QT+Yolov8 推理部署，ONNX模型 ，实例分割+目标检测

QT+Yolov8 实例分割、目标检测推理。QT源码。

程序准备/版本:QT creator QT6.8 编译器:MSVC2022 opencv:4.7 onnxruntime:1.16.0 cpu版本

程序部分源码:

#include "aitoolinterface.h"
#include "ui_aitoolinterface.h"
#include <QDebug>
#include <QDateTime>
#include <QFileInfo>AIToolInterface::AIToolInterface(QWidget *parent): QWidget(parent), ui(new Ui::AIToolInterface), m_yoloModel(nullptr), m_modelLoaded(false), m_yoloModel2(nullptr), m_modelLoaded2(false)
{ui->setupUi(this);// 初始化状态ui->label_status->setText("状态: 未加载模型");ui->label_status->setStyleSheet("color: red;");// 禁用测试按钮，直到模型加载ui->pushButton_test->setEnabled(false);ui->pushButton_test_2->setEnabled(false);logMessage("AI分割检测工具已启动");
}AIToolInterface::~AIToolInterface()
{if (m_yoloModel) {delete m_yoloModel;m_yoloModel = nullptr;}if (m_yoloModel2) {delete m_yoloModel2;m_yoloModel2 = nullptr;}delete ui;
}void AIToolInterface::on_pushButton_browseModel_clicked()
{QString modelPath = QFileDialog::getOpenFileName(this,"选择ONNX模型文件","./Bin/x64/Models/","ONNX Files (*.onnx);;All Files (*)");if (!modelPath.isEmpty()) {ui->lineEdit_modelPath->setText(modelPath);logMessage("已选择模型文件: " + modelPath);}
}void AIToolInterface::on_pushButton_loadModel_clicked()
{QString modelPath = ui->lineEdit_modelPath->text();if (modelPath.isEmpty()) {QMessageBox::warning(this, "警告", "请先选择模型文件路径");return;}// 检查文件是否存在QFileInfo fileInfo(modelPath);if (!fileInfo.exists()) {QMessageBox::critical(this, "错误", "模型文件不存在: " + modelPath);return;}try {// 创建YOLO模型实例if (m_yoloModel) {delete m_yoloModel;}m_yoloModel = new Yolov8SegOnnx();// 加载模型logMessage("正在加载模型...");bool success = m_yoloModel->ReadModel(modelPath.toStdString(), false, 0, false);if (success) {m_modelLoaded = true;ui->label_status->setText("状态: 模型已加载");ui->label_status->setStyleSheet("color: green;");ui->pushButton_test->setEnabled(true);logMessage("模型加载成功!");} else {m_modelLoaded = false;ui->label_status->setText("状态: 模型加载失败");ui->label_status->setStyleSheet("color: red;");ui->pushButton_test->setEnabled(false);logMessage("模型加载失败!");delete m_yoloModel;m_yoloModel = nullptr;}} catch (const std::exception& e) {m_modelLoaded = false;ui->label_status->setText("状态: 模型加载异常");ui->label_status->setStyleSheet("color: red;");ui->pushButton_test->setEnabled(false);logMessage("模型加载异常: " + QString(e.what()));if (m_yoloModel) {delete m_yoloModel;m_yoloModel = nullptr;}}
}void AIToolInterface::on_pushButton_browseImage_clicked()
{QString imagePath = QFileDialog::getOpenFileName(this,"选择图片文件","./Bin/x64/Pic/","Image Files (*.jpg *.jpeg *.png *.bmp);;All Files (*)");if (!imagePath.isEmpty()) {ui->lineEdit_imagePath->setText(imagePath);m_currentImagePath = imagePath;// 加载并显示图片m_currentImage = cv::imread(imagePath.toStdString());if (m_currentImage.empty()) {QMessageBox::warning(this, "警告", "无法加载图片: " + imagePath);logMessage("图片加载失败: " + imagePath);return;}// 检查图片是否有效if (m_currentImage.rows <= 0 || m_currentImage.cols <= 0) {QMessageBox::warning(this, "警告", "图片尺寸无效: " + imagePath);logMessage("图片尺寸无效: " + imagePath);m_currentImage = cv::Mat();return;}logMessage(QString("已加载图片: %1 (尺寸: %2x%3)").arg(imagePath).arg(m_currentImage.cols).arg(m_currentImage.rows));displayImage(m_currentImage, ui->label_originalImage, "原始图片");}
}void AIToolInterface::on_pushButton_test_clicked()
