建设医院网站的重点与难点在于,专业网站设计开发网站,重庆整合网络营销,上海市工程质量建设协会网站项目背景 最近停在门前的车被人开走了#xff0c;虽然有监控#xff0c;但是看监控太麻烦了#xff0c;于是想着框选一个区域用yolov8直接检测闯入到这个区域的所有目标#xff0c;这样1ms一帧#xff0c;很快就可以跑完一天的视频 用到的技术 COpenCVYolov8 OnnxRunt…项目背景 最近停在门前的车被人开走了虽然有监控但是看监控太麻烦了于是想着框选一个区域用yolov8直接检测闯入到这个区域的所有目标这样1ms一帧很快就可以跑完一天的视频 用到的技术 COpenCVYolov8 OnnxRuntime yolov8介绍 YOLOv8支持Pose和Segment,在使用TensorRT可以跑到1-2ms一帧YOLOv8提供了一个全新的SOTA模型包括P5 640和P6 1280分辨率的目标检测网络和基于YOLACT的实例分割模型。YOLOv8和YOLOv5一样基于缩放系数也提供了N/S/M/L/X尺度的不同大小模型用于满足不同场景需求。YOLOv8骨干网络和Neck部分可能参考了YOLOv7 ELAN设计思想将YOLOv5的C3结构换成了梯度流更丰富的C2f结构并对不同尺度模型调整了不同的通道数。YOLOv8 Head部分相比YOLOv5改动较大换成了目前主流的解耦头结构将分类和检测头分离同时也从Anchor-Based换成了Anchor-Free。YOLOv8 Loss计算方面采用了TaskAlignedAssigner正样本分配策略并引入了Distribution Focal Loss。YOLOv8训练的数据增强部分引入了YOLOX中的最后10 epoch关闭Mosiac增强的操作可以有效地提升精度。 实现步骤 首先打开视频第一帧框选区域我们直接使用opencv实现这个功能加载模型检测画面中的所有对象计算IOU如果有重合就保存这一帧具体信息跟踪闯入画面的目标否则会重复保存信息 使用opencv打开视频并框选区域 #include opencv2/opencv.hpp #include inference.husing namespace cv;// 定义一个全局变量用于存放鼠标框选的矩形区域 Rect g_rect; // 定义一个全局变量用于标记鼠标是否按下 bool g_bDrawingBox false;// 定义一个回调函数用于处理鼠标事件 void on_MouseHandle(int event, int x, int y, int flags, void* param) {// 将param转换为Mat类型的指针Mat image *(Mat*) param;// 根据不同的鼠标事件进行处理switch (event){// 鼠标左键按下事件case EVENT_LBUTTONDOWN:{// 标记鼠标已按下g_bDrawingBox true;// 记录矩形框的起始点g_rect.x x;g_rect.y y;break;}// 鼠标移动事件case EVENT_MOUSEMOVE:{// 如果鼠标已按下更新矩形框的宽度和高度if (g_bDrawingBox){g_rect.width x - g_rect.x;g_rect.height y - g_rect.y;}break;}// 鼠标左键松开事件case EVENT_LBUTTONUP:{// 标记鼠标已松开g_bDrawingBox false;// 如果矩形框的宽度和高度为正绘制矩形框到图像上if (g_rect.width 0 g_rect.height 0){rectangle(image, g_rect, Scalar(0, 255, 0));}break;}} }int main(int argc, char* argv[]) {// 读取视频文件cv::VideoCapture vc;vc.open(argv[1]);if(vc.isOpened()){cv::Mat frame;vc frame;if(!frame.empty()){// 创建一个副本图像用于显示框选过程Mat temp;frame.copyTo(temp);// 创建一个窗口显示图像namedWindow(image);// 设置鼠标回调函数传入副本图像作为参数setMouseCallback(image, on_MouseHandle, (void*)temp);while (1){// 如果鼠标正在框选绘制一个虚线矩形框到副本图像上并显示框的大小和坐标if (g_bDrawingBox){temp.copyTo(frame);rectangle(frame, g_rect, Scalar(0, 255, 0), 1, LINE_AA);char text[32];sprintf(text, w%d, h%d, g_rect.width, g_rect.height);putText(frame, text, Point(g_rect.x 5, g_rect.y - 5), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0));}// 显示副本图像imshow(image, frame);// 等待按键如果按下ESC键退出循环if (waitKey(10) 27){break;}}while(!frame.empty()){cv::imshow(image, frame);cv::waitKey(1);vc frame;}}}return 0; }使用YoloV8检测目标 inference.h #pragma once#define RET_OK nullptr#ifdef _WIN32 #include Windows.h #include direct.h #include io.h #endif#include string #include vector #include cstdio #include opencv2/opencv.hpp #include onnxruntime_cxx_api.h#ifdef USE_CUDA #include cuda_fp16.h #endifenum MODEL_TYPE {//FLOAT32 MODELYOLO_ORIGIN_V5 0,YOLO_ORIGIN_V8 1,//only support v8 detector currentlyYOLO_POSE_V8 2,YOLO_CLS_V8 3,YOLO_ORIGIN_V8_HALF 4,YOLO_POSE_V8_HALF 5,YOLO_CLS_V8_HALF 6 };typedef struct _DCSP_INIT_PARAM {std::string ModelPath;MODEL_TYPE