github.com/dolotech/hongbao@v0.0.0-20191130105438-fd59d7a5dda5/src/utils/imgo/util.go

//package imgo is a golang image process lib
package imgo

import (
	"bytes"
	"errors"
	"math"
	"image"
	"runtime"
)

type resamplingFilter struct {
	Support float64
	Kernel  func(float64) float64
}


func ResizeForMatrixBytes(filepath []byte, width int, height int)(imgMatrix [][][]uint8 , err error){
	reader:=bytes.NewReader(filepath)
	img, _, err := image.Decode(reader)
	if err != nil {
		return nil,err
	}

	nrgba:=convertToNRGBA(img)
	src:=Resize(nrgba,width,height)

	imgMatrix = NewRGBAMatrix(height,width)


	for i:=0;i<height;i++{
		for j:=0;j<width;j++{
			c:=src.At(j,i)
			r,g,b,a:=c.RGBA()
			imgMatrix[i][j][0]=uint8(r)
			imgMatrix[i][j][1]=uint8(g)
			imgMatrix[i][j][2]=uint8(b)
			imgMatrix[i][j][3]=uint8(a)

		}
	}

	return
}

func ResizeForMatrix(filepath string, width int, height int)(imgMatrix [][][]uint8 , err error){
	img,err1:=DecodeImage(filepath)
	
	if err1 != nil {
		err = err1
		return
	}
	
	nrgba:=convertToNRGBA(img)
	src:=Resize(nrgba,width,height)
	
	imgMatrix = NewRGBAMatrix(height,width)
	
	
	for i:=0;i<height;i++{
		for j:=0;j<width;j++{
			c:=src.At(j,i)
			r,g,b,a:=c.RGBA()
			imgMatrix[i][j][0]=uint8(r)
			imgMatrix[i][j][1]=uint8(g)
			imgMatrix[i][j][2]=uint8(b)
			imgMatrix[i][j][3]=uint8(a)
			
		}
	}
	
	return
}

// resize size of image
func Resize(src *image.NRGBA,width int, height int) *image.NRGBA {
	dstW, dstH := width, height

	if dstW < 0 || dstH < 0 {
		return src
	}
	if dstW == 0 && dstH == 0 {
		return src
	}

	srcW := src.Rect.Max.X
	srcH := src.Rect.Max.Y

	if srcW <= 0 || srcH <= 0 {
		return src
	}

	// 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
	
	var sinc = func(x float64) float64 {
		if x == 0 {
			return 1
		}
		return math.Sin(math.Pi*x) / (math.Pi * x)
	}
	
	var filter resamplingFilter = resamplingFilter{
		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
		},
	}

	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 resamplingFilter) *image.NRGBA {
	srcBounds := src.Bounds()
	srcW := srcBounds.Dx()
	srcH := srcBounds.Dy()
	srcMinX := srcBounds.Min.X
	srcMinY := srcBounds.Min.Y
	srcMaxX := srcBounds.Max.X

	dstW := width
	dstH := srcH

	dst := image.NewNRGBA(image.Rect(0, 0, dstW, dstH))

	dX := float64(srcW) / float64(dstW)
	scaleX := math.Max(dX, 1.0)
	rX := math.Ceil(scaleX * filter.Support)

	// divide image to parts for parallel processing
	numGoroutines := runtime.NumCPU()
	goMaxProcs := runtime.GOMAXPROCS(0)
	if numGoroutines > goMaxProcs {
		numGoroutines = goMaxProcs
	}
	if numGoroutines > dstW {
		numGoroutines = dstW
	}
	partSize := dstW / numGoroutines

	doneChan := make(chan bool, numGoroutines)

	for part := 0; part < numGoroutines; part++ {
		partStart := part * partSize
		partEnd := (part + 1) * partSize
		if part == numGoroutines-1 {
			partEnd = dstW
		}

		go func(partStart, partEnd int) {

			for dstX := partStart; dstX < partEnd; dstX++ {
				fX := float64(srcMinX) + (float64(dstX)+0.5)*dX - 0.5

				startX := int(math.Ceil(fX - rX))
				if startX < srcMinX {
					startX = srcMinX
				}
				endX := int(math.Floor(fX + rX))
				if endX > srcMaxX-1 {
					endX = srcMaxX - 1
				}

