OpenCV for Unity 2.6.4
Enox Software / Please refer to OpenCV official document ( http://docs.opencv.org/4.10.0/index.html ) for the details of the argument of the method.
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OpenCVForUnity.FaceModule.FacemarkTrain Class Reference

Abstract base class for trainable facemark models. More...

Static Public Member Functions

static new FacemarkTrain __fromPtr__ (IntPtr addr)
 
- Static Public Member Functions inherited from OpenCVForUnity.FaceModule.Facemark
static new Facemark __fromPtr__ (IntPtr addr)
 
- Static Public Member Functions inherited from OpenCVForUnity.CoreModule.Algorithm
static Algorithm __fromPtr__ (IntPtr addr)
 
- Static Public Member Functions inherited from OpenCVForUnity.DisposableObject
static IntPtr ThrowIfNullIntPtr (IntPtr ptr)
 

Protected Member Functions

override void Dispose (bool disposing)
 
- Protected Member Functions inherited from OpenCVForUnity.FaceModule.Facemark
- Protected Member Functions inherited from OpenCVForUnity.CoreModule.Algorithm
- Protected Member Functions inherited from OpenCVForUnity.DisposableOpenCVObject
 DisposableOpenCVObject ()
 
 DisposableOpenCVObject (bool isEnabledDispose)
 
 DisposableOpenCVObject (IntPtr ptr)
 
 DisposableOpenCVObject (IntPtr ptr, bool isEnabledDispose)
 
- Protected Member Functions inherited from OpenCVForUnity.DisposableObject
 DisposableObject ()
 
 DisposableObject (bool isEnabledDispose)
 

Additional Inherited Members

- Public Member Functions inherited from OpenCVForUnity.FaceModule.Facemark
bool fit (Mat image, MatOfRect faces, List< MatOfPoint2f > landmarks)
 Detect facial landmarks from an image.
 
void loadModel (string model)
 A function to load the trained model before the fitting process.
 
- Public Member Functions inherited from OpenCVForUnity.CoreModule.Algorithm
virtual void clear ()
 Clears the algorithm state.
 
virtual bool empty ()
 Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read.
 
virtual string getDefaultName ()
 
IntPtr getNativeObjAddr ()
 
void save (string filename)
 
- Public Member Functions inherited from OpenCVForUnity.DisposableObject
void Dispose ()
 
void ThrowIfDisposed ()
 
- Package Functions inherited from OpenCVForUnity.FaceModule.Facemark
- Package Functions inherited from OpenCVForUnity.CoreModule.Algorithm
- Package Attributes inherited from OpenCVForUnity.DisposableOpenCVObject
- Properties inherited from OpenCVForUnity.DisposableObject
bool IsDisposed [get, protected set]
 
bool IsEnabledDispose [get, set]
 

Detailed Description

Abstract base class for trainable facemark models.

To utilize this API in your program, please take a look at the tutorial_table_of_content_facemark

Description

The AAM and LBF facemark models in OpenCV are derived from the abstract base class FacemarkTrain, which provides a unified access to those facemark algorithms in OpenCV.

Here is an example on how to declare facemark algorithm:

// Using Facemark in your code:
Ptr<Facemark> facemark = FacemarkLBF::create();

The typical pipeline for facemark detection is listed as follows:

  • (Non-mandatory) Set a user defined face detection using FacemarkTrain::setFaceDetector. The facemark algorithms are designed to fit the facial points into a face. Therefore, the face information should be provided to the facemark algorithm. Some algorithms might provides a default face recognition function. However, the users might prefer to use their own face detector to obtains the best possible detection result.
  • (Non-mandatory) Training the model for a specific algorithm using FacemarkTrain::training. In this case, the model should be automatically saved by the algorithm. If the user already have a trained model, then this part can be omitted.
  • Load the trained model using Facemark.loadModel.
  • Perform the fitting via the Facemark.fit.

Member Function Documentation

◆ __fromPtr__()

static new FacemarkTrain OpenCVForUnity.FaceModule.FacemarkTrain.__fromPtr__ ( IntPtr addr)
static

◆ Dispose()

override void OpenCVForUnity.FaceModule.FacemarkTrain.Dispose ( bool disposing)
protectedvirtual

The documentation for this class was generated from the following file: