程序运行逻辑
在进行聚类前，首先使用
Blur(src, src, new Size(15, 15));
对图形进行了均值滤波
[TencentCloudSDKException] code:FailedOperation.ServiceIsolate message:service is stopped due to arrears, please recharge your account in Tencent Cloud requestId:54432bf3-6c74-4dde-9a83-0395b7f8ccc5
[En]
[TencentCloudSDKException] code:FailedOperation.ServiceIsolate message:service is stopped due to arrears, please recharge your account in Tencent Cloud requestId:d865d400-ba92-4906-ba65-780451729a21
1、var columnVector = src.Reshape(cn: 3, rows: src.Rows * src.Cols);
将图形转变为3通道的单列矩阵
2、columnVector.ConvertTo(samples, MatType.CV_32FC3);
将矩阵转变成浮点矩阵
3、调用kmean函数，并查看不同参数k：2，4，5，8的效果
Cv2.Kmeans(
data: samples,
k: clustersCount,
bestLabels: bestLabels,
criteria:
new TermCriteria(type: CriteriaType.Eps | CriteriaType.MaxIter, maxCount: 10, epsilon: 1.0),
attempts: 3,
flags: KMeansFlags.PpCenters,
centers: centers);
4、读取生成矩阵bestLabel，并重写成和原图同类型矩阵
var clusteredImage = new Mat(src.Rows, src.Cols, src.Type());
for (var size = 0; size < src.Cols * src.Rows; size++)
{
var clusterIndex = bestLabels.At<int>(0, size);
var newPixel = new Vec3b
{
Item0 = (byte)(centers.At<float>(clusterIndex, 0)), // B
Item1 = (byte)(centers.At<float>(clusterIndex, 1)), // G
Item2 = (byte)(centers.At<float>(clusterIndex, 2)) // R
};
clusteredImage.Set(size / src.Cols, size % src.Cols, newPixel);
}</float></float></float></int>
5、图形显示。
Original: https://blog.csdn.net/zzlwl/article/details/123138649
Author: 健忘的松鼠
Title: OpenCV#11 Kmeans聚类的使用
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