/// <summary>
/// Back-projects (creates probability map) from histogram values.
/// </summary>
/// <param name="srcs">Image channels.</param>
/// <returns>Back-projection image (probability image) </returns>
public Gray <byte>[,] BackProject(Gray <byte>[][,] srcs)
{
var destImg = srcs.First().CopyBlank();
using (var uDestImg = destImg.Lock())
{
var uChannels = srcs.Select(x => x.Lock()).ToArray();
backProjectByte(this, uChannels, uDestImg);
uChannels.ForEach(x => x.Dispose());
}
return(destImg);
}
/// <summary>
/// Back-projects (creates probability map) from histogram values.
/// </summary>
/// <param name="srcs">Image channels.</param>
/// <returns>Back-projection image (probability image) </returns>
public Gray<byte>[,] BackProject(Gray<byte>[][,] srcs)
{
var destImg = srcs.First().CopyBlank();
using (var uDestImg = destImg.Lock())
{
var uChannels = srcs.Select(x => x.Lock()).ToArray();
backProjectByte(this, uChannels, uDestImg);
uChannels.ForEach(x => x.Dispose());
}
return destImg;
}
/// <summary>
/// Computes the Bag of Words model.
/// </summary>
/// <typeparam name="TPoint">
/// The <see cref="Accord.Imaging.IFeaturePoint{TFeature}"/> type to be used with this class,
/// such as <see cref="Accord.Imaging.SpeededUpRobustFeaturePoint"/>.
/// </typeparam>
/// <typeparam name="TFeature">
/// The feature type of the <typeparamref name="TPoint"/>, such
/// as <see cref="T:double[]"/>.
/// </typeparam>
/// <param name="bow">Bag of Visual Words.</param>
/// <param name="images">The set of images to initialize the model.</param>
/// <param name="threshold">Convergence rate for the k-means algorithm. Default is 1e-5.</param>
/// <returns>The list of feature points detected in all images.</returns>
public static List <TPoint>[] Compute <TPoint, TFeature>(this BagOfVisualWords <TPoint, TFeature> bow,
Gray <byte>[][,] images, double threshold = 1e-5)
where TPoint : IFeatureDescriptor <TFeature>
{
var uImages = images.Select(x => x.Lock());
var featurePoints = bow.Compute
(
uImages.Select(x => x.AsBitmap()).ToArray(),
threshold
);
uImages.ForEach(x => x.Dispose());
return(featurePoints);
}
/// <summary>
/// Calculates histogram.
/// </summary>
/// <param name="channels">Image channels.</param>
/// <param name="accumulate">Accumulate or erase histogram before.</param>
/// <param name="mask">Mask for image color locations.</param>
/// <param name="maskOffset">The location offset for the mask. The mask area will be [offsetX, offsetY, channelWidth, channelHeight].</param>
public void Calculate(Gray<byte>[][,] channels, bool accumulate, Gray<byte>[,] mask, Point maskOffset)
{
if (!accumulate)
Array.Clear(histogram, 0, this.NumberOfElements);
if (mask == null)
{
mask = new Gray<byte>[channels[0].Width(), channels[0].Height()];
mask.SetValue<Gray<byte>>(Byte.MaxValue);
}
var maskArea = new Rectangle(maskOffset, channels.First().Size());
using (var uMask = mask.Lock(maskArea))
{
var uChannels = channels.Select(x => x.Lock()).ToArray();
calculateHistByte(this, uChannels, uMask);
uChannels.ForEach(x => x.Dispose());
}
}
/// <summary>
/// Calculates histogram.
/// </summary>
/// <param name="channels">Image channels.</param>
/// <param name="accumulate">Accumulate or erase histogram before.</param>
/// <param name="mask">Mask for image color locations.</param>
/// <param name="maskOffset">The location offset for the mask. The mask area will be [offsetX, offsetY, channelWidth, channelHeight].</param>
public void Calculate(Gray <byte>[][,] channels, bool accumulate, Gray <byte>[,] mask, Point maskOffset)
{
if (!accumulate)
{
Array.Clear(histogram, 0, this.NumberOfElements);
}
if (mask == null)
{
mask = new Gray <byte> [channels[0].Width(), channels[0].Height()];
mask.SetValue <Gray <byte> >(Byte.MaxValue);
}
var maskArea = new Rectangle(maskOffset, channels.First().Size());
using (var uMask = mask.Lock(maskArea))
{
var uChannels = channels.Select(x => x.Lock()).ToArray();
calculateHistByte(this, uChannels, uMask);
uChannels.ForEach(x => x.Dispose());
}
}
