/// <summary>
/// Finds an object center, size, and orientation.
/// </summary>
/// <param name="probImage">Back projection of the object histogram. </param>
/// <param name="window">Initial search window.</param>
/// <param name="criteria">Stop criteria for the underlying MeanShift() .</param>
/// <returns></returns>
public static RotatedRect CamShift(
InputArray probImage, ref Rect window, TermCriteria criteria)
{
if (probImage == null)
throw new ArgumentNullException(nameof(probImage));
probImage.ThrowIfDisposed();
RotatedRect result = NativeMethods.video_CamShift(
probImage.CvPtr, ref window, criteria);
return result;
}
/// <summary>
/// the constructor that performs SVD
/// </summary>
/// <param name="src"></param>
/// <param name="flags"></param>
public SVD(InputArray src, Flags flags = 0)
{
if (src == null)
throw new ArgumentNullException("src");
src.ThrowIfDisposed();
ptr = NativeMethods.core_SVD_new(src.CvPtr, (int)flags);
}
/// <summary>
/// Calculates all of the moments
/// up to the third order of a polygon or rasterized shape.
/// </summary>
/// <param name="array">A raster image (single-channel, 8-bit or floating-point
/// 2D array) or an array ( 1xN or Nx1 ) of 2D points ( Point or Point2f )</param>
/// <param name="binaryImage">If it is true, then all the non-zero image pixels are treated as 1’s</param>
/// <returns></returns>
public Moments(InputArray array, bool binaryImage = false)
{
if (array == null)
throw new ArgumentNullException(nameof(array));
array.ThrowIfDisposed();
InitializeFromInputArray(array, binaryImage);
}
/// <summary>
/// computes moments of the rasterized shape or a vector of points
/// </summary>
/// <param name="array"></param>
/// <param name="binaryImage"></param>
public Moments(InputArray array, bool binaryImage = false)
{
if(array == null)
throw new ArgumentNullException("array");
array.ThrowIfDisposed();
WCvMoments m = NativeMethods.imgproc_moments(array.CvPtr, binaryImage ? 1 : 0);
Initialize(m.m00, m.m10, m.m01, m.m20, m.m11, m.m02, m.m30, m.m21, m.m12, m.m03);
}
/// <summary>
/// Detect ChArUco Diamond markers.
/// </summary>
/// <param name="image">input image necessary for corner subpixel.</param>
/// <param name="markerCorners">list of detected marker corners from detectMarkers function.</param>
/// <param name="markerIds">list of marker ids in markerCorners.</param>
/// <param name="squareMarkerLengthRate">rate between square and marker length: squareMarkerLengthRate = squareLength/markerLength. The real units are not necessary.</param>
/// <param name="diamondCorners">output list of detected diamond corners (4 corners per diamond). The order is the same than in marker corners: top left, top right, bottom right and bottom left. Similar format than the corners returned by detectMarkers (e.g std::vector<std::vector<cv::Point2f>>).</param>
/// <param name="diamondIds">ids of the diamonds in diamondCorners. The id of each diamond is in fact of type Vec4i, so each diamond has 4 ids, which are the ids of the aruco markers composing the diamond.</param>
/// <param name="cameraMatrix">Optional camera calibration matrix.</param>
/// <param name="distCoeffs">Optional camera distortion coefficients.</param>
public static void DetectCharucoDiamond(InputArray image, Point2f[][] markerCorners, IEnumerable <int> markerIds,
float squareMarkerLengthRate, out Point2f[][] diamondCorners, out Vec4i[] diamondIds,
InputArray?cameraMatrix = null, InputArray?distCoeffs = null)
{
if (image == null)
{
throw new ArgumentNullException(nameof(image));
}
if (markerCorners == null)
{
throw new ArgumentNullException(nameof(markerCorners));
}
if (markerIds == null)
{
throw new ArgumentNullException(nameof(markerIds));
}
if (cameraMatrix == null && distCoeffs != null)
{
throw new ArgumentNullException(nameof(cameraMatrix));
}
if (cameraMatrix != null && distCoeffs == null)
{
throw new ArgumentNullException(nameof(distCoeffs));
}
image.ThrowIfDisposed();
cameraMatrix?.ThrowIfDisposed();
distCoeffs?.ThrowIfDisposed();
using var markerCornersAddress = new ArrayAddress2 <Point2f>(markerCorners);
using var markerIdsVec = new VectorOfInt32(markerIds);
using var diamondCornersVec = new VectorOfVectorPoint2f();
using var diamondIdsVec = new VectorOfVec4i();