ModelType YOLO_ORIGIN_V8;std::vectorint imgSize {640, 640};float RectConfidenceThreshold 0.6;float iouThreshold 0.5;bool CudaEnable false;int LogSeverityLevel 3;int IntraOpNumThreads 1; } DCSP_INIT_PARAM;typedef struct _DCSP_RESULT {int classId;float confidence;cv::Rect box; } DCSP_RESULT;class DCSP_CORE { public:DCSP_CORE();~DCSP_CORE();public:char *CreateSession(DCSP_INIT_PARAM iParams);char *RunSession(cv::Mat iImg, std::vectorDCSP_RESULT oResult);char *WarmUpSession();templatetypename Nchar *TensorProcess(clock_t starttime_1, cv::Mat iImg, N blob, std::vectorint64_t inputNodeDims,std::vectorDCSP_RESULT oResult);std::vectorstd::string classes{};private:Ort::Env env;Ort::Session *session;bool cudaEnable;Ort::RunOptions options;std::vectorconst char * inputNodeNames;std::vectorconst char * outputNodeNames;MODEL_TYPE modelType;std::vectorint imgSize;float rectConfidenceThreshold;float iouThreshold; };inference.cpp #include inference.h #include regex#define benchmarkDCSP_CORE::DCSP_CORE() {}DCSP_CORE::~DCSP_CORE() {delete session; }#ifdef USE_CUDA namespace Ort {templatestruct TypeToTensorTypehalf { static constexpr ONNXTensorElementDataType type ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT16; }; } #endiftemplatetypename T char *BlobFromImage(cv::Mat iImg, T iBlob) {int channels iImg.channels();int imgHeight iImg.rows;int imgWidth iImg.cols;for (int c 0; c channels; c) {for (int h 0; h imgHeight; h) {for (int w 0; w imgWidth; w) {iBlob[c * imgWidth * imgHeight h * imgWidth w] typename std::remove_pointerT::type((iImg.atcv::Vec3b(h, w)[c]) / 255.0f);}}}return RET_OK; }char *PostProcess(cv::Mat iImg, std::vectorint iImgSize, cv::Mat oImg) {cv::Mat img iImg.clone();cv::resize(iImg, oImg, cv::Size(iImgSize.at(0), iImgSize.at(1)));if (img.channels() 1) {cv::cvtColor(oImg, oImg, cv::COLOR_GRAY2BGR);}cv::cvtColor(oImg, oImg, cv::COLOR_BGR2RGB);return RET_OK; }char *DCSP_CORE::CreateSession(DCSP_INIT_PARAM iParams) {char *Ret RET_OK;std::regex pattern([\u4e00-\u9fa5]);bool result std::regex_search(iParams.ModelPath, pattern);if (result) {Ret [DCSP_ONNX]:Model path error.Change your model path without chinese characters.;std::cout Ret std::endl;return Ret;}try {rectConfidenceThreshold iParams.RectConfidenceThreshold;iouThreshold iParams.iouThreshold;imgSize iParams.imgSize;modelType iParams.ModelType;env Ort::Env(ORT_LOGGING_LEVEL_WARNING, Yolo);Ort::SessionOptions sessionOption;if (iParams.CudaEnable) {cudaEnable iParams.CudaEnable;OrtCUDAProviderOptions cudaOption;cudaOption.device_id 0;sessionOption.AppendExecutionProvider_CUDA(cudaOption);}sessionOption.SetGraphOptimizationLevel(GraphOptimizationLevel::ORT_ENABLE_ALL);sessionOption.SetIntraOpNumThreads(iParams.IntraOpNumThreads);sessionOption.SetLogSeverityLevel(iParams.LogSeverityLevel);#ifdef _WIN32int ModelPathSize MultiByteToWideChar(CP_UTF8, 0, iParams.ModelPath.c_str(), static_castint(iParams.ModelPath.length()), nullptr, 