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yolov7-face.cpp
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yolov7-face.cpp
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#include <fstream>
#include <iostream>
#include <sstream>
#include <numeric>
#include <chrono>
#include <vector>
#include <opencv2/opencv.hpp>
#include <dirent.h>
#include "NvInfer.h"
#include "cuda_runtime_api.h"
#include "logging.h"
#include "include/utils.hpp"
#include "preprocess.h"
#include "postprocess.h"
#define MAX_IMAGE_INPUT_SIZE_THRESH 5000 * 5000
#define MAX_OBJECTS 2048
struct affineMatrix //letter_box 仿射变换矩阵
{
float i2d[6]; //仿射变换正变换
float d2i[6]; //仿射变换逆变换
};
struct bbox
{
float x1,x2,y1,y2;
float landmarks[10]; //5个关键点
float score;
};
#define CHECK(status) \
do\
{\
auto ret = (status);\
if (ret != 0)\
{\
std::cerr << "Cuda failure: " << ret << std::endl;\
abort();\
}\
} while (0)
#define DEVICE 0 // GPU id
#define NMS_THRESH 0.45
#define BBOX_CONF_THRESH 0.3
using namespace nvinfer1;
static const int INPUT_W = 640;
static const int INPUT_H = 640;
static const int NUM_CLASSES = 1; //类别数
static const int CKPT_NUM=5; //关键点个数
const char* INPUT_BLOB_NAME = "images"; //onnx 输入 名字
const char* OUTPUT_BLOB_NAME = "output"; //onnx 输出 名字
static Logger gLogger;
void affine_project(float *d2i,float x,float y,float *ox,float *oy) //通过仿射变换逆矩阵,恢复成原图的坐标
{
*ox = d2i[0]*x+d2i[1]*y+d2i[2];
*oy = d2i[3]*x+d2i[4]*y+d2i[5];
}
const float color_list[5][3] =
{
{255, 0, 0},
{0, 255, 0},
{0, 0, 255},
{0, 255, 255},
{255,255,0},
};
void getd2i(affineMatrix &afmt,cv::Size to,cv::Size from) //计算仿射变换的矩阵和逆矩阵
{
float scale = std::min(1.0*to.width/from.width, 1.0*to.height/from.height);
afmt.i2d[0]=scale;
afmt.i2d[1]=0;
afmt.i2d[2]=-scale*from.width*0.5+to.width*0.5;
afmt.i2d[3]=0;
afmt.i2d[4]=scale;
afmt.i2d[5]=-scale*from.height*0.5+to.height*0.5;
cv::Mat i2d_mat(2,3,CV_32F,afmt.i2d);
cv::Mat d2i_mat(2,3,CV_32F,afmt.d2i);
cv::invertAffineTransform(i2d_mat,d2i_mat);
memcpy(afmt.d2i, d2i_mat.ptr<float>(0), sizeof(afmt.d2i));
}
int main(int argc, char** argv)
{
int NUM_BOX_ELEMENT=7+CKPT_NUM*2;
cudaSetDevice(DEVICE);
char *trtModelStreamDet{nullptr};
size_t size{0};
const std::string engine_file_path {argv[1]};
std::ifstream file(engine_file_path, std::ios::binary);
int batch_size = 1;
if (file.good()) {
file.seekg(0, file.end);
size = file.tellg();
file.seekg(0, file.beg);
trtModelStreamDet = new char[size];
assert(trtModelStreamDet);
file.read(trtModelStreamDet, size);
file.close();
}
//det模型trt初始化
IRuntime* runtime_det = createInferRuntime(gLogger);
assert(runtime_det != nullptr);
ICudaEngine* engine_det = runtime_det->deserializeCudaEngine(trtModelStreamDet, size);
assert(engine_det != nullptr);
IExecutionContext* context_det = engine_det->createExecutionContext();
assert(context_det != nullptr);
delete[] trtModelStreamDet;
float *buffers[2];
const int inputIndex = engine_det->getBindingIndex(INPUT_BLOB_NAME);
const int outputIndex = engine_det->getBindingIndex(OUTPUT_BLOB_NAME);
assert(inputIndex == 0);
assert(outputIndex == 1);
// Create GPU buffers on device
auto out_dims = engine_det->getBindingDimensions(1);
auto output_size = 1;
int OUTPUT_CANDIDATES = out_dims.d[1];
for(int j=0;j<out_dims.nbDims;j++) {
output_size *= out_dims.d[j];
}
CHECK(cudaMalloc((void**)&buffers[inputIndex], 3 * INPUT_H * INPUT_W * sizeof(float)));
CHECK(cudaMalloc((void**)&buffers[outputIndex], output_size * sizeof(float)));
// Create stream
cudaStream_t stream;
CHECK(cudaStreamCreate(&stream));
uint8_t* img_host = nullptr;
uint8_t* img_device = nullptr;
float *affine_matrix_d2i_host = nullptr;
float *affine_matrix_d2i_device = nullptr;
float *decode_ptr_device = nullptr;
float *decode_ptr_host = nullptr;
decode_ptr_host = new float[1+MAX_OBJECTS*NUM_BOX_ELEMENT];
// prepare input data cache in pinned memory
CHECK(cudaMallocHost((void**)&img_host, MAX_IMAGE_INPUT_SIZE_THRESH * 3));
// prepare input data cache in device memory
CHECK(cudaMalloc((void**)&img_device, MAX_IMAGE_INPUT_SIZE_THRESH * 3));
CHECK(cudaMallocHost(&affine_matrix_d2i_host,sizeof(float)*6));
CHECK(cudaMalloc(&affine_matrix_d2i_device,sizeof(float)*6));
