public void Run()
{
var gray = new Mat(FilePath.Image.Lenna, ImreadModes.GrayScale);
var dst = new Mat(FilePath.Image.Lenna, ImreadModes.Color);
BRISK brisk = BRISK.Create();
KeyPoint[] keypoints = brisk.Detect(gray);
if (keypoints != null)
{
var color = new Scalar(0, 255, 0);
foreach (KeyPoint kpt in keypoints)
{
float r = kpt.Size / 2;
Cv2.Circle(dst, kpt.Pt, (int)r, color);
Cv2.Line(dst,
new Point2f(kpt.Pt.X + r, kpt.Pt.Y + r),
new Point2f(kpt.Pt.X - r, kpt.Pt.Y - r),
color);
Cv2.Line(dst,
new Point2f(kpt.Pt.X - r, kpt.Pt.Y + r),
new Point2f(kpt.Pt.X + r, kpt.Pt.Y - r),
color);
}
}
using (new Window("BRISK features", dst))
{
Cv2.WaitKey();
}
}
public override ScalarValue GetValueToEncode(ScalarValue value, ScalarValue priorValue, Scalar field)
{
if (value == null)
return ScalarValue.Null;
return value;
}
public void setHsvColor (Scalar hsvColor)
{
double minH = (hsvColor.val [0] >= mColorRadius.val [0]) ? hsvColor.val [0] - mColorRadius.val [0] : 0;
double maxH = (hsvColor.val [0] + mColorRadius.val [0] <= 255) ? hsvColor.val [0] + mColorRadius.val [0] : 255;
mLowerBound.val [0] = minH;
mUpperBound.val [0] = maxH;
mLowerBound.val [1] = hsvColor.val [1] - mColorRadius.val [1];
mUpperBound.val [1] = hsvColor.val [1] + mColorRadius.val [1];
mLowerBound.val [2] = hsvColor.val [2] - mColorRadius.val [2];
mUpperBound.val [2] = hsvColor.val [2] + mColorRadius.val [2];
mLowerBound.val [3] = 0;
mUpperBound.val [3] = 255;
Mat spectrumHsv = new Mat (1, (int)(maxH - minH), CvType.CV_8UC3);
for (int j = 0; j < maxH-minH; j++) {
byte[] tmp = {(byte)(minH + j), (byte)255, (byte)255};
spectrumHsv.put (0, j, tmp);
}
Imgproc.cvtColor (spectrumHsv, mSpectrum, Imgproc.COLOR_HSV2RGB_FULL, 4);
}
public override Field Parse(System.Xml.XmlElement fieldNode, bool optional, ParsingContext context)
{
Operator.Operator operator_Renamed = Operator.Operator.NONE;
string defaultValue = null;
string key = null;
string ns = "";
System.Xml.XmlElement operatorElement = GetOperatorElement(fieldNode);
if (operatorElement != null)
{
if (operatorElement.HasAttribute("value"))
defaultValue = operatorElement.GetAttribute("value");
operator_Renamed = Operator.Operator.GetOperator(operatorElement.Name);
if (operatorElement.HasAttribute("key"))
key = operatorElement.GetAttribute("key");
if (operatorElement.HasAttribute("ns"))
ns = operatorElement.GetAttribute("ns");
if (operatorElement.HasAttribute("dictionary"))
context.Dictionary = operatorElement.GetAttribute("dictionary");
}
FASTType type = GetType(fieldNode, context);
var scalar = new Scalar(GetName(fieldNode, context), type, operator_Renamed, type.GetValue(defaultValue), optional);
if (fieldNode.HasAttribute("id"))
scalar.Id = fieldNode.GetAttribute("id");
if (key != null)
scalar.Key = new QName(key, ns);
scalar.Dictionary = context.Dictionary;
ParseExternalAttributes(fieldNode, scalar);
return scalar;
}
public override ScalarValue DecodeEmptyValue(ScalarValue priorValue, Scalar field)
{
if (!field.IsOptional)
return field.DefaultValue;
return null;
}
public override ScalarValue DecodeValue(ScalarValue newValue, ScalarValue priorValue, Scalar field)
{
if (priorValue == null && !field.IsOptional)
{
Global.ErrorHandler.OnError(null, DynError.MandatoryFieldNotPresent, "");
return null;
}
var baseValue = (StringValue) (priorValue == null || priorValue.IsUndefined
? field.BaseValue
: priorValue);
if (newValue == null || newValue.IsNull)
{
if (!field.IsOptional)
throw new ArgumentException("");
return null;
}
string delta = ((StringValue) newValue).Value;
int length = Math.Max(baseValue.Value.Length - delta.Length, 0);
string root = baseValue.Value.Substring(0, (length) - (0));
return new StringValue(root + delta);
}
public override ScalarValue GetValueToEncode(ScalarValue value_Renamed, ScalarValue priorValue, Scalar field)
{
if (priorValue == null)
{
return value_Renamed;
}
