/// <summary>
///
/// </summary>
/// <param name="rowRange"></param>
/// <param name="colRange"></param>
/// <returns></returns>
public virtual Mat this[CvSlice rowRange, CvSlice colRange]
{
get
{
Mat result = new Mat();
GpuInvoke.GpuMat_opRange2(ptr, rowRange, colRange, result.CvPtr);
return(result);
}
}
/// <summary>
///
/// </summary>
/// <param name="m"></param>
/// <param name="rowRange"></param>
/// <param name="colRange"></param>
public GpuMat(GpuMat m, CvSlice rowRange, CvSlice colRange)
{
if (m == null)
{
throw new ArgumentNullException("m");
}
ptr = GpuInvoke.GpuMat_new9(m.CvPtr, rowRange, colRange);
if (ptr == IntPtr.Zero)
{
throw new OpenCvSharpException();
}
}
/// <summary>
/// 入力サンプルに対する応答を予測する
/// </summary>
/// <param name="sample">入力サンプル</param>
/// <param name="missing">データ欠損マスク（オプション）．データ欠損を扱うためには，弱い決定木が代理分岐を含まなければならない．</param>
/// <param name="slice">予測に用いられる弱い決定木シーケンスの連続的部分集合（スライス）．デフォルトでは，全ての弱い分類器が用いられる．</param>
/// <param name="rawMode">falseは通常の入力を意味する．true の場合，このメソッドは離散入力変数の全ての値があらかじめ， 0..<num_of_categories_i>-1 の範囲に正規化されていることを仮定する（決定木は内部的にはこのような正規化された表現を用いている）．これは決定木集合の高速な予測に役立つ．連続変数の入力変数に対しては，このフラグは利用されない．</param>
/// <param name="returnSum"></param>
/// <returns>重み付き投票に基づく出力クラスラベル</returns>
#else
/// <summary>
/// Predicts response for the input sample
/// </summary>
/// <param name="sample">The input sample. </param>
/// <param name="missing">The optional mask of missing measurements. To handle missing measurements, the weak classifiers must include surrogate splits. </param>
/// <param name="slice">The continuous subset of the sequence of weak classifiers to be used for prediction. By default, all the weak classifiers are used. </param>
/// <param name="rawMode">The last parameter is normally set to false that implies a regular input. If it is true, the method assumes that all the values of the discrete input variables have been already normalized to 0..<num_of_categoriesi>-1 ranges. (as the decision tree uses such normalized representation internally). It is useful for faster prediction with tree ensembles. For ordered input variables the flag is not used. </param>
/// <param name="returnSum"></param>
/// <returns>the output class label based on the weighted voting. </returns>
#endif
public float Predict(
Mat sample,
Mat missing = null,
Range?slice = null,
bool rawMode = false,
bool returnSum = false)
{
if (sample == null)
{
throw new ArgumentNullException("sample");
}
CvSlice slice0 = slice.GetValueOrDefault(CvSlice.WholeSeq);
return(NativeMethods.ml_CvBoost_predict_Mat(
ptr,
sample.CvPtr,
Cv2.ToPtr(missing),
slice0,
rawMode ? 1 : 0,
returnSum ? 1 : 0));
}
/// <summary>
/// 入力サンプルに対する応答を予測する
/// </summary>
/// <param name="sample">入力サンプル</param>
/// <param name="missing">データ欠損マスク（オプション）．データ欠損を扱うためには，弱い決定木が代理分岐を含まなければならない．</param>
/// <param name="weakResponses">個々の弱い決定木からの応答の出力パラメータ（オプション）で，これは浮動小数点型ベクトルである．ベクトルの要素数は，slice 長と等しくなければならない．</param>
/// <param name="slice">予測に用いられる弱い決定木シーケンスの連続的部分集合（スライス）．デフォルトでは，全ての弱い分類器が用いられる．</param>
/// <param name="rawMode">falseは通常の入力を意味する．true の場合，このメソッドは離散入力変数の全ての値があらかじめ， 0..<num_of_categories_i>-1 の範囲に正規化されていることを仮定する（決定木は内部的にはこのような正規化された表現を用いている）．これは決定木集合の高速な予測に役立つ．連続変数の入力変数に対しては，このフラグは利用されない．</param>
/// <param name="returnSum"></param>
/// <returns>重み付き投票に基づく出力クラスラベル</returns>
#else
/// <summary>
/// Predicts response for the input sample
/// </summary>
/// <param name="sample">The input sample. </param>
/// <param name="missing">The optional mask of missing measurements. To handle missing measurements, the weak classifiers must include surrogate splits. </param>
