vendor: add all of our dependencies

vendor/github.com/disintegration/imaging/resize.go

@@ -0,0 +1,583 @@
+package imaging
+
+import (
+	"image"
+	"math"
+)
+
+type iwpair struct {
+	i int
+	w int32
+}
+
+type pweights struct {
+	iwpairs []iwpair
+	wsum    int32
+}
+
+func precomputeWeights(dstSize, srcSize int, filter ResampleFilter) []pweights {
+	du := float64(srcSize) / float64(dstSize)
+	scale := du
+	if scale < 1.0 {
+		scale = 1.0
+	}
+	ru := math.Ceil(scale * filter.Support)
+
+	out := make([]pweights, dstSize)
+
+	for v := 0; v < dstSize; v++ {
+		fu := (float64(v)+0.5)*du - 0.5
+
+		startu := int(math.Ceil(fu - ru))
+		if startu < 0 {
+			startu = 0
+		}
+		endu := int(math.Floor(fu + ru))
+		if endu > srcSize-1 {
+			endu = srcSize - 1
+		}
+
+		wsum := int32(0)
+		for u := startu; u <= endu; u++ {
+			w := int32(0xff * filter.Kernel((float64(u)-fu)/scale))
+			if w != 0 {
+				wsum += w
+				out[v].iwpairs = append(out[v].iwpairs, iwpair{u, w})
+			}
+		}
+		out[v].wsum = wsum
+	}
+
+	return out
+}
+
+// Resize resizes the image to the specified width and height using the specified resampling
+// filter and returns the transformed image. If one of width or height is 0, the image aspect
+// ratio is preserved.
+//
+// Supported resample filters: NearestNeighbor, Box, Linear, Hermite, MitchellNetravali,
+// CatmullRom, BSpline, Gaussian, Lanczos, Hann, Hamming, Blackman, Bartlett, Welch, Cosine.
+//
+// Usage example:
+//
+//		dstImage := imaging.Resize(srcImage, 800, 600, imaging.Lanczos)
+//
+func Resize(img image.Image, width, height int, filter ResampleFilter) *image.NRGBA {
+	dstW, dstH := width, height
+
+	if dstW < 0 || dstH < 0 {
+		return &image.NRGBA{}
+	}
+	if dstW == 0 && dstH == 0 {
+		return &image.NRGBA{}
+	}
+
+	src := toNRGBA(img)
+
+	srcW := src.Bounds().Max.X
+	srcH := src.Bounds().Max.Y
+
+	if srcW <= 0 || srcH <= 0 {
+		return &image.NRGBA{}
+	}
+
+	// if new width or height is 0 then preserve aspect ratio, minimum 1px
+	if dstW == 0 {
+		tmpW := float64(dstH) * float64(srcW) / float64(srcH)
+		dstW = int(math.Max(1.0, math.Floor(tmpW+0.5)))
+	}
+	if dstH == 0 {
+		tmpH := float64(dstW) * float64(srcH) / float64(srcW)
+		dstH = int(math.Max(1.0, math.Floor(tmpH+0.5)))
+	}
+
+	var dst *image.NRGBA
+
+	if filter.Support <= 0.0 {
+		// nearest-neighbor special case
+		dst = resizeNearest(src, dstW, dstH)
+
+	} else {
+		// two-pass resize
+		if srcW != dstW {
+			dst = resizeHorizontal(src, dstW, filter)
+		} else {
+			dst = src
+		}
+
+		if srcH != dstH {
+			dst = resizeVertical(dst, dstH, filter)
+		}
+	}
+
+	return dst
+}
+
+func resizeHorizontal(src *image.NRGBA, width int, filter ResampleFilter) *image.NRGBA {
+	srcBounds := src.Bounds()
+	srcW := srcBounds.Max.X
+	srcH := srcBounds.Max.Y
+
+	dstW := width
+	dstH := srcH
+
+	dst := image.NewNRGBA(image.Rect(0, 0, dstW, dstH))
+
+	weights := precomputeWeights(dstW, srcW, filter)
+
+	parallel(dstH, func(partStart, partEnd int) {
