Face landmark detailed information

Posted by bryan

Referring back to the post on face landmark detection, the command to retrieve face landmark information is

fm.fit(im.getBGR(), faces, shapes);

where im.getBGR() is the Mat variable of the input image; faces is the MatOfRect variable (a number of Rect) obtained from the face detection; shapes is the ArrayList<MatOfPoint2f> variable returning the face landmark details for each face detected.

Each face is a MatOfPoint2f value. We can convert it to an array of Point. The array has length 68. Each point in the array corresponds to a face landmark feature point in the face as shown in the below image.
 

Delaunay triangulation of the face contour in OpenCV with Processing

Posted by bryan

The 4th exercise is a demonstration of the planar subdivision function in OpenCV to retrieve the Delaunay triangulation of the face convex hull outline that we obtain from the last post. The program will use the Subdiv2D class from the Imgproc module in OpenCV.

Subdiv2D subdiv = new Subdiv2D(r);

where r is am OpenCV Rect object instance defining the size of the region. It is usually the size of the image we are working on. For every point on the convex hull, we add it to the subdiv object by,

subdiv.insert(pt);

where pt is an OpenCV Point object instance. To obtain the Delaunay triangles, we use the following codes,

MatOfFloat6 triangleList = new MatOfFloat6();
subdiv.getTriangleList(triangleList);
float [] triangleArray = triangleList.toArray();

The function getTriangleList() will compute the Delaunay triangulation based on all the points inserted. It will return the result in the variable, triangleList. This variable is an instance of MatOfFloat6, and which is a collection of 6 numbers. The first pair of numbers are the x and y position of the first vertex of the triangle. The second pair of numbers are for the second vertex. The third pair of numbers are for the third vertex of the triangle. Based on this, we can draw each triangle in the Delaunay triangulation process, as shown in the image below.

Complete source code is now available in my GitHub repository at ml20180819b.

Face landmark convex hull detection in OpenCV with Processing

Posted by bryan

The 3rd exercise is the demonstration of obtaining the convex hull of the face landmark points in the OpenCV Face module. The program based on the face landmark information collected from the last post to find out the convex hull of the face detected.

The function is provided by the Imgproc (image processing) module of OpenCV. In the sample program, the following command will obtain the each point information of those points on the convex hull of the polygon.

Imgproc.convexHull(new MatOfPoint(p), index, false);

The first parameter, variable p is an array of type Point in OpenCV. The second parameter, index, is the returned value of type MatOfInt indicating all the points along the convex hull boundary. The integer value is the index in the original array p. The third parameter, false, indicates the clockwise orientation is false. By traversing the array index, we can obtain all the points along the convex hull.

The complete source code is now in my GitHub repository ml20180819a.

Face landmark detection in OpenCV Face module with Processing

Posted by bryan

The 2nd exercise is a demonstration using the Face module of the OpenCV contribution libraries. The official documentation for OpenCV 3.4.2 has a tutorial on face landmark detection. The Face module distribution also has a sample – Facemark.java.  This exercise is derived from this sample. There are 2 extra parameter files. One is the Haar Cascades file,  haarcascade_frontalface_default.xml we used in the last post for general face detection. The other one is the face landmark model file face_landmark_model.dat that will be downloaded during the building process of the OpenCV. Otherwise, it is also available at this GitHub link.

The program uses the Facemark class with the instance variable fm.

Facemark fm;

It is created by the command.

fm = Face.createFacemarkKazemi();

And load in the model file with the following,

fm.loadModel(datPath(modelFile));

where modelFile is the string variable containing the model file name.

Complete source code is in this GitHub repository.