public void TestHughCircle()
{
if (CudaInvoke.HasCuda)
{
Mat m = new Mat(480, 480, DepthType.Cv8U, 1);
m.SetTo(new MCvScalar(0));
CvInvoke.Circle(m, new Point(240, 240), 100, new MCvScalar(150), 10);
GpuMat gm = new GpuMat();
gm.Upload(m);
using (CudaHoughCirclesDetector detector = new CudaHoughCirclesDetector(1, 30, 120, 30, 10, 400))
using (GpuMat circlesGpu = new GpuMat())
using (Mat circlesMat = new Mat())
{
detector.Detect(gm, circlesGpu);
circlesGpu.Download(circlesMat);
CircleF[] circles = new CircleF[circlesMat.Cols];
GCHandle circlesHandle = GCHandle.Alloc(circles, GCHandleType.Pinned);
Emgu.CV.Util.CvToolbox.Memcpy(circlesHandle.AddrOfPinnedObject(), circlesMat.DataPointer, Marshal.SizeOf(typeof(CircleF)) * circles.Length);
circlesHandle.Free();
foreach (var circle in circles)
{
CvInvoke.Circle(m, Point.Round(circle.Center), (int)circle.Radius, new MCvScalar(255));
}
}
}
}
public void cuda_MeanShift()
{
Mat temp = Image("lenna.png", ImreadModes.AnyColor);
Mat src = temp.CvtColor(ColorConversionCodes.RGB2RGBA);
Size size = src.Size();
int spatialRad = 30;
int colorRad = 30;
using (GpuMat g_src = new GpuMat(size, MatType.CV_8UC4))
using (GpuMat d_dst = new GpuMat()) {
g_src.Upload(src);
Cuda.cuda.meanShiftFiltering(g_src, d_dst, spatialRad, colorRad);
Mat dst_gold = new Mat();
Cv2.PyrMeanShiftFiltering(temp, dst_gold, spatialRad, colorRad);
Mat dst = new Mat();
d_dst.Download(dst);
Mat result = new Mat();
Cv2.CvtColor(dst, result, ColorConversionCodes.RGBA2RGB);
ImageEquals(result, dst_gold, 3);
ShowImagesWhenDebugMode(dst, dst_gold);
}
}
public void TestCudaUploadDownload()
{
if (!CudaInvoke.HasCuda)
{
return;
}
Mat m = new Mat(new Size(480, 320), DepthType.Cv8U, 3);
CvInvoke.Randu(m, new MCvScalar(), new MCvScalar(255, 255, 255));
#region test for async download & upload
Stream stream = new Stream();
GpuMat gm1 = new GpuMat();
gm1.Upload(m, stream);
Mat m2 = new Mat();
gm1.Download(m2, stream);
stream.WaitForCompletion();
EmguAssert.IsTrue(m.Equals(m2));
#endregion
#region test for blocking download & upload
GpuMat gm2 = new GpuMat();
gm2.Upload(m);
Mat m3 = new Mat();
gm2.Download(m3);
EmguAssert.IsTrue(m.Equals(m3));
#endregion
}
// uses cuda if available
public static Bitmap ResizeImage(Bitmap imgToResize, Size size)
{
// emgu cuda detection can be a little finicy sometimes, but try catching cost virtually nothing compared to actually changing the size of the img
if (CudaInvoke.HasCuda)
{
try {
// determine ratio between current and desired size
double ratio = (double)size.Height / (imgToResize.Height);
Mat dst = new Mat();
Image <Bgr, Byte> imageCV = new Image <Bgr, byte>(imgToResize);
var result = imageCV.CopyBlank();
var handle = GCHandle.Alloc(result);
Mat matToResize = imageCV.Mat;
using (GpuMat gMatSrc = new GpuMat())
using (GpuMat gMatDst = new GpuMat()) {
gMatSrc.Upload(matToResize);
Emgu.CV.Cuda.CudaInvoke.Resize(gMatSrc, gMatDst, new Size(0, 0), ratio, ratio, Inter.Area);
gMatDst.Download(dst);
}
handle.Free();
return(dst.Bitmap);
}
catch {
return(ResizeImageNoCuda(imgToResize, size));
}
}
else
{
return(ResizeImageNoCuda(imgToResize, size));
}
}
/// <summary>
/// Converts to UIImage.
/// </summary>
/// <returns>The UIImage.</returns>
public static UIImage ToUIImage(this GpuMat gpuMat)
{
using (Mat tmp = new Mat())
{
gpuMat.Download(tmp);
return(tmp.ToUIImage());
}
}
/// <summary>
/// Convert the gpuMat into Bitmap, the pixel values are copied over to the Bitmap
/// </summary>
public static Bitmap ToBitmap(this GpuMat gpuMat)
{
using (Mat tmp = new Mat())
{
gpuMat.Download(tmp);
return(tmp.ToBitmap());
}
}
private void ProcessFrame(object sender, EventArgs e)
{
if (_capture != null && _capture.Ptr != IntPtr.Zero)
{
_capture.Retrieve(frame, 0);
gpuFrame.Upload(frame);
cudaBgMOG2.Apply(gpuFrame, gpuSub);
CudaInvoke.Threshold(gpuSub, gpuSub, 12, 255, Emgu.CV.CvEnum.ThresholdType.Binary);
gpuSub.Download(outSub);
CvInvoke.FindContours(outSub, contours, hiererachy, Emgu.CV.CvEnum.RetrType.List, Emgu.CV.CvEnum.ChainApproxMethod.ChainApproxNone);
for (int i = 0; i < contours.Size; i++)
{
if (CvInvoke.ContourArea(contours[i]) > 50)
{
contoursGood.Push(contours[i]);
}
}
grayImage = new Image <Gray, byte>(frame.Width, frame.Height, new Gray(0));
grayImage.SetZero();
CvInvoke.DrawContours(grayImage, contoursGood, -1, new MCvScalar(255, 255, 255), -1);
CvInvoke.Dilate(grayImage, grayImage, element, new Point(-1, -1), 6, Emgu.CV.CvEnum.BorderType.Constant, new MCvScalar(255, 255, 255));
contoursGood.Clear();
CvInvoke.FindContours(grayImage, contours, hiererachy, Emgu.CV.CvEnum.RetrType.List, Emgu.CV.CvEnum.ChainApproxMethod.ChainApproxNone);
List <Point> points = new List <Point>();
for (int i = 0; i < contours.Size; i++)
{
MCvMoments moments = CvInvoke.Moments(contours[i], false);
Point WeightedCentroid = new Point((int)(moments.M10 / moments.M00), (int)(moments.M01 / moments.M00));
points.Add(WeightedCentroid);
}
blobList.AssignToBlobs(points);
blobList.Draw(frame);
blobList.Draw(mask);
blobList.Update();
CvInvoke.DrawContours(frame, contours, -1, new MCvScalar(0, 0, 255));
imageBox1.Image = frame;
imageBox2.Image = mask;
grayImage.Dispose();
indexFrame++;
}
}
public static Bitmap ToBitmap(this GpuMat gpuMat)
{
//IL_0000: Unknown result type (might be due to invalid IL or missing references)
//IL_0006: Expected O, but got Unknown
Mat val = (Mat)(object)new Mat();
try
{
gpuMat.Download((IOutputArray)(object)val, (Stream)null);
return(val.ToBitmap());
}
finally
{
((IDisposable)val)?.Dispose();
}
}
public override Mat ComputeDepthMap(Image <Bgr, byte> leftImage, Image <Bgr, byte> rightImage)
{
CudaStereoBM _cudaStereoBM = CreateCudaStereoBM();
ConvertImageToGray(leftImage, rightImage);
GpuMat imageDisparity = new GpuMat();
GpuMat imageToSave = new GpuMat();
Image <Bgr, byte> disparityToSave = new Image <Bgr, byte>(leftImage.Size);
Mat disparity = new Mat();
_cudaStereoBM.FindStereoCorrespondence(LeftGrayImage.ImageToGpuMat(), RightGrayImage.ImageToGpuMat(), imageDisparity);
imageDisparity.ConvertTo(imageToSave, DepthType.Cv8U);
imageToSave.Download(disparityToSave);
imageDisparity.Download(disparity);
return(disparity);
}
public void TestCudaPyrLKOpticalFlow()
{
if (!CudaInvoke.HasCuda)
{
return;
}
Image <Gray, Byte> prevImg, currImg;
AutoTestVarious.OpticalFlowImage(out prevImg, out currImg);
Mat flow = new Mat();
CudaDensePyrLKOpticalFlow opticalflow = new CudaDensePyrLKOpticalFlow(new Size(21, 21), 3, 30, false);
using (CudaImage <Gray, Byte> prevGpu = new CudaImage <Gray, byte>(prevImg))
using (CudaImage <Gray, byte> currGpu = new CudaImage <Gray, byte>(currImg))
using (GpuMat flowGpu = new GpuMat())
{
opticalflow.Calc(prevGpu, currGpu, flowGpu);
flowGpu.Download(flow);
}
}
public void TestCudaBroxOpticalFlow()
{
if (!CudaInvoke.HasCuda)
{
return;
}
Image <Gray, Byte> prevImg, currImg;
AutoTestVarious.OpticalFlowImage(out prevImg, out currImg);
Mat flow = new Mat();
CudaBroxOpticalFlow opticalflow = new CudaBroxOpticalFlow();
using (CudaImage <Gray, float> prevGpu = new CudaImage <Gray, float>(prevImg.Convert <Gray, float>()))
using (CudaImage <Gray, float> currGpu = new CudaImage <Gray, float>(currImg.Convert <Gray, float>()))
using (GpuMat flowGpu = new GpuMat())
{
opticalflow.Calc(prevGpu, currGpu, flowGpu);
flowGpu.Download(flow);
}
}
public void cuda_buildWarpPerspectiveMaps()
{
Mat src = Image("lenna.png", ImreadModes.Grayscale);
Size srcSize = src.Size();
double angle = Cv2.PI / 4;
Mat M = new Mat(3, 3, MatType.CV_64FC1);
M.Set <double>(0, 0, System.Math.Cos(angle));
M.Set <double>(0, 1, -System.Math.Sin(angle));
M.Set <double>(0, 2, srcSize.Width / 2.0);
M.Set <double>(1, 0, System.Math.Sin(angle));
M.Set <double>(1, 1, System.Math.Cos(angle));
M.Set <double>(1, 2, 0.0);
M.Set <double>(2, 0, 0.0);
M.Set <double>(2, 1, 0.0);
M.Set <double>(2, 2, 1.0);
using (Mat dst = new Mat())
using (Mat dst_gold = new Mat()) {
GpuMat g_xmap = new GpuMat();
GpuMat g_ymap = new GpuMat();
Cuda.cuda.buildWarpPerspectiveMaps(M, false, srcSize, g_xmap, g_ymap);
Cv2.WarpPerspective(src, dst_gold, M, srcSize, InterpolationFlags.Nearest, BorderTypes.Constant);
Mat xmap = new Mat();
Mat ymap = new Mat();
g_xmap.Download(xmap);
g_ymap.Download(ymap);
Cv2.Remap(src, dst, xmap, ymap, InterpolationFlags.Nearest, BorderTypes.Constant);
ShowImagesWhenDebugMode(dst_gold, dst);
