【图像算法】彩色图像分割专题五：提取彩色图像上特定色彩

【图像算法】彩色图像分割专题五：提取彩色图像特定色彩

SkySeraph Jun 8th 2011 HQU

Email：zgzhaobo@gmail.com QQ：452728574

Latest Modified Date：Jun 8th 2011 HQU

一 原理及说明：

1 RGB(red,green,blue)模式是一种与设备相关的色彩空间，最常用的用途就是显示器系统。RGB下，各分量关联性太大，每个通道都编入了亮度信息，容易受周围环境影响(光照等)，其与人眼认知颜色的过程不太匹配，并不适合用来对彩色图像进行分析和分割，相比下HSV空间是从人的视觉系统除法的，更适于图像分析等。更多关于各种彩色空间模型请参考/article/4890847.html

2 国内很多关于车牌识别的论文中，当利用到颜色信息时，一般都是在HSV/YIQ/Lab模式下，根据特定的车牌颜色信息(常见车牌颜色有：白底黑字、黑底白字、蓝底白字、黄底黑字等)，进行车牌分割进行的。 颜色的提取方法即本文所述。 这种方法只适合特定颜色的提取，用PR术语，类似"有监督学习"；反之，无监督，对任意图像进行颜色分割，属于彩色分割领域。

3 关于HSV范围的划分：

<1> 论文：Car color recognition from CCTV camera image：http://www.docin.com/p-211572110.html

作者采用的是如下方式：



<2>论文：利用支持向量机识别汽车颜色：http://www.cnki.com.cn/Article/CJFDTotal-JSJF200405018.htm

作者首先是在Lab空间下分出16类颜色，然后再HSV下进行样本空间分解，采用如下方式：



<3>本文根据实验，采取划分方式如源码所示，在这种方式下，测试结果较好。

二 源码：

/////////////////////////////////////////////////////////////////////////////
// Note：		颜色分割：提取特定颜色
// Version：	5/11/2011 skyseraph/zhaobo  zgzhaobo@gmail.com
/////////////////////////////////////////////////////////////////////////////
void CColorSegDlg::ColorSegByHSV(IplImage* img)
// 提取特定颜色
{
//====================== 变量定义====================//
int x,y; //循环

//====================== 输入彩色图像信息====================//
IplImage* pSrc = NULL;
pSrc = cvCreateImage(cvGetSize(img),img->depth,img->nChannels);
cvCopyImage(img,pSrc);

int width = pSrc->width;			//图像宽度
int height = pSrc->height;			//图像高度
int depth = pSrc->depth;			//图像位深(IPL_DEPTH_8U...)
int channels = pSrc->nChannels;		//图像通道数(1、2、3、4)
int imgSize = pSrc->imageSize;		//图像大小 imageSize = height*widthStep
int step = pSrc->widthStep/sizeof(uchar);    //相邻行的同列点之间的字节数: 注意widthStep != width*nChannels (有字节填零补充)
uchar* data    = (uchar *)pSrc->imageData;
int imageLen = width*height;		//

//=========================================//
double B=0.0,G=0.0,R=0.0,H=0.0,S=0.0,V=0.0;
IplImage* dstColorSegByColor = cvCreateImage(cvGetSize(pSrc),IPL_DEPTH_8U,3);
IplImage* dstColorSegByColorGray = cvCreateImage(cvGetSize(pSrc),IPL_DEPTH_8U,1);

//CvFont font = cvFont( 1, 1 );

for (y=0; y<height; y++)
{
for ( x=0; x<width; x++)
{
// 获取BGR值
B = ((uchar*)(pSrc->imageData + y*pSrc->widthStep))[x*pSrc->nChannels];
G = ((uchar*)(pSrc->imageData + y*pSrc->widthStep))[x*pSrc->nChannels+1];
R = ((uchar*)(pSrc->imageData + y*pSrc->widthStep))[x*pSrc->nChannels+2];

//  RGB-HSV
pMyColorSpace.RGB2HSV(R,G,B,H,S,V);

H = (360*H)/(2*PI);

