背景：

        最近由于项目原因，需要用C++做一些目标检测的任务，就捣鼓一下YOLOv5，发现部署确实很方便，将YOLOv5模型转为onnx模型后，可以用OpenCV的dnn.readNetFromONNX读取该模型，接着就是输入预处理和输出结果解析的事情。

       然而，当我将tf15训练得到的FasterRCNN模型并利用tf2onnx成功转为onnx模型后，却不能用OpenCV读取，报出以下错误，而onnxruntime可以成功调用该模型。

cv2.error: OpenCV(4.5.4) D:\a\opencv-python\opencv-python\opencv\modules\dnn\src\onnx\onnx_graph_simplifier.cpp:692: 
error: (-210:Unsupported format or combination of formats) 
Unsupported data type: BOOL in function 'cv::dnn::dnn4_v20211004::getMatFromTensor'

大概意思可能是：不支持的数据类型从而导致不支持该操作吧

程序：

       所以，只好采用onnxruntime的C++接口进行模型调用，废话不多说，直接上代码：

//FRCNN.h
#pragma once

#include<iostream>
#include<fstream>
#include<numeric>
#include<opencv.hpp>
#include"../commonStruct.h"
#include"../BaseShipDetectionModel.h"
#include <onnxruntime_cxx_api.h>
#
class FRCNN:public BaseShipDetectionModel
{
public:
	FRCNN();
	~FRCNN();

	bool readModel(std::string &netPath, bool isCuda=false);
	bool DetectShip(cv::Mat &SrcImg, std::vector<Output> &output);
	void drawPredShip(cv::Mat &img, std::vector<Output>& result);

	
private:
	enum OutputFlag
	{
		//NOTHING,
		BOXES,
		SCORES,
		CLSIDS
	};


	Ort::Env *OnnxEnv;
	Ort::SessionOptions OnnxSessionOp;
	Ort::Session* OnnxSession;
	Ort::AllocatorWithDefaultOptions allocator;
	Ort::MemoryInfo *memory_info;
	cv::Size2f factor;
	const int netWidth = 1067;
	const int netHeight = 600;
	float nmsThreshold = 0.45;
	float boxThreshold = 0.31;
	float classThreshold = 0.25;
	size_t num_input_nodes, num_output_nodes;
	std::vector<const char*> input_node_names, output_node_names;
	std::vector<OutputFlag> output_node_namesFlag;
	
	//Ort::Value *input_tensor;
	std::vector<int64_t> input_node_dims = { netHeight, netWidth,3 };
	size_t input_tensor_size = 3 * netHeight * netWidth;

	void parseOnnxOutput(std::vector<Ort::Value>&inputTensors, std::vector<Output> &results);
	
};

//FRCNN.cpp
#include "FRCNN.h"

using namespace std;
using namespace cv;
using namespace dnn;

#if 1
FRCNN::FRCNN()
{
	num_input_nodes = 0;
	num_output_nodes = 0;
	//Ort::Env env(ORT_LOGGING_LEVEL_VERBOSE, "test");
	
	OnnxEnv = new Ort::Env(ORT_LOGGING_LEVEL_WARNING, "test");
	//Ort::MemoryInfo memory_info = Ort::MemoryInfo::CreateCpu(OrtArenaAllocator, OrtMemTypeDefault);
	memory_info=new Ort::MemoryInfo(Ort::MemoryInfo::CreateCpu(OrtArenaAllocator, OrtMemTypeDefault));
	//memory_info =new Ort::MemoryInfo::CreateCpu(OrtArenaAllocator, OrtMemTypeDefault);
	if (OnnxEnv == nullptr) {
		std::cout << "new Error for " << VNAME(OnnxEnv) << std::endl;
		throw OnnxEnv;
	}
	if (memory_info == nullptr) {
		std::cout << "new Error for " << VNAME(memory_info) << std::endl;
		throw memory_info;
	}
	OnnxSessionOp.SetIntraOpNumThreads(5);
	OnnxSessionOp.SetGraphOptimizationLevel(GraphOptimizationLevel::ORT_ENABLE_ALL);
	flag_onnx = true;
	/*cout << OnnxEnv << '\t' << *OnnxEnv << endl;
	cout << &env << '\t' << env << endl;*/
	//test(*OnnxEnv, env, OnnxEnv);
	//std::cout<<env.operator const OrtEnv *
}
FRCNN::~FRCNN()
{
	if(OnnxEnv!=nullptr)
		delete OnnxEnv;
	if (OnnxSession != nullptr)
		delete OnnxSession;
}

