Added initial version of shape training example

examples/CMakeLists.txt

@@ -94,5 +94,6 @@ add_example(thread_pool_ex)
 add_example(threads_ex)
 add_example(timer_ex)
 add_example(train_object_detector)
+add_example(train_shape_predictor_ex)
 add_example(using_custom_kernels_ex)
 add_example(xml_parser_ex)

examples/train_shape_predictor_ex.cpp

@@ -0,0 +1,139 @@
+// The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
+/*
+
+
+
+    The pose estimator was created by using dlib's implementation of the paper:
+        One Millisecond Face Alignment with an Ensemble of Regression Trees by
+        Vahid Kazemi and Josephine Sullivan, CVPR 2014
+
+*/
+
+
+#include <dlib/image_processing.h>
+#include <dlib/data_io.h>
+#include <iostream>
+
+using namespace dlib;
+using namespace std;
+
+// ----------------------------------------------------------------------------------------
+
+std::vector<std::vector<double> > get_interocular_distances (
+    const std::vector<std::vector<full_object_detection> >& objects
+);
+
+// ----------------------------------------------------------------------------------------
+
+int main(int argc, char** argv)
+{  
+    try
+    {
+        // In this example we are going to train a shape_predictor based on the
+        // small faces dataset in the examples/faces directory.  So the first
+        // thing we do is load that dataset.  This means you need to supply the
+        // path to this faces folder as a command line argument so we will know
+        // where it is.
+        if (argc != 2)
+        {
+            cout << "Give the path to the examples/faces directory as the argument to this" << endl;
+            cout << "program.  For example, if you are in the examples folder then execute " << endl;
+            cout << "this program by running: " << endl;
+            cout << "   ./train_shape_predictor_ex faces" << endl;
+            cout << endl;
+            return 0;
+        }
+        const std::string faces_directory = argv[1];
+        // The faces directory contains a training dataset and a separate
+        // testing dataset.  The training data consists of 4 images, each
+        // annotated with rectangles that bound each human face along with 68
+        // face landmarks on each face.  The idea is to use this training data
+        // to learn to identify the position of landmarks on human faces in new
+        // images. 
+        // 
+        // Once you have trained a shape_predictor it is always important to
+        // test it on data it wasn't trained on.  Therefore, we will also load
+        // a separate testing set of 5 images.  Once we have a shape_predictor 
+        // created from the training data we will see how well it works by
+        // running it on the testing images. 
+        // 
+        // So here we create the variables that will hold our dataset.
+        // images_train will hold the 4 training images and face_boxes_train
+        // holds the locations of the faces in the training images.  So for
+        // example, the image images_train[0] has the faces given by the
+        // full_object_detections in face_boxes_train[0].
+        dlib::array<array2d<unsigned char> > images_train, images_test;
+        std::vector<std::vector<full_object_detection> > faces_train, faces_test;
+
+        // Now we load the data.  These XML files list the images in each
+        // dataset and also contain the positions of the face boxes and landmark
+        // (called parts in the XML file).  Obviously you can use any kind of
+        // input format you like so long as you store the data into images_train
+        // and faces_train.  
+        load_image_dataset(images_train, faces_train, faces_directory+"/training_with_face_landmarks.xml");
+        load_image_dataset(images_test, faces_test, faces_directory+"/testing_with_face_landmarks.xml");
+
+        shape_predictor_trainer trainer;
+        shape_predictor sp = trainer.train(images_train, faces_train);
+
+
+        cout << "mean training error: "<< test_shape_predictor(sp, images_train, faces_train, get_interocular_distances(faces_train)) << endl;
+        cout << "mean testing error:  "<< test_shape_predictor(sp, images_test, faces_test, get_interocular_distances(faces_test)) << endl;
+
+        serialize("sp.dat") << sp;
+    }
+    catch (exception& e)
+    {
+        cout << "\nexception thrown!" << endl;
+        cout << e.what() << endl;
+    }
+}
+
+// ----------------------------------------------------------------------------------------
+
+double interocular_distance (
+    const full_object_detection& det
+)
+{
+    dlib::vector<double,2> l, r;
+    double cnt = 0;
+    // Find the center of the left eye by averaging the points around 
+    // the eye.
+    for (unsigned long i = 36; i <= 41; ++i) 
+    {
+        l += det.part(i);
+        ++cnt;
+    }
+    l /= cnt;
+
+    // Find the center of the right eye by averaging the points around 
+    // the eye.
+    cnt = 0;
+    for (unsigned long i = 42; i <= 47; ++i) 
+    {
+        r += det.part(i);
+        ++cnt;
+    }
+    r /= cnt;
+
+    // Now return the distance between the centers of the eyes
+    return length(l-r);
+}
+
+std::vector<std::vector<double> > get_interocular_distances (
+    const std::vector<std::vector<full_object_detection> >& objects
+)
+{
+    std::vector<std::vector<double> > temp(objects.size());
+    for (unsigned long i = 0; i < objects.size(); ++i)
+    {
+        for (unsigned long j = 0; j < objects[i].size(); ++j)
+        {
+            temp[i].push_back(interocular_distance(objects[i][j]));
+        }
+    }
+    return temp;
+}
+
+// ----------------------------------------------------------------------------------------
+