{if (!m_modelLoaded || !m_yoloModel) {QMessageBox::warning(this, "警告", "请先加载模型");logMessage("检测失败: 模型未加载");return;}if (m_currentImage.empty()) {QMessageBox::warning(this, "警告", "请先选择图片");logMessage("检测失败: 未选择图片");return;}// 额外检查图片有效性if (m_currentImage.rows <= 0 || m_currentImage.cols <= 0) {QMessageBox::warning(this, "警告", "当前图片无效，请重新选择");logMessage("检测失败: 当前图片无效");return;}try {logMessage("开始检测...");// 执行检测std::vector<OutputParams> results;bool success = m_yoloModel->OnnxDetect(m_currentImage, results);if (success) {logMessage(QString("检测完成，发现 %1 个目标").arg(results.size()));qDebug()<<"准备绘制结果";// 绘制检测结果cv::Mat resultImage = drawDetectionResult(m_currentImage, results);qDebug()<<"绘制结果完成";cv::imwrite("D:/OtherFiles/QTFile/AI_Solder/AI_Solder_v0/Bin/x64/Pic/result.jpg",resultImage);// 显示结果displayImage(resultImage, ui->label_resultImage, "检测结果");qDebug()<<"显示结果完成";// 输出检测详情for (size_t i = 0; i < results.size(); ++i) {const auto& result = results[i];QString className = QString::fromStdString(m_yoloModel->_className[result.id]);logMessage(QString("目标 %1: %2, 置信度: %3, 位置: (%4,%5,%6,%7)").arg(i + 1).arg(className).arg(result.confidence, 0, 'f', 3).arg(result.box.x).arg(result.box.y).arg(result.box.width).arg(result.box.height));}} else {logMessage("检测失败!");QMessageBox::warning(this, "错误", "检测失败");}} catch (const std::exception& e) {logMessage("检测异常: " + QString(e.what()));QMessageBox::critical(this, "错误", "检测过程中发生异常: " + QString(e.what()));}
}//检测模型加载---
void AIToolInterface::on_pushButton_browseModel_2_clicked()
{QString modelPath = QFileDialog::getOpenFileName(this,"选择ONNX模型文件","./Bin/x64/Models/","ONNX Files (*.onnx);;All Files (*)");if (!modelPath.isEmpty()) {ui->lineEdit_modelPath_2->setText(modelPath);logMessage("已选择模型文件: " + modelPath);}
}void AIToolInterface::on_pushButton_browseImage_2_clicked()
{QString imagePath = QFileDialog::getOpenFileName(this,"选择图片文件","./Bin/x64/Pic/","Image Files (*.jpg *.jpeg *.png *.bmp);;All Files (*)");if (!imagePath.isEmpty()) {ui->lineEdit_imagePath_2->setText(imagePath);m_currentImagePath2 = imagePath;// 加载并显示图片m_currentImage2 = cv::imread(imagePath.toStdString());if (m_currentImage2.empty()) {QMessageBox::warning(this, "警告", "无法加载图片: " + imagePath);logMessage("图片加载失败: " + imagePath);return;}// 检查图片是否有效if (m_currentImage2.rows <= 0 || m_currentImage2.cols <= 0) {QMessageBox::warning(this, "警告", "图片尺寸无效: " + imagePath);logMessage("图片尺寸无效: " + imagePath);m_currentImage2 = cv::Mat();return;}logMessage(QString("已加载图片: %1 (尺寸: %2x%3)").arg(imagePath).arg(m_currentImage2.cols).arg(m_currentImage2.rows));displayImage(m_currentImage2, ui->label_originalImage, "原始图片");}
}void AIToolInterface::on_pushButton_loadModel_2_clicked()
{QString modelPath = ui->lineEdit_modelPath_2->text();if (modelPath.isEmpty()) {QMessageBox::warning(this, "警告", "请先选择模型文件路径");return;}// 检查文件是否存在QFileInfo fileInfo(modelPath);if (!fileInfo.exists()) {QMessageBox::critical(this, "错误", "模型文件不存在: " + modelPath);return;}try {// 创建YOLO模型实例if (m_yoloModel2) {delete m_yoloModel2;}m_yoloModel2 = new Yolov8Onnx();qDebug()<<"实例化检测模型";// 加载模型logMessage("正在加载模型...");bool success = m_yoloModel2->ReadModel(modelPath.toStdString(), false, 0, false);if (success) {m_modelLoaded2 = true;ui->label_status_2->setText("状态: 模型已加载");ui->label_status_2->setStyleSheet("color: green;");ui->pushButton_test_2->setEnabled(true);logMessage("模型加载成功!");} else {m_modelLoaded2 = false;ui->label_status_2->setText("状态: 模型加载失败");ui->label_status_2->setStyleSheet("color: red;");ui->pushButton_test_2->setEnabled(false);logMessage("模型加载失败!");delete m_yoloModel2;m_yoloModel2 = nullptr;}} catch (const std::exception& e) {m_modelLoaded2 = false;ui->label_status_2->setText("状态: 模型加载异常");ui->label_status_2->setStyleSheet("color: red;");ui->pushButton_test_2->setEnabled(false);logMessage("模型加载异常: " + QString(e.what()));if (m_yoloModel2) {delete m_yoloModel2;m_yoloModel2 = nullptr;}}
