View Source Evision.Face (Evision v0.2.9)
Summary
Functions
createFacemarkAAM
createFacemarkKazemi
createFacemarkLBF
Utility to draw the detected facial landmark points
Utility to draw the detected facial landmark points
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.
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.
A utility to load list of paths to training image and annotation file.
A utility to load facial landmark information from a given file.
A utility to load facial landmark information from a given file.
A utility to load facial landmark dataset from a single file.
Variant 1:
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.
A utility to load facial landmark information from the dataset.
Types
@type t() :: %Evision.Face{ref: reference()}
Type that represents an Face
struct.
ref.
reference()
The underlying erlang resource variable.
Functions
@spec createFacemarkAAM() :: Evision.Face.Facemark.t() | {:error, String.t()}
createFacemarkAAM
Return
- retval:
Evision.Face.Facemark.t()
Python prototype (for reference only):
createFacemarkAAM() -> retval
@spec createFacemarkKazemi() :: Evision.Face.Facemark.t() | {:error, String.t()}
createFacemarkKazemi
Return
- retval:
Evision.Face.Facemark.t()
Python prototype (for reference only):
createFacemarkKazemi() -> retval
@spec createFacemarkLBF() :: Evision.Face.Facemark.t() | {:error, String.t()}
createFacemarkLBF
Return
- retval:
Evision.Face.Facemark.t()
Python prototype (for reference only):
createFacemarkLBF() -> retval
@spec drawFacemarks(Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in()) :: Evision.Mat.t() | {:error, String.t()}
Utility to draw the detected facial landmark points
Positional Arguments
points:
Evision.Mat
.Contains the data of points which will be drawn.
Keyword Arguments
color:
Evision.scalar()
.The color of points in BGR format represented by cv::Scalar.
Return
image:
Evision.Mat.t()
.The input image to be processed.
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));
}
Python prototype (for reference only):
drawFacemarks(image, points[, color]) -> image
@spec drawFacemarks( Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in(), [{:color, term()}] | nil ) :: Evision.Mat.t() | {:error, String.t()}
Utility to draw the detected facial landmark points
Positional Arguments
points:
Evision.Mat
.Contains the data of points which will be drawn.
Keyword Arguments
color:
Evision.scalar()
.The color of points in BGR format represented by cv::Scalar.
Return
image:
Evision.Mat.t()
.The input image to be processed.
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));
}
Python prototype (for reference only):
drawFacemarks(image, points[, color]) -> image
@spec getFacesHAAR(Evision.Mat.maybe_mat_in(), binary()) :: Evision.Mat.t() | false | {:error, String.t()}
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.
Positional Arguments
image:
Evision.Mat
.The input image to be processed.
face_cascade_name:
String
Return
retval:
bool
faces:
Evision.Mat.t()
.Output of the function which represent region of interest of the detected faces. Each face is stored in cv::Rect container.
std::vector<cv::Rect> faces;
CParams params("haarcascade_frontalface_alt.xml");
cv::face::getFaces(frame, faces, ¶ms);
for(int j=0;j<faces.size();j++){
cv::rectangle(frame, faces[j], cv::Scalar(255,0,255));
}
cv::imshow("detection", frame);
Python prototype (for reference only):
getFacesHAAR(image, face_cascade_name[, faces]) -> retval, faces
@spec getFacesHAAR( Evision.Mat.maybe_mat_in(), binary(), [{atom(), term()}, ...] | nil ) :: Evision.Mat.t() | false | {:error, String.t()}
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.
Positional Arguments
image:
Evision.Mat
.The input image to be processed.
face_cascade_name:
String
Return
retval:
bool
faces:
Evision.Mat.t()
.Output of the function which represent region of interest of the detected faces. Each face is stored in cv::Rect container.
std::vector<cv::Rect> faces;
CParams params("haarcascade_frontalface_alt.xml");
cv::face::getFaces(frame, faces, ¶ms);
for(int j=0;j<faces.size();j++){
cv::rectangle(frame, faces[j], cv::Scalar(255,0,255));
}
cv::imshow("detection", frame);
Python prototype (for reference only):
getFacesHAAR(image, face_cascade_name[, faces]) -> retval, faces
@spec loadDatasetList(binary(), binary(), [binary()], [binary()]) :: boolean() | {:error, String.t()}
A utility to load list of paths to training image and annotation file.
Positional Arguments
imageList:
String
.The specified file contains paths to the training images.
annotationList:
String
.The specified file contains paths to the training annotations.
images:
[String]
.The loaded paths of training images.
annotations:
[String]
.The loaded paths of annotation files.
Return
- retval:
bool
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);
Python prototype (for reference only):
loadDatasetList(imageList, annotationList, images, annotations) -> retval
@spec loadFacePoints(Keyword.t()) :: any() | {:error, String.t()}
@spec loadFacePoints(binary()) :: Evision.Mat.t() | false | {:error, String.t()}
A utility to load facial landmark information from a given file.
