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opencv+yolov8实现监控画面报警功能

news 2025/7/28 4:09:14

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

C++
OpenCV
Yolov8 + 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.h"using 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::vector<int> 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::vector<DCSP_RESULT> &oResult);char *WarmUpSession();template<typename N>char *TensorProcess(clock_t &starttime_1, cv::Mat &iImg, N &blob, std::vector<int64_t> &inputNodeDims,std::vector<DCSP_RESULT> &oResult);std::vector<std::string> classes{};private:Ort::Env env;Ort::Session *session;bool cudaEnable;Ort::RunOptions options;std::vector<const char *> inputNodeNames;std::vector<const char *> outputNodeNames;MODEL_TYPE modelType;std::vector<int> 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
{template<>struct TypeToTensorType<half> { static constexpr ONNXTensorElementDataType type = ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT16; };
}
#endiftemplate<typename 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_pointer<T>::type((iImg.at<cv::Vec3b>(h, w)[c]) / 255.0f);}}}return RET_OK;
}char *PostProcess(cv::Mat &iImg, std::vector<int> 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_cast<int>(iParams.ModelPath.length()), nullptr, 0);wchar_t* wide_cstr = new wchar_t[ModelPathSize + 1];MultiByteToWideChar(CP_UTF8, 0, iParams.ModelPath.c_str(), static_cast<int>(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::vector<DCSP_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::vector<int64_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::vector<int64_t> inputNodeDims = { 1,3,imgSize.at(0),imgSize.at(1) };TensorProcess(starttime_1, iImg, blob, inputNodeDims, oResult);
#endif}return Ret;
}template<typename N>
char *DCSP_CORE::TensorProcess(clock_t &starttime_1, cv::Mat &iImg, N &blob, std::vector<int64_t> &inputNodeDims,std::vector<DCSP_RESULT> &oResult) {Ort::Value inputTensor = Ort::Value::CreateTensor<typename std::remove_pointer<N>::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::vector<int64_t> outputNodeDims = tensor_info.GetShape();auto output = outputTensor.front().GetTensorMutableData<typename std::remove_pointer<N>::type>();delete blob;switch (modelType) {case 1://V8_ORIGIN_FP32case 4://V8_ORIGIN_FP16{int strideNum = outputNodeDims[2];int signalResultNum = outputNodeDims[1];std::vector<int> class_ids;std::vector<float> confidences;std::vector<cv::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::vector<int> 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::vector<int64_t> YOLO_input_node_dims = {1, 3, imgSize.at(0), imgSize.at(1)};Ort::Value input_tensor = Ort::Value::CreateTensor<float>(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::vector<int64_t> YOLO_input_node_dims = { 1,3,imgSize.at(0),imgSize.at(1) };Ort::Value input_tensor = Ort::Value::CreateTensor<half>(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::vector<std::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::vector<std::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::vector<DCSP_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.h"using 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::vector<std::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::vector<std::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::vector<DCSP_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.h"using 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::vector<std::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::vector<std::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::vector<DCSP_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;
}