public void TestSimpleBlobDetector() { Mat box = EmguAssert.LoadMat("box.png"); SimpleBlobDetectorParams p = new SimpleBlobDetectorParams(); SimpleBlobDetector detector = new SimpleBlobDetector(p); MKeyPoint[] keypoints = detector.Detect(box); }
public void TestOclKernel() { if (CvInvoke.HaveOpenCL && CvInvoke.UseOpenCL) { Ocl.Device defaultDevice = Ocl.Device.Default; Mat img = EmguAssert.LoadMat("lena.jpg"); Mat imgGray = new Mat(); CvInvoke.CvtColor(img, imgGray, ColorConversion.Bgr2Gray); Mat imgFloat = new Mat(); imgGray.ConvertTo(imgFloat, DepthType.Cv32F, 1.0 / 255); UMat umat = imgFloat.GetUMat(AccessType.Read, UMat.Usage.AllocateDeviceMemory); UMat umatDst = new UMat(); umatDst.Create(umat.Rows, umat.Cols, DepthType.Cv32F, umat.NumberOfChannels, UMat.Usage.AllocateDeviceMemory); String buildOpts = String.Format("-D dstT={0}", Ocl.OclInvoke.TypeToString(umat.Depth)); String sourceStr = @" __constant sampler_t samplerLN = CLK_NORMALIZED_COORDS_FALSE | CLK_ADDRESS_CLAMP_TO_EDGE | CLK_FILTER_LINEAR; __kernel void shift(const image2d_t src, float shift_x, float shift_y, __global uchar* dst, int dst_step, int dst_offset, int dst_rows, int dst_cols) { int x = get_global_id(0); int y = get_global_id(1); if (x >= dst_cols) return; int dst_index = mad24(y, dst_step, mad24(x, (int)sizeof(dstT), dst_offset)); __global dstT *dstf = (__global dstT *)(dst + dst_index); float2 coord = (float2)((float)x+0.5f+shift_x, (float)y+0.5f+shift_y); dstf[0] = (dstT)read_imagef(src, samplerLN, coord).x; }"; using (CvString errorMsg = new CvString()) using (Ocl.ProgramSource ps = new Ocl.ProgramSource(sourceStr)) using (Ocl.Kernel kernel = new Ocl.Kernel()) using (Ocl.Image2D image2d = new Ocl.Image2D(umat)) using (Ocl.KernelArg ka = new Ocl.KernelArg(Ocl.KernelArg.Flags.ReadWrite, umatDst)) { float shiftX = 100.5f; float shiftY = -50.0f; bool success = kernel.Create("shift", ps, buildOpts, errorMsg); EmguAssert.IsTrue(success, errorMsg.ToString()); int idx = 0; idx = kernel.Set(idx, image2d); idx = kernel.Set(idx, ref shiftX); idx = kernel.Set(idx, ref shiftY); idx = kernel.Set(idx, ka); IntPtr[] globalThreads = new IntPtr[] { new IntPtr(umat.Cols), new IntPtr(umat.Rows), new IntPtr(1) }; success = kernel.Run(globalThreads, null, true); EmguAssert.IsTrue(success, "Failed to run the kernel"); using (Mat matDst = umatDst.GetMat(AccessType.Read)) using (Mat saveMat = new Mat()) { matDst.ConvertTo(saveMat, DepthType.Cv8U, 255.0); saveMat.Save("tmp.jpg"); } } } }
public void TestChessboardCalibrationSolvePnPRansac() { Size patternSize = new Size(9, 6); Mat chessboardImage = EmguAssert.LoadMat("left01.jpg", ImreadModes.Grayscale); Util.VectorOfPointF corners = new Util.VectorOfPointF(); bool patternWasFound = CvInvoke.FindChessboardCorners(chessboardImage, patternSize, corners); CvInvoke.CornerSubPix( chessboardImage, corners, new Size(10, 10), new Size(-1, -1), new MCvTermCriteria(0.05)); MCvPoint3D32f[] objectPts = CalcChessboardCorners(patternSize, 1.0f); using (VectorOfVectorOfPoint3D32F ptsVec = new VectorOfVectorOfPoint3D32F(new MCvPoint3D32f[][] { objectPts })) using (VectorOfVectorOfPointF