/// <summary>
/// Searches the image for the good features to track.
/// <para>For each location a Hessian matrix is made and min eig-value is compared against threshold.</para>
/// </summary>
/// <param name="image">Image.</param>
/// <param name="winSize">Window size.</param>
/// <param name="minEigVal">Minimum eigen value.</param>
/// <param name="minimalDistance">Minimum distance from two features.</param>
/// <returns>List of locations that have eigen value larger than <paramref name="minEigVal"/>.</returns>
public static List <Point> GoodFeaturesToTrack(this Gray <float>[,] image, int winSize = 10, float minEigVal = 0.3f, float minimalDistance = 3)
{
var strengthImg = image.CopyBlank();
var Dx = image.Sobel(1, 0, 3);
var Dy = image.Sobel(0, 1, 3);
var Dxx = Dx.MulFloat(Dx).MakeIntegral();
var Dxy = Dx.MulFloat(Dy).MakeIntegral();
var Dyy = Dy.MulFloat(Dy).MakeIntegral();
goodFeaturesToTrack(Dxx, Dxy, Dyy,
winSize, minEigVal, strengthImg);
var filteredStrengthImg = strengthImg.SupressNonMaxima();
//var filteredStrengthImg = strengthImg;
IList <float> values;
var locations = filteredStrengthImg.FindNonZero(out values);
var sortedFeatures = locations.Zip(values, (f, s) => new { f, s })
.OrderByDescending(x => x.s)
.Select(x => x.f)
.ToList();
sortedFeatures = sortedFeatures.EnforceMinimalDistance(minimalDistance);
return(sortedFeatures);
}
/// <summary>
/// Does non-maxima supression for the following gray image. Can be useful for detections filtering (e.g. post-processing output from Harris detector).
/// </summary>
/// <param name="img">Image.</param>
/// <param name="radius">Non-maxima supression radius (kernel radius).</param>
/// <param name="discardValue">The value will be discarded (0 - for black).</param>
/// <returns>Processed image.</returns>
public static Gray <float>[,] SupressNonMaxima(this Gray <float>[,] img, int radius = 3, int discardValue = 0)
{
var dest = img.CopyBlank();
SupressNonMaxima(img, dest, radius);
return(dest);
}
/// <summary>
/// Computes gradient orientations from the color image. Orientation from the channel which has the maximum gradient magnitude is taken as the orientation for a location.
/// </summary>
/// <param name="frame">Image.</param>
/// <param name="magnitudeSqrImage">Squared magnitude image.</param>
/// <param name="minValidMagnitude">Minimal valid magnitude.</param>
/// <returns>Orientation image (angles are in degrees).</returns>
public static unsafe Gray<int>[,] Compute(Bgr<byte>[,] frame, out Gray<int>[,] magnitudeSqrImage, int minValidMagnitude)
{
var minSqrMagnitude = minValidMagnitude * minValidMagnitude;
var orientationImage = new Gray<int>[frame.Height(), frame.Width()];
var _magnitudeSqrImage = orientationImage.CopyBlank();
using (var uFrame = frame.Lock())
{
ParallelLauncher.Launch(thread =>
{
computeColor(thread, (byte*)uFrame.ImageData, uFrame.Stride, orientationImage, _magnitudeSqrImage, minSqrMagnitude);
},
frame.Width() - 2 * kernelRadius, frame.Height() - 2 * kernelRadius);
}
magnitudeSqrImage = _magnitudeSqrImage;
return orientationImage;
}
private static Gray<byte>[,] SpreadOrientations(Gray<byte>[,] quantizedOrientationImage, int neghborhood)
{
var destImg = quantizedOrientationImage.CopyBlank();
using (var uQuantizedOrientationImage = quantizedOrientationImage.Lock())
using(var uDestImg = destImg.Lock())
{
byte* srcImgPtr = (byte*)uQuantizedOrientationImage.ImageData;
int imgStride = uQuantizedOrientationImage.Stride;
byte* destImgPtr = (byte*)uDestImg.ImageData;
int imgHeight = uDestImg.Height;
int imgWidth = uDestImg.Width;
for (int row = 0; row < neghborhood; row++)
{
int subImageHeight = imgHeight - row;
for (int col = 0; col < neghborhood; col++)
{
OrImageBits(&srcImgPtr[col], destImgPtr,
imgStride,
imgWidth - col, subImageHeight);
}
srcImgPtr += imgStride;
}
}
return destImg;
}
/// <summary>
/// Take only those orientations that have MINIMAL_NUM_OF_SAME_ORIENTED_PIXELS in 3x3 negborhood.