NativeMethods.HandleException(
NativeMethods.aruco_detectCharucoDiamond(
image.CvPtr, markerCornersAddress.GetPointer(), markerCornersAddress.GetDim1Length(), markerCornersAddress.GetDim2Lengths(),
markerIdsVec.CvPtr, squareMarkerLengthRate,
diamondCornersVec.CvPtr, diamondIdsVec.CvPtr,
cameraMatrix?.CvPtr ?? IntPtr.Zero, distCoeffs?.CvPtr ?? IntPtr.Zero));
diamondCorners = diamondCornersVec.ToArray();
diamondIds = diamondIdsVec.ToArray();
GC.KeepAlive(image);
if (cameraMatrix != null)
{
GC.KeepAlive(cameraMatrix);
}
if (distCoeffs != null)
{
GC.KeepAlive(distCoeffs);
}
}
/// <summary>
///
/// </summary>
/// <param name="data"></param>
/// <param name="mean"></param>
/// <param name="flags"></param>
/// <param name="retainedVariance"></param>
public PCA(InputArray data, InputArray mean, Flags flags, double retainedVariance)
{
if (data == null)
throw new ArgumentNullException(nameof(data));
if (mean == null)
throw new ArgumentNullException(nameof(mean));
data.ThrowIfDisposed();
mean.ThrowIfDisposed();
ptr = NativeMethods.core_PCA_new3(data.CvPtr, mean.CvPtr, (int)flags, retainedVariance);
}
/// <summary>
///
/// </summary>
/// <param name="data"></param>
/// <param name="mean"></param>
/// <param name="flags"></param>
/// <param name="maxComponents"></param>
public PCA(InputArray data, InputArray mean, Flags flags, int maxComponents = 0)
{
if (data == null)
throw new ArgumentNullException(nameof(data));
if (mean == null)
throw new ArgumentNullException(nameof(mean));
data.ThrowIfDisposed();
mean.ThrowIfDisposed();
ptr = NativeMethods.core_PCA_new2(data.CvPtr, mean.CvPtr, (int)flags, maxComponents);
}
/// <summary>
/// Finds an object on a back projection image.
/// </summary>
/// <param name="probImage">Back projection of the object histogram.</param>
/// <param name="window">Initial search window.</param>
/// <param name="criteria">Stop criteria for the iterative search algorithm.</param>
/// <returns>Number of iterations CAMSHIFT took to converge.</returns>
public static int MeanShift(
InputArray probImage, ref Rect window, TermCriteria criteria)
{
if (probImage == null)
throw new ArgumentNullException("probImage");
probImage.ThrowIfDisposed();
int result = NativeMethods.video_meanShift(
probImage.CvPtr, ref window, criteria);
return result;
}
/// <summary>
///
/// </summary>
/// <param name="src"></param>
/// <param name="dst"></param>
/// <param name="colormap"></param>
public static void ApplyColorMap(InputArray src, OutputArray dst, ColorMapMode colormap)
{
if (src == null)
throw new ArgumentNullException("src");
if (dst == null)
throw new ArgumentNullException("dst");
src.ThrowIfDisposed();
dst.ThrowIfNotReady();
NativeMethods.contrib_applyColorMap(src.CvPtr, dst.CvPtr, (int)colormap);
dst.Fix();
}
/// <summary>
/// detects corners using FAST algorithm by E. Rosten
/// </summary>
/// <param name="image"></param>
/// <param name="keypoints"></param>
/// <param name="threshold"></param>
/// <param name="nonmaxSupression"></param>
/// <param name="type"></param>
public static void FASTX(InputArray image, out KeyPoint[] keypoints, int threshold, bool nonmaxSupression, int type)
{
if (image == null)
throw new ArgumentNullException("image");
image.ThrowIfDisposed();
using (var kp = new VectorOfKeyPoint())
{
NativeMethods.features2d_FASTX(image.CvPtr, kp.CvPtr, threshold, nonmaxSupression ? 1 : 0, type);
keypoints = kp.ToArray();
}
}
/// <summary>
/// Forms a border around the image
/// </summary>
/// <param name="src">The source image</param>
/// <param name="dst">The destination image; will have the same type as src and
/// the size Size(src.cols+left+right, src.rows+top+bottom)</param>
/// <param name="top">Specify how much pixels in each direction from the source image rectangle one needs to extrapolate</param>
/// <param name="bottom">Specify how much pixels in each direction from the source image rectangle one needs to extrapolate</param>
/// <param name="left">Specify how much pixels in each direction from the source image rectangle one needs to extrapolate</param>
/// <param name="right">Specify how much pixels in each direction from the source image rectangle one needs to extrapolate</param>
/// <param name="borderType">The border type</param>