+		for dstY := partStart; dstY < partEnd; dstY++ {
+			for dstX := 0; dstX < dstW; dstX++ {
+				var c [4]int32
+				for _, iw := range weights[dstX].iwpairs {
+					i := dstY*src.Stride + iw.i*4
+					c[0] += int32(src.Pix[i+0]) * iw.w
+					c[1] += int32(src.Pix[i+1]) * iw.w
+					c[2] += int32(src.Pix[i+2]) * iw.w
+					c[3] += int32(src.Pix[i+3]) * iw.w
+				}
+				j := dstY*dst.Stride + dstX*4
+				sum := weights[dstX].wsum
+				dst.Pix[j+0] = clampint32(int32(float32(c[0])/float32(sum) + 0.5))
+				dst.Pix[j+1] = clampint32(int32(float32(c[1])/float32(sum) + 0.5))
+				dst.Pix[j+2] = clampint32(int32(float32(c[2])/float32(sum) + 0.5))
+				dst.Pix[j+3] = clampint32(int32(float32(c[3])/float32(sum) + 0.5))
+			}
+		}
+	})
+
+	return dst
+}
+
+func resizeVertical(src *image.NRGBA, height int, filter ResampleFilter) *image.NRGBA {
+	srcBounds := src.Bounds()
+	srcW := srcBounds.Max.X
+	srcH := srcBounds.Max.Y
+
+	dstW := srcW
+	dstH := height
+
+	dst := image.NewNRGBA(image.Rect(0, 0, dstW, dstH))
+
+	weights := precomputeWeights(dstH, srcH, filter)
+
+	parallel(dstW, func(partStart, partEnd int) {
+
+		for dstX := partStart; dstX < partEnd; dstX++ {
+			for dstY := 0; dstY < dstH; dstY++ {
+				var c [4]int32
+				for _, iw := range weights[dstY].iwpairs {
+					i := iw.i*src.Stride + dstX*4
+					c[0] += int32(src.Pix[i+0]) * iw.w
+					c[1] += int32(src.Pix[i+1]) * iw.w
+					c[2] += int32(src.Pix[i+2]) * iw.w
+					c[3] += int32(src.Pix[i+3]) * iw.w
+				}
+				j := dstY*dst.Stride + dstX*4
+				sum := weights[dstY].wsum
+				dst.Pix[j+0] = clampint32(int32(float32(c[0])/float32(sum) + 0.5))
+				dst.Pix[j+1] = clampint32(int32(float32(c[1])/float32(sum) + 0.5))
+				dst.Pix[j+2] = clampint32(int32(float32(c[2])/float32(sum) + 0.5))
+				dst.Pix[j+3] = clampint32(int32(float32(c[3])/float32(sum) + 0.5))
+			}
+		}
+
+	})
+
+	return dst
+}
+
+// fast nearest-neighbor resize, no filtering
+func resizeNearest(src *image.NRGBA, width, height int) *image.NRGBA {
+	dstW, dstH := width, height
+
+	srcBounds := src.Bounds()
+	srcW := srcBounds.Max.X
+	srcH := srcBounds.Max.Y
+
+	dst := image.NewNRGBA(image.Rect(0, 0, dstW, dstH))
+
+	dx := float64(srcW) / float64(dstW)
+	dy := float64(srcH) / float64(dstH)
+
+	parallel(dstH, func(partStart, partEnd int) {
+
+		for dstY := partStart; dstY < partEnd; dstY++ {
+			fy := (float64(dstY)+0.5)*dy - 0.5
+
+			for dstX := 0; dstX < dstW; dstX++ {
+				fx := (float64(dstX)+0.5)*dx - 0.5
+
+				srcX := int(math.Min(math.Max(math.Floor(fx+0.5), 0.0), float64(srcW)))
+				srcY := int(math.Min(math.Max(math.Floor(fy+0.5), 0.0), float64(srcH)))
+
+				srcOff := srcY*src.Stride + srcX*4
+				dstOff := dstY*dst.Stride + dstX*4
+
+				copy(dst.Pix[dstOff:dstOff+4], src.Pix[srcOff:srcOff+4])
+			}
+		}
+
+	})
+
+	return dst
+}
+
+// Fit scales down the image using the specified resample filter to fit the specified
+// maximum width and height and returns the transformed image.
