【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：[email protected] 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/zhaobo [email protected] ///////////////////////////////////////////////////////////////////////////// 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：
Author: SKySeraph
Email/GTalk: [email protected] QQ：452728574
From： http://www.cnblogs.com/skyseraph/
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