yolov8数据标注、模型训练到模型部署全过程-编程知识

文章目录

一、数据标注（x-anylabeling）
- 1. 安装方式
- - 1.1 直接通过Releases安装
  - 1.2 clone源码后采用终端运行
- 2. 如何使用
二、模型训练
三、模型部署
- 3.1 onnx转engine
- 3.2 c++调用engine模型
- - 3.2.1 main_tensorRT.cpp
  - 3.2.2 segmentationModel.cpp

一、数据标注（x-anylabeling）

1. 安装方式

1.1 直接通过Releases安装

https://github.com/CVHub520/X-AnyLabeling/releases
根据自己的系统选择CPU还是GPU推理以及Linux或者win系统
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1.2 clone源码后采用终端运行

https://github.com/CVHub520/X-AnyLabeling
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在项目中打开终端安装所需的环境依赖：

pip install -r requirements.txt

安装完成后运行app.py

python anylabeling/app.py

注：当直接使用exe运行失败的话，最好就是采用第二种方式，可以通过终端知道报错的原因。

2. 如何使用

注：由于x-anylabeling是可以使用自己训练后的模型，然后自动生成标注数据的，但是第一次的话就需要自己标注数据。

首次打开可以进行语言的选择
打开需要标注数据的文件夹
点击矩形框或者使用快捷键（R）
直接进行标记并自己定义类
打开左上角“文件”选项，点击自动保存
保存的文件类型是json，你可以自己选择导出的类型。
yolov8训练的标签格式是txt，通常我标记的时候都是选择导出voc（xml格式）。
注：你也可以直接导出yolo标签格式

将标记好的数据进行训练的格式进行划分

import os
import random
import shutil# 输入文件夹路径和划分比例
folder_path = input("请输入文件夹路径：")
train_ratio = float(input("请输入训练集比例："))# 检查文件夹是否存在
if not os.path.exists(folder_path):print("文件夹不存在！")exit()# 获取所有jpg和txt文件
jpg_files = [file for file in os.listdir(folder_path) if file.endswith(".jpg")]
txt_files = [file for file in os.listdir(folder_path) if file.endswith(".txt")]# 检查文件数量是否相等
if len(jpg_files) != len(txt_files):print("图片和标签数量不匹配！")exit()# 打乱文件顺序
random.shuffle(jpg_files)# 划分训练集和验证集
train_size = int(len(jpg_files) * train_ratio)
train_jpg = jpg_files[:train_size]
train_txt = [file.replace(".jpg", ".txt") for file in train_jpg]
val_jpg = jpg_files[train_size:]
val_txt = [file.replace(".jpg", ".txt") for file in val_jpg]# 创建文件夹和子文件夹
if not os.path.exists("images/train"):os.makedirs("images/train")
if not os.path.exists("images/val"):os.makedirs("images/val")
if not os.path.exists("labels/train"):os.makedirs("labels/train")
if not os.path.exists("labels/val"):os.makedirs("labels/val")# 复制文件到目标文件夹
for file in train_jpg:shutil.copy(os.path.join(folder_path, file), "images/train")
for file in train_txt:shutil.copy(os.path.join(folder_path, file), "labels/train")
for file in val_jpg:shutil.copy(os.path.join(folder_path, file), "images/val")
for file in val_txt:shutil.copy(os.path.join(folder_path, file), "labels/val")print("处理完成！")

生成images和labels的文件夹
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我这里没有加入测试集，只使用了训练集和验证集

训练好自己的模型后
将生成的.onnx和.yaml放在一个路径下

yaml文件的配置，注意这个类不要用数字，会被认定为int型，然后导致无法生成框，也就是报错。这个类的名称和个数一定要与训练的时候进行配置的一样

就是这里面的class names，这里填的什么，那么上面配置的yaml文件也要一样。

二、模型训练

yolov8的源代码：
https://github.com/ultralytics/ultralytics

首先安装yolov8运行所依赖的库
```
pip install ultralytics
```
根据代码进行
首先下载一个预训练模型：https://docs.ultralytics.com/tasks/detect/

