二值化代碼java 二值化python

如何用java去除圖片水??？

//運(yùn)行以下程序即可

站在用戶(hù)的角度思考問(wèn)題，與客戶(hù)深入溝通，找到祿勸網(wǎng)站設(shè)計(jì)與祿勸網(wǎng)站推廣的解決方案，憑借多年的經(jīng)驗(yàn)，讓設(shè)計(jì)與互聯(lián)網(wǎng)技術(shù)結(jié)合，創(chuàng)造個(gè)性化、用戶(hù)體驗(yàn)好的作品，建站類(lèi)型包括：網(wǎng)站建設(shè)、成都網(wǎng)站設(shè)計(jì)、企業(yè)官網(wǎng)、英文網(wǎng)站、手機(jī)端網(wǎng)站、網(wǎng)站推廣、域名與空間、網(wǎng)絡(luò)空間、企業(yè)郵箱。業(yè)務(wù)覆蓋祿勸地區(qū)。

public?class?ImageInit?{

BufferedImage?image;

private?int?iw,?ih;

private?int[]?pixels;

public?ImageInit(BufferedImage?image)?{

this.image?=?image;

iw?=?image.getWidth();

ih?=?image.getHeight();

pixels?=?new?int[iw?*?ih];

}

public?BufferedImage?changeGrey()?{

PixelGrabber?pg?=?new?PixelGrabber(image.getSource(),?0,?0,?iw,?ih,

pixels,?0,?iw);

try?{

pg.grabPixels();

}?catch?(InterruptedException?e)?{

e.printStackTrace();

}

//?設(shè)定二值化的域值，默認(rèn)值為100

int?grey?=?100;

//?對(duì)圖像進(jìn)行二值化處理，Alpha值保持不變

ColorModel?cm?=?ColorModel.getRGBdefault();

for?(int?i?=?0;?i??iw?*?ih;?i++)?{

int?red,?green,?blue;

int?alpha?=?cm.getAlpha(pixels[i]);

if?(cm.getRed(pixels[i])??grey)?{

red?=?255;

}?else?{

red?=?0;

}

if?(cm.getGreen(pixels[i])??grey)?{

green?=?255;

}?else?{

green?=?0;

}

if?(cm.getBlue(pixels[i])??grey)?{

blue?=?255;

}?else?{

blue?=?0;

}

pixels[i]?=?alpha??24?|?red??16?|?green??8?|?blue;?//?通過(guò)移位重新構(gòu)成某一點(diǎn)像素的RGB值

}

//?將數(shù)組中的象素產(chǎn)生一個(gè)圖像

Image?tempImg?=?Toolkit.getDefaultToolkit().createImage(

new?MemoryImageSource(iw,?ih,?pixels,?0,?iw));

image?=?new?BufferedImage(tempImg.getWidth(null),

tempImg.getHeight(null),?BufferedImage.TYPE_INT_BGR);

image.createGraphics().drawImage(tempImg,?0,?0,?null);

return?image;

}

public?BufferedImage?getMedian()?{

PixelGrabber?pg?=?new?PixelGrabber(image.getSource(),?0,?0,?iw,?ih,

pixels,?0,?iw);

try?{

pg.grabPixels();

}?catch?(InterruptedException?e)?{

e.printStackTrace();

}

//?對(duì)圖像進(jìn)行中值濾波，Alpha值保持不變

ColorModel?cm?=?ColorModel.getRGBdefault();

for?(int?i?=?1;?i??ih?-?1;?i++)?{

for?(int?j?=?1;?j??iw?-?1;?j++)?{

int?red,?green,?blue;

int?alpha?=?cm.getAlpha(pixels[i?*?iw?+?j]);

//?int?red2?=?cm.getRed(pixels[(i?-?1)?*?iw?+?j]);

int?red4?=?cm.getRed(pixels[i?*?iw?+?j?-?1]);

int?red5?=?cm.getRed(pixels[i?*?iw?+?j]);

int?red6?=?cm.getRed(pixels[i?*?iw?+?j?+?1]);

//?int?red8?=?cm.getRed(pixels[(i?+?1)?*?iw?+?j]);

//?水平方向進(jìn)行中值濾波

if?(red4?=?red5)?{

if?(red5?=?red6)?{

red?=?red5;

}?else?{

if?(red4?=?red6)?{

red?=?red6;

}?else?{

red?=?red4;

}

}

}?else?{

if?(red4??red6)?{

red?=?red4;

}?else?{

if?(red5??red6)?{

red?=?red6;

}?else?{

red?=?red5;

}

}

}

int?green4?=?cm.getGreen(pixels[i?*?iw?+?j?-?1]);

int?green5?=?cm.getGreen(pixels[i?*?iw?+?j]);

int?green6?=?cm.getGreen(pixels[i?*?iw?+?j?+?1]);

//?水平方向進(jìn)行中值濾波

if?(green4?=?green5)?{

if?(green5?=?green6)?{

green?=?green5;

}?else?{

if?(green4?=?green6)?{

green?=?green6;

}?else?{

green?=?green4;

}

}

}?else?{

if?(green4??green6)?{

green?=?green4;

}?else?{

if?(green5??green6)?{

green?=?green6;

}?else?{

green?=?green5;

}

}

}

//?int?blue2?=?cm.getBlue(pixels[(i?-?1)?*?iw?+?j]);

int?blue4?=?cm.getBlue(pixels[i?*?iw?+?j?-?1]);

int?blue5?=?cm.getBlue(pixels[i?*?iw?+?j]);

int?blue6?=?cm.getBlue(pixels[i?*?iw?+?j?+?1]);