bool FRCNN::readModel(string &netPath, bool isCuda) {
	try 
	{		
		std::ifstream f(netPath.c_str());
		std::cout << f.good() << std::endl;
		
		std::wstring wnetPath = std::wstring(netPath.begin(), netPath.end());
		
		OnnxSession=new Ort::Session((*OnnxEnv), wnetPath.c_str(), OnnxSessionOp);
		if (OnnxSession == nullptr) {
			std::cout << "new Error for " << VNAME(OnnxSession) << std::endl;
			throw OnnxSession;
		}
		
		// print model input layer (node names, types, shape etc.)
		// print number of model input nodes
		num_input_nodes = OnnxSession->GetInputCount();
		num_output_nodes = OnnxSession->GetOutputCount();
		
		for (int i = 0; i < num_input_nodes; ++i) {
			input_node_names.push_back(OnnxSession->GetInputName(0, allocator));
			//= { "image:0"};
			std::cout << input_node_names[i] << std::endl;
		}
		for (int i = 0; i < num_output_nodes; ++i) {
			char* name = OnnxSession->GetOutputName(i, allocator);
			output_node_names.push_back(name);
			if (strstr(name, "boxes") != nullptr) {
				output_node_namesFlag.push_back(BOXES);
			}
			else if (strstr(name, "scores") != nullptr) {
				output_node_namesFlag.push_back(SCORES);
			}
			else if (strstr(name, "labels") != nullptr) {
				output_node_namesFlag.push_back(CLSIDS);
			}
			else {
				//output_node_namesFlag.push_back(NOTHING);
				throw(name);
			}
			//= { "output/boxes:0", "output/scores:0","output/labels:0"};
			std::cout << output_node_names[i] << std::endl;
		}
		
	}
	catch (const std::exception& e) {
		return false;
	}

	return true;
}


bool FRCNN::DetectShip(cv::Mat &SrcImg,  std::vector<Output> &results) {

	if (SrcImg.empty()) {
		std::cout << "empty image error!" << std::endl;
		return false;
	}
	int col = SrcImg.cols;
	int row = SrcImg.rows;
	int i, j;
	results.clear();

	Mat netInputImg,Img;
	
	std::vector<int> indices;
	Output result;
	//netInputImg.create(SrcImg.size,SrcImg.depth());
	
	//SrcImg.copyTo(netInputImg);
	cv::resize(SrcImg, Img, cv::Size(netWidth, netHeight), 0.0, 0.0, cv::INTER_LINEAR);
	factor= cv::Size2f((float)SrcImg.cols / netWidth, (float)SrcImg.rows / netHeight);
	try {
		netInputImg.create(cv::Size(netWidth, netHeight), CV_32FC3);//allocate the continuous Mat
		Img.convertTo(netInputImg, CV_32F);
		assert(netInputImg.isContinuous());//
		
		Ort::Value input_tensor = Ort::Value::CreateTensor<float>(*memory_info, (float*)netInputImg.data, input_tensor_size, input_node_dims.data(), 3);
		assert(input_tensor.IsTensor());
		std::vector<Ort::Value> ort_inputs;
		ort_inputs.push_back(std::move(input_tensor));
							
		//Run the Detection
		std::vector<Ort::Value> output_tensors = OnnxSession->Run(Ort::RunOptions{ nullptr }, input_node_names.data(), ort_inputs.data(), ort_inputs.size(), output_node_names.data(), 3);
		
		parseOnnxOutput(output_tensors,  results);
	}
	catch (...) {
		std::cout << "prediction error!" << std::endl;
		return false;
	}

	if (results.size())
		return true;
	else
		return false;