/// <param name="value">The border value if borderType == Constant</param>
public static void CopyMakeBorder(InputArray src, OutputArray dst, int top, int bottom, int left, int right, BorderType borderType, Scalar? value = null)
{
if (src == null)
throw new ArgumentNullException("src");
if (dst == null)
throw new ArgumentNullException("dst");
src.ThrowIfDisposed();
dst.ThrowIfNotReady();
Scalar value0 = value.GetValueOrDefault(new Scalar());
NativeMethods.imgproc_copyMakeBorder(src.CvPtr, dst.CvPtr, top, bottom, left, right, (int)borderType, value0);
dst.Fix();
}
/// <summary>
/// Perform image denoising using Non-local Means Denoising algorithm
/// with several computational optimizations. Noise expected to be a gaussian white noise
/// </summary>
/// <param name="src">Input 8-bit 1-channel, 2-channel or 3-channel image.</param>
/// <param name="dst">Output image with the same size and type as src .</param>
/// <param name="h">
/// Parameter regulating filter strength. Big h value perfectly removes noise but also removes image details,
/// smaller h value preserves details but also preserves some noise</param>
/// <param name="templateWindowSize">
/// Size in pixels of the template patch that is used to compute weights. Should be odd. Recommended value 7 pixels</param>
/// <param name="searchWindowSize">
/// Size in pixels of the window that is used to compute weighted average for given pixel.
/// Should be odd. Affect performance linearly: greater searchWindowsSize - greater denoising time. Recommended value 21 pixels</param>
public static void FastNlMeansDenoising(InputArray src, OutputArray dst, float h = 3,
int templateWindowSize = 7, int searchWindowSize = 21)
{
if (src == null)
throw new ArgumentNullException("src");
if (dst == null)
throw new ArgumentNullException("dst");
src.ThrowIfDisposed();
dst.ThrowIfNotReady();
NativeMethods.photo_fastNlMeansDenoising(src.CvPtr, dst.CvPtr, h, templateWindowSize, searchWindowSize);
dst.Fix();
}
/// <summary>
/// Detects corners using the FAST algorithm
/// </summary>
/// <param name="image">grayscale image where keypoints (corners) are detected.</param>
/// <param name="threshold">threshold on difference between intensity of the central pixel
/// and pixels of a circle around this pixel.</param>
/// <param name="nonmaxSupression">if true, non-maximum suppression is applied to
/// detected corners (keypoints).</param>
/// <param name="type">one of the three neighborhoods as defined in the paper</param>
/// <returns>keypoints detected on the image.</returns>
public static KeyPoint[] FAST(InputArray image, int threshold, bool nonmaxSupression, FASTType type)
{
if (image == null)
throw new ArgumentNullException(nameof(image));
image.ThrowIfDisposed();
using (var kp = new VectorOfKeyPoint())
{
NativeMethods.features2d_FAST2(image.CvPtr, kp.CvPtr, threshold, nonmaxSupression ? 1 : 0, (int)type);
GC.KeepAlive(image);
return kp.ToArray();
}
}
/// <summary>
/// converts rotation vector to rotation matrix or vice versa using Rodrigues transformation
/// </summary>
/// <param name="src">Input rotation vector (3x1 or 1x3) or rotation matrix (3x3).</param>
/// <param name="dst">Output rotation matrix (3x3) or rotation vector (3x1 or 1x3), respectively.</param>
/// <param name="jacobian">Optional output Jacobian matrix, 3x9 or 9x3, which is a matrix of partial derivatives of the output array components with respect to the input array components.</param>
public static void Rodrigues(InputArray src, OutputArray dst, OutputArray jacobian = null)
{
if (src == null)
throw new ArgumentNullException("src");
if (dst == null)
throw new ArgumentNullException("dst");
src.ThrowIfDisposed();
dst.ThrowIfNotReady();
NativeMethods.calib3d_Rodrigues(src.CvPtr, dst.CvPtr, ToPtr(jacobian));
dst.Fix();
if (jacobian != null)
jacobian.Fix();
}
/// <summary>
/// Updates motion history image using the current silhouette
/// </summary>
/// <param name="silhouette">Silhouette mask that has non-zero pixels where the motion occurs.</param>
/// <param name="mhi">Motion history image that is updated by the function (single-channel, 32-bit floating-point).</param>
/// <param name="timestamp">Current time in milliseconds or other units.</param>