//  黑白
//黑色
if(V<0.35)
{
((uchar*)(dstColorSegByColorGray->imageData + y*dstColorSegByColorGray->widthStep))[x]
= 0;  //灰度
((uchar*)(dstColorSegByColor->imageData + y*dstColorSegByColor->widthStep))[x*dstColorSegByColor->nChannels]
= 0;  //B
((uchar*)(dstColorSegByColor->imageData + y*dstColorSegByColor->widthStep))[x*dstColorSegByColor->nChannels+1]
= 0;  //G
((uchar*)(dstColorSegByColor->imageData + y*dstColorSegByColor->widthStep))[x*dstColorSegByColor->nChannels+2]
= 0;  //R
}
//白色
if(S<0.15 && V>0.75)
{
((uchar*)(dstColorSegByColorGray->imageData + y*dstColorSegByColorGray->widthStep))[x]
= 255;  //灰度
((uchar*)(dstColorSegByColor->imageData + y*dstColorSegByColor->widthStep))[x*dstColorSegByColor->nChannels]
= 255;  //B
((uchar*)(dstColorSegByColor->imageData + y*dstColorSegByColor->widthStep))[x*dstColorSegByColor->nChannels+1]
= 255;  //G
((uchar*)(dstColorSegByColor->imageData + y*dstColorSegByColor->widthStep))[x*dstColorSegByColor->nChannels+2]
= 255;  //R
}

//灰色
if(S<0.15 && 0.35<V && V<0.75)
{
((uchar*)(dstColorSegByColorGray->imageData + y*dstColorSegByColorGray->widthStep))[x]
= 128;  //灰度
((uchar*)(dstColorSegByColor->imageData + y*dstColorSegByColor->widthStep))[x*dstColorSegByColor->nChannels]
= 128;  //B
((uchar*)(dstColorSegByColor->imageData + y*dstColorSegByColor->widthStep))[x*dstColorSegByColor->nChannels+1]
= 128;  //G
((uchar*)(dstColorSegByColor->imageData + y*dstColorSegByColor->widthStep))[x*dstColorSegByColor->nChannels+2]
= 128;  //R
}

//  彩色
if(V>=0.35 && S>=0.15)
{
//红色相近
if((H>=0 && H<15) || (H>=340 && H<360))
{
((uchar*)(dstColorSegByColorGray->imageData + y*dstColorSegByColorGray->widthStep))[x]
= 40;  //灰度
((uchar*)(dstColorSegByColor->imageData + y*dstColorSegByColor->widthStep))[x*dstColorSegByColor->nChannels]
= 0;  //B
((uchar*)(dstColorSegByColor->imageData + y*dstColorSegByColor->widthStep))[x*dstColorSegByColor->nChannels+1]
= 0;  //G
((uchar*)(dstColorSegByColor->imageData + y*dstColorSegByColor->widthStep))[x*dstColorSegByColor->nChannels+2]
= 255;  //R
}
//黄色相近
else if(H>=15 && H<75)
{
((uchar*)(dstColorSegByColorGray->imageData + y*dstColorSegByColorGray->widthStep))[x]
= 80;  //灰度
((uchar*)(dstColorSegByColor->imageData + y*dstColorSegByColor->widthStep))[x*dstColorSegByColor->nChannels]
= 0;  //B
((uchar*)(dstColorSegByColor->imageData + y*dstColorSegByColor->widthStep))[x*dstColorSegByColor->nChannels+1]
= 255;  //G
((uchar*)(dstColorSegByColor->imageData + y*dstColorSegByColor->widthStep))[x*dstColorSegByColor->nChannels+2]
= 255;  //R
}
//绿色相近
else if(H>=75 && H<150)
{
((uchar*)(dstColorSegByColorGray->imageData + y*dstColorSegByColorGray->widthStep))[x]
= 120;  //灰度
((uchar*)(dstColorSegByColor->imageData + y*dstColorSegByColor->widthStep))[x*dstColorSegByColor->nChannels]
= 0;  //B
((uchar*)(dstColorSegByColor->imageData + y*dstColorSegByColor->widthStep))[x*dstColorSegByColor->nChannels+1]
= 255;  //G
((uchar*)(dstColorSegByColor->imageData + y*dstColorSegByColor->widthStep))[x*dstColorSegByColor->nChannels+2]
= 0;  //R
}
///*//青色相近
else if(H>=150 && H<185)
{
((uchar*)(dstColorSegByColorGray->imageData + y*dstColorSegByColorGray->widthStep))[x]
= 160;  //灰度
((uchar*)(dstColorSegByColor->imageData + y*dstColorSegByColor->widthStep))[x*dstColorSegByColor->nChannels]
= 255;  //B
((uchar*)(dstColorSegByColor->imageData + y*dstColorSegByColor->widthStep))[x*dstColorSegByColor->nChannels+1]
= 255;  //G
((uchar*)(dstColorSegByColor->imageData + y*dstColorSegByColor->widthStep))[x*dstColorSegByColor->nChannels+2]
= 0;  //R
}//*/
//蓝色相近
else if(H>=185 && H<270)
{
((uchar*)(dstColorSegByColorGray->imageData + y*dstColorSegByColorGray->widthStep))[x]
= 200;  //灰度
((uchar*)(dstColorSegByColor->imageData + y*dstColorSegByColor->widthStep))[x*dstColorSegByColor->nChannels]
= 255;  //B
((uchar*)(dstColorSegByColor->imageData + y*dstColorSegByColor->widthStep))[x*dstColorSegByColor->nChannels+1]
= 0;  //G
((uchar*)(dstColorSegByColor->imageData + y*dstColorSegByColor->widthStep))[x*dstColorSegByColor->nChannels+2]
= 0;  //R
}
//	/*	//洋红：270-340
else if(H>=270 && H<340)
{
((uchar*)(dstColorSegByColorGray->imageData + y*dstColorSegByColorGray->widthStep))[x]
= 220;  //灰度
((uchar*)(dstColorSegByColor->imageData + y*dstColorSegByColor->widthStep))[x*dstColorSegByColor->nChannels]
= 255;  //B
((uchar*)(dstColorSegByColor->imageData + y*dstColorSegByColor->widthStep))[x*dstColorSegByColor->nChannels+1]
= 0;  //G
((uchar*)(dstColorSegByColor->imageData + y*dstColorSegByColor->widthStep))[x*dstColorSegByColor->nChannels+2]
= 255;  //R
}//*/
else
{
((uchar*)(dstColorSegByColorGray->imageData + y*dstColorSegByColorGray->widthStep))[x]
= 180;  //灰度
((uchar*)(dstColorSegByColor->imageData + y*dstColorSegByColor->widthStep))[x*dstColorSegByColor->nChannels]
= 128;  //B  //紫色Purple
((uchar*)(dstColorSegByColor->imageData + y*dstColorSegByColor->widthStep))[x*dstColorSegByColor->nChannels+1]
= 0;  //G
((uchar*)(dstColorSegByColor->imageData + y*dstColorSegByColor->widthStep))[x*dstColorSegByColor->nChannels+2]
= 128;  //R
}
}
}
}
//cvNamedWindow("src",1);
//cvShowImage("src",pSrc);
cvNamedWindow("dstColorSegByColor",1);
cvShowImage("dstColorSegByColor",dstColorSegByColor);
cvNamedWindow("dstColorSegByColorGray",1);
cvShowImage("dstColorSegByColorGray",dstColorSegByColorGray);
cvSaveImage(".\\dstColorSegByColor.jpg",dstColorSegByColor);
cvSaveImage(".\\dstColorSegByColorGray.jpg",dstColorSegByColorGray);

