sync.Mutex
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phaseShifts [4][]PhaseShiftM
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deltaXCoeffs [4][]float64 // 图像内畸变（线性变换），捕捉图像在水平方向上引起的X方向的变形
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deltaYCoeffs [4][]float64 // 图像内畸变（非线性变换），捕捉图像在水平方向上引起的Y方向的变形
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registeredMssImages [4]gocv.Mat // 配准后的MSS图像
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rgbirImage gocv.Mat
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resampleMethod ResampleMethod
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}
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func NewRegistrator(rsmethod ResampleMethod) *Registrator {
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var r Registrator
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r.resampleMethod = rsmethod
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return &r
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}
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func (r *Registrator) LoadPanRaw() error {
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data, err := os.ReadFile(r.Params.PanRawFile)
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if err != nil {
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log.Error("Error reading raw file: ", err)
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return err
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}
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height := len(data) / (PanWidth * PixelBytes)
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r.PanImage, err = gocv.NewMatFromBytes(height, PanWidth, 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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r.PanHeight = height
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r.PanWidth = PanWidth
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godal.RegisterAll()
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hDriver, ok := godal.RasterDriver("Gtiff")
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if !ok {
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panic("Gtiff not found")
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}
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md := hDriver.Metadatas()
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if md["DCAP_CREATE"] == "YES" {
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fmt.Printf("Driver GTiff supports Create() method.\n")
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}
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if md["DCAP_CREATECOPY"] == "YES" {
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fmt.Printf("Driver Gtiff supports CreateCopy() method.\n")
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}
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fmt.Println("Gtiff driver name:", hDriver.LongName(), hDriver.ShortName())
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return nil
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}
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func (r *Registrator) LoadMssRaw() error {
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data, err := os.ReadFile(r.Params.MssRawFile)
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if err != nil {
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log.Error("Error reading raw file: ", err)
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return err
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}
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height := len(data) / (MssWidth * PixelBytes * MssBands)
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mssData := make([][]byte, MssBands)
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for h := 0; h < height; h++ {
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row := data[h*MssWidth*MssBands*PixelBytes : (h+1)*MssWidth*MssBands*PixelBytes]
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for i := 0; i < MssBands; i++ {
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mssData[i] = append(mssData[i], row[i*MssWidth*PixelBytes:(i+1)*MssWidth*PixelBytes]...)
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}
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}
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for i := 0; i < MssBands; i++ {
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r.MssImages[i], err = gocv.NewMatFromBytes(height, MssWidth, 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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}
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r.MssHeight = height
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r.MssWidth = MssWidth
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return nil
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}
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func (r *Registrator) DoPhaseCorrelation() error {
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switch r.resampleMethod {
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case UpSampled:
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return r.CalcUpPhaseCorrelation()
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default:
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return r.CalcDownPhaseCorrelation()
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}
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}
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// 将PAN降采样后计算相位相关的偏移量
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func (r *Registrator) CalcDownPhaseCorrelation() error {
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// 确保 MSS 高度是 PAN 高度的 1/4
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if r.MssHeight*4 != r.PanHeight {
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err := fmt.Errorf("MSS height is not 1/4 of PAN height, invalid raw file")
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log.Error(err)
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return err
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}
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// 将PAN将采样作为轮廓匹配基准图像
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downsampledPanImage := gocv.NewMat()
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gocv.Resize(r.PanImage, &downsampledPanImage,
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image.Point{X: r.MssWidth, Y: r.MssHeight}, 0, 0, gocv.InterpolationCubic)
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log.Println("down sampled PAN images size:", downsampledPanImage.Size())
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// 分块高度
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blockHeight := r.MssHeight / BlockNH
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blockWidth := r.MssWidth / BlockNW
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return r.calcPhaseCorrelation(downsampledPanImage, r.MssImages, r.MssHeight, r.MssWidth, blockHeight, blockWidth)
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}
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// 将MSS升采样采样后计算相位相关的偏移量
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func (r *Registrator) CalcUpPhaseCorrelation() error {
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// 确保 MSS 高度是 PAN 高度的 1/4
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if r.MssHeight*4 != r.PanHeight {