/// <param name="duration">Maximal duration of the motion track in the same units as timestamp .</param>
public static void UpdateMotionHistory(
InputArray silhouette, InputOutputArray mhi,
double timestamp, double duration)
{
if (silhouette == null)
throw new ArgumentNullException("silhouette");
if (mhi == null)
throw new ArgumentNullException("mhi");
silhouette.ThrowIfDisposed();
mhi.ThrowIfNotReady();
NativeMethods.video_updateMotionHistory(
silhouette.CvPtr, mhi.CvPtr, timestamp, duration);
mhi.Fix();
}
/// <summary>
/// Detects corners using the AGAST algorithm
/// </summary>
/// <param name="image">grayscale image where keypoints (corners) are detected.</param>
/// <param name="threshold">threshold on difference between intensity of the central pixel
/// and pixels of a circle around this pixel.</param>
/// <param name="nonmaxSuppression">if true, non-maximum suppression is applied to
/// detected corners (keypoints).</param>
/// <param name="type">one of the four neighborhoods as defined in the paper</param>
/// <returns>keypoints detected on the image.</returns>
public static KeyPoint[] AGAST(InputArray image, int threshold, bool nonmaxSuppression, AGASTType type)
{
if (image == null)
throw new ArgumentNullException("image");
image.ThrowIfDisposed();
using (var vector = new VectorOfKeyPoint())
{
NativeMethods.features2d_AGAST(image.CvPtr, vector.CvPtr, threshold, nonmaxSuppression ? 1 : 0,
(int) type);
GC.KeepAlive(image);
return vector.ToArray();
}
}
/// <summary>
/// the update operator that takes the next video frame and returns the current foreground mask as 8-bit binary image.
/// </summary>
/// <param name="image"></param>
/// <param name="fgmask"></param>
/// <param name="learningRate"></param>
public virtual void Apply(InputArray image, OutputArray fgmask, double learningRate = -1)
{
if (image == null)
throw new ArgumentNullException("image");
if (fgmask == null)
throw new ArgumentNullException("fgmask");
image.ThrowIfDisposed();
fgmask.ThrowIfNotReady();
NativeMethods.video_BackgroundSubtractor_apply(ptr, image.CvPtr, fgmask.CvPtr, learningRate);
fgmask.Fix();
GC.KeepAlive(image);
}
/// <summary>
/// restores the damaged image areas using one of the available intpainting algorithms
/// </summary>
/// <param name="src"></param>
/// <param name="inpaintMask"></param>
/// <param name="dst"></param>
/// <param name="inpaintRadius"></param>
/// <param name="flags"></param>
public static void Inpaint(InputArray src, InputArray inpaintMask,
OutputArray dst, double inpaintRadius, InpaintMethod flags)
{
if (src == null)
throw new ArgumentNullException("src");
if (inpaintMask == null)
throw new ArgumentNullException("inpaintMask");
if (dst == null)
throw new ArgumentNullException("dst");
src.ThrowIfDisposed();
inpaintMask.ThrowIfDisposed();
dst.ThrowIfNotReady();
NativeMethods.photo_inpaint(src.CvPtr, inpaintMask.CvPtr, dst.CvPtr, inpaintRadius, (int)flags);
dst.Fix();
}
/// <summary>
/// Computes the global orientation of the selected motion history image part
/// </summary>
/// <param name="orientation">Motion gradient orientation image calculated by the function CalcMotionGradient() .</param>
/// <param name="mask">Mask image. It may be a conjunction of a valid gradient mask, also calculated by CalcMotionGradient() ,
/// and the mask of a region whose direction needs to be calculated.</param>
/// <param name="mhi">Motion history image calculated by UpdateMotionHistory() .</param>
/// <param name="timestamp">Timestamp passed to UpdateMotionHistory() .</param>
/// <param name="duration">Maximum duration of a motion track in milliseconds, passed to UpdateMotionHistory() .</param>
/// <returns></returns>
public static double CalcGlobalOrientation(
InputArray orientation, InputArray mask, InputArray mhi,
double timestamp, double duration)
{
if (orientation == null)
throw new ArgumentNullException("orientation");
if (mask == null)
throw new ArgumentNullException("mask");
if (mhi == null)
throw new ArgumentNullException("mhi");
orientation.ThrowIfDisposed();
mask.ThrowIfDisposed();
mhi.ThrowIfDisposed();
return NativeMethods.optflow_motempl_calcGlobalOrientation(
orientation.CvPtr, mask.CvPtr, mhi.CvPtr, timestamp, duration);
}
/// <summary>
/// Compute the shape distance between two shapes defined by its contours.