				// cache weights
				weightSum := 0.0
				weights := make([]float64, int(rX+2)*2)
				for x := startX; x <= endX; x++ {
					w := filter.Kernel((float64(x) - fX) / scaleX)
					weightSum += w
					weights[x-startX] = w
				}

				for dstY := 0; dstY < dstH; dstY++ {
					srcY := srcMinY + dstY

					r, g, b, a := 0.0, 0.0, 0.0, 0.0
					for x := startX; x <= endX; x++ {
						weight := weights[x-startX]
						i := src.PixOffset(x, srcY)
						r += float64(src.Pix[i+0]) * weight
						g += float64(src.Pix[i+1]) * weight
						b += float64(src.Pix[i+2]) * weight
						a += float64(src.Pix[i+3]) * weight
					}

					r = math.Min(math.Max(r/weightSum, 0.0), 255.0)
					g = math.Min(math.Max(g/weightSum, 0.0), 255.0)
					b = math.Min(math.Max(b/weightSum, 0.0), 255.0)
					a = math.Min(math.Max(a/weightSum, 0.0), 255.0)

					j := dst.PixOffset(dstX, dstY)
					dst.Pix[j+0] = uint8(r + 0.5)
					dst.Pix[j+1] = uint8(g + 0.5)
					dst.Pix[j+2] = uint8(b + 0.5)
					dst.Pix[j+3] = uint8(a + 0.5)
				}
			}

			doneChan <- true
		}(partStart, partEnd)

	}

	// wait for goroutines to finish
	for part := 0; part < numGoroutines; part++ {
		<-doneChan
	}

	return dst
}

func resizeVertical(src *image.NRGBA, height int, filter resamplingFilter) *image.NRGBA {
	srcBounds := src.Bounds()
	srcW := srcBounds.Dx()
	srcH := srcBounds.Dy()
	srcMinX := srcBounds.Min.X
	srcMinY := srcBounds.Min.Y
	srcMaxY := srcBounds.Max.Y

	dstW := srcW
	dstH := height

	dst := image.NewNRGBA(image.Rect(0, 0, dstW, dstH))

	dY := float64(srcH) / float64(dstH)
	scaleY := math.Max(dY, 1.0)
	rY := math.Ceil(scaleY * filter.Support)

	// divide image to parts for parallel processing
	numGoroutines := runtime.NumCPU()
	goMaxProcs := runtime.GOMAXPROCS(0)
	if numGoroutines > goMaxProcs {
		numGoroutines = goMaxProcs
	}
	if numGoroutines > dstH {
		numGoroutines = dstH
	}
	partSize := dstH / numGoroutines

	doneChan := make(chan bool, numGoroutines)

	for part := 0; part < numGoroutines; part++ {
		partStart := part * partSize
		partEnd := (part + 1) * partSize
		if part == numGoroutines-1 {
			partEnd = dstH
		}

		go func(partStart, partEnd int) {

			for dstY := partStart; dstY < partEnd; dstY++ {
				fY := float64(srcMinY) + (float64(dstY)+0.5)*dY - 0.5

				startY := int(math.Ceil(fY - rY))
				if startY < srcMinY {
					startY = srcMinY
				}
				endY := int(math.Floor(fY + rY))
				if endY > srcMaxY-1 {
					endY = srcMaxY - 1
				}

				// cache weights
				weightSum := 0.0
				weights := make([]float64, int(rY+2)*2)
				for y := startY; y <= endY; y++ {
					w := filter.Kernel((float64(y) - fY) / scaleY)
					weightSum += w
					weights[y-startY] = w
				}

				for dstX := 0; dstX < dstW; dstX++ {
					srcX := srcMinX + dstX

					r, g, b, a := 0.0, 0.0, 0.0, 0.0
					for y := startY; y <= endY; y++ {
						weight := weights[y-startY]
						i := src.PixOffset(srcX, y)
						r += float64(src.Pix[i+0]) * weight
						g += float64(src.Pix[i+1]) * weight
						b += float64(src.Pix[i+2]) * weight
						a += float64(src.Pix[i+3]) * weight
					}

					r = math.Min(math.Max(r/weightSum, 0.0), 255.0)
					g = math.Min(math.Max(g/weightSum, 0.0), 255.0)
					b = math.Min(math.Max(b/weightSum, 0.0), 255.0)
					a = math.Min(math.Max(a/weightSum, 0.0), 255.0)

					j := dst.PixOffset(dstX, dstY)
					dst.Pix[j+0] = uint8(r + 0.5)
					dst.Pix[j+1] = uint8(g + 0.5)
					dst.Pix[j+2] = uint8(b + 0.5)
					dst.Pix[j+3] = uint8(a + 0.5)
				}
			}

			doneChan <- true
		}(partStart, partEnd)

