commit 7a4cb1fef1c81e8c89de9f581ff6a62a90ffb758 Author: ty <1431301249@qq.com> Date: Sun Apr 6 17:27:22 2025 +0800 origin diff --git a/YOLOPv2.cpp b/YOLOPv2.cpp new file mode 100644 index 0000000..35bc929 --- /dev/null +++ b/YOLOPv2.cpp @@ -0,0 +1,302 @@ +#include "YOLOPv2.h" +#include + +YOLOPv2::YOLOPv2(Net_config config) +{ + this->confThreshold = config.confThreshold; + this->nmsThreshold = config.nmsThreshold; + //string model_path = config.modelpath; + //std::wstring widestr = std::wstring(model_path.begin(), model_path.end()); + + //CUDA option set + OrtCUDAProviderOptions cuda_option; + cuda_option.device_id = 0; + cuda_option.arena_extend_strategy = 0; + cuda_option.cudnn_conv_algo_search = OrtCudnnConvAlgoSearchExhaustive; + cuda_option.gpu_mem_limit = SIZE_MAX; + cuda_option.do_copy_in_default_stream = 1; + //CUDA 加速 + sessionOptions.SetIntraOpNumThreads(1);//设置线程数 + sessionOptions.SetGraphOptimizationLevel(GraphOptimizationLevel::ORT_ENABLE_ALL); //函数用于设置在使用 ORT 库执行模型时应用的图优化级别。ORT_ENABLE_ALL 选项启用所有可用的优化，这可以导致更快速和更高效的执行。 + sessionOptions.AppendExecutionProvider_CUDA(cuda_option); + + + const char *modelpath = "/home/wuxianfu/Projects/Fast-YolopV2/build/onnx/yolopv2_192x320.onnx" ; + ort_session = new Session(env, modelpath, sessionOptions); + + + size_t numInputNodes = ort_session->GetInputCount(); + size_t numOutputNodes = ort_session->GetOutputCount(); + + + + AllocatorWithDefaultOptions allocator; + for (int i = 0; i < numInputNodes; i++) + { + //input_names.push_back(ort_session->GetInputName(i, allocator)); + AllocatedStringPtr input_name_Ptr = ort_session->GetInputNameAllocated(i, allocator); + input_names.push_back(input_name_Ptr.get()); + qDebug() << "Input Name: " << input_name_Ptr.get(); + Ort::TypeInfo input_type_info = ort_session->GetInputTypeInfo(i); + auto input_tensor_info = input_type_info.GetTensorTypeAndShapeInfo(); + auto input_dims = input_tensor_info.GetShape(); + input_node_dims.push_back(input_dims); + + + } + + for (int i = 0; i < numOutputNodes; i++) + { + //output_names.push_back(ort_session->GetOutputName(i, allocator)); + AllocatedStringPtr output_name_Ptr= ort_session->GetOutputNameAllocated(i, allocator); + output_names.push_back(output_name_Ptr.get()); + qDebug() << "Output Name: " << output_name_Ptr.get(); + Ort::TypeInfo output_type_info = ort_session->GetOutputTypeInfo(i); + auto output_tensor_info = output_type_info.GetTensorTypeAndShapeInfo(); + auto output_dims = output_tensor_info.GetShape(); + output_node_dims.push_back(output_dims); + } + + + this->inpHeight = input_node_dims[0][2]; + this->inpWidth = input_node_dims[0][3]; + + string classesFile = "/home/wuxianfu/Projects/Fast-YolopV2/build/coco.names"; + ifstream ifs(classesFile.c_str()); + string line; + while (getline(ifs, line)) this->class_names.push_back(line); + this->num_class = class_names.size(); + +} + +void YOLOPv2::normalize_(Mat img) +{ + // img.convertTo(img, CV_32F); + int row = img.rows; + int col = img.cols; + this->input_image_.resize(row * col * img.channels()); + for (int c = 0; c < 3; c++) + { + for (int i = 0; i < row; i++) + { + for (int j = 0; j < col; j++) + { + float pix = img.ptr(i)[j * 3 + 2 - c]; + this->input_image_[c * row * col + i * col + j] = pix / 255.0; + } + } + } +} + +void YOLOPv2::nms(vector& input_boxes) +{ + sort(input_boxes.begin(), input_boxes.end(), [](BoxInfo a, BoxInfo b) { return a.score > b.score; }); + vector vArea(input_boxes.size()); + for (int i = 0; i < int(input_boxes.size()); ++i) + { + vArea[i] = (input_boxes.at(i).x2 - input_boxes.at(i).x1 + 1) + * (input_boxes.at(i).y2 - input_boxes.at(i).y1 + 1); + } + + vector isSuppressed(input_boxes.size(), false); + for (int i = 0; i < int(input_boxes.size()); ++i) + { + if (isSuppressed[i]) { continue; } + for (int j = i + 1; j < int(input_boxes.size()); ++j) + { + if (isSuppressed[j]) { continue; } + float xx1 = (max)(input_boxes[i].x1, input_boxes[j].x1); + float yy1 = (max)(input_boxes[i].y1, input_boxes[j].y1); + float xx2 = (min)(input_boxes[i].x2, input_boxes[j].x2); + float yy2 = (min)(input_boxes[i].y2, input_boxes[j].y2); + + float w = (max)(float(0), xx2 - xx1 + 1); + float h = (max)(float(0), yy2 - yy1 + 1); + float inter = w * h; + float ovr = inter / (vArea[i] + vArea[j] - inter); + + if (ovr >= this->nmsThreshold) + { + isSuppressed[j] = true; + } + } + } + // return post_nms; + int idx_t = 0; + input_boxes.erase(remove_if(input_boxes.begin(), input_boxes.end(), [&idx_t, &isSuppressed](const BoxInfo& f) { return isSuppressed[idx_t++]; }), input_boxes.end()); +} + +inline float sigmoid(float x) +{ + return 1.0 / (1 + exp(-x)); +} + +Mat YOLOPv2::detect(Mat& frame) +{ + Mat dstimg; + resize(frame, dstimg, Size(this->inpWidth, this->inpHeight)); + this->normalize_(dstimg); + array input_shape_{ 1, 3, this->inpHeight, this->inpWidth }; + + auto allocator_info = MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeDefault); + Value input_tensor_ = Value::CreateTensor(allocator_info, input_image_.data(), input_image_.size(), input_shape_.data(), input_shape_.size()); + + // 开始推理 + + /*qDebug() << " output_names size: " << output_names.size()<< " sec \n"; + qDebug() << " input_names: " << input_names[0]<< " sec \n"; + qDebug() << " output_names: " << output_names[1]<< " sec \n"; + + + vector ort_outputs = ort_session->Run(RunOptions{nullptr}, input_names.data(), &input_tensor_, 1, output_names.data(), output_names.size());*/ + + const char* inputNames[] = { "input" };//这两个值是根据netron查看onnx格式得到的输入输出名称 + const char* outputNames[] = { "seg" , "ll" , "pred0" , "pred1" , "pred2" , }; + vector ort_outputs = ort_session->Run(RunOptions{nullptr}, inputNames, &input_tensor_, 1, outputNames, 5); + + /////generate proposals + + vector generate_boxes; + float ratioh = (float)frame.rows / this->inpHeight, ratiow = (float)frame.cols / this->inpWidth; + int n = 0, q = 0, i = 0, j = 0, nout = this->class_names.size() + 5, c = 0, area = 0; + for (n = 0; n < 3; n++) ///尺度 + { + int num_grid_x = (int)(this->inpWidth / this->stride[n]); + int num_grid_y = (int)(this->inpHeight / this->stride[n]); + area = num_grid_x * num_grid_y; + const float* pdata = ort_outputs[n + 2].GetTensorMutableData(); + for (q = 0; q < 3; q++) ///anchor数 + { + const float anchor_w = this->anchors[n][q * 2]; + const float anchor_h = this->anchors[n][q * 2 + 1]; + for (i = 0; i < num_grid_y; i++) + { + for (j = 0; j < num_grid_x; j++) + { + float box_score = sigmoid(pdata[4 * area + i * num_grid_x + j]); + if (box_score > this->confThreshold) + { + float max_class_socre = -100000, class_socre = 0; + int max_class_id = 0; + for (c = 0; c < this->class_names.size(); c++) //// get max socre + { + class_socre = pdata[(c + 5) * area + i * num_grid_x + j]; + if (class_socre > max_class_socre) + { + max_class_socre = class_socre; + max_class_id = c; + } + } + max_class_socre = sigmoid(max_class_socre) * box_score; + if (max_class_socre > this->confThreshold) + { + float cx = (sigmoid(pdata[i * num_grid_x + j]) * 2.f - 0.5f + j) * this->stride[n]; ///cx + float cy = (sigmoid(pdata[area + i * num_grid_x + j]) * 2.f - 0.5f + i) * this->stride[n]; ///cy + float w = powf(sigmoid(pdata[2 * area + i * num_grid_x + j]) * 2.f, 2.f) * anchor_w; ///w + float h = powf(sigmoid(pdata[3 * area + i * num_grid_x + j]) * 2.f, 2.f) * anchor_h; ///h + + float xmin = (cx - 0.5*w)*ratiow; + float ymin = (cy - 0.5*h)*ratioh; + float xmax = (cx + 0.5*w)*ratiow; + float ymax = (cy + 0.5*h)*ratioh; + + generate_boxes.push_back(BoxInfo{ xmin, ymin, xmax, ymax, max_class_socre, max_class_id }); + } + } + } + } + pdata += area * nout; + } + } + nms(generate_boxes); + + Mat outimg = frame.clone(); + for (size_t i = 0; i < generate_boxes.size(); ++i) + { + int xmin = int(generate_boxes[i].x1); + int ymin = int(generate_boxes[i].y1); + rectangle(outimg, Point(xmin, ymin), Point(int(generate_boxes[i].x2), int(generate_boxes[i].y2)), Scalar(0, 0, 255), 2); + string label = format("%.2f", generate_boxes[i].score); + label = this->class_names[generate_boxes[i].label-1] + ":" + label; + putText(outimg, label, Point(xmin, ymin - 5), FONT_HERSHEY_SIMPLEX, 0.75, Scalar(0, 255, 0), 1); + } + + const float* pdrive_area = ort_outputs[0].GetTensorMutableData(); + const float* plane_line = ort_outputs[1].GetTensorMutableData(); + area = this->inpHeight*this->inpWidth; + int min_y = -1; + vector points_L, points_R; + for (i = 0; i < frame.rows; i++) + { + bool flg = false; + int left = -1, right = -1; + for (j = 0; j < frame.cols; j++) + { + const int x = int(j / ratiow); + const int y = int(i / ratioh); + if (pdrive_area[y * this->inpWidth + x] < pdrive_area[area + y * this->inpWidth + x]) + { + outimg.at(i, j)[0] = 0; + outimg.at(i, j)[1] = 255; + outimg.at(i, j)[2] = 0; + } + if (plane_line[y * this->inpWidth + x] > 0.5) + { + outimg.at(i, j)[0] = 255; + outimg.at(i, j)[1] = 0; + outimg.at(i, j)[2] = 0; + if (!flg && j >= frame.cols / 2 && right == -1) { // 记录图像右半部分最靠左的车道线的左边缘坐标 + right = j; + } + flg = true; + } else { + if (flg && j - 1 < frame.cols / 2) { //记录图像左半部分最靠右的车道线的右边缘坐标 + left = j - 1; + } + flg = false; + } + } + if (min_y == -1 && (left != -1 || right != -1)) { + min_y = i; + } + if (left != -1){ + points_L.push_back(Point2f(left, i)); + } + if (right != -1){ + points_R.push_back(Point2f(right, i)); + } + //若左右参考车道线均存在，计算并标记中心点 + if (left > -1 && right > -1) { + int mid = (left + right) / 2; + for (int k = -5; k <= 5; k++) { + outimg.at(i, mid+k)[0] = 255; + outimg.at(i, mid+k)[1] = 255; + outimg.at(i, mid+k)[2] = 0; + } + } +//(需要考虑的问题 1.双车道3条线 2.拐角处曲线 3.近处显示不全 4.两条线粘连) + } + //备选方案，对左右车道线分别拟合直线并计算中心线解析式 泛化 鲁棒 （目前有bug + if (points_L.size() && points_R.size()) { + Vec4f line_L, line_R; + float kL, bL, kR, bR, kM, bM; // x=ky+b + fitLine(points_L, line_L, DIST_WELSCH, 0, 0.01, 0.01); + fitLine(points_R, line_R, DIST_WELSCH, 0, 0.01, 0.01); + kL = line_L[0] / line_L[1]; + bL = line_L[2] - kL * line_L[3]; + kR = line_R[0] / line_R[1]; + bR = line_R[2] - kR * line_R[3]; + kM = (kL + kR) / 2; + bM = (bL + bR) / 2; + for (int i = min_y; i < frame.rows; i++) { + int mid = round(kM * i + bM); + for (int k = -5; k <= 5; k++) { + outimg.at(i, mid+k)[0] = 255; + outimg.at(i, mid+k)[1] = 0; + outimg.at(i, mid+k)[2] = 255; + } + } + } + + return outimg; +} diff --git a/YOLOPv2.h b/YOLOPv2.h new file mode 100644 index 