C# GpuMat类代码示例

 private Rectangle[] FindEyes(string eyeFileName, CudaImage<Gray, Byte> image)
 {
     using (CudaCascadeClassifier eye = new CudaCascadeClassifier(eyeFileName))
     using (GpuMat eyeRegionMat = new GpuMat())
     {
         eye.DetectMultiScale(image, eyeRegionMat);
         Rectangle[] eyeRegion = eye.Convert(eyeRegionMat);
         return eyeRegion;
     }
 }

 public void TestGpuMatContinuous()
 {
    if (!CudaInvoke.HasCuda)
       return;
    GpuMat<Byte> mat = new GpuMat<byte>(1200, 640, 1, true);
    Assert.IsTrue(mat.IsContinuous);
 }

        public Mat Calculate(Bitmap referenceBitmap, Bitmap currentBitmap)
        {
            Mat homography;

            using (var detector = new CudaSURF(threshold))
            using (var model = new Image<Gray, byte>(referenceBitmap))
            using (var observed = new Image<Gray, byte>(currentBitmap))
            using (var modelMat = new GpuMat(model))
            using (var modelKeyPointsRaw = detector.DetectKeyPointsRaw(modelMat))
            using (var modelKeyPoints = new VectorOfKeyPoint())
            using (var modelDescriptorsRaw = detector.ComputeDescriptorsRaw(modelMat, null, modelKeyPointsRaw))
            using (var observedMat = new GpuMat(observed))
            using (var observedKeyPointsRaw = detector.DetectKeyPointsRaw(observedMat))
            using (var observedKeyPoints = new VectorOfKeyPoint())
            using (var observedDescriptorsRaw = detector.ComputeDescriptorsRaw(observedMat, null, observedKeyPointsRaw))
            using (
                var matcher =
                    new CudaBFMatcher(DistanceType.L2))
            using (var matches = new VectorOfVectorOfDMatch())
            {
                matcher.KnnMatch(observedDescriptorsRaw, modelDescriptorsRaw, matches, k);

                detector.DownloadKeypoints(modelKeyPointsRaw, modelKeyPoints);
                detector.DownloadKeypoints(observedKeyPointsRaw, observedKeyPoints);

                homography = TryFindHomography(modelKeyPoints, observedKeyPoints, matches);
            }

            return homography;
        }

      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 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);
            }
        }

      public void TestCudaImageAsyncOps()
      {
         if (CudaInvoke.HasCuda)
         {
            int counter = 0;
            Stopwatch watch = Stopwatch.StartNew();
            using (GpuMat img1 = new GpuMat(3000, 2000, DepthType.Cv8U, 3))
            using (GpuMat img2 = new GpuMat(3000, 2000, DepthType.Cv8U, 3))
            using (GpuMat img3 = new GpuMat())
            using (Stream stream = new Stream())
            using (GpuMat mat1 = new GpuMat())
            {
               img1.ConvertTo(mat1, DepthType.Cv8U, 1, 0, stream);
               while (!stream.Completed)
               {
                  if (counter <= int.MaxValue) counter++;
               }
               Trace.WriteLine(String.Format("Counter has been incremented {0} times", counter));

               counter = 0;
               CudaInvoke.CvtColor(img2, img3, CvToolbox.GetColorCvtCode(typeof(Bgr), typeof(Gray)), 1, stream);
               while (!stream.Completed)
               {
                  if (counter <= int.MaxValue) counter++;
               }
               Trace.WriteLine(String.Format("Counter has been incremented {0} times", counter));
            }
            watch.Stop();
            Trace.WriteLine(String.Format("Total time: {0} milliseconds", watch.ElapsedMilliseconds));
         }
      }

      /// <summary>
      /// Create a Cuda cascade classifier using the specific file
      /// </summary>
      /// <param name="fileName">The file to create the classifier from</param>
      public CudaCascadeClassifier(String fileName)
      {
#if !NETFX_CORE
         Debug.Assert(File.Exists(fileName), String.Format("The Cascade file {0} does not exist.", fileName));
#endif
         using (CvString s = new CvString(fileName))
            _ptr = CudaInvoke.cudaCascadeClassifierCreate(s);
         _buffer = new GpuMat(1, 100, DepthType.Cv32S, 4);
      }

 /// <summary>
 /// Detect keypoints in the CudaImage
 /// </summary>
 /// <param name="img">The image where keypoints will be detected from</param>
 /// <param name="mask">The optional mask, can be null if not needed</param>
 /// <returns>An array of keypoints</returns>
 public MKeyPoint[] DetectKeyPoints(GpuMat img, GpuMat mask)
 {
    using (GpuMat tmp = DetectKeyPointsRaw(img, mask))
    using (VectorOfKeyPoint kpts = new VectorOfKeyPoint())
    {
       DownloadKeypoints(tmp, kpts);
       return kpts.ToArray();
    }
 }

