1 files changed, 0 insertions, 96 deletions
diff --git a/ml/dlib/examples/dnn_mmod_find_cars2_ex.cpp b/ml/dlib/examples/dnn_mmod_find_cars2_ex.cpp
deleted file mode 100644
index b9fffbba0..000000000
--- a/ml/dlib/examples/dnn_mmod_find_cars2_ex.cpp
+++ /dev/null
@@ -1,96 +0,0 @@
-// The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
-/*
-    This example shows how to run a CNN based vehicle detector using dlib.  The
-    example loads a pretrained model and uses it to find the front and rear ends
-    of cars in an image.  The model used by this example was trained by the
-    dnn_mmod_train_find_cars_ex.cpp example program on this dataset:   
-        http://dlib.net/files/data/dlib_front_and_rear_vehicles_v1.tar
-    
-    Users who are just learning about dlib's deep learning API should read
-    the dnn_introduction_ex.cpp and dnn_introduction2_ex.cpp examples to learn
-    how the API works.  For an introduction to the object detection method you
-    should read dnn_mmod_ex.cpp.
-
-    You can also see a video of this vehicle detector running on YouTube:
-        https://www.youtube.com/watch?v=OHbJ7HhbG74
-*/
-
-
-#include <iostream>
-#include <dlib/dnn.h>
-#include <dlib/image_io.h>
-#include <dlib/gui_widgets.h>
-#include <dlib/image_processing.h>
-
-using namespace std;
-using namespace dlib;
-
-
-
-// The front and rear view vehicle detector network
-template <long num_filters, typename SUBNET> using con5d = con<num_filters,5,5,2,2,SUBNET>;
-template <long num_filters, typename SUBNET> using con5  = con<num_filters,5,5,1,1,SUBNET>;
-template <typename SUBNET> using downsampler  = relu<affine<con5d<32, relu<affine<con5d<32, relu<affine<con5d<16,SUBNET>>>>>>>>>;
-template <typename SUBNET> using rcon5  = relu<affine<con5<55,SUBNET>>>;
-using net_type = loss_mmod<con<1,9,9,1,1,rcon5<rcon5<rcon5<downsampler<input_rgb_image_pyramid<pyramid_down<6>>>>>>>>;
-
-// ----------------------------------------------------------------------------------------
-
-int main() try
-{
-    net_type net;
-    shape_predictor sp;
-    // You can get this file from http://dlib.net/files/mmod_front_and_rear_end_vehicle_detector.dat.bz2
-    // This network was produced by the dnn_mmod_train_find_cars_ex.cpp example program.
-    // As you can see, the file also includes a separately trained shape_predictor.  To see
-    // a generic example of how to train those refer to train_shape_predictor_ex.cpp.
-    deserialize("mmod_front_and_rear_end_vehicle_detector.dat") >> net >> sp;
-
-    matrix<rgb_pixel> img;
-    load_image(img, "../mmod_cars_test_image2.jpg");
-
-    image_window win;
-    win.set_image(img);
-
-    // Run the detector on the image and show us the output.
-    for (auto&& d : net(img))
-    {
-        // We use a shape_predictor to refine the exact shape and location of the detection
-        // box.  This shape_predictor is trained to simply output the 4 corner points of
-        // the box.  So all we do is make a rectangle that tightly contains those 4 points
-        // and that rectangle is our refined detection position.
-        auto fd = sp(img,d);
-        rectangle rect;
-        for (unsigned long j = 0; j < fd.num_parts(); ++j)
-            rect += fd.part(j);
-
-        if (d.label == "rear")
-            win.add_overlay(rect, rgb_pixel(255,0,0), d.label);
-        else 
-            win.add_overlay(rect, rgb_pixel(255,255,0), d.label);
-    }
-
-
-
-
-    cout << "Hit enter to end program" << endl;
-    cin.get();
-}
-catch(image_load_error& e)
-{
-    cout << e.what() << endl;
-    cout << "The test image is located in the examples folder.  So you should run this program from a sub folder so that the relative path is correct." << endl;
-}
-catch(serialization_error& e)
-{
-    cout << e.what() << endl;
-    cout << "The correct model file can be obtained from: http://dlib.net/files/mmod_front_and_rear_end_vehicle_detector.dat.bz2" << endl;
-}
-catch(std::exception& e)
-{
-    cout << e.what() << endl;
-}
-
-
-
-