public DebuggerProxy(GpuMat v)
{
_v = v;
}
/// <summary>
/// Get the GpuMat from the input array
/// </summary>
/// <returns>The GpuMat</returns>
public Cuda.GpuMat GetGpuMat()
{
Cuda.GpuMat m = new Cuda.GpuMat();
CvInvoke.cveInputArrayGetGpuMat(Ptr, m);
return(m);
}
/// <summary>
/// Grabs, decodes and returns the next video frame.
/// </summary>
/// <param name="frame">The frame</param>
/// <param name="stream">Use a Stream to call the function asynchronously (non-blocking) or null to call the function synchronously (blocking).</param>
/// <returns>If no frames has been grabbed (there are no more frames in video file), the methods return false . </returns>
public bool NextFrame(GpuMat frame, Stream stream = null)
{
return(CudaInvoke.cudaVideoReaderNextFrame(_ptr, frame, stream));
}
/// <summary>
/// Push a value into the standard vector
/// </summary>
/// <param name="value">The value to be pushed to the vector</param>
public void Push(GpuMat value)
{
VectorOfGpuMatPush(_ptr, value.Ptr);
}
/// <summary>
/// Compute the dense optical flow.
/// </summary>
/// <param name="frame0">Source frame</param>
/// <param name="frame1">Frame to track (with the same size as <paramref name="frame0"/>)</param>
/// <param name="u">Flow horizontal component (along x axis)</param>
/// <param name="v">Flow vertical component (along y axis)</param>
public void Dense(GpuMat frame0, GpuMat frame1, GpuMat u, GpuMat v, GpuMat error = null)
{
CudaInvoke.cudaPyrLKOpticalFlowDense(_ptr, frame0, frame1, u, v, error);
}
/// <summary>
/// Calculate an optical flow for a sparse feature set.
/// </summary>
/// <param name="frame0">First 8-bit input image (supports both grayscale and color images).</param>
/// <param name="frame1">Second input image of the same size and the same type as <paramref name="frame0"/></param>
/// <param name="points0">
/// Vector of 2D points for which the flow needs to be found. It must be one row
/// matrix with 2 channels
/// </param>
/// <param name="points1">
/// Output vector of 2D points (with single-precision two channel floating-point coordinates)
/// containing the calculated new positions of input features in the second image.</param>
/// <param name="status">
/// Output status vector (CV_8UC1 type). Each element of the vector is set to 1 if the
/// flow for the corresponding features has been found. Otherwise, it is set to 0.
/// </param>
/// <param name="err">
/// Output vector (CV_32FC1 type) that contains the difference between patches around
/// the original and moved points or min eigen value if getMinEigenVals is checked. It can be
/// null, if not needed.
/// </param>
public void Sparse(GpuMat frame0, GpuMat frame1, GpuMat points0, GpuMat points1, GpuMat status, GpuMat err = null)
{
CudaInvoke.cudaPyrLKOpticalFlowSparse(_ptr, frame0, frame1, points0, points1, status, err);
}
/// <summary>
/// Compute the optical flow.
/// </summary>
/// <param name="frame0">Source frame</param>
/// <param name="frame1">Frame to track (with the same size as <paramref name="frame0"/>)</param>
/// <param name="u">Flow horizontal component (along x axis)</param>
/// <param name="v">Flow vertical component (along y axis)</param>
/// <param name="stream">Use a Stream to call the function asynchronously (non-blocking) or null to call the function synchronously (blocking).</param>
public void Compute(GpuMat frame0, GpuMat frame1, GpuMat u, GpuMat v, Stream stream = null)
{
CudaInvoke.cudaBroxOpticalFlowCompute(_ptr, frame0, frame1, u, v, stream);
}
/// <summary>
/// Compute the optical flow.
/// </summary>
/// <param name="frame0">Source frame</param>
/// <param name="frame1">Frame to track (with the same size as <paramref name="frame0"/>)</param>
/// <param name="u">Flow horizontal component (along x axis)</param>
/// <param name="v">Flow vertical component (along y axis)</param>
/// <param name="stream">Use a Stream to call the function asynchronously (non-blocking) or null to call the function synchronously (blocking).</param>
public void Compute(GpuMat frame0, GpuMat frame1, GpuMat u, GpuMat v)
{
CudaInvoke.cudaOpticalFlowDualTvl1Compute(_ptr, frame0, frame1, u, v);
}
/// <summary>
/// Find the good features to track
/// </summary>
public void Detect(GpuMat image, GpuMat corners, GpuMat mask = null)
{
CudaInvoke.cudaCornersDetectorDetect(_ptr, image, corners, mask);
}
/// <summary>
/// Obtain the keypoints array from GpuMat
/// </summary>
/// <param name="src">The keypoints obtained from DetectKeyPointsRaw</param>
/// <param name="dst">The vector of keypoints</param>
public void DownloadKeypoints(GpuMat src, VectorOfKeyPoint dst)
{
CudaInvoke.cudaFASTDownloadKeypoints(_ptr, src, dst);
}
/// <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>
/// <param name="keypoints">
/// The keypoints GpuMat that will have 1 row.
/// keypoints.at<float[6]>(1, i) contains i'th keypoint
/// format: (x, y, size, response, angle, octave)
/// </param>
public void DetectKeyPointsRaw(GpuMat img, GpuMat mask, GpuMat keypoints)
{
CudaInvoke.cudaFASTDetectorDetectKeyPoints(_ptr, img, mask, keypoints);
}
