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windows 使用yolov5训练模型，之后opencv加载模型进行推理。

一，搭建环境

安装 Anaconda

二，创建虚拟环境并安装yolov5

conda create -n yolov5 python=3.9 -y
conda activate yolov5
git clone https://github.com/ultralytics/yolov5
cd yolov5
pip install -r requirements.txt

三，安装LabelImg 进行标注

四，准备训练配置

1，data.yaml

train: ./images/train
val: ./images/valnc: 1
names: ['erha'] #类别名称，比如二哈

2，确保图像和标签对应

images/train/img001.jpg
labels/train/img001.txt

3，训练

python train.py --img 640 --batch 16 --epochs 50 --data  ./keiler/datasets/data.yaml --weights yolov5s.pt --name erha

输出模型路径：

runs/train/erha4/weights/best.pt

4,将模型 转成 onnx格式，这样才能给到opencv  加载

 五，opencv 推理

#include <iostream>
#include <Thread/semaphore.h>
#include <signal.h>
#include "core/Engine.h"
#include <opencv2/opencv.hpp>
#include <opencv2/dnn.hpp>
#include <iostream>
using namespace toolkit;using namespace cv;
using namespace dnn;
using namespace std;int main()
{// 加载模型Net net = readNetFromONNX("best.onnx");net.setPreferableBackend(DNN_BACKEND_OPENCV);net.setPreferableTarget(DNN_TARGET_CPU); // 可改为 DNN_TARGET_CUDAcout << "Net is empty? " << net.empty() << endl;// 读取图像Mat image = imread("test.jpeg");if (image.empty()){cerr << "Image not found!" << endl;return -1;}// YOLOv5 输入大小int input_width = 640;int input_height = 640;int num_classes = 1;// 原图尺寸int original_width = image.cols;int original_height = image.rows;// 预处理Mat blob;resize(image, image, Size(input_width, input_height));blobFromImage(image, blob, 1.0 / 255.0, Size(input_width, input_height), Scalar(), true, false);// 设置输入net.setInput(blob);// 前向推理std::vector<Mat> outputs;net.forward(outputs, net.getUnconnectedOutLayersNames());// 后处理float confThreshold = 0.001;float nmsThreshold = 0.001;vector<int> classIds;vector<float> confidences;vector<Rect> boxes;// 输出维度 [1, N, 85]Mat output = outputs[0];const int num_detections = output.size[1];const int dimensions = output.size[2];float* data = (float*)output.data;float x_factor = (float)original_width / input_width;float y_factor = (float)original_height / input_height;std::cout<<"num_detections "<<num_detections<<std::endl;for (int i = 0; i < num_detections; ++i) {float obj_conf = data[i * dimensions + 4];std::cout<<" obj_conf"<<obj_conf<<std::endl;if (obj_conf < confThreshold) continue;float* class_scores = data + i * dimensions + 5;Mat scores(1, num_classes, CV_32F, class_scores);Point classIdPoint;double max_class_score;minMaxLoc(scores, 0, &max_class_score, 0, &classIdPoint);float confidence = obj_conf * (float)max_class_score;std::cout<<" confidence"<<confidence<<std::endl;if (confidence > confThreshold) {// 解码框坐标float cx = data[i * dimensions + 0];float cy = data[i * dimensions + 1];float w = data[i * dimensions + 2];float h = data[i * dimensions + 3];int left = (int)((cx - w / 2) * x_factor);int top = (int)((cy - h / 2) * y_factor);int width = (int)(w * x_factor);int height = (int)(h * y_factor);boxes.push_back(Rect(left, top, width, height));confidences.push_back(confidence);classIds.push_back(classIdPoint.x);}}// NMS 抑制vector<int> indices;NMSBoxes(boxes, confidences, confThreshold, nmsThreshold, indices);for (int idx : indices) {Rect box = boxes[idx];rectangle(image, box, Scalar(0, 255, 0), 2);putText(image, to_string(classIds[idx]), box.tl(), FONT_HERSHEY_SIMPLEX, 0.6, Scalar(0, 0, 255), 2);}cv::imwrite("result.jpg", image);}

失败了，没有检测出来，稍后再查查。