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from ultralytics import YOLO# Load a models
model = YOLO("D:\MyProject\yolov8s.pt")  # load a pretrained model (recommended for training)# Use the model
model.train(data="D:\MyProject\data\myData.yaml", epochs=3)  # train the model
metrics = model.val()  # evaluate model performance on the validation set
results = model("https://ultralytics.com/images/bus.jpg")  # predict on an image
path = model.export(format="onnx")  # export the model to ONNX format

将前面标记好的数据放在路径下，配置好myData.yaml，如下：
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三、模型部署

3.1 onnx转engine

首先把前面训练好的模型pt通过model.export(format="onnx")转换成onnx。
工程文件如下：
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环境配置（一）：需要配置anaconda、opencv、cuda以及tensorRT。
tensorRT的安装与使用：链接

环境配置（二）：
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环境配置（三）：

opencv_world455.lib
myelin64_1.lib
nvinfer.lib
nvonnxparser.lib
nvparsers.lib
nvinfer_plugin.lib
cuda.lib
cudadevrt.lib
cudart_static.lib

由于x64中Release里面的依赖项太大，所以进行了分卷上传
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yolov8使用tensorRT部署的环境依赖项（一）
yolov8使用tensorRT部署的环境依赖项（二）

将上面两个压缩包下载后放到一个文件夹里面，直接解压001，就可以将两个压缩包里面的依赖项全部解压出来。把解压的dll所有文件复制到/x64/Release这个路径下。

代码执行：

#include <iostream>
#include "logging.h"
#include "NvOnnxParser.h"
#include "NvInfer.h"
#include <fstream>using namespace nvinfer1;
using namespace nvonnxparser;static Logger gLogger;int main(int argc, char** argv) {const char* onnx_filename = "D://TR_YOLOV8_DLL//zy_onnx2engine//models//best.onnx";const char* engine_filename = "D://TR_YOLOV8_DLL//zy_onnx2engine//models//best.engine";// 1 onnx解析器IBuilder* builder = createInferBuilder(gLogger);const auto explicitBatch = 1U << static_cast<uint32_t>(NetworkDefinitionCreationFlag::kEXPLICIT_BATCH);INetworkDefinition* network = builder->createNetworkV2(explicitBatch);nvonnxparser::IParser* parser = nvonnxparser::createParser(*network, gLogger);parser->parseFromFile(onnx_filename, static_cast<int>(Logger::Severity::kWARNING));for (int i = 0; i < parser->getNbErrors(); ++i){std::cout << parser->getError(i)->desc() << std::endl;}std::cout << "successfully load the onnx model" << std::endl;// 2build the engineunsigned int maxBatchSize = 1;builder->setMaxBatchSize(maxBatchSize);IBuilderConfig* config = builder->createBuilderConfig();config->setMaxWorkspaceSize(1 << 20);//config->setMaxWorkspaceSize(128 * (1 << 20));  // 16MBconfig->setFlag(BuilderFlag::kFP16);ICudaEngine* engine = builder->buildEngineWithConfig(*network, *config);// 3serialize ModelIHostMemory* gieModelStream = engine->serialize();std::ofstream p(engine_filename, std::ios::binary);if (!p){std::cerr << "could not open plan output file" << std::endl;return -1;}p.write(reinterpret_cast<const char*>(gieModelStream->data()), gieModelStream->size());gieModelStream->destroy();std::cout << "successfully generate the trt engine model" << std::endl;return 0;
}

完整项目：https://download.csdn.net/download/qq_44747572/88791740

3.2 c++调用engine模型

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这里的两个工程环境部署都跟上面部署的方式一样。进行重复的动作即可

