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4a94151f by 乔峰昇
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submmit all code

1 parent ed52fddf
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Showing 50 changed files with 2106 additions and 250 deletions
.vscode/settings.json 0 → 100644
{
"files.associations": {
"*.py": "python",
"string": "cpp",
"iostream": "cpp",
"memory": "cpp"
}
}
\ No newline at end of file
CMakeLists.txt
......@@ -6,8 +6,13 @@ set(CMAKE_CXX_STANDARD 11)
find_package(OpenCV REQUIRED)
set(MNN_DIR /home/situ/MNN/MNN1.0/MNN)
include_directories(${MNN_DIR}/include)
include_directories(/home/situ/qfs/sdk_project/mnn_projects/face_recognize_mnn/include)
LINK_DIRECTORIES(${MNN_DIR}/build)
add_executable(main main.cpp faceLandmarks.cpp)
set(FACECOMPARISON_DIR /home/situ/qfs/sdk_project/mnn_projects/face_recognize_mnn/lib)
# add_library(facecomparison SHARED facerecognize.cpp retinaface.cpp facelandmarks.cpp facecomparison.cpp)
# add_executable(main main.cpp facerecognize.cpp retinaface.cpp facelandmarks.cpp facecomparison.cpp)
add_executable(main main.cpp)
# add_executable(main z.cpp)
target_link_libraries(main -lMNN ${OpenCV_LIBS})
target_link_libraries(main ${FACECOMPARISON_DIR}/libfacecomparison.so -lMNN ${OpenCV_LIBS})
# target_link_libraries(facecomparison -lMNN ${OpenCV_LIBS})
......
README.md 0 → 100644
MNN1.0.0+opencv
模型初始化
FaceComparison face_rec=FaceComparison(det_model_path,landm_model_path,rec_model_path)
det_model_path:人脸检测模型retinaface的模型路径
landm_model_path:106人脸关键点模型的模型路径
rec_model_path:人脸识别模型的模型路径
重要参数(include/facecomparison.h文件)
// 人脸检测阈值
float confidence_threshold = 0.5;
// 是否进行人脸外廓
bool is_bbox_process = true;
// 人脸比对相似度阈值
float face_recongnize_thr = 0.5;
// 线程个数
int num_thread = 2;
接口(返回结果 bool:true/false)
bool face_compare(string image_path1,string image_path2);参数为两张图像地址，其中iamge1_path为face_id图像输入
bool face_compare_image(Mat image1,Mat image2);参数为两张opencv读取的图像矩阵，其中iamge1为face_id图像输入
编译
将CMakeLists.txt 中 set(FACECOMPARISON_DIR /home/situ/qfs/sdk_project/mnn_projects/face_recognize_mnn/lib)
改为lib文件夹所在的绝对地址
mkdir build
cd build
cmake ..
make
./main
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#include "faceLandmarks.h"
vector<vector<float>> FaceLandmarks::detect_landmarks(std::string image_path){
Mat input_data_=cv::imread(image_path);
float w_r=float(input_data_.cols)/112.0f;
float h_r=float(input_data_.rows)/112.0f;
Mat input_data;
cv::resize(input_data_,input_data,Size2d(112,112));
input_data.convertTo(input_data, CV_32F);
input_data = input_data /256.0f;
std::vector<std::vector<cv::Mat>> nChannels;
std::vector<cv::Mat> rgbChannels(3);
cv::split(input_data, rgbChannels);
nChannels.push_back(rgbChannels); // NHWC 转NCHW
auto *pvData = malloc(1 * 3 * 112 * 112 *sizeof(float));
int nPlaneSize = 112 * 112;
for (int c = 0; c < 3; ++c)
{
cv::Mat matPlane = nChannels[0][c];
memcpy((float *)(pvData) + c * nPlaneSize,\
matPlane.data, nPlaneSize * sizeof(float));
}
auto inTensor = net->getSessionInput(session, NULL);
net->resizeTensor(inTensor, {1, 3, 112,112});
net->resizeSession(session);
auto nchwTensor = new Tensor(inTensor, Tensor::CAFFE);
::memcpy(nchwTensor->host<float>(), pvData, nPlaneSize * 3 * sizeof(float));
inTensor->copyFromHostTensor(nchwTensor);
// //推理
net->runSession(session);
auto output= net->getSessionOutput(session, NULL);
MNN::Tensor feat_tensor(output, output->getDimensionType());
output->copyToHostTensor(&feat_tensor);
vector<vector<float>> landmarks;
for(int idx =0;idx<106;++idx){
float x_= *(feat_tensor.host<float>()+2*idx)*w_r;
float y_= *(feat_tensor.host<float>()+2*idx+1)*h_r;
vector<float> tmp={x_,y_};
landmarks.push_back(tmp);
}
return landmarks;
}
vector<vector<float>> FaceLandmarks::detect_image_landmarks(Mat image){
Mat input_data_=image;
float w_r=float(input_data_.cols)/112.0f;
float h_r=float(input_data_.rows)/112.0f;
Mat input_data;
cv::resize(input_data_,input_data,Size2d(112,112));
