79 lines
2.0 KiB
Go
79 lines
2.0 KiB
Go
package imageproc
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import (
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"image"
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"math"
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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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func PANFilter(panImage16UC1 gocv.Mat) gocv.Mat {
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log.Println("Applying PAN filter...")
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// 转换为浮点数类型进行傅里叶变换
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imgFloat := gocv.NewMat()
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panImage16UC1.ConvertTo(&imgFloat, gocv.MatTypeCV32F)
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// 使用傅里叶变换
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planes := gocv.NewMat()
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gocv.Merge([]gocv.Mat{imgFloat, gocv.NewMatWithSize(imgFloat.Rows(), imgFloat.Cols(), gocv.MatTypeCV32F)}, &planes)
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dft := gocv.NewMat()
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gocv.DFT(planes, &dft, gocv.DftComplexOutput)
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// 转换DFT图像，使低频分量位于中心
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dftShifted := shiftDFT(dft)
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// 创建掩膜
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rows, cols := panImage16UC1.Rows(), panImage16UC1.Cols()
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crow, ccol := rows/2, cols/2
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mask := gocv.NewMatWithSize(rows, cols, gocv.MatTypeCV32FC2)
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mask.SetTo(gocv.NewScalar(1.0, 1.0, 0.0, 0.0))
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r := 30 // 调整这个参数来改变滤波效果
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for i := 0; i < rows; i++ {
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for j := 0; j < cols; j++ {
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if math.Abs(float64(i-crow)) <= float64(r) && math.Abs(float64(j-ccol)) <= float64(r) {
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mask.SetFloatAt(i, j*2, 0)
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mask.SetFloatAt(i, j*2+1, 0)
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}
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}
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}
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// 应用掩膜并进行反向DFT
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filtered := gocv.NewMat()
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gocv.MulSpectrums(dftShifted, mask, &filtered, 0)
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filteredShifted := shiftDFT(filtered)
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idft := gocv.NewMat()
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gocv.DFT(filteredShifted, &idft, gocv.DftInverse|gocv.DftRealOutput)
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// 标准化图像到0-65535范围
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gocv.Normalize(idft, &idft, 0, 65535, gocv.NormMinMax)
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idft.ConvertTo(&idft, gocv.MatTypeCV16U)
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return idft
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}
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// shiftDFT 将DFT结果进行频谱平移
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func shiftDFT(src gocv.Mat) gocv.Mat {
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rows, cols := src.Rows(), src.Cols()
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crow, ccol := rows/2, cols/2
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q0 := src.Region(image.Rect(0, 0, ccol, crow))
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q1 := src.Region(image.Rect(ccol, 0, cols, crow))
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q2 := src.Region(image.Rect(0, crow, ccol, rows))
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q3 := src.Region(image.Rect(ccol, crow, cols, rows))
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tmp := gocv.NewMatWithSize(crow, ccol, src.Type())
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q0.CopyTo(&tmp)
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q3.CopyTo(&q0)
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tmp.CopyTo(&q3)
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q1.CopyTo(&tmp)
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q2.CopyTo(&q1)
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tmp.CopyTo(&q2)
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return src
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}
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