/// </summary>
/// <param name="contour1">Contour defining first shape.</param>
/// <param name="contour2">Contour defining second shape.</param>
/// <returns></returns>
public virtual float ComputeDistance(InputArray contour1, InputArray contour2)
{
if (ptr == IntPtr.Zero)
throw new ObjectDisposedException(GetType().Name);
if (contour1 == null)
throw new ArgumentNullException(nameof(contour1));
if (contour2 == null)
throw new ArgumentNullException(nameof(contour2));
contour1.ThrowIfDisposed();
contour2.ThrowIfDisposed();
float ret = NativeMethods.shape_ShapeDistanceExtractor_computeDistance(
ptr, contour1.CvPtr, contour2.CvPtr);
GC.KeepAlive(contour1);
GC.KeepAlive(contour2);
return ret;
}
/// <summary>
/// Computes the motion gradient orientation image from the motion history image
/// </summary>
/// <param name="mhi">Motion history single-channel floating-point image.</param>
/// <param name="mask">Output mask image that has the type CV_8UC1 and the same size as mhi.
/// Its non-zero elements mark pixels where the motion gradient data is correct.</param>
/// <param name="orientation">Output motion gradient orientation image that has the same type and the same size as mhi.
/// Each pixel of the image is a motion orientation, from 0 to 360 degrees.</param>
/// <param name="delta1">Minimal (or maximal) allowed difference between mhi values within a pixel neighborhood.</param>
/// <param name="delta2">Maximal (or minimal) allowed difference between mhi values within a pixel neighborhood.
/// That is, the function finds the minimum ( m(x,y) ) and maximum ( M(x,y) ) mhi values over 3x3 neighborhood of each pixel
/// and marks the motion orientation at (x, y) as valid only if:
/// min(delta1, delta2) <= M(x,y)-m(x,y) <= max(delta1, delta2).</param>
/// <param name="apertureSize"></param>
public static void CalcMotionGradient(
InputArray mhi, OutputArray mask, OutputArray orientation,
double delta1, double delta2, int apertureSize = 3)
{
if (mhi == null)
throw new ArgumentNullException("mhi");
if (mask == null)
throw new ArgumentNullException("mask");
if (orientation == null)
throw new ArgumentNullException("orientation");
mhi.ThrowIfDisposed();
mask.ThrowIfNotReady();
orientation.ThrowIfNotReady();
NativeMethods.video_calcMotionGradient(
mhi.CvPtr, mask.CvPtr, orientation.CvPtr, delta1, delta2, apertureSize);
mask.Fix();
orientation.Fix();
}
/// <summary>
/// Constructs a pyramid which can be used as input for calcOpticalFlowPyrLK
/// </summary>
/// <param name="img">8-bit input image.</param>
/// <param name="pyramid">output pyramid.</param>
/// <param name="winSize">window size of optical flow algorithm.
/// Must be not less than winSize argument of calcOpticalFlowPyrLK().
/// It is needed to calculate required padding for pyramid levels.</param>
/// <param name="maxLevel">0-based maximal pyramid level number.</param>
/// <param name="withDerivatives">set to precompute gradients for the every pyramid level.
