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删除 'Code/TestTRTInterDll/TestTRTInterDll.cpp'

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熊继淙 1 year ago
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Code/TestTRTInterDll/TestTRTInterDll.cpp
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// TestTRTInterDll.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 <filesystem>
#include <iostream>
#include <string>
#include "utils.h"
#include "MI_Interface.h"
#include <future>
namespace fs = std::filesystem;
bool buffer2Mat(const MN_VisionImage::MS_ImageParam& _inImg, cv::Mat& _mat);
void test01()
{
std::vector<std::string> imagePath;
imagePath.emplace_back("./imageDatas/daisy.jpg");
imagePath.emplace_back("./imageDatas/dandelion.jpg");
imagePath.emplace_back("./imageDatas/sunflower.jpg");
imagePath.emplace_back("./imageDatas/tulip.jpg");
std::vector<cv::Mat> matImgs;
for (auto _var : imagePath)
{
matImgs.emplace_back(cv::imread(_var, 1));
}
utils::InitParameter params;
params.m_modelType = utils::ME_ModelType::E_RESNET34;
params.class_names = utils::dataSets::flower_labels;
//initParameters.class_names = utils::dataSets::voc20;
params.num_class = 5; // for flower_labels
//initParameters.num_class = 20; // for voc2012
params.batch_size = 1;
params.dst_h = 224;
params.dst_w = 224;
memcpy(&params.mImage.m_data, matImgs[0].data, sizeof(matImgs[0].rows * matImgs[0].cols * matImgs[0].channels()));
params.mImage.m_height = matImgs[0].rows;
params.mImage.m_width = matImgs[0].cols;
params.mImage.m_channels = 3;
params.input_output_names = { "input", "output" };
params.conf_thresh = 0.25f;
params.iou_thresh = 0.45f;
params.save_path = "./imageDatas";
params.meanVec = { 0.406, 0.456, 0.485 };
params.stdVec = { 0.225, 0.224, 0.229 };
MI_VisionInterface* resnet34Ptr = getInterfacePtr(params);
if (!resnet34Ptr)
{
return;
}
utils::InitParameter params1;
params1.m_modelType = utils::ME_ModelType::E_RESNET50;
params1.class_names = utils::dataSets::flower_labels;
//initParameters.class_names = utils::dataSets::voc20;
params1.num_class = 5; // for flower_labels
//initParameters.num_class = 20; // for voc2012
params1.batch_size = 1;
params1.dst_h = 224;
params1.dst_w = 224;
memcpy(&params1.mImage.m_data, matImgs[0].data, sizeof(matImgs[0].rows * matImgs[0].cols * matImgs[0].channels()));
params1.mImage.m_height = matImgs[0].rows;
params1.mImage.m_width = matImgs[0].cols;
params1.mImage.m_channels = 3;
params1.input_output_names = { "input", "output" };
params1.conf_thresh = 0.25f;
params1.iou_thresh = 0.45f;
params1.save_path = "./imageDatas";
params1.meanVec = { 0.406, 0.456, 0.485 };
params1.stdVec = { 0.225, 0.224, 0.229 };
MI_VisionInterface* resnet50Ptr = getInterfacePtr(params1);
if (!resnet50Ptr)
{
return;
}
// 线程测试，初始化在一个线程，推理在另外一个线程内
std::string onnxFile = "./imageDatas/resnet34_0407.onnx";
std::string onnxFile1 = "./imageDatas/resnet50.onnx";
#if 0
bool bRet = false;