3.2.1 main_tensorRT.cpp

// Xray_test.cpp : 定义控制台应用程序的入口点。
#define _AFXDLL
#include <iomanip>
#include <string>    
#include <fstream>  
#include "opencv2/core/core.hpp"
#include "opencv2/highgui/highgui.hpp"
#include <io.h>
#include "segmentationModel.h"// stuff we know about the network and the input/output blobs
#define input_h 640
#define input_w  640
#define	channel  3
#define classe  2       // 80个类
#define segWidth  160
#define segHeight  160
#define segChannels  32
#define Num_box  34000   //8400    1280 33600MODELDLL predictClasse;
#pragma comment(lib, "..//x64//Release//segmentationModel.lib")using namespace cv;
using namespace std;int main(int argc, char** argv)
{//检测测试string engine_filename = "D://TR_YOLOV8_DLL//zy_Xray_inspection//models//best.engine";string img_filename = "D://TR_YOLOV8_DLL//zy_Xray_inspection//imgs//";predictClasse.LoadYoloV8DetectEngine(engine_filename);string pattern_jpg = img_filename + "*.jpg";  // test_imagesvector<cv::String> image_files;glob(pattern_jpg, image_files);vector<ObjectTR> output;float confTh = 0.25;for (int i = 0; i < image_files.size(); i++){Mat src = imread(image_files[i], 1);Mat dst;clock_t start = clock();predictClasse.YoloV8DetectPredict(src, dst, channel, classe, input_h, input_w, Num_box, confTh, output);clock_t end = clock();std::cout << "总时间：" << end - start << "ms" << std::endl;cv::namedWindow("output.jpg", 0);cv::imshow("output.jpg", dst);cv::waitKey(0);}分割测试//string engine_filename = "D://Users//6536//Desktop//TR_YOLOV8_DLL//zy_Xray_inspection//models//yolov8n-seg.engine";//string img_filename = "D://Users//6536//Desktop//TR_YOLOV8_DLL//zy_Xray_inspectionimgs//bus.jpg";//predictClasse.LoadYoloV8SegEngine(engine_filename);//Mat src = imread(img_filename, 1);//Mat dst;//predictClasse.YoloV8SegPredict(src, dst, channel, classe, input_h, input_w, segChannels, segWidth, segHeight, Num_box);//cv::imshow("output.jpg", dst);//cv::waitKey(0);return 0;
}

3.2.2 segmentationModel.cpp

#include "pch.h"
#include "segmentationModel.h"#define DEVICE 0  // GPU idstatic const float CONF_THRESHOLD = 0.25;
static const float NMS_THRESHOLD = 0.5;
static const float MASK_THRESHOLD = 0.5;
const char* INPUT_BLOB_NAME = "images";
const char* OUTPUT_BLOB_NAME = "output0";//detect
const char* OUTPUT_BLOB_NAME1 = "output1";//maskstatic Logger gLogger;IRuntime* runtimeYolov8Seg;
ICudaEngine* engineYolov8Seg;
IExecutionContext* contextYolov8Seg;MODELDLL::MODELDLL()
{}
MODELDLL::~MODELDLL()
{}
//yolov8检测推理
bool MODELDLL::LoadYoloV8DetectEngine(const std::string& engineName)
{// create a model using the API directly and serialize it to a streamchar* trtModelStream{ nullptr }; //char* trtModelStream==nullptr;  开辟空指针后 要和new配合使用，比如 trtModelStream = new char[size]size_t size{ 0 };//与int固定四个字节不同有所不同,size_t的取值range是目标平台下最大可能的数组尺寸,一些平台下size_t的范围小于int的正数范围,又或者大于unsigned int. 使用Int既有可能浪费，又有可能范围不够大。std::ifstream file(engineName, std::ios::binary);if (file.good()) {std::cout << "load engine success" << std::endl;file.seekg(0, file.end);//指向文件的最后地址size = file.tellg();//把文件长度告诉给size//std::cout << "\nfile:" << argv[1] << " size is";//std::cout << size << "";file.seekg(0, file.beg);//指回文件的开始地址trtModelStream = new char[size];//开辟一个char 长度是文件的长度assert(trtModelStream);//file.read(trtModelStream, size);//将文件内容传给trtModelStreamfile.close();//关闭}else {std::cout << "load engine failed" << std::endl;return 1;}runtimeYolov8Seg = createInferRuntime(gLogger);assert(runtimeYolov8Seg != nullptr);bool didInitPlugins = initLibNvInferPlugins(nullptr, "");engineYolov8Seg = runtimeYolov8Seg->deserializeCudaEngine(trtModelStream, size, nullptr);assert(engineYolov8Seg != nullptr);contextYolov8Seg = engineYolov8Seg->createExecutionContext();assert(contextYolov8Seg != nullptr);delete[] trtModelStream;return true;
}bool MODELDLL::YoloV8DetectPredict(const Mat& src, Mat& dst, const int& channel, const int& classe, const int& input_h, const int& input_w, const int& Num_box, float& CONF_THRESHOLD, vector<ObjectTR>& output)
{cudaSetDevice(DEVICE);if (src.empty()) { std::cout << "image load faild" << std::endl; return 1; }int img_width = src.cols;int img_height = src.rows;std::cout << "宽高：" << img_width << " " << img_height << std::endl;// Subtract mean from imagefloat* data = new float[channel * input_h * input_w];Mat pr_img0, pr_img;std::vector<int> padsize;Mat tempImg = src.clone(); pr_img = preprocess_img(tempImg, input_h, input_w, padsize);       // Resizeint newh = padsize[0], neww = padsize[1], padh = padsize[2], padw = padsize[3];float ratio_h = (float)src.rows / newh;float ratio_w = (float)src.cols / neww;int i = 0;// [1,3,INPUT_H,INPUT_W]//std::cout << "pr_img.step" << pr_img.step << std::endl;clock_t start_p = clock();for (int row = 0; row < input_h; ++row) {uchar* uc_pixel = pr_img.data + row * pr_img.step;//pr_img.step=widthx3 就是每一行有width个3通道的值for (int col = 0; col < input_w; ++col){data[i] = (float)uc_pixel[2] / 255.0;data[i + input_h * input_w] = (float)uc_pixel[1] / 255.0;data[i + 2 * input_h * input_w] = (float)uc_pixel[0] / 255.;uc_pixel += 3;++i;}}//优化一：从30多ms降速到20多，仅提速10ms左右，效果不明显
//#pragma omp parallel for
//    for (int row = 0; row < input_h; ++row) {
//        const uchar* uc_pixel = pr_img.data + row * pr_img.step;
//        int i = row * input_w;
//        for (int col = 0; col < input_w; ++col) {
//            float r = static_cast<float>(uc_pixel[2]) / 255.0f;
//            float g = static_cast<float>(uc_pixel[1]) / 255.0f;
//            float b = static_cast<float>(uc_pixel[0]) / 255.0f;
//            data[i] = r;
