//javadoc: LBPHFaceRecognizer::create(radius, neighbors, grid_x)
public static LBPHFaceRecognizer create(int radius, int neighbors, int grid_x)
{
#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
LBPHFaceRecognizer retVal = LBPHFaceRecognizer.__fromPtr__(face_LBPHFaceRecognizer_create_12(radius, neighbors, grid_x));
return(retVal);
#else
return(null);
#endif
}
//javadoc: LBPHFaceRecognizer::create()
public static LBPHFaceRecognizer create()
{
#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
LBPHFaceRecognizer retVal = LBPHFaceRecognizer.__fromPtr__(face_LBPHFaceRecognizer_create_15());
return(retVal);
#else
return(null);
#endif
}
void DoProcess()
{
if (!(owner.Value is OpenCVForUnityPlayMakerActions.LBPHFaceRecognizer))
{
LogError("owner is not initialized. Add Action \"newLBPHFaceRecognizer\".");
return;
}
OpenCVForUnity.FaceModule.LBPHFaceRecognizer wrapped_owner = OpenCVForUnityPlayMakerActionsUtils.GetWrappedObject <OpenCVForUnityPlayMakerActions.LBPHFaceRecognizer, OpenCVForUnity.FaceModule.LBPHFaceRecognizer>(owner);
storeResult.Value = wrapped_owner.getGridX();
}
void DoProcess()
{
if (!(owner.Value is OpenCVForUnityPlayMakerActions.LBPHFaceRecognizer))
{
LogError("owner is not initialized. Add Action \"newLBPHFaceRecognizer\".");
return;
}
OpenCVForUnity.FaceModule.LBPHFaceRecognizer wrapped_owner = OpenCVForUnityPlayMakerActionsUtils.GetWrappedObject <OpenCVForUnityPlayMakerActions.LBPHFaceRecognizer, OpenCVForUnity.FaceModule.LBPHFaceRecognizer>(owner);
wrapped_owner.setThreshold((float)val.Value);
}
void DoProcess()
{
if (!(owner.Value is OpenCVForUnityPlayMakerActions.LBPHFaceRecognizer))
{
LogError("owner is not initialized. Add Action \"newLBPHFaceRecognizer\".");
return;
}
OpenCVForUnity.FaceModule.LBPHFaceRecognizer wrapped_owner = OpenCVForUnityPlayMakerActionsUtils.GetWrappedObject <OpenCVForUnityPlayMakerActions.LBPHFaceRecognizer, OpenCVForUnity.FaceModule.LBPHFaceRecognizer>(owner);
if (!(storeResult.Value is OpenCVForUnityPlayMakerActions.Double))
{
storeResult.Value = new OpenCVForUnityPlayMakerActions.Double();
}
((OpenCVForUnityPlayMakerActions.Double)storeResult.Value).wrappedObject = wrapped_owner.getThreshold();
}
void DoProcess()
{
if (!(owner.Value is OpenCVForUnityPlayMakerActions.LBPHFaceRecognizer))
{
LogError("owner is not initialized. Add Action \"newLBPHFaceRecognizer\".");
return;
}
OpenCVForUnity.FaceModule.LBPHFaceRecognizer wrapped_owner = OpenCVForUnityPlayMakerActionsUtils.GetWrappedObject <OpenCVForUnityPlayMakerActions.LBPHFaceRecognizer, OpenCVForUnity.FaceModule.LBPHFaceRecognizer>(owner);
List <OpenCVForUnity.CoreModule.Mat> wrapped_storeResult = wrapped_owner.getHistograms();
if (!storeResult.IsNone)
{
OpenCVForUnityPlayMakerActionsUtils.ConvertListToFsmArray <OpenCVForUnity.CoreModule.Mat, OpenCVForUnityPlayMakerActions.Mat>(wrapped_storeResult, storeResult);
}
}
void DoProcess()
{
if (!(owner.Value is OpenCVForUnityPlayMakerActions.LBPHFaceRecognizer))
{
LogError("owner is not initialized. Add Action \"newLBPHFaceRecognizer\".");
return;
}
OpenCVForUnity.FaceModule.LBPHFaceRecognizer wrapped_owner = OpenCVForUnityPlayMakerActionsUtils.GetWrappedObject <OpenCVForUnityPlayMakerActions.LBPHFaceRecognizer, OpenCVForUnity.FaceModule.LBPHFaceRecognizer>(owner);
if (!(val.Value is OpenCVForUnityPlayMakerActions.Double))
{
LogError("val is not initialized. Add Action \"newDouble\".");
return;
}
System.Double wrapped_val = OpenCVForUnityPlayMakerActionsUtils.GetWrappedObject <OpenCVForUnityPlayMakerActions.Double, System.Double>(val);
wrapped_owner.setThreshold(wrapped_val);
}
public LBPHFaceRecognizer(OpenCVForUnity.FaceModule.LBPHFaceRecognizer nativeObj) : base(nativeObj)
{
}
/**
* radius, the smoother the image but more spatial information you can get.
