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DFT 低通滤波器

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This commit is contained in:
nuknal
2024-06-19 22:42:31 +08:00
parent 6e5ffe1ab2
commit e13038474a
8 changed files with 337 additions and 2 deletions
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9
pkg/producer/scenes.go
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@@ -13,6 +13,7 @@ import (
"github.com/paulmach/orb/geojson"
log "github.com/sirupsen/logrus"
"gocv.io/x/gocv"
"starwiz.cn/sjy01/image-proc/pkg/rrc"
)
type Scene struct {
@@ -66,7 +67,9 @@ func (r *Registrator) SubScenes() (panScenes []*Scene, mssScenes []*Scene, err e
scene.SceneId = fmt.Sprintf("%s_%03d", name, i+1)
mat := r.PanImage.Region(image.Rect(0, i*hPAN, 9344, y1))
scene.Mat = append(scene.Mat, mat)
matFiltered := rrc.HFNoiseFilter(mat, float64(mat.Cols())*0.45)
scene.Mat = append(scene.Mat, matFiltered)
mat.Close()
panScenes = append(panScenes, scene)
}
@@ -94,7 +97,9 @@ func (r *Registrator) SubScenes() (panScenes []*Scene, mssScenes []*Scene, err e
for band := 0; band < 4; band++ {
mat := r.registeredMssImages[band].Region(image.Rect(0, i*hMSS, 2336, y1))
scene.Mat = append(scene.Mat, mat)
matFiltered := rrc.HFNoiseFilter(mat, float64(mat.Cols())*0.45)
mat.Close()
scene.Mat = append(scene.Mat, matFiltered)
}
name := filepath.Base(r.Params.MssTiffFile)

43
pkg/rrc/filter_test.go Normal file
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@@ -0,0 +1,43 @@
package rrc
import (
"testing"
"github.com/airbusgeo/godal"
"gocv.io/x/gocv"
"starwiz.cn/sjy01/image-proc/pkg/utils"
)
func TestFilter(t *testing.T) {
godal.RegisterAll()
tif := "/Users/lan/workspace/sjy01/image-proc/data/052022-no-rrc/010/PAN/SJY01_PAN_20240520_115428_052022_103_010_L1A.tiff"
tif = "/Users/lan/workspace/sjy01/image-proc/data/051622/007/PAN/SJY01_PAN_20240516_101236_051622_096_007_L1A.tiff"
// tif = "/Users/lan/workspace/sjy01/image-proc/data/060622-mm/003/PAN/SJY01_PAN_20240606_012004_060622_065_003_L1A.tiff"
ds, err := godal.Open(tif)
if err != nil {
t.Error(err)
}
bands := ds.Bands()
width := bands[0].Structure().SizeX
height := bands[0].Structure().SizeY
img := gocv.NewMatWithSize(height, width, gocv.MatTypeCV16UC1)
data := make([]uint16, width*height)
err = bands[0].Read(0, 0, data, width, height)
if err != nil {
t.Error(err)
}
// 将 16 位数据存储到图像的相应通道
for y := 0; y < height; y++ {
for x := 0; x < width; x++ {
value := data[y*width+x]
img.SetShortAt(y, x, int16(value))
}
}
m0 := HFNoiseFilter(img, float64(img.Cols())*0.45)
defer m0.Close()
utils.SavePanToGDALGTiff(m0, 0, 0, "/Users/lan/workspace/sjy01/image-proc/data/fft/filtered.tiff", 1.3)
}