CHECK(cudaMalloc(&decode_ptr_device,sizeof(float)*(1+MAX_OBJECTS*NUM_BOX_ELEMENT)));
static float* prob = new float[output_size];
// std::string imgPath ="/mnt/Gpan/Mydata/pytorchPorject/Chinese_license_plate_detection_recognition/imgs";
std::string input_image_path=argv[2];
std::string imgPath=argv[2];
std::vector<std::string> imagList;
std::vector<std::string>fileType{"jpg","png"};
readFileList(const_cast<char *>(imgPath.c_str()),imagList,fileType);
double sumTime = 0;
int index = 0;
cv::Size to(INPUT_W,INPUT_H);
for (auto &input_image_path:imagList)
{
affineMatrix afmt;
cv::Mat img = cv::imread(input_image_path);
getd2i(afmt,to,cv::Size(img.cols,img.rows));
double begin_time = cv::getTickCount();
float *buffer_idx = (float*)buffers[inputIndex];
size_t size_image = img.cols * img.rows * 3;
size_t size_image_dst = INPUT_H * INPUT_W * 3;
memcpy(affine_matrix_d2i_host,afmt.d2i,sizeof(afmt.d2i));
memcpy(img_host, img.data, size_image);
CHECK(cudaMemcpyAsync(img_device, img_host, size_image, cudaMemcpyHostToDevice, stream));
CHECK(cudaMemcpyAsync(affine_matrix_d2i_device,affine_matrix_d2i_host,sizeof(afmt.d2i),cudaMemcpyHostToDevice,stream));
preprocess_kernel_img(img_device, img.cols, img.rows, buffer_idx, INPUT_W, INPUT_H,affine_matrix_d2i_device, stream); //前处理 ,相当于letter_box
double time_pre = cv::getTickCount();
double time_pre_=(time_pre-begin_time)/cv::getTickFrequency()*1000;
// std::cout<<"preprocessing time is "<<time_pre_<<" ms"<<std::endl;
// doInference_cu(*context_det,stream, (void**)buffers,prob,1,output_size);
(*context_det).enqueueV2((void**)buffers, stream, nullptr);
float *predict = (float *)buffers[outputIndex];
CHECK(cudaMemsetAsync(decode_ptr_device,0,sizeof(int),stream));
decode_kernel_invoker(predict,NUM_BOX_ELEMENT,OUTPUT_CANDIDATES,NUM_CLASSES,CKPT_NUM,BBOX_CONF_THRESH,affine_matrix_d2i_device,decode_ptr_device,MAX_OBJECTS,stream); //cuda 后处理
nms_kernel_invoker(decode_ptr_device, NMS_THRESH, MAX_OBJECTS, stream,NUM_BOX_ELEMENT);//cuda nms
CHECK(cudaMemcpyAsync(decode_ptr_host,decode_ptr_device,sizeof(float)*(1+MAX_OBJECTS*NUM_BOX_ELEMENT),cudaMemcpyDeviceToHost,stream));
cudaStreamSynchronize(stream);
double end_time = cv::getTickCount();
std::vector<bbox> boxes;
int boxes_count=0;
int count = std::min((int)*decode_ptr_host,MAX_OBJECTS);
for (int i = 0; i<count;i++)
{
int basic_pos = 1+i*NUM_BOX_ELEMENT;
int keep_flag= decode_ptr_host[basic_pos+6];
if (keep_flag==1)
{
boxes_count+=1;
bbox box;
box.x1 = decode_ptr_host[basic_pos+0];
box.y1 = decode_ptr_host[basic_pos+1];
box.x2 = decode_ptr_host[basic_pos+2];
box.y2 = decode_ptr_host[basic_pos+3];
box.score=decode_ptr_host[basic_pos+4];
int landmark_pos = basic_pos+7;
for (int id = 0; id<CKPT_NUM; id+=1)
{
box.landmarks[2*id]=decode_ptr_host[landmark_pos+2*id];
box.landmarks[2*id+1]=decode_ptr_host[landmark_pos+2*id+1];
}
boxes.push_back(box);
}
}
std::cout<<input_image_path<<" ";
for (int i = 0; i<boxes_count; i++)
{
cv::Rect roi_area(boxes[i].x1,boxes[i].y1,boxes[i].x2-boxes[i].x1,boxes[i].y2-boxes[i].y1);
cv::rectangle(img, roi_area, cv::Scalar(0,255,0), 2);
for (int j= 0; j<CKPT_NUM; j++)
{
cv::Scalar color = cv::Scalar(color_list[j][0], color_list[j][1], color_list[j][2]);
cv::circle(img,cv::Point(boxes[i].landmarks[2*j], boxes[i].landmarks[2*j+1]),2,color,-1);
}
}
auto time_gap = (end_time-begin_time)/cv::getTickFrequency()*1000;
std::cout<<" time_gap: "<<time_gap<<"ms ";
if (index)
{
sumTime+=time_gap;
}
std::cout<<std::endl;
index+=1;
int pos = input_image_path.find_last_of("/");
std::string image_name = input_image_path.substr(pos+1);
cv::imwrite(image_name,img);
}
// destroy the engine
std::cout<<"averageTime:"<<(sumTime/(imagList.size()-1))<<"ms"<<std::endl;
context_det->destroy();
engine_det->destroy();
runtime_det->destroy();
cudaStreamDestroy(stream);
CHECK(cudaFree(affine_matrix_d2i_device));
CHECK(cudaFreeHost(affine_matrix_d2i_host));
CHECK(cudaFree(img_device));
CHECK(cudaFreeHost(img_host));
CHECK(cudaFree(buffers[inputIndex]));
CHECK(cudaFree(buffers[outputIndex]));
CHECK(cudaFree(decode_ptr_device));
delete [] decode_ptr_host;
return 0;
}