}

void FRCNN::drawPredShip(cv::Mat & img, std::vector<Output>& result)
{
	BaseShipDetectionModel::drawPredShip(img, result);
}

void FRCNN::parseOnnxOutput(std::vector<Ort::Value>& inputTensors, std::vector<Output>& results)
{
	std::vector<int64_t> classIds;
	std::vector<float> confidences;
	std::vector<cv::Rect> boxes;
	int i, j;

	std::vector<int64_t> shape;
	size_t eleCount;
	size_t DimCount;
	int xmin, xmax, ymin, ymax;
	for (i = 0; i < num_output_nodes; ++i) {
		Ort::TensorTypeAndShapeInfo Info = inputTensors[i].GetTensorTypeAndShapeInfo();
		//std::cout << ":GetDimensionsCount:" << Info.GetDimensionsCount() << '\t';
		shape = Info.GetShape();
		DimCount = shape.size();
		//std::cout << i << "shape:";
		//for (int j = 0; j < shape.size(); ++j) {
		//	std::cout << shape[j] << '\t';
		//}
		eleCount = Info.GetElementCount();
		//std::cout << ":GetElementCount:" << eleCount << '\t';

		ONNXTensorElementDataType onnxType = Info.GetElementType();
		void* ptr = nullptr;
		//std::cout << "GetElementType:" << onnxType << '\t' << std::endl;
		switch (onnxType) {
		case ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT:// maps to c type float
		{
			ptr = inputTensors[i].GetTensorMutableData<float>();
		}
		break;
		case ONNX_TENSOR_ELEMENT_DATA_TYPE_INT64:// maps to c type int64_t
		{
			ptr = inputTensors[i].GetTensorMutableData<int64_t>();
		}
		break;
		default:
			throw("Unknown DataType!");
			break;
		}

		//std::cout << sizeof(ptr) << sizeof((float*)ptr) << sizeof((int64_t*)ptr)<<sizeof((uint8_t*)ptr)<< sizeof((uint16_t*)ptr)<< sizeof((int32_t*)ptr) << std::endl;
		//= { "output/boxes:0", "output/scores:0","output/labels:0"};
		/*
	output[0]//(44->(11,4))
	output[1]//(11->(11))
	output[2]//(11->(11))
	*/
		switch (output_node_namesFlag[i]) {
		case BOXES://xmin,ymin,xmax,ymax
		{
			float* p_boxes = (float*)ptr;
			for (j = 0; j < eleCount; j += 4) {
				xmin = p_boxes[j] *factor.width;
				ymin = p_boxes[j + 1] * factor.height;
				xmax = p_boxes[j + 2] *factor.width;
				ymax = p_boxes[j + 3] *factor.height;

				boxes.push_back(cv::Rect(xmin, ymin, xmax - xmin, ymax - ymin));
			}
			break;
		}
		case SCORES:
		{
			float* p_scores = (float*)ptr;
			for (j = 0; j < eleCount; j++) {
				confidences.push_back(p_scores[j]);
			}
			break;
		}
		case CLSIDS:
		{
			int64_t* p_clsids = (int64_t*)ptr;
			for (j = 0; j < eleCount; j++) {
				classIds.push_back(p_clsids[j]);
			}
			break;
		}	
		}

	}
	Output result;
	assert((boxes.size() == classIds.size())&&(boxes.size()==confidences.size()));
	for (i = 0; i < boxes.size(); ++i) {
		//j = indices[i];
		if (confidences[i] > boxThreshold) {
			result.ClsId = classIds[i]-1;//except background
			result.confidence = confidences[i];
			result.box = boxes[i];
			results.push_back(result);
		}
	}
		
}

#endif

代码调用顺序：

1.readModel//读取模型

2.DetectShip//检测目标，函数名根据需要修改

3.drawPredShip//画图，函数名根据需要修改

该代码对应FasterRCNN模型下载链接： fasterRCNN.model-深度学习文档类资源-CSDN文库

 后记：

本文仅为onnxruntime的C++调用作个笔记，特别是对输入数据准备与输出数据解析这两部分，如有疑问，请不吝指教！