OpenCV中使用vulkan 进行dnn模块推理。

核心代码仅3行 

	m_model = readNetFromDarknet(m_modelConfig, m_modelWeights);
	m_model.setPreferableBackend(DNN_BACKEND_VKCOM);
	m_model.setPreferableTarget(DNN_TARGET_VULKAN);

以下代码为核心代码，用于opencv加载darknet模型：

#pragma once
#ifndef  __DETECTION_H__
#define  __DETECTION_H__

#include <opencv2/opencv.hpp>
#include <opencv2/dnn.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/highgui.hpp>
#include <string.h>
#include <vector>
#include <fstream>

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

class Detection
{
public:
	//构造、析构函数
	Detection();
	Detection( const char* szModelConfig, const char* szModelWeights, const char* szClassFile=NULL);
	bool InitData(const char* szModelConfig, const char* szModelWeights, const char* szClassFile);
	~Detection();
	//初始化函数
	void Initialize(int width, int height);
	//读取网络模型
	void ReadModel();
	//行人与车辆检测
	bool Detecting(Mat frame);
	//获取网络输出层名称
	vector<String> GetOutputsNames();
	//对输出进行处理，使用NMS选出最合适的框
	void PostProcess();
	//画检测结果
	void Drawer();
	//画出检测框和相关信息
	void DrawBoxes(int classId, float conf, int left, int top, int right, int bottom);
	//获取Mat对象
	Mat GetFrame();
	//获取图像宽度
	int GetResWidth();
	//获取图像高度
	int GetResHeight();
	void SetGPU(bool bUseGPU) { m_bUseGPU = bUseGPU; }

private:
	//图像属性
	int m_width;			//图像宽度
	int m_height;			//图像高度
	//网络处理相关
	Net m_model;			//网络模型
	Mat m_frame;			//每一帧
	Mat m_blob;				//从每一帧创建一个4D的blob用于网络输入
	vector<Mat> m_outs;		//网络输出
	vector<float> m_confs;	//置信度
	vector<Rect> m_boxes;	//检测框左上角坐标、宽、高
	vector<int> m_classIds;	//类别id
	vector<int> m_perfIndx;	//非极大阈值处理后边界框的下标
	//检测超参数
	int m_inpWidth;			//网络输入图像宽度
	int m_inpHeight;		//网络输入图像高度
	float m_confThro;		//置信度阈值
	float m_NMSThro;		//NMS非极大抑制阈值
	vector<string> m_classes; //类别名称

	bool	m_bUseGPU = false;

private:
	//内存释放
	void Dump();
private:
	string m_classesFile = "../data/objdetect.names";
	String m_modelConfig = "../data/objdetect.cfg";
	String m_modelWeights = "../data/objdetect.pattern";

	bool m_bReady = false;
};

#endif

#include "Detection.h"

using namespace cv;
using namespace dnn;


//构造函数，成员变量初始化
Detection::Detection()
{
	//图像属性
	m_width = 0;
	m_height = 0;
	m_inpWidth = 416;
	m_inpHeight = 416;

	//其他成员变量
	m_confThro = 0.25;
	m_NMSThro = 0.4;

	网络模型加载
	//ReadModel();
}


//析构函数
Detection::~Detection()
{
	Dump();
}


//内存释放
void Detection::Dump()
{
	//网络输出相关清零
	m_outs.clear();
	m_boxes.clear();
	m_confs.clear();
	m_classIds.clear();
	m_perfIndx.clear();
}


//初始化函数
void Detection::Initialize(int width, int height)
{
	//图像属性
	m_width = width;
	m_height = height;
}

Detection::Detection( const char* szModelConfig, const char* szModelWeights, const char* szClassFile)
{


	m_bReady=InitData(szModelConfig, szModelWeights, szClassFile);

}

bool Detection::InitData(const char* szModelConfig, const char* szModelWeights, const char* szClassFile)
{

	m_classesFile = szClassFile;
	m_modelConfig = szModelConfig;
	m_modelWeights = szModelWeights;
	ReadModel();

	return true;

}
//读取网络模型和类别
void Detection::ReadModel()
{	

    	//加载类别名
	if (!m_classesFile.empty())
	{
		ifstream ifs(m_classesFile.c_str());
		string line;
		while (getline(ifs, line)) m_classes.push_back(line);
	}

	//加载网络模型
	m_model = readNetFromDarknet(m_modelConfig, m_modelWeights);

	if (m_bUseGPU)
	{
		m_model.setPreferableBackend(DNN_BACKEND_VKCOM);
		m_model.setPreferableTarget(DNN_TARGET_VULKAN);



		