//            data[i + input_h * input_w] = g;
//            data[i + 2 * input_h * input_w] = b;
//            uc_pixel += 3;
//            ++i;
//        }
//    }clock_t end_p = clock();std::cout << "preprocess_img时间：" << end_p - start_p << "ms" << std::endl;// Run inferencestatic const int OUTPUT_SIZE = Num_box * (classe + 4);//output0float* prob = new float[OUTPUT_SIZE];//for (int i = 0; i < 10; i++) {//计算10次的推理速度//       auto start = std::chrono::system_clock::now();//       doInference(*context, data, prob, prob1, 1);//       auto end = std::chrono::system_clock::now();//       std::cout << std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count() << "ms" << std::endl;//   }//auto start = std::chrono::system_clock::now();clock_t start = clock();//推理int batchSize = 1;const ICudaEngine& engine = (*contextYolov8Seg).getEngine();// Pointers to input and output device buffers to pass to engine.// Engine requires exactly IEngine::getNbBindings() number of buffers.assert(engine.getNbBindings() == 3);void* buffers[3];// In order to bind the buffers, we need to know the names of the input and output tensors.// Note that indices are guaranteed to be less than IEngine::getNbBindings()const int inputIndex = engine.getBindingIndex(INPUT_BLOB_NAME);const int outputIndex = engine.getBindingIndex(OUTPUT_BLOB_NAME);// Create GPU buffers on deviceCHECK(cudaMalloc(&buffers[inputIndex], batchSize * 3 * input_h * input_w * sizeof(float)));//CHECK(cudaMalloc(&buffers[outputIndex], batchSize * OUTPUT_SIZE * sizeof(float)));// cudaMalloc分配内存 cudaFree释放内存 cudaMemcpy或 cudaMemcpyAsync 在主机和设备之间传输数据// cudaMemcpy cudaMemcpyAsync 显式地阻塞传输 显式地非阻塞传输 // Create streamcudaStream_t stream;CHECK(cudaStreamCreate(&stream));// DMA input batch data to device, infer on the batch asynchronously, and DMA output back to hostCHECK(cudaMemcpyAsync(buffers[inputIndex], data, batchSize * 3 * input_h * input_w * sizeof(float), cudaMemcpyHostToDevice, stream));(*contextYolov8Seg).enqueue(batchSize, buffers, stream, nullptr);CHECK(cudaMemcpyAsync(prob, buffers[outputIndex], batchSize * OUTPUT_SIZE * sizeof(float), cudaMemcpyDeviceToHost, stream));cudaStreamSynchronize(stream);// Release stream and bufferscudaStreamDestroy(stream);CHECK(cudaFree(buffers[inputIndex]));CHECK(cudaFree(buffers[outputIndex]));////auto end = std::chrono::system_clock::now();clock_t end = clock();//std::cout << "推理时间：" << std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count() << "ms" << std::endl;std::cout << "推理时间：" << end - start << "ms" << std::endl;std::vector<int> classIds;//结果id数组std::vector<float> confidences;//结果每个id对应置信度数组std::vector<cv::Rect> boxes;//每个id矩形框// 处理boxint net_length = classe + 4;cv::Mat out1 = cv::Mat(net_length, Num_box, CV_32F, prob);//start = std::chrono::system_clock::now();start = clock();for (int i = 0; i < Num_box; i++) {//输出是1*net_length*Num_box;所以每个box的属性是每隔Num_box取一个值，共net_length个值cv::Mat scores = out1(Rect(i, 4, 1, classe)).clone();Point classIdPoint;double max_class_socre;minMaxLoc(scores, 0, &max_class_socre, 0, &classIdPoint);max_class_socre = (float)max_class_socre;if (max_class_socre >= CONF_THRESHOLD) {float x = (out1.at<float>(0, i) - padw) * ratio_w;  //cxfloat y = (out1.at<float>(1, i) - padh) * ratio_h;  //cyfloat w = out1.at<float>(2, i) * ratio_w;  //wfloat h = out1.at<float>(3, i) * ratio_h;  //hint left = MAX((x - 0.5 * w), 0);int top = MAX((y - 0.5 * h), 0);int width = (int)w;int height = (int)h;if (width <= 0 || height <= 0) { continue; }classIds.push_back(classIdPoint.y);confidences.push_back(max_class_socre);boxes.push_back(Rect(left, top, width, height));}}//执行非最大抑制以消除具有较低置信度的冗余重叠框（NMS）std::vector<int> nms_result;cv::dnn::NMSBoxes(boxes, confidences, CONF_THRESHOLD, NMS_THRESHOLD, nms_result);ObjectTR