/// <param name="weakResponses">The optional output parameter, a floating-point vector, of responses from each individual weak classifier. The number of elements in the vector must be equal to the slice length. </param>
/// <param name="slice">The continuous subset of the sequence of weak classifiers to be used for prediction. By default, all the weak classifiers are used. </param>
/// <param name="rawMode">The last parameter is normally set to false that implies a regular input. If it is true, the method assumes that all the values of the discrete input variables have been already normalized to 0..<num_of_categoriesi>-1 ranges. (as the decision tree uses such normalized representation internally). It is useful for faster prediction with tree ensembles. For ordered input variables the flag is not used. </param>
/// <param name="returnSum"></param>
/// <returns>the output class label based on the weighted voting. </returns>
#endif
public float Predict(
CvMat sample,
CvMat missing = null,
CvMat weakResponses = null,
CvSlice?slice = null,
bool rawMode = false,
bool returnSum = false)
{
if (sample == null)
{
throw new ArgumentNullException(nameof(sample));
}
CvSlice slice0 = slice.GetValueOrDefault(CvSlice.WholeSeq);
return(NativeMethods.ml_Boost_predict_CvMat(
ptr,
sample.CvPtr,
Cv2.ToPtr(missing),
Cv2.ToPtr(weakResponses),
slice0,
rawMode ? 1 : 0,
returnSum ? 1 : 0));
}
public static extern void cvSeqRemoveSlice(IntPtr seq, CvSlice slice);
public static extern double cvContourArea(IntPtr contour, CvSlice slice);
public static extern void ml_CvBoost_prune(IntPtr obj, CvSlice slice);
public static extern IntPtr gpu_GpuMat_new9(IntPtr m, CvSlice rowRange, CvSlice colRange);
public static extern void GpuMat_opRange2(IntPtr src, CvSlice rowRange, CvSlice colRange, IntPtr dst);
public static extern float ml_CvBoost_predict_Mat(
IntPtr obj, IntPtr sample, IntPtr missing,
CvSlice slice, int rawMode, int returnSum);
public static extern IntPtr core_Mat_new(IntPtr mat, CvSlice rowRange);
public static extern float CvBoost_predict(IntPtr obj, IntPtr _sample, IntPtr _missing,
IntPtr weak_responses, CvSlice slice, bool raw_mode);
public static extern float ml_Boost_predict_CvMat(
IntPtr obj, IntPtr sample, IntPtr missing, IntPtr weakResponses,
CvSlice slice, int rawMode, int returnSum);
public static extern IntPtr cvSeqSlice(IntPtr seq, CvSlice slice, IntPtr storage, int copy_data);
public static extern IntPtr core_Mat_subMat(IntPtr self, int nRanges, CvSlice[] ranges);
public static extern int cvSliceLength(CvSlice slice, IntPtr seq);
/// <summary>
/// 入力サンプルに対する応答を予測する
/// </summary>
/// <param name="sample">入力サンプル</param>
/// <param name="missing">データ欠損マスク（オプション）．データ欠損を扱うためには，弱い決定木が代理分岐を含まなければならない．</param>
/// <param name="weak_responses">個々の弱い決定木からの応答の出力パラメータ（オプション）で，これは浮動小数点型ベクトルである．ベクトルの要素数は，slice 長と等しくなければならない．</param>
/// <param name="slice">予測に用いられる弱い決定木シーケンスの連続的部分集合（スライス）．デフォルトでは，全ての弱い分類器が用いられる．</param>
/// <param name="raw_mode">falseは通常の入力を意味する．true の場合，このメソッドは離散入力変数の全ての値があらかじめ， 0..<num_of_categories_i>-1 の範囲に正規化されていることを仮定する（決定木は内部的にはこのような正規化された表現を用いている）．これは決定木集合の高速な予測に役立つ．連続変数の入力変数に対しては，このフラグは利用されない．</param>
/// <returns>重み付き投票に基づく出力クラスラベル</returns>
#else
/// <summary>
/// Predicts response for the input sample
/// </summary>
/// <param name="sample">The input sample. </param>
/// <param name="missing">The optional mask of missing measurements. To handle missing measurements, the weak classifiers must include surrogate splits. </param>
/// <param name="weakResponses">The optional output parameter, a floating-point vector, of responses from each individual weak classifier. The number of elements in the vector must be equal to the slice length. </param>