		
	}
	else
	{
		m_model.setPreferableBackend(DNN_BACKEND_OPENCV);
		//m_model.setPreferableTarget(DNN_TARGET_CPU);
		m_model.setPreferableTarget(DNN_TARGET_OPENCL);  // opencl

	}
}


//行人与车辆检测
bool Detection::Detecting(Mat frame)
{
    	m_frame = frame.clone();

	//创建4D的blob用于网络输入
	blobFromImage(m_frame, m_blob, 1 / 255.0,Size(m_inpWidth, m_inpHeight), Scalar(0, 0, 0), true, false);

	//设置网络输入
	m_model.setInput(m_blob);

	//前向预测得到网络输出，forward需要知道输出层，这里用了一个函数找到输出层
	m_model.forward(m_outs, GetOutputsNames());

	//使用非极大抑制NMS删除置信度较低的边界框
	PostProcess();
	//画检测框
    //Drawer();

	return true;
}


//获取网络输出层名称
vector<String> Detection::GetOutputsNames()
{
	static vector<String> names;
	if (names.empty())
	{
		//得到输出层索引号
		vector<int> outLayers = m_model.getUnconnectedOutLayers();
		
		//得到网络中所有层名称
		vector<String> layersNames = m_model.getLayerNames();
		
		//获取输出层名称
		names.resize(outLayers.size());
		for (int i = 0; i < outLayers.size(); ++i)
			names[i] = layersNames[outLayers[i] - 1];
	}
	return names;
}


//使用非极大抑制NMS去除置信度较低的边界框
void Detection::PostProcess()
{
	for (int num = 0; num < m_outs.size(); num++)
	{
		Point Position;
		double confidence;

		//得到每个输出的数据
		float* data = (float*)m_outs[num].data;
		for (int j = 0; j < m_outs[num].rows; j++, data += m_outs[num].cols)
		{
			//得到该输出的所有类别的
			Mat scores = m_outs[num].row(j).colRange(5, m_outs[num].cols);

			//获取最大置信度对应的值和位置
			minMaxLoc(scores, 0, &confidence, 0, &Position);
			
			//对置信度大于阈值的边界框进行相关计算和保存
			if (confidence > m_confThro)
			{
				//data[0],data[1],data[2],data[3]都是相对于原图像的比例
				int centerX = (int)(data[0] * m_width);
				int centerY = (int)(data[1] * m_height);
				int width = (int)(data[2] * m_width);
				int height = (int)(data[3] * m_height);
				int left = centerX - width / 2;
				int top = centerY - height / 2;
				//保存信息
				m_classIds.push_back(Position.x);
				m_confs.push_back((float)confidence);
				m_boxes.push_back(Rect(left, top, width, height));
			}
		}
	}
	//非极大值抑制，以消除具有较低置信度的冗余重叠框
	NMSBoxes(m_boxes, m_confs, m_confThro, m_NMSThro, m_perfIndx);
}


//画出检测结果
void Detection::Drawer()
{
	//获取所有最佳检测框信息
	for (int i = 0; i < m_perfIndx.size(); i++)
	{
		int idx = m_perfIndx[i];
		Rect box = m_boxes[idx];
		DrawBoxes(m_classIds[idx], m_confs[idx], box.x, box.y,
			box.x + box.width, box.y + box.height);
	}
}


//画出检测框和相关信息
void Detection::DrawBoxes(int classId, float conf, int left, int top, int right, int bottom)
{
	//画检测框
	rectangle(m_frame, Point(left, top), Point(right, bottom), Scalar(255, 178, 50), 3);

	//该检测框对应的类别和置信度
	string label = format("%.2f", conf);
	if (!m_classes.empty())
	{
		CV_Assert(classId < (int)m_classes.size());
		label = m_classes[classId] + ":" + label;
	}

	//将标签显示在检测框顶部
	int baseLine;
	Size labelSize = getTextSize(label, FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);
	top = max(top, labelSize.height);
	rectangle(m_frame, Point(left, top - round(1.5*labelSize.height)), Point(left + round(1.5*labelSize.width), top + baseLine), Scalar(255, 255, 255), FILLED);
	putText(m_frame, label, Point(left, top), FONT_HERSHEY_SIMPLEX, 0.75, Scalar(0, 0, 0), 1);
}


//获取Mat对象
Mat Detection::GetFrame()
{
	return m_frame;
}


//获取结果图像宽度
int Detection::GetResWidth()
{
	return m_width;
}


//获取结果图像高度
int Detection::GetResHeight()
{
	return m_height;
}

标签：width,int,dnn,height,Detection,OpenCV,model,vulkan,data
来源： https://blog.csdn.net/znsoft/article/details/117382586