0);wchar_t* wide_cstr new wchar_t[ModelPathSize 1];MultiByteToWideChar(CP_UTF8, 0, iParams.ModelPath.c_str(), static_castint(iParams.ModelPath.length()), wide_cstr, ModelPathSize);wide_cstr[ModelPathSize] L\0;const wchar_t* modelPath wide_cstr; #elseconst char *modelPath iParams.ModelPath.c_str(); #endif // _WIN32session new Ort::Session(env, modelPath, sessionOption);Ort::AllocatorWithDefaultOptions allocator;size_t inputNodesNum session-GetInputCount();for (size_t i 0; i inputNodesNum; i) {Ort::AllocatedStringPtr input_node_name session-GetInputNameAllocated(i, allocator);char *temp_buf new char[50];strcpy(temp_buf, input_node_name.get());inputNodeNames.push_back(temp_buf);}size_t OutputNodesNum session-GetOutputCount();for (size_t i 0; i OutputNodesNum; i) {Ort::AllocatedStringPtr output_node_name session-GetOutputNameAllocated(i, allocator);char *temp_buf new char[10];strcpy(temp_buf, output_node_name.get());outputNodeNames.push_back(temp_buf);}options Ort::RunOptions{nullptr};WarmUpSession();return RET_OK;}catch (const std::exception e) {const char *str1 [DCSP_ONNX]:;const char *str2 e.what();std::string result std::string(str1) std::string(str2);char *merged new char[result.length() 1];std::strcpy(merged, result.c_str());std::cout merged std::endl;delete[] merged;return [DCSP_ONNX]:Create session failed.;}}char *DCSP_CORE::RunSession(cv::Mat iImg, std::vectorDCSP_RESULT oResult) { #ifdef benchmarkclock_t starttime_1 clock(); #endif // benchmarkchar *Ret RET_OK;cv::Mat processedImg;PostProcess(iImg, imgSize, processedImg);if (modelType 4) {float *blob new float[processedImg.total() * 3];BlobFromImage(processedImg, blob);std::vectorint64_t inputNodeDims {1, 3, imgSize.at(0), imgSize.at(1)};TensorProcess(starttime_1, iImg, blob, inputNodeDims, oResult);} else { #ifdef USE_CUDAhalf* blob new half[processedImg.total() * 3];BlobFromImage(processedImg, blob);std::vectorint64_t inputNodeDims { 1,3,imgSize.at(0),imgSize.at(1) };TensorProcess(starttime_1, iImg, blob, inputNodeDims, oResult); #endif}return Ret; }templatetypename N char *DCSP_CORE::TensorProcess(clock_t starttime_1, cv::Mat iImg, N blob, std::vectorint64_t inputNodeDims,std::vectorDCSP_RESULT oResult) {Ort::Value inputTensor Ort::Value::CreateTensortypename std::remove_pointerN::type(Ort::MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeCPU), blob, 3 * imgSize.at(0) * imgSize.at(1),inputNodeDims.data(), inputNodeDims.size()); #ifdef benchmarkclock_t starttime_2 clock(); #endif // benchmarkauto outputTensor session-Run(options, inputNodeNames.data(), inputTensor, 1, outputNodeNames.data(),outputNodeNames.size()); #ifdef benchmarkclock_t starttime_3 clock(); #endif // benchmarkOrt::TypeInfo typeInfo outputTensor.front().GetTypeInfo();auto tensor_info typeInfo.GetTensorTypeAndShapeInfo();std::vectorint64_t outputNodeDims tensor_info.GetShape();auto output outputTensor.front().GetTensorMutableDatatypename