ImageEquals(dst_gold, dst);
}
}
public void TestSplitMerge()
{
if (CudaInvoke.HasCuda)
{
using (Image <Bgr, Byte> img1 = new Image <Bgr, byte>(1200, 640))
{
img1.SetRandUniform(new MCvScalar(0, 0, 0), new MCvScalar(255, 255, 255));
using (GpuMat gpuImg1 = new GpuMat(img1))
{
GpuMat[] channels = gpuImg1.Split(null);
for (int i = 0; i < channels.Length; i++)
{
Mat imgL = channels[i].ToMat();
Image <Gray, Byte> imgR = img1[i];
Assert.IsTrue(imgL.Equals(imgR.Mat), "failed split GpuMat");
}
using (GpuMat gpuImg2 = new GpuMat())
{
gpuImg2.MergeFrom(channels, null);
using (Image <Bgr, byte> img2 = new Image <Bgr, byte>(img1.Size))
{
gpuImg2.Download(img2);
Assert.IsTrue(img2.Equals(img1), "failed split and merge test");
}
}
for (int i = 0; i < channels.Length; i++)
{
channels[i].Dispose();
}
}
}
}
}
// calculates the optical flow according to the Farneback algorithm
public Bitmap Dense_Optical_Flow(Bitmap bmp, OpticalFlowVariable optiVariables, Camera cam)
{
frameReduction = optiVariables.frameReduction < 1 ? 1 : optiVariables.frameReduction;
// frame becomes previous frame (i.e., prev_frame stores information about current frame)
prev_frame = matframe;
Image <Bgr, Byte> imageCV = new Image <Bgr, byte>(bmp); //Image Class from Emgu.CV
matframe = imageCV.Mat; //This is your Image converted to Mat
if (prev_frame == null)
{
return(bmp);
}
// frame_nr increment by number of steps given in textfield on user interface
frame_nr += 1;
// intialize this Image Matrix before resizing (see below), so it remains at original size
img_average_vectors = new Image <Bgr, byte>(matframe.Width, matframe.Height);
orig_height = matframe.Height;
Size n_size = new Size(matframe.Width / frameReduction,
matframe.Height / frameReduction);
// Resize frame and previous frame (smaller to reduce processing load)
//Source
Mat matFramDst = new Mat();
using (GpuMat gMatSrc = new GpuMat())
using (GpuMat gMatDst = new GpuMat()) {
gMatSrc.Upload(matframe);
Emgu.CV.Cuda.CudaInvoke.Resize(gMatSrc, gMatDst, new Size(0, 0), (double)1 / frameReduction, (double)1 / frameReduction);
gMatDst.Download(matFramDst);
}
matframe = matFramDst;
if (prev_frame.Height != matframe.Height)
{
return(bmp);
}
// images that are compared during the flow operations (see below)
// these need to be greyscale images
Image <Gray, Byte> prev_grey_img, curr_grey_img;
prev_grey_img = new Image <Gray, byte>(prev_frame.Width, prev_frame.Height);
curr_grey_img = new Image <Gray, byte>(matframe.Width, matframe.Height);
// Image arrays to store information of flow vectors (one image array for each direction, which is x and y)
Image <Gray, float> flow_x;
Image <Gray, float> flow_y;
flow_x = new Image <Gray, float>(matframe.Width, matframe.Height);
flow_y = new Image <Gray, float>(matframe.Width, matframe.Height);
// assign information stored in frame and previous frame in greyscale images (works without convert function)
CvInvoke.CvtColor(matframe, curr_grey_img, ColorConversion.Bgr2Gray);
CvInvoke.CvtColor(prev_frame, prev_grey_img, ColorConversion.Bgr2Gray);
// Apply Farneback dense optical flow
// parameters are the two greyscale images (these are compared)
// and two image arrays storing the flow information
// the results of the procedure are stored
// the rest of the parameters are:
// pryScale: specifies image scale to build pyramids: 0.5 means that each next layer is twice smaller than the former
// levels: number of pyramid levels: 1 means no extra layers
// winSize: the average window size; larger values = more robust to noise but more blur
// iterations: number of iterations at each pyramid level
// polyN: size of pixel neighbourhood: higher = more precision but more blur
// polySigma
// flags
CvInvoke.CalcOpticalFlowFarneback(prev_grey_img, curr_grey_img, flow_x, flow_y, 0.5, 3, 10, 3, 6, 1.3, 0);
// call function that shows results of Farneback algorithm
Image <Bgr, Byte> farnebackImg = Draw_Farneback_flow_map(matframe.ToImage <Bgr, Byte>(), flow_x, flow_y, optiVariables);// given in global variables section
// Release memory
prev_grey_img.Dispose();
curr_grey_img.Dispose();
flow_x.Dispose();
flow_y.Dispose();
//return farnebackImg.ToBitmap();
Image <Bgra, Byte> alphaImgShape = new Image <Bgra, byte>(imageCV.Size.Width, imageCV.Size.Height, new Bgra(0, 0, 0, .5));
CvInvoke.AddWeighted(alphaImgShape, .5, BlackTransparent(farnebackImg), .5, 0, alphaImgShape);
Mat alphaimg = new Mat();
CvInvoke.CvtColor(imageCV, alphaimg, ColorConversion.Bgr2Bgra);
if (CudaInvoke.HasCuda)
{
using (GpuMat gMatSrc = new GpuMat())
using (GpuMat gMatSrc2 = new GpuMat())
using (GpuMat gMatDst = new GpuMat()) {
gMatSrc.Upload(alphaimg);
gMatSrc2.Upload(alphaImgShape);
CudaInvoke.AlphaComp(gMatSrc, gMatSrc2, gMatDst, AlphaCompTypes.Plus);
gMatDst.Download(alphaimg);
}
return(alphaimg.Bitmap);
}
else
{
return(Overlay(imageCV, alphaImgShape).ToBitmap());
}
}
// function that depicts results of optical flow operations
// requires reference to image being processed, the results of Farneback algorithm stored in flow_x and flow_y
// step gives the distance between pixels that are depicted, shift_that_counts is threshold for vector length that is used for calculations
private Image <Bgr, Byte> Draw_Farneback_flow_map(Image <Bgr, Byte> img_curr, Image <Gray, float> flow_x, Image <Gray, float> flow_y, OpticalFlowVariable optiVars)
{
// NOTE: flow Images (flow_x and flow_y) are organized like this:
// at index (is position of pixel before optical flow operation) of Image array
// the shift of this specific pixel after the flow operation is stored
// if no shift has occured value stored at index is zero
// (i.e., pixel[index] = 0
GC.Collect(0, GCCollectionMode.Forced);
Image <Bgr, Byte> blackFrame = new Image <Bgr, Byte>(new Bitmap(1280 / frameReduction, 720 / frameReduction));
System.Drawing.Point from_dot_xy = new System.Drawing.Point(); // point variable to draw lines between dots before and after flow (=vectors)
System.Drawing.Point to_dot_xy = new System.Drawing.Point(); // point variable, which will be endpoint of line between dots before and after flow
MCvScalar col; // variable to store color values of lines representing flow vectors
col.V0 = 100;
col.V1 = 255;
col.V2 = 0;
col.V3 = 100;
// for drawing central line based on window size
System.Drawing.Point[] window_centre = new System.Drawing.Point[2];
window_centre[0].X = img_curr.Width / 2;// * Convert.ToInt32(txt_resize_factor.Text)/ 2;
window_centre[0].Y = 0;
window_centre[1].X = img_curr.Width / 2; //* Convert.ToInt32(txt_resize_factor.Text) / 2;
window_centre[1].Y = orig_height;
// Point variables that constitute starting point for drawing summed and mean vectors onto image
System.Drawing.Point vector_right = new System.Drawing.Point();
System.Drawing.Point vector_left = new System.Drawing.Point();
// variables used for summing vectors to left and to the right of the window's centre
System.Drawing.Point vector_right_end_window = new System.Drawing.Point();
System.Drawing.Point vector_left_end_window = new System.Drawing.Point();
// determine centre of output window (needed for summed vectors)
int mid_point_horz = 1280 * frameReduction / 2; // width
int mid_point_vert = 720 * frameReduction / 2; // height
// landmark coordinates that are origin of direction vectors
// near centre of image window; to depict motion of left and right half of "body" (or more precisely, window)
vector_right.X = (mid_point_horz + 10) * optiVars.stepRate;
vector_right.Y = mid_point_vert * optiVars.stepRate;
vector_left.X = (mid_point_horz - 10) * optiVars.stepRate;
vector_left.Y = mid_point_vert * optiVars.stepRate;
// counting landmarks in flow field that exceed a certain value (shift_that_counts); left and right is based on centre of window (half of width)
double count_X_right = 0;
double count_Y_right = 0;
double count_X_left = 0;
double count_Y_left = 0;
// loops over image matrix; position of dots before and after optical flow operations are compared and vector is drawn between the old and the new position
for (int i = 0; i < flow_x.Rows; i += optiVars.stepRate) // NOTE: steps are given by step variable in arguments of method
{
for (int j = 0; j < flow_x.Cols; j += optiVars.stepRate) // BEGIN FOR
// pixel shift measured by optical flow is transferred to point variables
// storing starting point of motion (from_dot..) and its end points (to_dot...)