/// If pyramid is constructed without the gradients then calcOpticalFlowPyrLK() will
/// calculate them internally.</param>
/// <param name="pyrBorder">the border mode for pyramid layers.</param>
/// <param name="derivBorder">the border mode for gradients.</param>
/// <param name="tryReuseInputImage">put ROI of input image into the pyramid if possible.
/// You can pass false to force data copying.</param>
/// <returns>number of levels in constructed pyramid. Can be less than maxLevel.</returns>
public static int BuildOpticalFlowPyramid(
InputArray img, OutputArray pyramid,
Size winSize, int maxLevel,
bool withDerivatives = true,
BorderTypes pyrBorder = BorderTypes.Reflect101,
BorderTypes derivBorder = BorderTypes.Constant,
bool tryReuseInputImage = true)
{
if (img == null)
throw new ArgumentNullException("img");
if (pyramid == null)
throw new ArgumentNullException("pyramid");
img.ThrowIfDisposed();
pyramid.ThrowIfNotReady();
int result = NativeMethods.video_buildOpticalFlowPyramid1(
img.CvPtr, pyramid.CvPtr, winSize, maxLevel, withDerivatives ? 1 : 0,
(int)pyrBorder, (int)derivBorder, tryReuseInputImage ? 1 : 0);
pyramid.Fix();
return result;
}
/// <summary>
/// Returns array containing proposal boxes.
/// </summary>
/// <param name="edgeMap">edge image.</param>
/// <param name="orientationMap">orientation map.</param>
/// <param name="boxes">proposal boxes.</param>
public virtual void GetBoundingBoxes(InputArray edgeMap, InputArray orientationMap, out Rect[] boxes)
{
ThrowIfDisposed();
if (edgeMap == null)
{
throw new ArgumentNullException(nameof(edgeMap));
}
if (orientationMap == null)
{
throw new ArgumentNullException(nameof(orientationMap));
}
edgeMap.ThrowIfDisposed();
orientationMap.ThrowIfDisposed();
using (var boxesVec = new VectorOfRect())
{
NativeMethods.ximgproc_EdgeBoxes_getBoundingBoxes(ptr, edgeMap.CvPtr, orientationMap.CvPtr, boxesVec.CvPtr);
boxes = boxesVec.ToArray();
}
GC.KeepAlive(this);
GC.KeepAlive(edgeMap);
GC.KeepAlive(orientationMap);
}
/// <summary>
/// Apply high-dimensional filtering using adaptive manifolds.
/// </summary>
/// <param name="src">filtering image with any numbers of channels.</param>
/// <param name="dst">output image.</param>
/// <param name="joint">optional joint (also called as guided) image with any numbers of channels.</param>
public virtual void Filter(InputArray src, OutputArray dst, InputArray?joint = null)
{
ThrowIfDisposed();
if (src == null)
{
throw new ArgumentNullException(nameof(src));
}
if (dst == null)
{
throw new ArgumentNullException(nameof(dst));
}
src.ThrowIfDisposed();
dst.ThrowIfNotReady();
joint?.ThrowIfDisposed();
NativeMethods.HandleException(
NativeMethods.ximgproc_AdaptiveManifoldFilter_filter(
ptr, src.CvPtr, dst.CvPtr, joint?.CvPtr ?? IntPtr.Zero));
GC.KeepAlive(this);
GC.KeepAlive(src);
dst.Fix();
GC.KeepAlive(joint);
}
/// <summary>
/// Trains the statistical model
/// </summary>
/// <param name="samples">training samples</param>
/// <param name="layout">SampleTypes value</param>
/// <param name="responses">vector of responses associated with the training samples.</param>
/// <returns></returns>
public virtual bool Train(InputArray samples, SampleTypes layout, InputArray responses)
{
if (ptr == IntPtr.Zero)
{
throw new ObjectDisposedException(GetType().Name);
}
if (samples == null)
{
throw new ArgumentNullException(nameof(samples));
}
if (responses == null)
{
throw new ArgumentNullException(nameof(responses));
}
samples.ThrowIfDisposed();
responses.ThrowIfDisposed();
int ret = NativeMethods.ml_StatModel_train2(ptr, samples.CvPtr, (int)layout, responses.CvPtr);
GC.KeepAlive(this);
GC.KeepAlive(samples);
GC.KeepAlive(responses);
return(ret != 0);
}
/// <summary>
/// Predicts the label and confidence for a given sample.