        /// <summary>
        /// Find the pedestrian in the image
        /// </summary>
        /// <param name="image">The image</param>
        /// <param name="processingTime">The pedestrian detection time in milliseconds</param>
        /// <returns>The region where pedestrians are detected</returns>
        public static Rectangle[] Find(Mat image, bool tryUseCuda, bool tryUseOpenCL, out long processingTime)
        {
            Stopwatch watch;
            Rectangle[] regions;

#if !(IOS || NETFX_CORE)
            //check if there is a compatible Cuda device to run pedestrian detection
            if (tryUseCuda && CudaInvoke.HasCuda)
            {  //this is the Cuda version
                using (CudaHOG des = new CudaHOG(new Size(64, 128), new Size(16, 16), new Size(8, 8), new Size(8, 8)))
                {
                    des.SetSVMDetector(des.GetDefaultPeopleDetector());

                    watch = Stopwatch.StartNew();
                    using (GpuMat cudaBgr = new GpuMat(image))
                    using (GpuMat cudaBgra = new GpuMat())
                    using (VectorOfRect vr = new VectorOfRect())
                    {
                        CudaInvoke.CvtColor(cudaBgr, cudaBgra, ColorConversion.Bgr2Bgra);
                        des.DetectMultiScale(cudaBgra, vr);
                        regions = vr.ToArray();
                    }
                }
            }
            else
#endif
            {
                //Many opencl functions require opencl compatible gpu devices. 
                //As of opencv 3.0-alpha, opencv will crash if opencl is enable and only opencv compatible cpu device is presented
                //So we need to call CvInvoke.HaveOpenCLCompatibleGpuDevice instead of CvInvoke.HaveOpenCL (which also returns true on a system that only have cpu opencl devices).
                CvInvoke.UseOpenCL = tryUseOpenCL && CvInvoke.HaveOpenCLCompatibleGpuDevice;

                //this is the CPU/OpenCL version
                using (HOGDescriptor des = new HOGDescriptor())
                {
                    des.SetSVMDetector(HOGDescriptor.GetDefaultPeopleDetector());

                    //load the image to umat so it will automatically use opencl is available
                    UMat umat = image.ToUMat(AccessType.Read);

                    watch = Stopwatch.StartNew();

                    MCvObjectDetection[] results = des.DetectMultiScale(umat);
                    regions = new Rectangle[results.Length];
                    for (int i = 0; i < results.Length; i++)
                        regions[i] = results[i].Rect;
                    watch.Stop();
                }
            }

            processingTime = watch.ElapsedMilliseconds;

            return regions;
        }

      /// <summary>
      /// Find the pedestrian in the image
      /// </summary>
      /// <param name="image">The image</param>
      /// <param name="processingTime">The pedestrian detection time in milliseconds</param>
      /// <returns>The region where pedestrians are detected</returns>
      public static Rectangle[] Find(Mat image, bool tryUseCuda, out long processingTime)
      {
         Stopwatch watch;
         Rectangle[] regions;

#if !(__IOS__ || NETFX_CORE)
         //check if there is a compatible Cuda device to run pedestrian detection
         if (tryUseCuda && CudaInvoke.HasCuda)
         {  //this is the Cuda version
            using (CudaHOG des = new CudaHOG(new Size(64, 128), new Size(16, 16), new Size(8,8), new Size(8,8)))
            {
               des.SetSVMDetector(des.GetDefaultPeopleDetector());

               watch = Stopwatch.StartNew();
               using (GpuMat cudaBgr = new GpuMat(image))
               using (GpuMat cudaBgra = new GpuMat() )
               using (VectorOfRect vr = new VectorOfRect())
               {
                  CudaInvoke.CvtColor(cudaBgr, cudaBgra, ColorConversion.Bgr2Bgra);
                  des.DetectMultiScale(cudaBgra, vr);
                  regions = vr.ToArray();
               }
            }
         }
         else
#endif
         {  
            //this is the CPU/OpenCL version
            using (HOGDescriptor des = new HOGDescriptor())
            {
               des.SetSVMDetector(HOGDescriptor.GetDefaultPeopleDetector());
               
               //load the image to umat so it will automatically use opencl is available
               UMat umat = image.ToUMat(AccessType.Read);

               watch = Stopwatch.StartNew();
               
