1 files changed, 159 insertions, 0 deletions
diff --git a/ml/dlib/tools/archive/train_face_5point_model.cpp b/ml/dlib/tools/archive/train_face_5point_model.cpp
new file mode 100644
index 00000000..0cd35467
--- /dev/null
+++ b/ml/dlib/tools/archive/train_face_5point_model.cpp
@@ -0,0 +1,159 @@
+
+/*
+
+    This is the program that created the http://dlib.net/files/shape_predictor_5_face_landmarks.dat.bz2 model file.
+
+*/
+
+
+#include <dlib/image_processing/frontal_face_detector.h>
+#include <dlib/image_processing.h>
+#include <dlib/console_progress_indicator.h>
+#include <dlib/data_io.h>
+#include <dlib/statistics.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
+);
+/*!
+    ensures
+        - returns an object D such that:    
+            - D[i][j] == the distance, in pixels, between the eyes for the face represented
+              by objects[i][j].
+!*/
+
+// ----------------------------------------------------------------------------------------
+
+template <
+    typename image_array_type,
+    typename T
+    >
+void add_image_left_right_flips_5points (
+    image_array_type& images,
+    std::vector<std::vector<T> >& objects
+)
+{
+    // make sure requires clause is not broken
+    DLIB_ASSERT( images.size() == objects.size(),
+        "\t void add_image_left_right_flips()"
+        << "\n\t Invalid inputs were given to this function."
+        << "\n\t images.size():  " << images.size() 
+        << "\n\t objects.size(): " << objects.size() 
+        );
+
+    typename image_array_type::value_type temp;
+    std::vector<T> rects;
+
+    const unsigned long num = images.size();
+    for (unsigned long j = 0; j < num; ++j)
+    {
+        const point_transform_affine tran = flip_image_left_right(images[j], temp);
+
+        rects.clear();
+        for (unsigned long i = 0; i < objects[j].size(); ++i)
+        {
+            rects.push_back(impl::tform_object(tran, objects[j][i]));
+
+            DLIB_CASSERT(rects.back().num_parts() == 5);
+            swap(rects.back().part(0), rects.back().part(2));
+            swap(rects.back().part(1), rects.back().part(3));
+        }
+
+        images.push_back(temp);
+        objects.push_back(rects);
+    }
+}
+
+// ----------------------------------------------------------------------------------------
+
+int main(int argc, char** argv)
+{
+    try
+    {
+        if (argc != 2)
+        {
+            cout << "give the path to the training data folder" << endl;
+            return 0;
+        }
+        const std::string faces_directory = argv[1];
+        dlib::array<array2d<unsigned char> > images_train, images_test;
+        std::vector<std::vector<full_object_detection> > faces_train, faces_test;
+
+        std::vector<std::string> parts_list;
+        load_image_dataset(images_train, faces_train, faces_directory+"/train_cleaned.xml", parts_list);
+        load_image_dataset(images_test, faces_test, faces_directory+"/test_cleaned.xml");
+
+        add_image_left_right_flips_5points(images_train, faces_train);
+        add_image_left_right_flips_5points(images_test, faces_test);
+        add_image_rotations(linspace(-20,20,3)*pi/180.0,images_train, faces_train);
+
+        cout << "num training images: "<< images_train.size() << endl;
+
+        for (auto& part : parts_list)
+            cout << part << endl;
+
+        shape_predictor_trainer trainer;
+        trainer.set_oversampling_amount(40);
+        trainer.set_num_test_splits(150);
+        trainer.set_feature_pool_size(800);
+        trainer.set_num_threads(4);
+        trainer.set_cascade_depth(15);
+        trainer.be_verbose();
+
+        // Now finally generate the shape model
+        shape_predictor sp = trainer.train(images_train, faces_train);
+
+        serialize("shape_predictor_5_face_landmarks.dat") << sp;
+
+        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;
+
+    }
+    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;
+    // left eye
+    l = (det.part(0) + det.part(1))/2;
+    // right eye
+    r = (det.part(2) + det.part(3))/2;
+
+    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;
+}
+
+// ----------------------------------------------------------------------------------------
+