Yolov4的onnx模型C++推导

1、环境：win10+cuda11.4+onnxruntime-gpu1.10+opencv3.4.4+vs2019 c++

GPU:本人使用的NVIDIA GeForce GTX 1650  4GB显存。

2、yolov4模型的训练参考：

https://edu.51cto.com/course/22877.html

https://github.com/AlexeyAB/darknet

darknet模型转onnx模型参考：

https://github.com/Tianxiaomo/pytorch-YOLOv4

3、C++调用测试：

#include"deeplearning_yolo_interface.h"
#include <vector>
#include <stdlib.h>
#include <iostream> 
#include <codecvt>
#include <numeric>
#include "encoding.h"


static std::string wstringToUtf8(const std::wstring& str)
{
	std::wstring_convert<std::codecvt_utf8<wchar_t>> strCnv;
	return strCnv.to_bytes(str);
}

int main(int argc,char** argv)
{
	std::string modelpath = argv[1];

	std::string image_file = argv[2];

	std::string gpu_id = argv[3];

	std::string yolo_type = argv[4];
	int yoloType = atoi(yolo_type.c_str());

	int gpuId= atoi(gpu_id.c_str());
	//初始化OCR模型
	void* modelHandles = DL_YOLO_LIB_CPP::DL_YOLO_Init(modelpath, gpuId, yoloType);
	if (modelHandles == NULL)
	{
		cout << "DL_YOLO_Init failed !!!" << endl;
		return -1;
	}
	std::string image_dir = image_file;

	cv::String pattern = image_dir + "/*.jpg";
	vector<cv::String> image_list;
	cv::glob(pattern, image_list, false);
	if (image_list.empty())
	{
		cout << "images list is null,please check your image dir !!!" << endl;
		return -1;
	}
	for (int i = 0; i < image_list.size(); i++)
	{
		std::cout << "[" << i + 1 << "/" << image_list.size() << "]" << image_list[i] << endl;
		cv::Mat inputImage = cv::imread(image_list[i]);
		std::vector<detect_rlt> detResult;

		int flg = DL_YOLO_LIB_CPP::DL_YOLO_Detect(modelHandles, inputImage, detResult);

		for (auto& rlt: detResult)
		{
			cv::Rect tmp_rect;
			tmp_rect.x = rlt.box.x;
			tmp_rect.y = rlt.box.y;
			tmp_rect.width = rlt.box.w;
			tmp_rect.height = rlt.box.h;
			cv::rectangle(inputImage, tmp_rect, cv::Scalar(0, 0, 255), 1);
			float score = rlt.box.score;
			int class_id = rlt.class_id;
		}

		/*std::wstring yoloResult = DL_YOLO_LIB_CPP::DL_YOLO_Detect(modelHandles, inputImage);
		cout << "DL_OCR_Rec_bankcard_id=" << utf8_to_gbk(wstr_to_utf8(yoloResult)) << endl;*/
	}

	//模型释放
	DL_YOLO_LIB_CPP::DL_YOLO_Free(modelHandles);

	return 0;
}

yolo目标检测 C++ onnx使用示例工程分享：

链接： https://pan.baidu.com/s/1Ga_x8auJ9220Nubx0Mw6gA

提取码：kpe9

自己要学习Onnx的C++推导，可学习以下课程：

https://edu.51cto.com/course/30388.html