0000000..25e3fe7 --- /dev/null +++ b/YOLOPv2.h @@ -0,0 +1,62 @@ +#ifndef YOLOPV2_H +#define YOLOPV2_H + +#include +#include +#include +#include +#include +#include + +using namespace cv; +using namespace Ort; +using namespace std; + +struct Net_config +{ + float confThreshold; // Confidence threshold + float nmsThreshold; // Non-maximum suppression threshold + string modelpath; +}; + +typedef struct BoxInfo +{ + float x1; + float y1; + float x2; + float y2; + float score; + int label; +} BoxInfo; + +class YOLOPv2 +{ +public: + YOLOPv2(Net_config config); + Mat detect(Mat& frame); +private: + int inpWidth; + int inpHeight; + int nout; + int num_proposal; + vector class_names; + int num_class; + + float confThreshold; + float nmsThreshold; + vector input_image_; + void normalize_(Mat img); + void nms(vector& input_boxes); + const float anchors[3][6] = { {12, 16, 19, 36, 40, 28}, {36, 75, 76, 55, 72, 146},{142, 110, 192, 243, 459, 401} }; + const float stride[3] = { 8.0, 16.0, 32.0 }; + + Env env = Env(ORT_LOGGING_LEVEL_ERROR, "YOLOPv2"); + Ort::Session *ort_session = nullptr; + SessionOptions sessionOptions = SessionOptions(); + vector input_names; + vector output_names; + vector> input_node_dims; // >=1 outputs + vector> output_node_dims; // >=1 outputs +}; + +#endif // YOLOPV2_H diff --git a/build/ui_mainwindow.h b/build/ui_mainwindow.h new file mode 100644 index 0000000..4b38114 --- /dev/null +++ b/build/ui_mainwindow.h @@ -0,0 +1,61 @@ +/******************************************************************************** +** Form generated from reading UI file 'mainwindow.ui' +** +** Created by: Qt User Interface Compiler version 5.15.3 +** +** WARNING! All changes made in this file will be lost when recompiling UI file! +********************************************************************************/ + +#ifndef UI_MAINWINDOW_H +#define UI_MAINWINDOW_H + +#include +#include +#include +#include +#include +#include + +QT_BEGIN_NAMESPACE + +class Ui_MainWindow +{ +public: + QWidget *centralwidget; + QMenuBar *menubar; + QStatusBar *statusbar; + + void setupUi(QMainWindow *MainWindow) + { + if (MainWindow->objectName().isEmpty()) + MainWindow->setObjectName(QString::fromUtf8("MainWindow")); + MainWindow->resize(800, 600); + centralwidget = new QWidget(MainWindow); + centralwidget->setObjectName(QString::fromUtf8("centralwidget")); + MainWindow->setCentralWidget(centralwidget); + menubar = new QMenuBar(MainWindow); + menubar->setObjectName(QString::fromUtf8("menubar")); + MainWindow->setMenuBar(menubar); + statusbar = new QStatusBar(MainWindow); + statusbar->setObjectName(QString::fromUtf8("statusbar")); + MainWindow->setStatusBar(statusbar); + + retranslateUi(MainWindow); + + QMetaObject::connectSlotsByName(MainWindow); + } // setupUi + + void retranslateUi(QMainWindow *MainWindow) + { + MainWindow->setWindowTitle(QCoreApplication::translate("MainWindow", "MainWindow", nullptr)); + } // retranslateUi + +}; + +namespace Ui { + class MainWindow: public Ui_MainWindow {}; +} // namespace Ui + +QT_END_NAMESPACE + +#endif // UI_MAINWINDOW_H diff --git a/fast-yolopv2.pro b/fast-yolopv2.pro new file mode 100644 index 0000000..40ef06a --- /dev/null +++ b/fast-yolopv2.pro @@ -0,0 +1,51 @@ +QT += core gui widgets + +greaterThan(QT_MAJOR_VERSION, 4): QT += widgets + +CONFIG += c++11 +DEFINES += OPENCV +DEFINES += GPU +DEFINES += CUDNN + + + +# You can make your code fail to compile if it uses deprecated