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https://github.com/Kagami/go-face.git
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130 lines
4.1 KiB
C++
130 lines
4.1 KiB
C++
#include <dlib/dnn.h>
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#include <dlib/image_io.h>
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#include <dlib/image_processing/frontal_face_detector.h>
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#include "facerec.h"
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using namespace dlib;
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template <template <int,template<typename>class,int,typename> class block, int N, template<typename>class BN, typename SUBNET>
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using residual = add_prev1<block<N,BN,1,tag1<SUBNET>>>;
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template <template <int,template<typename>class,int,typename> class block, int N, template<typename>class BN, typename SUBNET>
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using residual_down = add_prev2<avg_pool<2,2,2,2,skip1<tag2<block<N,BN,2,tag1<SUBNET>>>>>>;
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template <int N, template <typename> class BN, int stride, typename SUBNET>
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using block = BN<con<N,3,3,1,1,relu<BN<con<N,3,3,stride,stride,SUBNET>>>>>;
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template <int N, typename SUBNET> using ares = relu<residual<block,N,affine,SUBNET>>;
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template <int N, typename SUBNET> using ares_down = relu<residual_down<block,N,affine,SUBNET>>;
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template <typename SUBNET> using alevel0 = ares_down<256,SUBNET>;
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template <typename SUBNET> using alevel1 = ares<256,ares<256,ares_down<256,SUBNET>>>;
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template <typename SUBNET> using alevel2 = ares<128,ares<128,ares_down<128,SUBNET>>>;
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template <typename SUBNET> using alevel3 = ares<64,ares<64,ares<64,ares_down<64,SUBNET>>>>;
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template <typename SUBNET> using alevel4 = ares<32,ares<32,ares<32,SUBNET>>>;
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using anet_type = loss_metric<fc_no_bias<128,avg_pool_everything<
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alevel0<
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alevel1<
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alevel2<
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alevel3<
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alevel4<
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max_pool<3,3,2,2,relu<affine<con<32,7,7,2,2,
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input_rgb_image_sized<150>
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>>>>>>>>>>>>;
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typedef matrix<float,0,1> descriptor;
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static const size_t DESCR_SIZE = 128 * sizeof(float);
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class FaceRec {
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public:
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FaceRec(const char* model_dir) {
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detector_ = get_frontal_face_detector();
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std::string dir = model_dir;
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std::string shape_predictor_path = dir + "/shape_predictor_5_face_landmarks.dat";
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std::string resnet_path = dir + "/dlib_face_recognition_resnet_model_v1.dat";
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deserialize(shape_predictor_path) >> sp_;
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deserialize(resnet_path) >> net_;
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}
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// TODO(Kagami): Jittering?
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std::vector<descriptor> Recognize(const char* img_path) {
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matrix<rgb_pixel> img;
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load_image(img, img_path);
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std::vector<matrix<rgb_pixel>> faces;
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for (auto face : detector_(img)) {
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auto shape = sp_(img, face);
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matrix<rgb_pixel> face_chip;
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extract_image_chip(img, get_face_chip_details(shape, 150, 0.25), face_chip);
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faces.push_back(std::move(face_chip));
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}
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// Short circuit.
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if (faces.size() == 0)
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return {};
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std::vector<descriptor> descriptors = net_(faces);
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return descriptors;
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}
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private:
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frontal_face_detector detector_;
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shape_predictor sp_;
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anet_type net_;
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};
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// Plain C interface for Go.
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facerec* facerec_init(const char* model_dir) {
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facerec* rec = (facerec*)calloc(1, sizeof(facerec));
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try {
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FaceRec* cls = new FaceRec(model_dir);
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rec->cls = (void*)cls;
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} catch(serialization_error& e) {
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rec->err_str = strdup(e.what());
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rec->err_code = SERIALIZATION_ERROR;
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} catch (std::exception& e) {
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rec->err_str = strdup(e.what());
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rec->err_code = UNKNOWN_ERROR;
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}
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return rec;
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}
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faceret* facerec_recognize(facerec* rec, const char* img_path) {
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faceret* ret = (faceret*)calloc(1, sizeof(faceret));
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FaceRec* cls = (FaceRec*)(rec->cls);
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try {
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std::vector<descriptor> descriptors = cls->Recognize(img_path);
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ret->num_faces = descriptors.size();
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if (ret->num_faces == 0)
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return ret;
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ret->descriptors = (float*)malloc(ret->num_faces * DESCR_SIZE);
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for (int i = 0; i < ret->num_faces; i++) {
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void* dst = (uint8_t*)(ret->descriptors) + i * DESCR_SIZE;
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void* src = (void*)&descriptors[i](0,0);
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memcpy(dst, src, DESCR_SIZE);
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}
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} catch(image_load_error& e) {
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ret->err_str = strdup(e.what());
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ret->err_code = IMAGE_LOAD_ERROR;
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} catch (std::exception& e) {
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ret->err_str = strdup(e.what());
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ret->err_code = UNKNOWN_ERROR;
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}
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return ret;
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}
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void facerec_free(facerec* rec) {
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if (rec) {
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if (rec->cls) {
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FaceRec* cls = (FaceRec*)(rec->cls);
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delete cls;
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rec->cls = NULL;
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}
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free(rec);
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}
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}
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