if (value_Renamed == null)
{
if (field.Optional)
{
if (priorValue == ScalarValue.UNDEFINED && field.DefaultValue.Undefined)
{
return null;
}
return ScalarValue.NULL;
}
throw new ArgumentException();
}
if (priorValue.Undefined)
{
if (value_Renamed.Equals(field.DefaultValue))
{
return null;
}
return value_Renamed;
}
if (!value_Renamed.Equals(((NumericValue) priorValue).Increment()))
{
return value_Renamed;
}
return null;
}
// Update is called once per frame
void Update () {
cap.Read (frame);
if (!frame.Empty()){
//assume this part of the frame contains only background
smoothed_img = frame.Blur(new Size(5,5));
frame_hsv = frame.CvtColor (ColorConversionCodes.BGR2HSV);
Scalar lb = new Scalar (0, 0, 50);
Scalar ub = new Scalar (180, 70, 180);
Mat disc = Cv2.GetStructuringElement (MorphShapes.Ellipse, new Size (7, 7));
Cv2.MorphologyEx (thresh, thresh, MorphTypes.Close, disc,null,3);
contours = Cv2.FindContoursAsMat (thresh , RetrievalModes.List, ContourApproximationModes.ApproxSimple);
mask = new Mat (thresh.Size (), thresh.Type (), Scalar.All (0));
Cv2.Merge(new Mat[]{mask,mask,mask},mask);
Cv2.BitwiseAnd (mask, frame, mask);
//Cv2.Merge(new Mat[]{frame_backproj,frame_backproj,frame_backproj},frame_backproj);
tex.LoadImage (smoothed_img.ToBytes (".png", new int[]{ 0 }));
}
}
public RaiseArmTrigger(XmlNode node)
{
mHeightThreshold = Nui.magnitude(Nui.joint(Nui.Shoulder_Centre) - Nui.joint(Nui.Hip_Centre));
mAngleThreshold = Scalar.Create(.48f);
mDepthThreshold = Scalar.Create(GetFloat(node, 3.6f, "DepthThreshold"));
mWidthThreshold = Scalar.Create(GetFloat(node, 1f, "WidthThreshold"));
mBody = Nui.joint(Nui.Hip_Centre);
Condition inWidth = Nui.abs(Nui.x(Nui.joint(Nui.Hip_Centre))) < mWidthThreshold;
Condition inDepth = Nui.z(Nui.joint(Nui.Hip_Centre)) < mDepthThreshold;
Condition inRange = C.And(inWidth, inDepth);
Vector up = Vector.Create(0f, 1f, 0f);
mArmR = Nui.joint(Nui.Hand_Right) - Nui.joint(Nui.Shoulder_Right);
mArmL = Nui.joint(Nui.Hand_Left) - Nui.joint(Nui.Shoulder_Left);
mAngleR = Nui.dot(up, mArmR);
mAngleL = Nui.dot(up, mArmL);
mTriggerR = C.And(Nui.y(mArmR) > mHeightThreshold, mAngleR > mAngleThreshold);
mTriggerL = C.And(Nui.y(mArmL) > mHeightThreshold, mAngleL > mAngleThreshold);
mTrigger = C.And(C.Or(mTriggerR, mTriggerL), inRange);
mTrigger.OnChange += new ChangeDelegate(mTrigger_OnChange);
}
public override ScalarValue DecodeValue(ScalarValue val, ScalarValue priorVal, Scalar field)
{
if (priorVal == null)
{
Global.HandleError(Error.FastConstants.D6_MNDTRY_FIELD_NOT_PRESENT, "The field " + field + " must have a priorValue defined.");
return null;
}
if (val == null)
{
return null;
}
DecimalValue priorValue;
if (priorVal.Undefined)
{
if (field.DefaultValue.Undefined)
{
priorValue = (DecimalValue) field.BaseValue;
}
else
{
priorValue = (DecimalValue) field.DefaultValue;
}
}
else
{
priorValue = (DecimalValue) priorVal;
}
var value_Renamed = (DecimalValue) val;
return new DecimalValue(value_Renamed.mantissa + priorValue.mantissa, value_Renamed.exponent + priorValue.exponent);
}
public void Run()
{
var dst = new Mat(FilePath.Lenna, LoadMode.Color);
var gray = new Mat(FilePath.Lenna, LoadMode.GrayScale);
StarDetector detector = new StarDetector(45);
KeyPoint[] keypoints = detector.Run(gray);
if (keypoints != null)
{
var color = new Scalar(0, 255, 0);
foreach (KeyPoint kpt in keypoints)
{
float r = kpt.Size / 2;
Cv2.Circle(dst, kpt.Pt, (int)r, color, 1, LineType.Link8, 0);
Cv2.Line(dst,
new Point2f(kpt.Pt.X + r, kpt.Pt.Y + r),
new Point2f(kpt.Pt.X - r, kpt.Pt.Y - r),
color, 1, LineType.Link8, 0);
Cv2.Line(dst,
new Point2f(kpt.Pt.X - r, kpt.Pt.Y + r),
new Point2f(kpt.Pt.X + r, kpt.Pt.Y - r),
color, 1, LineType.Link8, 0);
}
}
using (new Window("StarDetector features", dst))
{
Cv.WaitKey();
}
}
public override ScalarValue DecodeEmptyValue(ScalarValue previousValue, Scalar field)
{