std::remove_pointerN::type();delete blob;switch (modelType) {case 1://V8_ORIGIN_FP32case 4://V8_ORIGIN_FP16{int strideNum outputNodeDims[2];int signalResultNum outputNodeDims[1];std::vectorint class_ids;std::vectorfloat confidences;std::vectorcv::Rect boxes;cv::Mat rawData;if (modelType 1) {// FP32rawData cv::Mat(signalResultNum, strideNum, CV_32F, output);} else {// FP16rawData cv::Mat(signalResultNum, strideNum, CV_16F, output);rawData.convertTo(rawData, CV_32F);}rawData rawData.t();float *data (float *) rawData.data;float x_factor iImg.cols / 640.;float y_factor iImg.rows / 640.;for (int i 0; i strideNum; i) {float *classesScores data 4;cv::Mat scores(1, this-classes.size(), CV_32FC1, classesScores);cv::Point class_id;double maxClassScore;cv::minMaxLoc(scores, 0, maxClassScore, 0, class_id);if (maxClassScore rectConfidenceThreshold) {confidences.push_back(maxClassScore);class_ids.push_back(class_id.x);float x data[0];float y data[1];float w data[2];float h data[3];int left int((x - 0.5 * w) * x_factor);int top int((y - 0.5 * h) * y_factor);int width int(w * x_factor);int height int(h * y_factor);boxes.emplace_back(left, top, width, height);}data signalResultNum;}std::vectorint nmsResult;cv::dnn::NMSBoxes(boxes, confidences, rectConfidenceThreshold, iouThreshold, nmsResult);for (int i 0; i nmsResult.size(); i) {int idx nmsResult[i];DCSP_RESULT result;result.classId class_ids[idx];result.confidence confidences[idx];result.box boxes[idx];oResult.push_back(result);}#ifdef benchmarkclock_t starttime_4 clock();double pre_process_time (double) (starttime_2 - starttime_1) / CLOCKS_PER_SEC * 1000;double process_time (double) (starttime_3 - starttime_2) / CLOCKS_PER_SEC * 1000;double post_process_time (double) (starttime_4 - starttime_3) / CLOCKS_PER_SEC * 1000;if (cudaEnable) {std::cout [DCSP_ONNX(CUDA)]: pre_process_time ms pre-process, process_time ms inference, post_process_time ms post-process. std::endl;} else {std::cout [DCSP_ONNX(CPU)]: pre_process_time ms pre-process, process_time ms inference, post_process_time ms post-process. std::endl;} #endif // benchmarkbreak;}}return RET_OK; }char *DCSP_CORE::WarmUpSession() {clock_t starttime_1 clock();cv::Mat iImg cv::Mat(cv::Size(imgSize.at(0), imgSize.at(1)), CV_8UC3);cv::Mat processedImg;PostProcess(iImg, imgSize, processedImg);if (modelType 4) {float *blob new float[iImg.total() * 3];BlobFromImage(processedImg, blob);std::vectorint64_t YOLO_input_node_dims {1, 3, imgSize.at(0), imgSize.at(1)};Ort::Value input_tensor Ort::Value::CreateTensorfloat(Ort::MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeCPU), blob, 3 * imgSize.at(0) * imgSize.at(1),YOLO_input_node_dims.data(), YOLO_input_node_dims.size());auto output_tensors session-Run(options, inputNodeNames.data(), input_tensor, 1, outputNodeNames.data(),outputNodeNames.size());delete[] blob;clock_t starttime_4 clock();double post_process_time (double) (starttime_4 - starttime_1) / CLOCKS_PER_SEC * 1000;if (cudaEnable) {std::cout [DCSP_ONNX(CUDA)]: Cuda warm-up cost post_process_time ms. std::endl;}} else { #ifdef USE_CUDAhalf* blob new half[iImg.total() * 3];BlobFromImage(processedImg, blob);std::vectorint64_t YOLO_input_node_dims { 1,3,imgSize.at(0),imgSize.at(1) };Ort::Value input_tensor Ort::Value::CreateTensorhalf(Ort::MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeCPU), blob, 3 * imgSize.at(0) * imgSize.at(1), YOLO_input_node_dims.data(), YOLO_input_node_dims.size());auto output_tensors session-Run(options, inputNodeNames.data(), input_tensor, 1, outputNodeNames.data(), outputNodeNames.size());delete[] blob;clock_t starttime_4 clock();double post_process_time (double)(starttime_4 - starttime_1) / CLOCKS_PER_SEC * 1000;if (cudaEnable){std::cout [DCSP_ONNX(CUDA)]: Cuda warm-up cost post_process_time ms. std::endl;} #endif}return RET_OK; }main.cpp int read_coco_yaml(DCSP_CORE *p) {// Open the YAML filestd::ifstream file(coco.yaml);if (!file.is_open()) {std::cerr Failed to open file std::endl;return 1;}// Read the file line by linestd::string line;std::vectorstd::string lines;while (std::getline(file, line)) {lines.push_back(line);}// Find the start and end of the names sectionstd::size_t start 0;std::size_t end 0;for (std::size_t i 0; i lines.size(); i) {if (lines[i].find(names:) ! std::string::npos) {start i 1;} else if (start 0 lines[i].find(:) std::string::npos) {end i;break;}}// Extract the namesstd::vectorstd::string names;for (std::size_t i start; i end; i) {std::stringstream ss(lines[i]);std::string name;std::getline(ss, name, :); // Extract the number before the delimiterstd::getline(ss, name); // Extract the string after the delimiternames.push_back(name);}p-classes names;return 0; }int main(int argc, char* argv[]) {DCSP_CORE *yoloDetector new DCSP_CORE;//std::string model_path yolov8n.onnx;std::string model_path argv[1];read_coco_yaml(yoloDetector);#ifdef USE_CUDA// GPU FP32 inferenceDCSP_INIT_PARAM params{ model_path, YOLO_ORIGIN_V8, {640, 640}, 0.1, 0.5, true };// GPU FP16 inference// DCSP_INIT_PARAM params{ model_path, YOLO_ORIGIN_V8_HALF, {640, 640}, 0.1, 0.5, true };#else// CPU inferenceDCSP_INIT_PARAM params{model_path, YOLO_ORIGIN_V8, {640, 640}, 0.1, 0.5, false};#endifyoloDetector-CreateSession(params);cv::VideoCapture vc;vc.open(argv[2]);if(vc.isOpened()){cv::Mat frame;vc frame;while(!frame.empty()){std::vectorDCSP_RESULT res;yoloDetector-RunSession(frame, res);for (int i 0; i res.size(); i){DCSP_RESULT detection res[i];cv::Rect box detection.box;cv::RNG rng(cv::getTickCount());cv::Scalar color(rng.uniform(0, 256), rng.uniform(0, 256), rng.uniform(0, 256));;// Detection boxcv::rectangle(frame, box, color, 2);// Detection box textstd::string classString yoloDetector-classes[detection.classId] std::to_string(detection.confidence).substr(0, 4);cv::Size textSize cv::getTextSize(classString, cv::FONT_HERSHEY_DUPLEX, 1, 2, 0);cv::Rect