| private cvCalcBackProject ( IntPtr image, IntPtr backProject, IntPtr hist ) : void | ||
| image | IntPtr | |
| backProject | IntPtr | |
| hist | IntPtr | |
| Результат | void | |
///<summary>
/// Backproject the histogram into a matrix
///</summary>
///<param name="srcs">Source matrices, all are of the same size and type</param>
///<returns>Destination back projection matrix of the sametype as the source matrices</returns>
///<typeparam name="TDepth">The type of depth of the matrix</typeparam>
public Matrix <TDepth> BackProject <TDepth>(Matrix <TDepth>[] srcs) where TDepth : new()
{
Debug.Assert(srcs.Length == Dimension, Properties.StringTable.IncompatibleDimension);
IntPtr[] imgPtrs =
Array.ConvertAll <Matrix <TDepth>, IntPtr>(
srcs,
delegate(Matrix <TDepth> img) { return(img.Ptr); });
Matrix <TDepth> res = new Matrix <TDepth>(srcs[0].Size);
CvInvoke.cvCalcBackProject(imgPtrs, res.Ptr, _ptr);
return(res);
}
///<summary>
/// Backproject the histogram into a matrix
///</summary>
///<param name="srcs">Source matrices, all are of the same size and type</param>
///<returns>Destination back projection matrix of the sametype as the source matrices</returns>
///<typeparam name="TDepth">The type of depth of the matrix</typeparam>
public Matrix <TDepth> BackProject <TDepth>(Matrix <TDepth>[] srcs) where TDepth : new()
{
Debug.Assert(srcs.Length == Dimension, Properties.StringTable.IncompatibleDimension);
IntPtr[] imgPtrs = new IntPtr[srcs.Length];
for (int i = 0; i < srcs.Length; i++)
{
imgPtrs[i] = srcs[i].Ptr;
}
Matrix <TDepth> res = new Matrix <TDepth>(srcs[0].Size);
CvInvoke.cvCalcBackProject(imgPtrs, res.Ptr, _ptr);
return(res);
}
///<summary>
/// Backproject the histogram into a matrix
///</summary>
///<param name="srcs">Source matrices, all are of the same size and type</param>
///<returns>Destination back projection matrix of the sametype as the source matrices</returns>
///<typeparam name="TDepth">The type of depth of the matrix</typeparam>
public Matrix <TDepth> BackProject <TDepth>(Matrix <TDepth>[] srcs) where TDepth : new()
{
#if !NETFX_CORE
Debug.Assert(srcs.Length == Dimension, "IncompatibleDimension");
#endif
IntPtr[] imgPtrs = new IntPtr[srcs.Length];
for (int i = 0; i < srcs.Length; i++)
{
imgPtrs[i] = srcs[i].Ptr;
}
Matrix <TDepth> res = new Matrix <TDepth>(srcs[0].Size);
CvInvoke.cvCalcBackProject(imgPtrs, res.Ptr, _ptr);
return(res);
}
///<summary>
/// Backproject the histogram into a gray scale image
///</summary>
///<param name="srcs">Source images, all are of the same size and type</param>
///<returns>Destination back projection image of the same type as the source images</returns>
///<typeparam name="TDepth">The type of depth of the image</typeparam>
public Image <Gray, TDepth> BackProject <TDepth>(Image <Gray, TDepth>[] srcs) where TDepth : new()
{
#if !NETFX_CORE
Debug.Assert(srcs.Length == Dimension, Properties.StringTable.IncompatibleDimension);
#endif
IntPtr[] imgPtrs =
#if NETFX_CORE
Extensions.
#else
Array.
#endif
ConvertAll <Image <Gray, TDepth>, IntPtr>(
srcs,
delegate(Image <Gray, TDepth> img) {
return(img.Ptr);
});
Image <Gray, TDepth> res = srcs[0].CopyBlank();
CvInvoke.cvCalcBackProject(imgPtrs, res.Ptr, _ptr);
return(res);
}
/// <summary>
/// Eliminate the matched features whose scale and rotation do not aggree with the majority's scale and rotation.
/// </summary>
/// <param name="rotationBins">The numbers of bins for rotation, a good value might be 20 (which means each bin covers 18 degree)</param>
/// <param name="scaleIncrement">This determins the different in scale for neighbour hood bins, a good value might be 1.5 (which means matched features in bin i+1 is scaled 1.5 times larger than matched features in bin i</param>
/// <param name="matchedFeatures">The matched feature that will be participated in the voting. For each matchedFeatures, only the zero indexed ModelFeature will be considered.</param>
public static MatchedSURFFeature[] VoteForSizeAndOrientation(MatchedSURFFeature[] matchedFeatures, double scaleIncrement, int rotationBins)
{
int elementsCount = matchedFeatures.Length;
float[] scales = new float[elementsCount];
float[] rotations = new float[elementsCount];
float[] flags = new float[elementsCount];
float minScale = float.MaxValue;
float maxScale = float.MinValue;
for (int i = 0; i < matchedFeatures.Length; i++)
{
float scale = (float)matchedFeatures[i].ObservedFeature.Point.size / (float)matchedFeatures[i].SimilarFeatures[0].Feature.Point.size;
scale = (float)Math.Log10(scale);
scales[i] = scale;
if (scale < minScale)
{
minScale = scale;
}
if (scale > maxScale)
{
maxScale = scale;
}
float rotation = matchedFeatures[i].ObservedFeature.Point.dir - matchedFeatures[i].SimilarFeatures[0].Feature.Point.dir;
rotations[i] = rotation < 0.0 ? rotation + 360 : rotation;
}
int scaleBinSize = (int)Math.Max(((maxScale - minScale) / Math.Log10(scaleIncrement)), 1);
int count;
using (DenseHistogram h = new DenseHistogram(new int[] { scaleBinSize, rotationBins }, new RangeF[] { new RangeF(minScale, maxScale), new RangeF(0, 360) }))
{
GCHandle scaleHandle = GCHandle.Alloc(scales, GCHandleType.Pinned);
GCHandle rotationHandle = GCHandle.Alloc(rotations, GCHandleType.Pinned);
GCHandle flagsHandle = GCHandle.Alloc(flags, GCHandleType.Pinned);
using (Matrix <float> flagsMat = new Matrix <float>(1, elementsCount, flagsHandle.AddrOfPinnedObject()))
using (Matrix <float> scalesMat = new Matrix <float>(1, elementsCount, scaleHandle.AddrOfPinnedObject()))
using (Matrix <float> rotationsMat = new Matrix <float>(1, elementsCount, rotationHandle.AddrOfPinnedObject()))
{
h.Calculate(new Matrix <float>[] { scalesMat, rotationsMat }, true, null);
float minVal, maxVal;
int[] minLoc, maxLoc;
h.MinMax(out minVal, out maxVal, out minLoc, out maxLoc);
h.Threshold(maxVal * 0.5);
CvInvoke.cvCalcBackProject(new IntPtr[] { scalesMat.Ptr, rotationsMat.Ptr }, flagsMat.Ptr, h.Ptr);
count = CvInvoke.cvCountNonZero(flagsMat);
}
scaleHandle.Free();
rotationHandle.Free();
flagsHandle.Free();
MatchedSURFFeature[] matchedGoodFeatures = new MatchedSURFFeature[count];
int index = 0;
for (int i = 0; i < matchedFeatures.Length; i++)
{
if (flags[i] != 0)
{
matchedGoodFeatures[index++] = matchedFeatures[i];
}
}
return(matchedGoodFeatures);
}
}