/// <param name="slice">The continuous subset of the sequence of weak classifiers to be used for prediction. By default, all the weak classifiers are used. </param>
/// <param name="rawMode">The last parameter is normally set to false that implies a regular input. If it is true, the method assumes that all the values of the discrete input variables have been already normalized to 0..<num_of_categoriesi>-1 ranges. (as the decision tree uses such normalized representation internally). It is useful for faster prediction with tree ensembles. For ordered input variables the flag is not used. </param>
/// <returns>the output class label based on the weighted voting. </returns>
#endif
public float Predict(CvMat sample, CvMat missing, CvMat weakResponses, CvSlice slice, bool rawMode)
{
if (sample == null)
throw new ArgumentNullException("sample");
IntPtr missingPtr = (missing == null) ? IntPtr.Zero : missing.CvPtr;
IntPtr weakResponsesPtr = (weakResponses == null) ? IntPtr.Zero : weakResponses.CvPtr;
return MLInvoke.CvBoost_predict(
ptr,
sample.CvPtr,
missingPtr,
weakResponsesPtr,
slice,
rawMode
);
}
/// <summary>
/// returns a new matrix header for the specified row span
/// </summary>
/// <param name="r"></param>
/// <returns></returns>
public GpuMat RowRange(CvSlice r)
{
return(RowRange(r.StartIndex, r.EndIndex));
}
/// <summary>
/// 輪郭の周囲長または曲線の長さを計算する
/// </summary>
/// <param name="curve">配列</param>
/// <param name="slice">曲線の始点と終点．デフォルトでは曲線の全ての長さが計算される．</param>
/// <param name="isClosed">閉曲線かどうかを示す．次の3つの状態がある：
/// is_closed=0 - 曲線は閉曲線として扱われない．
/// is_closed>0 - 曲線は閉曲線として扱われる．
/// is_closed<0 - 曲線がシーケンスの場合， ((CvSeq*)curve)->flagsのフラグCV_SEQ_FLAG_CLOSEDから閉曲線かどうかを判別する．そうでない（曲線が点の配列（CvMat*）で表現される）場合，閉曲線として扱われない．
/// </param>
/// <returns>輪郭の周囲長または曲線の長さ</returns>
#else
/// <summary>
/// Calculates contour perimeter or curve length
/// </summary>
/// <param name="curve">Sequence or array of the curve points. </param>
/// <param name="slice">Starting and ending points of the curve, by default the whole curve length is calculated. </param>
/// <param name="isClosed">Indicates whether the curve is closed or not. There are 3 cases:
/// * is_closed=0 - the curve is assumed to be unclosed.
/// * is_closed>0 - the curve is assumed to be closed.
/// * is_closed<0 - if curve is sequence, the flag CV_SEQ_FLAG_CLOSED of ((CvSeq*)curve)->flags is checked to determine if the curve is closed or not, otherwise (curve is represented by array (CvMat*) of points) it is assumed to be unclosed. </param>
/// <returns></returns>
#endif
public static double ArcLength(CvArr curve, CvSlice slice, int isClosed)
{
if (curve == null)
throw new ArgumentNullException("curve");
double ret = NativeMethods.cvArcLength(curve.CvPtr, slice, isClosed);
GC.KeepAlive(curve);
return ret;
}
/// <summary>
/// returns a new matrix header for the specified column span
/// </summary>
/// <param name="r"></param>
/// <returns></returns>
public GpuMat ColRange(CvSlice r)
{
return(ColRange(r.StartIndex, r.EndIndex));
}
/// <summary>
/// sub-array selection; only the header is copied
/// </summary>
/// <param name="ranges"></param>
/// <returns></returns>
public virtual MatND this[CvSlice[] ranges]
{
get
{
MatND result = new MatND();
CppInvoke.MatND_opRange(ptr, ranges, result.CvPtr);
return result;
}
}
public static extern IntPtr GpuMat_new9(IntPtr m, CvSlice rowRange, CvSlice colRange);
public static extern IntPtr gpu_GpuMat_opRange2(IntPtr src, CvSlice rowRange, CvSlice colRange);
public static extern void GpuMat_opRange2(IntPtr src, CvSlice rowRange, CvSlice colRange, IntPtr dst);