* appropriate value is to use {code 8} sample points. Keep in mind: the more sample points you include,
* the higher the computational cost.
* publications. The more cells, the finer the grid, the higher the dimensionality of the resulting
* feature vector.
* publications. The more cells, the finer the grid, the higher the dimensionality of the resulting
* feature vector.
* is larger than the threshold, this method returns -1.
*
* ### Notes:
*
* <ul>
* <li>
* The Circular Local Binary Patterns (used in training and prediction) expect the data given as
* grayscale images, use cvtColor to convert between the color spaces.
* </li>
* <li>
* This model supports updating.
* </li>
* </ul>
*
* ### Model internal data:
*
* <ul>
* <li>
* radius see LBPHFaceRecognizer::create.
* </li>
* <li>
* neighbors see LBPHFaceRecognizer::create.
* </li>
* <li>
* grid_x see LLBPHFaceRecognizer::create.
* </li>
* <li>
* grid_y see LBPHFaceRecognizer::create.
* </li>
* <li>
* threshold see LBPHFaceRecognizer::create.
* </li>
* <li>
* histograms Local Binary Patterns Histograms calculated from the given training data (empty if
* none was given).
* </li>
* <li>
* labels Labels corresponding to the calculated Local Binary Patterns Histograms.
* </li>
* </ul>
* return automatically generated
*/
public static LBPHFaceRecognizer create()
{
return(LBPHFaceRecognizer.__fromPtr__(face_LBPHFaceRecognizer_create_15()));
}
/**
* param radius The radius used for building the Circular Local Binary Pattern. The greater the
* radius, the smoother the image but more spatial information you can get.
* appropriate value is to use {code 8} sample points. Keep in mind: the more sample points you include,
* the higher the computational cost.
* publications. The more cells, the finer the grid, the higher the dimensionality of the resulting
* feature vector.
* publications. The more cells, the finer the grid, the higher the dimensionality of the resulting
* feature vector.
* is larger than the threshold, this method returns -1.
*
* ### Notes:
*
* <ul>
* <li>
* The Circular Local Binary Patterns (used in training and prediction) expect the data given as
* grayscale images, use cvtColor to convert between the color spaces.
* </li>
* <li>
* This model supports updating.
* </li>
* </ul>
*
* ### Model internal data:
*
* <ul>
* <li>
* radius see LBPHFaceRecognizer::create.
* </li>
* <li>
* neighbors see LBPHFaceRecognizer::create.
* </li>
* <li>
* grid_x see LLBPHFaceRecognizer::create.
* </li>
* <li>
* grid_y see LBPHFaceRecognizer::create.
* </li>
* <li>
* threshold see LBPHFaceRecognizer::create.
* </li>
* <li>
* histograms Local Binary Patterns Histograms calculated from the given training data (empty if
* none was given).
* </li>
* <li>
* labels Labels corresponding to the calculated Local Binary Patterns Histograms.
* </li>
* </ul>
* return automatically generated
*/
public static LBPHFaceRecognizer create(int radius)
{
return(LBPHFaceRecognizer.__fromPtr__(face_LBPHFaceRecognizer_create_14(radius)));
}
/**
* param radius The radius used for building the Circular Local Binary Pattern. The greater the
* radius, the smoother the image but more spatial information you can get.
* param neighbors The number of sample points to build a Circular Local Binary Pattern from. An
* appropriate value is to use {code 8} sample points. Keep in mind: the more sample points you include,
* the higher the computational cost.
* publications. The more cells, the finer the grid, the higher the dimensionality of the resulting
* feature vector.
* publications. The more cells, the finer the grid, the higher the dimensionality of the resulting
* feature vector.
* is larger than the threshold, this method returns -1.
*
* ### Notes:
*
* <ul>
* <li>
* The Circular Local Binary Patterns (used in training and prediction) expect the data given as
* grayscale images, use cvtColor to convert between the color spaces.
* </li>
* <li>
* This model supports updating.
* </li>
* </ul>
*
* ### Model internal data:
*
* <ul>
* <li>
* radius see LBPHFaceRecognizer::create.
* </li>
* <li>
* neighbors see LBPHFaceRecognizer::create.
* </li>
* <li>
* grid_x see LLBPHFaceRecognizer::create.
* </li>
* <li>
* grid_y see LBPHFaceRecognizer::create.
* </li>
* <li>
* threshold see LBPHFaceRecognizer::create.
