public Doctor(Block block)
{
Block = block;
int size = block.Size;
spare = new byte[size];
Buffer.BlockCopy(block.data, 0, spare, 0, size);
xor = new byte[8];
RNG.Fill(xor);
//block.BeforeWrite += Block_BeforeWrite;
block.AfterWrite += Block_AfterWrite;
//block.BeforeRead += Block_BeforeRead;
//block.AfterRead += Block_AfterRead;
}
/// <summary>
/// https://github.com/Itseez/opencv_extra/blob/master/learning_opencv_v2/ch13_ex13_1.cpp
/// </summary>
private static void example01()
{
using (var window = new Window("Clusters", flags: WindowMode.AutoSize | WindowMode.FreeRatio))
{
const int maxClusters = 5;
var rng = new RNG(state: (ulong)DateTime.Now.Ticks);
for (; ;)
{
var clustersCount = rng.Uniform(a: 2, b: maxClusters + 1);
var samplesCount = rng.Uniform(a: 1, b: 1001);
var points = new Mat(rows: samplesCount, cols: 1, type: MatType.CV_32FC2);
clustersCount = Math.Min(clustersCount, samplesCount);
var img = new Mat(rows: 500, cols: 500, type: MatType.CV_8UC3, s: Scalar.All(0));
// generate random sample from multi-gaussian distribution
for (var k = 0; k < clustersCount; k++)
{
var pointChunk = points.RowRange(
startRow: k * samplesCount / clustersCount,
endRow: (k == clustersCount - 1)
? samplesCount
: (k + 1) * samplesCount / clustersCount);
var center = new Point
{
X = rng.Uniform(a: 0, b: img.Cols),
Y = rng.Uniform(a: 0, b: img.Rows)
};
rng.Fill(
mat: pointChunk,
distType: DistributionType.Normal,
a: new Scalar(center.X, center.Y),
b: new Scalar(img.Cols * 0.05f, img.Rows * 0.05f));
}
Cv2.RandShuffle(dst: points, iterFactor: 1, rng: rng);
var labels = new Mat();
var centers = new Mat(rows: clustersCount, cols: 1, type: points.Type());
Cv2.Kmeans(
data: points,
k: clustersCount,
bestLabels: labels,
criteria: new TermCriteria(CriteriaType.Epsilon | CriteriaType.Iteration, 10, 1.0),
attempts: 3,
flags: KMeansFlag.PpCenters,
centers: centers);
Scalar[] colors =
{
new Scalar(0, 0, 255),
new Scalar(0, 255, 0),
new Scalar(255, 100, 100),
new Scalar(255, 0, 255),
new Scalar(0, 255, 255)
};
for (var i = 0; i < samplesCount; i++)
{
var clusterIdx = labels.At <int>(i);
Point ipt = points.At <Point2f>(i);
Cv2.Circle(
img: img,
center: ipt,
radius: 2,
color: colors[clusterIdx],
lineType: LineType.AntiAlias,
thickness: Cv.FILLED);
}
window.Image = img;
var key = (char)Cv2.WaitKey();
if (key == 27 || key == 'q' || key == 'Q') // 'ESC'
{
break;
}
}
}
}
/// <summary>
/// https://github.com/Itseez/opencv_extra/blob/master/learning_opencv_v2/ch13_ex13_1.cpp
/// </summary>
private static void example01()
{
using (var window = new Window("Clusters", flags: WindowMode.AutoSize | WindowMode.FreeRatio))
{
const int maxClusters = 5;
var rng = new RNG(state: (ulong)DateTime.Now.Ticks);
for (; ; )
{
var clustersCount = rng.Uniform(a: 2, b: maxClusters + 1);
var samplesCount = rng.Uniform(a: 1, b: 1001);
var points = new Mat(rows: samplesCount, cols: 1, type: MatType.CV_32FC2);
clustersCount = Math.Min(clustersCount, samplesCount);
var img = new Mat(rows: 500, cols: 500, type: MatType.CV_8UC3, s: Scalar.All(0));
// generate random sample from multi-gaussian distribution
for (var k = 0; k < clustersCount; k++)
{
var pointChunk = points.RowRange(
startRow: k * samplesCount / clustersCount,
endRow: (k == clustersCount - 1)
? samplesCount
: (k + 1) * samplesCount / clustersCount);
var center = new Point
{
X = rng.Uniform(a: 0, b: img.Cols),
Y = rng.Uniform(a: 0, b: img.Rows)
};
rng.Fill(
mat: pointChunk,
distType: DistributionType.Normal,
a: new Scalar(center.X, center.Y),
b: new Scalar(img.Cols * 0.05f, img.Rows * 0.05f));
}
Cv2.RandShuffle(dst: points, iterFactor: 1, rng: rng);
var labels = new Mat();
var centers = new Mat(rows: clustersCount, cols: 1, type: points.Type());
Cv2.Kmeans(
data: points,
k: clustersCount,
bestLabels: labels,
criteria: new TermCriteria(CriteriaType.Epsilon | CriteriaType.Iteration, 10, 1.0),
attempts: 3,
flags: KMeansFlag.PpCenters,
centers: centers);
Scalar[] colors =
{
new Scalar(0, 0, 255),
new Scalar(0, 255, 0),
new Scalar(255, 100, 100),
new Scalar(255, 0, 255),
new Scalar(0, 255, 255)
};
for (var i = 0; i < samplesCount; i++)
{
var clusterIdx = labels.At<int>(i);
Point ipt = points.At<Point2f>(i);
Cv2.Circle(
img: img,
center: ipt,
radius: 2,
color: colors[clusterIdx],
lineType: LineType.AntiAlias,
thickness: Cv.FILLED);
}
window.Image = img;
var key = (char)Cv2.WaitKey();
if (key == 27 || key == 'q' || key == 'Q') // 'ESC'
{
break;
}
}
}
}