input_data.convertTo(input_data, CV_32F);
input_data = input_data /256.0f;
std::vector<std::vector<cv::Mat>> nChannels;
std::vector<cv::Mat> rgbChannels(3);
cv::split(input_data, rgbChannels);
nChannels.push_back(rgbChannels); // NHWC 转NCHW
auto *pvData = malloc(1 * 3 * 112 * 112 *sizeof(float));
int nPlaneSize = 112 * 112;
for (int c = 0; c < 3; ++c)
{
cv::Mat matPlane = nChannels[0][c];
memcpy((float *)(pvData) + c * nPlaneSize,\
matPlane.data, nPlaneSize * sizeof(float));
}
auto inTensor = net->getSessionInput(session, NULL);
net->resizeTensor(inTensor, {1, 3, 112,112});
net->resizeSession(session);
auto nchwTensor = new Tensor(inTensor, Tensor::CAFFE);
::memcpy(nchwTensor->host<float>(), pvData, nPlaneSize * 3 * sizeof(float));
inTensor->copyFromHostTensor(nchwTensor);
// //推理
net->runSession(session);
auto output= net->getSessionOutput(session, NULL);
MNN::Tensor feat_tensor(output, output->getDimensionType());
output->copyToHostTensor(&feat_tensor);
vector<vector<float>> landmarks;
for(int idx =0;idx<106;++idx){
float x_= *(feat_tensor.host<float>()+2*idx)*w_r;
float y_= *(feat_tensor.host<float>()+2*idx+1)*h_r;
vector<float> tmp={x_,y_};
landmarks.push_back(tmp);
}
return landmarks;
}
\ No newline at end of file
facecomparison.cpp 0 → 100644
#include "facecomparison.h"
bool FaceComparison::face_compare(string image1_path,string image2_path){
bool result=false;
cv::Mat image1=cv::imread(image1_path);
cv::Mat image2=cv::imread(image2_path);
if(image1.empty()||image2.empty()){
return false;
}
vector<Bbox> box1=face_det.detect_image(image1);
vector<Bbox> box2=face_det.detect_image(image2);
if(box1.empty()||box2.empty()){
return false;
}
int max_box1=0;
double max_area1=0,max_area2=0;
for(int i=0;i<box1.size();++i){
double tmp_area1=(box1[i].ymax-box1[i].ymin)*(box1[i].xmax-box1[i].xmin);
if(tmp_area1>max_area1){
max_box1=i;
max_area1=tmp_area1;
}
}
Rect rect1=Rect(box1[max_box1].xmin,box1[max_box1].ymin,box1[max_box1].xmax-box1[max_box1].xmin,box1[max_box1].ymax-box1[max_box1].ymin);
Mat face_area1=image1(rect1);
vector<vector<float>> landms1=face_landm.detect_landmarks_image(face_area1);
vector<vector<float>> land1={
{float(landms1[104][0]),float(landms1[104][1])},
{float(landms1[105][0]),float(landms1[105][1])},
{float(landms1[46][0]),float(landms1[46][1])},
{float(landms1[84][0]),float(landms1[84][1])},
{float(landms1[90][0]),float(landms1[90][1])}
};
Mat align_resize_image1=face_rec.preprocess_face(face_area1,land1);
for(int j=0;j<box2.size();++j){
Rect rect2=Rect(box2[j].xmin,box2[j].ymin,box2[j].xmax-box2[j].xmin,box2[j].ymax-box2[j].ymin);
Mat face_area2=image2(rect2);
vector<vector<float>> landms2=face_landm.detect_landmarks_image(face_area2);
vector<vector<float>> land2={
{float(landms2[104][0]),float(landms2[104][1])},
{float(landms2[105][0]),float(landms2[105][1])},
{float(landms2[46][0]),float(landms2[46][1])},
{float(landms2[84][0]),float(landms2[84][1])},
{float(landms2[90][0]),float(landms2[90][1])}
};
Mat align_resize_image2=face_rec.preprocess_face(face_area2,land2);
double samilar_score=face_rec.get_samilar_image(align_resize_image1,align_resize_image2);
if(samilar_score>face_recongnize_thr){
result=true;
}
}
return result;
}
bool FaceComparison::face_compare_image(Mat image1,Mat image2){
bool result=false;
vector<Bbox> box1=face_det.detect_image(image1);
vector<Bbox> box2=face_det.detect_image(image2);
if(box1.empty()||box2.empty()){
return false;
}
int max_box1=0;
double max_area1=0,max_area2=0;
for(int i=0;i<box1.size();++i){
double tmp_area1=(box1[i].ymax-box1[i].ymin)*(box1[i].xmax-box1[i].xmin);
if(tmp_area1>max_area1){
max_box1=i;
max_area1=tmp_area1;
}
}
Rect rect1=Rect(box1[max_box1].xmin,box1[max_box1].ymin,box1[max_box1].xmax-box1[max_box1].xmin,box1[max_box1].ymax-box1[max_box1].ymin);
Mat face_area1=image1(rect1);
vector<vector<float>> landms1=face_landm.detect_landmarks_image(face_area1);
