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Diffstat (limited to 'ml/dlib/examples/dnn_face_recognition_ex.cpp')
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diff --git a/ml/dlib/examples/dnn_face_recognition_ex.cpp b/ml/dlib/examples/dnn_face_recognition_ex.cpp new file mode 100644 index 000000000..4c0a2a02b --- /dev/null +++ b/ml/dlib/examples/dnn_face_recognition_ex.cpp @@ -0,0 +1,220 @@ +// 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 deep learning tools from the dlib C++ + Library. In it, we will show how to do face recognition. This example uses the + pretrained dlib_face_recognition_resnet_model_v1 model which is freely available from + the dlib web site. This model has a 99.38% accuracy on the standard LFW face + recognition benchmark, which is comparable to other state-of-the-art methods for face + recognition as of February 2017. + + In this example, we will use dlib to do face clustering. Included in the examples + folder is an image, bald_guys.jpg, which contains a bunch of photos of action movie + stars Vin Diesel, The Rock, Jason Statham, and Bruce Willis. We will use dlib to + automatically find their faces in the image and then to automatically determine how + many people there are (4 in this case) as well as which faces belong to each person. + + Finally, this example uses a network with the loss_metric loss. Therefore, if you want + to learn how to train your own models, or to get a general introduction to this loss + layer, you should read the dnn_metric_learning_ex.cpp and + dnn_metric_learning_on_images_ex.cpp examples. +*/ + +#include <dlib/dnn.h> +#include <dlib/gui_widgets.h> +#include <dlib/clustering.h> +#include <dlib/string.h> +#include <dlib/image_io.h> +#include <dlib/image_processing/frontal_face_detector.h> + +using namespace dlib; +using namespace std; + +// ---------------------------------------------------------------------------------------- + +// The next bit of code defines a ResNet network. It's basically copied +// and pasted from the dnn_imagenet_ex.cpp example, except we replaced the loss +// layer with loss_metric and made the network somewhat smaller. Go read the introductory +// dlib DNN examples to learn what all this stuff means. +// +// Also, the dnn_metric_learning_on_images_ex.cpp example shows how to train this network. +// The dlib_face_recognition_resnet_model_v1 model used by this example was trained using +// essentially the code shown in dnn_metric_learning_on_images_ex.cpp except the +// mini-batches were made larger (35x15 instead of 5x5), the iterations without progress +// was set to 10000, and the training dataset consisted of about 3 million images instead of +// 55. Also, the input layer was locked to images of size 150. +template <template <int,template<typename>class,int,typename> class block, int N, template<typename>class BN, typename SUBNET> +using residual = add_prev1<block<N,BN,1,tag1<SUBNET>>>; + +template <template <int,template<typename>class,int,typename> class block, int N, template<typename>class BN, typename SUBNET> +using residual_down = add_prev2<avg_pool<2,2,2,2,skip1<tag2<block<N,BN,2,tag1<SUBNET>>>>>>; + +template <int N, template <typename> class BN, int stride, typename SUBNET> +using block = BN<con<N,3,3,1,1,relu<BN<con<N,3,3,stride,stride,SUBNET>>>>>; + +template <int N, typename SUBNET> using ares = relu<residual<block,N,affine,SUBNET>>; +template <int N, typename SUBNET> using ares_down = relu<residual_down<block,N,affine,SUBNET>>; + +template <typename SUBNET> using alevel0 = ares_down<256,SUBNET>; +template <typename SUBNET> using alevel1 = ares<256,ares<256,ares_down<256,SUBNET>>>; +template <typename SUBNET> using alevel2 = ares<128,ares<128,ares_down<128,SUBNET>>>; +template <typename SUBNET> using alevel3 = ares<64,ares<64,ares<64,ares_down<64,SUBNET>>>>; +template <typename SUBNET> using alevel4 = ares<32,ares<32,ares<32,SUBNET>>>; + +using anet_type = loss_metric<fc_no_bias<128,avg_pool_everything< + alevel0< + alevel1< + alevel2< + alevel3< + alevel4< + max_pool<3,3,2,2,relu<affine<con<32,7,7,2,2, + input_rgb_image_sized<150> + >>>>>>>>>>>>; + +// ---------------------------------------------------------------------------------------- + +std::vector<matrix<rgb_pixel>> jitter_image( + const matrix<rgb_pixel>& img +); + +// ---------------------------------------------------------------------------------------- + +int main(int argc, char** argv) try +{ + if (argc != 2) + { + cout << "Run this example by invoking it like this: " << endl; + cout << " ./dnn_face_recognition_ex faces/bald_guys.jpg" << endl; + cout << endl; + cout << "You will also need to get the face landmarking model file as well as " << endl; + cout << "the face recognition model file. Download and then decompress these files from: " << endl; + cout << "http://dlib.net/files/shape_predictor_5_face_landmarks.dat.bz2" << endl; + cout << "http://dlib.net/files/dlib_face_recognition_resnet_model_v1.dat.bz2" << endl; + cout << endl; + return 