下面給大家介紹JAVA代碼怎么實現圖像六邊形網格分割效果，希望能給大家提供幫助。

一：原理

根據輸入參數blockSize的大小，將圖像分塊，決定每塊的中心通過該像素塊內所有像素之和的均值與該塊內部每個像素比較，RGB值之間幾何距離最小為新的中心，迭代更新運算，直到達到輸入參數聲明的最大循環(huán)次數為止，然后輸出結果圖像即可。

二：程序實現

類MyCluster，存儲每個像素塊中心的信息，計算中心位置。

類SuperPixelsFilter, 濾鏡實現，完成六邊形網格分割的主要功能，其中距離計算，基于歐幾里德距離公式。

三：效果

原圖：

效果：

四：完全源代碼

package?com.gloomyfish.image.cluster.effect;

import?java.awt.image.BufferedImage;

import?java.util.ArrayList;

import?java.util.Arrays;

import?java.util.List;

import?com.gloomyfish.filter.study.AbstractBufferedImageOp;

public?class?SuperPixelsFilter?extends?AbstractBufferedImageOp?{

private?double[]?distances;

private?int[]?labels;

private?MyCluster[]?clusters;

private?intmaxClusteringLoops=50;

private?double?blockSize;

private?double?modifier;

public?SuperPixelsFilter()

{

blockSize=16;

modifier=130;

}

public?double?getBlockSize()?{

return?blockSize;

}

public?void?setBlockSize(double?blockSize)?{

this.blockSize=?blockSize;

}

public?double?getModifier()?{

return?modifier;

}

public?void?setModifier(double?modifier)?{

this.modifier=?modifier;

}

@Override

public?BufferedImage?filter(BufferedImage?src,?BufferedImage?dest)?{

intwidth=src.getWidth();

intheight=src.getHeight();

if?(dest==?null?)

dest=createCompatibleDestImage(?src,?null?);

int[]inPixels=newint[width*height];

getRGB(?src,?0,?0,?width,?height,?inPixels?);

intindex=0;

//?initialization

distances=newdouble[width*height];

labels=newint[width*height];

Arrays.fill(distances,?Integer.MAX_VALUE);

Arrays.fill(labels,?-1);

initClusters(width,?height,?inPixels,?blockSize,?modifier);

//?loop?to?get?all?block/cells,?image?segmentation

intloops=0;

booleanpixelChangedCluster=true;

while?(pixelChangedCluster&&loops

pixelChangedCluster=false;

loops++;

//?for?each?cluster?center?C

for?(inti=0;i

MyClusterc=clusters[i];

//?for?each?pixel?i?in?2S?region?around

//?cluster?center

intxs=Math.max((int)(c.avg_x-blockSize),0);

intys=Math.max((int)(c.avg_y-blockSize),0);

intxe=Math.min((int)(c.avg_x+blockSize),width);

intye=Math.min((int)(c.avg_y+blockSize),height);

for?(inty=ys;y

for?(intx=xs;x

intpos=x+width*y;

inttr=?(inPixels[pos]>>16)?&?0xff;

inttg=?(inPixels[pos]>>8)?&?0xff;

inttb=inPixels[pos]?&?0xff;

doubleD=c.distance(x,?y,?tr,

tg,

tb,

blockSize,?modifier,?width,?height);

if?((D

distances[pos]?????????=?D;

labels[pos]????????????=?c.id;

pixelChangedCluster=true;

}

}?//?end?for?x

}?//?end?for?y

}?//?end?for?clusters

//?reset?clusters

for?(index=0;index

clusters[index].reset();

}

//?add?every?pixel?to?cluster?based?on?label

for?(inty=0;y

for?(intx=0;x

intpos=x+y*width;

inttr=?(inPixels[pos]>>16)?&?0xff;

inttg=?(inPixels[pos]>>8)?&?0xff;

inttb=inPixels[pos]?&?0xff;

clusters[labels[pos]].addPixel(x,?y,

tr,?tg,?tb);

}

}

//?calculate?centers

for?(index=0;index

clusters[index].calculateCenter();

}

}

//?Create?output?image?with?pixel?edges

for?(inty=1;y

for?(intx=1;x

intid1=labels[x+y*width];

intid2=labels[(x+1)+y*width];

intid3=labels[x+(y+1)*width];

if?(id1!=id2||id1!=id3)?{

intpos=x+y*width;

inPixels[pos]?=?(255<<24)?|?(0<<16)?|?(0<<8)?|?0;

