目录

一、Yolov8简介

1、yolov8 源码地址：

2、官方文档：

3、预训练模型百度网盘地址：

二、模型训练

1、标定红绿灯数据：

2、训练环境：

3、数据转化：

4、构造训练数据：

5、训练样本：

三、验证模型：

1、图像测试：

2、视频测试：

四、导出ONNX

五、Opencv实现Yolov8 C++ 识别

1、开发环境：

2、main函数代码：

3、yolov8 头文件inference.h代码：

4、yolov8 cpp文件inference.cpp代码：

---

一、Yolov8简介

1、yolov8 源码地址：

工程链接：https://github.com/ultralytics/ultralytics

2、官方文档：

CLI - Ultralytics YOLOv8 Docs

3、预训练模型百度网盘地址：

训练时需要用到，下载的网址较慢：

如果模型下载不了，加QQ：187100248.

链接: https://pan.baidu.com/s/1YfMxRPGk8LF75a4cbgYxGg 提取码: rd7b

二、模型训练

1、标定红绿灯数据：

         类别为23类，分别为：

红绿灯类别

red_light	green_light	yellow_light	off_light	part_ry_light	part_rg_light
part_yg_light	ryg_light	countdown_off_light	countdown_on_light	shade_light	zero
one	two	three	four	five	six
seven	eight	nine	brokeNumber	brokenLight

        标注工具地址：AI标注工具Labelme和LabelImage Labelme和LabelImage集成工具_labelimage与labelme-CSDN博客

2、训练环境：

1）、Ubuntu18.04；

2）、Cuda11.7 + CUDNN8.0.6；

3）、opencv4.5.5；

4）、PyTorch1.8.1-GPU；

5）、python3.9

3、数据转化：

 1）、需要把上面标定的数据集中的.xml文件转换为.txt，转换代码为：

import os
import shutil
import xml.etree.ElementTree as ET
from xml.etree.ElementTree import Element, SubElement
from PIL import Image
import cv2

classes = ['red_light', 'green_light', 'yellow_light', 'off_light', 'part_ry_light', 'part_rg_light', 'part_yg_light', 'ryg_light',
           'countdown_off_light', 'countdown_on_light','shade_light','zero','one','two','three','four','five','six','seven',
           'eight','nine','brokeNumber','brokenLight']

class Xml_make(object):
    def __init__(self):
        super().__init__()

    def __indent(self, elem, level=0):
        i = "\n" + level * "\t"
        if len(elem):
            if not elem.text or not elem.text.strip():
                elem.text = i + "\t"
            if not elem.tail or not elem.tail.strip():
                elem.tail = i
            for elem in elem:
                self.__indent(elem, level + 1)
            if not elem.tail or not elem.tail.strip():
                elem.tail = i
        else:
            if level and (not elem.tail or not elem.tail.strip()):
                elem.tail = i

    def _imageinfo(self, list_top):
        annotation_root = ET.Element('annotation')
        annotation_root.set('verified', 'no')
        tree = ET.ElementTree(annotation_root)
        '''
        0:xml_savepath 1:folder,2:filename,3:path
        4:checked,5:width,6:height,7:depth
        '''
        folder_element = ET.Element('folder')
        folder_element.text = list_top[1]
        annotation_root.append(folder_element)

        filename_element = ET.Element('filename')
        filename_element.text = list_top[2]
        annotation_root.append(filename_element)

        path_element = ET.Element('path')
        path_element.text = list_top[3]
        annotation_root.append(path_element)

        # checked_element = ET.Element('checked')
        # checked_element.text = list_top[4]
        # annotation_root.append(checked_element)

        source_element = ET.Element('source')
        database_element = SubElement(source_element, 'database')
        database_element.text = 'Unknown'
        annotation_root.append(source_element)

        size_element = ET.Element('size')
        width_element = SubElement(size_element, 'width')
        width_element.text = str(list_top[5])
        height_element = SubElement(size_element, 'height')
        height_element.text = str(list_top[6])
        depth_element = SubElement(size_element, 'depth')
        depth_element.text = str(list_top[7])
        annotation_root.append(size_element)

        segmented_person_element = ET.Element('segmented')
        segmented_person_element.text = '0'
        annotation_root.append(segmented_person_element)

        return tree, annotation_root

    def _bndbox(self, annotation_root, list_bndbox):
        for i in range(0, len(list_bndbox), 9):
            object_element = ET.Element('object')
            name_element = SubElement(object_element, 'name')
            name_element.text = list_bndbox[i]