}void AIToolInterface::on_pushButton_test_2_clicked()
{if (!m_modelLoaded2 || !m_yoloModel2) {QMessageBox::warning(this, "警告", "请先加载模型");logMessage("检测失败: 模型未加载");return;}if (m_currentImage2.empty()) {QMessageBox::warning(this, "警告", "请先选择图片");logMessage("检测失败: 未选择图片");return;}// 额外检查图片有效性if (m_currentImage2.rows <= 0 || m_currentImage2.cols <= 0) {QMessageBox::warning(this, "警告", "当前图片无效，请重新选择");logMessage("检测失败: 当前图片无效");return;}try {logMessage("开始检测...");// 执行检测std::vector<OutputParams> results;bool success = m_yoloModel2->OnnxDetect(m_currentImage2, results);if (success) {logMessage(QString("检测完成，发现 %1 个目标").arg(results.size()));qDebug()<<"准备绘制结果";// 绘制检测结果 - 使用专门的目标检测绘制函数cv::Mat resultImage = drawDetectionResult2(m_currentImage2, results);qDebug()<<"绘制结果完成";cv::imwrite("D:/OtherFiles/QTFile/AI_Solder/AI_Solder_v0/Bin/x64/Pic/result2.jpg",resultImage);// 显示结果displayImage(resultImage, ui->label_DetecResult, "检测结果");qDebug()<<"显示结果完成";// 输出检测详情for (size_t i = 0; i < results.size(); ++i) {const auto& result = results[i];QString className = QString::fromStdString(m_yoloModel2->_className[result.id]);logMessage(QString("目标 %1: %2, 置信度: %3, 位置: (%4,%5,%6,%7)").arg(i + 1).arg(className).arg(result.confidence, 0, 'f', 3).arg(result.box.x).arg(result.box.y).arg(result.box.width).arg(result.box.height));}} else {logMessage("检测失败!");QMessageBox::warning(this, "错误", "检测失败");}} catch (const std::exception& e) {logMessage("检测异常: " + QString(e.what()));QMessageBox::critical(this, "错误", "检测过程中发生异常: " + QString(e.what()));}
}//日志打印
void AIToolInterface::logMessage(const QString& message)
{QString timestamp = QDateTime::currentDateTime().toString("hh:mm:ss");ui->textEdit_log->append(QString("[%1] %2").arg(timestamp, message));// 自动滚动到底部QTextCursor cursor = ui->textEdit_log->textCursor();cursor.movePosition(QTextCursor::End);ui->textEdit_log->setTextCursor(cursor);
}void AIToolInterface::displayImage(const cv::Mat& image, QLabel* label, const QString& text)
{if (image.empty()) {label->setText(text.isEmpty() ? "无图片" : text);return;}// 转换为QImageQImage qimg = cvMatToQImage(image);// 缩放图片以适应标签大小QPixmap pixmap = QPixmap::fromImage(qimg);QPixmap scaledPixmap = pixmap.scaled(label->size(), Qt::KeepAspectRatio, Qt::SmoothTransformation);label->setPixmap(scaledPixmap);label->setAlignment(Qt::AlignCenter);
}
//绘制分割结果--：在黑色背景图像上绘制分割掩膜
cv::Mat AIToolInterface::drawDetectionResult(const cv::Mat& image, const std::vector<OutputParams>& results)
{// 创建黑色背景图片cv::Mat resultImage = cv::Mat::zeros(image.size(), CV_8UC3);qDebug()<<"绘制1111111111111111";// 定义颜色// 随机数生成器初始化static std::mt19937 rng(std::time(0));static std::uniform_int_distribution<int> dist(50, 205); // 避免太暗或太亮的颜色// 颜色缓存（避免重复生成）static std::unordered_map<int, cv::Scalar> colorMap;// 创建累积掩码，用于收集所有目标的掩码cv::Mat accumulatedMask = cv::Mat::zeros(image.size(), CV_8UC3);for (const auto& result : results) {// 自动生成或获取颜色if (colorMap.find(result.id) == colorMap.end()) {// 生成高对比度颜色（确保在图像中可见）colorMap[result.id] = cv::Scalar(dist(rng),       // Bdist(rng),       // Gdist(rng)        // R);}cv::Scalar color = colorMap[result.id];// // 绘制边界框---可选// if (result.box.area() > 0) {//     