textBox(box.x, box.y - 40, textSize.width 10, textSize.height 20);cv::rectangle(frame, textBox, color, cv::FILLED);cv::putText(frame, classString, cv::Point(box.x 5, box.y - 10), cv::FONT_HERSHEY_DUPLEX, 1, cv::Scalar(0, 0, 0), 2, 0);}cv::rectangle(frame, g_rect, Scalar(0, 255, 0), 3, cv::LINE_AA);cv::imshow(image, frame);cv::waitKey(1);vc frame;}} } opencv的框选区域和yolov8检测目标框融合 #include opencv2/opencv.hpp #include fstream #include inference.husing namespace cv;// 定义一个全局变量用于存放鼠标框选的矩形区域 Rect g_rect; // 定义一个全局变量用于标记鼠标是否按下 bool g_bDrawingBox false;// 定义一个回调函数用于处理鼠标事件 void on_MouseHandle(int event, int x, int y, int flags, void* param) {// 将param转换为Mat类型的指针Mat image *(Mat*) param;// 根据不同的鼠标事件进行处理switch (event){// 鼠标左键按下事件case EVENT_LBUTTONDOWN:{// 标记鼠标已按下g_bDrawingBox true;// 记录矩形框的起始点g_rect.x x;g_rect.y y;break;}// 鼠标移动事件case EVENT_MOUSEMOVE:{// 如果鼠标已按下更新矩形框的宽度和高度if (g_bDrawingBox){g_rect.width x - g_rect.x;g_rect.height y - g_rect.y;}break;}// 鼠标左键松开事件case EVENT_LBUTTONUP:{// 标记鼠标已松开g_bDrawingBox false;// 如果矩形框的宽度和高度为正绘制矩形框到图像上if (g_rect.width 0 g_rect.height 0){rectangle(image, g_rect, Scalar(0, 255, 0));}break;}} }int read_coco_yaml(DCSP_CORE *p) {// Open the YAML filestd::ifstream file(coco.yaml);if (!file.is_open()) {std::cerr Failed to open file std::endl;return 1;}// Read the file line by linestd::string line;std::vectorstd::string lines;while (std::getline(file, line)) {lines.push_back(line);}// Find the start and end of the names sectionstd::size_t start 0;std::size_t end 0;for (std::size_t i 0; i lines.size(); i) {if (lines[i].find(names:) ! std::string::npos) {start i 1;} else if (start 0 lines[i].find(:) std::string::npos) {end i;break;}}// Extract the namesstd::vectorstd::string names;for (std::size_t i start; i end; i) {std::stringstream ss(lines[i]);std::string name;std::getline(ss, name, :); // Extract the number before the delimiterstd::getline(ss, name); // Extract the string after the delimiternames.push_back(name);}p-classes names;return 0; }int main(int argc, char* argv[]) {// 读取原始图像// Mat src imread(argv[1]);DCSP_CORE *yoloDetector new DCSP_CORE;//std::string model_path yolov8n.onnx;std::string model_path argv[1];read_coco_yaml(yoloDetector); #ifdef USE_CUDA// GPU FP32 inferenceDCSP_INIT_PARAM params{ model_path, YOLO_ORIGIN_V8, {640, 640}, 0.1, 0.5, true };// GPU FP16 inference// DCSP_INIT_PARAM params{ model_path, YOLO_ORIGIN_V8_HALF, {640, 640}, 0.1, 0.5, true }; #else// CPU inferenceDCSP_INIT_PARAM params{model_path, YOLO_ORIGIN_V8, {640, 640}, 0.1, 0.5, false}; #endifyoloDetector-CreateSession(params);cv::VideoCapture vc;vc.open(argv[2]);if(vc.isOpened()){cv::Mat frame;vc frame;if(!frame.empty()){// 创建一个副本图像用于显示框选过程Mat temp;frame.copyTo(temp);// 创建一个窗口显示图像namedWindow(image);// 设置鼠标回调函数传入副本图像作为参数setMouseCallback(image, on_MouseHandle, (void*)temp);while (1){// 如果鼠标正在框选绘制一个虚线矩形框到副本图像上并显示框的大小和坐标if (g_bDrawingBox){temp.copyTo(frame);rectangle(frame, g_rect, Scalar(0, 