            # flag_element = SubElement(object_element, 'flag')
            # flag_element.text = list_bndbox[i + 1]

            pose_element = SubElement(object_element, 'pose')
            pose_element.text = list_bndbox[i + 2]

            truncated_element = SubElement(object_element, 'truncated')
            truncated_element.text = list_bndbox[i + 3]

            difficult_element = SubElement(object_element, 'difficult')
            difficult_element.text = list_bndbox[i + 4]

            bndbox_element = SubElement(object_element, 'bndbox')
            xmin_element = SubElement(bndbox_element, 'xmin')
            xmin_element.text = str(list_bndbox[i + 5])

            ymin_element = SubElement(bndbox_element, 'ymin')
            ymin_element.text = str(list_bndbox[i + 6])

            xmax_element = SubElement(bndbox_element, 'xmax')
            xmax_element.text = str(list_bndbox[i + 7])

            ymax_element = SubElement(bndbox_element, 'ymax')
            ymax_element.text = str(list_bndbox[i + 8])

            annotation_root.append(object_element)

        return annotation_root

    def txt_to_xml(self, list_top, list_bndbox):
        tree, annotation_root = self._imageinfo(list_top)
        annotation_root = self._bndbox(annotation_root, list_bndbox)
        self.__indent(annotation_root)
        tree.write(list_top[0], encoding='utf-8', xml_declaration=True)

def txt_2_xml(source_path, xml_save_dir, jpg_save_dir,txt_dir):

    COUNT = 0
    for folder_path_tuple, folder_name_list, file_name_list in os.walk(source_path):
        for file_name in file_name_list:
            file_suffix = os.path.splitext(file_name)[-1]
            if file_suffix != '.jpg':
                continue
            list_top = []
            list_bndbox = []
            path = os.path.join(folder_path_tuple, file_name)
            xml_save_path = os.path.join(xml_save_dir, file_name.replace(file_suffix, '.xml'))
            txt_path = os.path.join(txt_dir, file_name.replace(file_suffix, '.txt'))
            filename = file_name#os.path.splitext(file_name)[0]
            checked = 'NO'
            #print(file_name)
            im = Image.open(path)
            im_w = im.size[0]
            im_h = im.size[1]

            shutil.copy(path, jpg_save_dir)


            if im_w*im_h > 34434015:
                print(file_name)
            if im_w < 100:
                print(file_name)

            width = str(im_w)
            height = str(im_h)
            depth = '3'
            flag = 'rectangle'
            pose = 'Unspecified'
            truncated = '0'
            difficult = '0'
            list_top.extend([xml_save_path, folder_path_tuple, filename, path, checked, width, height, depth])
            for line in open(txt_path, 'r'):
                line = line.strip()
                info = line.split(' ')
                name = classes[int(info[0])]
                x_cen = float(info[1]) * im_w
                y_cen = float(info[2]) * im_h
                w = float(info[3]) * im_w
                h = float(info[4]) * im_h
                xmin = int(x_cen - w / 2) - 1
                ymin = int(y_cen - h / 2) - 1
                xmax = int(x_cen + w / 2) + 3
                ymax = int(y_cen + h / 2) + 3

                if xmin < 0:
                    xmin = 0
                if ymin < 0:
                    ymin = 0

                if xmax > im_w - 1:
                    xmax = im_w - 1
                if ymax > im_h - 1:
                    ymax = im_h - 1

                if w > 5 and h > 5:
                  list_bndbox.extend([name, flag, pose, truncated, difficult,str(xmin), str(ymin), str(xmax), str(ymax)])

                if xmin < 0 or xmax > im_w - 1 or ymin < 0 or ymax > im_h - 1:
                    print(xml_save_path)

