Golang版本的图像二值化分析范例

golang_image_filter.go

package main

import(
	"fmt"
	"os"
	"image"
    "image/jpeg"
    "image/draw"
)


func main(){
	for i:=1; i<=5; i++ {
		fromFile := fmt.Sprintf("%d.jpg", i)
		toFile := fmt.Sprintf("gray_%d.jpg", i)
		convertJpegToGray(fromFile, toFile)
	}
}

func createGrayImageFromJpegFile(file string) (image.Image, error) {
	f, err := os.Open(file)
	if err!=nil {
		return nil, err
	}
	defer f.Close()

	colorImage, err := jpeg.Decode(f)
	if err!=nil {
		return nil, err
	}

	grayImage := image.NewGray(colorImage.Bounds())
	draw.Draw(grayImage, colorImage.Bounds(), colorImage, colorImage.Bounds().Min, draw.Src)

	return grayImage,nil
}

func convertJpegToGray(fromFile,toFile string) error {
	grayImage, err := createGrayImageFromJpegFile(fromFile)
	if err !=nil {
		return err
	}

	w, err := os.Create(toFile)
	if err!=nil {
		return err
	}

	defer w.Close()
	return jpeg.Encode(w, grayImage, nil)    //保存文件

	//统计
	//width := size.Dx()
	//height := size.Dy()
	//zft := make([]int, 256)//用于保存每个像素的数量，注意这里用了int类型，在某些图像上可能会溢出。
	//var idx int
	//for i := 0; i < width; i++ {
	//	for j := 0; j < height; j++ {
	//		idx = i*height + j
	//		zft[pic.Pix[idx]]++    //image对像有一个Pix属性，它是一个slice，里面保存的是所有像素的数据。
	//	}
	//}

	//灰度化结果

	//fz := uint8(GetOSTUThreshold(zft))
	//for fz := uint8(0); fz<255; fz++ {
		//do(pic, fz)
	//}
}

//二值化
func toValued(oriPic *image.Gray, fz uint8) {
	pic := new(image.Gray)
	pic.Stride = oriPic.Stride
	pic.Rect = oriPic.Rect
	pic.Pix = make([]uint8, len(oriPic.Pix))
	copy(pic.Pix, oriPic.Pix)

	zeroCount := 0
	for i := 0; i < len(pic.Pix); i++ {
		//以下是内外区间法，像素差5以内算同一像素
		if (pic.Pix[i] - fz) > 5 {
			pic.Pix[i] = 255
		} else {
			zeroCount += 1
			pic.Pix[i] = 0
		}

		//以下是区间法，大于fz为白，否则为黑
		//if pic.Pix[i] > fz {
		//	pic.Pix[i] = 255
		//} else {
		//	pic.Pix[i] = 0
		//}
	}

	//只保留白色为1%～2%的图像
	r := float32(zeroCount)/float32(len(pic.Pix))
	if r > 0.02 || r < 0.01 {
		return
	}

	w, _ := os.Create(fmt.Sprintf("result/black_white%03d_%.0f.jpg", fz, 100 * float32(zeroCount)/float32(len(pic.Pix))))
	defer w.Close()
	jpeg.Encode(w, pic, nil)
}


//直方图获取二值化中间值
func GetOSTUThreshold(HistGram []int) int {
	var Y, Amount int
	var PixelBack, PixelFore, PixelIntegralBack, PixelIntegralFore, PixelIntegral int
	var OmegaBack, OmegaFore, MicroBack, MicroFore, SigmaB, Sigma float64 // 类间方差;
	var MinValue, MaxValue int
	var Threshold int = 0
	for MinValue = 0; MinValue < 256 && HistGram[MinValue] == 0; MinValue++ {
	}
	for MaxValue = 255; MaxValue > MinValue && HistGram[MinValue] == 0; MaxValue-- {
	}
	if MaxValue == MinValue {
		return MaxValue // 图像中只有一个颜色
	}
	if MinValue+1 == MaxValue {
		return MinValue // 图像中只有二个颜色
	}
	for Y = MinValue; Y <= MaxValue; Y++ {
		Amount += HistGram[Y] //  像素总数
	}
	PixelIntegral = 0
	for Y = MinValue; Y <= MaxValue; Y++ {
		PixelIntegral += HistGram[Y] * Y
	}
	SigmaB = -1
	for Y = MinValue; Y < MaxValue; Y++ {
		PixelBack = PixelBack + HistGram[Y]
		PixelFore = Amount - PixelBack
		OmegaBack = float64(PixelBack) / float64(Amount)
		OmegaFore = float64(PixelFore) / float64(Amount)
		PixelIntegralBack += HistGram[Y] * Y
		PixelIntegralFore = PixelIntegral - PixelIntegralBack
		MicroBack = float64(PixelIntegralBack) / float64(PixelBack)
		MicroFore = float64(PixelIntegralFore) / float64(PixelFore)
		Sigma = OmegaBack * OmegaFore * (MicroBack - MicroFore) * (MicroBack - MicroFore)
		if Sigma > SigmaB {
			SigmaB = Sigma
			Threshold = Y
		}
	}
	return Threshold
}