vector<vector<float>> land1={
{float(landms1[104][0]),float(landms1[104][1])},
{float(landms1[105][0]),float(landms1[105][1])},
{float(landms1[46][0]),float(landms1[46][1])},
{float(landms1[84][0]),float(landms1[84][1])},
{float(landms1[90][0]),float(landms1[90][1])}
};
Mat align_resize_image1=face_rec.preprocess_face(face_area1,land1);
for(int j=0;j<box2.size();++j){
Rect rect2=Rect(box2[j].xmin,box2[j].ymin,box2[j].xmax-box2[j].xmin,box2[j].ymax-box2[j].ymin);
Mat face_area2=image2(rect2);
vector<vector<float>> landms2=face_landm.detect_landmarks_image(face_area2);
vector<vector<float>> land2={
{float(landms2[104][0]),float(landms2[104][1])},
{float(landms2[105][0]),float(landms2[105][1])},
{float(landms2[46][0]),float(landms2[46][1])},
{float(landms2[84][0]),float(landms2[84][1])},
{float(landms2[90][0]),float(landms2[90][1])}
};
Mat align_resize_image2=face_rec.preprocess_face(face_area2,land2);
double samilar_score=face_rec.get_samilar_image(align_resize_image1,align_resize_image2);
if(samilar_score>face_recongnize_thr){
result=true;
}
}
return result;
}
\ No newline at end of file
facelandmarks.cpp 0 → 100644
#include "facelandmarks.h"
FaceLandmarks::FaceLandmarks(){
model_init=false;
}
// FaceLandmarks::~FaceLandmarks(){
// pfld_interpreter->releaseModel();
// pfld_interpreter->releaseSession(session);
// }
bool FaceLandmarks::init_model(string model_path){
pfld_interpreter = unique_ptr<MNN::Interpreter>(MNN::Interpreter::createFromFile(model_path.c_str()));
if(nullptr==pfld_interpreter){
return false;
}
//创建session
MNN::ScheduleConfig schedule_config;
schedule_config.type = MNN_FORWARD_CPU;
schedule_config.numThread = num_thread;
MNN::BackendConfig backend_config;
backend_config.memory = MNN::BackendConfig::Memory_Normal;
backend_config.power = MNN::BackendConfig::Power_Normal;
backend_config.precision = MNN::BackendConfig::Precision_Normal;
schedule_config.backendConfig = &backend_config;
session = pfld_interpreter->createSession(schedule_config);
input_tensor = pfld_interpreter->getSessionInput(session,NULL);
pfld_interpreter->resizeTensor(input_tensor,{1,3,112,112});
pfld_interpreter->resizeSession(session);
//数据预处理
MNN::CV::ImageProcess::Config image_config;
::memcpy(image_config.normal,normal,sizeof(normal));
image_config.sourceFormat = MNN::CV::BGR;
image_config.destFormat = MNN::CV::BGR;
pretreat = shared_ptr<MNN::CV::ImageProcess>(MNN::CV::ImageProcess::create(image_config));
// pretreat->setMatrix(transforms);
model_init = true;
}
vector<vector<float>> FaceLandmarks::detect_landmarks(string image_path){
Mat image = cv::imread(image_path);
vector<vector<float>> landmarks;
int width = image.cols;
int height = image.rows;
Mat resize_image;
cv::resize(image,resize_image,Size(112,112));
float ws = float(width)/float(112.0);
float hs = float(height)/float(112.0);
pretreat->convert(resize_image.data,112,112,0,input_tensor);
pfld_interpreter->runSession(session);
auto output_landmark = pfld_interpreter->getSessionOutput(session, NULL);
MNN::Tensor landmark_tensor(output_landmark, output_landmark->getDimensionType());
output_landmark->copyToHostTensor(&landmark_tensor);
float* result = landmark_tensor.host<float>();
for (int i = 0; i < 106; ++i) {
vector<float> curr_pt={result[2 * i + 0] * ws,result[2 * i + 1] * hs};
landmarks.push_back(curr_pt);
}
return landmarks;
}
vector<vector<float>> FaceLandmarks::detect_landmarks_image(Mat image){
vector<vector<float>> landmarks;
int width = image.cols;
int height = image.rows;
Mat resize_image;
cv::resize(image,resize_image,Size(112,112));
float ws = float(width)/float(112.0);
float hs = float(height)/float(112.0);
pretreat->convert(resize_image.data,112,112,0,input_tensor);
pfld_interpreter->runSession(session);
auto output_landmark = pfld_interpreter->getSessionOutput(session, NULL);
MNN::Tensor landmark_tensor(output_landmark, output_landmark->getDimensionType());
output_landmark->copyToHostTensor(&landmark_tensor);
