1 files changed, 148 insertions, 0 deletions
diff --git a/vendor/github.com/disintegration/imaging/convolution.go b/vendor/github.com/disintegration/imaging/convolution.go
new file mode 100644
index 00000000..11eddc16
--- /dev/null
+++ b/vendor/github.com/disintegration/imaging/convolution.go
@@ -0,0 +1,148 @@
+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
+		}
+	}
+}