public static extern float ml_CvBoost_predict_Mat(
IntPtr obj, IntPtr sample, IntPtr missing,
CvSlice slice, int rawMode, int returnSum);
public static extern IntPtr Mat_new5(IntPtr m, CvSlice rowRange, CvSlice colRange);
public static extern double cvArcLength(IntPtr curve, CvSlice slice, int is_closed);
public static extern void MatND_opRange(IntPtr src, CvSlice[] ranges, IntPtr dst);
public static extern IntPtr cvCvtSeqToArray(IntPtr seq, IntPtr elements, CvSlice slice);
public static extern float ml_Boost_predict_CvMat(
IntPtr obj, IntPtr sample, IntPtr missing, IntPtr weakResponses,
CvSlice slice, int rawMode, int returnSum);
public static extern IntPtr cvSeqSlice(IntPtr seq, CvSlice slice, IntPtr storage, [MarshalAs(UnmanagedType.Bool)] bool copy_data);
/// <summary>
/// 輪郭の周囲長または曲線の長さを計算する
/// </summary>
/// <param name="curve">配列</param>
/// <param name="slice">曲線の始点と終点．デフォルトでは曲線の全ての長さが計算される．</param>
/// <returns>輪郭の周囲長または曲線の長さ</returns>
#else
/// <summary>
/// Calculates contour perimeter or curve length
/// </summary>
/// <param name="curve">Sequence or array of the curve points. </param>
/// <param name="slice">Starting and ending points of the curve, by default the whole curve length is calculated. </param>
/// <returns></returns>
#endif
public static double ArcLength(CvArr curve, CvSlice slice)
{
return ArcLength(curve, slice, -1);
}
/// <summary>
/// 入力サンプルに対する応答を予測する
/// </summary>
/// <param name="sample">入力サンプル</param>
/// <param name="missing">データ欠損マスク（オプション）．データ欠損を扱うためには，弱い決定木が代理分岐を含まなければならない．</param>
/// <param name="weak_responses">個々の弱い決定木からの応答の出力パラメータ（オプション）で，これは浮動小数点型ベクトルである．ベクトルの要素数は，slice 長と等しくなければならない．</param>
/// <param name="slice">予測に用いられる弱い決定木シーケンスの連続的部分集合（スライス）．デフォルトでは，全ての弱い分類器が用いられる．</param>
/// <returns>重み付き投票に基づく出力クラスラベル</returns>
#else
/// <summary>
/// Predicts response for the input sample
/// </summary>
/// <param name="sample">The input sample. </param>
/// <param name="missing">The optional mask of missing measurements. To handle missing measurements, the weak classifiers must include surrogate splits. </param>
/// <param name="weakResponses">The optional output parameter, a floating-point vector, of responses from each individual weak classifier. The number of elements in the vector must be equal to the slice length. </param>
/// <param name="slice">The continuous subset of the sequence of weak classifiers to be used for prediction. By default, all the weak classifiers are used. </param>
/// <returns>the output class label based on the weighted voting. </returns>
#endif
public float Predict(CvMat sample, CvMat missing, CvMat weakResponses, CvSlice slice)
{
return Predict(sample, missing, weakResponses, slice, false);
}
/// <summary>
/// 輪郭の周囲長または曲線の長さを計算する
/// </summary>
/// <param name="curve">配列</param>
/// <param name="slice">曲線の始点と終点．デフォルトでは曲線の全ての長さが計算される．</param>
/// <param name="isClosed">閉曲線かどうかを示す．次の3つの状態がある：
/// is_closed=0 - 曲線は閉曲線として扱われない．
/// is_closed>0 - 曲線は閉曲線として扱われる．
/// is_closed<0 - 曲線がシーケンスの場合， ((CvSeq*)curve)->flagsのフラグCV_SEQ_FLAG_CLOSEDから閉曲線かどうかを判別する．そうでない（曲線が点の配列（CvMat*）で表現される）場合，閉曲線として扱われない．
/// </param>
/// <returns>輪郭の周囲長または曲線の長さ</returns>
#else
/// <summary>
/// Calculates contour perimeter or curve length
/// </summary>
/// <param name="curve">Sequence or array of the curve points. </param>
/// <param name="slice">Starting and ending points of the curve, by default the whole curve length is calculated. </param>
/// <param name="isClosed">Indicates whether the curve is closed or not. There are 3 cases:
/// * is_closed=0 - the curve is assumed to be unclosed.
