image segmentation based on colors using k means, opencv [closed] - k-means

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I want to
1) Read a 3 channel image
2) Using K-Means method, create k different classes
3) I will tag pixels accordingly to show which class they belong to and store them in a matrix.
4) And after that I'm thinking using Connected Component Labeling to determine if they are in the same segment or not.
I'm new in opencv so I wanted to ask you for the implementation , some code snippet to to get me started.
Any help will be appreciated. Thank you

Answer 1

typedef struct {
int red;
int green;
int blue;
int b64;
int groupNo;
} IMAGE;
typedef struct _MEANS
{
int r, g, b;
int groupNo;
} point_t, *point;
Mat img = imread("C:\\253027.jpg",CV_LOAD_IMAGE_COLOR);
int clusterCount=5;
IMAGE **strct= {0};
strct = allocate_matrix(img.rows, img.cols);
int i,j,k;
if(! img.data )
{
cout << "Could not open or find the image" << std::endl ;
return -1;
}
for ( i = 0; i < img.rows; i++) {
for ( j = 0; j < img.cols; j++) {
strct[i][j].red = img.at<Vec3b>(i,j)[2];
strct[i][j].green = img.at<Vec3b>(i,j)[1];
strct[i][j].blue = img.at<Vec3b>(i,j)[0];
/* printf("%u",strct[i][j].red);
printf("%u",strct[i][j].green);
printf("%u",strct[i][j].blue);
*/
}
}
double *tempuz= (double *)malloc(sizeof(double) * clusterCount);
point_t* p = (point_t *)malloc(sizeof(point_t) * clusterCount);
for( k = 0; k < clusterCount; k++ )
{
p[k].r= rand()%255;
p[k].g=rand()%255;
p[k].b=rand()%255;
p[k].groupNo=k;
}
int max=0;
for (i = 0; i < img.rows; i++) {
for ( j = 0; j < img.cols; j++) {
for(k = 0; k < clusterCount; k++ ){
tempuz[k]= abs(strct[i][j].red - p[k].r)+abs(strct[i][j].green -p[k].g)+abs(strct[i][j].blue - p[k].b);
// cout<<"tempuz"<<tempuz[k]<<endl;
} strct[i][j].groupNo=min_element(tempuz,clusterCount);
// cout<<"grup no"<<strct[i][j].groupNo<<endl;
}
}
i=0;
int r=0,b=0,g=0,counter=0;
while(i<200){
r=0,b=0,g=0,counter=0;
for(k=0;k<clusterCount;k++){
for (i = 0; i < img.rows; i++) {
for ( j = 0; j < img.cols; j++) {
if(strct[i][j].groupNo==k){
r+=strct[i][j].red;
b+=strct[i][j].blue;
g+=strct[i][j].green;
counter++;
}
}
}
if(counter!=0){
p[k].r=r/counter;
p[k].b=b/counter;
p[k].g=g/=counter;
}
}
i++;
max=0;
for (i = 0; i < img.rows; i++) {
for ( j = 0; j < img.cols; j++) {
for(k = 0; k < clusterCount; k++ ){
tempuz[k]= abs(strct[i][j].red - p[k].r)+abs(strct[i][j].green -p[k].g)+abs(strct[i][j].blue - p[k].b);
} strct[i][j].groupNo=min_element(tempuz,clusterCount);
}
}
}//end of while
for(int k=0;k<clusterCount;k++){
for (int i = 0; i < img.rows; i++) {
for (int j = 0; j < img.cols; j++) {
if(strct[i][j].groupNo==k){
strct[i][j].red=p[k].r;
strct[i][j].blue=p[k].b;
strct[i][j].green=p[k].r;
}
}
}
}
for (int i = 0; i < img.rows; i++) {
for (int j = 0; j < img.cols; j++) {
img.at<Vec3b>(i,j)[2] = strct[i][j].red;
img.at<Vec3b>(i,j)[1] = strct[i][j].green;
img.at<Vec3b>(i,j)[0] = strct[i][j].blue;
}
}