【模型部署】c++部署yolov5使用openvino
c++部署yolov5使用openvino
·
1. 推理方式
- CPU推理
- GPU推理（要求电脑具备核显，即CPU中有嵌入显卡）
2. openvino依赖项下载
https://github.com/openvinotoolkit/openvino/releases
直接解压（随便放到哪个路径）
环境配置
********（openvino所在路径）\runtime\bin\intel64\Release
******** \runtime\3rdparty\tbb\bin
3. c++推理
3.1 Zy_OV_YOLOV5
3.1.1 Zy_OV_YOLOV5.cpp
// Zy_OV_YOLOV5.cpp : 定义 DLL 应用程序的导出函数。
//
#include "stdafx.h"
#include "Zy_OV_YOLOV5.h"
// 这是导出变量的一个示例
ZY_OV_YOLOV5_API int nZy_OV_YOLOV5=0;
// 这是导出函数的一个示例。
ZY_OV_YOLOV5_API int fnZy_OV_YOLOV5(void)
{
return 42;
}
// 这是已导出类的构造函数。
// 有关类定义的信息，请参阅 Zy_OV_YOLOV5.h
CZy_OV_YOLOV5::CZy_OV_YOLOV5()
{
return;
}
extern "C"
{
YoloModel::YoloModel()
{
detector = new Detector;
}
/*bool YoloModel::init(string xml_path, string bin_path, double cof_threshold, double nms_area_threshold, int classNum)
{
bool initflag = detector->init(xml_path, bin_path, cof_threshold, nms_area_threshold, classNum);
return initflag;
}
bool YoloModel::YoloInfer(const Mat& srcImg,vector<Object>& vecObj)
{
bool inferflag = detector->yoloInfer(srcImg, vecObj);
return inferflag;
}*/
bool YoloModel::init(string xml_path, string bin_path, double cof_threshold, double nms_area_threshold)
{
bool initflag = detector->init(xml_path, bin_path, cof_threshold, nms_area_threshold);
return initflag;
}
bool YoloModel::YoloInfer(const Mat& srcImg, vector<Rect>& vecObj)
{
bool inferflag = detector->yoloInfer(srcImg, vecObj);
return inferflag;
}
}
3.1.2 detector.cpp
#include "detector.h"
Detector::Detector(){}
Detector::~Detector(){}
bool Detector::parse_yolov5(const Blob::Ptr &blob, int net_grid, float cof_thr, vector<Rect>& o_rect, vector<float>& o_rect_cof, vector<int>& classids)
{
vector<int> anchors = get_anchors(net_grid);
LockedMemory<const void> blobMapped = as<MemoryBlob>(blob)->rmap();
const float *output_blob = blobMapped.as<float *>();
int item_size = _classNum + 5;
for(int n=0; n<3; ++n)
{
for(int i=0; i<net_grid; ++i)
{
for(int j=0; j<net_grid; ++j)
{
double box_prob = output_blob[n*net_grid*net_grid*item_size + i*net_grid*item_size + j*item_size + 4];
box_prob = sigmoid(box_prob);
if(box_prob < cof_thr)
continue;
//注意此处输出为中心点坐标,需要转化为角点坐标
double x = output_blob[n*net_grid*net_grid*item_size + i*net_grid*item_size + j*item_size + 0];
double y = output_blob[n*net_grid*net_grid*item_size + i*net_grid*item_size + j*item_size + 1];
double w = output_blob[n*net_grid*net_grid*item_size + i*net_grid*item_size + j*item_size + 2];
double h = output_blob[n*net_grid*net_grid*item_size + i*net_grid*item_size + j *item_size+ 3];
double max_prob = 0;
int idx = 0;
for(int t=5; t< item_size; ++t){
double tp = output_blob[n*net_grid*net_grid*item_size + i*net_grid*item_size + j*item_size + t];
tp = sigmoid(tp);
if(tp > max_prob){
max_prob = tp;
idx = t;
}
}
float cof = box_prob * max_prob;
if(cof < cof_thr)
continue;
/*x = (sigmoid(x) * 2 - 0.5 + j) * 640.0f / net_grid;
