| OpenCV 3.4.1 Open Source Computer Vision

Classes | Typedefs | Functions
Face Analysis

## Classes
class | cv::face::BasicFaceRecognizer
struct | cv::face::CParams
class | cv::face::EigenFaceRecognizer
class | cv::face::Facemark
| Abstract base class for all facemark models. More...
class | cv::face::FacemarkAAM
class | cv::face::FacemarkLBF
class | cv::face::FaceRecognizer
| Abstract base class for all face recognition models. More...
class | cv::face::FisherFaceRecognizer
class | cv::face::LBPHFaceRecognizer
class | cv::face::PredictCollector
| Abstract base class for all strategies of prediction result handling. More...
class | cv::face::StandardCollector
| Default predict collector. More...

## Typedefs
typedef bool(* | cv::face::FN_FaceDetector) (InputArray, OutputArray, void *userData)

## Functions
void | cv::face::drawFacemarks (InputOutputArray image, InputArray points, Scalar color=Scalar(255, 0, 0))
| Utility to draw the detected facial landmark points. More...
bool | cv::face::getFaces (InputArray image, OutputArray faces, CParams *params)
| 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. More...
bool | cv::face::getFacesHAAR (InputArray image, OutputArray faces, const String &face_cascade_name)
bool | cv::face::loadDatasetList (String imageList, String annotationList, std::vector< String > &images, std::vector< String > &annotations)
| A utility to load list of paths to training image and annotation file. More...
bool | cv::face::loadFacePoints (String filename, OutputArray points, float offset=0.0f)
| A utility to load facial landmark information from a given file. More...
bool | cv::face::loadTrainingData (String filename, std::vector< String > &images, OutputArray facePoints, char delim=' ', float offset=0.0f)
| A utility to load facial landmark dataset from a single file. More...
bool | cv::face::loadTrainingData (String imageList, String groundTruth, std::vector< String > &images, OutputArray facePoints, float offset=0.0f)
| A utility to load facial landmark information from the dataset. More...
bool | cv::face::loadTrainingData (std::vector< String > filename, std::vector< std::vector< Point2f > > &trainlandmarks, std::vector< String > &trainimages)
| 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. More...

## Detailed Description

- Tutorial on Facial Landmark Detector API
- The Facemark API
- Face module changelog
- Face Recognition with OpenCV

## Typedef Documentation

## § FN_FaceDetector

typedef bool(* cv::face::FN_FaceDetector) (InputArray, OutputArray, void *userData)

## Function Documentation

## § drawFacemarks()

void cv::face::drawFacemarks | ( | InputOutputArray | image,
|  | InputArray | points,
|  | Scalar | color = `Scalar(255, 0, 0)`
| ) |  |

Python:
| image | = | cv.face.drawFacemarks( | image, points[, color] | )

Utility to draw the detected facial landmark points.

Parameters

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

Example of usage

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));
}

## § getFaces()

bool cv::face::getFaces | ( | InputArray | image,
|  | OutputArray | faces,
|  | CParams * | params
| ) |  |

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.

Parameters

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

Example of usage

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);

## § getFacesHAAR()

bool cv::face::getFacesHAAR | ( | InputArray | image,
|  | OutputArray | faces,
|  | const String & | face_cascade_name
| ) |  |

Python:
| retval, faces | = | cv.face.getFacesHAAR( | image, face_cascade_name[, faces] | )

## § loadDatasetList()

bool cv::face::loadDatasetList | ( | String | imageList,
|  | String | annotationList,
|  | std::vector< String > & | images,
|  | std::vector< String > & | annotations
| ) |  |

Python:
| retval | = | cv.face.loadDatasetList( | imageList, annotationList, images, annotations | )

A utility to load list of paths to training image and annotation file.

Parameters

  imageList | The specified file contains paths to the training images.
  annotationList | The specified file contains paths to the training annotations.
  images | The loaded paths of training images.
  annotations | The loaded paths of annotation files.

Example of usage:

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);

## § loadFacePoints()

bool cv::face::loadFacePoints | ( | String | filename,
|  | OutputArray | points,
|  | float | offset = `0.0f`
| ) |  |

Python:
| retval, points | = | cv.face.loadFacePoints( | filename[, points[, offset]] | )

A utility to load facial landmark information from a given file.

Parameters

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

Example of usage

std::vector<Point2f> points;
face::loadFacePoints("filename.txt", points, 0.0f);

The annotation file should follow the default format which is

version: 1
n_points: 68
{
212.716603 499.771793
230.232816 566.290071
...
}

where n_points is the number of points considered and each point is represented as its position in x and y.

## § loadTrainingData() [1/3]

bool cv::face::loadTrainingData | ( | String | filename,
|  | std::vector< String > & | images,
|  | OutputArray | facePoints,
|  | char | delim = `' '`,
|  | float | offset = `0.0f`
| ) |  |

Python:
| retval, facePoints | = | cv.face.loadTrainingData( | filename, images[, facePoints[, delim[, offset]]] | )
| retval, facePoints | = | cv.face.loadTrainingData( | imageList, groundTruth, images[, facePoints[, offset]] | )
| retval | = | cv.face.loadTrainingData( | filename, trainlandmarks, trainimages | )

A utility to load facial landmark dataset from a single file.

Parameters

  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 /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 ...
  images | A vector where each element represent the filename of image in the dataset. Images are not loaded by default to save the memory.
  facePoints | The loaded landmark points for all training data.
  delim | Delimiter between each element, the default value is a whitespace.
  offset | An offset value to adjust the loaded points.

Example of usage

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);

## § loadTrainingData() [2/3]

bool cv::face::loadTrainingData | ( | String | imageList,
|  | String | groundTruth,
|  | std::vector< String > & | images,
|  | OutputArray | facePoints,
|  | float | offset = `0.0f`
| ) |  |

Python:
| retval, facePoints | = | cv.face.loadTrainingData( | filename, images[, facePoints[, delim[, offset]]] | )
| retval, facePoints | = | cv.face.loadTrainingData( | imageList, groundTruth, images[, facePoints[, offset]] | )
| retval | = | cv.face.loadTrainingData( | filename, trainlandmarks, trainimages | )

A utility to load facial landmark information from the dataset.

Parameters

  imageList | A file contains the list of image filenames in the training dataset.
  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).
  images | A vector where each element represent the filename of image in the dataset. Images are not loaded by default to save the memory.
  facePoints | The loaded landmark points for all training data.
  offset | An offset value to adjust the loaded points.

Example of usage

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);

example of content in the images_train.txt

/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

/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

## § loadTrainingData() [3/3]

bool cv::face::loadTrainingData | ( | std::vector< String > | filename,
|  | std::vector< std::vector< Point2f > > & | trainlandmarks,
|  | std::vector< String > & | trainimages
| ) |  |

Python:
| retval, facePoints | = | cv.face.loadTrainingData( | filename, images[, facePoints[, delim[, offset]]] | )
| retval, facePoints | = | cv.face.loadTrainingData( | imageList, groundTruth, images[, facePoints[, offset]] | )
| retval | = | cv.face.loadTrainingData( | filename, trainlandmarks, trainimages | )

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 HELEN dataset

Parameters

  filename | A vector of type cv::String containing name of the .txt files.
  trainlandmarks | A vector of type cv::Point2f that would store shape or landmarks of all images.
  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