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|
package imaging
import (
"image"
)
// ConvolveOptions are convolution parameters.
type ConvolveOptions struct {
// If Normalize is true the kernel is normalized before convolution.
Normalize bool
// If Abs is true the absolute value of each color channel is taken after convolution.
Abs bool
// Bias is added to each color channel value after convolution.
Bias int
}
// Convolve3x3 convolves the image with the specified 3x3 convolution kernel.
// Default parameters are used if a nil *ConvolveOptions is passed.
func Convolve3x3(img image.Image, kernel [9]float64, options *ConvolveOptions) *image.NRGBA {
return convolve(img, kernel[:], options)
}
// Convolve5x5 convolves the image with the specified 5x5 convolution kernel.
// Default parameters are used if a nil *ConvolveOptions is passed.
func Convolve5x5(img image.Image, kernel [25]float64, options *ConvolveOptions) *image.NRGBA {
return convolve(img, kernel[:], options)
}
func convolve(img image.Image, kernel []float64, options *ConvolveOptions) *image.NRGBA {
src := toNRGBA(img)
w := src.Bounds().Max.X
h := src.Bounds().Max.Y
dst := image.NewNRGBA(image.Rect(0, 0, w, h))
if w < 1 || h < 1 {
return dst
}
if options == nil {
options = &ConvolveOptions{}
}
if options.Normalize {
normalizeKernel(kernel)
}
type coef struct {
x, y int
k float64
}
var coefs []coef
var m int
switch len(kernel) {
case 9:
m = 1
case 25:
m = 2
}
i := 0
for y := -m; y <= m; y++ {
for x := -m; x <= m; x++ {
if kernel[i] != 0 {
coefs = append(coefs, coef{x: x, y: y, k: kernel[i]})
}
i++
}
}
parallel(0, h, func(ys <-chan int) {
for y := range ys {
for x := 0; x < w; x++ {
var r, g, b float64
for _, c := range coefs {
ix := x + c.x
if ix < 0 {
ix = 0
} else if ix >= w {
ix = w - 1
}
iy := y + c.y
if iy < 0 {
iy = 0
} else if iy >= h {
iy = h - 1
}
off := iy*src.Stride + ix*4
s := src.Pix[off : off+3 : off+3]
r += float64(s[0]) * c.k
g += float64(s[1]) * c.k
b += float64(s[2]) * c.k
}
if options.Abs {
if r < 0 {
r = -r
}
if g < 0 {
g = -g
}
if b < 0 {
b = -b
}
}
if options.Bias != 0 {
r += float64(options.Bias)
g += float64(options.Bias)
b += float64(options.Bias)
}
srcOff := y*src.Stride + x*4
dstOff := y*dst.Stride + x*4
d := dst.Pix[dstOff : dstOff+4 : dstOff+4]
d[0] = clamp(r)
d[1] = clamp(g)
d[2] = clamp(b)
d[3] = src.Pix[srcOff+3]
}
}
})
return dst
}
func normalizeKernel(kernel []float64) {
var sum, sumpos float64
for i := range kernel {
sum += kernel[i]
if kernel[i] > 0 {
sumpos += kernel[i]
}
}
if sum != 0 {
for i := range kernel {
kernel[i] /= sum
}
} else if sumpos != 0 {
for i := range kernel {
kernel[i] /= sumpos
}
}
}
|