}

}

}

setRGB(?dest,?0,?0,?width,?height,?inPixels?);

return?dest;

}

public?void?initClusters(int?width,?int?height,?int[]?input,

double?S,?double?m)?{

Listtemp=newArrayList();

booleaneven=false;

doublexstart=0;

intid=0;

for?(doubley=S/?2;?y

//?創(chuàng)建六邊形網格

if?(even)?{

xstart=S/?2.0;

even=false;

}?else?{

xstart=S;

even=true;

}

for?(doublex=xstart;?x

intindex=?(int)?(x?+?y?*?width);

inttr=?(input[index]>>16)?&?0xff;

inttg=?(input[index]>>8)?&?0xff;

inttb=input[index]?&?0xff;

MyClusterc=newMyCluster(id,?tr,?tg,?tb,

(int)?x,?(int)?y,?S,?m);

temp.add(c);

id++;

}

}

clusters=newMyCluster[temp.size()];

for?(inti=0;?i

clusters[i]?=?temp.get(i);

}

}

}

MyCluster類代碼：

package?com.gloomyfish.image.cluster.effect;

public?class?MyCluster?{

int?id;

doubleinv=0;?//?inv?variable?for?optimization

double?pixelCount;?//?pixels?in?this?cluster

double?avg_red;?//?average?red?value

double?avg_green;?//?average?green?value

double?avg_blue;?//?average?blue?value

double?sum_red;?//?sum?red?values

double?sum_green;?//?sum?green?values

double?sum_blue;?//?sum?blue?values

double?sum_x;?//?sum?x

double?sum_y;?//?sum?y

double?avg_x;?//?average?x

double?avg_y;?//?average?y

public?MyCluster(int?id,?int?in_red,?int?in_green,?int?in_blue,?int?x,

int?y,?double?S,?double?m)?{

//?inverse?for?distance?calculation

this.inv=1.0?/?((S?/?m)?*?(S?/?m));

this.id=?id;

addPixel(x,?y,?in_red,?in_green,?in_blue);

//?calculate?center?with?initial?one?pixel

calculateCenter();

}

public?void?reset()?{

avg_red=0;

avg_green=0;

avg_blue=0;

sum_red=0;

sum_green=0;

sum_blue=0;

pixelCount=0;

avg_x=0;

avg_y=0;

sum_x=0;

sum_y=0;

}

/*

*?Add?pixel?color?values?to?sum?of?previously?added?color?values.

*/

void?addPixel(int?x,?int?y,?int?in_red,?int?in_green,?int?in_blue)?{

sum_x?+=?x;

sum_y?+=?y;

sum_red?+=?in_red;

sum_green?+=?in_green;

sum_blue?+=?in_blue;

pixelCount++;

}

public?void?calculateCenter()?{

//?Optimization:?using?"inverse"

//?to?change?divide?to?multiply

doubleinv=1/?pixelCount;

avg_red=sum_red*?inv;

avg_green=sum_green*?inv;

avg_blue=sum_blue*?inv;

avg_x=sum_x*?inv;

avg_y=sum_y*?inv;

}

double?distance(int?x,?int?y,?int?red,?int?green,?int?blue,?double?S,

double?m,?int?w,?int?h)?{

//?power?of?color?difference?between

//?given?pixel?and?cluster?center

doubledx_color=?(avg_red?-?red)?*?(avg_red?-?red)

+?(avg_green?-?green)?*?(avg_green?-?green)?+?(avg_blue?-?blue)

*?(avg_blue?-?blue);

//?power?of?spatial?difference?between

//?given?pixel?and?cluster?center

doubledx_spatial=?(avg_x?-?x)?*?(avg_x?-?x)?+?(avg_y?-?y)

*?(avg_y?-?y);

//?Calculate?approximate?distance?D

//?doubleD=dx_color+dx_spatial*inv;

//?Calculate?squares?to?get?more?accurate?results

doubleD=Math.sqrt(dx_color)?+?Math.sqrt(dx_spatial?*?inv);

return?D;

}

}

五：參考這里

該濾鏡是SuperPixel算法的簡單應用，多數時候，我們可能更熟悉K-Means等圖像分割算法，其實SuperPixel是圖像分割算法之一。

(責任編輯：6g下載網)

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