if (!field.Optional)
{
return field.DefaultValue;
}
return null;
}
//LEDの位置検出
public Microsoft.Kinect.CameraSpacePoint GetLEDPositinon(int imgW, int imgH, byte[] colors, Scalar color, ushort[] depthBuffer)
{
//二値化 → 輪郭抽出・中心位置取得 → depth位置に変換 → 座標取得
//Mat ColorImage = this.CreatMat(colors, imgW, imgH);
//Mat GrayScaleImage = this.Converter(ColorImage);
Mat GrayScaleImage = this.Converter(colors, imgW, imgH, 4);
Point pt = this.GetCenterPointofLED(GrayScaleImage);
return this.GetCenterPosition(pt, depthBuffer, imgW, imgH);
}
public override ScalarValue GetValueToEncode(ScalarValue value_Renamed, ScalarValue priorValue, Scalar field)
{
if (value_Renamed == null)
{
return field.DefaultValue.Undefined ? null : ScalarValue.NULL;
}
return value_Renamed.Equals(field.DefaultValue)?null:value_Renamed;
}
public override ScalarValue GetValueToEncode(ScalarValue value_Renamed, ScalarValue priorValue, Scalar field)
{
if (value_Renamed == null)
{
return ScalarValue.NULL;
}
return value_Renamed;
}
public KinectScaledAxis(string name, AxisBinding binding)
: base(name, new ScalarUpdater(sAnchor * MakeDZ()), new ScalarUpdater(sAnchor * MakeScale()), binding)
{
mDeadzoneScale = sTmpDZScale;
mScaleScale = sTmpSScale;
mDeadzoneScale.Value = G.Cfg.GetDeadzone(name);
mScaleScale.Value = G.Cfg.GetScale(name);
Scale.ManuallyChanged += new Action<float>(Scale_ManuallyChanged);
Deadzone.ManuallyChanged += new Action<float>(Deadzone_ManuallyChanged);
}
protected override ScalarValue GetInitialValue(Scalar field)
{
if (!field.DefaultValue.IsUndefined)
return field.DefaultValue;
if (field.IsOptional)
return null;
Global.ErrorHandler.OnError(null, DynError.NoDefaultValue, "No default value for {0}", field);
return null;
}
public override ScalarValue DecodeEmptyValue(ScalarValue priorValue, Scalar field)
{
ScalarValue value = priorValue;
if (value != null && value.IsUndefined)
value = (field.DefaultValue.IsUndefined) ? null : field.DefaultValue;
if (value == null && !field.IsOptional)
{
Global.ErrorHandler.OnError(null, DynError.MandatoryFieldNotPresent, "The field {0} was not present.",
field);
}
return value;
}
public void TestScalar()
{
var a = new Scalar(2);
var b = a.Pow(new Scalar(3));
Assert.True(b.Value == 8);
var c = new Scalar(1);
var d = c.Log();
Assert.True(d.Value == 0);
}
/// <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();
}
public CrouchAxis(AxisBinding binding)
: base("Crouch", binding)
{
Scalar hip = Nui.y(Nui.joint(Nui.Hip_Centre));
Scalar feet = Nui.y((Nui.joint(Nui.Ankle_Left) + Nui.joint(Nui.Ankle_Right))) / 2f;
//How far pushing forward
Scalar raw = feet - hip;
Scalar anchor = Nui.smooth(Nui.magnitude(Nui.joint(Nui.Shoulder_Centre) - Nui.joint(Nui.Hip_Centre)), 50);
mRaw = (anchor * 3) + raw;
mRaw *= -1f;
}
public void draw(Mat im, Scalar pts_color, Scalar con_color)
{
foreach (var point in points) {
int n = point.Length;
if (n == 0)
return;
for (int i = 0; i < smodel.C.rows(); i++) {
int j = (int)smodel.C.get (i, 0) [0], k = (int)smodel.C.get (i, 1) [0];
Core.line (im, point [j], point [k], con_color, 1);
}
for (int i = 0; i < n; i++) {
Core.circle (im, point [i], 1, pts_color, 2, Core.LINE_AA, 0);
}
}
}
private Scalar ParseSequenceLengthField(QName name, System.Xml.XmlElement sequence, bool optional, ParsingContext parent)
{
System.Xml.XmlNodeList lengthElements = sequence.GetElementsByTagName("length");
if (lengthElements.Count == 0)
{
var implicitLength = new Scalar(Global.CreateImplicitName(name), FASTType.U32, Operator.Operator.NONE, ScalarValue.UNDEFINED, optional)
{Dictionary = parent.Dictionary};
return implicitLength;
}
var length = (System.Xml.XmlElement) lengthElements.Item(0);
var context = new ParsingContext(length, parent);
return (Scalar) sequenceLengthParser.Parse(length, optional, context);
}
