yolov8数据标注、模型训练到模型部署全过程
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
1.2 clone源码后采用终端运行
https://github.com/CVHub520/X-AnyLabeling
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
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/
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：
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：
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）：
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
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 完整项目：https://download.csdn.net/download/qq_44747572/88791740
3.2 c++调用engine模型
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 3.2.2 segmentationModel.cpp
#include "pch.h"
#include "segmentationModel.h"
#define DEVICE 0 // GPU id
static 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";//mask
static 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 stream
char* 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trtModelStream
file.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 image
float* 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); // Resize
int 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(uc_pixel[2]) / 255.0f;
// float g = static_cast(uc_pixel[1]) / 255.0f;
// float b = static_cast(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 inference
static const int OUTPUT_SIZE = Num_box * (classe + 4);//output0
float* 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(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 device
CHECK(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 stream
cudaStream_t stream;
CHECK(cudaStreamCreate(&stream));
// DMA input batch data to device, infer on the batch asynchronously, and DMA output back to host
CHECK(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 buffers
cudaStreamDestroy(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(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box
int 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; //cx
float y = (out1.at<float>(1, i) - padh) * ratio_h; //cy
float w = out1.at<float>(2, i) * ratio_w; //w
float h = out1.at<float>(3, i) * ratio_h; //h
int 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 stream
char* 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trtModelStream
file.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 image
float* 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); // Resize
int 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 inference
static const int OUTPUT_SIZE = Num_box * (classe + 4 + segChannels);//output0
static const int OUTPUT_SIZE1 = segChannels * segWidth * segHeight;//output1
float* 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(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 device
CHECK(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 stream
cudaStream_t stream;
CHECK(cudaStreamCreate(&stream));
// DMA input batch data to device, infer on the batch asynchronously, and DMA output back to host
CHECK(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 buffers
cudaStreamDestroy(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box
int 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; //cx
float y = (out1.at<float>(1, i) - padh) * ratio_h; //cy
float w = out1.at<float>(2, i) * ratio_w; //w
float h = out1.at<float>(3, i) * ratio_h; //h
int 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mask
Mat 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*160
std::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);//sigmoid
dest = 1.0 / (1.0 + dest);//160*160
dest = 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mask
Rect 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 engine
contextYolov8Seg->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
}
完整项目：https://download.csdn.net/download/qq_44747572/88791748