255, 0), 1, LINE_AA);char text[32];sprintf(text, w%d, h%d, g_rect.width, g_rect.height);putText(frame, text, Point(g_rect.x 5, g_rect.y - 5), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0));}// 显示副本图像imshow(image, frame);// 等待按键如果按下ESC键退出循环if (waitKey(10) 27){break;}}while(!frame.empty()){std::vectorDCSP_RESULT res;yoloDetector-RunSession(frame, res);for (int i 0; i res.size(); i){DCSP_RESULT detection res[i];cv::Rect box detection.box;cv::RNG rng(cv::getTickCount());cv::Scalar color(rng.uniform(0, 256), rng.uniform(0, 256), rng.uniform(0, 256));;// Detection boxcv::rectangle(frame, box, color, 2);// Detection box textstd::string classString yoloDetector-classes[detection.classId] std::to_string(detection.confidence).substr(0, 4);cv::Size textSize cv::getTextSize(classString, cv::FONT_HERSHEY_DUPLEX, 1, 2, 0);cv::Rect textBox(box.x, box.y - 40, textSize.width 10, textSize.height 20);cv::rectangle(frame, textBox, color, cv::FILLED);cv::putText(frame, classString, cv::Point(box.x 5, box.y - 10), cv::FONT_HERSHEY_DUPLEX, 1, cv::Scalar(0, 0, 0), 2, 0);}cv::rectangle(frame, g_rect, Scalar(0, 255, 0), 3, cv::LINE_AA);cv::imshow(image, frame);cv::waitKey(1);vc frame;}}}return 0; }计算预警区域和目标框重合度 double calIou(const cv::Rect rc1, const cv::Rect rc2) {cv::Rect intersection rc1 rc2;if (!intersection.empty()) {double intersectionArea intersection.width * intersection.height;double rect1Area rc1.width * rc1.height;double rect2Area rc2.width * rc2.height;// 计算IOUdouble iou intersectionArea / (rect1Area rect2Area - intersectionArea);return iou;} else {// 没有重叠IOU为0return 0.0;} } 跟踪实现 不断的去循环激活的目标来过滤掉重复的代码这块以后实现 完整代码 #include opencv2/opencv.hpp #include fstream #include inference.husing namespace cv;// 定义一个全局变量用于存放鼠标框选的矩形区域 Rect g_rect; // 定义一个全局变量用于标记鼠标是否按下 bool g_bDrawingBox false;// 定义一个回调函数用于处理鼠标事件 void on_MouseHandle(int event, int x, int y, int flags, void* param) {// 将param转换为Mat类型的指针Mat image *(Mat*) param;// 根据不同的鼠标事件进行处理switch (event){// 鼠标左键按下事件case EVENT_LBUTTONDOWN:{// 标记鼠标已按下g_bDrawingBox true;// 记录矩形框的起始点g_rect.x x;g_rect.y y;break;}// 鼠标移动事件case EVENT_MOUSEMOVE:{// 如果鼠标已按下更新矩形框的宽度和高度if (g_bDrawingBox){g_rect.width x - g_rect.x;g_rect.height y - g_rect.y;}break;}// 鼠标左键松开事件case EVENT_LBUTTONUP:{// 标记鼠标已松开g_bDrawingBox false;// 如果矩形框的宽度和高度为正绘制矩形框到图像上if (g_rect.width 0 g_rect.height 0){rectangle(image, g_rect, Scalar(0, 255, 0));}break;}} }int read_coco_yaml(DCSP_CORE *p) {// Open the YAML filestd::ifstream file(coco.yaml);if (!file.is_open()) {std::cerr Failed to open file std::endl;return 1;}// Read the file line by linestd::string line;std::vectorstd::string lines;while (std::getline(file, line)) {lines.push_back(line);}// Find the start and end of the names sectionstd::size_t start 0;std::size_t end 0;for (std::size_t i 0; i lines.size(); i) {if (lines[i].find(names:) ! std::string::npos) {start i 1;} else if (start 0 lines[i].find(:) std::string::npos) {end