float* result = landmark_tensor.host<float>();
for (int i = 0; i < 106; ++i) {
vector<float> curr_pt={result[2 * i + 0] * ws,result[2 * i + 1] * hs};
landmarks.push_back(curr_pt);
}
return landmarks;
}
\ No newline at end of file
facerecognize.cpp
#include "facerecognize.h"
FaceRecognize::FaceRecognize(){}
// FaceRecognize::~FaceRecognize(){
// net->releaseModel();
// net->releaseSession(session1);
// net->releaseSession(session2);
// }
bool FaceRecognize::init_model(string model_path){
net=shared_ptr<MNN::Interpreter>(MNN::Interpreter::createFromFile(model_path.c_str()));
ScheduleConfig config;
config.numThread=num_thread;
config.type=MNN_FORWARD_CPU;
session1 = net->createSession(config);
session2 = net->createSession(config);
input_tensor1 = net->getSessionInput(session1,NULL);
input_tensor2 = net->getSessionInput(session2,NULL);
net->resizeTensor(input_tensor1,{1,3,input_size[1],input_size[0]});
net->resizeSession(session1);
net->resizeTensor(input_tensor2,{1,3,input_size[1],input_size[0]});
net->resizeSession(session2);
//数据预处理
MNN::CV::ImageProcess::Config image_config;
image_config.sourceFormat = MNN::CV::BGR;
image_config.destFormat = MNN::CV::BGR;
::memcpy(image_config.mean,mean,sizeof(mean));
::memcpy(image_config.normal,normal,sizeof(normal));
pretreat = shared_ptr<MNN::CV::ImageProcess>(MNN::CV::ImageProcess::create(image_config));
model_init = true;
return model_init;
}
cv::Mat FaceRecognize::meanAxis0(const cv::Mat &src)
{
int num = src.rows;
......@@ -168,61 +205,52 @@ double FaceRecognize::cos_distance(const vector<double>& base, const vector<doub
return simility;
}
double FaceRecognize::get_samilar(Mat image1,Mat image2){
double FaceRecognize::get_samilar_image(Mat image1,Mat image2){
cv::resize(image1,image1,Size2d(input_size[0],input_size[1]));
cv::resize(image2,image2,Size2d(input_size[0],input_size[1]));
image1.convertTo(image1, CV_32F);
image2.convertTo(image2, CV_32F);
image1 = (image1-mean)*scale;
image2 = (image2-mean)*scale;
pretreat->convert(image1.data,input_size[0],input_size[1],0,input_tensor1);
pretreat->convert(image2.data,input_size[0],input_size[1],0,input_tensor2);
//推理
net->runSession(session1);
auto output1= net->getSessionOutput(session1, NULL);
//推理
net->runSession(session2);
auto output2= net->getSessionOutput(session2, NULL);
MNN::Tensor feat_tensor1(output1, output1->getDimensionType());
MNN::Tensor feat_tensor2(output2, output2->getDimensionType());
output1->copyToHostTensor(&feat_tensor1);
output2->copyToHostTensor(&feat_tensor2);
auto feature1 = feat_tensor1.host<float>();
auto feature2 = feat_tensor2.host<float>();
std::vector<std::vector<cv::Mat>> nChannels1;
std::vector<cv::Mat> rgbChannels1(3);
cv::split(image1, rgbChannels1);
nChannels1.push_back(rgbChannels1); // NHWC 转NCHW
auto *pvData1 = malloc(1 * 3 * input_size[1] * input_size[0] *sizeof(float));
int nPlaneSize = input_size[0] * input_size[1];
for (int c = 0; c < 3; ++c)
{
cv::Mat matPlane1 = nChannels1[0][c];
memcpy((float *)(pvData1) + c * nPlaneSize,\
matPlane1.data, nPlaneSize * sizeof(float));
vector<double> v1,v2;
for(int i=0;i<int(feat_tensor1.size()/4);i++){
v1.push_back((double)feature1[i]);
v2.push_back((double)feature2[i]);
}
auto inTensor1 = net->getSessionInput(session1, NULL);
net->resizeTensor(inTensor1, {1, 3, input_size[1],input_size[0]});
net->resizeSession(session1);
double cos_score=cos_distance(v1,v2);
return cos_score;
}
auto nchwTensor1 = new Tensor(inTensor1, Tensor::CAFFE);
::memcpy(nchwTensor1->host<float>(), pvData1, nPlaneSize * 3 * sizeof(float));
inTensor1->copyFromHostTensor(nchwTensor1);
// //推理
double FaceRecognize::get_samilar(string image_path1,string image_path2){
Mat image1 = cv::imread(image_path1);
Mat image2 = cv::imread(image_path2);
cv::resize(image1,image1,Size2d(input_size[0],input_size[1]));
cv::resize(image2,image2,Size2d(input_size[0],input_size[1]));