               MCvObjectDetection[] results = des.DetectMultiScale(umat);
               regions = new Rectangle[results.Length];
               for (int i = 0; i < results.Length; i++)
                  regions[i] = results[i].Rect;
               watch.Stop();
            }
         }
        
         processingTime = watch.ElapsedMilliseconds;

         return regions;
      }

      /// <summary>
      /// Find the pedestrian in the image
      /// </summary>
      /// <param name="image">The image</param>
      /// <param name="processingTime">The processing time in milliseconds</param>
      /// <returns>The region where pedestrians are detected</returns>
      public static Rectangle[] Find(IInputArray image, out long processingTime)
      {
         Stopwatch watch;
         Rectangle[] regions;

         using (InputArray iaImage = image.GetInputArray())
         {
#if !(__IOS__ || NETFX_CORE)
            //if the input array is a GpuMat
            //check if there is a compatible Cuda device to run pedestrian detection
            if (iaImage.Kind == InputArray.Type.CudaGpuMat)
            {
               //this is the Cuda version
               using (CudaHOG des = new CudaHOG(new Size(64, 128), new Size(16, 16), new Size(8, 8), new Size(8, 8)))
               {
                  des.SetSVMDetector(des.GetDefaultPeopleDetector());

                  watch = Stopwatch.StartNew();
                  using (GpuMat cudaBgra = new GpuMat())
                  using (VectorOfRect vr = new VectorOfRect())
                  {
                     CudaInvoke.CvtColor(image, cudaBgra, ColorConversion.Bgr2Bgra);
                     des.DetectMultiScale(cudaBgra, vr);
                     regions = vr.ToArray();
                  }
               }
            }
            else
#endif
            {
               //this is the CPU/OpenCL version
               using (HOGDescriptor des = new HOGDescriptor())
               {
                  des.SetSVMDetector(HOGDescriptor.GetDefaultPeopleDetector());
                  watch = Stopwatch.StartNew();

                  MCvObjectDetection[] results = des.DetectMultiScale(image);
                  regions = new Rectangle[results.Length];
                  for (int i = 0; i < results.Length; i++)
                     regions[i] = results[i].Rect;
                  watch.Stop();
               }
            }

            processingTime = watch.ElapsedMilliseconds;

            return regions;
         }
      }

        /// <summary>
        /// the update operator [MOG_GPU::operator()]
        /// </summary>
        /// <param name="frame"></param>
        /// <param name="fgmask"></param>
        /// <param name="learningRate"></param>
        /// <param name="stream"></param>
        public void Update(
            GpuMat frame, GpuMat fgmask, float learningRate = 0.0f, Stream stream = null)
        {
            if (disposed)
                throw new ObjectDisposedException(GetType().Name);
            if (frame == null)
                throw new ArgumentNullException("frame");
            if (fgmask == null)
                throw new ArgumentNullException("fgmask");

            stream = stream ?? Stream.Null;

            NativeMethods.gpu_MOG_GPU_operator(
                ptr, frame.CvPtr, fgmask.CvPtr, learningRate, stream.CvPtr);

            GC.KeepAlive(frame);
            GC.KeepAlive(fgmask);
            GC.KeepAlive(stream);
        }

        /// <summary>
        /// Computes a background image which are the mean of all background gaussians
        /// </summary>
        /// <param name="backgroundImage"></param>
        /// <param name="stream"></param>
        public void GetBackgroundImage(
            GpuMat backgroundImage, Stream stream = null)
        {
            if (disposed)
                throw new ObjectDisposedException(GetType().Name);
            if (backgroundImage == null)
                throw new ArgumentNullException("backgroundImage");

            stream = stream ?? Stream.Null;

            NativeMethods.gpu_MOG_GPU_getBackgroundImage(
                ptr, backgroundImage.CvPtr, stream.CvPtr);

            GC.KeepAlive(backgroundImage);
            GC.KeepAlive(stream);
        }

        /// <summary>
        /// converts matrix type, ex from float to uchar depending on type
        /// </summary>
        /// <param name="src"></param>
        /// <param name="dst"></param>
        /// <param name="dtype"></param>
        /// <param name="a"></param>
        /// <param name="b"></param>
        public void EnqueueConvert(GpuMat src, GpuMat dst, int dtype, double a = 1, double b = 0)
        {
            ThrowIfDisposed();
            if (src == null)
                throw new ArgumentNullException("src");
            if (dst == null)
                throw new ArgumentNullException("dst");
            src.ThrowIfDisposed();
            dst.ThrowIfDisposed();

            NativeMethods.cuda_Stream_enqueueConvert(ptr, src.CvPtr, dst.CvPtr, dtype, a, b);
        }

        /// <summary>
        /// 
        /// </summary>
        /// <param name="left"></param>
        /// <param name="right"></param>
        /// <param name="disparity"></param>
#else
        /// <summary>
        /// 
        /// </summary>
        /// <param name="left"></param>
        /// <param name="right"></param>
        /// <param name="disparity"></param>
#endif
        public void Run(GpuMat left, GpuMat right, GpuMat disparity)
        {
            if (disposed)
                throw new ObjectDisposedException("StereoBM_GPU");
            if(left == null)
                throw new ArgumentNullException("left");
            if(right == null)
                throw new ArgumentNullException("right");
            if (disparity == null)
                throw new ArgumentNullException("disparity");

            NativeMethods.StereoBM_GPU_run1(ptr, left.CvPtr, right.CvPtr, disparity.CvPtr);
        }