{
to_dot_xy.X = (int)flow_x.Data[i, j, 0]; // access single pixel of flow matrix where x-coords of pixel after flow are stored; only gives the shift
to_dot_xy.Y = (int)flow_y.Data[i, j, 0]; // access single pixel of flow matrix where y-coords of pixel after flow are stored; only gives the shift
from_dot_xy.X = j; // index of loop is position on image (here: x-coord); X is cols
from_dot_xy.Y = i; // index of of loop is position on image (here: y-coord); Y is rows
// LEFT SIDE OF WINDOW BASED CENTRE
if (j < window_centre[0].X)
{
// count the x and y indices and sum them when they exceed the value given by shift_that_counts (here:0)
if (Math.Abs(to_dot_xy.X) > optiVars.shiftThatCounts)
{
count_X_left++;
}
if (Math.Abs(to_dot_xy.Y) > optiVars.shiftThatCounts)
{
count_Y_left++;
}
// sum up vectors
vector_left_end_window.Y += to_dot_xy.Y;
vector_left_end_window.X += to_dot_xy.X;
}
else //(j > window_centre[0].X)// WINDOW BASED CENTRE
{
// like above; count the x and y indices and sum them
if (Math.Abs(to_dot_xy.X) > optiVars.shiftThatCounts)
{
count_X_right++;
}
if (Math.Abs(to_dot_xy.Y) > optiVars.shiftThatCounts)
{
count_Y_right++;
}
// sum vectors
vector_right_end_window.Y += to_dot_xy.Y;
vector_right_end_window.X += to_dot_xy.X;
}
to_dot_xy.X = from_dot_xy.X + to_dot_xy.X; // new x-coord position of pixel (taking into account distance from the origin)
to_dot_xy.Y = from_dot_xy.Y + to_dot_xy.Y; // new y-coord postion of pixel
// draw line between coords on image and pixel shift stored in flow field after applying optical-flow
if (GetDistance(from_dot_xy.X, from_dot_xy.Y, to_dot_xy.X, to_dot_xy.Y) > optiVars.shiftThatCounts)
{
CvInvoke.Line(blackFrame, from_dot_xy, to_dot_xy, col, 1);
}
//CvInvoke.Imshow("Flow field vectors", img_curr); // show image with flow depicted as lines
} // END of both for loops
}
Mat blackDst = new Mat();
Mat BlackMat = blackFrame.Mat;
using (GpuMat gMatSrc = new GpuMat())
using (GpuMat gMatDst = new GpuMat()) {
gMatSrc.Upload(BlackMat);
Emgu.CV.Cuda.CudaInvoke.Resize(gMatSrc, gMatDst, new Size(0, 0), frameReduction, frameReduction, Inter.Area);
gMatDst.Download(blackDst);
}
GC.Collect();
return(blackDst.ToImage <Bgr, Byte>());
}
public static void FindMatch(Image <Gray, Byte> modelImage, Image <Gray, byte> observedImage, out long matchTime, out VectorOfKeyPoint modelKeyPoints, out VectorOfKeyPoint observedKeyPoints, out Matrix <int> indices, out Matrix <byte> mask, out HomographyMatrix homography)
{
int k = 2;
double uniquenessThreshold = 0.8;
SURFDetector surfCPU = new SURFDetector(500, false);
Stopwatch watch;
homography = null;
if (GpuInvoke.HasCuda)
{
GpuSURFDetector surfGPU = new GpuSURFDetector(surfCPU.SURFParams, 0.01f);
using (GpuImage <Gray, Byte> gpuModelImage = new GpuImage <Gray, byte>(modelImage))
//extract features from the object image
using (GpuMat <float> gpuModelKeyPoints = surfGPU.DetectKeyPointsRaw(gpuModelImage, null))
using (GpuMat <float> gpuModelDescriptors = surfGPU.ComputeDescriptorsRaw(gpuModelImage, null, gpuModelKeyPoints))
using (GpuBruteForceMatcher <float> matcher = new GpuBruteForceMatcher <float>(DistanceType.L2))
{
modelKeyPoints = new VectorOfKeyPoint();
surfGPU.DownloadKeypoints(gpuModelKeyPoints, modelKeyPoints);
watch = Stopwatch.StartNew();
// extract features from the observed image
using (GpuImage <Gray, Byte> gpuObservedImage = new GpuImage <Gray, byte>(observedImage))
using (GpuMat <float> gpuObservedKeyPoints = surfGPU.DetectKeyPointsRaw(gpuObservedImage, null))
using (GpuMat <float> 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 (Stream stream = new Stream())
{
matcher.KnnMatchSingle(gpuObservedDescriptors, gpuModelDescriptors, gpuMatchIndices, gpuMatchDist, k, null, stream);
indices = new Matrix <int>(gpuMatchIndices.Size);
mask = new Matrix <byte>(gpuMask.Size);
//gpu implementation of voteForUniquess
using (GpuMat <float> col0 = gpuMatchDist.Col(0))
using (GpuMat <float> col1 = gpuMatchDist.Col(1))
{
GpuInvoke.Multiply(col1, new MCvScalar(uniquenessThreshold), col1, stream);
GpuInvoke.Compare(col0, col1, gpuMask, CMP_TYPE.CV_CMP_LE, stream);
}
observedKeyPoints = new VectorOfKeyPoint();
surfGPU.DownloadKeypoints(gpuObservedKeyPoints, observedKeyPoints);
//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();
gpuMask.Download(mask);
gpuMatchIndices.Download(indices);
if (GpuInvoke.CountNonZero(gpuMask) >= 4)
{
int nonZeroCount = Features2DToolbox.VoteForSizeAndOrientation(modelKeyPoints, observedKeyPoints, indices, mask, 1.5, 20);
if (nonZeroCount >= 4)
{
homography = Features2DToolbox.GetHomographyMatrixFromMatchedFeatures(modelKeyPoints, observedKeyPoints, indices, mask, 2);
}
}
watch.Stop();
}
}
}
else
{
//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);
BruteForceMatcher <float> matcher = new BruteForceMatcher <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 = watch.ElapsedMilliseconds;
}
private void ProcessFrame(object sender, EventArgs arg)
{
Image <Bgr, Byte> frame = _capture.QueryFrame().Resize(320, 240, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
Image <Gray, Byte> grayframe = frame.Convert <Gray, Byte>();
Image <Gray, Byte> modelImage = new Image <Gray, byte>("DataPlate/" + 10 + ".jpg");
Image <Gray, Byte> observedImage = grayframe;
Stopwatch watch;
HomographyMatrix homography = null;
SURFDetector surfCPU = new SURFDetector(500, false);
VectorOfKeyPoint modelKeyPoints;
VectorOfKeyPoint observedKeyPoints;
Matrix <int> indices;
Matrix <float> dist;
Matrix <byte> mask;
if (GpuInvoke.HasCuda)
{
GpuSURFDetector surfGPU = new GpuSURFDetector(surfCPU.SURFParams, 0.01f);
using (GpuImage <Gray, Byte> gpuModelImage = new GpuImage <Gray, byte>(modelImage))
#region SURF
//extract features from the object image
using (GpuMat <float> gpuModelKeyPoints = surfGPU.DetectKeyPointsRaw(gpuModelImage, null))
using (GpuMat <float> gpuModelDescriptors = surfGPU.ComputeDescriptorsRaw(gpuModelImage, null, gpuModelKeyPoints))
using (GpuBruteForceMatcher matcher = new GpuBruteForceMatcher(GpuBruteForceMatcher.DistanceType.L2))
{
modelKeyPoints = new VectorOfKeyPoint();
surfGPU.DownloadKeypoints(gpuModelKeyPoints, modelKeyPoints);
watch = Stopwatch.StartNew();
// extract features from the observed image
using (GpuImage <Gray, Byte> gpuObservedImage = new GpuImage <Gray, byte>(observedImage))
using (GpuMat <float> gpuObservedKeyPoints = surfGPU.DetectKeyPointsRaw(gpuObservedImage, null))
using (GpuMat <float> gpuObservedDescriptors = surfGPU.ComputeDescriptorsRaw(gpuObservedImage, null, gpuObservedKeyPoints))
using (GpuMat <int> gpuMatchIndices = new GpuMat <int>(gpuObservedDescriptors.Size.Height, 2, 1))
using (GpuMat <float> gpuMatchDist = new GpuMat <float>(gpuMatchIndices.Size, 1))
{
observedKeyPoints = new VectorOfKeyPoint();
surfGPU.DownloadKeypoints(gpuObservedKeyPoints, observedKeyPoints);
matcher.KnnMatch(gpuObservedDescriptors, gpuModelDescriptors, gpuMatchIndices, gpuMatchDist, 2, null);
indices = new Matrix <int>(gpuMatchIndices.Size);
dist = new Matrix <float>(indices.Size);
gpuMatchIndices.Download(indices);
gpuMatchDist.Download(dist);
mask = new Matrix <byte>(dist.Rows, 1);
mask.SetValue(255);
Features2DTracker.VoteForUniqueness(dist, 0.8, mask);
int nonZeroCount = CvInvoke.cvCountNonZero(mask);
if (nonZeroCount >= 4)
{
nonZeroCount = Features2DTracker.VoteForSizeAndOrientation(modelKeyPoints, observedKeyPoints, indices, mask, 1.5, 20);
if (nonZeroCount >= 4)
{
homography = Features2DTracker.GetHomographyMatrixFromMatchedFeatures(modelKeyPoints, observedKeyPoints, indices, mask, 3);
}
}
watch.Stop();
}
}
#endregion
}
else
{
//extract features from the object image
modelKeyPoints = surfCPU.DetectKeyPointsRaw(modelImage, null);
//MKeyPoint[] kpts = modelKeyPoints.ToArray();
Matrix <float> modelDescriptors = surfCPU.ComputeDescriptorsRaw(modelImage, null, modelKeyPoints);
watch = Stopwatch.StartNew();
// extract features from the observed image
observedKeyPoints = surfCPU.DetectKeyPointsRaw(observedImage, null);
Matrix <float> observedDescriptors = surfCPU.ComputeDescriptorsRaw(observedImage, null, observedKeyPoints);
BruteForceMatcher matcher = new BruteForceMatcher(BruteForceMatcher.DistanceType.L2F32);
matcher.Add(modelDescriptors);
int k = 2;
indices = new Matrix <int>(observedDescriptors.Rows, k);
dist = new Matrix <float>(observedDescriptors.Rows, k);
matcher.KnnMatch(observedDescriptors, indices, dist, k, null);
mask = new Matrix <byte>(dist.Rows, 1);
mask.SetValue(255);
Features2DTracker.VoteForUniqueness(dist, 0.8, mask);
int nonZeroCount = CvInvoke.cvCountNonZero(mask);
if (nonZeroCount >= 20)
