fast-yolo4/3rdparty/opencv/inc/opencv2/face/facemark_train.hpp

// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.

/*
This file was part of GSoC Project: Facemark API for OpenCV
Final report: https://gist.github.com/kurnianggoro/74de9121e122ad0bd825176751d47ecc
Student: Laksono Kurnianggoro
Mentor: Delia Passalacqua
*/

#ifndef __OPENCV_FACELANDMARKTRAIN_HPP__
#define __OPENCV_FACELANDMARKTRAIN_HPP__

/**
@defgroup face Face Analysis
- @ref tutorial_table_of_content_facemark
- The Facemark API
*/

#include "opencv2/face/facemark.hpp"
#include "opencv2/objdetect.hpp"
#include <vector>
#include <string>


namespace cv {
namespace face {

//! @addtogroup face
//! @{

typedef bool(*FN_FaceDetector)(InputArray, OutputArray, void* userData);

struct CParams{
    String cascade; //!<  the face detector
    double scaleFactor; //!< Parameter specifying how much the image size is reduced at each image scale.
    int minNeighbors; //!< Parameter specifying how many neighbors each candidate rectangle should have to retain it.
    Size minSize; //!< Minimum possible object size.
    Size maxSize; //!< Maximum possible object size.

    CV_EXPORTS CParams(
        String cascade_model,
        double sf = 1.1,
        int minN = 3,
        Size minSz = Size(30, 30),
        Size maxSz = Size()
    );

    CascadeClassifier face_cascade;
};

/** @brief Default face detector
This function is mainly utilized by the implementation of a Facemark Algorithm.
End users are advised to use function Facemark::getFaces which can be manually defined
and circumvented to the algorithm by Facemark::setFaceDetector.

@param image The input image to be processed.
@param faces Output of the function which represent region of interest of the detected faces.
Each face is stored in cv::Rect container.
@param params detector parameters

<B>Example of usage</B>
@code
std::vector<cv::Rect> faces;
CParams params("haarcascade_frontalface_alt.xml");
cv::face::getFaces(frame, faces, &params);
for(int j=0;j<faces.size();j++){
    cv::rectangle(frame, faces[j], cv::Scalar(255,0,255));
}
cv::imshow("detection", frame);
@endcode
*/
CV_EXPORTS bool getFaces(InputArray image, OutputArray faces, CParams* params);

CV_EXPORTS_W bool getFacesHAAR(InputArray image, OutputArray faces, const String& face_cascade_name);

/** @brief A utility to load list of paths to training image and annotation file.
@param imageList The specified file contains paths to the training images.
@param annotationList The specified file contains paths to the training annotations.
@param images The loaded paths of training images.
@param annotations The loaded paths of annotation files.

Example of usage:
@code
String imageFiles = "images_path.txt";
String ptsFiles = "annotations_path.txt";
std::vector<String> images_train;
std::vector<String> landmarks_train;
loadDatasetList(imageFiles,ptsFiles,images_train,landmarks_train);
@endcode
*/
CV_EXPORTS_W bool loadDatasetList(String imageList,
                                  String annotationList,
                                  std::vector<String> & images,
                                  std::vector<String> & annotations);

/** @brief A utility to load facial landmark dataset from a single file.

@param filename The filename of a file that contains the dataset information.
Each line contains the filename of an image followed by
pairs of x and y values of facial landmarks points separated by a space.
Example
@code
/home/user/ibug/image_003_1.jpg 336.820955 240.864510 334.238298 260.922709 335.266918 ...
/home/user/ibug/image_005_1.jpg 376.158428 230.845712 376.736984 254.924635 383.265403 ...
@endcode
@param images A vector where each element represent the filename of image in the dataset.
Images are not loaded by default to save the memory.
@param facePoints The loaded landmark points for all training data.
@param delim Delimiter between each element, the default value is a whitespace.
@param offset An offset value to adjust the loaded points.

<B>Example of usage</B>
@code
cv::String imageFiles = "../data/images_train.txt";
cv::String ptsFiles = "../data/points_train.txt";
std::vector<String> images;
std::vector<std::vector<Point2f> > facePoints;
loadTrainingData(imageFiles, ptsFiles, images, facePoints, 0.0f);
@endcode
*/
CV_EXPORTS_W bool loadTrainingData( String filename , std::vector<String> & images,
                                    OutputArray facePoints,
                                    char delim = ' ', float offset = 0.0f);

/** @brief A utility to load facial landmark information from the dataset.

@param imageList A file contains the list of image filenames in the training dataset.
@param groundTruth A file contains the list of filenames
where the landmarks points information are stored.
The content in each file should follow the standard format (see face::loadFacePoints).
@param images A vector where each element represent the filename of image in the dataset.
Images are not loaded by default to save the memory.
@param facePoints The loaded landmark points for all training data.
@param offset An offset value to adjust the loaded points.