+//
+// Supported resample filters: NearestNeighbor, Box, Linear, Hermite, MitchellNetravali,
+// CatmullRom, BSpline, Gaussian, Lanczos, Hann, Hamming, Blackman, Bartlett, Welch, Cosine.
+//
+// Usage example:
+//
+//		dstImage := imaging.Fit(srcImage, 800, 600, imaging.Lanczos)
+//
+func Fit(img image.Image, width, height int, filter ResampleFilter) *image.NRGBA {
+	maxW, maxH := width, height
+
+	if maxW <= 0 || maxH <= 0 {
+		return &image.NRGBA{}
+	}
+
+	srcBounds := img.Bounds()
+	srcW := srcBounds.Dx()
+	srcH := srcBounds.Dy()
+
+	if srcW <= 0 || srcH <= 0 {
+		return &image.NRGBA{}
+	}
+
+	if srcW <= maxW && srcH <= maxH {
+		return Clone(img)
+	}
+
+	srcAspectRatio := float64(srcW) / float64(srcH)
+	maxAspectRatio := float64(maxW) / float64(maxH)
+
+	var newW, newH int
+	if srcAspectRatio > maxAspectRatio {
+		newW = maxW
+		newH = int(float64(newW) / srcAspectRatio)
+	} else {
+		newH = maxH
+		newW = int(float64(newH) * srcAspectRatio)
+	}
+
+	return Resize(img, newW, newH, filter)
+}
+
+// Fill scales the image to the smallest possible size that will cover the specified dimensions,
+// crops the resized image to the specified dimensions using the given anchor point and returns
+// the transformed image.
+//
+// Supported resample filters: NearestNeighbor, Box, Linear, Hermite, MitchellNetravali,
+// CatmullRom, BSpline, Gaussian, Lanczos, Hann, Hamming, Blackman, Bartlett, Welch, Cosine.
+//
+// Usage example:
+//
+//		dstImage := imaging.Fill(srcImage, 800, 600, imaging.Center, imaging.Lanczos)
+//
+func Fill(img image.Image, width, height int, anchor Anchor, filter ResampleFilter) *image.NRGBA {
+	minW, minH := width, height
+
+	if minW <= 0 || minH <= 0 {
+		return &image.NRGBA{}
+	}
+
+	srcBounds := img.Bounds()
+	srcW := srcBounds.Dx()
+	srcH := srcBounds.Dy()
+
+	if srcW <= 0 || srcH <= 0 {
+		return &image.NRGBA{}
+	}
+
+	if srcW == minW && srcH == minH {
+		return Clone(img)
+	}
+
+	srcAspectRatio := float64(srcW) / float64(srcH)
+	minAspectRatio := float64(minW) / float64(minH)
+
+	var tmp *image.NRGBA
+	if srcAspectRatio < minAspectRatio {
+		tmp = Resize(img, minW, 0, filter)
+	} else {
+		tmp = Resize(img, 0, minH, filter)
+	}
+
+	return CropAnchor(tmp, minW, minH, anchor)
+}
+
+// Thumbnail scales the image up or down using the specified resample filter, crops it
+// to the specified width and hight and returns the transformed image.
+//
+// Supported resample filters: NearestNeighbor, Box, Linear, Hermite, MitchellNetravali,
+// CatmullRom, BSpline, Gaussian, Lanczos, Hann, Hamming, Blackman, Bartlett, Welch, Cosine.