{
nonZeroCount = Features2DTracker.VoteForSizeAndOrientation(modelKeyPoints, observedKeyPoints, indices, mask, 1.5, 20);
if (nonZeroCount >= 20)
{
homography = Features2DTracker.GetHomographyMatrixFromMatchedFeatures(modelKeyPoints, observedKeyPoints, indices, mask, 3);
XMLData();
}
else
{
textBox1.Text = string.Empty;
textBox2.Text = string.Empty;
textBox3.Text = string.Empty;
textBox4.Text = string.Empty;
textBox5.Text = string.Empty;
}
}
watch.Stop();
#region draw the projected region on the image
if (homography != null)
{ //draw a rectangle along the projected model
Rectangle rect = modelImage.ROI;
PointF[] pts = new PointF[] {
new PointF(rect.Left, rect.Bottom),
new PointF(rect.Right, rect.Bottom),
new PointF(rect.Right, rect.Top),
new PointF(rect.Left, rect.Top)
};
homography.ProjectPoints(pts);
frame.DrawPolyline(Array.ConvertAll <PointF, Point>(pts, Point.Round), true, new Bgr(Color.Red), 2);
}
#endregion
CaptureImageBox.Image = frame;
DataImageBox.Image = modelImage;
}
}
public void TestSplitMerge()
{
if (CudaInvoke.HasCuda)
{
using (Image<Bgr, Byte> img1 = new Image<Bgr, byte>(1200, 640))
{
img1.SetRandUniform(new MCvScalar(0, 0, 0), new MCvScalar(255, 255, 255));
using (GpuMat gpuImg1 = new GpuMat(img1))
{
GpuMat[] channels = gpuImg1.Split(null);
for (int i = 0; i < channels.Length; i++)
{
Mat imgL = channels[i].ToMat();
Image<Gray, Byte> imgR = img1[i];
Assert.IsTrue(imgL.Equals(imgR.Mat), "failed split GpuMat");
}
using (GpuMat gpuImg2 = new GpuMat())
{
gpuImg2.MergeFrom(channels, null);
using (Image<Bgr, byte> img2 = new Image<Bgr, byte>(img1.Size))
{
gpuImg2.Download(img2);
Assert.IsTrue(img2.Equals(img1), "failed split and merge test");
}
}
for (int i = 0; i < channels.Length; i++)
{
channels[i].Dispose();
}
}
}
}
}
public static Bitmap PerformShapeDetection(Bitmap frame, ShapeDetectionVariables detectionVars)
{
StringBuilder msgBuilder = new StringBuilder("Performance: ");
Image <Bgr, Byte> img = new Image <Bgr, byte>(frame);
Mat MatImg = img.Mat;
Mat outputImg = new Mat();
if (CudaInvoke.HasCuda)
{
using (GpuMat gMatSrc = new GpuMat())
using (GpuMat gMatDst = new GpuMat()) {
gMatSrc.Upload(MatImg);
CudaGaussianFilter noiseReducetion = new CudaGaussianFilter(MatImg.Depth, img.NumberOfChannels, MatImg.Depth, img.NumberOfChannels, new Size(1, 1), 0);
noiseReducetion.Apply(gMatSrc, gMatDst);
gMatDst.Download(outputImg);
}
}
else
{
Mat pyrDown = new Mat();
CvInvoke.PyrDown(img, pyrDown);
CvInvoke.PyrUp(pyrDown, img);
outputImg = img.Mat;
}
UMat uimage = new UMat();
CvInvoke.CvtColor(outputImg, uimage, ColorConversion.Bgr2Gray);
CircleF[] circles = new CircleF[0];
if (detectionVars.calcCircles)
{
circles = CvInvoke.HoughCircles(
uimage,
HoughType.Gradient, 1.0, 20.0,
detectionVars.circleCannyThreshold,
detectionVars.circleAccumulatorThreshold == 0 ? 1 : detectionVars.circleAccumulatorThreshold,
detectionVars.minradius,
detectionVars.maxRadius);
}
#region Canny and edge detection
UMat cannyEdges = new UMat();
CvInvoke.Canny(uimage, cannyEdges, detectionVars.lineCannyThreshold, detectionVars.cannyThresholdLinking);
LineSegment2D[] lines = new LineSegment2D[0];
if (detectionVars.calcLines)
{
lines = CvInvoke.HoughLinesP(
cannyEdges,
1, //Distance resolution in pixel-related units
Math.PI / 45.0, //Angle resolution measured in radians.
detectionVars.lineThreshold, //threshold
detectionVars.minLineWidth, //min Line width
10); //gap between lines
}
#endregion
#region Find triangles and rectangles
List <RotatedRect> boxList = new List <RotatedRect>(); //a box is a rotated rectangle
if (detectionVars.calcRectTri)
{
using (VectorOfVectorOfPoint contours = new VectorOfVectorOfPoint()) {
CvInvoke.FindContours(cannyEdges, contours, null, RetrType.List, ChainApproxMethod.ChainApproxSimple);
int count = contours.Size;
for (int i = 0; i < count; i++)
{
using (VectorOfPoint contour = contours[i])
using (VectorOfPoint approxContour = new VectorOfPoint()) {
CvInvoke.ApproxPolyDP(contour, approxContour, CvInvoke.ArcLength(contour, true) * 0.05, true);
if (CvInvoke.ContourArea(approxContour, false) > 250) //only consider contours with area greater than 250
{
if (approxContour.Size == 4) //The contour has 4 vertices.
{
#region determine if all the angles in the contour are within [80, 100] degree
bool isRectangle = true;
Point[] pts = approxContour.ToArray();
LineSegment2D[] edges = PointCollection.PolyLine(pts, true);
for (int j = 0; j < edges.Length; j++)
{
double angle = Math.Abs(
edges[(j + 1) % edges.Length].GetExteriorAngleDegree(edges[j]));
if (angle < 80 || angle > 100)
{
isRectangle = false;
break;
}
}
#endregion
if (isRectangle)
{
boxList.Add(CvInvoke.MinAreaRect(approxContour));
}
}
}
}
}
}
}
#endregion
Image <Bgra, Byte> alphaImgShape = new Image <Bgra, byte>(img.Size.Width, img.Size.Height, new Bgra(0, 0, 0, .5));
Mat alphaimg = new Mat();
CvInvoke.CvtColor(img, alphaimg, ColorConversion.Bgr2Bgra);
#region draw rectangles and triangles
if (detectionVars.calcRectTri)
{
Image <Bgr, Byte> triangleRectangleImage = new Image <Bgr, Byte>(img.Size);
foreach (RotatedRect box in boxList)
{
CvInvoke.Polylines(triangleRectangleImage, Array.ConvertAll(box.GetVertices(), Point.Round), true, new Bgr(0, 255, 0).MCvScalar, 2);
}
CvInvoke.AddWeighted(alphaImgShape, .5, BlackTransparent(triangleRectangleImage), .5, 0, alphaImgShape);
if (CudaInvoke.HasCuda)
{
using (GpuMat gMatSrc = new GpuMat())
using (GpuMat gMatSrc2 = new GpuMat())
using (GpuMat gMatDst = new GpuMat()) {
gMatSrc.Upload(alphaimg);
gMatSrc2.Upload(alphaImgShape);
CudaInvoke.AlphaComp(gMatSrc, gMatSrc2, gMatDst, AlphaCompTypes.Plus);
gMatDst.Download(alphaimg);
}
}
else
{
img = Overlay(img, alphaImgShape);
}
}
#endregion
#region draw circles
if (detectionVars.calcCircles)
{
Image <Bgr, Byte> circleImage = new Image <Bgr, Byte>(img.Size);
foreach (CircleF circle in circles.Take(10))
{
CvInvoke.Circle(circleImage, Point.Round(circle.Center), (int)circle.Radius, new Bgr(0, 255, 0).MCvScalar, 2);
}
alphaImgShape = new Image <Bgra, byte>(img.Size.Width, img.Size.Height, new Bgra(0, 0, 0, .5));
CvInvoke.AddWeighted(alphaImgShape, .7, BlackTransparent(circleImage), .5, 0, alphaImgShape);
if (CudaInvoke.HasCuda)
{
using (GpuMat gMatSrc = new GpuMat())
using (GpuMat gMatSrc2 = new GpuMat())
using (GpuMat gMatDst = new GpuMat()) {
gMatSrc.Upload(alphaimg);
gMatSrc2.Upload(alphaImgShape);
CudaInvoke.AlphaComp(gMatSrc, gMatSrc2, gMatDst, AlphaCompTypes.Plus);
gMatDst.Download(alphaimg);
}
}
else
{
img = Overlay(img, alphaImgShape);
}
}
#endregion
#region draw lines
if (detectionVars.calcLines)
{
Image <Bgr, Byte> lineImage = new Image <Bgr, Byte>(img.Size);
foreach (LineSegment2D line in lines)
{
CvInvoke.Line(lineImage, line.P1, line.P2, new Bgr(0, 255, 0).MCvScalar, 2);
}
alphaImgShape = new Image <Bgra, byte>(img.Size.Width, img.Size.Height, new Bgra(0, 0, 0, .5));
CvInvoke.AddWeighted(alphaImgShape, .5, BlackTransparent(lineImage), .5, 0, alphaImgShape);
if (CudaInvoke.HasCuda)
{
using (GpuMat gMatSrc = new GpuMat())
using (GpuMat gMatSrc2 = new GpuMat())
using (GpuMat gMatDst = new GpuMat()) {
gMatSrc.Upload(alphaimg);
gMatSrc2.Upload(alphaImgShape);
CudaInvoke.AlphaComp(gMatSrc, gMatSrc2, gMatDst, AlphaCompTypes.Plus);
gMatDst.Download(alphaimg);
}
}
else
{
img = Overlay(img, alphaImgShape);
}
}
#endregion
GC.Collect(); // first time I've had to use this but this program will use as much memory as possible, resulting in corrptions
return(alphaimg.Bitmap ?? frame);
}
public Image<Gray, byte> Solve(Image<Gray, byte> left, Image<Gray, byte> right)
{
var size = left.Size;
using (var leftGpu = new GpuMat(left.Rows, left.Cols, DepthType.Cv16S, 1))
using (var rightGpu = new GpuMat(left.Rows, left.Cols, DepthType.Cv16S, 1))
using (var disparityGpu = new GpuMat(left.Rows, left.Cols, DepthType.Cv16S, 1))
using (var filteredDisparityGpu = new GpuMat(left.Rows, left.Cols, DepthType.Cv16S, 1))
using (var filteredDisparity16S = new Mat(size, DepthType.Cv16S, 1))
using (var filteredDisparity8U = new Mat(size, DepthType.Cv8U, 1))
{
leftGpu.Upload(left.Mat);
rightGpu.Upload(right.Mat);
algorithm.FindStereoCorrespondence(leftGpu, rightGpu, disparityGpu);
filter.Apply(disparityGpu, leftGpu, filteredDisparityGpu);
filteredDisparityGpu.Download(filteredDisparity16S);
CvInvoke.MinMaxLoc(filteredDisparity16S, ref min, ref max, ref minPosition, ref maxPosition);
filteredDisparity16S.ConvertTo(filteredDisparity8U, DepthType.Cv8U, 255.0/(Max - Min));
return new Image<Gray, byte>(filteredDisparity8U.Bitmap);
}
}
static void Run()
{