public TestForm(Vector topLeft, Vector normal, Vector top, Vector side, Vector pointStart, Vector pointDir, Vector intersection, Scalar w, Scalar h, Scalar x, Scalar y)
: this()
{
topLeftPanel.Vector = new VectorUpdater(topLeft);
normalPanel.Vector = new VectorUpdater(normal);
topPanel.Vector = new VectorUpdater(top);
sidePanel.Vector = new VectorUpdater(side);
pointStartPanel.Vector = new VectorUpdater(pointStart);
pointDirPanel.Vector = new VectorUpdater(pointDir);
intersectionPanel.Vector = new VectorUpdater(intersection);
wPanel.Scalar = new ScalarUpdater(w);
hPanel.Scalar = new ScalarUpdater(h);
xPanel.Scalar = new ScalarUpdater(x);
yPanel.Scalar = new ScalarUpdater(y);
}
public override ScalarValue DecodeEmptyValue(ScalarValue priorValue, Scalar field)
{
if (field.DefaultValue.IsUndefined)
{
if (field.IsOptional)
return ScalarValue.Null;
if (priorValue.IsUndefined)
throw new ArgumentException(
"Mandatory fields without a previous value or default value must be present.",
"priorValue");
return priorValue;
}
return field.DefaultValue;
}
public override ScalarValue DecodeEmptyValue(ScalarValue previousValue, Scalar field)
{
if (field.DefaultValue.Undefined)
{
if (field.Optional)
{
return ScalarValue.NULL;
}
if (previousValue.Undefined)
{
throw new SystemException("Mandatory fields without a previous value or default value must be present.");
}
return previousValue;
}
return field.DefaultValue;
}
public override ScalarValue DecodeValue(ScalarValue newValue, ScalarValue previousValue, Scalar field)
{
if ((newValue == null) || newValue.Null)
{
return null;
}
var diffValue = (TwinValue) newValue;
ScalarValue base_Renamed = (previousValue.Undefined)?field.BaseValue:previousValue;
if (diffValue.first.ToInt() > base_Renamed.ToString().Length)
{
Global.HandleError(Error.FastConstants.D7_SUBTRCTN_LEN_LONG, "The string diff <" + diffValue + "> cannot be applied to the base value \"" + base_Renamed + "\" because the subtraction length is too long.");
}
return Util.ApplyDifference((StringValue) base_Renamed, diffValue);
}
protected internal override ScalarValue GetInitialValue(Scalar field)
{
if (!field.DefaultValue.Undefined)
{
return field.DefaultValue;
}
if (field.Optional)
{
return null;
}
Global.HandleError(Error.FastConstants.D5_NO_DEFAULT_VALUE, "No default value for " + field);
return null;
}
public override ScalarValue GetValueToEncode(ScalarValue value_Renamed, ScalarValue priorValue, Scalar field)
{
if (value_Renamed == null)
{
return ScalarValue.NULL;
}
if (priorValue == null)
{
Global.HandleError(Error.FastConstants.D6_MNDTRY_FIELD_NOT_PRESENT, "The field " + field + " must have a priorValue defined.");
return null;
}
ScalarValue base_Renamed = (priorValue.Undefined)?field.BaseValue:priorValue;
return Util.GetDifference((StringValue) value_Renamed, (StringValue) base_Renamed);
}
public override ScalarValue DecodeValue(ScalarValue newValue, ScalarValue priorValue, Scalar field)
{
if (newValue == null || newValue.IsNull)
return null;
var diffValue = (TwinValue) newValue;
ScalarValue v = (priorValue.IsUndefined) ? field.BaseValue : priorValue;
if (diffValue.First.ToInt() > v.ToString().Length)
{
Global.ErrorHandler.OnError(null, DynError.SubtrctnLenLong,
"The string diff <{0}> cannot be applied to the base value '{1}' because the subtraction length is too long.",
diffValue, v);
}
byte[] bytes = Util.ApplyDifference(v, diffValue);
return field.FastType.GetValue(bytes);
//return Util.ApplyDifference((StringValue) v, diffValue);
}
public static CubicBezierFunction2 Create(SnVector2 controlPoint0, SnVector2 controlPoint1, SnVector2 controlPoint2, SnVector2 controlPoint3, Scalar t)
{
return(new CubicBezierFunction2(controlPoint0, controlPoint1, controlPoint2, controlPoint3, t));
}
/**
* Set preprocessing parameters for frame.
* param size New input size.
* param mean Scalar with mean values which are subtracted from channels.
* param scale Multiplier for frame values.