y = (sigmoid(y) * 2 - 0.5 + i) * 640.0f / net_grid;*/
x = (sigmoid(x) * 2 - 0.5 + j) * static_cast<float>(imgSize) / net_grid;
y = (sigmoid(y) * 2 - 0.5 + i) * static_cast<float>(imgSize) / net_grid;
int left = n * 2;
int right = n * 2 + 1;
w = pow(sigmoid(w) * 2, 2) * anchors[left];
h = pow(sigmoid(h) * 2, 2) * anchors[right];
double r_x = x - w / 2;
double r_y = y - h / 2;
Rect rect = Rect(round(r_x), round(r_y), round(w), round(h));
o_rect.push_back(rect);
o_rect_cof.push_back(cof);
classids.push_back(idx - 5);
}
}
}
if (o_rect.size() == 0)
{
return false;
}
return true;
}
//bool Detector::init(string xml_path, string bin_path, double cof_threshold, double nms_area_threshold, int classNum)
//{
// _xml_path = xml_path;
// _bin_path = bin_path;
// _cof_threshold = cof_threshold;
// _nms_area_threshold = nms_area_threshold;
// InferenceEngine::Core ie;
// auto cnnNetwork = ie.ReadNetwork(_xml_path, _bin_path);
//
//
// // 输入设置
// InputsDataMap inputInfo(cnnNetwork.getInputsInfo());
// InputInfo::Ptr& input = inputInfo.begin()->second;
// _input_name = inputInfo.begin()->first;
// cout << "input_name: " << _input_name << endl;
// input->setPrecision(Precision::FP32);
// input->getInputData()->setLayout(InferenceEngine::Layout::NCHW);
// ICNNNetwork::InputShapes inputShapes = cnnNetwork.getInputShapes();
//
// int InputImgSize = inputShapes["images"].back();
// imgSize = InputImgSize;
// _classNum = classNum;
//
// cout << "InputImgSize:" << imgSize << endl;
// if (imgSize != 160 && imgSize != 640 && imgSize != 1280)
// {
// return false;
// }
//
//
// SizeVector& inSizeVector = inputShapes.begin()->second;
// cnnNetwork.reshape(inputShapes);
// // 输出设置
// _outputinfo = OutputsDataMap(cnnNetwork.getOutputsInfo());
// for (auto &output : _outputinfo)
// {
// cout << "output_name: " << output.first << endl;
// output.second->setPrecision(Precision::FP32);
// }
// //_network = ie.LoadNetwork(cnnNetwork, "CPU");
// _network = ie.LoadNetwork(cnnNetwork, "GPU");
// return true;
//}
//bool Detector::process_frame(Mat& inframe, vector<Object>& detected_objects)
//{
// resize(inframe, inframe, Size(imgSize, imgSize));
// cvtColor(inframe, inframe, COLOR_BGR2RGB);
// size_t img_size = imgSize * imgSize;
//
// InferenceEngine::InferRequest infer_request = _network.CreateInferRequest();
// Blob::Ptr frameBlob = infer_request.GetBlob(_input_name);
//
// InferenceEngine::LockedMemory<void> blobMapped = InferenceEngine::as<InferenceEngine::MemoryBlob>(frameBlob)->wmap();
// float* blob_data = blobMapped.as<float*>();
//
// for(size_t row = 0; row < imgSize; row++) {
// for(size_t col = 0; col < imgSize; col++) {
// for(size_t ch = 0; ch < 3; ch++) {
// blob_data[img_size * ch + row * imgSize + col] = float(inframe.at<Vec3b>(row, col)[ch]) / 255.0f;
// }
// }
// }
//
// infer_request.Infer();
// vector<Rect> origin_rect;