imgPtsVec = new VectorOfVectorOfPointF(corners)) using (Mat cameraMatrix = new Mat()) using (Mat distortionCoeff = new Mat()) using (VectorOfMat rotations = new VectorOfMat()) using (VectorOfMat translations = new VectorOfMat()) { Mat calMat = CvInvoke.InitCameraMatrix2D(ptsVec, imgPtsVec, chessboardImage.Size, 0); Matrix <double> calMatF = new Matrix <double>(calMat.Rows, calMat.Cols, calMat.NumberOfChannels); calMat.CopyTo(calMatF); double error = CvInvoke.CalibrateCamera(ptsVec, imgPtsVec, chessboardImage.Size, cameraMatrix, distortionCoeff, rotations, translations, CalibType.Default, new MCvTermCriteria(30, 1.0e-10)); using (Mat rotation = new Mat()) using (Mat translation = new Mat()) using (VectorOfPoint3D32F vpObject = new VectorOfPoint3D32F(objectPts)) { CvInvoke.SolvePnPRansac( vpObject, corners, cameraMatrix, distortionCoeff, rotation, translation); } CvInvoke.DrawChessboardCorners(chessboardImage, patternSize, corners, patternWasFound); using (Mat undistorted = new Mat()) { CvInvoke.Undistort(chessboardImage, undistorted, cameraMatrix, distortionCoeff); String title = String.Format("Reprojection error: {0}", error); //CvInvoke.NamedWindow(title); //CvInvoke.Imshow(title, undistorted); //CvInvoke.WaitKey(); //UI.ImageViewer.Show(undistorted, String.Format("Reprojection error: {0}", error)); } } }
public async Task TestWeChatQRCode() { using (Mat m = EmguAssert.LoadMat("link_github_ocv.jpg")) using (Emgu.CV.Models.WeChatQRCodeDetector detector = new WeChatQRCodeDetector()) { await detector.Init(DownloadManager_OnDownloadProgressChanged); String text = detector.ProcessAndRender(m, m); } }
public async Task TestPedestrianDetector() { using (Mat m = EmguAssert.LoadMat("pedestrian")) using (Emgu.CV.Models.PedestrianDetector detector = new PedestrianDetector()) { await detector.Init(DownloadManager_OnDownloadProgressChanged); String text = detector.ProcessAndRender(m, m); } }
public void TestMatPixelAccess() { Mat m1 = EmguAssert.LoadMat("lena.jpg"); byte[] data = new byte[m1.Width * m1.Height * 3]; //3 channel bgr image data GCHandle handle = GCHandle.Alloc(data, GCHandleType.Pinned); using (Mat m2 = new Mat(m1.Size, DepthType.Cv8U, 3, handle.AddrOfPinnedObject(), m1.Width * 3)) CvInvoke.BitwiseNot(m1, m2); handle.Free(); //now the data array contains the pixel data of the inverted lena image. //note that if the m2 Mat was allocated with the wrong size, data[] array will contains all 0s, and no exception will be thrown //so be really careful when performing the above operations. }
public static bool TestFeature2DTracker(Feature2D keyPointDetector, Feature2D descriptorGenerator) { //for (int k = 0; k < 1; k++) { Feature2D feature2D = null; if (keyPointDetector == descriptorGenerator) { feature2D = keyPointDetector as Feature2D; } Mat modelImage = EmguAssert.LoadMat("box.png"); //Image<Gray, Byte> modelImage = new Image<Gray, byte>("stop.jpg"); //modelImage = modelImage.Resize(400, 400, true); //modelImage._EqualizeHist(); #region extract features from the object image Stopwatch stopwatch = Stopwatch.StartNew(); VectorOfKeyPoint modelKeypoints = new