* blob(n, c, y, x) = scale * resize( frame(y, x, c) ) - mean(c) )
*/
public void setInputParams(double scale, Size size, Scalar mean)
{
ThrowIfDisposed();
dnn_Model_setInputParams_12(nativeObj, scale, size.width, size.height, mean.val[0], mean.val[1], mean.val[2], mean.val[3]);
}
public static Key <uint, string> Hash <T>(this Scalar <T> me, int invertHashTokens) => _rec.Key <uint, string>(me);
public bool TrySignBIP340(ReadOnlySpan <byte> msg32, ECPubKey?pubkey, INonceFunctionHardened?nonceFunction, [System.Diagnostics.CodeAnalysis.MaybeNullWhen(false)] out SecpSchnorrSignature signature)
{
signature = null;
if (msg32.Length != 32)
{
return(false);
}
using var sha = new Secp256k1.SHA256();
Span <byte> buf = stackalloc byte[32];
Span <byte> sig64 = stackalloc byte[64];
Span <byte> pk_buf = stackalloc byte[32];
Span <byte> sec_key = stackalloc byte[32];
if (nonceFunction == null)
{
nonceFunction = new BIP340NonceFunction(true);
}
var pk = (pubkey ?? CreatePubKey()).Q;
var sk = this.sec;
/* Because we are signing for a x-only pubkey, the secret key is negated
* before signing if the point corresponding to the secret key does not
* have an even Y. */
if (pk.y.IsOdd)
{
sk = sk.Negate();
}
sk.WriteToSpan(sec_key);
pk.x.WriteToSpan(pk_buf);
var ret = nonceFunction.TryGetNonce(buf, msg32, sec_key, pk_buf, BIP340NonceFunction.ALGO_BIP340);
var k = new Scalar(buf, out _);
ret &= !k.IsZero;
Scalar.CMov(ref k, Scalar.One, ret ? 0 : 1);
var rj = ctx.EcMultGenContext.MultGen(k);
var r = rj.ToGroupElement();
var ry = r.y.NormalizeVariable();
if (ry.IsOdd)
{
k = k.Negate();
}
var rx = r.x.NormalizeVariable();
rx.WriteToSpan(sig64);
/* tagged hash(r.x, pk.x, msg32) */
sha.InitializeTagged(ECXOnlyPubKey.TAG_BIP0340Challenge);
sha.Write(sig64.Slice(0, 32));
sha.Write(pk_buf);
sha.Write(msg32);
sha.GetHash(buf);
/* Set scalar e to the challenge hash modulo the curve order as per
* BIP340. */
var e = new Scalar(buf, out _);
e = e * sk;
e = e + k;
e.WriteToSpan(sig64.Slice(32));
ret &= SecpSchnorrSignature.TryCreate(sig64, out signature);
k = default;
sk = default;
sec_key.Fill(0);
sig64.Fill(0);
if (!ret)
{
signature = null;
}
return(signature is SecpSchnorrSignature);
}
public override object ConvertFrom(ref ObjectContext context, Scalar scalar)
{
var primitiveType = (PrimitiveDescriptor)context.Descriptor;
var type = primitiveType.Type;
var text = scalar.Value;
// Return null if expected type is an object and scalar is null
if (text == null)
{
switch (Type.GetTypeCode(type))
{
case TypeCode.Object:
case TypeCode.Empty:
case TypeCode.String:
return(null);
default:
// TODO check this
throw new YamlException(scalar.Start, scalar.End, "Unexpected null scalar value");
}
}
// If type is an enum, try to parse it
if (type.IsEnum)
{
return(Enum.Parse(type, text, false));
}
// Parse default types
switch (Type.GetTypeCode(type))
{
case TypeCode.Boolean:
object value;
context.SerializerContext.Schema.TryParse(scalar, type, out value);
return(value);
case TypeCode.DateTime:
return(DateTime.Parse(text, CultureInfo.InvariantCulture));
case TypeCode.String:
return(text);
}
if (type == typeof(TimeSpan))
{
return(TimeSpan.Parse(text, CultureInfo.InvariantCulture));
}
// Remove _ character from numeric values
text = text.Replace("_", string.Empty);
// Parse default types
switch (Type.GetTypeCode(type))
{
case TypeCode.Char:
if (text.Length != 1)
{
throw new YamlException(scalar.Start, scalar.End, "Unable to decode char from [{0}]. Expecting a string of length == 1".DoFormat(text));
}
return(text.ToCharArray()[0]);
break;
case TypeCode.Byte:
return(byte.Parse(text, CultureInfo.InvariantCulture));
case TypeCode.SByte:
return(sbyte.Parse(text, CultureInfo.InvariantCulture));
case TypeCode.Int16:
return(short.Parse(text, CultureInfo.InvariantCulture));
case TypeCode.UInt16:
return(ushort.Parse(text, CultureInfo.InvariantCulture));
case TypeCode.Int32:
return(int.Parse(text, CultureInfo.InvariantCulture));
case TypeCode.UInt32:
return(uint.Parse(text, CultureInfo.InvariantCulture));
case TypeCode.Int64:
return(long.Parse(text, CultureInfo.InvariantCulture));
case TypeCode.UInt64:
return(ulong.Parse(text, CultureInfo.InvariantCulture));
case TypeCode.Single:
return(float.Parse(text, CultureInfo.InvariantCulture));
case TypeCode.Double:
return(double.Parse(text, CultureInfo.InvariantCulture));
case TypeCode.Decimal:
return(decimal.Parse(text, CultureInfo.InvariantCulture));
}
// If we are expecting a type object, return directly the string
if (type == typeof(object))
{