Image <Gray, Byte> modelImage = new Image <Gray, byte>("box.png");
Image <Gray, Byte> observedImage = new Image <Gray, byte>("box_in_scene.png");
Stopwatch watch;
HomographyMatrix homography = null;
SURFDetector surfCPU = new SURFDetector(500, false);
VectorOfKeyPoint modelKeyPoints;
VectorOfKeyPoint observedKeyPoints;
Matrix <int> indices;
Matrix <float> dist;
Matrix <byte> mask;
if (GpuInvoke.HasCuda)
{
GpuSURFDetector surfGPU = new GpuSURFDetector(surfCPU.SURFParams, 0.01f);
using (GpuImage <Gray, Byte> gpuModelImage = new GpuImage <Gray, byte>(modelImage))
//extract features from the object image
using (GpuMat <float> gpuModelKeyPoints = surfGPU.DetectKeyPointsRaw(gpuModelImage, null))
using (GpuMat <float> gpuModelDescriptors = surfGPU.ComputeDescriptorsRaw(gpuModelImage, null, gpuModelKeyPoints))
using (GpuBruteForceMatcher matcher = new GpuBruteForceMatcher(GpuBruteForceMatcher.DistanceType.L2))
{
modelKeyPoints = new VectorOfKeyPoint();
surfGPU.DownloadKeypoints(gpuModelKeyPoints, modelKeyPoints);
watch = Stopwatch.StartNew();
// extract features from the observed image
using (GpuImage <Gray, Byte> gpuObservedImage = new GpuImage <Gray, byte>(observedImage))
using (GpuMat <float> gpuObservedKeyPoints = surfGPU.DetectKeyPointsRaw(gpuObservedImage, null))
using (GpuMat <float> gpuObservedDescriptors = surfGPU.ComputeDescriptorsRaw(gpuObservedImage, null, gpuObservedKeyPoints))
using (GpuMat <int> gpuMatchIndices = new GpuMat <int>(gpuObservedDescriptors.Size.Height, 2, 1))
using (GpuMat <float> gpuMatchDist = new GpuMat <float>(gpuMatchIndices.Size, 1))
{
observedKeyPoints = new VectorOfKeyPoint();
surfGPU.DownloadKeypoints(gpuObservedKeyPoints, observedKeyPoints);
matcher.KnnMatch(gpuObservedDescriptors, gpuModelDescriptors, gpuMatchIndices, gpuMatchDist, 2, null);
indices = new Matrix <int>(gpuMatchIndices.Size);
dist = new Matrix <float>(indices.Size);
gpuMatchIndices.Download(indices);
gpuMatchDist.Download(dist);
mask = new Matrix <byte>(dist.Rows, 1);
mask.SetValue(255);
Features2DTracker.VoteForUniqueness(dist, 0.8, mask);
int nonZeroCount = CvInvoke.cvCountNonZero(mask);
if (nonZeroCount >= 4)
{
nonZeroCount = Features2DTracker.VoteForSizeAndOrientation(modelKeyPoints, observedKeyPoints, indices, mask, 1.5, 20);
if (nonZeroCount >= 4)
{
homography = Features2DTracker.GetHomographyMatrixFromMatchedFeatures(modelKeyPoints, observedKeyPoints, indices, mask, 3);
}
}
watch.Stop();
}
}
}
else
{
//extract features from the object image
modelKeyPoints = surfCPU.DetectKeyPointsRaw(modelImage, null);
//MKeyPoint[] kpts = modelKeyPoints.ToArray();
Matrix <float> modelDescriptors = surfCPU.ComputeDescriptorsRaw(modelImage, null, modelKeyPoints);
watch = Stopwatch.StartNew();
// extract features from the observed image
observedKeyPoints = surfCPU.DetectKeyPointsRaw(observedImage, null);
Matrix <float> observedDescriptors = surfCPU.ComputeDescriptorsRaw(observedImage, null, observedKeyPoints);
BruteForceMatcher matcher = new BruteForceMatcher(BruteForceMatcher.DistanceType.L2F32);
matcher.Add(modelDescriptors);
int k = 2;
indices = new Matrix <int>(observedDescriptors.Rows, k);
dist = new Matrix <float>(observedDescriptors.Rows, k);
matcher.KnnMatch(observedDescriptors, indices, dist, k, null);
mask = new Matrix <byte>(dist.Rows, 1);
mask.SetValue(255);
Features2DTracker.VoteForUniqueness(dist, 0.8, mask);
int nonZeroCount = CvInvoke.cvCountNonZero(mask);
if (nonZeroCount >= 4)
{
nonZeroCount = Features2DTracker.VoteForSizeAndOrientation(modelKeyPoints, observedKeyPoints, indices, mask, 1.5, 20);
if (nonZeroCount >= 4)
{
homography = Features2DTracker.GetHomographyMatrixFromMatchedFeatures(modelKeyPoints, observedKeyPoints, indices, mask, 3);
}
}
watch.Stop();
}
//Draw the matched keypoints
Image <Bgr, Byte> result = Features2DTracker.DrawMatches(modelImage, modelKeyPoints, observedImage, observedKeyPoints,
indices, new Bgr(255, 255, 255), new Bgr(255, 255, 255), mask, Features2DTracker.KeypointDrawType.NOT_DRAW_SINGLE_POINTS);
#region draw the projected region on the image
if (homography != null)
{ //draw a rectangle along the projected model
Rectangle rect = modelImage.ROI;
PointF[] pts = new PointF[] {
new PointF(rect.Left, rect.Bottom),
new PointF(rect.Right, rect.Bottom),
new PointF(rect.Right, rect.Top),
new PointF(rect.Left, rect.Top)
};
homography.ProjectPoints(pts);
result.DrawPolyline(Array.ConvertAll <PointF, Point>(pts, Point.Round), true, new Bgr(Color.Red), 5);
}
#endregion
ImageViewer.Show(result, String.Format("Matched using {0} in {1} milliseconds", GpuInvoke.HasCuda ? "GPU" : "CPU", watch.ElapsedMilliseconds));
}
/*
public void TestPyrLK()
{
const int MAX_CORNERS = 500;
Capture c = new Capture();
ImageViewer viewer = new ImageViewer();
Image<Gray, Byte> oldImage = null;
Image<Gray, Byte> currentImage = null;
Application.Idle += new EventHandler(delegate(object sender, EventArgs e)
{
if (oldImage == null)
{
oldImage = c.QueryGrayFrame();
}
currentImage = c.QueryGrayFrame();
Features2D.GFTTDetector detector = new Features2D.GFTTDetector(MAX_CORNERS, 0.05, 3, 3);
//PointF[] features = oldImage.GoodFeaturesToTrack(MAX_CORNERS, 0.05, 3.0, 3, false, 0.04)[0];
PointF[] shiftedFeatures;
Byte[] status;
float[] trackErrors;
CvInvoke.CalcOpticalFlowPyrLK(oldImage, currentImage, features, new Size(9, 9), 3, new MCvTermCriteria(20, 0.05),
out shiftedFeatures, out status, out trackErrors);
Image<Gray, Byte> displayImage = currentImage.Clone();
for (int i = 0; i < features.Length; i++)
displayImage.Draw(new LineSegment2DF(features[i], shiftedFeatures[i]), new Gray(), 2);
oldImage = currentImage;
viewer.Image = displayImage;
});
viewer.ShowDialog();
}*/
public void TestPyrLKGPU()
{
if (!CudaInvoke.HasCuda)
return;
const int MAX_CORNERS = 500;
Capture c = new Capture();
ImageViewer viewer = new ImageViewer();
GpuMat oldImage = null;
GpuMat currentImage = null;
using (CudaGoodFeaturesToTrackDetector detector = new CudaGoodFeaturesToTrackDetector(DepthType.Cv8U, 1, MAX_CORNERS, 0.05, 3.0, 3, false, 0.04))
using (CudaDensePyrLKOpticalFlow flow = new CudaDensePyrLKOpticalFlow(new Size(21, 21), 3, 30, false))
{
Application.Idle += new EventHandler(delegate(object sender, EventArgs e)
{
if (oldImage == null)
{
Mat bgrFrame = c.QueryFrame();
using (GpuMat oldBgrImage = new GpuMat(bgrFrame))
{
oldImage = new GpuMat();
CudaInvoke.CvtColor(oldBgrImage, oldImage, ColorConversion.Bgr2Gray);
}
}
using (Mat tmpFrame = c.QueryFrame())
using (GpuMat tmp = new GpuMat(tmpFrame))
{
currentImage = new GpuMat();
CudaInvoke.CvtColor(tmp, currentImage, ColorConversion.Bgr2Gray);
}
using (GpuMat f = new GpuMat())
using (GpuMat vertex = new GpuMat())
using (GpuMat colors = new GpuMat())
using(GpuMat corners = new GpuMat())
{
flow.Calc(oldImage, currentImage, f);
//CudaInvoke.CreateOpticalFlowNeedleMap(u, v, vertex, colors);
detector.Detect(oldImage, corners, null);
//GpuMat<float> detector.Detect(oldImage, null);
/*
//PointF[] features = oldImage.GoodFeaturesToTrack(MAX_CORNERS, 0.05, 3.0, 3, false, 0.04)[0];
PointF[] shiftedFeatures;
Byte[] status;
float[] trackErrors;
OpticalFlow.PyrLK(oldImage, currentImage, features, new Size(9, 9), 3, new MCvTermCriteria(20, 0.05),
out shiftedFeatures, out status, out trackErrors);
*/
Mat displayImage = new Mat();
currentImage.Download(displayImage);
/*
for (int i = 0; i < features.Length; i++)
displayImage.Draw(new LineSegment2DF(features[i], shiftedFeatures[i]), new Gray(), 2);
*/
oldImage = currentImage;
viewer.Image = displayImage;
}
});
viewer.ShowDialog();
}
}
public static bool FindModelImageInObservedImage( Image<Gray, byte> modelImage, Image<Gray, byte> observedImage )
{
var surfCpu = new SURFDetector(500, false);
VectorOfKeyPoint modelKeyPoints;
VectorOfKeyPoint observedKeyPoints;
Matrix<int> indices;
Matrix<byte> mask;
int k = 2;
double uniquenessThreshold = 0.8;
if ( GpuInvoke.HasCuda )
{
GpuSURFDetector surfGpu = new GpuSURFDetector(surfCpu.SURFParams, 0.01f);
using ( GpuImage<Gray, byte> gpuModelImage = new GpuImage<Gray, byte>( modelImage ) )
//extract features from the object image
using ( GpuMat<float> gpuModelKeyPoints = surfGpu.DetectKeyPointsRaw( gpuModelImage, null ) )
using ( GpuMat<float> gpuModelDescriptors = surfGpu.ComputeDescriptorsRaw( gpuModelImage, null, gpuModelKeyPoints ) )
using ( GpuBruteForceMatcher<float> matcher = new GpuBruteForceMatcher<float>( DistanceType.L2 ) )
{
modelKeyPoints = new VectorOfKeyPoint();
surfGpu.DownloadKeypoints( gpuModelKeyPoints, modelKeyPoints );
// extract features from the observed image
using ( GpuImage<Gray, byte> gpuObservedImage = new GpuImage<Gray, byte>( observedImage ) )
