package images

import (
	"bufio"
	"image"
	"io"
	"math"
	"os"
)

// Histogram 相似图片识别（直方图）
type Histogram struct {
	redBins   int
	greenBins int
	blueBins  int
	histogram []float64
}

func NewHistogram() *Histogram {
	binCount := 4
	h := &Histogram{
		redBins:   binCount,
		greenBins: binCount,
		blueBins:  binCount,
		histogram: make([]float64, binCount*binCount*binCount),
	}
	return h
}

func (hm *Histogram) OpenFile(filename string) error {
	file, err := os.Open(filename)
	if err != nil {
		return err
	}
	return hm.Open(bufio.NewReader(file))
}

func (hm *Histogram) Open(reader io.Reader) error {

	img, _, err := image.Decode(reader)
	if err != nil {
		return err
	}
	return hm.OpenImage(img)
}

func (hm *Histogram) OpenImage(img image.Image) error {

	width := img.Bounds().Dx()
	height := img.Bounds().Dy()

	img.ColorModel()
	total := 0
	for y := 0; y < height; y++ {
		for x := 0; x < width; x++ {
			r, g, b, _ := img.At(x, y).RGBA()
			red := index(hm.redBins, r>>8, 255)
			green := index(hm.greenBins, g>>8, 255)
			blue := index(hm.blueBins, b>>8, 255)
			idx := red + green*hm.redBins + blue*hm.redBins*hm.greenBins
			hm.histogram[idx]++
			total += 1
		}
	}

	// start to normalize the histogram data
	for i := 0; i < len(hm.histogram); i++ {
		hm.histogram[i] = hm.histogram[i] / float64(total)
	}
	return nil
}

func (hm *Histogram) Data() []float64 {
	return hm.histogram
}

// Match 返回值大于等于0.8可以简单判断这两张图片内容一致
func (hm *Histogram) Match(dest *Histogram) float64 {
	source := hm.Data()
	candidate := dest.Data()

	mixed := make([]float64, len(source))
	for i := 0; i < len(source); i++ {
		mixed[i] = math.Sqrt(source[i] * candidate[i])
	}
	similarity := 0.0
	for i := 0; i < len(mixed); i++ {
		similarity += mixed[i]
	}
	// The degree of similarity
	return similarity
}

func index(binCount int, color uint32, maxColor uint32) int {
	binIndex := int(float32(color) / float32(maxColor) * float32(binCount))
	if binIndex >= binCount {
		binIndex = binCount - 1
	}
	return binIndex
}