APIs. +# In order to do so, uncomment the following line. +#DEFINES += QT_DISABLE_DEPRECATED_BEFORE=0x060000 # disables all the APIs deprecated before Qt 6.0.0 + +# 包含 OpenCV 的头文件路径 +INCLUDEPATH += /usr/local/Opencv-4.10.0/include/opencv4 +# 包含 Cuda 的头文件路径 +INCLUDEPATH += /usr/local/cuda-12.6/include +# 包含 onnx 的头文件路径 +INCLUDEPATH += /usr/local/include/onnxruntime + + +# 链接到 OpenCV 的库文件 +LIBS += -L/usr/local/Opencv-4.10.0/lib \ + -lopencv_core \ + -lopencv_imgproc \ + -lopencv_highgui \ + -lopencv_imgcodecs \ + -lopencv_videoio + +# 链接到 onnx 的库文件 +LIBS += -L/usr/local/lib -lonnxruntime + + +SOURCES += \ + YOLOPv2.cpp \ + main.cpp \ + mainwindow.cpp + +HEADERS += \ + YOLOPv2.h \ + mainwindow.h + +FORMS += \ + mainwindow.ui + +# Default rules for deployment. +qnx: target.path = /tmp/$${TARGET}/bin +else: unix:!android: target.path = /opt/$${TARGET}/bin +!isEmpty(target.path): INSTALLS += target diff --git a/main.cpp b/main.cpp new file mode 100644 index 0000000..fd3e533 --- /dev/null +++ b/main.cpp @@ -0,0 +1,11 @@ +#include "mainwindow.h" + +#include + +int main(int argc, char *argv[]) +{ + QApplication a(argc, argv); + MainWindow w; + w.show(); + return a.exec(); +} diff --git a/mainwindow.cpp b/mainwindow.cpp new file mode 100644 index 0000000..c8edfe0 --- /dev/null +++ b/mainwindow.cpp @@ -0,0 +1,201 @@ +#include "mainwindow.h" +#include "ui_mainwindow.h" +#include "YOLOPv2.h" +#include +#include +#include + + +string gstreamer_pipeline (int capture_width, int capture_height, int display_width, int display_height, int framerate, int flip_method) +{ + return "nvarguscamerasrc ! video/x-raw(memory:NVMM), width=(int)" + to_string(capture_width) + ", height=(int)" + + to_string(capture_height) + ", format=(string)NV12, framerate=(fraction)" + to_string(framerate) + + "/1 ! nvvidconv flip-method=" + to_string(flip_method) + " ! video/x-raw, width=(int)" + to_string(display_width) + ", height=(int)" + + to_string(display_height) + ", format=(string)BGRx ! videoconvert ! video/x-raw, format=(string)BGR ! appsink"; +} + + +MainWindow::MainWindow(QWidget *parent) + : QMainWindow(parent) + , ui(new Ui::MainWindow) +{ + ui->setupUi(this); + //ShowImage(); + ShowVideo(); + //OpenCSICamera(); + +} + + +MainWindow::~MainWindow() +{ + delete ui; +} + + +void MainWindow::ShowImage() +{ + Net_config YOLOPv2_nets = { 0.5, 0.5, "/home/wuxianfu/Projects/Fast-YolopV2/build/onnx/yolopv2_192x320.onnx" }; ////choices = onnx文件夹里的文件 + YOLOPv2 net(YOLOPv2_nets); + string imgpath = "/home/wuxianfu/Projects/Fast-YolopV2/build/images/3c0e7240-96e390d2.jpg"; + static const string kWinName = "Deep learning object detection in ONNXRuntime"; + namedWindow(kWinName, WINDOW_NORMAL); + + Mat srcimg = imread(imgpath); + imshow(kWinName, srcimg); + + + Mat outimg = net.detect(srcimg); + + imshow(kWinName, outimg); + waitKey(0); + destroyAllWindows(); +} + +void MainWindow::ShowVideo() +{ + Net_config YOLOPv2_nets = { 0.5, 0.5, "/home/wuxianfu/Projects/Fast-YolopV2/build/onnx/yolopv2_736x1280.onnx" }; ////choices = onnx文件夹里的文件 + YOLOPv2 net(YOLOPv2_nets); + static const string kWinName = "Deep learning object detection in ONNXRuntime"; + namedWindow(kWinName, WINDOW_NORMAL); + + cv::VideoCapture cap("/home/wuxianfu/Projects/Fast-YolopV2/build/videos/566a351c29c00924a337e91e85fa7dec.mp4"); + if (!cap.isOpened()) { + std::cerr << "Error opening video stream" << std::endl; + return; + } + + cv::Mat frame; + while (true) { + cap >> frame; // 读取一帧 + if (frame.empty()) { + std::cerr << "Error reading frame" << std::endl; + break; + } + auto start = std::chrono::steady_clock::now(); + Mat outimg = net.detect(frame); + auto end = std::chrono::steady_clock::now(); + std::chrono::duration spent = end - start; + qDebug()<< " Time: " << spent.count() << " sec \n"; + + imshow(kWinName, outimg); + if (cv::waitKey(5) >= 0) break; // 按任意键退出循环 + } + cap.release(); // 释放资源 + cv::destroyAllWindows(); // 关闭所有OpenCV窗口 +} + + +void MainWindow::OpenUSBCamera() { + + Net_config YOLOPv2_nets = { 0.5, 0.5, "/home/wuxianfu/Projects/Fast-YolopV2/build/onnx/yolopv2_192x320.onnx" }; ////choices = onnx文件夹里的文件 + YOLOPv2 net(YOLOPv2_nets); + cv::VideoCapture cap(1); // 使用默认的摄像头索引（通常是0） + if (!cap.isOpened()) { + std::cerr << "Error opening video stream" << std::endl; + return; + } + + cv::Mat frame; + while (true) { + cap >> frame; // 读取一帧 + if (frame.empty()) { + std::cerr << "Error reading frame" << std::endl; + break; + } + + + Mat outimg = net.detect(frame); + cv::imshow("USB Camera", outimg); // 显示帧 + if (cv::waitKey(10) >= 0) break; // 按任意键退出循环 + } + cap.release(); // 释放资源 + cv::destroyAllWindows(); // 关闭所有OpenCV窗口 + +} + +void MainWindow::OpenCSICamera() { + + + Net_config YOLOPv2_nets = { 0.5, 0.5, "/home/wuxianfu/Projects/Fast-YolopV2/build/onnx/yolopv2_736x1280.onnx" }; ////choices = onnx文件夹里的文件 + YOLOPv2 net(YOLOPv2_nets); + string imgpath = "/home/wuxianfu/Projects/Fast-YolopV2/build/images/0ace96c3-48481887.jpg"; + Mat srcimg = imread(imgpath); + + + int capture_width = 3280 ; + int capture_height = 1848 ; + int display_width = 3280 ; + int display_height = 1848 ; + + //3280*2464最大支持21帧 + //3280*1848最大支持28帧 + //1920*1080最大支持29帧 + //1640*1232最大支持29帧 + //1280*720 最大支持59帧 + int framerate = 10; + int flip_method = 0; + + //创建管道 + string pipeline = gstreamer_pipeline(capture_width, + capture_height, + display_width, + display_height, + framerate, + flip_method); + std::cout << "使用gstreamer管道: \n\t" << pipeline << "\n"; + + //管道与视频流绑定 + VideoCapture cap(pipeline, CAP_GSTREAMER); + if(!cap.isOpened()) + { + std::cout<<"打开摄像头失败."<800) + { + new_height=int(new_width*1.0/width*height); + } + cv::resize(img, img, cv::Size(new_width, new_height)); + + Mat outimg = net.detect(img); + auto end = std::chrono::steady_clock::now(); + std::chrono::duration spent = end - start; + qDebug()<< " Time: " << spent.count() << " sec \n"; + + imshow("CSI Camera",outimg); + + int keycode = cv::waitKey(2) & 0xff ; //ESC键退出 + if (keycode == 27) break ; + } + + cap.release(); + destroyAllWindows() ; + +} + + diff --git a/mainwindow.h b/mainwindow.h new file mode 100644 index 0000000..547ffbd --- /dev/null +++ b/mainwindow.h @@ -0,0 +1,32 @@ +#ifndef MAINWINDOW_H +#define MAINWINDOW_H + +#include + +QT_BEGIN_NAMESPACE +namespace Ui { class MainWindow; } +QT_END_NAMESPACE + +class Detector; + +class MainWindow : public QMainWindow +{ + Q_OBJECT + +public: + MainWindow(QWidget *parent = nullptr); + ~MainWindow(); + +public: + void OpenUSBCamera(); + void OpenCSICamera(); + void ShowImage(); + void ShowVideo(); + + std::vector m_vObjects_Names; //检测目标名称 + Detector *m_pDetector; + +private: + Ui::MainWindow *ui; +}; +#endif // MAINWINDOW_H diff --git a/mainwindow.ui b/mainwindow.ui new file mode 100644 index 0000000..b232854 --- /dev/null +++ b/mainwindow.ui @@ -0,0 +1,22 @@ + + + MainWindow + + + + 0 + 0 + 800 + 600 + + + + MainWindow + + + + + + + +