本文整理汇总了C#中Index.KnnSearch方法的典型用法代码示例。如果您正苦于以下问题：C# Index.KnnSearch方法的具体用法？C# Index.KnnSearch怎么用？C# Index.KnnSearch使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类Index的用法示例。
在下文中一共展示了Index.KnnSearch方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的C#代码示例。
示例1: FlannTest
public FlannTest()
{
Console.WriteLine("===== FlannTest =====");
// creates data set
using (Mat features = new Mat(10000, 2, MatrixType.F32C1))
{
Random rand = new Random();
for (int i = 0; i < features.Rows; i++)
{
features.Set<float>(i, 0, rand.Next(10000));
features.Set<float>(i, 1, rand.Next(10000));
}
// query
CvPoint2D32f queryPoint = new CvPoint2D32f(7777, 7777);
Mat queries = new Mat(1, 2, MatrixType.F32C1);
queries.Set<float>(0, 0, queryPoint.X);
queries.Set<float>(0, 1, queryPoint.Y);
Console.WriteLine("query:({0}, {1})", queryPoint.X, queryPoint.Y);
Console.WriteLine("-----");
// knnSearch
using (Index nnIndex = new Index(features, new KDTreeIndexParams(4)))
{
int knn = 1;
int[] indices;
float[] dists;
nnIndex.KnnSearch(queries, out indices, out dists, knn, new SearchParams(32));
for (int i = 0; i < knn; i++)
{
int index = indices[i];
float dist = dists[i];
CvPoint2D32f pt = new CvPoint2D32f(features.Get<float>(index, 0), features.Get<float>(index, 1));
Console.Write("No.{0}\t", i);
Console.Write("index:{0}", index);
Console.Write(" distance:{0}", dist);
Console.Write(" data:({0}, {1})", pt.X, pt.Y);
Console.WriteLine();
}
Console.Read();
}
}
}示例2: Run
public void Run()
{
Console.WriteLine("===== FlannTest =====");
// creates data set
using (var features = new Mat(10000, 2, MatType.CV_32FC1))
{
var rand = new Random();
for (int i = 0; i < features.Rows; i++)
{
features.Set<float>(i, 0, rand.Next(10000));
features.Set<float>(i, 1, rand.Next(10000));
}
// query
var queryPoint = new Point2f(7777, 7777);
var queries = new Mat(1, 2, MatType.CV_32FC1);
queries.Set<float>(0, 0, queryPoint.X);
queries.Set<float>(0, 1, queryPoint.Y);
Console.WriteLine("query:({0}, {1})", queryPoint.X, queryPoint.Y);
Console.WriteLine("-----");
// knnSearch
using (var nnIndex = new Index(features, new KDTreeIndexParams(4)))
{
const int Knn = 1;
int[] indices;
float[] dists;
nnIndex.KnnSearch(queries, out indices, out dists, Knn, new SearchParams(32));
for (int i = 0; i < Knn; i++)
{
int index = indices[i];
float dist = dists[i];
var pt = new Point2f(features.Get<float>(index, 0), features.Get<float>(index, 1));
Console.Write("No.{0}\t", i);
Console.Write("index:{0}", index);
Console.Write(" distance:{0}", dist);
Console.Write(" data:({0}, {1})", pt.X, pt.Y);
Console.WriteLine();
}
}
}
Console.Read();
}