pretreat->convert(image1.data,input_size[0],input_size[1],0,input_tensor1);
pretreat->convert(image2.data,input_size[0],input_size[1],0,input_tensor2);
//推理
net->runSession(session1);
auto output1= net->getSessionOutput(session1, NULL);
std::vector<std::vector<cv::Mat>> nChannels2;
std::vector<cv::Mat> rgbChannels2(3);
cv::split(image2, rgbChannels2);
nChannels2.push_back(rgbChannels2); // NHWC 转NCHW
auto *pvData2 = malloc(1 * 3 * input_size[1] * input_size[0] *sizeof(float));
for (int c = 0; c < 3; ++c)
{
cv::Mat matPlane2 = nChannels2[0][c];
memcpy((float *)(pvData2) + c * nPlaneSize,\
matPlane2.data, nPlaneSize * sizeof(float));
}
auto inTensor2 = net->getSessionInput(session2, NULL);
net->resizeTensor(inTensor2, {1, 3, input_size[1],input_size[0]});
net->resizeSession(session2);
auto nchwTensor2 = new Tensor(inTensor2, Tensor::CAFFE);
::memcpy(nchwTensor2->host<float>(), pvData2, nPlaneSize * 3 * sizeof(float));
inTensor2->copyFromHostTensor(nchwTensor2);
// //推理
//推理
net->runSession(session2);
auto output2= net->getSessionOutput(session2, NULL);
MNN::Tensor feat_tensor1(output1, MNN::Tensor::CAFFE);
MNN::Tensor feat_tensor2(output2, MNN::Tensor::CAFFE);
MNN::Tensor feat_tensor1(output1, output1->getDimensionType());
MNN::Tensor feat_tensor2(output2, output2->getDimensionType());
output1->copyToHostTensor(&feat_tensor1);
output2->copyToHostTensor(&feat_tensor2);
auto feature1 = feat_tensor1.host<float>();
......@@ -236,4 +264,3 @@ double FaceRecognize::get_samilar(Mat image1,Mat image2){
double cos_score=cos_distance(v1,v2);
return cos_score;
}
......
include/facecomparison.h 0 → 100644
#ifndef FACECOMPARISON_H
#define FACECOMPARISON_H
#include "facerecognize.h"
#include "retinaface.h"
#include "facelandmarks.h"
class FaceComparison{
public:
// 人脸检测阈值
float confidence_threshold = 0.2;
// 是否进行人脸外廓
bool is_bbox_process = true;
// 人脸比对相似度阈值
float face_recongnize_thr = 0.5;
//线程个数
int num_thread = 2;
//接口
bool face_compare(string image_path1,string image_path2);
bool face_compare_image(Mat image1,Mat image2);
private:
RetinaFace face_det;
FaceLandmarks face_landm;
FaceRecognize face_rec;
public:
FaceComparison(){};
FaceComparison(string face_det_model,string face_landm_model,string face_rec_model){
face_det = RetinaFace(face_det_model);
face_det.confidence_threshold = confidence_threshold;
face_det.is_bbox_process = is_bbox_process;
face_det.num_thread=num_thread;
face_landm =FaceLandmarks(face_landm_model);
face_landm.num_thread=num_thread;
face_rec =FaceRecognize(face_rec_model);
face_rec.num_thread=num_thread;
}
};
#endif
\ No newline at end of file
faceLandmarks.h → include/facelandmarks.h
#ifndef FACELANDMARKS_H
#define FACELANDMARKS_H
#include <opencv2/opencv.hpp>
#include<MNN/Interpreter.hpp>
#include<MNN/ImageProcess.hpp>
#include<iostream>
#include<memory>
using namespace std;
using namespace cv;
using namespace MNN;
class FaceLandmarks{
private:
vector<float> input_size={112,112};
std::shared_ptr<MNN::Interpreter> net;
Session *session = nullptr;
ScheduleConfig config;
public:
FaceLandmarks(){};
FaceLandmarks(string model_path){
net = std::shared_ptr<MNN::Interpreter>(MNN::Interpreter::createFromFile(model_path.c_str()));//创建解释器
config.numThread = 8;
config.type = MNN_FORWARD_CPU;
session = net->createSession(config);//创建session
}
int num_thread = 2;
public:
FaceLandmarks();
// ~FaceLandmarks();
FaceLandmarks(string model_path){
init_model(model_path);
};
bool init_model(string model_path);
vector<vector<float>> detect_landmarks(string image_path);
vector<vector<float>> detect_image_landmarks(cv::Mat image);
vector<vector<float>> detect_landmarks_image(Mat image);
private:
bool model_init;
float normal[3]={1.0f/256.f,1.0f/256.f,1.0f/256.f};
std::shared_ptr<MNN::Interpreter> pfld_interpreter = nullptr;
MNN::Session* session = nullptr;
MNN::Tensor* input_tensor = nullptr;
shared_ptr<MNN::CV::ImageProcess> pretreat;
};
#endif
\ No newline at end of file
#endif
......
