org.bytedeco.opencv.opencv_face.FacemarkTrain Maven / Gradle / Ivy
// Targeted by JavaCPP version 1.5.7: DO NOT EDIT THIS FILE
package org.bytedeco.opencv.opencv_face;
import java.nio.*;
import org.bytedeco.javacpp.*;
import org.bytedeco.javacpp.annotation.*;
import static org.bytedeco.javacpp.presets.javacpp.*;
import static org.bytedeco.openblas.global.openblas_nolapack.*;
import static org.bytedeco.openblas.global.openblas.*;
import org.bytedeco.opencv.opencv_core.*;
import static org.bytedeco.opencv.global.opencv_core.*;
import org.bytedeco.opencv.opencv_imgproc.*;
import static org.bytedeco.opencv.global.opencv_imgproc.*;
import static org.bytedeco.opencv.global.opencv_imgcodecs.*;
import org.bytedeco.opencv.opencv_videoio.*;
import static org.bytedeco.opencv.global.opencv_videoio.*;
import org.bytedeco.opencv.opencv_highgui.*;
import static org.bytedeco.opencv.global.opencv_highgui.*;
import org.bytedeco.opencv.opencv_flann.*;
import static org.bytedeco.opencv.global.opencv_flann.*;
import org.bytedeco.opencv.opencv_features2d.*;
import static org.bytedeco.opencv.global.opencv_features2d.*;
import org.bytedeco.opencv.opencv_calib3d.*;
import static org.bytedeco.opencv.global.opencv_calib3d.*;
import org.bytedeco.opencv.opencv_objdetect.*;
import static org.bytedeco.opencv.global.opencv_objdetect.*;
import org.bytedeco.opencv.opencv_photo.*;
import static org.bytedeco.opencv.global.opencv_photo.*;
import static org.bytedeco.opencv.global.opencv_face.*;
/** \brief Abstract base class for trainable facemark models
To utilize this API in your program, please take a look at the \ref 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:
{@code
// Using Facemark in your code:
Ptr 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.
*/
@Namespace("cv::face") @Properties(inherit = org.bytedeco.opencv.presets.opencv_face.class)
public class FacemarkTrain extends Facemark {
static { Loader.load(); }
/** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */
public FacemarkTrain(Pointer p) { super(p); }
/** \brief Add one training sample to the trainer.
@param image Input image.
@param landmarks The ground-truth of facial landmarks points corresponds to the image.
Example of usage
{@code
String imageFiles = "../data/images_train.txt";
String ptsFiles = "../data/points_train.txt";
std::vector images_train;
std::vector landmarks_train;
// load the list of dataset: image paths and landmark file paths
loadDatasetList(imageFiles,ptsFiles,images_train,landmarks_train);
Mat image;
std::vector facial_points;
for(size_t i=0;iaddTrainingSample(image, facial_points);
}
}
The contents in the training files should follows the standard format.
Here are examples for the contents in these files.
example of content in the images_train.txt
{@code
/home/user/ibug/image_003_1.jpg
/home/user/ibug/image_004_1.jpg
/home/user/ibug/image_005_1.jpg
/home/user/ibug/image_006.jpg
}
example of content in the points_train.txt
{@code
/home/user/ibug/image_003_1.pts
/home/user/ibug/image_004_1.pts
/home/user/ibug/image_005_1.pts
/home/user/ibug/image_006.pts
}
*/
public native @Cast("bool") boolean addTrainingSample(@ByVal Mat image, @ByRef Point2fVector landmarks);
/** \brief Trains a Facemark algorithm using the given dataset.
Before the training process, training samples should be added to the trainer
using face::addTrainingSample function.
@param parameters Optional extra parameters (algorithm dependent).
Example of usage
{@code
FacemarkLBF::Params params;
params.model_filename = "ibug68.model"; // filename to save the trained model
Ptr facemark = FacemarkLBF::create(params);
// add training samples (see Facemark::addTrainingSample)
facemark->training();
}
*/
public native void training(Pointer parameters/*=0*/);
public native void training();
/** \brief Set a user defined face detector for the Facemark algorithm.
@param detector The user defined face detector function
@param userData Detector parameters
Example of usage
{@code
MyDetectorParameters detectorParameters(...);
facemark->setFaceDetector(myDetector, &detectorParameters);
}
Example of a user defined face detector
{@code
bool myDetector( InputArray image, OutputArray faces, void* userData)
{
MyDetectorParameters* params = (MyDetectorParameters*)userData;
// -------- do something --------
}
}
TODO Lifetime of detector parameters is uncontrolled. Rework interface design to "Ptr".
*/
public native @Cast("bool") boolean setFaceDetector(@Cast("cv::face::FN_FaceDetector") Pointer detector, Pointer userData/*=0*/);
public native @Cast("bool") boolean setFaceDetector(@Cast("cv::face::FN_FaceDetector") Pointer detector);
/** \brief Detect faces from a given image using default or user defined face detector.
Some Algorithm might not provide a default face detector.
@param image Input image.
@param faces Output of the function which represent region of interest of the detected faces. Each face is stored in cv::Rect container.
Example of usage
{@code
std::vector faces;
facemark->getFaces(img, faces);
for(int j=0;j
*/
public native @Cast("bool") boolean getFaces(@ByVal Mat image, @ByRef RectVector faces);
/** \brief Get data from an algorithm
@param items The obtained data, algorithm dependent.
Example of usage
{@code
Ptr facemark = FacemarkAAM::create();
facemark->loadModel("AAM.yml");
FacemarkAAM::Data data;
facemark->getData(&data);
std::vector s0 = data.s0;
cout<
*/
public native @Cast("bool") boolean getData(Pointer items/*=0*/);
public native @Cast("bool") boolean getData(); // FIXIT
}