OpenCV - How to apply Kmeans on a grayscale image? - c++

Question

I am trying to cluster a grayscale image using Kmeans.
First, I have a question:
Is Kmeans the best way to cluster a Mat or are there newer more efficient approaches?
Second, when I try this:
Mat degrees = imread("an image" , IMREAD_GRAYSCALE);
const unsigned int singleLineSize = degrees.rows * degrees.cols;
Mat data = degrees.reshape(1, singleLineSize);
data.convertTo(data, CV_32F);
std::vector<int> labels;
cv::Mat1f colors;
cv::kmeans(data, 3, labels, cv::TermCriteria(cv::TermCriteria::EPS + cv::TermCriteria::COUNT, 10, 1.), 2, cv::KMEANS_PP_CENTERS, colors);
for (unsigned int i = 0; i < singleLineSize; i++) {
data.at<float>(i) = colors(labels[i]);
}
Mat outputImage = data.reshape(1, degrees.rows);
outputImage.convertTo(outputImage, CV_8U);
imshow("outputImage", outputImage);
The result (outputImage) is empty.
When I try to multiply colors in the for loop like data.at<float>(i) = 255 * colors(labels[i]);
I get this error:
Unhandled exception : Integer division by zero.
How can I cluster a grayscale image properly?

Answer 1

It looks to me that you are wrongly parsing the labels and colors info to your output matrix.
K-means returns this info:
Labels - This is an int matrix with all the cluster labels. It is a "column" matrix of size TotalImagePixels x 1.
Centers - This what you refer to as "Colors". This is a float matrix that contains the cluster centers. The matrix is of size
NumberOfClusters x featureMean.
In this case, as you are using BGR pixels as "features" consider that Centers has 3 columns: One mean for the B channel, one mean for the G channel and finally, a mean for the R channel.
So, basically you loop through the (plain) label matrix, retrieve the label, use this value as index in the Centers matrix to retrieve the 3 colors.
One way to do this is as follows, using the auto data specifier and looping through the input image instead (that way we can index each input label easier):
//prepare an empty output matrix
cv::Mat outputImage( inputImage.size(), inputImage.type() );
//loop thru the input image rows...
for( int row = 0; row != inputImage.rows; ++row ){
//obtain a pointer to the beginning of the row
//alt: uchar* outputImageBegin = outputImage.ptr<uchar>(row);
auto outputImageBegin = outputImage.ptr<uchar>(row);
//obtain a pointer to the end of the row
auto outputImageEnd = outputImageBegin + outputImage.cols * 3;
//obtain a pointer to the label:
auto labels_ptr = labels.ptr<int>(row * inputImage.cols);
//while the end of the image hasn't been reached...
while( outputImageBegin != outputImageEnd ){
//current label index:
int const cluster_idx = *labels_ptr;
//get the center of that index:
auto centers_ptr = centers.ptr<float>(cluster_idx);
//we got an implicit VEC3B vector, we must map the BGR items to the
//output mat:
clusteredImageBegin[0] = centers_ptr[0];
clusteredImageBegin[1] = centers_ptr[1];
clusteredImageBegin[2] = centers_ptr[2];
//increase the row "iterator" of our matrices:
clusteredImageBegin += 3; ++labels_ptr;
}
}