<B>Example of usage</B>
@code
cv::String imageFiles = "../data/images_train.txt";
cv::String ptsFiles = "../data/points_train.txt";
std::vector<String> images;
std::vector<std::vector<Point2f> > facePoints;
loadTrainingData(imageFiles, ptsFiles, images, facePoints, 0.0f);
@endcode

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
@endcode

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
@endcode
*/
CV_EXPORTS_W bool loadTrainingData( String imageList, String groundTruth,
                                    std::vector<String> & images,
                                    OutputArray facePoints,
                                    float offset = 0.0f);

/** @brief This function extracts the data for training from .txt files which contains the corresponding image name and landmarks.
*The first file in each file should give the path of the image whose
*landmarks are being described in the file. Then in the subsequent
*lines there should be coordinates of the landmarks in the image
*i.e each line should be of the form x,y
*where x represents the x coordinate of the landmark and y represents
*the y coordinate of the landmark.
*
*For reference you can see the files as provided in the
*<a href="http://www.ifp.illinois.edu/~vuongle2/helen/">HELEN dataset</a>
*
* @param filename A vector of type cv::String containing name of the .txt files.
* @param trainlandmarks A vector of type cv::Point2f that would store shape or landmarks of all images.
* @param trainimages A vector of type cv::String which stores the name of images whose landmarks are tracked
* @returns A boolean value. It returns true when it reads the data successfully and false otherwise
*/
CV_EXPORTS_W bool loadTrainingData(std::vector<String> filename,std::vector< std::vector<Point2f> >
                          &trainlandmarks,std::vector<String> & trainimages);

/** @brief A utility to load facial landmark information from a given file.

@param filename The filename of file contains the facial landmarks data.
@param points The loaded facial landmark points.
@param offset An offset value to adjust the loaded points.

<B>Example of usage</B>
@code
std::vector<Point2f> points;
face::loadFacePoints("filename.txt", points, 0.0f);
@endcode

The annotation file should follow the default format which is
@code
version: 1
n_points:  68
{
212.716603 499.771793
230.232816 566.290071
...
}
@endcode
where n_points is the number of points considered
and each point is represented as its position in x and y.
*/
CV_EXPORTS_W bool loadFacePoints( String filename, OutputArray points,
                                  float offset = 0.0f);

/** @brief Utility to draw the detected facial landmark points

@param image The input image to be processed.
@param points Contains the data of points which will be drawn.
@param color The color of points in BGR format represented by cv::Scalar.

<B>Example of usage</B>
@code
std::vector<Rect> faces;
std::vector<std::vector<Point2f> > landmarks;
facemark->getFaces(img, faces);
facemark->fit(img, faces, landmarks);
for(int j=0;j<rects.size();j++){
    face::drawFacemarks(frame, landmarks[j], Scalar(0,0,255));
}
@endcode
*/
CV_EXPORTS_W void drawFacemarks( InputOutputArray image, InputArray points,
                                 Scalar color = Scalar(255,0,0));

/** @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> facemark = FacemarkLBF::create();
@endcode


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.
*/
class CV_EXPORTS_W FacemarkTrain : public Facemark
{
public:
    /** @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.

    <B>Example of usage</B>
    @code
    String imageFiles = "../data/images_train.txt";
    String ptsFiles = "../data/points_train.txt";
    std::vector<String> images_train;
    std::vector<String> landmarks_train;

    // load the list of dataset: image paths and landmark file paths
    loadDatasetList(imageFiles,ptsFiles,images_train,landmarks_train);

    Mat image;
    std::vector<Point2f> facial_points;
    for(size_t i=0;i<images_train.size();i++){
        image = imread(images_train[i].c_str());
        loadFacePoints(landmarks_train[i],facial_points);
        facemark->addTrainingSample(image, facial_points);
    }
    @endcode

    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
    @endcode

    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
    @endcode

    */
    virtual bool addTrainingSample(InputArray image, InputArray landmarks)=0;

    /** @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).

    <B>Example of usage</B>
    @code
    FacemarkLBF::Params params;
    params.model_filename = "ibug68.model"; // filename to save the trained model
    Ptr<Facemark> facemark = FacemarkLBF::create(params);

    // add training samples (see Facemark::addTrainingSample)

    facemark->training();
    @endcode
    */

    virtual void training(void* parameters=0)=0;

    /** @brief Set a user defined face detector for the Facemark algorithm.
    @param detector The user defined face detector function
    @param userData Detector parameters

    <B>Example of usage</B>
    @code
    MyDetectorParameters detectorParameters(...);
    facemark->setFaceDetector(myDetector, &detectorParameters);
    @endcode

    Example of a user defined face detector
    @code
    bool myDetector( InputArray image, OutputArray faces, void* userData)
    {
        MyDetectorParameters* params = (MyDetectorParameters*)userData;
        // -------- do something --------
    }
    @endcode

    TODO Lifetime of detector parameters is uncontrolled. Rework interface design to "Ptr<FaceDetector>".
    */
    virtual bool setFaceDetector(FN_FaceDetector detector, void* userData = 0)=0;

    /** @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.

    <B>Example of usage</B>
    @code
    std::vector<cv::Rect> faces;
    facemark->getFaces(img, faces);
    for(int j=0;j<faces.size();j++){
        cv::rectangle(img, faces[j], cv::Scalar(255,0,255));
    }
    @endcode
    */
    virtual bool getFaces(InputArray image, OutputArray faces)=0;

    /** @brief Get data from an algorithm

    @param items The obtained data, algorithm dependent.

    <B>Example of usage</B>
    @code
    Ptr<FacemarkAAM> facemark = FacemarkAAM::create();
    facemark->loadModel("AAM.yml");

    FacemarkAAM::Data data;
    facemark->getData(&data);
    std::vector<Point2f> s0 = data.s0;

    cout<<s0<<endl;
    @endcode
    */
    virtual bool getData(void * items=0)=0; // FIXIT
}; /* Facemark*/

//! @}
} /* namespace face */
} /* namespace cv */
#endif //__OPENCV_FACELANDMARKTRAIN_HPP__