            Xml_make().txt_to_xml(list_top, list_bndbox)
            COUNT += 1
            #print(COUNT, xml_save_path)

if __name__ == "__main__":

    out_xml_path = "/home/TL_TrainData/"  # .xml输出文件存放地址
    out_jpg_path = "/home/TL_TrainData/"  # .jpg输出文件存放地址
    txt_path = "/home/Data/TrafficLight/trainData"  # yolov3标注.txt和图片文件夹
    images_path = "/home/TrafficLight/trainData"  # image文件存放地址
    txt_2_xml(images_path, out_xml_path, out_jpg_path, txt_path)

4、构造训练数据：

2）、训练样本数据构造，需要把分成images和labels，images下面放入图片，labels下面放入.txt文件:

5、训练样本：

 1）、首先安装训练包：

pip install ultralytics

2）、修改训练数据参数coco128_light.yaml文件，这个是自己修改的。

# Ultralytics YOLO 🚀, AGPL-3.0 license
# COCO128 dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics
# Example usage: yolo train data=coco128.yaml
# parent
# ├── ultralytics
# └── datasets
#     └── coco128  ← downloads here (7 MB)


# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
path: /home/Data/TrafficLight/datasets  # dataset root dir
train: images  # train images (relative to 'path') 128 images
val: images  # val images (relative to 'path') 128 images
test:  # test images (optional)

# Parameters
nc: 23  # number of classes

# Classes
names:
  0: red_light
  1: green_light
  2: yellow_light
  3: off_light
  4: part_ry_light
  5: part_rg_light
  6: part_yg_light
  7: ryg_light
  8: countdown_off_light
  9: countdown_on_light
  10: shade_light
  11: zero
  12: one
  13: two
  14: three
  15: four
  16: five
  17: six
  18: seven
  19: eight
  20: nine
  21: brokeNumber
  22: brokenLight

# Download script/URL (optional)
#download: https://ultralytics.com/assets/coco128.zip

3）、执行 train_yolov8x_light.sh，内容为：

yolo detect train data=coco128_light.yaml model=./runs/last.pt epochs=100 imgsz=640 workers=16 batch=32

        开始启动训练：

        

三、验证模型：

1、图像测试：

from ultralytics import YOLO

# Load a model
#model = YOLO('yolov8n.pt')  # load an official model
model = YOLO('best.pt')  # load a custom model

# Predict with the model
results = model('bus.jpg')  # predict on an image

# View results
for r in results:
    print(r.boxes)  # print the Boxes object containing the detection bounding boxes

2、视频测试：

import cv2
from ultralytics import YOLO

# Load the YOLOv8 model
model = YOLO('best.pt')

# Open the video file
video_path = "test_car_person_1080P.mp4"
cap = cv2.VideoCapture(video_path)

# Loop through the video frames
while cap.isOpened():
    # Read a frame from the video
    success, frame = cap.read()

    if success:
        # Run YOLOv8 inference on the frame
        results = model(frame)

        # Visualize the results on the frame
        annotated_frame = results[0].plot()

        # Display the annotated frame
        cv2.imshow("YOLOv8 Inference", annotated_frame)
        cv2.waitKey(10)

四、导出ONNX

1、训练输出，经过上面的训练后，得到训练生成文件，weights下生成了best.pt和last.pt：

2、等训练完毕后，利用best.pt生成best.onnx，执行命令如下：

yolo export model=best.pt imgsz=640 format=onnx opset=12

五、Opencv实现Yolov8 C++ 识别

1、开发环境：

1）、win7/win10；

2）、vs2019；

3）、opencv4.7.0；

2、main函数代码：

#include <iostream>
#include <vector>
#include "opencv2/opencv.hpp"
#include "inference.h"
#include <io.h>
#include <thread>
#define socklen_t int
#pragma comment (lib, "ws2_32.lib")

using namespace std;
using namespace cv;

int getFiles(std::string path, std::vector<std::string>& files, std::vector<std::string>& names)
{
    int i = 0;
    intptr_t hFile = 0;
    struct _finddata_t c_file;
    std::string imageFile = path + "*.*";