/// * is_closed>0 - the curve is assumed to be closed.
/// * is_closed<0 - if curve is sequence, the flag CV_SEQ_FLAG_CLOSED of ((CvSeq*)curve)->flags is checked to determine if the curve is closed or not, otherwise (curve is represented by array (CvMat*) of points) it is assumed to be unclosed. </param>
/// <returns></returns>
#endif
public static double ArcLength(CvArr curve, CvSlice slice, int isClosed)
{
if (curve == null)
{
throw new ArgumentNullException("curve");
}
return NativeMethods.cvArcLength(curve.CvPtr, slice, isClosed);
}
/// <summary>
/// 指定された弱い決定木を削除する
/// </summary>
/// <param name="slice"></param>
#else
/// <summary>
/// Removes specified weak classifiers
/// </summary>
/// <param name="slice"></param>
#endif
public virtual void Prune(CvSlice slice)
{
MLInvoke.CvBoost_prune(ptr, slice);
}
/// <summary>
/// 入力サンプルに対する応答を予測する
/// </summary>
/// <param name="sample">入力サンプル</param>
/// <param name="missing">データ欠損マスク（オプション）．データ欠損を扱うためには，弱い決定木が代理分岐を含まなければならない．</param>
/// <param name="weakResponses">個々の弱い決定木からの応答の出力パラメータ（オプション）で，これは浮動小数点型ベクトルである．ベクトルの要素数は，slice 長と等しくなければならない．</param>
/// <param name="slice">予測に用いられる弱い決定木シーケンスの連続的部分集合（スライス）．デフォルトでは，全ての弱い分類器が用いられる．</param>
/// <param name="rawMode">falseは通常の入力を意味する．true の場合，このメソッドは離散入力変数の全ての値があらかじめ， 0..<num_of_categories_i>-1 の範囲に正規化されていることを仮定する（決定木は内部的にはこのような正規化された表現を用いている）．これは決定木集合の高速な予測に役立つ．連続変数の入力変数に対しては，このフラグは利用されない．</param>
/// <param name="returnSum"></param>
/// <returns>重み付き投票に基づく出力クラスラベル</returns>
#else
/// <summary>
/// Predicts response for the input sample
/// </summary>
/// <param name="sample">The input sample. </param>
/// <param name="missing">The optional mask of missing measurements. To handle missing measurements, the weak classifiers must include surrogate splits. </param>
/// <param name="weakResponses">The optional output parameter, a floating-point vector, of responses from each individual weak classifier. The number of elements in the vector must be equal to the slice length. </param>
/// <param name="slice">The continuous subset of the sequence of weak classifiers to be used for prediction. By default, all the weak classifiers are used. </param>
/// <param name="rawMode">The last parameter is normally set to false that implies a regular input. If it is true, the method assumes that all the values of the discrete input variables have been already normalized to 0..<num_of_categoriesi>-1 ranges. (as the decision tree uses such normalized representation internally). It is useful for faster prediction with tree ensembles. For ordered input variables the flag is not used. </param>
/// <param name="returnSum"></param>
/// <returns>the output class label based on the weighted voting. </returns>
#endif
public float Predict(
CvMat sample,
CvMat missing = null,
CvMat weakResponses = null,
CvSlice? slice = null,
bool rawMode = false,
bool returnSum = false)
{
if (sample == null)
throw new ArgumentNullException("sample");
CvSlice slice0 = slice.GetValueOrDefault(CvSlice.WholeSeq);
return NativeMethods.ml_CvBoost_predict_CvMat(
ptr,
sample.CvPtr,
Cv2.ToPtr(missing),
Cv2.ToPtr(weakResponses),
slice0,
rawMode ? 1 : 0,
returnSum ? 1 : 0);
}
public static extern void ml_CvBoost_prune(IntPtr obj, CvSlice slice);
public static extern IntPtr gpu_GpuMat_opRange2(IntPtr src, CvSlice rowRange, CvSlice colRange);