using ( GpuMat<float> gpuObservedKeyPoints = surfGpu.DetectKeyPointsRaw( gpuObservedImage, null ) )
using ( GpuMat<float> 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 ( var stream = new Emgu.CV.GPU.Stream() )
{
matcher.KnnMatchSingle( gpuObservedDescriptors, gpuModelDescriptors, gpuMatchIndices, gpuMatchDist, k, null, stream );
indices = new Matrix<int>( gpuMatchIndices.Size );
mask = new Matrix<byte>( gpuMask.Size );
//gpu implementation of voteForUniquess
using ( GpuMat<float> col0 = gpuMatchDist.Col( 0 ) )
using ( GpuMat<float> col1 = gpuMatchDist.Col( 1 ) )
{
GpuInvoke.Multiply( col1, new MCvScalar( uniquenessThreshold ), col1, stream );
GpuInvoke.Compare( col0, col1, gpuMask, CMP_TYPE.CV_CMP_LE, stream );
}
observedKeyPoints = new VectorOfKeyPoint();
surfGpu.DownloadKeypoints( gpuObservedKeyPoints, observedKeyPoints );
//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();
gpuMask.Download( mask );
gpuMatchIndices.Download( indices );
if ( GpuInvoke.CountNonZero( gpuMask ) >= 4 )
{
int nonZeroCount = Features2DToolbox.VoteForSizeAndOrientation(modelKeyPoints, observedKeyPoints, indices, mask, 1.5, 20);
if ( nonZeroCount >= 4 )
{
Features2DToolbox.GetHomographyMatrixFromMatchedFeatures( modelKeyPoints, observedKeyPoints, indices, mask, 2 );
}
if ( (double)nonZeroCount / mask.Height > 0.02 )
{
return true;
}
}
}
}
}
else
{
//extract features from the object image
modelKeyPoints = surfCpu.DetectKeyPointsRaw( modelImage, null );
Matrix<float> modelDescriptors = surfCpu.ComputeDescriptorsRaw(modelImage, null, modelKeyPoints);
// extract features from the observed image
observedKeyPoints = surfCpu.DetectKeyPointsRaw( observedImage, null );
Matrix<float> observedDescriptors = surfCpu.ComputeDescriptorsRaw(observedImage, null, observedKeyPoints);
BruteForceMatcher<float> matcher = new BruteForceMatcher<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 )
{
Features2DToolbox.GetHomographyMatrixFromMatchedFeatures( modelKeyPoints, observedKeyPoints, indices, mask, 2 );
}
}
if ( (double)nonZeroCount/mask.Height > 0.02 )
{
return true;
}
}
//Draw the matched keypoints
//var result = Features2DToolbox.DrawMatches(modelImage, modelKeyPoints, observedImage, observedKeyPoints, indices, new Bgr(0, 0, 255), new Bgr(255, 0, 0), mask, Features2DToolbox.KeypointDrawType.DEFAULT);
//result.Save( @"C:\Users\D.Markachev\Desktop\bleh-keypoints.jpg" );
return false;
}
public void TestCudaPyrLKOpticalFlow()
{
if (!CudaInvoke.HasCuda)
return;
Image<Gray, Byte> prevImg, currImg;
AutoTestVarious.OpticalFlowImage(out prevImg, out currImg);
Mat flow = new Mat();
CudaDensePyrLKOpticalFlow opticalflow = new CudaDensePyrLKOpticalFlow(new Size(21, 21), 3, 30, false);
using (CudaImage<Gray, Byte> prevGpu = new CudaImage<Gray, byte>(prevImg))
using (CudaImage<Gray, byte> currGpu = new CudaImage<Gray, byte>(currImg))
using (GpuMat flowGpu = new GpuMat())
{
opticalflow.Calc(prevGpu, currGpu, flowGpu);
flowGpu.Download(flow);
}
}
/// <summary>
/// Draw the model image and observed image, the matched features and homography projection.
/// </summary>
/// <param name="modelImage">The model image</param>
/// <param name="observedImage">The observed image</param>
/// <param name="matchTime">The output total time for computing the homography matrix.</param>
/// <returns>The model image and observed image, the matched features and homography projection.</returns>
public static Image<Bgr, Byte> Draw(Image<Gray, Byte> modelImage, Image<Gray, byte> observedImage, out long matchTime)
{
Stopwatch watch;
HomographyMatrix homography = null;
SURFDetector surfCPU = new SURFDetector (500, false);
VectorOfKeyPoint modelKeyPoints;
VectorOfKeyPoint observedKeyPoints;
Matrix<int> indices;
Matrix<byte> mask;
int k = 2;
double uniquenessThreshold = 0.8;
if (GpuInvoke.HasCuda) {
GpuSURFDetector surfGPU = new GpuSURFDetector (surfCPU.SURFParams, 0.01f);
using (GpuImage<Gray, Byte> gpuModelImage = new GpuImage<Gray, byte> (modelImage))
//extract features from the object image
using (GpuMat<float> gpuModelKeyPoints = surfGPU.DetectKeyPointsRaw (gpuModelImage, null))
using (GpuMat<float> gpuModelDescriptors = surfGPU.ComputeDescriptorsRaw (gpuModelImage, null, gpuModelKeyPoints))
using (GpuBruteForceMatcher<float> matcher = new GpuBruteForceMatcher<float> (DistanceType.L2)) {
modelKeyPoints = new VectorOfKeyPoint ();
surfGPU.DownloadKeypoints (gpuModelKeyPoints, modelKeyPoints);
watch = Stopwatch.StartNew ();
// extract features from the observed image
using (GpuImage<Gray, Byte> gpuObservedImage = new GpuImage<Gray, byte> (observedImage))
using (GpuMat<float> gpuObservedKeyPoints = surfGPU.DetectKeyPointsRaw (gpuObservedImage, null))
using (GpuMat<float> 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 (Stream stream = new Stream ()) {
matcher.KnnMatchSingle (gpuObservedDescriptors, gpuModelDescriptors, gpuMatchIndices, gpuMatchDist, k, null, stream);
indices = new Matrix<int> (gpuMatchIndices.Size);
mask = new Matrix<byte> (gpuMask.Size);
//gpu implementation of voteForUniquess
using (GpuMat<float> col0 = gpuMatchDist.Col (0))
using (GpuMat<float> col1 = gpuMatchDist.Col (1)) {
GpuInvoke.Multiply (col1, new MCvScalar (uniquenessThreshold), col1, stream);
GpuInvoke.Compare (col0, col1, gpuMask, CMP_TYPE.CV_CMP_LE, stream);
}
observedKeyPoints = new VectorOfKeyPoint ();
surfGPU.DownloadKeypoints (gpuObservedKeyPoints, observedKeyPoints);
//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 ();
gpuMask.Download (mask);
gpuMatchIndices.Download (indices);
if (GpuInvoke.CountNonZero (gpuMask) >= 4) {
int nonZeroCount = Features2DToolbox.VoteForSizeAndOrientation (modelKeyPoints, observedKeyPoints, indices, mask, 1.5, 20);
if (nonZeroCount >= 4)
homography = Features2DToolbox.GetHomographyMatrixFromMatchedFeatures (modelKeyPoints, observedKeyPoints, indices, mask, 2);
}
watch.Stop ();
}
}
} else {
//extract features from the object image
modelKeyPoints = surfCPU.DetectKeyPointsRaw (modelImage, null);
Matrix<float> modelDescriptors = surfCPU.ComputeDescriptorsRaw (modelImage, null, modelKeyPoints);
watch = Stopwatch.StartNew ();
// extract features from the observed image
observedKeyPoints = surfCPU.DetectKeyPointsRaw (observedImage, null);
Matrix<float> observedDescriptors = surfCPU.ComputeDescriptorsRaw (observedImage, null, observedKeyPoints);
BruteForceMatcher<float> matcher = new BruteForceMatcher<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 ();
}
//Draw the matched keypoints
Image<Bgr, Byte> result = Features2DToolbox.DrawMatches (modelImage, modelKeyPoints, observedImage, observedKeyPoints,
indices, new Bgr (255, 255, 255), new Bgr (255, 255, 255), mask, Features2DToolbox.KeypointDrawType.DEFAULT);
#region draw the projected region on the image
if (homography != null) { //draw a rectangle along the projected model
Rectangle rect = modelImage.ROI;
PointF[] pts = new PointF[] {
new PointF (rect.Left, rect.Bottom),
new PointF (rect.Right, rect.Bottom),
new PointF (rect.Right, rect.Top),
new PointF (rect.Left, rect.Top)
};
homography.ProjectPoints (pts);
result.DrawPolyline (Array.ConvertAll<PointF, Point> (pts, Point.Round), true, new Bgr (Color.Red), 5);
}
#endregion
matchTime = watch.ElapsedMilliseconds;
return result;
}
public static void FindMatch(Image<Gray, Byte> modelImage, Image<Gray, byte> observedImage, out long matchTime, out VectorOfKeyPoint modelKeyPoints, out VectorOfKeyPoint observedKeyPoints, out Matrix<int> indices, out Matrix<byte> mask, out HomographyMatrix homography)
{
int k = 2;
double uniquenessThreshold = 0.8;
SURFDetector surfCPU = new SURFDetector(500, false);
Stopwatch watch;
homography = null;
#if !IOS
if (GpuInvoke.HasCuda)
{
GpuSURFDetector surfGPU = new GpuSURFDetector(surfCPU.SURFParams, 0.01f);
using (GpuImage<Gray, Byte> gpuModelImage = new GpuImage<Gray, byte>(modelImage))
//extract features from the object image
using (GpuMat<float> gpuModelKeyPoints = surfGPU.DetectKeyPointsRaw(gpuModelImage, null))
using (GpuMat<float> gpuModelDescriptors = surfGPU.ComputeDescriptorsRaw(gpuModelImage, null, gpuModelKeyPoints))
using (GpuBruteForceMatcher<float> matcher = new GpuBruteForceMatcher<float>(DistanceType.L2))
{
modelKeyPoints = new VectorOfKeyPoint();
surfGPU.DownloadKeypoints(gpuModelKeyPoints, modelKeyPoints);
watch = Stopwatch.StartNew();
// extract features from the observed image
using (GpuImage<Gray, Byte> gpuObservedImage = new GpuImage<Gray, byte>(observedImage))
using (GpuMat<float> gpuObservedKeyPoints = surfGPU.DetectKeyPointsRaw(gpuObservedImage, null))