+//
+// Usage example:
+//
+//		dstImage := imaging.Thumbnail(srcImage, 100, 100, imaging.Lanczos)
+//
+func Thumbnail(img image.Image, width, height int, filter ResampleFilter) *image.NRGBA {
+	return Fill(img, width, height, Center, filter)
+}
+
+// Resample filter struct. It can be used to make custom filters.
+//
+// Supported resample filters: NearestNeighbor, Box, Linear, Hermite, MitchellNetravali,
+// CatmullRom, BSpline, Gaussian, Lanczos, Hann, Hamming, Blackman, Bartlett, Welch, Cosine.
+//
+//	General filter recommendations:
+//
+//	- Lanczos
+//		Probably the best resampling filter for photographic images yielding sharp results,
+//		but it's slower than cubic filters (see below).
+//
+//	- CatmullRom
+//		A sharp cubic filter. It's a good filter for both upscaling and downscaling if sharp results are needed.
+//
+//	- MitchellNetravali
+//		A high quality cubic filter that produces smoother results with less ringing than CatmullRom.
+//
+//	- BSpline
+//		A good filter if a very smooth output is needed.
+//
+//	- Linear
+//		Bilinear interpolation filter, produces reasonably good, smooth output. It's faster than cubic filters.
+//
+//	- Box
+//		Simple and fast resampling filter appropriate for downscaling.
+//		When upscaling it's similar to NearestNeighbor.
+//
+//	- NearestNeighbor
+//		Fastest resample filter, no antialiasing at all. Rarely used.
+//
+type ResampleFilter struct {
+	Support float64
+	Kernel  func(float64) float64
+}
+
+// Nearest-neighbor filter, no anti-aliasing.
+var NearestNeighbor ResampleFilter
+
+// Box filter (averaging pixels).
+var Box ResampleFilter
+
+// Linear filter.
+var Linear ResampleFilter
+
+// Hermite cubic spline filter (BC-spline; B=0; C=0).
+var Hermite ResampleFilter
+
+// Mitchell-Netravali cubic filter (BC-spline; B=1/3; C=1/3).
+var MitchellNetravali ResampleFilter
+
+// Catmull-Rom - sharp cubic filter (BC-spline; B=0; C=0.5).
+var CatmullRom ResampleFilter
+
+// Cubic B-spline - smooth cubic filter (BC-spline; B=1; C=0).
+var BSpline ResampleFilter
+
+// Gaussian Blurring Filter.
+var Gaussian ResampleFilter
+
+// Bartlett-windowed sinc filter (3 lobes).
+var Bartlett ResampleFilter
+
+// Lanczos filter (3 lobes).
+var Lanczos ResampleFilter
+
+// Hann-windowed sinc filter (3 lobes).
+var Hann ResampleFilter
+
+// Hamming-windowed sinc filter (3 lobes).
+var Hamming ResampleFilter
+
+// Blackman-windowed sinc filter (3 lobes).
+var Blackman ResampleFilter
+
+// Welch-windowed sinc filter (parabolic window, 3 lobes).
+var Welch ResampleFilter
+
+// Cosine-windowed sinc filter (3 lobes).