// vector<float> origin_rect_cof;
// vector<int> classids;
//
//
// int s[3];
// if (640 == imgSize)
// {
// s[0] = 80;
// s[1] = 40;
// s[2] = 20;
// }
// else if (160 == imgSize)
// {
// s[0] = 20;
// s[1] = 10;
// s[2] = 5;
// }
//
// int i = 0;
// for (auto &output : _outputinfo) {
// auto output_name = output.first;
// // cout << "output_name: " << output_name << endl;
// Blob::Ptr blob = infer_request.GetBlob(output_name);
// parse_yolov5(blob, s[i], _cof_threshold, origin_rect, origin_rect_cof, classids);
// ++i;
// if (i == 3)
// {
// break;
// }
// }
//
// vector<int> final_id;
// dnn::NMSBoxes(origin_rect, origin_rect_cof, _cof_threshold, _nms_area_threshold, final_id);
// for(int i=0; i<final_id.size(); ++i)
// {
// Rect resize_rect = origin_rect[final_id[i]];
// detected_objects.push_back(Object{ origin_rect_cof[final_id[i]], resize_rect,classids[final_id[i]] });
// }
//
// return true;
//}
//bool Detector::yoloInfer(const Mat& srcImg, vector<Object>& vecObj)
//{
// Mat src_copy;
// int width = srcImg.cols;
// int height = srcImg.rows;
// int channel = srcImg.channels();
// double scale = min(static_cast<double>(imgSize) / width, static_cast<double>(imgSize) / height);
// int w = round(width * scale);
// int h = round(height * scale);
// resize(srcImg, src_copy, Size(w, h));
// int top = 0, bottom = 0, left = 0, right = 0;
// if (w > h)
// {
// top = (w - h) / 2;
// bottom = (w - h) - top;
// }
// else if (h > w)
// {
// left = (h - w) / 2;
// right = (h - w) - left;
// }
// copyMakeBorder(src_copy, src_copy, top, bottom, left, right, BORDER_CONSTANT, Scalar(114, 114, 114));
// vector<Object> detected_objects;
// auto start = chrono::high_resolution_clock::now();
// process_frame(src_copy, detected_objects); // infer
// auto end = chrono::high_resolution_clock::now();
// std::chrono::duration<double> diff = end - start;
// cout << "use " << diff.count() << " s" << endl;
// for (int i = 0; i < detected_objects.size(); ++i) {
// int xmin = max(detected_objects[i].rect.x - left, 0);
// int ymin = max(detected_objects[i].rect.y - top, 0);
// int width = detected_objects[i].rect.width;
// int height = detected_objects[i].rect.height;
// Rect rect(int(xmin / scale), int(ymin / scale), int(width / scale), int(height / scale));
// vecObj.push_back(Object{ detected_objects[i].prob,rect,detected_objects[i].classid });
// }
//
// return true;
//}
bool Detector::init(string xml_path, string bin_path, double cof_threshold, double nms_area_threshold)
{
int classNum = 1; // 外面增加类的时候 此处需修改
_xml_path = xml_path;
_bin_path = bin_path;
_cof_threshold = cof_threshold;
_nms_area_threshold = nms_area_threshold;
InferenceEngine::Core ie;
auto cnnNetwork = ie.ReadNetwork(_xml_path, _bin_path);
// 输入设置
InputsDataMap inputInfo(cnnNetwork.getInputsInfo());
InputInfo::Ptr& input = inputInfo.begin()->second;