result;Rect holeImgRect(0, 0, src.cols, src.rows);for (int i = 0; i < nms_result.size(); ++i) {int idx = nms_result[i];result.classid = classIds[idx];result.prob = confidences[idx];result.rect = boxes[idx] & holeImgRect;output.push_back(result);}//end = std::chrono::system_clock::now();end = clock();std::cout << "后处理时间：" << end - start << "ms" << std::endl;Mat finalImg = src.clone();DrawPredDetect(finalImg, classe, output);dst = finalImg.clone();// Destroy the engine/* contextYolov8Seg->destroy();engineYolov8Seg->destroy();runtimeYolov8Seg->destroy();*/delete data;delete prob;return 0;}void MODELDLL::DrawPredDetect(const Mat& img, const int& classe, std::vector<ObjectTR> result) {//生成随机颜色std::vector<Scalar> color;srand(time(0));for (int i = 0; i < classe; i++) {int b = rand() % 256;int g = rand() % 256;int r = rand() % 256;color.push_back(Scalar(b, g, r));}for (int i = 0; i < result.size(); i++) {int left, top;left = result[i].rect.x;top = result[i].rect.y;int color_num = i;rectangle(img, result[i].rect, Scalar(0, 0, 255), 2, 8);//rectangle(img, result[i].box, color[result[i].id], 2, 8);char label[100];sprintf(label, "%d:%.2f", result[i].classid, result[i].prob);int baseLine;Size labelSize = getTextSize(label, FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);top = max(top, labelSize.height);/*putText(img, label, Point(left, top), FONT_HERSHEY_SIMPLEX, 1, color[result[i].id], 2);*/putText(img, label, Point(left, top), FONT_HERSHEY_SIMPLEX, 1, Scalar(0, 0, 255), 2);}}// yolov8分割推理
bool MODELDLL::LoadYoloV8SegEngine(const std::string& engineName)
{// create a model using the API directly and serialize it to a streamchar* trtModelStream{ nullptr }; //char* trtModelStream==nullptr;  开辟空指针后 要和new配合使用，比如 trtModelStream = new char[size]size_t size{ 0 };//与int固定四个字节不同有所不同,size_t的取值range是目标平台下最大可能的数组尺寸,一些平台下size_t的范围小于int的正数范围,又或者大于unsigned int. 使用Int既有可能浪费，又有可能范围不够大。std::ifstream file(engineName, std::ios::binary);if (file.good()) {std::cout << "load engine success" << std::endl;file.seekg(0, file.end);//指向文件的最后地址size = file.tellg();//把文件长度告诉给size//std::cout << "\nfile:" << argv[1] << " size is";//std::cout << size << "";file.seekg(0, file.beg);//指回文件的开始地址trtModelStream = new char[size];//开辟一个char 长度是文件的长度assert(trtModelStream);//file.read(trtModelStream, size);//将文件内容传给trtModelStreamfile.close();//关闭}else {std::cout << "load engine failed" << std::endl;return 1;}runtimeYolov8Seg = createInferRuntime(gLogger);assert(runtimeYolov8Seg != nullptr);bool didInitPlugins = initLibNvInferPlugins(nullptr, "");engineYolov8Seg = runtimeYolov8Seg->deserializeCudaEngine(trtModelStream, size, nullptr);assert(engineYolov8Seg != nullptr);contextYolov8Seg = engineYolov8Seg->createExecutionContext();assert(contextYolov8Seg != nullptr);delete[] trtModelStream;return true;
}bool MODELDLL::YoloV8SegPredict(const Mat& src, Mat& dst, const int& channel, const int& classe, const int& input_h, const int& input_w, const int& segChannels, const int& segWidth, const int& segHeight, const int& Num_box)
