/// <summary>
/// Apply cascade to an input frame and return the array of decection objects.
/// </summary>
/// <param name="image">A frame on which detector will be applied.</param>
/// <param name="rois">A regions of interests mask generated by genRoi. Only the objects that fall into one of the regions will be returned.</param>
/// <param name="stream">Use a Stream to call the function asynchronously (non-blocking) or null to call the function synchronously (blocking).</param>
/// <returns>An array of decection objects</returns>
public GpuMat Detect(CudaImage <Bgr, Byte> image, GpuMat <int> rois, Emgu.CV.Cuda.Stream stream = null)
{
GpuMat result = new GpuMat();
SoftCascadeInvoke.cudaSoftCascadeDetectorDetect(_ptr, image, rois, result, stream);
return(result);
}
/// <summary>
/// Finds matching points in the faces using SURF
/// </summary>
/// <param name="modelImage">
/// The model image.
/// </param>
/// <param name="observedImage">
/// The observed image.
/// </param>
/// <param name="matchTime">
/// The match time.
/// </param>
/// <param name="modelKeyPoints">
/// The model key points.
/// </param>
/// <param name="observedKeyPoints">
/// The observed key points.
/// </param>
/// <param name="matches">
/// The matches.
/// </param>
/// <param name="mask">
/// The mask.
/// </param>
/// <param name="homography">
/// The homography.
/// </param>
/// <param name="score">
/// The score.
/// </param>
private void FindMatch(
Mat modelImage,
Mat observedImage,
out long matchTime,
out VectorOfKeyPoint modelKeyPoints,
out VectorOfKeyPoint observedKeyPoints,
VectorOfVectorOfDMatch matches,
out Mat mask,
out Mat homography,
out long score)
{
int k = 2;
double uniquenessThreshold = 5;
Stopwatch watch;
homography = null;
mask = null;
score = 0;
modelKeyPoints = new VectorOfKeyPoint();
observedKeyPoints = new VectorOfKeyPoint();
if (Controller.Instance.Cuda)
{
CudaSURF surfGPU = new CudaSURF(700f, 4, 2, false);
using (CudaImage <Gray, byte> gpuModelImage = new CudaImage <Gray, byte>(modelImage))
//extract features from the object image
using (GpuMat gpuModelKeyPoints = surfGPU.DetectKeyPointsRaw(gpuModelImage, null))
using (GpuMat gpuModelDescriptors =
surfGPU.ComputeDescriptorsRaw(gpuModelImage, null, gpuModelKeyPoints))
using (CudaBFMatcher matcher = new CudaBFMatcher(DistanceType.L2))
{
surfGPU.DownloadKeypoints(gpuModelKeyPoints, modelKeyPoints);
watch = Stopwatch.StartNew();
// extract features from the observed image
using (CudaImage <Gray, Byte> gpuObservedImage = new CudaImage <Gray, byte>(observedImage))
using (GpuMat gpuObservedKeyPoints = surfGPU.DetectKeyPointsRaw(gpuObservedImage, null))
using (GpuMat gpuObservedDescriptors = surfGPU.ComputeDescriptorsRaw(
gpuObservedImage,
null,
gpuObservedKeyPoints))
using (GpuMat <int> gpuMatchIndices = new GpuMat <int>(
gpuObservedDescriptors.Size.Height,
k,
1,
true))
using (GpuMat <float> gpuMatchDist = new GpuMat <float>(
gpuObservedDescriptors.Size.Height,
k,
1,
true))
//using (GpuMat<Byte> gpuMask = new GpuMat<byte>(gpuMatchIndices.Size.Height, 1, 1))
using (Emgu.CV.Cuda.Stream stream = new Emgu.CV.Cuda.Stream())
{
matcher.KnnMatch(gpuObservedDescriptors, gpuModelDescriptors, matches, k, null);
//indices = new Matrix<int>(gpuMatchIndices.Size);
//mask = new Matrix<byte>(gpuMask.Size);
mask = new Mat(matches.Size, 1, DepthType.Cv8U, 1);
mask.SetTo(new MCvScalar(255));
surfGPU.DownloadKeypoints(gpuObservedKeyPoints, observedKeyPoints);
/*//gpu implementation of voteForUniquess
* using (GpuMat col0 = gpuMatchDist.Col(0))
* using (GpuMat col1 = gpuMatchDist.Col(1))
* {
* CudaInvoke.Multiply(col1, new GpuMat(), col1, 1, DepthType.Default, stream);
* CudaInvoke.Compare(col0, col1, mask, CmpType.LessEqual, stream);
* }*/
Features2DToolbox.VoteForUniqueness(matches, uniquenessThreshold, mask);
//wait for the stream to complete its tasks
//We can perform some other CPU intesive stuffs here while we are waiting for the stream to complete.
stream.WaitForCompletion();
//gpuMatchIndices.Download(indices);
if (CudaInvoke.CountNonZero(mask) >= 4)
{
int nonZeroCount = Features2DToolbox.VoteForSizeAndOrientation(
modelKeyPoints,
observedKeyPoints,
matches,
mask,
1.5,
20);
if (nonZeroCount >= 4)
{
homography = Features2DToolbox.GetHomographyMatrixFromMatchedFeatures(
modelKeyPoints,
observedKeyPoints,
matches,
mask,
2);
}
}
watch.Stop();
}
for (int i = 0; i < matches.Size; i++)
{
score++;
}
}
}
//else
//{
// SURF surfCPU = new SURF(500, 4, 2, false);
// //extract features from the object image
// modelKeyPoints = new VectorOfKeyPoint();
// Matrix<float> modelDescriptors = surfCPU.DetectAndCompute(modelImage, null, modelKeyPoints);
// watch = Stopwatch.StartNew();
// // extract features from the observed image
// observedKeyPoints = new VectorOfKeyPoint();
// Matrix<float> observedDescriptors = surfCPU.DetectAndCompute(observedImage, null, observedKeyPoints);
// BFMatcher matcher = new BFMatcher<float>(DistanceType.L2);
// matcher.Add(modelDescriptors);
// indices = new Matrix<int>(observedDescriptors.Rows, k);
// using (Matrix<float> dist = new Matrix<float>(observedDescriptors.Rows, k))
// {
// matcher.KnnMatch(observedDescriptors, indices, dist, k, null);
// mask = new Matrix<byte>(dist.Rows, 1);
// mask.SetValue(255);
// Features2DToolbox.VoteForUniqueness(dist, uniquenessThreshold, mask);
// }
// int nonZeroCount = CvInvoke.cvCountNonZero(mask);
// if (nonZeroCount >= 4)
// {
// nonZeroCount = Features2DToolbox.VoteForSizeAndOrientation(modelKeyPoints, observedKeyPoints, indices, mask, 1.5, 20);
// if (nonZeroCount >= 4)
// homography = Features2DToolbox.GetHomographyMatrixFromMatchedFeatures(modelKeyPoints, observedKeyPoints, indices, mask, 2);
// }
// watch.Stop();
//}
matchTime = 0;
}