/// </summary>
/// <param name="src"></param>
/// <param name="label"></param>
/// <param name="confidence"></param>
public virtual void Predict(InputArray src, out int label, out double confidence)
{
if (src == null)
throw new ArgumentNullException("src");
src.ThrowIfDisposed();
NativeMethods.contrib_FaceRecognizer_predict2(ptr, src.CvPtr, out label, out confidence);
}
/// <summary>
/// Gets a prediction from a FaceRecognizer.
/// </summary>
/// <param name="src"></param>
/// <returns></returns>
public virtual int Predict(InputArray src)
{
if (src == null)
throw new ArgumentNullException("src");
src.ThrowIfDisposed();
return NativeMethods.contrib_FaceRecognizer_predict1(ptr, src.CvPtr);
}
/// <summary>
/// Draws two groups of lines in blue and red, counting the non overlapping (mismatching) pixels.
/// </summary>
/// <param name="size">The size of the image, where lines1 and lines2 were found.</param>
/// <param name="lines1">The first group of lines that needs to be drawn. It is visualized in blue color.</param>
/// <param name="lines2">The second group of lines. They visualized in red color.</param>
/// <param name="image">Optional image, where the lines will be drawn.
/// The image should be color(3-channel) in order for lines1 and lines2 to be drawn
/// in the above mentioned colors.</param>
/// <returns></returns>
public virtual int CompareSegments(
Size size, InputArray lines1, InputArray lines2, InputOutputArray image = null)
{
if (lines1 == null)
throw new ArgumentNullException(nameof(lines1));
if (lines2 == null)
throw new ArgumentNullException(nameof(lines2));
lines1.ThrowIfDisposed();
lines2.ThrowIfDisposed();
image?.ThrowIfNotReady();
var ret = NativeMethods.imgproc_LineSegmentDetector_compareSegments(
ptr, size, lines1.CvPtr, lines2.CvPtr, Cv2.ToPtr(image));
GC.KeepAlive(lines1);
GC.KeepAlive(lines2);
image?.Fix();
return ret;
}
/// <summary>
/// Draws the line segments on a given image.
/// </summary>
/// <param name="image">The image, where the liens will be drawn.
/// Should be bigger or equal to the image, where the lines were found.</param>
/// <param name="lines">A vector of the lines that needed to be drawn.</param>
public virtual void DrawSegments(InputOutputArray image, InputArray lines)
{
if (image == null)
throw new ArgumentNullException(nameof(image));
if (lines == null)
throw new ArgumentNullException(nameof(lines));
image.ThrowIfNotReady();
lines.ThrowIfDisposed();
NativeMethods.imgproc_LineSegmentDetector_drawSegments(ptr, image.CvPtr, lines.CvPtr);
image.Fix();
GC.KeepAlive(lines);
}
/// <summary>
/// Finds lines in the input image.
/// This is the output of the default parameters of the algorithm on the above shown image.
/// </summary>
/// <param name="image">A grayscale (CV_8UC1) input image. </param>
/// <param name="lines">A vector of Vec4i or Vec4f elements specifying the beginning and ending point of a line.
/// Where Vec4i/Vec4f is (x1, y1, x2, y2), point 1 is the start, point 2 - end. Returned lines are strictly oriented depending on the gradient.</param>
/// <param name="width">Vector of widths of the regions, where the lines are found. E.g. Width of line.</param>
/// <param name="prec">Vector of precisions with which the lines are found.</param>
/// <param name="nfa">Vector containing number of false alarms in the line region,
/// with precision of 10%. The bigger the value, logarithmically better the detection.</param>
public virtual void Detect(InputArray image, out Vec4f[] lines,
out double[] width, out double[] prec, out double[] nfa)
{
if (image == null)
throw new ArgumentNullException(nameof(image));
image.ThrowIfDisposed();
using (var linesVec = new VectorOfVec4f())
using (var widthVec = new VectorOfDouble())
using (var precVec = new VectorOfDouble())
using (var nfaVec = new VectorOfDouble())
{
NativeMethods.imgproc_LineSegmentDetector_detect_vector(ptr, image.CvPtr,
linesVec.CvPtr, widthVec.CvPtr, precVec.CvPtr, nfaVec.CvPtr);
lines = linesVec.ToArray();
width = widthVec.ToArray();
prec = precVec.ToArray();
nfa = nfaVec.ToArray();
}
GC.KeepAlive(image);
}
/// <summary>
/// Finds lines in the input image.
