基于K-MEANS聚类的胡萝卜纯色背景去除方法

IplImage* img = cvCloneImage(m_sourceImage);
		IplImage* grayImage = cvCreateImage(cvGetSize(img), IPL_DEPTH_8U, 1);
		//IplImage* redImage = cvCreateImage(cvGetSize(img), IPL_DEPTH_8U, 1);

		//cvSplit(img, NULL, NULL, redImage, NULL);

		cvCvtColor(img, grayImage, CV_RGB2GRAY);
		//IplImage* img = cvCreateImage(cvGetSize(m_sourceImage), IPL_DEPTH_8U, 1);
		//cvCvtColor(m_sourceImage, img, CV_RGB2GRAY);
		int total = img->height*img->width;
		int cluster_num = 2;
		CvMat *row = cvCreateMat(img->height, img->width, CV_32FC3);
		//cvConvert(redImage, row);
		cvConvert(img, row);//转一下类型!
		CvMat *clusters = cvCreateMat(total, 1, CV_32SC1);

// 		CvArr* cvReshapeMatND(const CvArr* arr,
// 			int sizeof_header, CvArr* header,
// 			int new_cn, int new_dims, int* new_sizes);
// 
// 		arr :输入数组
// 		sizeof_header :输出头的大小，对于IplImage, CvMat 和 CvMatND 各种结构输出的头均是不同的.
// 		header:被添充的输出头.
// 		new_cn:新的通道数，如果new_cn = 0 则通道数保持原样
//		new_dims:新的维数.如果new_dims = 0 则维数保持原样。
// 			new_sizes
// 			新的维大小.只有当 new_dims = 1值被使用，因为要保持数组的总数一致，因此如果 new_dims = 1, new_sizes 是不被使用的


		cvReshape(row, row, 0, total);						//把图像转成数据矩阵,但注意实际数据未变,只是访问顺序变了。
		cvKMeans2(row, cluster_num, clusters, cvTermCriteria(CV_TERMCRIT_EPS + CV_TERMCRIT_ITER, 10, 1.0));
		cvReshape(clusters, clusters, 0, img->width);		//聚类完的结果再reshape回来比较方便看~
		int i = 0, j = 0;
		CvScalar s;
		IplImage* resImg = cvCreateImage(cvSize(img->width, img->height), 8, 1);//生成用来显示结果的图像
		s = cvGet2D(img, i, j);

		vector <int> v1, v2;		//存放分类后的灰度值


		for (i = 0; i < img->height; i++)
		{
			for (j = 0; j < img->width; j++)
			{
				double val = cvGetReal2D(grayImage, i, j);

				if (clusters->data.i[i*img->width + j] == 0)
				{
					v1.push_back(val);				//处理完成后的图像的白色区域
					s.val[0] = 255;
 					cvSet2D(resImg, i, j, s);//注意循环顺序
				}
				else
				{
					v2.push_back(val);				//处理完成后的图像的黑色区域
					s.val[0] = 0;
					cvSet2D(resImg, i, j, s);
				}
			}
		}

		double thresh1 = accumulate(v1.begin(), v1.end(), 0) / v1.size();
		double thresh2 = accumulate(v2.begin(), v2.end(), 0) / v2.size();

		if (thresh2 > thresh1)		//如果黑色区域的灰度值均值大于白色区域
		{
			cvNot(resImg, resImg);	//图像取反
		}

		IplConvKernel *element = cvCreateStructuringElementEx(5, 5, 2, 2, CV_SHAPE_ELLIPSE);
		cvSmooth(resImg, resImg, CV_MEDIAN);
		cvErode(resImg, resImg, element, 1);
		cvDilate(resImg, resImg, element, 1);
		cvReleaseStructuringElement(&element);

		cvSaveImage("s2.jpg", m_sourceImage);
		cvSaveImage("b2.jpg", resImg);

		ShowImage(resImg, IDC_PIC2);

		int key = cvWaitKey(0);
		cvReleaseImage(&img);//记得释放内存
		cvReleaseImage(&resImg);
		cvReleaseMat(&row);
		cvReleaseMat(&clusters);