i;break;}}// Extract the namesstd::vectorstd::string names;for (std::size_t i start; i end; i) {std::stringstream ss(lines[i]);std::string name;std::getline(ss, name, :); // Extract the number before the delimiterstd::getline(ss, name); // Extract the string after the delimiternames.push_back(name);}p-classes names;return 0; }double calIou(const cv::Rect rc1, const cv::Rect rc2) {cv::Rect intersection rc1 rc2;if (!intersection.empty()) {double intersectionArea intersection.width * intersection.height;double rect1Area rc1.width * rc1.height;double rect2Area rc2.width * rc2.height;// 计算IOUdouble iou intersectionArea / (rect1Area rect2Area - intersectionArea);return iou;} else {// 没有重叠IOU为0return 0.0;} }int main(int argc, char* argv[]) {// 读取原始图像// Mat src imread(argv[1]);DCSP_CORE *yoloDetector new DCSP_CORE;//std::string model_path yolov8n.onnx;std::string model_path argv[1];read_coco_yaml(yoloDetector); #ifdef USE_CUDA// GPU FP32 inferenceDCSP_INIT_PARAM params{ model_path, YOLO_ORIGIN_V8, {640, 640}, 0.1, 0.5, true };// GPU FP16 inference// DCSP_INIT_PARAM params{ model_path, YOLO_ORIGIN_V8_HALF, {640, 640}, 0.1, 0.5, true }; #else// CPU inferenceDCSP_INIT_PARAM params{model_path, YOLO_ORIGIN_V8, {640, 640}, 0.1, 0.5, false}; #endifyoloDetector-CreateSession(params);cv::VideoCapture vc;vc.open(argv[2]);if(vc.isOpened()){cv::Mat frame;vc frame;if(!frame.empty()){// 创建一个副本图像用于显示框选过程Mat temp;frame.copyTo(temp);// 创建一个窗口显示图像namedWindow(image);// 设置鼠标回调函数传入副本图像作为参数setMouseCallback(image, on_MouseHandle, (void*)temp);while (1){// 如果鼠标正在框选绘制一个虚线矩形框到副本图像上并显示框的大小和坐标if (g_bDrawingBox){temp.copyTo(frame);rectangle(frame, g_rect, Scalar(0, 255, 0), 1, LINE_AA);char text[32];sprintf(text, w%d, h%d, g_rect.width, g_rect.height);putText(frame, text, Point(g_rect.x 5, g_rect.y - 5), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0));}// 显示副本图像imshow(image, frame);// 等待按键如果按下ESC键退出循环if (waitKey(10) 27){break;}}while(!frame.empty()){std::vectorDCSP_RESULT res;yoloDetector-RunSession(frame, res);for (int i 0; i res.size(); i){DCSP_RESULT detection res[i];cv::Rect box detection.box;cv::RNG rng(cv::getTickCount());cv::Scalar color(rng.uniform(0, 256), rng.uniform(0, 256), rng.uniform(0, 256));;// Detection boxcv::rectangle(frame, box, color, 2);// Detection box textstd::string classString yoloDetector-classes[detection.classId] std::to_string(detection.confidence).substr(0, 4);cv::Size textSize cv::getTextSize(classString, cv::FONT_HERSHEY_DUPLEX, 1, 2, 0);cv::Rect textBox(box.x, box.y - 40, textSize.width 10, textSize.height 20);cv::rectangle(frame, textBox, color, cv::FILLED);cv::putText(frame, classString, cv::Point(box.x 5, box.y - 10), cv::FONT_HERSHEY_DUPLEX, 1, cv::Scalar(0, 0, 0), 2, 0);double iou calIou(g_rect, box);if(iou 0)std::cout iou: iou std::endl;}cv::rectangle(frame, g_rect, Scalar(0, 255, 0), 3, cv::LINE_AA);cv::imshow(image, frame);cv::waitKey(1);vc frame;}}}return 0; }参考 yolov8