/// <summary>
/// Converts this image to a grayscale image.
/// </summary>
/// <returns>A grayscale image</returns>
public unsafe GrayscaleImage ToGrayscale()
{
int rows = Rows;
int columns = Columns;
GrayscaleImage grayscale = new GrayscaleImage(rows, columns);
fixed(int *src = _handler.RawArray)
{
fixed(float *dst = grayscale.RawArray)
{
int * srcPtr = src;
float *dstPtr = dst;
int count = rows * columns;
while (count-- != 0)
{
*dstPtr = *srcPtr;
dstPtr++;
srcPtr++;
}
}
}
grayscale.Normalize();
return(grayscale);
}
/// <summary>
/// Saves the image to a file, using the provided minimum and maximum values for scaling.
/// </summary>
/// <param name="image">The image to save</param>
/// <param name="filename">The destination filename</param>
/// <param name="min">The value to map to 0</param>
/// <param name="max">The value to map to 255</param>
public static void Save(GrayscaleImage image, string filename, float min, float max)
{
BitmapEncoder encoder = IO.GetEncoder(filename);
encoder.Frames.Add(BitmapFrame.Create(image.ToBitmap(min, max)));
encoder.Save(filename);
}
/// <summary>
/// Returns a GrayscaleImage representing the edge magnitude at each pixel.
/// </summary>
/// <returns>A magnitude map</returns>
public unsafe GrayscaleImage ToMagnitudeMap()
{
GrayscaleImage mono = new GrayscaleImage(Rows, Columns);
fixed(float *src = RawArray, dst = mono.RawArray)
{
float *srcPtr = src;
float min = float.MaxValue;
float max = float.MinValue;
int length = Rows * Columns;
for (int i = 0; i < length; i++, srcPtr += 4)
{
min = Math.Min(min, *srcPtr);
max = Math.Max(max, *srcPtr);
}
float scale = max - min;
srcPtr = src;
float *dstPtr = dst;
while (length-- > 0)
{
float val = *srcPtr;
val -= min;
val /= scale;
*dstPtr++ = val;
srcPtr += 4;
}
}
return(mono);
}
/// <summary>
/// Writes <paramref name="image"/> to <paramref name="filename"/> using the PGM file format.
/// </summary>
/// <param name="image">Image to write</param>
/// <param name="filename">Path to write to</param>
public static unsafe void Write(GrayscaleImage image, string filename)
{
FileStream output = File.OpenWrite(filename);
int rows = image.Height;
int columns = image.Width;
string header = string.Format("P5\n{0} {1}\n255\n", columns, rows);
byte[] buf = Encoding.ASCII.GetBytes(header);
output.Write(buf, 0, buf.Length);
fixed(float *src = image.RawArray)
{
float *ptr = src;
for (int r = 0; r < rows; r++)
{
byte[] row = new byte[columns];
for (int c = 0; c < columns; c++)
{
row[c] = (byte)(*ptr++ *255);
}
output.Write(row, 0, columns);
}
}
output.Close();
}
/// <summary>
/// Reads a PGM file from <paramref name="filename"/>.
/// </summary>
/// <param name="filename">The location of the PGM file</param>
/// <returns>An object encapsulating the image data</returns>
public static unsafe GrayscaleImage Read(string filename)
{
FileStream stream = File.OpenRead(filename);
StreamReader input = new StreamReader(stream);
string magicNumber = input.ReadLine();
if (magicNumber != "P5")
{
return(null);
}
string[] dims = input.ReadLine().Split();
string depth = input.ReadLine();
if (depth != "255")
{
return(null);
}
int columns = int.Parse(dims[0]);
int rows = int.Parse(dims[1]);
GrayscaleImage gray = new GrayscaleImage(rows, columns);
fixed(float *src = gray.RawArray)
{
float *ptr = src;
byte[] scanline = new byte[columns];
float norm = 1f / 255;
for (int r = 0; r < rows; r++)
{
int count = 0;
while (count < columns)
{
count += stream.Read(scanline, count, columns - count);
}
for (int c = 0; c < columns; c++)
{
float val = scanline[c] * norm;
* ptr++ = val;
}
}
}
return(gray);
}
/// <summary>
/// Converts the distance transform image to a grayscale representation.
/// </summary>
/// <returns>A grayscale representation of the distance transform</returns>
public unsafe GrayscaleImage ToGrayscale()
{
GrayscaleImage gray = new GrayscaleImage(Rows, Columns);
fixed(float *dst = gray.RawArray)
{
fixed(int *src = RawArray)
{
int * srcPtr = src;
float *dstPtr = dst;
for (int length = Rows * Columns; length > 0; length--, dstPtr++, srcPtr += Channels)
{
*dstPtr = *srcPtr;
}
}
}
return(gray);
}
/// <summary>
/// Returns a grayscale version of this image, with 1 for "true" and 0 for "false".