+var Cosine ResampleFilter
+
+func bcspline(x, b, c float64) float64 {
+	x = math.Abs(x)
+	if x < 1.0 {
+		return ((12-9*b-6*c)*x*x*x + (-18+12*b+6*c)*x*x + (6 - 2*b)) / 6
+	}
+	if x < 2.0 {
+		return ((-b-6*c)*x*x*x + (6*b+30*c)*x*x + (-12*b-48*c)*x + (8*b + 24*c)) / 6
+	}
+	return 0
+}
+
+func sinc(x float64) float64 {
+	if x == 0 {
+		return 1
+	}
+	return math.Sin(math.Pi*x) / (math.Pi * x)
+}
+
+func init() {
+	NearestNeighbor = ResampleFilter{
+		Support: 0.0, // special case - not applying the filter
+	}
+
+	Box = ResampleFilter{
+		Support: 0.5,
+		Kernel: func(x float64) float64 {
+			x = math.Abs(x)
+			if x <= 0.5 {
+				return 1.0
+			}
+			return 0
+		},
+	}
+
+	Linear = ResampleFilter{
+		Support: 1.0,
+		Kernel: func(x float64) float64 {
+			x = math.Abs(x)
+			if x < 1.0 {
+				return 1.0 - x
+			}
+			return 0
+		},
+	}
+
+	Hermite = ResampleFilter{
+		Support: 1.0,
+		Kernel: func(x float64) float64 {
+			x = math.Abs(x)
+			if x < 1.0 {
+				return bcspline(x, 0.0, 0.0)
+			}
+			return 0
+		},
+	}
+
+	MitchellNetravali = ResampleFilter{
+		Support: 2.0,
+		Kernel: func(x float64) float64 {
+			x = math.Abs(x)
+			if x < 2.0 {
+				return bcspline(x, 1.0/3.0, 1.0/3.0)
+			}
+			return 0
+		},
+	}
+
+	CatmullRom = ResampleFilter{
+		Support: 2.0,
+		Kernel: func(x float64) float64 {
+			x = math.Abs(x)
+			if x < 2.0 {
+				return bcspline(x, 0.0, 0.5)
+			}
+			return 0
+		},
+	}
+
+	BSpline = ResampleFilter{
+		Support: 2.0,
+		Kernel: func(x float64) float64 {
+			x = math.Abs(x)
+			if x < 2.0 {
+				return bcspline(x, 1.0, 0.0)
+			}
+			return 0
+		},
+	}
+
+	Gaussian = ResampleFilter{
+		Support: 2.0,
+		Kernel: func(x float64) float64 {
+			x = math.Abs(x)
+			if x < 2.0 {
+				return math.Exp(-2 * x * x)
+			}
+			return 0
+		},
+	}
+
+	Bartlett = ResampleFilter{
+		Support: 3.0,
+		Kernel: func(x float64) float64 {
+			x = math.Abs(x)
+			if x < 3.0 {
+				return sinc(x) * (3.0 - x) / 3.0
+			}
+			return 0
+		},
+	}
+
+	Lanczos = ResampleFilter{
+		Support: 3.0,
+		Kernel: func(x float64) float64 {
+			x = math.Abs(x)
+			if x < 3.0 {
+				return sinc(x) * sinc(x/3.0)
+			}
+			return 0
+		},
+	}
+
+	Hann = ResampleFilter{
+		Support: 3.0,
+		Kernel: func(x float64) float64 {
+			x = math.Abs(x)
+			if x < 3.0 {
+				return sinc(x) * (0.5 + 0.5*math.Cos(math.Pi*x/3.0))
+			}
+			return 0
+		},
+	}
+
+	Hamming = ResampleFilter{
+		Support: 3.0,
+		Kernel: func(x float64) float64 {
+			x = math.Abs(x)
+			if x < 3.0 {
+				return sinc(x) * (0.54 + 0.46*math.Cos(math.Pi*x/3.0))
+			}
+			return 0
+		},
+	}
+
+	Blackman = ResampleFilter{
+		Support: 3.0,
+		Kernel: func(x float64) float64 {
+			x = math.Abs(x)
+			if x < 3.0 {
+				return sinc(x) * (0.42 - 0.5*math.Cos(math.Pi*x/3.0+math.Pi) + 0.08*math.Cos(2.0*math.Pi*x/3.0))
+			}
+			return 0
+		},
+	}
+
+	Welch = ResampleFilter{
+		Support: 3.0,
+		Kernel: func(x float64) float64 {
+			x = math.Abs(x)
+			if x < 3.0 {
+				return sinc(x) * (1.0 - (x * x / 9.0))
+			}
+			return 0
+		},
+	}
+
+	Cosine = ResampleFilter{
+		Support: 3.0,
+		Kernel: func(x float64) float64 {
+			x = math.Abs(x)
+			if x < 3.0 {
+				return sinc(x) * math.Cos((math.Pi/2.0)*(x/3.0))
+			}
+			return 0
+		},
+	}
+}