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-rw-r--r-- | ml/dlib/examples/dnn_mmod_dog_hipsterizer.cpp | 180 |
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diff --git a/ml/dlib/examples/dnn_mmod_dog_hipsterizer.cpp b/ml/dlib/examples/dnn_mmod_dog_hipsterizer.cpp new file mode 100644 index 000000000..22829d338 --- /dev/null +++ b/ml/dlib/examples/dnn_mmod_dog_hipsterizer.cpp @@ -0,0 +1,180 @@ +// 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_size(40,40); + + 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; +} + + + + |