// Try to parse the scalar directly
string defaultTag;
object scalarValue;
if (context.SerializerContext.Schema.TryParse(scalar, true, out defaultTag, out scalarValue))
{
return(scalarValue);
}
return(text);
}
throw new YamlException(scalar.Start, scalar.End, "Unable to decode scalar [{0}] not supported by current schema".DoFormat(scalar));
}
internal IssuanceValidationData(Money amount, Scalar r, GroupElement ma)
{
Amount = amount;
Randomness = r;
Ma = ma;
}
public ScalarUpdater(Scalar scalar)
{
Scalar = scalar;
}
public override object ConvertFrom(ref ObjectContext context, Scalar fromScalar)
{
return(realScalarSerializer.ConvertFrom(ref context, fromScalar));
}
public void Run()
{
Mat img = Cv2.ImRead(FilePath.Lenna, LoadMode.GrayScale);
// expand input image to optimal size
Mat padded = new Mat();
int m = Cv2.GetOptimalDFTSize(img.Rows);
int n = Cv2.GetOptimalDFTSize(img.Cols); // on the border add zero values
Cv2.CopyMakeBorder(img, padded, 0, m - img.Rows, 0, n - img.Cols, BorderType.Constant, Scalar.All(0));
// Add to the expanded another plane with zeros
Mat paddedF32 = new Mat();
padded.ConvertTo(paddedF32, MatType.CV_32F);
Mat[] planes = { paddedF32, Mat.Zeros(padded.Size(), MatType.CV_32F) };
Mat complex = new Mat();
Cv2.Merge(planes, complex);
// this way the result may fit in the source matrix
Mat dft = new Mat();
Cv2.Dft(complex, dft);
// compute the magnitude and switch to logarithmic scale
// => log(1 + sqrt(Re(DFT(I))^2 + Im(DFT(I))^2))
Mat[] dftPlanes;
Cv2.Split(dft, out dftPlanes); // planes[0] = Re(DFT(I), planes[1] = Im(DFT(I))
// planes[0] = magnitude
Mat magnitude = new Mat();
Cv2.Magnitude(dftPlanes[0], dftPlanes[1], magnitude);
magnitude += Scalar.All(1); // switch to logarithmic scale
Cv2.Log(magnitude, magnitude);
// crop the spectrum, if it has an odd number of rows or columns
Mat spectrum = magnitude[
new Rect(0, 0, magnitude.Cols & -2, magnitude.Rows & -2)];
// rearrange the quadrants of Fourier image so that the origin is at the image center
int cx = spectrum.Cols / 2;
int cy = spectrum.Rows / 2;
Mat q0 = new Mat(spectrum, new Rect(0, 0, cx, cy)); // Top-Left - Create a ROI per quadrant
Mat q1 = new Mat(spectrum, new Rect(cx, 0, cx, cy)); // Top-Right
Mat q2 = new Mat(spectrum, new Rect(0, cy, cx, cy)); // Bottom-Left
Mat q3 = new Mat(spectrum, new Rect(cx, cy, cx, cy)); // Bottom-Right
// swap quadrants (Top-Left with Bottom-Right)
Mat tmp = new Mat();
q0.CopyTo(tmp);
q3.CopyTo(q0);
tmp.CopyTo(q3);
// swap quadrant (Top-Right with Bottom-Left)
q1.CopyTo(tmp);
q2.CopyTo(q1);
tmp.CopyTo(q2);
// Transform the matrix with float values into a
Cv2.Normalize(spectrum, spectrum, 0, 1, NormType.MinMax);
// Show the result
Cv2.ImShow("Input Image", img);
Cv2.ImShow("Spectrum Magnitude", spectrum);
// calculating the idft
Mat inverseTransform = new Mat();
Cv2.Dft(dft, inverseTransform, DftFlag2.Inverse | DftFlag2.RealOutput);
Cv2.Normalize(inverseTransform, inverseTransform, 0, 1, NormType.MinMax);
Cv2.ImShow("Reconstructed by Inverse DFT", inverseTransform);
Cv2.WaitKey();
}
public abstract object ConvertFrom(ref ObjectContext context, Scalar fromScalar);
public override void Show(object value)
{
Point point;
if (value is Point)
{
point = (Point)value;
}
else if (value is Point2f)
{
var point2f = (Point2f)value;
point.X = float.IsNaN(point2f.X) ? -1 : (int)point2f.X;
point.Y = float.IsNaN(point2f.Y) ? -1 : (int)point2f.Y;
}
else
{
var point2d = (Point2d)value;
point.X = double.IsNaN(point2d.X) ? -1 : (int)point2d.X;
point.Y = double.IsNaN(point2d.Y) ? -1 : (int)point2d.Y;
}
var image = visualizer.VisualizerImage;
if (point.X < 0 || point.Y < 0 ||
point.X >= image.Width || point.Y >= image.Height)
{
points = null;
}
else if (tracking != TrackingMode.None)
{
if (points == null)
{
points = new Queue <Point>(1);
polylines.Enqueue(points);
}
points.Enqueue(point);
if (tracking == TrackingMode.Fixed)
{
var head = polylines.Peek();
head.Dequeue();
if (head.Count == 0)
{
polylines.Dequeue();
}
}
}
if (polylines.Count > 0)
{
CV.PolyLine(image, polylines.Select(ps => ps.ToArray()).ToArray(), false, Scalar.Rgb(255, 0, 0), 2);
}
CV.Circle(image, point, 3, Scalar.Rgb(0, 255, 0), 3);
if (tracking == TrackingMode.None)
{
polylines.Clear();
points = null;
}
}
public static Key <uint, T> Dictionarize <T>(this Scalar <T> me) => _rec.Key <uint, T>(me);
public static VarVector <T> ConcatWith <T>(this Scalar <T> me, params ScalarOrVectorOrVarVector <T>[] i) => _rec.VarVector <T>(Helper(me, i));
/// <summary>
/// Raises the webcam texture initialized event.