VectorOfKeyPoint(); Mat modelDescriptors = new Mat(); if (feature2D != null) { feature2D.DetectAndCompute(modelImage, null, modelKeypoints, modelDescriptors, false); } else { keyPointDetector.DetectRaw(modelImage, modelKeypoints); descriptorGenerator.Compute(modelImage, modelKeypoints, modelDescriptors); } stopwatch.Stop(); EmguAssert.WriteLine(String.Format("Time to extract feature from model: {0} milli-sec", stopwatch.ElapsedMilliseconds)); #endregion //Image<Gray, Byte> observedImage = new Image<Gray, byte>("traffic.jpg"); Image <Gray, Byte> observedImage = EmguAssert.LoadImage <Gray, byte>("box_in_scene.png"); //Image<Gray, Byte> observedImage = modelImage.Rotate(45, new Gray(0.0)); //image = image.Resize(400, 400, true); //observedImage._EqualizeHist(); #region extract features from the observed image stopwatch.Reset(); stopwatch.Start(); VectorOfKeyPoint observedKeypoints = new VectorOfKeyPoint(); using (Mat observedDescriptors = new Mat()) { if (feature2D != null) { feature2D.DetectAndCompute(observedImage, null, observedKeypoints, observedDescriptors, false); } else { keyPointDetector.DetectRaw(observedImage, observedKeypoints); descriptorGenerator.Compute(observedImage, observedKeypoints, observedDescriptors); } stopwatch.Stop(); EmguAssert.WriteLine(String.Format("Time to extract feature from image: {0} milli-sec", stopwatch.ElapsedMilliseconds)); #endregion //Merge the object image and the observed image into one big image for display Image <Gray, Byte> res = modelImage.ToImage <Gray, Byte>().ConcateVertical(observedImage); Rectangle rect = new Rectangle(Point.Empty, modelImage.Size); 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) }; Mat homography = null; stopwatch.Reset(); stopwatch.Start(); int k = 2; DistanceType dt = modelDescriptors.Depth == CvEnum.DepthType.Cv8U ? DistanceType.Hamming : DistanceType.L2; //using (Matrix<int> indices = new Matrix<int>(observedDescriptors.Rows, k)) //using (Matrix<float> dist = new Matrix<float>(observedDescriptors.Rows, k)) using (VectorOfVectorOfDMatch matches = new VectorOfVectorOfDMatch()) using (BFMatcher matcher = new BFMatcher(dt)) { //ParamDef[] parameterDefs = matcher.GetParams(); matcher.Add(modelDescriptors); matcher.KnnMatch(observedDescriptors, matches, k, null); Mat mask = new Mat(matches.Size, 1, DepthType.Cv8U, 1); mask.SetTo(new MCvScalar(255)); //mask.SetValue(255); Features2DToolbox.VoteForUniqueness(matches, 0.8, mask); 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); } } } stopwatch.Stop(); EmguAssert.WriteLine(String.Format("Time for feature matching: {0} milli-sec", stopwatch.ElapsedMilliseconds)); bool success = false; if (homography != null) { PointF[] points = pts.Clone() as PointF[]; points = CvInvoke.PerspectiveTransform(points, homography); //homography.ProjectPoints(points); for (int i = 0; i < points.Length; i++) { points[i].Y += modelImage.Height; } res.DrawPolyline( #if NETFX_CORE Extensions. #else Array. #endif ConvertAll <PointF, Point>(points, Point.Round), true, new Gray(255.0), 5); success = true; } //Emgu.CV.UI.ImageViewer.Show(res); return(success); } /* * stopwatch.Reset(); stopwatch.Start(); * //set