Added an example for the new HOG feature extractor.

--HG--
rename : examples/image_ex.cpp => examples/fhog_ex.cpp

examples/CMakeLists.txt

@@ -32,6 +32,7 @@ add_example(config_reader_ex)
 add_example(custom_trainer_ex)
 add_example(dir_nav_ex)
 add_example(empirical_kernel_map_ex)
+add_example(fhog_ex)
 add_example(file_to_code_ex)
 add_example(graph_labeling_ex)
 add_example(gui_api_ex)

examples/fhog_ex.cpp

+// The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
+/*
+
+    This is an example illustrating the use of the extract_fhog_features() routine from
+    the dlib C++ Library.
+
+    
+    The extract_fhog_features() routine performs the style of HOG feature extraction
+    described in the paper:
+        Object Detection with Discriminatively Trained Part Based Models by
+        P. Felzenszwalb, R. Girshick, D. McAllester, D. Ramanan
+        IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 32, No. 9, Sep. 2010
+    This means that it takes an input image and outputs Felzenszwalb's
+    31 dimensional version of HOG features.  We show it's use below.
+*/
+
+
+
+#include <dlib/gui_widgets.h>
+#include <dlib/image_io.h>
+#include <dlib/image_transforms.h>
+
+
+using namespace std;
+using namespace dlib;
+
+//  ----------------------------------------------------------------------------
+
+int main(int argc, char** argv)
+{
+    try
+    {
+        // Make sure the user entered an argument to this program.  It should be the
+        // filename for an image.
+        if (argc != 2)
+        {
+            cout << "error, you have to enter a BMP file as an argument to this program" << endl;
+            return 1;
+        }
+
+        // Here we declare an image object that can store color rgb_pixels.    
+        array2d<rgb_pixel> img;
+
+        // Now load the image file into our image.  If something is wrong then
+        // load_image() will throw an exception.  Also, if you linked with libpng
+        // and libjpeg then load_image() can load PNG and JPEG files in addition
+        // to BMP files.
+        load_image(img, argv[1]);
+
+
+        // Now convert the image into a FHOG feature image.  The output, hog, is a 2D array
+        // of 31 dimensional vectors.
+        array2d<matrix<float,31,1> > hog;
+        extract_fhog_features(img, hog);
+
+        cout << "hog image has " << hog.nr() << " rows and " << hog.nc() << " columns." << endl;
+
+        // Lets see what the image and also the FHOG features look like.
+        image_window win(img);
+        image_window winhog(draw_fhog(hog));
+
+        // Another thing you might want to do is map between the pixels in img and the
+        // cells in the hog image.  dlib provides the image_to_fhog() and fhog_to_image()
+        // routines for this.  Their use is demonstrated in the following loop which
+        // responds to the user clicking on pixels in the image img.
+        point p;  // A 2D point, used to represent pixel locations.
+        while (win.get_next_double_click(p))
+        {
+            point hp = image_to_fhog(p);
+            cout << "The point " << p << " in the input image corresponds to " << hp << " in hog space." << endl;
+            cout << "FHOG features at this point: " << trans(hog[hp.y()][hp.x()]) << endl;
+        }
+
+        // Finally, sometimes you want to get a "planar" representation of the HOG features
+        // rather than the explicit vector (i.e. interlaced) representation used above.  
+        dlib::array<array2d<float> > planar_hog;
+        extract_fhog_features(img, planar_hog);
+        // Now we have an array of 31 float valued images, each representing one of the
+        // dimensions of the HOG feature vector.  
+    }
+    catch (exception& e)
+    {
+        cout << "exception thrown: " << e.what() << endl;
+    }
+}
+
+//  ----------------------------------------------------------------------------
+