是否有公式可以确定给定 BGR 值的整体颜色?(OpenCV 和 C++)

• A simple approach would be to divide (integer division) by a factor N the image, and then multiply by a factor N.

或者您可以使用一些聚类算法(例如此处显示的 kmeans 或中值切割算法)将图像划分为 K 种颜色.

Or you can divide your image into K colors, using some clustering algorithm such as kmeans showed here, or median-cut algorithm.

原图:

减少颜色(量化，N = 64):

减少颜色(聚集，K = 8):

代码量化:

#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;
}

代码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;
}

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