/// </summary>
/// <returns>A grayscale version of the image</returns>
public unsafe GrayscaleImage ToGrayscale()
{
GrayscaleImage mono = new GrayscaleImage(Rows, Columns);
fixed(float *dst = mono.RawArray)
{
fixed(bool *src = RawArray)
{
float *dstPtr = dst;
bool * srcPtr = src;
int length = Rows * Columns;
while (length-- > 0)
{
bool edge = *srcPtr++;
* dstPtr++ = edge ? 1 : 0;
}
}
}
return(mono);
}
/// <summary>
/// Extracts a channel as a Grayscale image. Uses the <see cref="M:IArrayHandler.ExtractChannel"/> method.
/// </summary>
/// <param name="handler">The image upon which to operate</param>
/// <param name="channel">The channel to extract</param>
/// <returns>An image representation of the channel</returns>
public static unsafe GrayscaleImage ExtractChannelAsImage(this IArrayHandler <float> handler, int channel)
{
float[,] buffer = handler.ExtractChannel(channel);
int rows = handler.Rows;
int columns = handler.Columns;
float[, ,] data = new float[rows, columns, 1];
fixed(float *channelSrc = buffer, dataSrc = data)
{
float *channelPtr = channelSrc;
float *dataPtr = dataSrc;
int count = rows * columns;
while (count-- > 0)
{
*dataPtr++ = *channelPtr++;
}
}
GrayscaleImage gray = new GrayscaleImage();
gray.SetData(data);
return(gray);
}
/// <summary>
/// Computes a gradient image from the source image.
/// </summary>
/// <param name="sigma">The sigma to use when blurring the source image.</param>
/// <param name="image">Source image</param>
/// <returns>Gradient image</returns>
public static unsafe GradientImage Compute(GrayscaleImage image, float sigma)
{
int rows = image.Rows;
int columns = image.Columns;
if (sigma > 0)
{
image = Convolution.ConvolveGaussian <GrayscaleImage>(image, sigma);
}
float[, ,] data = new float[rows, columns, 4];
fixed(float *src = image.RawArray, dst = data)
{
float *srcPtr = src;
float *srcPtrP = srcPtr + 1;
float *dstPtr = dst;
dstPtr += 2;
// X derivative
for (int r = 0; r < rows; r++)
{
*dstPtr = *srcPtrP - *srcPtr;
dstPtr += 4;
srcPtrP++;
for (int c = 1; c < columns - 1; c++, srcPtr++, srcPtrP++, dstPtr += 4)
{
*dstPtr = *srcPtrP - *srcPtr;
}
srcPtrP--;
*dstPtr = *srcPtrP - *srcPtr;
dstPtr += 4;
srcPtr += 2;
srcPtrP += 2;
}
srcPtr = src;
srcPtrP = srcPtr + columns;
dstPtr = dst;
dstPtr += 3;
int stride = 4 * columns;
for (int c = 0; c < columns; c++, srcPtr++, srcPtrP++, dstPtr += 4)
{
float *srcScan = srcPtr;
float *srcScanP = srcPtrP;
float *dstScan = dstPtr;
* dstScan = *srcScanP - *srcScan;
dstScan += stride;
srcScanP += columns;
for (int r = 1; r < rows - 1; r++, dstScan += stride, srcScan += columns, srcScanP += columns)
{
*dstScan = *srcScanP - *srcScan;
}
srcScanP -= columns;
*dstScan = *srcScanP - *srcScan;
}
dstPtr = dst;
int length = rows * columns;
for (int i = 0; i < length; i++, dstPtr += 4)
{
setData(dstPtr);
}
}
GradientImage result = new GradientImage();
result.SetData(data);
return(result);
}
/// <summary>
/// Computes a gradient image.
/// </summary>
/// <param name="image">Source image</param>
/// <param name="blurImage">Whether to blur the source image before computing the gradient</param>
/// <returns>Gradient image</returns>
public static GradientImage Compute(GrayscaleImage image, bool blurImage)
{
return(Compute(image, blurImage ? SIGMA : 0f));
}
/// <summary>
/// Computes a gradient image from the source image using the default sigma value.
/// </summary>
/// <param name="image">Source image</param>
/// <returns>Gradient image</returns>
public static GradientImage Compute(GrayscaleImage image)
{
return(Compute(image, SIGMA));
}