_input_name = inputInfo.begin()->first;
cout << "input_name: " << _input_name << endl;
input->setPrecision(Precision::FP32);
input->getInputData()->setLayout(InferenceEngine::Layout::NCHW);
ICNNNetwork::InputShapes inputShapes = cnnNetwork.getInputShapes();
int InputImgSize = inputShapes["images"].back();
imgSize = InputImgSize;
_classNum = classNum;
cout << "InputImgSize:" << imgSize << endl;
if (imgSize != 160 && imgSize != 640 && imgSize != 1280)
{
return false;
}
SizeVector& inSizeVector = inputShapes.begin()->second;
cnnNetwork.reshape(inputShapes);
// 输出设置
_outputinfo = OutputsDataMap(cnnNetwork.getOutputsInfo());
for (auto& output : _outputinfo)
{
cout << "output_name: " << output.first << endl;
output.second->setPrecision(Precision::FP32);
}
//_network = ie.LoadNetwork(cnnNetwork, "CPU");
_network = ie.LoadNetwork(cnnNetwork, "GPU");
return true;
}
bool Detector::process_frame(Mat& inframe, vector<Rect>& detected_objects)
{
resize(inframe, inframe, Size(imgSize, imgSize));
cvtColor(inframe, inframe, COLOR_BGR2RGB);
size_t img_size = imgSize * imgSize;
InferenceEngine::InferRequest infer_request = _network.CreateInferRequest();
Blob::Ptr frameBlob = infer_request.GetBlob(_input_name);
InferenceEngine::LockedMemory<void> blobMapped = InferenceEngine::as<InferenceEngine::MemoryBlob>(frameBlob)->wmap();
float* blob_data = blobMapped.as<float*>();
for (size_t row = 0; row < imgSize; row++) {
for (size_t col = 0; col < imgSize; col++) {
for (size_t ch = 0; ch < 3; ch++) {
blob_data[img_size * ch + row * imgSize + col] = float(inframe.at<Vec3b>(row, col)[ch]) / 255.0f;
}
}
}
infer_request.Infer();
vector<Rect> origin_rect;
vector<float> origin_rect_cof;
vector<int> classids;
int s[3];
if (640 == imgSize)
{
s[0] = 80;
s[1] = 40;
s[2] = 20;
}
else if (160 == imgSize)
{
s[0] = 20;
s[1] = 10;
s[2] = 5;
}
int i = 0;
for (auto& output : _outputinfo) {
auto output_name = output.first;
// cout << "output_name: " << output_name << endl;
Blob::Ptr blob = infer_request.GetBlob(output_name);
parse_yolov5(blob, s[i], _cof_threshold, origin_rect, origin_rect_cof, classids);
++i;
if (i == 3)
{
break;
}
}
vector<int> final_id;
dnn::NMSBoxes(origin_rect, origin_rect_cof, _cof_threshold, _nms_area_threshold, final_id);
for (int i = 0; i < final_id.size(); ++i)
{
Rect resize_rect = origin_rect[final_id[i]];
detected_objects.push_back(resize_rect);
}
return true;
}
bool Detector::yoloInfer(const Mat& srcImg, vector<Rect>& vecObj)
{
Mat src_copy;
int width = srcImg.cols;
int height = srcImg.rows;
int channel = srcImg.channels();
double scale = min(static_cast<double>(imgSize) / width, static_cast<double>(imgSize) / height);
int w = round(width * scale);
int h = round(height * scale);
resize(srcImg, src_copy, Size(w, h));
int top = 0, bottom = 0, left = 0, right = 0;
if (w > h)
{
top = (w - h) / 2;
bottom = (w - h) - top;
}
else if (h > w)