facerecognize.h → include/facerecognize.h
......@@ -4,29 +4,40 @@
#include<MNN/Interpreter.hpp>
#include<MNN/ImageProcess.hpp>
#include<iostream>
#include<memory>
using namespace MNN;
using namespace std;
using namespace cv;
class FaceRecognize{
public:
int num_thread = 2;
// 推理
double get_samilar(string image_path1,string image_path2);
double get_samilar_image(Mat image1,Mat image2);
private:
vector<float> input_size={112,112};
vector<int> input_size={112,112};
bool model_init=false;
std::shared_ptr<MNN::Interpreter> net;
Session *session1 = nullptr;
Session *session2 = nullptr;
ScheduleConfig config;
Scalar mean=Scalar(127.5f,127.5f,127.5f);
float scale = 1.0f/127.5f;
MNN::Tensor* input_tensor1=nullptr;
MNN::Tensor* input_tensor2=nullptr;
shared_ptr<MNN::CV::ImageProcess> pretreat;
float mean[3]={127.5f,127.5f,127.5f};
float normal[3] = {1.0f/127.5f,1.0f/127.5f,1.0f/127.5f};
public:
FaceRecognize(){};
FaceRecognize();
// ~FaceRecognize();
FaceRecognize(string model_path){
net = std::shared_ptr<MNN::Interpreter>(MNN::Interpreter::createFromFile(model_path.c_str()));//创建解释器
config.numThread = 8;
config.type = MNN_FORWARD_CPU;
session1 = net->createSession(config);//创建session
session2 = net->createSession(config);//创建session
init_model(model_path);
}
bool init_model(string model_path);
//预处理
cv::Mat meanAxis0(const cv::Mat &src);
cv::Mat elementwiseMinus(const cv::Mat &A,const cv::Mat &B);
......@@ -36,7 +47,5 @@ class FaceRecognize{
Mat preprocess_face(Mat image,vector<vector<float>> land);
double getMold(const vector<double>& vec);
double cos_distance(const vector<double>& base, const vector<double>& target);
// 推理
double get_samilar(Mat image1,Mat image2);
};
#endif
\ No newline at end of file
......
retinaface.h → include/retinaface.h
......@@ -4,6 +4,7 @@
#include<MNN/Interpreter.hpp>
#include<MNN/ImageProcess.hpp>
#include<iostream>
#include<memory>
using namespace MNN;
using namespace std;
......@@ -29,24 +30,26 @@ class RetinaFace{
public:
float confidence_threshold = 0.5;
bool is_bbox_process=true;
int num_thread = 2;
private:
bool use_gpu=true;
vector<float> input_size={640,640};
public:
bool model_init=false;
vector<int> input_size={640,640};
vector<float> variances={0.1,0.2};
Scalar mean = Scalar(104.0f, 117.0f, 123.0f);
float mean[3] = {104.0f, 117.0f, 123.0f};
float keep_top_k = 100;
float nms_threshold = 0.4;
float resize_scale = 1.0;
std::shared_ptr<MNN::Interpreter> net;
Session *session = nullptr;
ScheduleConfig config;
MNN::Tensor* input_tensor=nullptr;
shared_ptr<MNN::CV::ImageProcess> pretreat;
vector<vector<float>> anchors;
private:
// 生成anchors
vector<vector<float>> priorBox(vector<float> image_size);
vector<vector<float>> priorBox(vector<int> image_size);
// 解析bounding box landmarks 包含置信度
vector<Bbox> decode(float *loc,float *score,float *pre,vector<vector<float>> priors,vector<float> variances);
// 解析landmarks
......@@ -60,14 +63,12 @@ class RetinaFace{
public:
RetinaFace(){};
RetinaFace();
// ~RetinaFace();
RetinaFace(string model_path){
net = std::shared_ptr<MNN::Interpreter>(MNN::Interpreter::createFromFile(model_path.c_str()));//创建解释器
config.numThread = 8;
config.type = MNN_FORWARD_CPU;
session = net->createSession(config);//创建session
anchors=priorBox(input_size);
init_model(model_path);
}
bool init_model(string model_path);
// 推理
vector<Bbox> detect(string image_path);
......