/// </summary>
private void OnInited()
{
prevPos = new Vector2(webCamTexture.width / 2, webCamTexture.height);
if (colors == null || colors.Length != webCamTexture.width * webCamTexture.height)
{
colors = new Color32[webCamTexture.width * webCamTexture.height];
}
if (texture == null || texture.width != webCamTexture.width || texture.height != webCamTexture.height)
{
//texture = new Texture2D (webCamTexture.width, webCamTexture.height, TextureFormat.RGBA32, false);
texture = new Texture2D(webCamTexture.width, webCamTexture.height, TextureFormat.RGBA32, false);
}
rgbaMat = new Mat(webCamTexture.height, webCamTexture.width, CvType.CV_8UC4);
/*
* rgbaMat2 = new Mat (webCamTexture.height, webCamTexture.width, CvType.CV_8UC4);
* rgbaMat3 = new Mat (webCamTexture.height, webCamTexture.width, CvType.CV_8UC4);
* rgbaMatFinal = new Mat (webCamTexture.height, webCamTexture.width, CvType.CV_8UC4);
*/
rgbaMat2 = new Mat(requestedHeight, requestedWidth, CvType.CV_8UC4);
rgbaMat3 = new Mat(requestedHeight, requestedWidth, CvType.CV_8UC4);
rgbaMatFinal = new Mat(requestedHeight, requestedWidth, CvType.CV_8UC4);
gameObject.GetComponent <Renderer> ().material.mainTexture = texture;
gameObject.transform.localScale = new Vector3(webCamTexture.width, webCamTexture.height, 1);
Debug.Log("Screen.width " + Screen.width + " Screen.height " + Screen.height + " Screen.orientation " + Screen.orientation);
if (fpsMonitor != null)
{
fpsMonitor.Add("width", rgbaMat.width().ToString());
fpsMonitor.Add("height", rgbaMat.height().ToString());
fpsMonitor.Add("orientation", Screen.orientation.ToString());
}
float width = rgbaMat.width();
float height = rgbaMat.height();
float widthScale = (float)Screen.width / width;
float heightScale = (float)Screen.height / height;
if (widthScale < heightScale)
{
Camera.main.orthographicSize = (width * (float)Screen.height / (float)Screen.width) / 2;
}
else
{
Camera.main.orthographicSize = height / 2;
}
KF = new KalmanFilter(4, 2, 0, CvType.CV_32FC1);
// intialization of KF...
Mat transitionMat = new Mat(4, 4, CvType.CV_32F);
transitionMat.put(0, 0, new float[] { 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1 });
KF.set_transitionMatrix(transitionMat);
measurement = new Mat(2, 1, CvType.CV_32FC1); measurement.setTo(Scalar.all(0));
cursorPos = new Point();
GetCursorPos(cursorPos);
// Set initial state estimate.