{
left = (h - w) / 2;
right = (h - w) - left;
}
copyMakeBorder(src_copy, src_copy, top, bottom, left, right, BORDER_CONSTANT, Scalar(114, 114, 114));
vector<Rect> detected_objects;
auto start = chrono::high_resolution_clock::now();
process_frame(src_copy, detected_objects); // infer
auto end = chrono::high_resolution_clock::now();
std::chrono::duration<double> diff = end - start;
cout << "use " << diff.count() << " s" << endl;
for (int i = 0; i < detected_objects.size(); ++i) {
int xmin = max(detected_objects[i].x - left, 0);
int ymin = max(detected_objects[i].y - top, 0);
int width = detected_objects[i].width;
int height = detected_objects[i].height;
Rect rect(int(xmin / scale), int(ymin / scale), int(width / scale), int(height / scale));
vecObj.push_back(rect);
}
return true;
}
double Detector::sigmoid(double x){
return (1 / (1 + exp(-x)));
}
vector<int> Detector::get_anchors(int net_grid){
vector<int> anchors(6);
int a80[6] = {10, 13, 16, 30, 33, 23};
int a40[6] = {30, 61, 62, 45, 59, 119};
int a20[6] = {116, 90, 156, 198, 373, 326};
if(net_grid == 80){
anchors.insert(anchors.begin(), a80, a80 + 6);
}
else if(net_grid == 40){
anchors.insert(anchors.begin(), a40, a40 + 6);
}
else if(net_grid == 20){
anchors.insert(anchors.begin(), a20, a20 + 6);
}
return anchors;
}
3.1.3 dllmain.cpp
// dllmain.cpp : 定义 DLL 应用程序的入口点。
#include "stdafx.h"
BOOL APIENTRY DllMain( HMODULE hModule,
DWORD ul_reason_for_call,
LPVOID lpReserved
)
{
switch (ul_reason_for_call)
{
case DLL_PROCESS_ATTACH:
case DLL_THREAD_ATTACH:
case DLL_THREAD_DETACH:
case DLL_PROCESS_DETACH:
break;
}
return TRUE;
}
3.1.4 stdafx.cpp
// stdafx.cpp : 只包括标准包含文件的源文件
// Zy_OV_YOLOV5.pch 将作为预编译头
// stdafx.obj 将包含预编译类型信息
#include "stdafx.h"
// TODO: 在 STDAFX.H 中
// 引用任何所需的附加头文件，而不是在此文件中引用
3.1.5 Zy_OV_YOLOV5.h
// 下列 ifdef 块是创建使从 DLL 导出更简单的
// 宏的标准方法。此 DLL 中的所有文件都是用命令行上定义的 ZY_OV_YOLOV5_EXPORTS
// 符号编译的。在使用此 DLL 的
// 任何其他项目上不应定义此符号。这样，源文件中包含此文件的任何其他项目都会将
// ZY_OV_YOLOV5_API 函数视为是从 DLL 导入的，而此 DLL 则将用此宏定义的
// 符号视为是被导出的。
#ifdef ZY_OV_YOLOV5_EXPORTS
#define ZY_OV_YOLOV5_API __declspec(dllexport)
#else
#define ZY_OV_YOLOV5_API __declspec(dllimport)
#endif
#include "detector.h"
// 此类是从 Zy_OV_YOLOV5.dll 导出的
class ZY_OV_YOLOV5_API CZy_OV_YOLOV5 {
public:
CZy_OV_YOLOV5(void);
// TODO: 在此添加您的方法。
};
extern ZY_OV_YOLOV5_API int nZy_OV_YOLOV5;
ZY_OV_YOLOV5_API int fnZy_OV_YOLOV5(void);
extern "C"
{
class ZY_OV_YOLOV5_API YoloModel
{
public:
YoloModel();
/*bool init(string xml_path, string bin_path, double cof_threshold, double nms_area_threshold, int classNum);
bool YoloInfer(const Mat& srcImg, vector<Object>& vecObj);*/
bool init(string xml_path, string bin_path, double cof_threshold, double nms_area_threshold);
bool YoloInfer(const Mat& srcImg, vector<Rect>& vecObj);
private:
Detector* detector;
};
}
3.1.6 detector.h
#ifndef DETECTOR_H
#define DETECTOR_H