1; + } + + // The first thing we are going to do is load all our models. First, since we need to + // find faces in the image we will need a face detector: + frontal_face_detector detector = get_frontal_face_detector(); + // We will also use a face landmarking model to align faces to a standard pose: (see face_landmark_detection_ex.cpp for an introduction) + shape_predictor sp; + deserialize("shape_predictor_5_face_landmarks.dat") >> sp; + // And finally we load the DNN responsible for face recognition. + anet_type net; + deserialize("dlib_face_recognition_resnet_model_v1.dat") >> net; + + matrix<rgb_pixel> img; + load_image(img, argv[1]); + // Display the raw image on the screen + image_window win(img); + + // Run the face detector on the image of our action heroes, and for each face extract a + // copy that has been normalized to 150x150 pixels in size and appropriately rotated + // and centered. + std::vector<matrix<rgb_pixel>> faces; + for (auto face : detector(img)) + { + auto shape = sp(img, face); + matrix<rgb_pixel> face_chip; + extract_image_chip(img, get_face_chip_details(shape,150,0.25), face_chip); + faces.push_back(move(face_chip)); + // Also put some boxes on the faces so we can see that the detector is finding + // them. + win.add_overlay(face); + } + + if (faces.size() == 0) + { + cout << "No faces found in image!" << endl; + return 1; + } + + // This call asks the DNN to convert each face image in faces into a 128D vector. + // In this 128D vector space, images from the same person will be close to each other + // but vectors from different people will be far apart. So we can use these vectors to + // identify if a pair of images are from the same person or from different people. + std::vector<matrix<float,0,1>> face_descriptors = net(faces); + + + // In particular, one simple thing we can do is face clustering. This next bit of code + // creates a graph of connected faces and then uses the Chinese whispers graph clustering + // algorithm to identify how many people there are and which faces belong to whom. + std::vector<sample_pair> edges; + for (size_t i = 0; i < face_descriptors.size(); ++i) + { + for (size_t j = i; j < face_descriptors.size(); ++j) + { + // Faces are connected in the graph if they are close enough. Here we check if + // the distance between two face descriptors is less than 0.6, which is the + // decision threshold the network was trained to use. Although you can + // certainly use any other threshold you find useful. + if (length(face_descriptors[i]-face_descriptors[j]) < 0.6) + edges.push_back(sample_pair(i,j)); + } + } + std::vector<unsigned long> labels; + const auto num_clusters = chinese_whispers(edges, labels); + // This will correctly indicate that there are 4 people in the image. + cout << "number of people found in the image: "<< num_clusters << endl; + + + // Now let's display the face clustering results on the screen. You will see that it + // correctly grouped all the faces. + std::vector<image_window> win_clusters(num_clusters); + for (size_t cluster_id = 0; cluster_id < num_clusters; ++cluster_id) + { + std::vector<matrix<rgb_pixel>> temp; + for (size_t j = 0; j < labels.size(); ++j) + { + if (cluster_id == labels[j]) + temp.push_back(faces[j]); + } + win_clusters[cluster_id].set_title("face cluster " + cast_to_string(cluster_id)); + win_clusters[cluster_id].set_image(tile_images(temp)); + } + + + + + // Finally, let's print one of the face descriptors to the screen. + cout << "face descriptor for one face: " << trans(face_descriptors[0]) << endl; + + // It should also be noted that face recognition accuracy can be improved if jittering + // is used when creating face descriptors. In particular, to get 99.38% on the LFW + // benchmark you need to use the jitter_image() routine to compute the descriptors, + // like so: + matrix<float,0,1> face_descriptor = mean(mat(net(jitter_image(faces[0])))); + cout << "jittered face descriptor for one face: " << trans(face_descriptor) << endl; + // If you use the model without jittering, as we did when clustering the bald guys, it + // gets an accuracy of 99.13% on the LFW benchmark. So jittering makes the whole + // procedure a little more accurate but makes face descriptor calculation slower. + + + cout << "hit enter to terminate" << endl; + cin.get(); +} +catch (std::exception& e) +{ + cout << e.what() << endl; +} + +// ---------------------------------------------------------------------------------------- + +std::vector<matrix<rgb_pixel>> jitter_image( + const matrix<rgb_pixel>& img +) +{ + // All this function does is make 100 copies of img, all slightly jittered by being + // zoomed, rotated, and translated a little bit differently. They are also randomly + // mirrored left to right. + thread_local dlib::rand rnd; + + std::vector<matrix<rgb_pixel>> crops; + for (int i = 0; i < 100; ++i) + crops.push_back(jitter_image(img,rnd)); + + return crops; +} + +// ---------------------------------------------------------------------------------------- + |