//?int?blue8?=?cm.getBlue(pixels[(i?+?1)?*?iw?+?j]);

//?水平方向進(jìn)行中值濾波

if?(blue4?=?blue5)?{

if?(blue5?=?blue6)?{

blue?=?blue5;

}?else?{

if?(blue4?=?blue6)?{

blue?=?blue6;

}?else?{

blue?=?blue4;

}

}

}?else?{

if?(blue4??blue6)?{

blue?=?blue4;

}?else?{

if?(blue5??blue6)?{

blue?=?blue6;

}?else?{

blue?=?blue5;

}

}

}

pixels[i?*?iw?+?j]?=?alpha??24?|?red??16?|?green??8

|?blue;

}

}

//?將數(shù)組中的象素產(chǎn)生一個(gè)圖像

Image?tempImg?=?Toolkit.getDefaultToolkit().createImage(

new?MemoryImageSource(iw,?ih,?pixels,?0,?iw));

image?=?new?BufferedImage(tempImg.getWidth(null),

tempImg.getHeight(null),?BufferedImage.TYPE_INT_BGR);

image.createGraphics().drawImage(tempImg,?0,?0,?null);

return?image;

}

public?BufferedImage?getGrey()?{

ColorConvertOp?ccp?=?new?ColorConvertOp(

ColorSpace.getInstance(ColorSpace.CS_GRAY),?null);

return?image?=?ccp.filter(image,?null);

}

//?Brighten?using?a?linear?formula?that?increases?all?color?values

public?BufferedImage?getBrighten()?{

RescaleOp?rop?=?new?RescaleOp(1.25f,?0,?null);

return?image?=?rop.filter(image,?null);

}

//?Blur?by?"convolving"?the?image?with?a?matrix

public?BufferedImage?getBlur()?{

float[]?data?=?{?.1111f,?.1111f,?.1111f,?.1111f,?.1111f,?.1111f,

.1111f,?.1111f,?.1111f,?};

ConvolveOp?cop?=?new?ConvolveOp(new?Kernel(3,?3,?data));

return?image?=?cop.filter(image,?null);

}

//?Sharpen?by?using?a?different?matrix

public?BufferedImage?getSharpen()?{

float[]?data?=?{?0.0f,?-0.75f,?0.0f,?-0.75f,?4.0f,?-0.75f,?0.0f,

-0.75f,?0.0f?};

ConvolveOp?cop?=?new?ConvolveOp(new?Kernel(3,?3,?data));

return?image?=?cop.filter(image,?null);

}

//?11)?Rotate?the?image?180?degrees?about?its?center?point

public?BufferedImage?getRotate()?{

AffineTransformOp?atop?=?new?AffineTransformOp(

AffineTransform.getRotateInstance(Math.PI,

image.getWidth()?/?2,?image.getHeight()?/?2),

AffineTransformOp.TYPE_NEAREST_NEIGHBOR);

return?image?=?atop.filter(image,?null);

}

public?BufferedImage?getProcessedImg()?{

return?image;

}

public?static?void?main(String[]?args)?throws?IOException?{

String?filePath="F:/k7qp5.png";

FileInputStream?fin?=?new?FileInputStream(filePath);

BufferedImage?bi?=?ImageIO.read(fin);

ImageInit?flt?=?new?ImageInit(bi);

flt.changeGrey();

flt.getGrey();

flt.getBrighten();

bi?=?flt.getProcessedImg();

String?pname?=?filePath.substring(0,?filePath.lastIndexOf("."));

File?file?=?new?File(pname?+?".jpg");

ImageIO.write(bi,?"jpg",?file);

}

}

java代碼怎么實(shí)現(xiàn)圖像二值化

較為常用的圖像二值化方法有：1）全局固定閾值；2）局部自適應(yīng)閾值；3）OTSU等。

局部自適應(yīng)閾值則是根據(jù)像素的鄰域塊的像素值分布來(lái)確定該像素位置上的二值化閾值。

這樣做的好處在于每個(gè)像素位置處的二值化閾值不是固定不變的，而是由其周?chē)徲蛳袼氐姆植紒?lái)決定的。

亮度較高的圖像區(qū)域的二值化閾值通常會(huì)較高，而亮度較低的圖像區(qū)域的二值化閾值則會(huì)相適應(yīng)地變小。不同亮度、對(duì)比度、紋理的局部圖像區(qū)域?qū)?huì)擁有相對(duì)應(yīng)的局部二值化閾值。

常用的局部自適應(yīng)閾值有：

1）局部鄰域塊的均值；

2）局部鄰域塊的高斯加權(quán)和。

急！！，二值化后的圖像，用JAVA中值濾波算法，去除椒鹽噪點(diǎn)?。?！

椒鹽噪聲的話(huà)一般可以用中值濾波器去除, 中值濾波器很容易實(shí)現(xiàn), 依此遍歷圖像中每個(gè)像素點(diǎn), 每個(gè)像素點(diǎn)與其周?chē)?個(gè)點(diǎn)像素值做一下排序操作, 找到這九個(gè)點(diǎn)中的中值點(diǎn)賦給當(dāng)前遍歷點(diǎn)的像素就可以了, 算法很簡(jiǎn)單吧. 我這有c++的源碼, 樓主要想要的話(huà)發(fā)郵件到我的郵箱769569350@qq.com我可以把程序發(fā)給你.

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