lib/libfacecomparison.so 0 → 100755
No preview for this file type
main.cpp
#include "faceLandmarks.h"
#include "facecomparison.h"
int main(){
FaceLandmarks face_landmarks1 = FaceLandmarks("/home/situ/qfs/sdk_project/gitlab_demo/face_recognize_mnn/model/det_landmarks_106_v0.0.1.mnn");
vector<string> filenames;
cv::glob("/home/situ/图片/img3", filenames, false);
for(auto path:filenames){
// FaceLandmarks face_landmarks1 = FaceLandmarks("/home/situ/qfs/sdk_project/mnn_projects/face_recognize_mnn2/model/det_landmarks_106_v0.0.1.mnn");
// vector<string> filenames;
// cv::glob("/home/situ/图片/img3", filenames, false);
// for(auto path:filenames){
// cout<<path<<endl;
Mat img1 =cv::imread(path);
auto landmarks1 = face_landmarks1.detect_landmarks(path);
for(auto landm:landmarks1){
cv::circle(img1,Point2d(landm[0],landm[1]),2,Scalar(255,0,0));
}
cv::imshow("img",img1);
cv::waitKey(0);
}
// // cout<<path<<endl;
// Mat img1 =cv::imread(path);
// auto landmarks1 = face_landmarks1.detect_landmarks(path);
// for(auto landm:landmarks1){
// cv::circle(img1,Point2d(landm[0],landm[1]),2,Scalar(255,0,0));
// }
// cv::imshow("img",img1);
// cv::waitKey(0);
// }
// Mat image1 = cv::imread("/home/situ/图片/4.jpg");
// Mat image2 = cv::imread("/home/situ/图片/img3/1.jpg");
// string face_det_model = "/home/situ/qfs/sdk_project/face_recognize_mnn/model/mnn/det_face_retina_mnn_1.0.0_v0.1.1.mnn";
// string face_landm_model = "/home/situ/qfs/sdk_project/face_recognize_mnn/model/mnn/det_landmarks_106_v0.0.1.mnn";
// string face_rec_model = "/home/situ/qfs/mobile_face_recognize/models/cls_face_mnn_1.0.0_v0.1.0.mnn";
Mat image1 = cv::imread("/data/face_recognize/pipeline_test/35326a760af111ec9f2300163e772630/310cardImageContent163047910320427.jpg");
Mat image2 = cv::imread("/data/face_recognize/pipeline_test/35326a760af111ec9f2300163e772630/310faceImageContent163047910353714.jpg");
string face_det_model = "/home/situ/qfs/sdk_project/mnn_projects/face_recognize_mnn/model/det_face_retina_mnn_1.0.0_v0.1.1.mnn";
string face_landm_model = "/home/situ/qfs/sdk_project/mnn_projects/face_recognize_mnn/model/det_landmarks_106_v0.0.1.mnn";
// string face_rec_model = "/home/situ/qfs/sdk_project/mnn_projects/face_recognize_mnn/model/cls_face_mnn_1.0.0_v0.0.2.mnn";
string face_rec_model = "/home/situ/qfs/sdk_project/mnn_projects/face_recognize_mnn/model/cls_face_mnn_1.0.0_v0.1.1.mnn";
// FaceComparison face_rec = FaceComparison(face_det_model,face_landm_model,face_rec_model);
// bool result = face_rec.face_compare("/home/situ/图片/2.png","/home/situ/图片/2.png");
// cout<<result<<endl;
FaceComparison face_rec = FaceComparison(face_det_model,face_landm_model,face_rec_model);
bool result = face_rec.face_compare_image(image1,image2);
cout<<result<<endl;
// string model_path = "/home/situ/qfs/sdk_project/mnn_projects/face_retinaface_mnn/det_face_retina_mnn_1.0.0_v0.1.1.mnn";
// RetinaFace face_det = RetinaFace(model_path.c_str());
// Mat image = cv::imread("/home/situ/图片/2.png");
// vector<Bbox> result = face_det.detect_image(image);
// for(auto res:result){
// cv::rectangle(image,cv::Rect(res.xmin,res.ymin,res.xmax-res.xmin,res.ymax-res.ymin),Scalar(0,255,0),2);
// }
// cv::imshow("image",image);
// cv::waitKey(0);
return 0;
}
......