/// This is the output of the default parameters of the algorithm on the above shown image.
/// </summary>
/// <param name="image">A grayscale (CV_8UC1) input image. </param>
/// <param name="lines">A vector of Vec4i or Vec4f elements specifying the beginning and ending point of a line.
/// Where Vec4i/Vec4f is (x1, y1, x2, y2), point 1 is the start, point 2 - end. Returned lines are strictly oriented depending on the gradient.</param>
/// <param name="width">Vector of widths of the regions, where the lines are found. E.g. Width of line.</param>
/// <param name="prec">Vector of precisions with which the lines are found.</param>
/// <param name="nfa">Vector containing number of false alarms in the line region,
/// with precision of 10%. The bigger the value, logarithmically better the detection.</param>
public virtual void Detect(InputArray image, OutputArray lines,
OutputArray width = null, OutputArray prec = null, OutputArray nfa = null)
{
if (image == null)
throw new ArgumentNullException(nameof(image));
if (lines == null)
throw new ArgumentNullException(nameof(lines));
image.ThrowIfDisposed();
lines.ThrowIfNotReady();
width?.ThrowIfNotReady();
prec?.ThrowIfNotReady();
nfa?.ThrowIfNotReady();
NativeMethods.imgproc_LineSegmentDetector_detect_OutputArray(ptr, image.CvPtr, lines.CvPtr,
Cv2.ToPtr(width), Cv2.ToPtr(prec), Cv2.ToPtr(nfa));
GC.KeepAlive(image);
lines.Fix();
width?.Fix();
prec?.Fix();
nfa?.Fix();
}
/// <summary>
/// inserts a single channel to dst (coi is 0-based index)
/// </summary>
/// <param name="src"></param>
/// <param name="dst"></param>
/// <param name="coi"></param>
public static void InsertChannel(InputArray src, InputOutputArray dst, int coi)
{
if (src == null)
throw new ArgumentNullException("src");
if (dst == null)
throw new ArgumentNullException("dst");
src.ThrowIfDisposed();
dst.ThrowIfNotReady();
NativeMethods.core_insertChannel(src.CvPtr, dst.CvPtr, coi);
GC.KeepAlive(src);
dst.Fix();
}
/// <summary>
/// transforms 2D matrix to 1D row or column vector by taking sum, minimum, maximum or mean value over all the rows
/// </summary>
/// <param name="src">The source 2D matrix</param>
/// <param name="dst">The destination vector.
/// Its size and type is defined by dim and dtype parameters</param>
/// <param name="dim">The dimension index along which the matrix is reduced.
/// 0 means that the matrix is reduced to a single row and 1 means that the matrix is reduced to a single column</param>
/// <param name="rtype"></param>
/// <param name="dtype">When it is negative, the destination vector will have
/// the same type as the source matrix, otherwise, its type will be CV_MAKE_TYPE(CV_MAT_DEPTH(dtype), mtx.channels())</param>
public static void Reduce(InputArray src, OutputArray dst, ReduceDimension dim, ReduceTypes rtype, int dtype)
{
if (src == null)
throw new ArgumentNullException("src");
if (dst == null)
throw new ArgumentNullException("dst");
src.ThrowIfDisposed();
dst.ThrowIfNotReady();
NativeMethods.core_reduce(src.CvPtr, dst.CvPtr, (int)dim, (int)rtype, dtype);
dst.Fix();
GC.KeepAlive(src);
}
/// <summary>
/// finds global minimum and maximum array elements and returns their values and their locations
/// </summary>
/// <param name="src">The source single-channel array</param>
/// <param name="minVal">Pointer to returned minimum value</param>
/// <param name="maxVal">Pointer to returned maximum value</param>
/// <param name="minIdx"></param>
/// <param name="maxIdx"></param>
/// <param name="mask"></param>
public static void MinMaxIdx(InputArray src, out double minVal, out double maxVal,
out int minIdx, out int maxIdx, InputArray mask = null)
{
if (src == null)
throw new ArgumentNullException("src");
src.ThrowIfDisposed();
NativeMethods.core_minMaxIdx(src.CvPtr, out minVal, out maxVal, out minIdx, out maxIdx, ToPtr(mask));
GC.KeepAlive(src);
}