bRet = resnetPtr->initEngine(onnxFile);
if (!bRet)
{
return;
}
bRet = resnetPtr->check();
if (!bRet)
{
return;
}
std::vector<MR_Result> detectResVec;
bRet = resnetPtr->doTRTInfer(matImgs, &detectResVec, nullptr);
if (!bRet)
{
return;
}
#endif // 0
// 初始化线程
auto initRes = std::async(std::launch::async, [&] {
bool bRet = false;
// 加载第一个模型初始化并推理
bRet = resnet34Ptr->initEngine(onnxFile);
if (!bRet)
{
return false;
}
// 加载第二个模型初始化并推理
bRet = resnet50Ptr->initEngine(onnxFile1);
if (!bRet)
{
return false;
}
return true;
});
initRes.wait();
auto initRes1 = std::async(std::launch::async, [&] {
bool bRet = false;
bRet = resnet34Ptr->check();
if (!bRet)
{
return false;
}
std::vector<utils::MR_Result> detectResVec1;
bRet = resnet34Ptr->doTRTInfer(matImgs, &detectResVec1, nullptr);
if (!bRet)
{
return false;
}
bRet = resnet50Ptr->check();
if (!bRet)
{
return false;
}
std::vector<utils::MR_Result> detectResVec2;
bRet = resnet50Ptr->doTRTInfer(matImgs, &detectResVec2, nullptr);
if (!bRet)
{
return false;
}
return true;
});
//initRes1.wait();
auto initRes2 = std::async(std::launch::async, [&] {
bool bRet = false;
bRet = resnet34Ptr->check();
if (!bRet)
{
return false;
}
std::vector<utils::MR_Result> detectResVec1;
bRet = resnet34Ptr->doTRTInfer(matImgs, &detectResVec1, nullptr);
if (!bRet)
{
return false;
}
bRet = resnet50Ptr->check();
if (!bRet)
{
return false;
}
std::vector<utils::MR_Result> detectResVec2;
bRet = resnet50Ptr->doTRTInfer(matImgs, &detectResVec2, nullptr);
if (!bRet)
{
return false;
}
return true;
});
//initRes2.wait();
}
void test02()
{
cv::Mat img = cv::imread("./imageDatas/daisy.jpg", 1);
MN_VisionImage::MS_ImageParam bufImg((uchar*)img.data, img.cols, img.rows, MN_VisionImage::ME_ImageType::E_RGB);
cv::Mat convertImg;
bool bRet = buffer2Mat(bufImg, convertImg);
cv::imshow("src", img);
cv::imshow("image", convertImg);
cv::waitKey(0);
}
int main()
{
test01();
system("pause");
return 0;
}
// 运行程序: Ctrl + F5 或调试 >“开始执行(不调试)”菜单
// 调试程序: F5 或调试 >“开始调试”菜单
bool buffer2Mat(const MN_VisionImage::MS_ImageParam& _inImg, cv::Mat& _mat)
{
uchar* pBuf = _inImg.m_data.get(); //获取图像数据首地址
int nW = _inImg.m_width;
int nH = _inImg.m_height;
int nChannel = _inImg.m_channels;
if (pBuf == nullptr || nW <= 1 || nH <= 1)
{
// LOG_ERROR("convert buffer to mat, input image error. \n");
return false;
}
if (_inImg.mImgType == MN_VisionImage::ME_ImageType::E_GRAY)
{
_mat = cv::Mat(nH, nW, CV_8UC1, pBuf);
}
else if (_inImg.mImgType == MN_VisionImage::ME_ImageType::E_RGBA)
{
_mat = cv::Mat(nH, nW, CV_8UC4, pBuf);
}
else
{
_mat = cv::Mat(nH, nW, CV_8UC3, pBuf);
}
if (_mat.data == nullptr || _mat.cols <= 1 || _mat.rows <= 1)
{
// LOG_ERROR("convert buffer to mat, convert image failed. \n");
return false;
}
return true;
}
// 0.3 * 0.3 - 12寸晶圆， 一次性识别9颗 -- 测试推理时间
/*
1. 芯片分类：正常，边缘，蓝膜
2. 对于正常的芯片进行检测--针对划痕和脏污分两个模型/或者一个模型；
3. 针对针测点检测 -- 传统算法；
*/
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