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package imaging
import (
"image"
"math"
)
func gaussianBlurKernel(x, sigma float64) float64 {
return math.Exp(-(x*x)/(2*sigma*sigma)) / (sigma * math.Sqrt(2*math.Pi))
}
// Blur produces a blurred version of the image using a Gaussian function.
// Sigma parameter must be positive and indicates how much the image will be blurred.
//
// Usage example:
//
// dstImage := imaging.Blur(srcImage, 3.5)
//
func Blur(img image.Image, sigma float64) *image.NRGBA {
if sigma <= 0 {
return Clone(img)
}
radius := int(math.Ceil(sigma * 3.0))
kernel := make([]float64, radius+1)
for i := 0; i <= radius; i++ {
kernel[i] = gaussianBlurKernel(float64(i), sigma)
}
return blurVertical(blurHorizontal(img, kernel), kernel)
}
func blurHorizontal(img image.Image, kernel []float64) *image.NRGBA {
src := newScanner(img)
dst := image.NewNRGBA(image.Rect(0, 0, src.w, src.h))
radius := len(kernel) - 1
parallel(0, src.h, func(ys <-chan int) {
scanLine := make([]uint8, src.w*4)
for y := range ys {
src.scan(0, y, src.w, y+1, scanLine)
for x := 0; x < src.w; x++ {
min := x - radius
if min < 0 {
min = 0
}
max := x + radius
if max > src.w-1 {
max = src.w - 1
}
var r, g, b, a, wsum float64
for ix := min; ix <= max; ix++ {
i := ix * 4
weight := kernel[absint(x-ix)]
wsum += weight
wa := float64(scanLine[i+3]) * weight
r += float64(scanLine[i+0]) * wa
g += float64(scanLine[i+1]) * wa
b += float64(scanLine[i+2]) * wa
a += wa
}
if a != 0 {
r /= a
g /= a
b /= a
}
j := y*dst.Stride + x*4
dst.Pix[j+0] = clamp(r)
dst.Pix[j+1] = clamp(g)
dst.Pix[j+2] = clamp(b)
dst.Pix[j+3] = clamp(a / wsum)
}
}
})
return dst
}
func blurVertical(img image.Image, kernel []float64) *image.NRGBA {
src := newScanner(img)
dst := image.NewNRGBA(image.Rect(0, 0, src.w, src.h))
radius := len(kernel) - 1
parallel(0, src.w, func(xs <-chan int) {
scanLine := make([]uint8, src.h*4)
for x := range xs {
src.scan(x, 0, x+1, src.h, scanLine)
for y := 0; y < src.h; y++ {
min := y - radius
if min < 0 {
min = 0
}
max := y + radius
if max > src.h-1 {
max = src.h - 1
}
var r, g, b, a, wsum float64
for iy := min; iy <= max; iy++ {
i := iy * 4
weight := kernel[absint(y-iy)]
wsum += weight
wa := float64(scanLine[i+3]) * weight
r += float64(scanLine[i+0]) * wa
g += float64(scanLine[i+1]) * wa
b += float64(scanLine[i+2]) * wa
a += wa
}
if a != 0 {
r /= a
g /= a
b /= a
}
j := y*dst.Stride + x*4
dst.Pix[j+0] = clamp(r)
dst.Pix[j+1] = clamp(g)
dst.Pix[j+2] = clamp(b)
dst.Pix[j+3] = clamp(a / wsum)
}
}
})
return dst
}
// Sharpen produces a sharpened version of the image.
// Sigma parameter must be positive and indicates how much the image will be sharpened.
//
// Usage example:
//
// dstImage := imaging.Sharpen(srcImage, 3.5)
//
func Sharpen(img image.Image, sigma float64) *image.NRGBA {
if sigma <= 0 {
return Clone(img)
}
src := newScanner(img)
dst := image.NewNRGBA(image.Rect(0, 0, src.w, src.h))
blurred := Blur(img, sigma)
parallel(0, src.h, func(ys <-chan int) {
scanLine := make([]uint8, src.w*4)
for y := range ys {
src.scan(0, y, src.w, y+1, scanLine)
j := y * dst.Stride
for i := 0; i < src.w*4; i++ {
val := int(scanLine[i])<<1 - int(blurred.Pix[j])
if val < 0 {
val = 0
} else if val > 0xff {
val = 0xff
}
dst.Pix[j] = uint8(val)
j++
}
}
})
return dst
}
|
