/// <summary>
/// Niblackの手法による二値化処理を行う。
/// </summary>
/// <param name="imgSrc">入力画像</param>
/// <param name="imgDst">出力画像</param>
/// <param name="kernelSize">局所領域のサイズ</param>
/// <param name="k">係数</param>
#else
/// <summary>
/// Binarizes by Niblack's method
/// </summary>
/// <param name="src">Input image</param>
/// <param name="dst">Output image</param>
/// <param name="kernelSize">Window size</param>
/// <param name="k">Adequate coefficient</param>
#endif
public static void Niblack(IplImage src, IplImage dst, int kernelSize, double k)
{
if (src == null)
{
throw new ArgumentNullException("src");
}
if (dst == null)
{
throw new ArgumentNullException("dst");
}
// グレースケールのみ
if (src.NChannels != 1)
{
throw new ArgumentException("src must be gray scale image");
}
if (dst.NChannels != 1)
{
throw new ArgumentException("dst must be gray scale image");
}
// サイズのチェック
if (kernelSize < 3)
{
throw new ArgumentOutOfRangeException("kernelSize", "size must be 3 and above");
}
if (kernelSize % 2 == 0)
{
throw new ArgumentOutOfRangeException("kernelSize", "size must be odd number");
}
CvRect roi = src.ROI;
int width = roi.Width;
int height = roi.Height;
if (width != dst.Width || height != dst.Height)
{
throw new ArgumentException("src.Size == dst.Size");
}
unsafe
{
byte *pSrc = src.ImageDataPtr;
byte *pDst = dst.ImageDataPtr;
int stepSrc = src.WidthStep;
int stepDst = dst.WidthStep;
//for (int y = 0; y < gray.Height; y++)
MyParallel.For(0, height, delegate(int y)
{
for (int x = 0; x < width; x++)
{
double m, s;
MeanStddev(src, x + roi.X, y + roi.Y, kernelSize, out m, out s);
double threshold = m + k * s;
int offsetSrc = stepSrc * (y + roi.Y) + (x + roi.X);
int offsetDst = stepDst * y + x;
if (pSrc[offsetSrc] < threshold)
{
pDst[offsetDst] = 0;
}
else
{
pDst[offsetDst] = 255;
}
}
}
);
}
}
/// <summary>
/// Niblackの手法による二値化処理を行う。
/// </summary>
/// <param name="imgSrc">入力画像</param>
/// <param name="imgDst">出力画像</param>
/// <param name="kernelSize">局所領域のサイズ</param>
/// <param name="k">係数</param>
#else
/// <summary>
/// Binarizes by Niblack's method
/// </summary>
/// <param name="src">Input image</param>
/// <param name="dst">Output image</param>
/// <param name="kernelSize">Window size</param>
/// <param name="k">Adequate coefficient</param>
#endif
public static void Niblack(Mat src, Mat dst, int kernelSize, double k)
{
if (src == null)
{
throw new ArgumentNullException("src");
}
if (dst == null)
{
throw new ArgumentNullException("dst");
}
// グレースケールのみ
if (src.Type() != MatType.CV_8UC1)
{
throw new ArgumentException("src must be gray scale image");
}
if (dst.Type() != MatType.CV_8UC1)
{
throw new ArgumentException("dst must be gray scale image");
}
// サイズのチェック
if (kernelSize < 3)
{
throw new ArgumentOutOfRangeException("kernelSize", "size must be 3 and above");
}
if (kernelSize % 2 == 0)
{
throw new ArgumentOutOfRangeException("kernelSize", "size must be odd number");
}
int width = src.Width;
int height = src.Height;
dst.Create(src.Size(), src.Type());
using (var tSrcMat = new MatOfByte(src))
using (var tDstMat = new MatOfByte(dst))
{
var tSrc = tSrcMat.GetIndexer();
var tDst = tDstMat.GetIndexer();
//for (int y = 0; y < gray.Height; y++)
MyParallel.For(0, height, delegate(int y)
{
for (int x = 0; x < width; x++)
{
double m, s;
MeanStddev(src, x, y, kernelSize, out m, out s);
double threshold = m + k * s;
if (tSrc[y, x] < threshold)
{
tDst[y, x] = 0;
}
else
{
tDst[y, x] = 255;
}
}
}
);
}
}
