[−][src]Trait opencv::face::Facemark
Abstract base class for all facemark models
To utilize this API in your program, please take a look at the @ref tutorial_table_of_content_facemark
Description
Facemark is a base class which provides universal access to any specific facemark algorithm. Therefore, the users should declare a desired algorithm before they can use it in their application.
Here is an example on how to declare a facemark algorithm:
ⓘ
// Using Facemark in your code: Ptr<Facemark> facemark = createFacemarkLBF();
The typical pipeline for facemark detection is as follows:
- Load the trained model using Facemark::loadModel.
- Perform the fitting on an image via Facemark::fit.
Required methods
pub fn as_raw_Facemark(&self) -> *const c_void
[src]
pub fn as_raw_mut_Facemark(&mut self) -> *mut c_void
[src]
Provided methods
pub fn load_model(&mut self, model: &str) -> Result<()>
[src]
A function to load the trained model before the fitting process.
Parameters
- model: A string represent the filename of a trained model.
Example of usage
ⓘ
facemark->loadModel("../data/lbf.model");
pub fn fit(
&mut self,
image: &dyn ToInputArray,
faces: &dyn ToInputArray,
landmarks: &mut dyn ToOutputArray
) -> Result<bool>
[src]
&mut self,
image: &dyn ToInputArray,
faces: &dyn ToInputArray,
landmarks: &mut dyn ToOutputArray
) -> Result<bool>
Detect facial landmarks from an image.
Parameters
- image: Input image.
- faces: Output of the function which represent region of interest of the detected faces. Each face is stored in cv::Rect container.
- landmarks: The detected landmark points for each faces.
Example of usage
ⓘ
Mat image = imread("image.jpg"); std::vector<Rect> faces; std::vector<std::vector<Point2f> > landmarks; facemark->fit(image, faces, landmarks);