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err := fmt.Errorf("MSS height is not 1/4 of PAN height, invalid raw file")
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log.Error(err)
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return err
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}
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// 将PAN将采样作为轮廓匹配基准图像
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var upsampledMssImages [MssBands]gocv.Mat
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for i := 0; i < MssBands; i++ {
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upsampledMssImages[i] = gocv.NewMat()
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gocv.Resize(r.MssImages[i], &upsampledMssImages[i],
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image.Point{X: r.PanWidth, Y: r.PanHeight}, 0, 0, gocv.InterpolationCubic)
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}
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fmt.Println("up sampled MSS images size:", upsampledMssImages[0].Size())
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// 分块高度 - BlockNH, BlockNW % 4 == 0
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blockHeight := r.PanHeight / BlockNH
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blockWidth := r.PanWidth / BlockNW
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log.Infof("blockHeight=%d, blockWidth=%d", blockHeight, blockWidth)
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return r.calcPhaseCorrelation(r.PanImage, upsampledMssImages, r.PanHeight, r.PanWidth, blockHeight, blockWidth)
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}
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func (r *Registrator) calcPhaseCorrelation(panImage gocv.Mat,
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mssImages [4]gocv.Mat,
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height, width,
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blockHeight, blockWidth int) error {
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var wg sync.WaitGroup
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for bh := 0; bh < BlockNH; bh++ {
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for bw := 0; bw < BlockNW; bw++ {
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wg.Add(1)
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go func(bh, bw int) {
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defer wg.Done()
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x0 := bw * blockWidth
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y0 := bh * blockHeight
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x1 := (bw + 1) * blockWidth
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y1 := (bh + 1) * blockHeight
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y1 += OverlappedBlockLines // Y偏移量过大，需要将重叠块的行数加上，以避免边界影响
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if x1 > width || y1 > height {
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log.Debugf("Block out of range. x0=%d, y0=%d, x1=%d, y1=%d", x0, y0, x1, y1)
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}
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if y1 > height {
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y1 = height
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}
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var shiftM PhaseShiftM
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shiftM.Block.width = x1 - x0
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shiftM.Block.height = y1 - y0
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shiftM.Block.coord.X = x0 // 块左上角x坐标
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shiftM.Block.coord.Y = y0 // 块左上角y坐标
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rect := image.Rect(
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x0, y0,
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x1, y1,
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)
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panBlock := panImage.Region(rect)
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for band := 0; band < MssBands; band++ {
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log.Debug("processing band:", band+1, ",block:", bh, rect)
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mssBlock := mssImages[band].Region(rect)
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// 处理每个分块
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phaseShift, response := r.calculateBlockPhaseShift(panBlock, mssBlock)
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shiftM.dx = phaseShift.X
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shiftM.dy = phaseShift.Y
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shiftM.response = response
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r.shiftMutex.Lock()
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r.phaseShifts[band] = append(r.phaseShifts[band], shiftM)
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r.shiftMutex.Unlock()
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mssBlock.Close()
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}
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panBlock.Close()
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}(bh, bw)
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}
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}
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wg.Wait()
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for i := 0; i < MssBands; i++ {
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for _, shift := range r.phaseShifts[i] {
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if shift.response > 0.4 || shift.dx > 8 || shift.dy > 8 {
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log.Debugf("Band %d, block %d, dx=%f, dy=%f, response=%f",
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i, shift.Block.coord.X, shift.dx, shift.dy, shift.response)
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}
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}
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}
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return r.calcDeltaCoeffs()
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}
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func (r *Registrator) Clean() {
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r.PanImage.Close()
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for i := 0; i < MssBands; i++ {
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r.MssImages[i].Close()
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}
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for i := 0; i < MssBands; i++ {
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r.registeredMssImages[i].Close()
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}
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r.rgbirImage.Close()
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}
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func (r *Registrator) calcDeltaCoeffs() error {
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// 计算每个通道的delta多项式拟合系数
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for i := 0; i < MssBands; i++ {
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var cx []float64