    if ((hFile = _findfirst(imageFile.c_str(), &c_file)) == -1L)
    {
        _findclose(hFile);
        return -1;
    }
    else
    {
        while (true)
        {
            std::string strname(c_file.name);
            if (std::string::npos != strname.find(".jpg") || std::string::npos != strname.find(".png") || std::string::npos != strname.find(".bmp"))
            {
                std::string fullName = path + c_file.name;

                files.push_back(fullName);

                std::string cutname = strname.substr(0, strname.rfind("."));
                names.push_back(cutname);
            }

            if (_findnext(hFile, &c_file) != 0)
            {
                _findclose(hFile);
                break;
            }
        }
    }

    return 0;
}

int main()
{
    std::string projectBasePath = "./"; // Set your ultralytics base path

    bool runOnGPU = true;

    //
    // Pass in either:
    //
    // "yolov8s.onnx" or "yolov5s.onnx"
    //
    // To run Inference with yolov8/yolov5 (ONNX)
    //

    // Note that in this example the classes are hard-coded and 'classes.txt' is a place holder.
    Inference inf(projectBasePath + "/best.onnx", cv::Size(640, 640), "classes.txt", runOnGPU);

    std::vector<std::string> files;
    std::vector<std::string> names;
    getFiles("./test/", files, names);

    //std::vector<std::string> imageNames;
    //imageNames.push_back(projectBasePath + "/test/20221104_8336.jpg");
    //imageNames.push_back(projectBasePath + "/test/20221104_8339.jpg");

    for (int i = 0; i < files.size(); ++i)
    {
        cv::Mat frame = cv::imread(files[i]);

        // Inference starts here...
        clock_t start, end;
        float time;
        start = clock();
        std::vector<Detection> output = inf.runInference(frame);
        end = clock();
        time = (float)(end - start);//CLOCKS_PER_SEC; 
        printf("timeCount = %f\n", time);

        int detections = output.size();
        std::cout << "Number of detections:" << detections << std::endl;

        for (int i = 0; i < detections; ++i)
        {
            Detection detection = output[i];

            cv::Rect box = detection.box;
            cv::Scalar color = detection.color;

            // Detection box
            cv::rectangle(frame, box, color, 2);

            // Detection box text
            std::string classString = detection.className + ' ' + 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);
        }
        // Inference ends here...

        // This is only for preview purposes
        float scale = 0.8;
        cv::resize(frame, frame, cv::Size(frame.cols * scale, frame.rows * scale));
        cv::imshow("Inference", frame);

        cv::waitKey(10);
    }
}

3、yolov8 头文件inference.h代码：

#ifndef INFERENCE_H
#define INFERENCE_H

// Cpp native
#include <fstream>
#include <vector>
#include <string>
#include <random>

// OpenCV / DNN / Inference
#include <opencv2/imgproc.hpp>
#include <opencv2/opencv.hpp>
#include <opencv2/dnn.hpp>

struct Detection
{
    int class_id{0};
    std::string className{};
    float confidence{0.0};
    cv::Scalar color{};
    cv::Rect box{};
};

class Inference
{
public:
    Inference(const std::string &onnxModelPath, const cv::Size &modelInputShape = {640, 640}, const std::string &classesTxtFile = "", const bool &runWithCuda = true);
    std::vector<Detection> runInference(const cv::Mat &input);

private:
    void loadClassesFromFile();
    void loadOnnxNetwork();
    cv::Mat formatToSquare(const cv::Mat &source);

    std::string modelPath{};
    std::string classesPath{};
    bool cudaEnabled{};

    std::vector<std::string> classes{ "red_light", "green_light", "yellow_light", "off_light", "part_ry_light", "part_rg_light", "part_yg_light", "ryg_light","countdown_off_light", "countdown_on_light","shade_light","zero","one","two","three","four","five","six","seven","eight","nine","brokeNumber","brokenLight" };

    cv::Size2f modelShape{};

    float modelConfidenceThreshold {0.25};
    float modelScoreThreshold      {0.45};
    float modelNMSThreshold        {0.50};

    bool letterBoxForSquare = true;

    cv::dnn::Net net;
};

#endif // INFERENCE_H

4、yolov8 cpp文件inference.cpp代码：

#include "inference.h"