#include <opencv2/opencv.hpp>
#include <inference_engine.hpp>
#include <opencv2/dnn/dnn.hpp>
#include <iostream>
#include <chrono>
#include <cmath>
#include <stdlib.h>
using namespace std;
using namespace cv;
using namespace InferenceEngine;
typedef struct {
float prob;
cv::Rect rect;
int classid;
}Object;
class Detector
{
public:
Detector();
~Detector();
//bool init(string xml_path, string bin_path, double cof_threshold, double nms_area_threshold,int classNum);
//bool process_frame(Mat& inframe, vector<Object> &detected_objects);
//bool yoloInfer(const Mat& srcImg, vector<Object>& vecObj);
bool init(string xml_path, string bin_path, double cof_threshold, double nms_area_threshold);
bool process_frame(Mat& inframe, vector<Rect>& detected_objects);
bool yoloInfer(const Mat& srcImg, vector<Rect>& vecObj);
private:
int imgSize = 640;
int _classNum = 2;
double sigmoid(double x);
vector<int> get_anchors(int net_grid);
bool parse_yolov5(const Blob::Ptr &blob, int net_grid, float cof_threshold, vector<Rect>& o_rect, vector<float>& o_rect_cof, vector<int>& classids);
ExecutableNetwork _network;
OutputsDataMap _outputinfo;
string _input_name;
string _xml_path;
string _bin_path;
double _cof_threshold = 0.1;
double _nms_area_threshold = 0.5;
};
#endif
3.1.7 targetver.h
#pragma once
// 包括 SDKDDKVer.h 将定义可用的最高版本的 Windows 平台。
// 如果要为以前的 Windows 平台生成应用程序，请包括 WinSDKVer.h，并将
// WIN32_WINNT 宏设置为要支持的平台，然后再包括 SDKDDKVer.h。
#include <SDKDDKVer.h>
3.1.8 stdafx.h
// stdafx.h : 标准系统包含文件的包含文件，
// 或是经常使用但不常更改的
// 特定于项目的包含文件
//
#pragma once
#include "targetver.h"
#define WIN32_LEAN_AND_MEAN // 从 Windows 头文件中排除极少使用的信息
// Windows 头文件:
#include <windows.h>
// TODO: 在此处引用程序需要的其他头文件
3.2 BatchTest
3.2.1 BatchTest.cpp
// BatchTest.cpp : 此文件包含 "main" 函数。程序执行将在此处开始并结束。
//
#include <iostream>
#include "Zy_OV_YOLOV5.h"
#pragma comment(lib,"..//x64//Release//Zy_OV_YOLOV5.lib")
int main()
{
YoloModel yolomodel;
string xml_path = "ScratchModel.xml";
string bin_path = "ScratchModel.bin";
/*string xml_path = "ScratchModel.xml";
string bin_path = "ScratchModel.bin";*/
double scratch_th = 0.25;
//bool initflag = yolomodel.init(xml_path, bin_path, scratch_th, 0.45, 2); // 分类是2
bool initflag = yolomodel.init(xml_path, bin_path, scratch_th, 0.45);
cout << "scratch th == " << scratch_th << endl;
if (initflag == true)
{
cout << "Yolo初始化成功" << endl;
}
string picPath = "./ScratchImg"; // classifyImg 分类数据
vector<cv::String> vecCVStr = {};
cv::glob(picPath, vecCVStr);
for (int i = 0; i < vecCVStr.size(); i++)
{
string each_pic_path = vecCVStr[i].c_str();
string picFileName = each_pic_path.substr(picPath.length()+1, each_pic_path.length() - picPath.length());
Mat src = imread(each_pic_path,IMREAD_GRAYSCALE);
//vector<Object> ResObj = {};
transpose(src, src);
//yolomodel.YoloInfer(src, ResObj);
vector<Rect> ResObj = {};
//transpose(src, src);
yolomodel.YoloInfer(src, ResObj);
