Image processing 在Golang中使用Lanczos重采样的粗糙边
我一直在写一些在Golang中调整图像大小的基本方法。我看过几篇关于调整图片大小的帖子,但就我个人而言,我不知道我错过了什么 本质上,我的问题是,在Golang中调整图像大小时,我的结果似乎有很多别名 我尝试过反复地对图像进行下采样,但这并没有带来多大的改进 这是我的密码:Image processing 在Golang中使用Lanczos重采样的粗糙边,image-processing,go,scaling,downsampling,lanczos,Image Processing,Go,Scaling,Downsampling,Lanczos,我一直在写一些在Golang中调整图像大小的基本方法。我看过几篇关于调整图片大小的帖子,但就我个人而言,我不知道我错过了什么 本质上,我的问题是,在Golang中调整图像大小时,我的结果似乎有很多别名 我尝试过反复地对图像进行下采样,但这并没有带来多大的改进 这是我的密码: func resize(original image.Image, edgeSize int, filterSize int) image.Image { oldBounds := original.Bou
func resize(original image.Image,
edgeSize int, filterSize int) image.Image {
oldBounds := original.Bounds()
if oldBounds.Dx() < edgeSize && oldBounds.Dy() < edgeSize {
// No resize necessary
return original
}
threshold := edgeSize * 4 / 3
if oldBounds.Dx() > threshold || oldBounds.Dy() > threshold {
fmt.Println("Upstream")
original = customResizeImageToFitBounds(original, threshold, filterSize)
oldBounds = original.Bounds()
}
newBounds := getNewBounds(oldBounds, edgeSize)
resized := image.NewRGBA(newBounds)
var ratioX = float64(oldBounds.Dx()) / float64(newBounds.Dx())
var ratioY = float64(oldBounds.Dy()) / float64(newBounds.Dy())
for x := 0; x < newBounds.Dx(); x++ {
for y := 0; y < newBounds.Dy(); y++ {
sourceX := ratioX * float64(x)
minX := int(math.Floor(sourceX))
sourceY := ratioY * float64(y)
minY := int(math.Floor(sourceY))
sampleSize := filterSize<<1 + 1
var xCoeffs = make([]float64, sampleSize)
var yCoeffs = make([]float64, sampleSize)
var sumX = 0.0
var sumY = 0.0
for i := 0; i < sampleSize; i++ {
xCoeffs[i] = lanczos(filterSize, sourceX-float64(minX+i-filterSize))
yCoeffs[i] = lanczos(filterSize, sourceY-float64(minY+i-filterSize))
sumX += xCoeffs[i]
sumY += yCoeffs[i]
}
for i := 0; i < sampleSize; i++ {
xCoeffs[i] /= sumX
yCoeffs[i] /= sumY
}
rgba := make([]float64, 4)
for i := 0; i < sampleSize; i++ {
if yCoeffs[i] == 0.0 {
continue
}
currY := minY + i - filterSize
rgbaRow := make([]float64, 4)
for j := 0; j < sampleSize; j++ {
if xCoeffs[j] == 0.0 {
continue
}
currX := minX + i - filterSize
rij, gij, bij, aij := original.At(
clamp(currX, currY, oldBounds)).RGBA()
rgbaRow[0] += float64(rij) * xCoeffs[j]
rgbaRow[1] += float64(gij) * xCoeffs[j]
rgbaRow[2] += float64(bij) * xCoeffs[j]
rgbaRow[3] += float64(aij) * xCoeffs[j]
}
rgba[0] += float64(rgbaRow[0]) * yCoeffs[i]
rgba[1] += float64(rgbaRow[1]) * yCoeffs[i]
rgba[2] += float64(rgbaRow[2]) * yCoeffs[i]
rgba[3] += float64(rgbaRow[3]) * yCoeffs[i]
}
rgba[0] = clampRangeFloat(0, rgba[0], 0xFFFF)
rgba[1] = clampRangeFloat(0, rgba[1], 0xFFFF)
rgba[2] = clampRangeFloat(0, rgba[2], 0xFFFF)
rgba[3] = clampRangeFloat(0, rgba[3], 0xFFFF)
var rgbaF [4]uint64
rgbaF[0] = (uint64(math.Floor(rgba[0]+0.5)) * 0xFF) / 0xFFFF
rgbaF[1] = (uint64(math.Floor(rgba[1]+0.5)) * 0xFF) / 0xFFFF
rgbaF[2] = (uint64(math.Floor(rgba[2]+0.5)) * 0xFF) / 0xFFFF
rgbaF[3] = (uint64(math.Floor(rgba[3]+0.5)) * 0xFF) / 0xFFFF
rf := uint8(clampRangeUint(0, uint32(rgbaF[0]), 255))
gf := uint8(clampRangeUint(0, uint32(rgbaF[1]), 255))
bf := uint8(clampRangeUint(0, uint32(rgbaF[2]), 255))
af := uint8(clampRangeUint(0, uint32(rgbaF[3]), 255))
resized.Set(x, y, color.RGBA{R: rf, G: gf, B: bf, A: af})
}
}
return resized
}
// Machine epsilon
var epsilon = math.Nextafter(1.0, 2.0) - 1
func lanczos(filterSize int, x float64) float64 {
x = math.Abs(x)
fs := float64(filterSize)
if x < epsilon {
return 1.0
}
if x > fs {
return 0
}
piX := math.Pi * x
piXOverFS := piX / fs
return (math.Sin(piX) / piX) * (math.Sin(piXOverFS) / (piXOverFS))
}
func调整大小(原始图像.image,
edgeSize int,filterSize int)图像。图像{
oldBounds:=原始的.Bounds()
如果oldBounds.Dx()阈值| | oldBounds.Dy()>阈值{
fmt.Println(“上游”)
原始=customResizeImageToFitBounds(原始、阈值、过滤器大小)
oldBounds=original.Bounds()
}
新边界:=getNewBounds(旧边界,边大小)
调整大小:=image.NewRGBA(新边界)
var ratioX=float64(oldBounds.Dx())/float64(newBounds.Dx())
var ratioY=float64(oldBounds.Dy())/float64(newBounds.Dy())
对于x:=0;x sampleSize:=filterSize好的,我想我找到了一种解决混叠问题的方法。我没有使用lanczos3,而是使用双线性插值对源图像进行重新采样,采样大小略高于我想要的大小(edgeSize=1080),高斯模糊图像,然后将图像缩小到目标大小(edgeSize=600),这次是双三次插值。这给了我和RMagick给我的结果差不多的结果