【图像算法】彩色图像分割专题五：提取彩色图像上特定色彩

SkySeraph Jun 8th 2011 HQU

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http://www.cnblogs.com/skyseraph/archive/2011/05/03/2035643.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  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);

}

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：

More in http://skyseraph.com/2011/08/27/CV/图像算法专题/

Author: SKySeraph

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

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

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原文地址：https://www.cnblogs.com/skyseraph/p/2075599.html