Positional Arguments
filename:
String
.The filename of file contains the facial landmarks data.
Keyword Arguments
offset:
float
.An offset value to adjust the loaded points.
Return
retval:
bool
points:
Evision.Mat.t()
.The loaded facial landmark points.
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.
Python prototype (for reference only):
loadFacePoints(filename[, points[, offset]]) -> retval, points
@spec loadFacePoints(binary(), [{:offset, term()}] | nil) :: Evision.Mat.t() | false | {:error, String.t()}
A utility to load facial landmark information from a given file.
Positional Arguments
filename:
String
.The filename of file contains the facial landmarks data.
Keyword Arguments
offset:
float
.An offset value to adjust the loaded points.
Return
retval:
bool
points:
Evision.Mat.t()
.The loaded facial landmark points.
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.
Python prototype (for reference only):
loadFacePoints(filename[, points[, offset]]) -> retval, points
@spec loadTrainingData(binary(), [binary()]) :: Evision.Mat.t() | false | {:error, String.t()}
A utility to load facial landmark dataset from a single file.
Positional Arguments
filename:
String
.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
images:
[String]
.A vector where each element represent the filename of image in the dataset. Images are not loaded by default to save the memory.
Keyword Arguments
delim:
char
.Delimiter between each element, the default value is a whitespace.
offset:
float
.An offset value to adjust the loaded points.
Return
retval:
bool
facePoints:
Evision.Mat.t()
.The loaded landmark points for all training data.
/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 ...
Example of usagecv::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);
Python prototype (for reference only):
loadTrainingData(filename, images[, facePoints[, delim[, offset]]]) -> retval, facePoints
@spec loadTrainingData(binary(), [binary()], [delim: term(), offset: term()] | nil) :: Evision.Mat.t() | false | {:error, String.t()}
@spec loadTrainingData([binary()], [[{number(), number()}]], [binary()]) :: boolean() | {:error, String.t()}
@spec loadTrainingData(binary(), binary(), [binary()]) :: Evision.Mat.t() | false | {:error, String.t()}
Variant 1:
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.
Positional Arguments
filename:
[String]
.A vector of type cv::String containing name of the .txt files.
trainlandmarks:
[[Point2f]]
.A vector of type cv::Point2f that would store shape or landmarks of all images.
trainimages:
[String]
.A vector of type cv::String which stores the name of images whose landmarks are tracked
Return
- retval:
bool
For reference you can see the files as provided in the
HELEN dataset@returns A boolean value. It returns true when it reads the data successfully and false otherwise
Python prototype (for reference only):
loadTrainingData(filename, trainlandmarks, trainimages) -> retval
Variant 2:
A utility to load facial landmark information from the dataset.
Positional Arguments
imageList:
String
.A file contains the list of image filenames in the training dataset.
groundTruth:
String
.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:
[String]
.A vector where each element represent the filename of image in the dataset. Images are not loaded by default to save the memory.
Keyword Arguments
offset:
float
.An offset value to adjust the loaded points.
Return
retval:
bool
facePoints:
Evision.Mat.t()
.The loaded landmark points for all training data.
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
Python prototype (for reference only):
loadTrainingData(imageList, groundTruth, images[, facePoints[, offset]]) -> retval, facePoints
Variant 3:
A utility to load facial landmark dataset from a single file.
Positional Arguments
filename:
String
.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
images:
[String]
.A vector where each element represent the filename of image in the dataset. Images are not loaded by default to save the memory.
Keyword Arguments
delim:
char
.Delimiter between each element, the default value is a whitespace.
offset:
float
.An offset value to adjust the loaded points.
Return
retval:
bool
facePoints:
Evision.Mat.t()
.The loaded landmark points for all training data.
/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 ...
Example of usagecv::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);
Python prototype (for reference only):
loadTrainingData(filename, images[, facePoints[, delim[, offset]]]) -> retval, facePoints
@spec loadTrainingData(binary(), binary(), [binary()], [{:offset, term()}] | nil) :: Evision.Mat.t() | false | {:error, String.t()}
A utility to load facial landmark information from the dataset.
Positional Arguments
imageList:
String
.A file contains the list of image filenames in the training dataset.
groundTruth:
String
.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:
[String]
.A vector where each element represent the filename of image in the dataset. Images are not loaded by default to save the memory.
Keyword Arguments
offset:
float
.An offset value to adjust the loaded points.
Return
retval:
bool
facePoints:
Evision.Mat.t()
.The loaded landmark points for all training data.
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
Python prototype (for reference only):
loadTrainingData(imageList, groundTruth, images[, facePoints[, offset]]) -> retval, facePoints