face_FacemarkTrain
import { face_FacemarkTrain } from '@banou/opencv-wasm'Use after await initOpenCV(). See the initialization and named imports guide.
Native object: release it with using or delete(). Factories can return null; check before calling methods. Inherits face_Facemark.
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.
Constructors and members
clone
Create another handle to the same native object. This retains the object without copying its pixels or algorithm state; dispose both handles separately.
clone(): this;The this result.
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