If you want to make your image simpler (i.e. with less colors), but good looking, you have a few options:
-
A simple approach would be to divide (integer division) by a factor
N
the image, and then multiply by a factorN
. -
Or you can divide your image into
K
colors, using some clustering algorithm such askmeans
showed here, or median-cut algorithm.
Original image:
Reduced colors (quantized, N = 64
):
Reduced colors (clustered, K = 8
):
Code Quantization:
#include <opencv2/opencv.hpp>
using namespace std;
using namespace cv;
int main()
{
Mat3b img = imread("path_to_image");
imshow("Original", img);
uchar N = 64;
img /= N;
img *= N;
imshow("Reduced", img);
waitKey();
return 0;
}
Code kmeans:
#include <opencv2/opencv.hpp>
using namespace std;
using namespace cv;
int main()
{
Mat3b img = imread("path_to_image");
imshow("Original", img);
// Cluster
int K = 8;
int n = img.rows * img.cols;
Mat data = img.reshape(1, n);
data.convertTo(data, CV_32F);
vector<int> labels;
Mat1f colors;
kmeans(data, K, labels, cv::TermCriteria(), 1, cv::KMEANS_PP_CENTERS, colors);
for (int i = 0; i < n; ++i)
{
data.at<float>(i, 0) = colors(labels[i], 0);
data.at<float>(i, 1) = colors(labels[i], 1);
data.at<float>(i, 2) = colors(labels[i], 2);
}
Mat reduced = data.reshape(3, img.rows);
reduced.convertTo(reduced, CV_8U);
imshow("Reduced", reduced);
waitKey();
return 0;
}