{cudaSetDevice(DEVICE);if (src.empty()) { std::cout << "image load faild" << std::endl; return 1; }int img_width = src.cols;int img_height = src.rows;std::cout << "宽高：" << img_width << " " << img_height << std::endl;// Subtract mean from imagefloat* data = new float[channel * input_h * input_w];Mat pr_img0, pr_img;std::vector<int> padsize;Mat tempImg = src.clone();pr_img = preprocess_img(tempImg, input_h, input_w, padsize);       // Resizeint newh = padsize[0], neww = padsize[1], padh = padsize[2], padw = padsize[3];float ratio_h = (float)src.rows / newh;float ratio_w = (float)src.cols / neww;int i = 0;// [1,3,INPUT_H,INPUT_W]//std::cout << "pr_img.step" << pr_img.step << std::endl;for (int row = 0; row < input_h; ++row) {uchar* uc_pixel = pr_img.data + row * pr_img.step;//pr_img.step=widthx3 就是每一行有width个3通道的值for (int col = 0; col < input_w; ++col){data[i] = (float)uc_pixel[2] / 255.0;data[i + input_h * input_w] = (float)uc_pixel[1] / 255.0;data[i + 2 * input_h * input_w] = (float)uc_pixel[0] / 255.;uc_pixel += 3;++i;}}// Run inferencestatic const int OUTPUT_SIZE = Num_box * (classe + 4 + segChannels);//output0static const int OUTPUT_SIZE1 = segChannels * segWidth * segHeight;//output1float* prob = new float[OUTPUT_SIZE];float* prob1 = new float[OUTPUT_SIZE1];//for (int i = 0; i < 10; i++) {//计算10次的推理速度//       auto start = std::chrono::system_clock::now();//       doInference(*context, data, prob, prob1, 1);//       auto end = std::chrono::system_clock::now();//       std::cout << std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count() << "ms" << std::endl;//   }auto start = std::chrono::system_clock::now();//推理int batchSize = 1;const ICudaEngine& engine = (*contextYolov8Seg).getEngine();// Pointers to input and output device buffers to pass to engine.// Engine requires exactly IEngine::getNbBindings() number of buffers.assert(engine.getNbBindings() == 3);void* buffers[3];// In order to bind the buffers, we need to know the names of the input and output tensors.// Note that indices are guaranteed to be less than IEngine::getNbBindings()const int inputIndex = engine.getBindingIndex(INPUT_BLOB_NAME);const int outputIndex = engine.getBindingIndex(OUTPUT_BLOB_NAME);const int outputIndex1 = engine.getBindingIndex(OUTPUT_BLOB_NAME1);// Create GPU buffers on deviceCHECK(cudaMalloc(&buffers[inputIndex], batchSize * 3 * input_h * input_w * sizeof(float)));//CHECK(cudaMalloc(&buffers[outputIndex], batchSize * OUTPUT_SIZE * sizeof(float)));CHECK(cudaMalloc(&buffers[outputIndex1], batchSize * OUTPUT_SIZE1 * sizeof(float)));// cudaMalloc分配内存 cudaFree释放内存 cudaMemcpy或 cudaMemcpyAsync 在主机和设备之间传输数据// cudaMemcpy cudaMemcpyAsync 显式地阻塞传输 显式地非阻塞传输 // Create streamcudaStream_t stream;CHECK(cudaStreamCreate(&stream));// DMA input batch data to device, infer on the batch asynchronously, and DMA output back to hostCHECK(cudaMemcpyAsync(buffers[inputIndex], data, batchSize * 3 * input_h * input_w * sizeof(float), cudaMemcpyHostToDevice, stream));(*contextYolov8Seg).enqueue(batchSize, buffers, stream, nullptr);CHECK(cudaMemcpyAsync(prob, buffers[outputIndex], batchSize * OUTPUT_SIZE * sizeof(float), cudaMemcpyDeviceToHost, stream));CHECK(cudaMemcpyAsync(prob1, buffers[outputIndex1], batchSize * OUTPUT_SIZE1 * sizeof(float), cudaMemcpyDeviceToHost, stream));cudaStreamSynchronize(stream);// Release stream and bufferscudaStreamDestroy(stream);CHECK(cudaFree(buffers[inputIndex]));CHECK(cudaFree(buffers[outputIndex]));CHECK(cudaFree(buffers[outputIndex1]));//auto end = std::chrono::system_clock::now();std::cout << "推理时间：" << std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count() << "ms" << std::endl;std::vector<int> classIds;//结果id数组std::vector<float> confidences;//结果每个id对应置信度数组std::vector<cv::Rect> boxes;//每个id矩形框std::vector<cv::Mat> picked_proposals;  //后续计算mask// 处理boxint net_length = classe + 4 + segChannels;cv::Mat out1 = cv::Mat(net_length, Num_box, CV_32F, prob);start = std::chrono::system_clock::now();for (int i = 0; i < Num_box; i++) {//输出是1*net_length*Num_box;所以每个box的属性是每隔Num_box取一个值，共net_length个值cv::Mat scores = out1(Rect(i, 4, 1, classe)).clone();Point classIdPoint;double max_class_socre;minMaxLoc(scores, 0, &max_class_socre, 0, &classIdPoint);max_class_socre = (float)max_class_socre;if (max_class_socre >= CONF_THRESHOLD) {cv::Mat temp_proto = out1(Rect(i, 4 + classe, 1, segChannels)).clone();picked_proposals.push_back(temp_proto.t());float x = (out1.at<float>(0, i) - padw) * ratio_w;  //cxfloat y = (out1.at<float>(1, i) - padh) * ratio_h;  //cyfloat w = out1.at<float>(2, i) * ratio_w;  //wfloat h = out1.at<float>(3, i) * ratio_h;  //hint left = MAX((x - 0.5 * w), 0);int top = MAX((y - 0.5 * h), 0);int width = (int)w;int height = (int)h;if (width <= 0 || height <= 0) { continue; }classIds.push_back(classIdPoint.y);confidences.push_back(max_class_socre);boxes.push_back(Rect(left, top, width, height));}}//执行非最大抑制以消除具有较低置信度的冗余重叠框（NMS）std::vector<int> nms_result;cv::dnn::NMSBoxes(boxes, confidences, CONF_THRESHOLD, NMS_THRESHOLD, nms_result);std::vector<cv::Mat> temp_mask_proposals;std::vector<OutputSeg> output;Rect holeImgRect(0, 0, src.cols, src.rows);for (int i = 0; i < nms_result.size(); ++i) {int idx = nms_result[i];OutputSeg result;result.id = classIds[idx];result.confidence = confidences[idx];result.box = boxes[idx] & holeImgRect;output.push_back(result);temp_mask_proposals.push_back(picked_proposals[idx]);}// 处理maskMat maskProposals;for (int i = 0; i < temp_mask_proposals.size(); ++i)maskProposals.push_back(temp_mask_proposals[i]);Mat protos = Mat(segChannels, segWidth * segHeight, CV_32F, prob1);Mat matmulRes = (maskProposals * protos).t();//n*32 32*25600 A*B是以数学运算中矩阵相乘的方式实现的，要求A的列数等于B的行数时Mat masks = matmulRes.reshape(output.size(), { segWidth, segHeight});//n*160*160std::vector<Mat> maskChannels;cv::split(masks, maskChannels);Rect roi(int((float)padw / input_w * segWidth), int((float)padh / input_h * segHeight), int(segWidth - padw / 2), int(segHeight - padh / 2));for (int i = 0; i < output.size(); ++i) {Mat dest, mask;cv::exp(-maskChannels[i], dest);//sigmoiddest = 1.0 / (1.0 + dest);//160*160dest = dest(roi);resize(dest, mask, cv::Size(src.cols, src.rows), INTER_NEAREST);//crop----截取box中的mask作为该box对应的maskRect temp_rect = output[i].box;mask = mask(temp_rect) > MASK_THRESHOLD;output[i].boxMask = mask;}end = std::chrono::system_clock::now();std::cout << "后处理时间：" << std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count() << "ms" << std::endl;Mat finalImg = src.clone();DrawPred(finalImg, classe, output);dst = finalImg.clone();// Destroy the enginecontextYolov8Seg->destroy();engineYolov8Seg->destroy();runtimeYolov8Seg->destroy();delete data;delete prob;delete prob1;return 0;}void MODELDLL::DrawPred(const Mat& img, const int& classe, std::vector<OutputSeg> result) {//生成随机颜色std::vector<Scalar> color;srand(time(0));for (int i = 0; i < classe; i++) {int b = rand() % 256;int g = rand() % 256;int r = rand() % 256;color.push_back(Scalar(b, g, r));}Mat mask = img.clone();for (int i = 0; i < result.size(); i++) {int left, top;left = result[i].box.x;top = result[i].box.y;int color_num = i;rectangle(img, result[i].box, color[result[i].id], 2, 8);mask(result[i].box).setTo(color[result[i].id], result[i].boxMask);char label[100];sprintf(label, "%d:%.2f", result[i].id, result[i].confidence);//std::string label = std::to_string(result[i].id) + ":" + std::to_string(result[i].confidence);int baseLine;Size labelSize = getTextSize(label, FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);top = max(top, labelSize.height);putText(img, label, Point(left, top), FONT_HERSHEY_SIMPLEX, 1, color[result[i].id], 2);}addWeighted(img, 0.5, mask, 0.8, 1, img); //将mask加在原图上面}