118
pkg/rrc/hf_filter.go Normal file
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@@ -0,0 +1,118 @@
package rrc
import (
"image"
"math"
"github.com/sirupsen/logrus"
"gocv.io/x/gocv"
)
// filter high frequence noise
func HFNoiseFilter(img gocv.Mat, radius float64) gocv.Mat {
logrus.Println("applying hf noise filter...")
imgFloat := gocv.NewMat()
img.ConvertTo(&imgFloat, gocv.MatTypeCV32F)
// rows, cols := img.Rows(), img.Cols()
// utils.SavePanToGDALGTiff(img, 0, 0, "/Users/lan/workspace/sjy01/image-proc/data/fft/original.tiff", 1.3)
// 将图像转换为32位浮点型
img32f := gocv.NewMat()
img.ConvertTo(&img32f, gocv.MatTypeCV32F)
defer img32f.Close()
// 获取图像尺寸
rows, cols := img32f.Rows(), img32f.Cols()
// zeros := gocv.NewMatFromScalar(gocv.NewScalar(0, 0, 0, 0), gocv.MatTypeCV32F)
// 执行 DFT
DFT := gocv.NewMat()
defer DFT.Close()
planes := gocv.NewMatWithSize(rows, cols, gocv.MatTypeCV32F)
img32f.ConvertTo(&planes, gocv.MatTypeCV32F)
gocv.Merge([]gocv.Mat{planes, gocv.Zeros(rows, cols, gocv.MatTypeCV32F)}, &DFT)
gocv.DFT(DFT, &DFT, gocv.DftComplexOutput)
fftshift(&DFT)
// 将复数部分分离并计算振幅光谱
pss := gocv.Split(DFT)
// SaveDFT(DFT, "/Users/lan/workspace/sjy01/image-proc/data/fft/dft.tiff")
// 应用滤波器
for y := 0; y < rows; y++ {
for x := 0; x < cols; x++ {
if math.Sqrt(math.Pow(float64(x-cols/2), 2)+math.Pow(float64(y-rows/2), 2)) > float64(radius) {
// if math.Abs(float64(x-cols/2)) < float64(radius) || math.Abs(float64(y-rows/2)) < float64(radius) {
pss[0].SetFloatAt(y, x, 0.0)
pss[1].SetFloatAt(y, x, 0.0)
}
}
}
gocv.Merge(pss, &DFT)
// SaveDFT(DFT, "/Users/lan/workspace/sjy01/image-proc/data/fft/dft_filtered.tiff")
fftshift(&DFT)
// 执行 IDFT
IDFT := gocv.NewMat()
gocv.DFT(DFT, &IDFT, gocv.DftInverse|gocv.DftRealOutput)
// 归一化到0-65535
gocv.Normalize(IDFT, &IDFT, 0, 8192, gocv.NormMinMax)
IDFT.ConvertTo(&IDFT, gocv.MatTypeCV16U)
// 保存 IDFT 结果
return IDFT
}
// 创建低通滤波器
func createLowPassFilter(filter *gocv.Mat, radius int) {
rows, cols := filter.Rows(), filter.Cols()
center := image.Pt(cols/2, rows/2)
for y := 0; y < rows; y++ {
for x := 0; x < cols; x++ {
dist := math.Sqrt(math.Pow(float64(x-center.X), 2) + math.Pow(float64(y-center.Y), 2))
if dist <= float64(radius) {
filter.SetFloatAt(y, x, 1.0)
} else {
filter.SetFloatAt(y, x, 0.0)
}
}
}
}
// fftshift 将频率直流分量移动到图像中心
func fftshift(input *gocv.Mat) {
cx, cy := input.Cols()/2, input.Rows()/2
q0 := input.Region(image.Rect(0, 0, cx, cy)) // Top-Left
q1 := input.Region(image.Rect(cx, 0, input.Cols(), cy)) // Top-Right
q2 := input.Region(image.Rect(0, cy, cx, input.Rows())) // Bottom-Left
q3 := input.Region(image.Rect(cx, cy, input.Cols(), input.Rows())) // Bottom-Right
tmp := gocv.NewMatWithSize(q0.Rows(), q0.Cols(), input.Type())
q0.CopyTo(&tmp)
q3.CopyTo(&q0)
tmp.CopyTo(&q3)
q1.CopyTo(&tmp)
q2.CopyTo(&q1)
tmp.CopyTo(&q2)
}
func SaveDFT(DFT gocv.Mat, file string) {
pss := gocv.Split(DFT)
planes := gocv.NewMat()
gocv.Magnitude(pss[0], pss[1], &planes)
// 进行对数尺度变换，便于可视化
ones := gocv.NewMatFromScalar(gocv.NewScalar(1, 0, 0, 0), gocv.MatTypeCV64F)
gocv.Add(planes, ones, &planes)
gocv.Log(planes, &planes)
// 归一化
gocv.Normalize(planes, &planes, 0, 1, gocv.NormMinMax)
// 保存DFT结果
gocv.IMWrite(file, planes)
}

