分景后配准

pkg/producer/phase_correlation.go

@@ -3,8 +3,10 @@ package producer
 import (
 	"errors"
 	"image"
+	"math/cmplx"
 
 	"github.com/duke-git/lancet/v2/slice"
+	"github.com/mjibson/go-dsp/fft"
 	log "github.com/sirupsen/logrus"
 	"gocv.io/x/gocv"
 )
@@ -31,9 +33,9 @@ func CV_PhaseCorrelate(panBlock, mssBlock gocv.Mat) (gocv.Point2f, float64) {
 	mssBlock.ConvertTo(&mss, gocv.MatTypeCV32FC1)
 	defer mss.Close()
 
-	hann := gocv.NewMat()
+	hann := gocv.NewMatWithSize(pan.Rows(), pan.Cols(), pan.Type())
 	defer hann.Close()
-
+	gocv.CreateHanningWindow(&hann, image.Point{X: pan.Cols(), Y: pan.Rows()}, pan.Type())
 	shift, response := gocv.PhaseCorrelate(pan, mss, hann)
 
 	dx := shift.X
@@ -64,3 +66,77 @@ func (r *ImgProc) fileterPhaseShift(thredholds []float64, greaterThan bool) erro
 
 	return nil
 }
+
+func PhaseCorrelate(panBlock, mssBlock gocv.Mat) (gocv.Point2f, float64) {
+	// 计算傅里叶变换
+	panFreqDomain, _, _, err := toFreqDomain(panBlock)
+	if err != nil {
+		log.Error(err)
+	}
+
+	mssFreqDomain, width, height, err := toFreqDomain(mssBlock)
+	if err != nil {
+		log.Error(err)
+	}
+
+	// 计算共轭乘积并积累相位信息
+	crossPowerSpectrum := make([][]complex128, height)
+	for i := range crossPowerSpectrum {
+		crossPowerSpectrum[i] = make([]complex128, width)
+	}
+
+	for y := 0; y < height; y++ {
+		for x := 0; x < width; x++ {
+			if panFreqDomain[y][x] != 0 && mssFreqDomain[y][x] != 0 {
+				crossPowerSpectrum[y][x] = panFreqDomain[y][x] * cmplx.Conj(mssFreqDomain[y][x])
+			}
+		}
+	}
+
+	// 归一化
+	for y := 0; y < height; y++ {
+		for x := 0; x < width; x++ {
+			if crossPowerSpectrum[y][x] != 0 {
+				magnitude := cmplx.Abs(crossPowerSpectrum[y][x])
+				crossPowerSpectrum[y][x] = crossPowerSpectrum[y][x] / complex(magnitude, 0)
+			}
+		}
+	}
+
+	ifftResult := fft.IFFT2(crossPowerSpectrum)
+
+	// 查找最大值及其对应的平移参数
+	maxVal := 0.0
+	var dx, dy float64
+	for y := 0; y < height; y++ {
+		for x := 0; x < width; x++ {
+			val := real(ifftResult[y][x])
+			if val > maxVal {
+				maxVal = val
+				dx = float64(x)
+				dy = float64(y)
+			}
+		}
+	}
+	log.Debugf("Block shift: dx = %f, dy = %f. response = %f", dx, dy, 0.0)
+
+	return gocv.Point2f{X: float32(dx), Y: float32(dy)}, 0.0
+}
+
+func toFreqDomain(input gocv.Mat) ([][]complex128, int, int, error) {
+	height := input.Rows()
+	width := input.Cols()
+
+	data := make([][]complex128, height)
+	for y := 0; y < height; y++ {
+		data[y] = make([]complex128, width)
+		for x := 0; x < width; x++ {
+			grayColor := float64(uint16(input.GetShortAt(y, x)))
+			data[y][x] = complex(grayColor, 0)
+		}
+	}
+
+	freqDomain := fft.FFT2(data)
+
+	return freqDomain, width, height, nil
+}