the initial region to be the whole image * using (Image<Gray, Single> priorMask = new Image<Gray, float>(observedImage.Size)) * { * priorMask.SetValue(1.0); * homography = tracker.CamShiftTrack( * observedFeatures, * (RectangleF)observedImage.ROI, * priorMask); * } * Trace.WriteLine(String.Format("Time for feature tracking: {0} milli-sec", stopwatch.ElapsedMilliseconds)); * * if (homography != null) //set the initial tracking window to be the whole image * { * PointF[] points = pts.Clone() as PointF[]; * homography.ProjectPoints(points); * * for (int i = 0; i < points.Length; i++) * points[i].Y += modelImage.Height; * res.DrawPolyline(Array.ConvertAll<PointF, Point>(points, Point.Round), true, new Gray(255.0), 5); * return true; * } * else * { * return false; * }*/ } }
public void TestOclKernel() { if (CvInvoke.HaveOpenCL && CvInvoke.UseOpenCL) { Ocl.Device defaultDevice = Ocl.Device.Default; Mat img = EmguAssert.LoadMat("lena.jpg"); Mat imgGray = new Mat(); CvInvoke.CvtColor(img, imgGray, ColorConversion.Bgr2Gray); Mat imgFloat = new Mat(); imgGray.ConvertTo(imgFloat, DepthType.Cv32F, 1.0 / 255); UMat umat = imgFloat.GetUMat(AccessType.Read, UMat.Usage.AllocateDeviceMemory); UMat umatDst = new UMat(); umatDst.Create(umat.Rows, umat.Cols, DepthType.Cv32F, umat.NumberOfChannels, UMat.Usage.AllocateDeviceMemory); String buildOpts = String.Format("-D dstT={0}", Ocl.OclInvoke.TypeToString(umat.Depth)); String sourceStr = @" __kernel void magnutude_filter_8u( __global const uchar* src, int src_step, int src_offset, __global uchar* dst, int dst_step, int dst_offset, int dst_rows, int dst_cols, float scale) { int x = get_global_id(0); int y = get_global_id(1); if (x < dst_cols && y < dst_rows) { int dst_idx = y * dst_step + x + dst_offset; if (x > 0 && x < dst_cols - 1 && y > 0 && y < dst_rows - 2) { int src_idx = y * src_step + x + src_offset; int dx = (int)src[src_idx]*2 - src[src_idx - 1] - src[src_idx + 1]; int dy = (int)src[src_idx]*2 - src[src_idx - 1*src_step] - src[src_idx + 1*src_step]; dst[dst_idx] = convert_uchar_sat(sqrt((float)(dx*dx + dy*dy)) * scale); } else { dst[dst_idx] = 0; } } }"; using (CvString errorMsg = new CvString()) using (Ocl.ProgramSource ps = new Ocl.ProgramSource(sourceStr)) using (Ocl.Kernel kernel = new Ocl.Kernel()) using (Ocl.Image2D image2d = new Ocl.Image2D(umat)) using (Ocl.KernelArg ka = new Ocl.KernelArg(Ocl.KernelArg.Flags.ReadWrite, umatDst)) { float shiftX = 100.5f; float shiftY = -50.0f; bool success = kernel.Create("myshift", ps, buildOpts, errorMsg); EmguAssert.IsTrue(success, errorMsg.ToString()); int idx = 0; idx = kernel.Set(idx, image2d); idx = kernel.Set(idx, ref shiftX); idx = kernel.Set(idx, ref shiftY); idx = kernel.Set(idx, ka); IntPtr[] globalThreads = new IntPtr[] { new IntPtr(umat.Cols), new IntPtr(umat.Rows), new IntPtr(1) }; success = kernel.Run(globalThreads, null, true); EmguAssert.IsTrue(success, "Failed to run the kernel"); using (Mat matDst = umatDst.GetMat(AccessType.Read)) using (Mat saveMat = new Mat()) { matDst.ConvertTo(saveMat, DepthType.Cv8U, 255.0); saveMat.Save("tmp.jpg"); } } } }