/// <summary>
/// Creates template from the input image by using provided parameters.
/// </summary>
/// <param name="sourceImage">Input image.</param>
/// <param name="minFeatureStrength">Minimum gradient value for the feature.</param>
/// <param name="maxNumberOfFeatures">Maximum number of features per template. The features will be extracted so that their locations are semi-uniformly spread.</param>
/// <param name="classLabel">Template class label.</param>
public virtual void Initialize(Bgr <byte>[,] sourceImage, int minFeatureStrength, int maxNumberOfFeatures, string classLabel)
{
Gray <int>[,] sqrMagImg;
Gray <int>[,] orientationImg = GradientComputation.Compute(sourceImage, out sqrMagImg, minFeatureStrength);
Func <Feature, int> featureImportanceFunc = (feature) => sqrMagImg[feature.Y, feature.X].Intensity;
Initialize(orientationImg, maxNumberOfFeatures, classLabel, featureImportanceFunc);
}
/// <summary>
/// Creates template from the input image by using provided parameters.
/// </summary>
/// <param name="sourceImage">Input image.</param>
/// <param name="minFeatureStrength">Minimum gradient value for the feature.</param>
/// <param name="maxNumberOfFeatures">Maximum number of features per template. The features will be extracted so that their locations are semi-uniformly spread.</param>
/// <param name="classLabel">Template class label.</param>
public virtual void Initialize(Image <Bgr, byte> sourceImage, int minFeatureStrength, int maxNumberOfFeatures, string classLabel)
{
Image <Gray, int> sqrMagImg;
Image <Gray, int> orientationImg = GradientComputation.Compute(sourceImage, out sqrMagImg, minFeatureStrength);
Func <Feature, int> featureImportanceFunc = (feature) => *(byte *)sqrMagImg.GetData(feature.Y, feature.X);
Initialize(orientationImg, maxNumberOfFeatures, classLabel, featureImportanceFunc);
}
/// <summary>
/// Creates linearized maps pyramid.
/// </summary>
/// <param name="sourceImage">Source image.</param>
/// <param name="minGradientMagnitude">Minimal gradient value threshold.</param>
/// <param name="neigborhoodPerLevel">Neighborhood per level. If null default neighborhood is going to be used.</param>
/// <returns>Pyramid of linearized maps.</returns>
public static LinearizedMapPyramid CreatePyramid(Bgr <byte>[,] sourceImage, int minGradientMagnitude = 35, params int[] neigborhoodPerLevel)
{
return(CreatePyramid(sourceImage,
source =>
{
Gray <int>[,] sqrMagImg;
return GradientComputation.Compute(sourceImage, out sqrMagImg, minGradientMagnitude);
},
neigborhoodPerLevel));
}
/// <summary>
/// Creates template from the input image by using provided parameters.
/// </summary>
/// <param name="sourceImage">Input image.</param>
/// <param name="minFeatureStrength">Minimum gradient value for the feature.</param>
/// <param name="maxNumberOfFeatures">Maximum number of features per template. The features will be extracted so that their locations are semi-uniformly spread.</param>
/// <param name="classLabel">Template class label.</param>
/// <param name="angle"></param>
public virtual void Initialize(Bgr <byte>[,] sourceImage, int minFeatureStrength, int maxNumberOfFeatures, string classLabel, float angle)
{
Gray <int>[,] sqrMagImg;
// so at this point oriImage is a image full of degrees? not gradient?
Gray <int>[,] orientationImg = GradientComputation.Compute(sourceImage, out sqrMagImg, minFeatureStrength);
Func <Feature, int> featureImportanceFunc = (feature) => sqrMagImg[feature.Y, feature.X].Intensity;
Initialize(orientationImg, maxNumberOfFeatures, classLabel, angle, featureImportanceFunc);
}
