package imgo

import (
	"github.com/BurntSushi/graphics-go/graphics"
	"image"
	"image/color"
	"sync"
)

// GaussianBlur returns a blurred image.
// ksize is Gaussian kernel size
// sigma is Gaussian kernel standard deviation.
func (i *Image) GaussianBlur(ksize int, sigma float64) *Image {
	if i.Error != nil {
		return i
	}

	dst := image.NewRGBA(i.image.Bounds())
	err := graphics.Blur(dst, i.image, &graphics.BlurOptions{
		StdDev: sigma,
		Size:   ksize,
	})

	if err != nil {
		i.addError(err)
		return i
	}

	i.image = dst
	i.width = dst.Bounds().Dx()
	i.height = dst.Bounds().Dy()

	return i
}

// normalizeKernel normalizes a kernel.
func (i Image) normalizeKernel(kernel [][]float64) {
	var sum float64
	for _, row := range kernel {
		for _, value := range row {
			sum += value
		}
	}
	for i, row := range kernel {
		for j, value := range row {
			kernel[i][j] = value / sum
		}
	}
}

// Blur returns a blurred image.
// ksize is filter kernel size, it must be a odd number.
func (i *Image) Blur(ksize int) *Image {
	if i.Error != nil {
		return i
	}

	if ksize < 2 {
		return i
	}

	if ksize%2 == 0 {
		ksize++
	}

	kernel := make([][]float64, ksize)
	for p := range kernel {
		row := make([]float64, ksize)
		for q := 0; q < ksize; q++ {
			row[q] = 1
		}
		kernel[p] = row
	}

	i.imageFilterFast(kernel)

	return i
}

// imageFilterFast returns a filtered image with Goroutine.
func (i *Image) imageFilterFast(kernel [][]float64) *Image {
	i.normalizeKernel(kernel)
	dst := image.NewRGBA(i.image.Bounds())
	kernelSize := len(kernel)
	radius := (kernelSize - 1) / 2
	wg := sync.WaitGroup{}
	convolution := func(x, y int, wg *sync.WaitGroup) {
		defer wg.Done()
		var sumR, sumG, sumB, sumA uint16
		for p := -radius; p <= radius; p++ {
			for q := -radius; q <= radius; q++ {
				trueX := x + q
				trueY := y + p
				if image.Pt(trueX, trueY).In(i.Bounds()) {
					thisR, thisG, thisB, thisA := i.image.At(trueX, trueY).RGBA()
					sumR += uint16(float64(thisR) * kernel[q+radius][p+radius])
					sumG += uint16(float64(thisG) * kernel[q+radius][p+radius])
					sumB += uint16(float64(thisB) * kernel[q+radius][p+radius])
					sumA += uint16(float64(thisA) * kernel[q+radius][p+radius])
				}
			}
		}
		dst.SetRGBA64(x, y, color.RGBA64{R: sumR, G: sumG, B: sumB, A: sumA})
	}
	wg.Add(i.width * i.height)
	for y := 0; y < i.height; y++ {
		for x := 0; x < i.width; x++ {
			go convolution(x, y, &wg)
		}
	}
	wg.Wait()
	i.image = dst
	return i
}

// imageFilter returns a filtered image.
func (i *Image) imageFilter(kernel [][]float64) *Image {
	i.normalizeKernel(kernel)
	dst := image.NewNRGBA64(i.image.Bounds())
	kernelSize := len(kernel)
	radius := (kernelSize - 1) / 2
	for y := 0; y < i.height; y++ {
		for x := 0; x < i.width; x++ {
			var sumR, sumG, sumB, sumA uint16
			for p := -radius; p <= radius; p++ {
				for q := -radius; q <= radius; q++ {
					trueX := x + q
					trueY := y + p
					if image.Pt(trueX, trueY).In(i.Bounds()) {
						thisR, thisG, thisB, thisA := i.image.At(trueX, trueY).RGBA()
						sumR += uint16(float64(thisR) * kernel[q+radius][p+radius])
						sumG += uint16(float64(thisG) * kernel[q+radius][p+radius])
						sumB += uint16(float64(thisB) * kernel[q+radius][p+radius])
						sumA += uint16(float64(thisA) * kernel[q+radius][p+radius])
					}
				}
			}
			dst.SetRGBA64(x, y, color.RGBA64{R: sumR, G: sumG, B: sumB, A: sumA})
		}
	}
	i.image = Image2RGBA(dst)
	return i
}