cvWaitKey(0);
cvDestroyAllWindows();
cvReleaseImage(&pSrc);
cvReleaseImage(&dstColorSegByColor);
cvReleaseImage(&dstColorSegByColorGray);

}

[/code]

三 效果：

（1）原图



(2)颜色分割后彩色图



(3)颜色分割后灰度图(利用不同灰度级显示)



四 补充(RGB模式下，来源网络)

1 源码

void CFindRGBDlg::OnFind()
{
int color=m_colorList.GetCurSel();

pic=cvCreateImage( cvSize(image->width,image->height), 8, 1 );
cvZero(pic);
for(int x=0;x<image->height;x++)
{
for(int y=0;y<image->width;y++)
{
uchar* ptrImg = &CV_IMAGE_ELEM(image,uchar,x,y*3);
//	uchar* ptrPic = &((uchar*)(pic->imageData + pic->widthStep*y))[x];
//red
if(color==0)
{
if((ptrImg[0]-ptrImg[1])>200&&(ptrImg[0]-ptrImg[2])>200)
CV_IMAGE_ELEM(pic,uchar,x,y)=255;
}
//Green
else if(color==1)
{
if((ptrImg[1]-ptrImg[0])>200&&(ptrImg[1]-ptrImg[2])>200)
CV_IMAGE_ELEM(pic,uchar,x,y)=255;
}
//blue
else if(color==2)
{
if((ptrImg[2]-ptrImg[0])>200&&(ptrImg[2]-ptrImg[1])>200)
CV_IMAGE_ELEM(pic,uchar,x,y)=255;
}

}
}

cvNamedWindow("temp",-1);
cvShowImage("temp",pic);
cvWaitKey();

storage = cvCreateMemStorage(0);
contour = 0;
mode = CV_RETR_EXTERNAL;
cvFindContours( pic, storage, &contour, sizeof(CvContour),
mode, CV_CHAIN_APPROX_SIMPLE);
cvDrawContours(image, contour,
CV_RGB(0,0,0), CV_RGB(0, 0, 0),
2, 2, 8);

CRect rect;
GetDlgItem(IDC_PICTURE)->GetClientRect(&rect);
InvalidateRect(rect,true);
}

2 效果：





Author: SKySeraph

Email/GTalk: zgzhaobo@gmail.com QQ：452728574

From： http://www.cnblogs.com/skyseraph/

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