model/cls_face_mnn_1.0.0_v0.0.2.mnn deleted 100644 → 0
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model/cls_face_mnn_1.0.0_v0.1.1.mnn 0 → 100644
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retinaface.cpp
#include "retinaface.h"
RetinaFace::RetinaFace(){}
// RetinaFace::~RetinaFace(){
// net->releaseModel();
// net->releaseSession(session);
// }
bool RetinaFace::init_model(string model_path){
net = std::shared_ptr<MNN::Interpreter>(MNN::Interpreter::createFromFile(model_path.c_str()));//创建解释器
if(nullptr==net){
return false;
}
ScheduleConfig config;
config.numThread = num_thread;
config.type = MNN_FORWARD_CPU;
session = net->createSession(config);//创建session
anchors=priorBox(input_size); //生成建议框
input_tensor = net->getSessionInput(session,NULL);
net->resizeTensor(input_tensor,{1,3,input_size[1],input_size[0]});
net->resizeSession(session);
//数据预处理
MNN::CV::ImageProcess::Config image_config;
image_config.sourceFormat = MNN::CV::BGR;
image_config.destFormat = MNN::CV::BGR;
::memcpy(image_config.mean,mean,sizeof(mean));
pretreat = shared_ptr<MNN::CV::ImageProcess>(CV::ImageProcess::create(image_config));
model_init = true;
}
// 生成anchors
vector<vector<float>> RetinaFace::priorBox(vector<float> image_size){
vector<vector<float>> RetinaFace::priorBox(vector<int> image_size){
vector<int> tmp1={16,32};
vector<int> tmp2={64,128};
vector<int> tmp3={256,512};
......@@ -171,27 +204,8 @@ vector<Bbox> RetinaFace::detect(string image_path){
float w_r=float(input_size[0])/float(image.cols);
float h_r=float(input_size[1])/float(image.rows);
Mat input_data;
cv::resize(image,input_data,Size(input_size[0],input_size[1]));
input_data = input_data-mean;
input_data.convertTo(input_data, CV_32F);
std::vector<std::vector<cv::Mat>> nChannels;
std::vector<cv::Mat> rgbChannels(3);
cv::split(input_data, rgbChannels);
nChannels.push_back(rgbChannels); // NHWC 转NCHW
auto *pvData = malloc(1 * 3 * input_size[1] * input_size[0] *sizeof(float));
int nPlaneSize = input_size[0] * input_size[1];
for (int c = 0; c < 3; ++c)
{
cv::Mat matPlane = nChannels[0][c];
memcpy((float *)(pvData) + c * nPlaneSize,\
matPlane.data, nPlaneSize * sizeof(float));
}
auto inTensor = net->getSessionInput(session, NULL);
net->resizeTensor(inTensor, {1, 3, input_size[1],input_size[0]});
net->resizeSession(session);
auto nchwTensor = new Tensor(inTensor, Tensor::CAFFE);
::memcpy(nchwTensor->host<float>(), pvData, nPlaneSize * 3 * sizeof(float));
inTensor->copyFromHostTensor(nchwTensor);
cv::resize(image,input_data,Size(input_size[1],input_size[0]));
pretreat->convert(input_data.data,input_size[0],input_size[1],0,input_tensor);
// //推理
net->runSession(session);
auto output0= net->getSessionOutput(session, "output0");
......@@ -222,36 +236,20 @@ vector<Bbox> RetinaFace::detect(string image_path){
vector<Bbox> RetinaFace::detect_image(Mat image){
float w_r=float(input_size[0])/float(image.cols);
float h_r=float(input_size[1])/float(image.rows);
Mat input_data;
cv::resize(image,input_data,Size(input_size[0],input_size[1]));
input_data = input_data-mean;
input_data.convertTo(input_data, CV_32F);
std::vector<std::vector<cv::Mat>> nChannels;
std::vector<cv::Mat> rgbChannels(3);
cv::split(input_data, rgbChannels);
nChannels.push_back(rgbChannels); // NHWC 转NCHW
auto *pvData = malloc(1 * 3 * input_size[1] * input_size[0] *sizeof(float));
int nPlaneSize = input_size[0] * input_size[1];
for (int c = 0; c < 3; ++c)
{
cv::Mat matPlane = nChannels[0][c];
memcpy((float *)(pvData) + c * nPlaneSize,\
matPlane.data, nPlaneSize * sizeof(float));
}
auto inTensor = net->getSessionInput(session, NULL);
net->resizeTensor(inTensor, {1, 3, input_size[1],input_size[0]});
net->resizeSession(session);
auto nchwTensor = new Tensor(inTensor, Tensor::CAFFE);
::memcpy(nchwTensor->host<float>(), pvData, nPlaneSize * 3 * sizeof(float));
inTensor->copyFromHostTensor(nchwTensor);
cv::resize(image,input_data,Size(input_size[1],input_size[0]));
pretreat->convert(input_data.data,input_size[0],input_size[1],0,input_tensor);
// //推理
net->runSession(session);
auto output0= net->getSessionOutput(session, "output0");
auto output1= net->getSessionOutput(session, "output1");
auto output2= net->getSessionOutput(session, "output2");
MNN::Tensor feat_tensor0(output0, MNN::Tensor::CAFFE);
MNN::Tensor feat_tensor1(output1, MNN::Tensor::CAFFE);
MNN::Tensor feat_tensor2(output2, MNN::Tensor::CAFFE);
MNN::Tensor feat_tensor0(output0, output0->getDimensionType());
MNN::Tensor feat_tensor1(output1, output1->getDimensionType());
MNN::Tensor feat_tensor2(output2, output2->getDimensionType());
output0->copyToHostTensor(&feat_tensor0);
output1->copyToHostTensor(&feat_tensor1);
output2->copyToHostTensor(&feat_tensor2);
......@@ -261,7 +259,6 @@ vector<Bbox> RetinaFace::detect_image(Mat image){
vector<Bbox> result_boxes = decode(loc,score,landm,anchors,variances);
vector<Bbox> results=select_score(result_boxes,confidence_threshold,w_r,h_r);
nms_cpu(results,nms_threshold);
if(is_bbox_process){
vector<Bbox> res_bboxes=bbox_process(results,image.cols,image.rows);
......
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