The process for using K-means clustering algorithm to convert a grayscale image into a binary image in C++ would involve the following steps:
Here is some example code in C++ that demonstrates this process using the OpenCV library:
#include <opencv2/opencv.hpp>
using namespace cv;
int main(int argc, char** argv)
{
// Load grayscale image
Mat image = imread("grayscale_image.jpg", IMREAD_GRAYSCALE);
// Set number of clusters
int K = 2;
// Initialize K-means algorithm
Mat labels, centers;
kmeans(image.reshape(1, image.rows * image.cols), K, labels, TermCriteria(TermCriteria::EPS + TermCriteria::COUNT, 10, 1.0), 3, KMEANS_PP_CENTERS, centers);
// Create binary image from centroid assignments
Mat binary_image = Mat::zeros(image.size(), CV_8UC1);
for (int i = 0; i < image.rows; i++) {
for (int j = 0; j < image.cols; j++) {
int label = labels.at<int>(i * image.cols + j);
if (label == 1) {
binary_image.at<uchar>(i, j) = 255;
}
}
}
// Display binary image
imshow("Binary Image", binary_image);
waitKey(0);
return 0;
}
Note that this code assumes the input grayscale image is a JPEG file named "grayscale_image.jpg" in the same directory as the executable. Also, the number of clusters (K) is set to 2 for simplicity, but this value can be adjusted depending on the specific image and application.
Asked: 2023-01-14 11:00:00 +0000
Seen: 9 times
Last updated: Apr 01 '23