/// Performs angle transformation into binary form ([0..7] -> [1, 2, 4, 8, ..., 128]) as well.
/// </summary>
/// <param name="qunatizedOrientionImg">Quantized orientation image where angles are represented by lables [0..GlobalParameters.NUM_OF_QUNATIZED_ORIENTATIONS] (invalid orientation label included).</param>
/// <param name="minSameOrientations">Minimal number of same orientations for 3x3 neigborhood. The range is: [0..9] (3x3 neigborhood).</param>
private static Gray<byte>[,] RetainImportantQuantizedOrientations(Gray<byte>[,] qunatizedOrientionImg, int minSameOrientations)
{
if (minSameOrientations < 0 || minSameOrientations > 9 /*3x3 neigborhood*/)
throw new Exception("Minimal number of same orientations should be in: [0..9].");
var quantizedFilteredOrient = qunatizedOrientionImg.CopyBlank();
using (var uQunatizedOrientionImg = qunatizedOrientionImg.Lock())
using (var uQuantizedFilteredOrient = quantizedFilteredOrient.Lock())
{
//debugImg = new Image<Hsv, byte>(orientDegImg.Width, orientDegImg.Height);
//debugImg = null;
int qOrinetStride = uQunatizedOrientionImg.Stride;
int qOrinetAllign = uQunatizedOrientionImg.Stride - uQunatizedOrientionImg.Width;
byte* qOrinetUnfilteredPtr = (byte*)uQunatizedOrientionImg.ImageData + qOrinetStride + 1;
byte* qOrinetFilteredPtr = (byte*)uQuantizedFilteredOrient.ImageData + qOrinetStride + 1;
//Debug.Assert(qunatizedOrientionImg.Stride == quantizedFilteredOrient.Stride);
int imgWidth = uQunatizedOrientionImg.Width;
int imgHeight = uQunatizedOrientionImg.Height;
for (int j = 1; j < imgHeight - 1; j++)
{
for (int i = 1; i < imgWidth - 1; i++)
{
if (*qOrinetUnfilteredPtr != INVALID_QUANTIZED_ORIENTATION)
{
byte[] histogram = new byte[INVALID_QUANTIZED_ORIENTATION + 1]; //gleda se susjedstvo 3x3
histogram[qOrinetUnfilteredPtr[-qOrinetStride - 1]]++; histogram[qOrinetUnfilteredPtr[-qOrinetStride + 0]]++; histogram[qOrinetUnfilteredPtr[-qOrinetStride + 1]]++;
histogram[qOrinetUnfilteredPtr[-1]]++; histogram[qOrinetUnfilteredPtr[0]]++; histogram[qOrinetUnfilteredPtr[+1]]++;
histogram[qOrinetUnfilteredPtr[+qOrinetStride - 1]]++; histogram[qOrinetUnfilteredPtr[+qOrinetStride + 0]]++; histogram[qOrinetUnfilteredPtr[+qOrinetStride + 1]]++;
int maxBinVotes = 0; byte quantizedAngle = 0;
for (byte histBinIdx = 0; histBinIdx < GlobalParameters.NUM_OF_QUNATIZED_ORIENTATIONS /*discard invalid orientation*/; histBinIdx++)
{
if (histogram[histBinIdx] > maxBinVotes)
{
maxBinVotes = histogram[histBinIdx];
quantizedAngle = histBinIdx;
}
}
if (maxBinVotes >= minSameOrientations)
*qOrinetFilteredPtr = (byte)(1 << quantizedAngle); //[1,2,4,8...128] (8 orientations)
//*qOrinetFilteredPtr = (byte)(1 << *qOrinetUnfilteredPtr); //[1,2,4,8...128] (8 orientations)
//debugImg[j, i] = new Hsv((*qOrinetFilteredPtr-1) * 35, 100, 100);
}
qOrinetUnfilteredPtr++;
qOrinetFilteredPtr++;
}
qOrinetUnfilteredPtr += 1 + qOrinetAllign + 1;
qOrinetFilteredPtr += 1 + qOrinetAllign + 1; //preskoči zadnji piksel, poravnanje, i početni piksel
}
}
//magnitudeImg.Save("magnitude.bmp");
//quantizedFilteredOrient.Save("quantizedImg.bmp");
return quantizedFilteredOrient;
}