func diffOp(mag, dir *image.Gray, src image.Image, opX, opY *convolve.SeparableKernel) error {
if src == nil {
return errors.New("graphics: src is nil")
}
b := src.Bounds()
srcg, ok := src.(*image.Gray)
if !ok {
srcg = image.NewGray(b)
draw.Draw(srcg, b, src, b.Min, draw.Src)
}
mx := image.NewGray(b)
if err := convolve.Convolve(mx, srcg, opX); err != nil {
return err
}
my := image.NewGray(b)
if err := convolve.Convolve(my, srcg, opY); err != nil {
return err
}
for y := b.Min.Y; y < b.Max.Y; y++ {
for x := b.Min.X; x < b.Max.X; x++ {
off := (y-mx.Rect.Min.Y)*mx.Stride + (x-mx.Rect.Min.X)*1
cx := float64(mx.Pix[off])
cy := float64(my.Pix[off])
if mag != nil {
off = (y-mag.Rect.Min.Y)*mag.Stride + (x-mag.Rect.Min.X)*1
mag.Pix[off] = uint8(math.Sqrt(cx*cx + cy*cy))
}
if dir != nil {
off = (y-dir.Rect.Min.Y)*dir.Stride + (x-dir.Rect.Min.X)*1
angle := math.Atan(cy / cx)
// Round the angle to 0, 45, 90, or 135 degrees.
angle = math.Mod(angle, 2*math.Pi)
var degree uint8
if angle <= math.Pi/8 {
degree = 0
} else if angle <= math.Pi*3/8 {
degree = 45
} else if angle <= math.Pi*5/8 {
degree = 90
} else if angle <= math.Pi*7/8 {
degree = 135
} else {
degree = 0
}
dir.Pix[off] = degree
}
}
}
return nil
}
// Blur produces a blurred version of the image, using a Gaussian blur.
func Blur(dst draw.Image, src image.Image, opt *BlurOptions) error {
if dst == nil {
return errors.New("graphics: dst is nil")
}
if src == nil {
return errors.New("graphics: src is nil")
}
sd := DefaultStdDev
size := 0
if opt != nil {
sd = opt.StdDev
size = opt.Size
}
if size < 1 {
size = int(math.Ceil(sd * 6))
}
kernel := make([]float64, 2*size+1)
for i := 0; i <= size; i++ {
x := float64(i) / sd
x = math.Pow(1/math.SqrtE, x*x)
kernel[size-i] = x
kernel[size+i] = x
}
// Normalize the weights to sum to 1.0.
kSum := 0.0
for _, k := range kernel {
kSum += k
}
for i, k := range kernel {
kernel[i] = k / kSum
}
return convolve.Convolve(dst, src, &convolve.SeparableKernel{
X: kernel,
Y: kernel,
})
}
// LaplacianOfGaussian approximates a 2D laplacian of gaussian with a convolution kernel.
func LaplacianOfGaussian(dst *image.Gray, src image.Image) {
srcg, ok := src.(*image.Gray)
if !ok {
b := src.Bounds()
srcg = image.NewGray(b)
draw.Draw(srcg, b, src, b.Min, draw.Src)
}
k, err := convolve.NewKernel([]float64{
0, 0, 1, 0, 0,
0, 1, 2, 1, 0,
1, 2, -16, 2, 1,
0, 1, 2, 1, 0,
0, 0, 1, 0, 0,
})
if err != nil {
panic(err) // impossible
}
convolve.Convolve(dst, srcg, k)
}