Mat colorMat = src.clone();
if (src.channels() == 1)
{
cvtColor(src, colorMat, COLOR_GRAY2BGR);
}
if (ResObj.size() == 0)
{
system("pause");
}
if (true)
{
float maxokprob = 0.0f;
float maxngprob = 0.0f;
/*for (int i = 0; i < ResObj.size(); i++)
{
if (ResObj[i].classid == 0)
{
maxngprob = std::max(ResObj[i].prob, maxngprob);
}
if (ResObj[i].classid == 1)
{
maxokprob = std::max(ResObj[i].prob, maxokprob);
}
}*/
if (maxokprob > maxngprob)
{
cv::putText(colorMat, "OK", Point(5, 30), 3, 1.2, Scalar(0, 255, 0));
cv::putText(colorMat, "prob:" + to_string(maxokprob), Point(5, 50), 3, 0.6, Scalar(0, 255, 0));
imwrite("D:\\Xray\\ok\\" + picFileName, colorMat);
}
else
{
cv::putText(colorMat, "NG", Point(5, 30), 3, 1.2, Scalar(0, 0, 255));
cv::putText(colorMat, "prob:" + to_string(maxngprob), Point(5, 50), 3, 0.6, Scalar(0, 0, 255));
imwrite("D:\\Xray\\ng\\" + picFileName, colorMat);
}
}
//if (ResObj.size() == 1)
//{
// if (ResObj[0].classid == 0)
// {
// cv::putText(colorMat, "NG", Point(5, 30), 3, 1.2, Scalar(0, 0, 255));
// imwrite("D:\\Xray\\ng\\" + picFileName, colorMat);
// //rectangle(colorMat, ResObj[i].rect, Scalar(0, 0, 255), 1, LINE_8, 0);
// }
// if (ResObj[0].classid == 1)
// {
// cv::putText(colorMat, "OK", Point(5, 30), 3, 1.2, Scalar(0, 255, 0));
// imwrite("D:\\Xray\\ok\\" + picFileName, colorMat);
// //rectangle(colorMat, ResObj[i].rect, Scalar(0, 255, 0), 1, LINE_8, 0);
// }
//}
//resize(colorMat, colorMat, Size(0, 0), 0.5, 0.5);
//transpose(colorMat, colorMat);
namedWindow("colorMat", WINDOW_GUI_NORMAL);
imshow("colorMat", colorMat);
waitKey(0);
}
}
3.2.2 Zy_OV_YOLOV5.h
// 下列 ifdef 块是创建使从 DLL 导出更简单的
// 宏的标准方法。此 DLL 中的所有文件都是用命令行上定义的 ZY_OV_YOLOV5_EXPORTS
// 符号编译的。在使用此 DLL 的
// 任何其他项目上不应定义此符号。这样，源文件中包含此文件的任何其他项目都会将
// ZY_OV_YOLOV5_API 函数视为是从 DLL 导入的，而此 DLL 则将用此宏定义的
// 符号视为是被导出的。
#ifdef ZY_OV_YOLOV5_EXPORTS
#define ZY_OV_YOLOV5_API __declspec(dllexport)
#else
#define ZY_OV_YOLOV5_API __declspec(dllimport)
#endif
#include <iostream>
#include <opencv2\opencv.hpp>
using namespace std;
using namespace cv;
// 此类是从 Zy_OV_YOLOV5.dll 导出的
class ZY_OV_YOLOV5_API CZy_OV_YOLOV5 {
public:
CZy_OV_YOLOV5(void);
// TODO: 在此添加您的方法。
};
extern ZY_OV_YOLOV5_API int nZy_OV_YOLOV5;
ZY_OV_YOLOV5_API int fnZy_OV_YOLOV5(void);
typedef struct {
float prob;
cv::Rect rect;
int classid;
} Object;
extern "C"
{
class ZY_OV_YOLOV5_API YoloModel
{
public:
YoloModel();
/*bool init(string xml_path, string bin_path, double cof_threshold, double nms_area_threshold, int classNum);
bool YoloInfer(const Mat& srcImg, vector<Object>& vecObj);*/
bool init(string xml_path, string bin_path, double cof_threshold, double nms_area_threshold);
bool YoloInfer(const Mat& srcImg, vector<Rect>& vecObj);
private:
class Detector* detector;
};
}
4. 模型onnx转换xml和bin
openvino转换： 下载链接
5. dll部署环境
腾讯云面向开发者汇聚海量精品云计算使用和开发经验，营造开放的云计算技术生态圈。
更多推荐
1
0- 0
已为社区贡献8条内容
所有评论(0)