using (GpuMat<float> 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 (Stream stream = new Stream())
{
matcher.KnnMatchSingle(gpuObservedDescriptors, gpuModelDescriptors, gpuMatchIndices, gpuMatchDist, k, null, stream);
indices = new Matrix<int>(gpuMatchIndices.Size);
mask = new Matrix<byte>(gpuMask.Size);
//gpu implementation of voteForUniquess
using (GpuMat<float> col0 = gpuMatchDist.Col(0))
using (GpuMat<float> col1 = gpuMatchDist.Col(1))
{
GpuInvoke.Multiply(col1, new MCvScalar(uniquenessThreshold), col1, stream);
GpuInvoke.Compare(col0, col1, gpuMask, CMP_TYPE.CV_CMP_LE, stream);
}
observedKeyPoints = new VectorOfKeyPoint();
surfGPU.DownloadKeypoints(gpuObservedKeyPoints, observedKeyPoints);
//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();
gpuMask.Download(mask);
gpuMatchIndices.Download(indices);
if (GpuInvoke.CountNonZero(gpuMask) >= 4)
{
int nonZeroCount = Features2DToolbox.VoteForSizeAndOrientation(modelKeyPoints, observedKeyPoints, indices, mask, 1.5, 20);
if (nonZeroCount >= 4)
homography = Features2DToolbox.GetHomographyMatrixFromMatchedFeatures(modelKeyPoints, observedKeyPoints, indices, mask, 2);
}
watch.Stop();
}
}
}
else
#endif
{
//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);
BruteForceMatcher<float> matcher = new BruteForceMatcher<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 = watch.ElapsedMilliseconds;
}
public void TestCudaUploadDownload()
{
if (!CudaInvoke.HasCuda)
return;
Mat m = new Mat(new Size(480, 320), DepthType.Cv8U, 3);
CvInvoke.Randu(m, new MCvScalar(), new MCvScalar(255, 255, 255) );
#region test for async download & upload
Stream stream = new Stream();
GpuMat gm1 = new GpuMat();
gm1.Upload(m, stream);
Mat m2 = new Mat();
gm1.Download(m2, stream);
stream.WaitForCompletion();
EmguAssert.IsTrue(m.Equals(m2));
#endregion
#region test for blocking download & upload
GpuMat gm2 = new GpuMat();
gm2.Upload(m);
Mat m3 = new Mat();
gm2.Download(m3);
EmguAssert.IsTrue(m.Equals(m3));
#endregion
}
public static bool FindModelImageInObservedImage(Image <Gray, byte> modelImage, Image <Gray, byte> observedImage)
{
var surfCpu = new SURFDetector(500, false);
VectorOfKeyPoint modelKeyPoints;
VectorOfKeyPoint observedKeyPoints;
Matrix <int> indices;
Matrix <byte> mask;
int k = 2;
double uniquenessThreshold = 0.8;
if (GpuInvoke.HasCuda)
{
GpuSURFDetector surfGpu = new GpuSURFDetector(surfCpu.SURFParams, 0.01f);
using (GpuImage <Gray, byte> gpuModelImage = new GpuImage <Gray, byte>(modelImage))
//extract features from the object image
using (GpuMat <float> gpuModelKeyPoints = surfGpu.DetectKeyPointsRaw(gpuModelImage, null))
using (GpuMat <float> gpuModelDescriptors = surfGpu.ComputeDescriptorsRaw(gpuModelImage, null, gpuModelKeyPoints))
using (GpuBruteForceMatcher <float> matcher = new GpuBruteForceMatcher <float>(DistanceType.L2))
{
modelKeyPoints = new VectorOfKeyPoint();
surfGpu.DownloadKeypoints(gpuModelKeyPoints, modelKeyPoints);
// extract features from the observed image
using (GpuImage <Gray, byte> gpuObservedImage = new GpuImage <Gray, byte>(observedImage))
using (GpuMat <float> gpuObservedKeyPoints = surfGpu.DetectKeyPointsRaw(gpuObservedImage, null))
using (GpuMat <float> 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 (var stream = new Emgu.CV.GPU.Stream())
{
matcher.KnnMatchSingle(gpuObservedDescriptors, gpuModelDescriptors, gpuMatchIndices, gpuMatchDist, k, null, stream);
indices = new Matrix <int>(gpuMatchIndices.Size);
mask = new Matrix <byte>(gpuMask.Size);
//gpu implementation of voteForUniquess
using (GpuMat <float> col0 = gpuMatchDist.Col(0))
using (GpuMat <float> col1 = gpuMatchDist.Col(1))
{
GpuInvoke.Multiply(col1, new MCvScalar(uniquenessThreshold), col1, stream);
GpuInvoke.Compare(col0, col1, gpuMask, CMP_TYPE.CV_CMP_LE, stream);
}
observedKeyPoints = new VectorOfKeyPoint();
surfGpu.DownloadKeypoints(gpuObservedKeyPoints, observedKeyPoints);
//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();
gpuMask.Download(mask);
gpuMatchIndices.Download(indices);
if (GpuInvoke.CountNonZero(gpuMask) >= 4)
{
int nonZeroCount = Features2DToolbox.VoteForSizeAndOrientation(modelKeyPoints, observedKeyPoints, indices, mask, 1.5, 20);
if (nonZeroCount >= 4)
{
Features2DToolbox.GetHomographyMatrixFromMatchedFeatures(modelKeyPoints, observedKeyPoints, indices, mask, 2);
}
if ((double)nonZeroCount / mask.Height > 0.02)
{
return(true);
}
}
}
}
}
else
{
//extract features from the object image
modelKeyPoints = surfCpu.DetectKeyPointsRaw(modelImage, null);
Matrix <float> modelDescriptors = surfCpu.ComputeDescriptorsRaw(modelImage, null, modelKeyPoints);
// extract features from the observed image
observedKeyPoints = surfCpu.DetectKeyPointsRaw(observedImage, null);
Matrix <float> observedDescriptors = surfCpu.ComputeDescriptorsRaw(observedImage, null, observedKeyPoints);
BruteForceMatcher <float> matcher = new BruteForceMatcher <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)
{
Features2DToolbox.GetHomographyMatrixFromMatchedFeatures(modelKeyPoints, observedKeyPoints, indices, mask, 2);
}
}
if ((double)nonZeroCount / mask.Height > 0.02)
{
return(true);
}
}
//Draw the matched keypoints
//var result = Features2DToolbox.DrawMatches(modelImage, modelKeyPoints, observedImage, observedKeyPoints, indices, new Bgr(0, 0, 255), new Bgr(255, 0, 0), mask, Features2DToolbox.KeypointDrawType.DEFAULT);
//result.Save( @"C:\Users\D.Markachev\Desktop\bleh-keypoints.jpg" );
return(false);
}
/// <summary>
/// Draw the model image and observed image, the matched features and homography projection.
/// </summary>
/// <param name="modelImage">The model image</param>
/// <param name="observedImage">The observed image</param>
/// <param name="matchTime">The output total time for computing the homography matrix.</param>
/// <returns>The model image and observed image, the matched features and homography projection.</returns>
public static Image <Bgr, Byte> Draw(Image <Gray, Byte> modelImage, Image <Gray, byte> observedImage, out long matchTime)
{
Stopwatch watch;
HomographyMatrix homography = null;
SURFDetector surfCPU = new SURFDetector(500, false);
VectorOfKeyPoint modelKeyPoints;
VectorOfKeyPoint observedKeyPoints;
Matrix <int> indices;
Matrix <byte> mask;
int k = 2;
double uniquenessThreshold = 0.8;
if (GpuInvoke.HasCuda)
{
GpuSURFDetector surfGPU = new GpuSURFDetector(surfCPU.SURFParams, 0.01f);
using (GpuImage <Gray, Byte> gpuModelImage = new GpuImage <Gray, byte>(modelImage))
//extract features from the object image
using (GpuMat <float> gpuModelKeyPoints = surfGPU.DetectKeyPointsRaw(gpuModelImage, null))
using (GpuMat <float> gpuModelDescriptors = surfGPU.ComputeDescriptorsRaw(gpuModelImage, null, gpuModelKeyPoints))
using (GpuBruteForceMatcher <float> matcher = new GpuBruteForceMatcher <float>(DistanceType.L2))
{
modelKeyPoints = new VectorOfKeyPoint();
surfGPU.DownloadKeypoints(gpuModelKeyPoints, modelKeyPoints);
watch = Stopwatch.StartNew();
// extract features from the observed image
using (GpuImage <Gray, Byte> gpuObservedImage = new GpuImage <Gray, byte>(observedImage))
using (GpuMat <float> gpuObservedKeyPoints = surfGPU.DetectKeyPointsRaw(gpuObservedImage, null))
using (GpuMat <float> 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 (Stream stream = new Stream())
{
matcher.KnnMatchSingle(gpuObservedDescriptors, gpuModelDescriptors, gpuMatchIndices, gpuMatchDist, k, null, stream);
indices = new Matrix <int>(gpuMatchIndices.Size);
mask = new Matrix <byte>(gpuMask.Size);
//gpu implementation of voteForUniquess
using (GpuMat <float> col0 = gpuMatchDist.Col(0))
using (GpuMat <float> col1 = gpuMatchDist.Col(1))
{
GpuInvoke.Multiply(col1, new MCvScalar(uniquenessThreshold), col1, stream);
GpuInvoke.Compare(col0, col1, gpuMask, CMP_TYPE.CV_CMP_LE, stream);
}
observedKeyPoints = new VectorOfKeyPoint();
surfGPU.DownloadKeypoints(gpuObservedKeyPoints, observedKeyPoints);
//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();
gpuMask.Download(mask);
gpuMatchIndices.Download(indices);
if (GpuInvoke.CountNonZero(gpuMask) >= 4)
{
int nonZeroCount = Features2DToolbox.VoteForSizeAndOrientation(modelKeyPoints, observedKeyPoints, indices, mask, 1.5, 20);
if (nonZeroCount >= 4)
{
homography = Features2DToolbox.GetHomographyMatrixFromMatchedFeatures(modelKeyPoints, observedKeyPoints, indices, mask, 2);
}
}
watch.Stop();
}
}
}
else
{
//extract features from the object image
modelKeyPoints = surfCPU.DetectKeyPointsRaw(modelImage, null);
Matrix <float> modelDescriptors = surfCPU.ComputeDescriptorsRaw(modelImage, null, modelKeyPoints);
