从前也练习使用过OpenCV的Kmean算法，但是那版本低，而且也是基于C的开发。这两天由于造论文的需要把它重新翻出来在研究一下C++，发现有了些改进
kmeans
- C++: doublekmeans(InputArraydata, int K, InputOutputArray bestLabels, TermCriteriacriteria, int attempts, int flags, OutputArraycenters=noArray() )
- data：输入样本，要分类的对象，浮点型，每行一个样本（我要对颜色分类则每行一个像素）；
- K： 类型数目；
- bestLabels： 分类后的矩阵，每个样本对应一个类型label；
- TermCriteria criteria：结束条件（最大迭代数和理想精度）
- int attempts：根据最后一个参数确定选取的最理想初始聚类中心（选取attempt次初始中心，选择compactness最小的）；
- int flags ：
Flag that can take the following values:
- KMEANS_RANDOM_CENTERS Select random initial centers in each attempt.
- KMEANS_PP_CENTERS Use kmeans++ center initialization by Arthur and Vassilvitskii [Arthur2007].
- KMEANS_USE_INITIAL_LABELS During the first (and possibly the only) attempt, use the user-supplied labels instead of computing them from the initial centers. For the second and further attempts, use the random or semi-random centers. Use one of KMEANS_*_CENTERS flag to specify the exact method.
centers：输出聚类中心，每行一个中心（第一列是聚类中心，但是还有其他列，这里不太明白，大家谁懂，求科普啊！~~）
compactness： 测试初始中心是否最优
上代码:
- #include <string>
- #include <iostream>
- #include <math.h>
- #include <vector>
- #include <map>
-
- #include "opencv/cv.h"
- #include "opencv/highgui.h"
- #include "opencv/cxcore.h"
-
- #define ClusterNum (6)
-
- using namespace cv;
- using namespace std;
-
- string filename="D:/demo1.jpg";
-
- Mat clustering(Mat src)
- {
- int row = src.rows;
- int col = src.cols;
- unsigned long int size = row*col;
-
- Mat clusters(size, 1, CV_32SC1);
-
-
- Mat srcPoint(size, 1, CV_32FC3);
-
- Vec3f* srcPoint_p = (Vec3f*)srcPoint.data;
- Vec3f* src_p = (Vec3f*)src.data;
- unsigned long int i;
-
- for(i = 0;i < size; i++)
- {
- *srcPoint_p = *src_p;
- srcPoint_p++;
- src_p++;
- }
- Mat center(ClusterNum,1,CV_32FC3);
- double compactness;
- compactness = kmeans(srcPoint, ClusterNum, clusters,
- cvTermCriteria (CV_TERMCRIT_EPS + CV_TERMCRIT_ITER, 10, 0.1),ClusterNum,
- KMEANS_PP_CENTERS , center);
-
- cout<<"center row:"<<center.rows<<" col:"<<center.cols<<endl;
- for (int y = 0; y < center.rows; y++)
- {
- Vec3f* imgData = center.ptr<Vec3f>(y);
- for (int x = 0; x < center.cols; x++)
- {
- cout<<imgData[x].val[0]<<" "<<imgData[x].val[1]<<" "<<imgData[x].val[2]<<endl;
- }
- cout<<endl;
- }
-
-
- double minH,maxH;
- minMaxLoc(clusters, &minH, &maxH);
- cout<<"H-channel min:"<<minH<<" max:"<<maxH<<endl;
-
- int* clusters_p = (int*)clusters.data;
-
- Mat label(src.size(), CV_32SC1);
- int* label_p = (int*)label.data;
-
- for(i = 0;i < size; i++)
- {
- *label_p = *clusters_p;
- label_p++;
- clusters_p++;
- }
-
- Mat label_show;
- label.convertTo(label_show,CV_8UC1);
- normalize(label_show,label_show,255,0,CV_MINMAX);
- imshow("label",label_show);
-
-
-
- map<int,int> count;
- map<int,Vec3f> avg;
-
-
- for (int y = 0; y < row; y++)
- {
- const Vec3f* imgData = src.ptr<Vec3f>(y);
- int* idx = label.ptr<int>(y);
- for (int x = 0; x < col; x++)
- {
-
- avg[idx[x]] += imgData[x];
- count[idx[x]] ++;
- }
- }
-
-
-
-
- for (i = 0; i < ClusterNum; i++)
- {
- avg[i] /= count[i];
- if (avg[i].val[0]>0&&avg[i].val[1]>0&&avg[i].val[2]>0)
- {
- cout<<i<<": "<<avg[i].val[0]<<" "<<avg[i].val[1]<<" "<<avg[i].val[2]<<" count:"<<count[i]<<endl;
-
- }
- }
-
- Mat showImg(src.size(),CV_32FC3);
- for (int y = 0; y < row; y++)
- {
- Vec3f* imgData = showImg.ptr<Vec3f>(y);
- int* idx = label.ptr<int>(y);
- for (int x = 0; x < col; x++)
- {
- int id = idx[x];
- imgData[x].val[0] = avg[id].val[0];
- imgData[x].val[1] = avg[id].val[1];
- imgData[x].val[2] = avg[id].val[2];
- }
- }
- normalize(showImg,showImg,1,0,CV_MINMAX);
- imshow("show",showImg);
- waitKey();
- return label;
- }
-
- int main()
- {
- Mat img=imread(filename,1);
- GaussianBlur(img,img,Size(3,3),0);
- img.convertTo(img,CV_32FC3);
- Mat pixId=clustering(img);
- }