Mat statePreMat = KF.get_statePre();
statePreMat.put(0, 0, new float[] { (float)cursorPos.x, (float)cursorPos.y, 0, 0 });
Mat statePostMat = KF.get_statePost();
statePostMat.put(0, 0, new float[] { (float)cursorPos.x, (float)cursorPos.y, 0, 0 });
Mat measurementMat = new Mat(2, 4, CvType.CV_32FC1);
Core.setIdentity(measurementMat);
KF.set_measurementMatrix(measurementMat);
Mat processNoiseCovMat = new Mat(4, 4, CvType.CV_32FC1);
Core.setIdentity(processNoiseCovMat, Scalar.all(1e-4));
KF.set_processNoiseCov(processNoiseCovMat);
Mat measurementNoiseCovMat = new Mat(2, 2, CvType.CV_32FC1);
Core.setIdentity(measurementNoiseCovMat, Scalar.all(10));
KF.set_measurementNoiseCov(measurementNoiseCovMat);
Mat errorCovPostMat = new Mat(4, 4, CvType.CV_32FC1);
Core.setIdentity(errorCovPostMat, Scalar.all(.1));
KF.set_errorCovPost(errorCovPostMat);
/*Imgproc.rectangle (
* rgbaMatFinal,
* new Point (0, 0),
* new Point (rgbaMatFinal.width (), rgbaMatFinal.height ()),
* new Scalar (0, 0, 0, 255), -1);
*/
}
public void PutText_NullText_ThrowsArgumentNullException()
{
var image = new IplImage(new Size(10, 10), IplDepth.U8, 1);
CV.PutText(image, null, Point.Zero, new Font(1), Scalar.All(0));
}
public static Proof Prove(Statement statement, Scalar witness, WasabiRandom random)
{
return(Prove(statement, new ScalarVector(witness), random));
}
internal ScalarMultiply(Scalar left, Vector2 right)
{
Left = left;
Right = right;
}
public static Key <uint> Hash <T>(this Scalar <T> me) => _rec.Key <uint>(me);
public HsvThreshold()
{
Upper = new Scalar(179, 255, 255, 255);
}
public static IScalarValidationErrors Validate(IScalarValidationErrors result, Scalar <INamedTypeSymbol> resourceType, Scalar <IPropertySymbol> resourceProperty,
Scalar <string> message)
{
result = Validate(result, resourceType, resourceProperty);
if (resourceType.Value == null && string.IsNullOrWhiteSpace(message.Value))
{
result = result.Add(new ScalarValidationError(UserMessages.MessageView_ValueIsRequired, message));
}
return(result);
}
protected _RegistrationPresenter()
{
_userName = NewScalar(string.Empty).AddValidator(ValidateUserNameRequired);
_passwordConfirmation = NewScalar(string.Empty).AddValidator(ValidatePasswordConfirmationLength);
_interests = NewScalar(Interests.None).AddValidator(ValidateInterests);
}
public static VarVector <string> Tokenize(this Scalar <string> me) => _rec.VarVector <string>(me);
internal Constructed(Scalar x, Scalar y)
{
X = x;
Y = y;
}
CubicBezierFunction2(SnVector2 controlPoint0, SnVector2 controlPoint1, SnVector2 controlPoint2, SnVector2 controlPoint3, Scalar t)
{
_p0 = controlPoint0;
_p1 = controlPoint1;
_p2 = controlPoint2;
_p3 = controlPoint3;
_t = t;
}
public void AddBinding <T>(TemplateBuilder <T> builder, Scalar <INamedTypeSymbol> resourceType, Scalar <IPropertySymbol> resourceProperty, Scalar <string> message)
where T : TemplateBuilder <T>
{
var resourceTypeValue = resourceType.Value;
builder.AddBinding(_comboBoxResourceType, resourceType.BindToComboBox(GetResourceTypeSelection(resourceTypeValue)).ApplyRefresh((v, p) =>
{
Refresh(resourceType.Value != null);
}));
if (resourceTypeValue != null)
{
builder.AddBinding(_comboBoxResourceProperty, resourceProperty.BindToComboBox(GetResourcePropertySelection(resourceTypeValue)));
}
else
{
_comboBoxResourceType.IsEnabled = false;
}
builder.AddBinding(_textBoxMessage, message.BindToTextBox());
}
/// <summary> /// Performs text featurization on the input text. This will tokenize, do n-gram featurization, /// dictionary based term mapping, and finally produce a word-bag vector for the output. /// </summary> /// <param name="me">The text to featurize</param> /// <returns></returns> public static Vector <float> TextFeaturizer(this Scalar <string> me, bool keepDiacritics = true) => _rec.Vector <float>(me);
public static IScalarValidationErrors Validate(IScalarValidationErrors result, Scalar <INamedTypeSymbol> resourceType, Scalar <IPropertySymbol> resourceProperty)
{
if (resourceType.Value != null && resourceProperty.Value == null)
{
result = result.Add(new ScalarValidationError(UserMessages.MessageView_ValueIsRequired, resourceProperty));
}
return(result);
}
/// <summary> /// Trains a linear predictor using logistic regression. /// </summary> /// <param name="label">The target label for this binary classification task</param> /// <param name="features">The features to train on. Should be normalized.</param> /// <returns>A tuple of columns representing the score, the calibrated score as a probability, and the boolean predicted label</returns> public static (Scalar <float> score, Scalar <float> probability, Scalar <bool> predictedLabel) TrainLinearClassification(this Scalar <bool> label, Vector <float> features) => (_rec.Scalar <float>(label, features), _rec.Scalar <float>(label, features), _rec.Scalar <bool>(label, features));
// Count creates a new metric based on the Scalar information that counts
// the number of times the supplied int64 measure is set.
// Metrics of this type will use Int64Data.
public static void Count(this Scalar info, ptr <Config> _addr_e, label.Key key)
{
ref Config e = ref _addr_e.val;
private void GotoTargetPoint(Scalar targetPoint)
{
var startime = DateTime.Now;
var isNarrowing = false;
}