dlib 13 dlib自带demo DNN狗脸检测

01 dlib的基于DNN的狗脸检测资源

代码:dlib\examples\dnn_mmod_dog_hipsterizer.cpp
工程名:dnn_mmod_dog_hipsterizer
测试图像文件:
dlib\examples\faces\bald_guys.jpg

从代码注释中可以获得model数据文件：
http://dlib.net/files/mmod_dog_hipsterizer.dat.bz2
把上面获得的压缩包内容分别解压到data目录下：
\dlib\data\mmod_dog_hipsterizer.dat

02 项目设置

把examples解决方案中的dnn_mmod_dog_hipsterizer工程设置为启动项。
如需调试，使用debug。使用release运行速度会快一些。

配置属性==>调试==>命令参数==>..\..\..\data\mmod_dog_hipsterizer.dat  ..\..\..\examples\faces\dogs.jpg配置属性==>调试==>工作目录==>$(OutDir)

03 运行结果

04 代码

dlib\examples\dnn_mmod_dog_hipsterizer.cpp

// 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;}