cv::rectangle(resultImage, result.box, color, 2);//     // 绘制标签//     std::string className = m_yoloModel->_className[result.id];//     std::string label = className + ":" + std::to_string(result.confidence);//     qDebug()<<"绘制3333333333333";//     int baseline = 0;//     cv::Size labelSize = cv::getTextSize(label, cv::FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseline);//     qDebug()<<"绘制44444444444444";//     cv::Point textPos(result.box.x, result.box.y - 5);//     if (textPos.y < labelSize.height) {//         textPos.y = result.box.y + labelSize.height;//     }//     qDebug()<<"绘制55555555555555";//     cv::putText(resultImage, label, textPos, cv::FONT_HERSHEY_SIMPLEX, 0.5, color, 1);// }// 绘制分割掩码if (result.boxMask.rows > 0 && result.boxMask.cols > 0) {cv::Mat mask = result.boxMask.clone();// 创建一个与原始图像相同尺寸的掩码cv::Mat fullMask = cv::Mat::zeros(image.size(), CV_8UC1);// 将小掩码复制到正确的位置cv::Rect roi = result.box;if (roi.x >= 0 && roi.y >= 0 &&roi.x + roi.width <= fullMask.cols &&roi.y + roi.height <= fullMask.rows) {mask.copyTo(fullMask(roi));}// 转换为彩色掩码cv::Mat coloredMask;cv::cvtColor(fullMask, coloredMask, cv::COLOR_GRAY2BGR);qDebug()<<"绘制6666666666666";// 应用颜色coloredMask.setTo(color, fullMask);qDebug()<<"绘制77777777777777";// 将当前掩码累积到总掩码上cv::add(accumulatedMask, coloredMask, accumulatedMask);qDebug()<<"绘制888888888888";}}// 最后将累积的掩码叠加到结果图像上cv::addWeighted(resultImage, 1.0, accumulatedMask, 1, 0, resultImage);return resultImage;
}//绘制目标检测结果--：在原图上直接绘制边界框和置信度
cv::Mat AIToolInterface::drawDetectionResult2(const cv::Mat& image, const std::vector<OutputParams>& results)
{// 克隆原图，避免修改原始图像cv::Mat resultImage = image.clone();// 随机数生成器初始化static std::mt19937 rng(std::time(0));static std::uniform_int_distribution<int> dist(50, 205); // 避免太暗或太亮的颜色// 颜色缓存（避免重复生成）static std::unordered_map<int, cv::Scalar> colorMap;for (const auto& result : results) {// 自动生成或获取颜色if (colorMap.find(result.id) == colorMap.end()) {// 生成高对比度颜色（确保在图像中可见）colorMap[result.id] = cv::Scalar(dist(rng),       // Bdist(rng),       // Gdist(rng)        // R);}cv::Scalar color = colorMap[result.id];// 绘制边界框if (result.box.area() > 0) {// 绘制矩形框cv::rectangle(resultImage, result.box, color, 2);// 绘制标签背景std::string className = m_yoloModel2->_className[result.id];std::string label = className + ":" + std::to_string(result.confidence);int baseline = 0;cv::Size labelSize = cv::getTextSize(label, cv::FONT_HERSHEY_SIMPLEX, 1, 4, &baseline);// 计算标签位置cv::Point textPos(result.box.x, result.box.y - 5);if (textPos.y < labelSize.height) {textPos.y = result.box.y + labelSize.height;}// // 绘制标签背景矩形// cv::Rect labelRect(textPos.x - 2, textPos.y - labelSize.height - 2,//                  labelSize.width + 4, labelSize.height + 4);// cv::rectangle(resultImage, labelRect, color, -1); // 填充矩形// 绘制标签文字（白色）cv::putText(resultImage, label, textPos, cv::FONT_HERSHEY_SIMPLEX, 1, cv::Scalar(0, 255, 255), 4);}}return resultImage;
}QImage AIToolInterface::cvMatToQImage(const cv::Mat& mat)
{if (mat.type() == CV_8UC3) {// BGR to RGBcv::Mat rgb;cv::cvtColor(mat, rgb, cv::COLOR_BGR2RGB);QImage img((uchar*)rgb.data, rgb.cols, rgb.rows, rgb.step, QImage::Format_RGB888);return img.copy();} else if (mat.type() == CV_8UC1) {// GrayscaleQImage img((uchar*)mat.data, mat.cols, mat.rows, mat.step, QImage::Format_Grayscale8);return img.copy();} else {return QImage();}
}
void AIToolInterface::Recv_Img(const QPixmap &p){}

效果图:

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