* </li>
* <li>
* histograms Local Binary Patterns Histograms calculated from the given training data (empty if
* none was given).
* </li>
* <li>
* labels Labels corresponding to the calculated Local Binary Patterns Histograms.
* </li>
* </ul>
* return automatically generated
*/
public static LBPHFaceRecognizer create(int radius, int neighbors)
{
return(LBPHFaceRecognizer.__fromPtr__(face_LBPHFaceRecognizer_create_13(radius, neighbors)));
}
/**
* param radius The radius used for building the Circular Local Binary Pattern. The greater the
* radius, the smoother the image but more spatial information you can get.
* param neighbors The number of sample points to build a Circular Local Binary Pattern from. An
* appropriate value is to use {code 8} sample points. Keep in mind: the more sample points you include,
* the higher the computational cost.
* param grid_x The number of cells in the horizontal direction, 8 is a common value used in
* publications. The more cells, the finer the grid, the higher the dimensionality of the resulting
* feature vector.
* param grid_y The number of cells in the vertical direction, 8 is a common value used in
* publications. The more cells, the finer the grid, the higher the dimensionality of the resulting
* feature vector.
* is larger than the threshold, this method returns -1.
*
* ### Notes:
*
* <ul>
* <li>
* The Circular Local Binary Patterns (used in training and prediction) expect the data given as
* grayscale images, use cvtColor to convert between the color spaces.
* </li>
* <li>
* This model supports updating.
* </li>
* </ul>
*
* ### Model internal data:
*
* <ul>
* <li>
* radius see LBPHFaceRecognizer::create.
* </li>
* <li>
* neighbors see LBPHFaceRecognizer::create.
* </li>
* <li>
* grid_x see LLBPHFaceRecognizer::create.
* </li>
* <li>
* grid_y see LBPHFaceRecognizer::create.
* </li>
* <li>
* threshold see LBPHFaceRecognizer::create.
* </li>
* <li>
* histograms Local Binary Patterns Histograms calculated from the given training data (empty if
* none was given).
* </li>
* <li>
* labels Labels corresponding to the calculated Local Binary Patterns Histograms.
* </li>
* </ul>
* return automatically generated
*/
public static LBPHFaceRecognizer create(int radius, int neighbors, int grid_x, int grid_y)
{
return(LBPHFaceRecognizer.__fromPtr__(face_LBPHFaceRecognizer_create_11(radius, neighbors, grid_x, grid_y)));
}
//
// C++: static Ptr_LBPHFaceRecognizer cv::face::LBPHFaceRecognizer::create(int radius = 1, int neighbors = 8, int grid_x = 8, int grid_y = 8, double threshold = DBL_MAX)
//
/**
* param radius The radius used for building the Circular Local Binary Pattern. The greater the
* radius, the smoother the image but more spatial information you can get.
* param neighbors The number of sample points to build a Circular Local Binary Pattern from. An
* appropriate value is to use {code 8} sample points. Keep in mind: the more sample points you include,
* the higher the computational cost.
* param grid_x The number of cells in the horizontal direction, 8 is a common value used in
* publications. The more cells, the finer the grid, the higher the dimensionality of the resulting
* feature vector.
* param grid_y The number of cells in the vertical direction, 8 is a common value used in
* publications. The more cells, the finer the grid, the higher the dimensionality of the resulting
* feature vector.
* param threshold The threshold applied in the prediction. If the distance to the nearest neighbor
* is larger than the threshold, this method returns -1.
*
* ### Notes:
*
* <ul>
* <li>
* The Circular Local Binary Patterns (used in training and prediction) expect the data given as
* grayscale images, use cvtColor to convert between the color spaces.
* </li>
* <li>
* This model supports updating.
* </li>
* </ul>
*
* ### Model internal data:
*
* <ul>
* <li>
* radius see LBPHFaceRecognizer::create.
* </li>
* <li>
* neighbors see LBPHFaceRecognizer::create.
* </li>
* <li>
* grid_x see LLBPHFaceRecognizer::create.
* </li>
* <li>
* grid_y see LBPHFaceRecognizer::create.
* </li>
* <li>
* threshold see LBPHFaceRecognizer::create.
* </li>
* <li>
* histograms Local Binary Patterns Histograms calculated from the given training data (empty if
* none was given).
* </li>
* <li>
* labels Labels corresponding to the calculated Local Binary Patterns Histograms.
* </li>
* </ul>
* return automatically generated
*/
public static LBPHFaceRecognizer create(int radius, int neighbors, int grid_x, int grid_y, double threshold)
{
return(LBPHFaceRecognizer.__fromPtr__(face_LBPHFaceRecognizer_create_10(radius, neighbors, grid_x, grid_y, threshold)));
}
