// 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 dog face detector using dlib. The
example loads a pretrained model and uses it to find dog faces in images.
We also use the dlib::shape_predictor to find the location of the eyes and
nose and then draw glasses and a mustache onto each dog found :)
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
TRAINING THE MODEL
Finally, users interested in how the dog face detector was trained should
read the dnn_mmod_ex.cpp example program. It should be noted that the
dog face detector used in this example uses a bigger training dataset and
larger CNN architecture than what is shown in dnn_mmod_ex.cpp, but
otherwise training is the same. If you compare the net_type statements
in this file and dnn_mmod_ex.cpp you will see that they are very similar
except that the number of parameters has been increased.
Additionally, the following training parameters were different during
training: The following lines in dnn_mmod_ex.cpp were changed from
mmod_options options(face_boxes_train, 40*40);
trainer.set_iterations_without_progress_threshold(300);
to the following when training the model used in this example:
mmod_options options(face_boxes_train, 80*80);
trainer.set_iterations_without_progress_threshold(8000);
Also, the random_cropper was left at its default settings, So we didn't
call these functions:
cropper.set_chip_dims(200, 200);
cropper.set_min_object_height(0.2);
The training data used to create the model is also available at
http://dlib.net/files/data/CU_dogs_fully_labeled.tar.gz
Lastly, the shape_predictor was trained with default settings except we
used the following non-default settings: cascade depth=20, tree
depth=5, padding=0.2
*/
#include <iostream>
#include <dlib/dnn.h>
#include <dlib/data_io.h>
#include <dlib/image_processing.h>
#include <dlib/gui_widgets.h>
using namespace std;
using namespace dlib;
// ----------------------------------------------------------------------------------------
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<45,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(int argc, char** argv) try
{
if (argc < 3)
{
cout << "Call this program like this:" << endl;
cout << "./dnn_mmod_dog_hipsterizer mmod_dog_hipsterizer.dat faces/dogs.jpg" << endl;
cout << "\nYou can get the mmod_dog_hipsterizer.dat file from:\n";
cout << "http://dlib.net/files/mmod_dog_hipsterizer.dat.bz2" << endl;
return 0;
}
// load the models as well as glasses and mustache.
net_type net;
shape_predictor sp;
matrix<rgb_alpha_pixel> glasses, mustache;
deserialize(argv[1]) >> net >> sp >> glasses >> mustache;
pyramid_up(glasses);
pyramid_up(mustache);
image_window win1(glasses);
image_window win2(mustache);
image_window win_wireframe, win_hipster;
// Now process each image, find dogs, and hipsterize them by drawing glasses and a
// mustache on each dog :)
for (int i = 2; i < argc; ++i)
{
matrix<rgb_pixel> img;
load_image(img, argv[i]);
// Upsampling the image will allow us to find smaller dog faces but will use more
// computational resources.
//pyramid_up(img);
auto dets = net(img);
win_wireframe.clear_overlay();
win_wireframe.set_image(img);
// We will also draw a wireframe on each dog's face so you can see where the
// shape_predictor is identifying face landmarks.
std::vector<image_window::overlay_line> lines;
for (auto&& d : dets)
{
// get the landmarks for this dog's face
auto shape = sp(img, d.rect);
const rgb_pixel color(0,255,0);
auto top = shape.part(0);
auto lear = shape.part(1);
auto leye = shape.part(2);
auto nose = shape.part(3);
auto rear = shape.part(4);
auto reye = shape.part(5);
// The locations of the left and right ends of the mustache.
auto lmustache = 1.3*(leye-reye)/2 + nose;
auto rmustache = 1.3*(reye-leye)/2 + nose;
// Draw the glasses onto the image.
std::vector<point> from = {2*point(176,36), 2*point(59,35)}, to = {leye, reye};
auto tform = find_similarity_transform(from, to);
for (long r = 0; r < glasses.nr(); ++r)
{
for (long c = 0; c < glasses.nc(); ++c)
{
point p = tform(point(c,r));
if (get_rect(img).contains(p))
assign_pixel(img(p.y(),p.x()), glasses(r,c));
}
}
// Draw the mustache onto the image right under the dog's nose.
auto mrect = get_rect(mustache);
from = {mrect.tl_corner(), mrect.tr_corner()};
to = {rmustache, lmustache};
tform = find_similarity_transform(from, to);
for (long r = 0; r < mustache.nr(); ++r)
{
for (long c = 0; c < mustache.nc(); ++c)
{
point p = tform(point(c,r));
if (get_rect(img).contains(p))
assign_pixel(img(p.y(),p.x()), mustache(r,c));
}
}
// Record the lines needed for the face wire frame.
lines.push_back(image_window::overlay_line(leye, nose, color));
lines.push_back(image_window::overlay_line(nose, reye, color));
lines.push_back(image_window::overlay_line(reye, leye, color));
lines.push_back(image_window::overlay_line(reye, rear, color));
lines.push_back(image_window::overlay_line(rear, top, color));
lines.push_back(image_window::overlay_line(top, lear, color));
lines.push_back(image_window::overlay_line(lear, leye, color));
}
win_wireframe.add_overlay(lines);
win_hipster.set_image(img);
cout << "Hit enter to process the next image." << endl;
cin.get();
}
}
catch(std::exception& e)
{
cout << e.what() << endl;
}