static void Main()
{
Application.EnableVisualStyles();
Application.SetCompatibleTextRenderingDefault(false);
using (Mat image = new Mat("pedestrian.png"))
{
long processingTime;
Rectangle[] results;
if (CudaInvoke.HasCuda)
{
using (GpuMat gpuMat = new GpuMat(image))
results = FindPedestrian.Find(gpuMat, out processingTime);
}
else
{
using (UMat uImage = image.GetUMat(AccessType.ReadWrite))
results = FindPedestrian.Find(uImage, out processingTime);
}
foreach (Rectangle rect in results)
{
CvInvoke.Rectangle(image, rect, new Bgr(Color.Red).MCvScalar);
}
ImageViewer.Show(
image,
String.Format("Pedestrian detection using {0} in {1} milliseconds.",
CudaInvoke.HasCuda ? "GPU" :
CvInvoke.UseOpenCL ? "OpenCL":
"CPU",
processingTime));
}
}
public static 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 = 0.80;
Stopwatch watch;
homography = null;
modelKeyPoints = new VectorOfKeyPoint();
observedKeyPoints = new VectorOfKeyPoint();
using (UMat uModelImage = modelImage.GetUMat(AccessType.Read))
using (UMat uObservedImage = observedImage.GetUMat(AccessType.Read))
{
KAZE featureDetector = new KAZE();
Mat modelDescriptors = new Mat();
featureDetector.DetectAndCompute(uModelImage, null, modelKeyPoints, modelDescriptors, false);
watch = Stopwatch.StartNew();
Mat observedDescriptors = new Mat();
featureDetector.DetectAndCompute(uObservedImage, null, observedKeyPoints, observedDescriptors, false);
// KdTree for faster results / less accuracy
using (var ip = new Emgu.CV.Flann.KdTreeIndexParams())
using (var sp = new SearchParams())
using (DescriptorMatcher matcher = new FlannBasedMatcher(ip, sp))
{
matcher.Add(modelDescriptors);
matcher.KnnMatch(observedDescriptors, matches, k, null);
mask = new Mat(matches.Size, 1, DepthType.Cv8U, 1);
mask.SetTo(new MCvScalar(255));
Features2DToolbox.VoteForUniqueness(matches, uniquenessThreshold, mask);
// Calculate score based on matches size
// ---------------------------------------------->
score = 0;
for (int i = 0; i < matches.Size; i++)
{
if (mask.GetData(i)[0] == 0)
{
continue;
}
foreach (var e in matches[i].ToArray())
{
++score;
}
}
// <----------------------------------------------
int nonZeroCount = CvInvoke.CountNonZero(mask);
if (nonZeroCount >= 4)
{
nonZeroCount = Features2DToolbox.VoteForSizeAndOrientation(modelKeyPoints, observedKeyPoints, matches, mask, 1.5, 20);
if (nonZeroCount >= 4)
{
homography = Features2DToolbox.GetHomographyMatrixFromMatchedFeatures(modelKeyPoints, observedKeyPoints, matches, mask, 2);
}
}
}
watch.Stop();
}
matchTime = watch.ElapsedMilliseconds;
}