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var dx []float64
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var dy []float64
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effectShift := 0
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for _, shift := range r.phaseShifts[i] {
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if math.IsNaN(float64(shift.dx)) || math.IsNaN(float64(shift.dy)) {
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continue
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}
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// 经验值过滤
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if shift.dy < 64.0 {
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continue
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}
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effectShift++
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cx = append(cx, float64(shift.Block.coord.X+shift.Block.width/2)) // MSS 块在X方向没有分块
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log.Debugf("effective shift value: %v, cx: %v, dx: %v, dy: %v",
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effectShift, shift.Block.coord.X, shift.dx, shift.dy)
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dx = append(dx, float64(shift.dx))
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dy = append(dy, float64(shift.dy))
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}
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if len(cx) < 3 {
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log.Errorf("No effective shift value found for band %d, skip delta coefficients calculation", i+1)
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continue
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} else {
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var err error
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if r.deltaXCoeffs[i], err = PolynomialFit(cx, dx, 1); err != nil {
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log.Error("Error fitting deltaX coeffs: ", err)
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return err
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}
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if r.deltaYCoeffs[i], err = PolynomialFit(cx, dy, 2); err != nil {
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log.Error("Error fitting deltaY coeffs: ", err)
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return err
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}
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}
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}
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for i := 0; i < MssBands; i++ {
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if len(r.deltaXCoeffs[i]) < 2 || len(r.deltaYCoeffs[i]) < 3 {
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continue
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}
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log.Printf("Band %d:\n delta_x = %.6f*x + %.6f, \n delta_y = %.6f*x^2 + %.6f*x + %.6f\n",
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i+1,
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r.deltaXCoeffs[i][1], r.deltaXCoeffs[i][0],
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r.deltaYCoeffs[i][2], r.deltaYCoeffs[i][1], r.deltaYCoeffs[i][0])
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}
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return nil
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}
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func (r *Registrator) DoCoRegestration() error {
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for band := 0; band < MssBands; band++ {
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if len(r.deltaXCoeffs[band]) < 2 || len(r.deltaYCoeffs[band]) < 3 {
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log.Error("delta coefficients not calculated, skip co-registration")
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r.registeredMssImages[band] = r.MssImages[band].Clone()
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continue
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}
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mapX := gocv.NewMatWithSize(r.MssHeight, r.MssWidth, gocv.MatTypeCV32FC1)
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mapY := gocv.NewMatWithSize(r.MssHeight, r.MssWidth, gocv.MatTypeCV32FC1)
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for y := 0; y < r.MssHeight; y++ {
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for x := 0; x < r.MssWidth; x++ {
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var dx, dy float64
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if r.resampleMethod == UpSampled {
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xx := float64(x * MssBands)
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yy := float64(y * MssBands)
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dx = (r.deltaXCoeffs[band][1]*float64(xx) + r.deltaXCoeffs[band][0] + xx) / MssBands
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dy = (r.deltaYCoeffs[band][2]*float64(xx*xx) + r.deltaYCoeffs[band][1]*float64(xx) + r.deltaYCoeffs[band][0] + yy) / MssBands
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} else {
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dx = r.deltaXCoeffs[band][1]*float64(x) + r.deltaXCoeffs[band][0] + float64(x)
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dy = r.deltaYCoeffs[band][2]*float64(x*x) + r.deltaYCoeffs[band][1]*float64(x) + r.deltaYCoeffs[band][0] + float64(y)
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}
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// if band+1 == 4 {
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// fmt.Println("band:", band+1, "x:", x, "map_x:", mx, "y:", y, "map_y:", my)
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// }
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// mapX.SetFloatAt(y, x, float32(x)+float32(r.deltaXCoeffs[band][0]))
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// mapY.SetFloatAt(y, x, float32(y)+float32(r.deltaYCoeffs[band][0]))
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mapX.SetFloatAt(y, x, float32(dx))
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mapY.SetFloatAt(y, x, float32(dy))
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}
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}
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log.Println("co-registration for band", band+1)
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r.registeredMssImages[band] = gocv.NewMatWithSize(r.MssHeight, r.MssWidth, gocv.MatTypeCV16UC1)
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gocv.Remap(r.MssImages[band],
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&r.registeredMssImages[band],
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&mapX, &mapY,
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gocv.InterpolationCubic,
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gocv.BorderConstant,
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color.RGBA{0, 0, 0, 0})
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}
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return nil
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}
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Block a user