56
pkg/rrc/hf_filter_fft.go Normal file
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@@ -0,0 +1,56 @@
package rrc
import (
"math"
"github.com/mjibson/go-dsp/fft"
log "github.com/sirupsen/logrus"
"gocv.io/x/gocv"
)
func HFNoiseFilterFFT(input gocv.Mat, radius int) gocv.Mat {
// 将灰度图像转换为浮点数数组
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)
}
}
// 进行二维DFT
log.Println("Performing 2D DFT...")
dft := fft.FFT2(data)
// 应用低通滤波器
log.Println("Applying low-frequency-pass filter...")
for y := 0; y < height; y++ {
for x := 0; x < width; x++ {
if math.Abs(float64(x-width/2)) < float64(radius) || math.Abs(float64(y-height/2)) < float64(radius) {
dft[y][x] = 0
}
}
}
// 进行二维逆DFT
log.Println("Performing 2D inverse DFT...")
idft := fft.IFFT2(dft)
// 将浮点数数组转换回灰度图像
for y := 0; y < height; y++ {
for x := 0; x < width; x++ {
realPart := real(idft[y][x])
if realPart < 0 {
realPart = 0
} else if realPart > 65535 {
realPart = 65535
}
input.SetShortAt(y, x, int16(uint16(realPart)))
}
}
return input
}

15
pkg/rrc/mean_filter.go Normal file
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@@ -0,0 +1,15 @@
package rrc
import (
"image"
log "github.com/sirupsen/logrus"
"gocv.io/x/gocv"
)
func MeanFilter(img gocv.Mat, ksize int) gocv.Mat {
log.Println("Applying mean filter, kernel size:", ksize)
meanFilteredImg := gocv.NewMat()
gocv.Blur(img, &meanFilteredImg, image.Pt(ksize, ksize))
return meanFilteredImg
}

95
pkg/utils/tiff.go Normal file
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@@ -0,0 +1,95 @@
package utils
import (
"time"
"github.com/airbusgeo/godal"
log "github.com/sirupsen/logrus"
"starwiz.cn/sjy01/image-proc/pkg/calculator"
"gocv.io/x/gocv"
)
func SavePanToGDALGTiff(pan gocv.Mat, topLeftX, topLeftY float64, tiffFile string, resolution float64) error {
// log.Println("Saving PAN image to TIFF file:", tiffFile)
width := pan.Cols()
height := pan.Rows()
ds, err := godal.Create(godal.GTiff, tiffFile, 1, godal.UInt16, width, height)
if err != nil {
log.Error("Error creating TIFF file: ", err)
return err
}
defer ds.Close()
setGeoTransform(ds, topLeftX, topLeftY, resolution)
ds.SetMetadata("NBITS", "16")
// 将通道的数据转换为uint16数组
data := make([]uint16, width*height)
for y := 0; y < height; y++ {
for x := 0; x < width; x++ {
data[y*width+x] = uint16(pan.GetShortAt(y, x))
}
}
band := ds.Bands()[0]
band.SetColorInterp(godal.CIGray)
err = band.IO(godal.IOWrite,
0, 0,
data,
width, height,
godal.PixelSpacing(2),
godal.LineSpacing(width*2))
if err != nil {
log.Error("Failed to write data to band:", err)
return err
}
log.Info("Saved pan image to ", tiffFile)
return nil
}
func setGeoTransform(ds *godal.Dataset, topLeftLng, topLeftLat, resolution float64) {
// 转换分辨率（米到度）
resLat := resolution / calculator.MetersPerDegreeLatitude
resLng := calculator.MetersToDegreesLongitude(resolution, topLeftLat)
// 设置地理变换（假设左上角坐标为(0,0)，PAN每个像素分辨率为1.2米）
geotransform := [6]float64{
topLeftLng, // top left x
resLng, // w-e pixel resolution
0, // rotation, 0 if image is "north up"
topLeftLat, // top left y
0, // rotation, 0 if image is "north up"
-resLat, // n-s pixel resolution (negative value)
}
err := ds.SetGeoTransform(geotransform)
if err != nil {
log.Errorf("Failed to set GeoTransform: %v", err)
return
}
// 设置投影为 WGS84
srs, err := godal.NewSpatialRefFromEPSG(4326) // gdal.CreateSpatialReference("")
if err != nil {
log.Errorf("Failed to set spatial reference: %v", err)
return
}
projWKT, err := srs.WKT()
if err != nil {
log.Errorf("Failed to convert spatial reference to WKT: %v", err)
return
}
err = ds.SetProjection(projWKT)
if err != nil {
log.Errorf("Failed to set projection: %v", err)
}
// 设置一些常见的元数据（可选）
ds.SetMetadata("TIFFTAG_DATETIME", time.Now().String())
ds.SetMetadata("TIFFTAG_SOFTWARE", "StarWiz-SJY01-IMAGE-PROC")
}