	}

	// wait for goroutines to finish
	for part := 0; part < numGoroutines; part++ {
		<-doneChan
	}

	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.Dx()
	srcH := srcBounds.Dy()
	srcMinX := srcBounds.Min.X
	srcMinY := srcBounds.Min.Y
	srcMaxX := srcBounds.Max.X
	srcMaxY := srcBounds.Max.Y

	dst := image.NewNRGBA(image.Rect(0, 0, dstW, dstH))

	dx := float64(srcW) / float64(dstW)
	dy := float64(srcH) / float64(dstH)

	// divide image to parts for parallel processing
	numGoroutines := runtime.NumCPU()
	goMaxProcs := runtime.GOMAXPROCS(0)
	if numGoroutines > goMaxProcs {
		numGoroutines = goMaxProcs
	}
	if numGoroutines > dstH {
		numGoroutines = dstH
	}
	partSize := dstH / numGoroutines

	doneChan := make(chan bool, numGoroutines)

	for part := 0; part < numGoroutines; part++ {
		partStart := part * partSize
		partEnd := (part + 1) * partSize
		if part == numGoroutines-1 {
			partEnd = dstH
		}

		go func(partStart, partEnd int) {

			for dstY := partStart; dstY < partEnd; dstY++ {
				fy := float64(srcMinY) + (float64(dstY)+0.5)*dy - 0.5

				for dstX := 0; dstX < dstW; dstX++ {
					fx := float64(srcMinX) + (float64(dstX)+0.5)*dx - 0.5

					srcX := int(math.Min(math.Max(math.Floor(fx+0.5), float64(srcMinX)), float64(srcMaxX)))
					srcY := int(math.Min(math.Max(math.Floor(fy+0.5), float64(srcMinY)), float64(srcMaxY)))

					srcOffset := src.PixOffset(srcX, srcY)
					dstOffset := dst.PixOffset(dstX, dstY)

					dst.Pix[dstOffset+0] = src.Pix[srcOffset+0]
					dst.Pix[dstOffset+1] = src.Pix[srcOffset+1]
					dst.Pix[dstOffset+2] = src.Pix[srcOffset+2]
					dst.Pix[dstOffset+3] = src.Pix[srcOffset+3]
				}
			}

			doneChan <- true
		}(partStart, partEnd)
	}

	// wait for goroutines to finish
	for part := 0; part < numGoroutines; part++ {
		<-doneChan
	}

	return dst
}

// create a three dimenson slice
func New3DSlice(x int , y int , z int)(theSlice [][][]uint8){
	theSlice = make([][][]uint8,x,x)
	for i := 0; i < x; i++ {
        s2 := make([][]uint8, y, y) 
        for j:=0 ; j < y; j++ {
			s3 := make([]uint8,z,z)
			s2[j] = s3 
		}
		theSlice[i] = s2  
    }
	return 
}

// create a new rgba matrix
func NewRGBAMatrix(height int,width int)(rgbaMatrix [][][]uint8){
	rgbaMatrix = New3DSlice(height,width,4)
	return
}

func Matrix2Vector(imgMatrix [][][]uint8)(vector []uint8){
	h:=len(imgMatrix)
	w:=len(imgMatrix[0])
	r:=len(imgMatrix[0][0])
	length:=h*w*r
	
	vector = make([]uint8,length)
	
	for i:=0; i<h; i++ {
		for j:=0; j<w; j++ {
			for k:=0; k<r-1; k++ {
				vector = append(vector,imgMatrix[i][j][k])
			}
		}
	}
	return
}

func Dot(x []uint8, y []uint8) float64 {
	xlen:=len(x)
	
	var sum float64 = 0
	for i:=0; i<xlen; i++ {
			sum = sum + float64(x[i])*float64(y[i])
			
	}
	return sum
}

type IterFunc func(i int, j int, k int, src [][][]uint8)[][][]uint8

func Iterator(filepath string, iter IterFunc)(imgMatrix [][][]uint8, err error ){
	imgMatrix,err = Read(filepath)
	if err != nil {
		return 
	}
	
	height:=len(imgMatrix)
	width:=len(imgMatrix[0])
	pix:=len(imgMatrix[0][0])
	if height == 0 || width == 0 {
		err = errors.New("The input of matrix is illegal!")
		return
	}
	
	for i:=0; i<height; i++ {
		for j:=0; j<width; j++ {
			for k:=0; k<pix; k++ {
				imgMatrix = iter(i,j,k,imgMatrix)
			}
		}
	}
	return
}