watch = Stopwatch.StartNew();
// extract features from the observed image
observedKeyPoints = surfCPU.DetectKeyPointsRaw(observedImage, null);
Matrix <float> observedDescriptors = surfCPU.ComputeDescriptorsRaw(observedImage, null, observedKeyPoints);
BruteForceMatcher <float> matcher = new BruteForceMatcher <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();
}
//Draw the matched keypoints
Image <Bgr, Byte> result = Features2DToolbox.DrawMatches(modelImage, modelKeyPoints, observedImage, observedKeyPoints,
indices, new Bgr(255, 255, 255), new Bgr(255, 255, 255), mask, Features2DToolbox.KeypointDrawType.DEFAULT);
#region draw the projected region on the image
if (homography != null)
{ //draw a rectangle along the projected model
Rectangle rect = modelImage.ROI;
PointF[] pts = new PointF[] {
new PointF(rect.Left, rect.Bottom),
new PointF(rect.Right, rect.Bottom),
new PointF(rect.Right, rect.Top),
new PointF(rect.Left, rect.Top)
};
homography.ProjectPoints(pts);
result.DrawPolyline(Array.ConvertAll <PointF, Point>(pts, Point.Round), true, new Bgr(Color.Red), 5);
}
#endregion
matchTime = watch.ElapsedMilliseconds;
return(result);
}
private Image <Bgr, byte> Match(Image <Bgr, byte> image1, Image <Bgr, byte> image2, int flag)
{
HomographyMatrix homography = null;
SURFDetector surfDetectorCPU = new SURFDetector(500, false);
int k = 2; //number of matches that we want ot find between image1 and image2
double uniquenessThreshold = 0.8;
Matrix <int> indices;
Matrix <byte> mask;
VectorOfKeyPoint KeyPointsImage1;
VectorOfKeyPoint KeyPointsImage2;
Image <Gray, Byte> Image1G = image1.Convert <Gray, Byte>();
Image <Gray, Byte> Image2G = image2.Convert <Gray, Byte>();
if (GpuInvoke.HasCuda) //Using CUDA, the GPUs can be used for general purpose processing (i.e., not exclusively graphics), speed up performance
{
Console.WriteLine("Here");
GpuSURFDetector surfDetectorGPU = new GpuSURFDetector(surfDetectorCPU.SURFParams, 0.01f);
// extract features from Image1
using (GpuImage <Gray, Byte> gpuImage1 = new GpuImage <Gray, byte>(Image1G)) //convert CPU input image to GPUImage(greyscale)
using (GpuMat <float> gpuKeyPointsImage1 = surfDetectorGPU.DetectKeyPointsRaw(gpuImage1, null)) //find key points for image
using (GpuMat <float> gpuDescriptorsImage1 = surfDetectorGPU.ComputeDescriptorsRaw(gpuImage1, null, gpuKeyPointsImage1)) //calculate descriptor for each key point
using (GpuBruteForceMatcher <float> matcher = new GpuBruteForceMatcher <float>(DistanceType.L2)) //create a new matcher object
{
KeyPointsImage1 = new VectorOfKeyPoint();
surfDetectorGPU.DownloadKeypoints(gpuKeyPointsImage1, KeyPointsImage1); //copy the Matrix from GPU to CPU
// extract features from Image2
using (GpuImage <Gray, Byte> gpuImage2 = new GpuImage <Gray, byte>(Image2G))
using (GpuMat <float> gpuKeyPointsImage2 = surfDetectorGPU.DetectKeyPointsRaw(gpuImage2, null))
using (GpuMat <float> gpuDescriptorsImage2 = surfDetectorGPU.ComputeDescriptorsRaw(gpuImage2, null, gpuKeyPointsImage2))
//for each descriptor of each image2 , we find k best matching points and their distances from image1 descriptors
using (GpuMat <int> gpuMatchIndices = new GpuMat <int>(gpuDescriptorsImage2.Size.Height, k, 1, true)) //stores indices of k best mathces
using (GpuMat <float> gpuMatchDist = new GpuMat <float>(gpuDescriptorsImage2.Size.Height, k, 1, true)) //stores distance of k best matches
using (GpuMat <Byte> gpuMask = new GpuMat <byte>(gpuMatchIndices.Size.Height, 1, 1)) //stores result of comparison
using (Stream stream = new Stream())
{
matcher.KnnMatchSingle(gpuDescriptorsImage2, gpuDescriptorsImage1, gpuMatchIndices, gpuMatchDist, k, null, stream); //matching descriptors of image2 to image1 and storing the k best indices and corresponding distances
indices = new Matrix <int>(gpuMatchIndices.Size);
mask = new Matrix <byte>(gpuMask.Size);
//gpu implementation of voteForUniquess
using (GpuMat <float> col0 = gpuMatchDist.Col(0))
using (GpuMat <float> col1 = gpuMatchDist.Col(1))
{
GpuInvoke.Multiply(col1, new MCvScalar(uniquenessThreshold), col1, stream); //by setting stream, we perform an Async Task
GpuInvoke.Compare(col0, col1, gpuMask, CMP_TYPE.CV_CMP_LE, stream); //col0 >= 0.8col1 , only then is it considered a good match
}
KeyPointsImage2 = new VectorOfKeyPoint();
surfDetectorGPU.DownloadKeypoints(gpuKeyPointsImage2, KeyPointsImage2);
//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();
gpuMask.Download(mask);
gpuMatchIndices.Download(indices);
if (GpuInvoke.CountNonZero(gpuMask) >= 4)
{
int nonZeroCount = Features2DToolbox.VoteForSizeAndOrientation(KeyPointsImage1, KeyPointsImage2, indices, mask, 1.5, 20); //count the number of nonzero points in the mask(this stored the comparison result of col0 >= 0.8col1)
//we can create a homography matrix only if we have atleast 4 matching points
if (nonZeroCount >= 4)
{
homography = Features2DToolbox.GetHomographyMatrixFromMatchedFeatures(KeyPointsImage1, KeyPointsImage2, indices, mask, 2);
}
}
}
}
}
else
{
Console.WriteLine("No CUDA");
//extract features from image2
KeyPointsImage1 = new VectorOfKeyPoint();
Matrix <float> DescriptorsImage1 = surfDetectorCPU.DetectAndCompute(Image1G, null, KeyPointsImage1);
//extract features from image1
KeyPointsImage2 = new VectorOfKeyPoint();
Matrix <float> DescriptorsImage2 = surfDetectorCPU.DetectAndCompute(Image2G, null, KeyPointsImage2);
BruteForceMatcher <float> matcher = new BruteForceMatcher <float>(DistanceType.L2);
matcher.Add(DescriptorsImage1);
indices = new Matrix <int>(DescriptorsImage2.Rows, k);
using (Matrix <float> dist = new Matrix <float>(DescriptorsImage2.Rows, k))
{
matcher.KnnMatch(DescriptorsImage2, 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(KeyPointsImage1, KeyPointsImage2, indices, mask, 1.5, 20);
if (nonZeroCount >= 4)
{
homography = Features2DToolbox.GetHomographyMatrixFromMatchedFeatures(KeyPointsImage1, KeyPointsImage2, indices, mask, 2);
}
}
}
Image <Bgr, Byte> mImage = image1.Convert <Bgr, Byte>();
Image <Bgr, Byte> oImage = image2.Convert <Bgr, Byte>();
Image <Bgr, Byte> result = new Image <Bgr, byte>(mImage.Width + oImage.Width, mImage.Height);
//Image<Bgr, Byte> temp = Features2DToolbox.DrawMatches(image1, KeyPointsImage1, image2, KeyPointsImage2, indices, new Bgr(255, 255, 255), new Bgr(255, 255, 255), mask, Features2DToolbox.KeypointDrawType.DEFAULT);
if (homography != null)
{ //draw a rectangle along the projected model
Rectangle rect = image1.ROI;
PointF[] pts = new PointF[] {
new PointF(rect.Left, rect.Bottom),
new PointF(rect.Right, rect.Bottom),
new PointF(rect.Right, rect.Top),
new PointF(rect.Left, rect.Top)
};
homography.ProjectPoints(pts);
HomographyMatrix origin = new HomographyMatrix(); //I perform a copy of the left image with a not real shift operation on the origin
origin.SetIdentity();
origin.Data[0, 2] = 0;
origin.Data[1, 2] = 0;
Image <Bgr, Byte> mosaic = new Image <Bgr, byte>(mImage.Width + oImage.Width, mImage.Height * 2);
Image <Bgr, byte> warp_image = mosaic.Clone();
mosaic = mImage.WarpPerspective(origin, mosaic.Width, mosaic.Height, Emgu.CV.CvEnum.INTER.CV_INTER_LINEAR, Emgu.CV.CvEnum.WARP.CV_WARP_DEFAULT, new Bgr(0, 0, 0));
warp_image = oImage.WarpPerspective(homography, warp_image.Width, warp_image.Height, Emgu.CV.CvEnum.INTER.CV_INTER_LINEAR, Emgu.CV.CvEnum.WARP.CV_WARP_INVERSE_MAP, new Bgr(200, 0, 0));
Image <Gray, byte> warp_image_mask = oImage.Convert <Gray, byte>();
warp_image_mask.SetValue(new Gray(255));
Image <Gray, byte> warp_mosaic_mask = mosaic.Convert <Gray, byte>();
warp_mosaic_mask.SetZero();
warp_mosaic_mask = warp_image_mask.WarpPerspective(homography, warp_mosaic_mask.Width, warp_mosaic_mask.Height, Emgu.CV.CvEnum.INTER.CV_INTER_LINEAR, Emgu.CV.CvEnum.WARP.CV_WARP_INVERSE_MAP, new Gray(0));
warp_image.Copy(mosaic, warp_mosaic_mask);
if (flag == 1)
{
Console.WriteLine("Using Image Blending");
return(blend(mosaic, warp_image, warp_mosaic_mask, 2));
}
else
{
Console.WriteLine("No Image Blending");
return(mosaic);
}
}
return(null);
}
public void TestCudaBroxOpticalFlow()
{
if (!CudaInvoke.HasCuda)
return;
Image<Gray, Byte> prevImg, currImg;
AutoTestVarious.OpticalFlowImage(out prevImg, out currImg);
Mat flow = new Mat();
CudaBroxOpticalFlow opticalflow = new CudaBroxOpticalFlow();
using (CudaImage<Gray, float> prevGpu = new CudaImage<Gray, float>(prevImg.Convert<Gray, float>()))
using (CudaImage<Gray, float> currGpu = new CudaImage<Gray, float>(currImg.Convert<Gray, float>()))
using (GpuMat flowGpu = new GpuMat())
{
opticalflow.Calc(prevGpu, currGpu, flowGpu);
flowGpu.Download(flow);
}
}