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