Inference::Inference(const std::string &onnxModelPath, const cv::Size &modelInputShape, const std::string &classesTxtFile, const bool &runWithCuda)
{
    modelPath = onnxModelPath;
    modelShape = modelInputShape;
    classesPath = classesTxtFile;
    cudaEnabled = runWithCuda;

    loadOnnxNetwork();
    // loadClassesFromFile(); The classes are hard-coded for this example
}

std::vector<Detection> Inference::runInference(const cv::Mat &input)
{
    cv::Mat modelInput = input;
    if (letterBoxForSquare && modelShape.width == modelShape.height)
        modelInput = formatToSquare(modelInput);

    cv::Mat blob;
    cv::dnn::blobFromImage(modelInput, blob, 1.0/255.0, modelShape, cv::Scalar(), true, false);
    net.setInput(blob);

    std::vector<cv::Mat> outputs;
    net.forward(outputs, net.getUnconnectedOutLayersNames());

    int rows = outputs[0].size[1];
    int dimensions = outputs[0].size[2];

    bool yolov8 = false;
    // yolov5 has an output of shape (batchSize, 25200, 85) (Num classes + box[x,y,w,h] + confidence[c])
    // yolov8 has an output of shape (batchSize, 84,  8400) (Num classes + box[x,y,w,h])
    if (dimensions > rows) // Check if the shape[2] is more than shape[1] (yolov8)
    {
        yolov8 = true;
        rows = outputs[0].size[2];
        dimensions = outputs[0].size[1];

        outputs[0] = outputs[0].reshape(1, dimensions);
        cv::transpose(outputs[0], outputs[0]);
    }
    float *data = (float *)outputs[0].data;

    float x_factor = modelInput.cols / modelShape.width;
    float y_factor = modelInput.rows / modelShape.height;

    std::vector<int> class_ids;
    std::vector<float> confidences;
    std::vector<cv::Rect> boxes;

    for (int i = 0; i < rows; ++i)
    {
        if (yolov8)
        {
            float *classes_scores = data+4;

            cv::Mat scores(1, classes.size(), CV_32FC1, classes_scores);
            cv::Point class_id;
            double maxClassScore;

            minMaxLoc(scores, 0, &maxClassScore, 0, &class_id);

            if (maxClassScore > modelScoreThreshold)
            {
                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.push_back(cv::Rect(left, top, width, height));
            }
        }
        else // yolov5
        {
            float confidence = data[4];

            if (confidence >= modelConfidenceThreshold)
            {
                float *classes_scores = data+5;

                cv::Mat scores(1, classes.size(), CV_32FC1, classes_scores);
                cv::Point class_id;
                double max_class_score;

                minMaxLoc(scores, 0, &max_class_score, 0, &class_id);

                if (max_class_score > modelScoreThreshold)
                {
                    confidences.push_back(confidence);
                    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.push_back(cv::Rect(left, top, width, height));
                }
            }
        }

        data += dimensions;
    }

    std::vector<int> nms_result;
    cv::dnn::NMSBoxes(boxes, confidences, modelScoreThreshold, modelNMSThreshold, nms_result);

    std::vector<Detection> detections{};
    for (unsigned long i = 0; i < nms_result.size(); ++i)
    {
        int idx = nms_result[i];

        Detection result;
        result.class_id = class_ids[idx];
        result.confidence = confidences[idx];

        std::random_device rd;
        std::mt19937 gen(rd());
        std::uniform_int_distribution<int> dis(100, 255);
        result.color = cv::Scalar(dis(gen),
                                  dis(gen),
                                  dis(gen));

        result.className = classes[result.class_id];
        result.box = boxes[idx];

        detections.push_back(result);
    }

    return detections;
}

void Inference::loadClassesFromFile()
{
    std::ifstream inputFile(classesPath);
    if (inputFile.is_open())
    {
        std::string classLine;
        while (std::getline(inputFile, classLine))
            classes.push_back(classLine);
        inputFile.close();
    }
}

void Inference::loadOnnxNetwork()
{
    net = cv::dnn::readNetFromONNX(modelPath);
    if (cudaEnabled)
    {
        std::cout << "\nRunning on CUDA" << std::endl;
        net.setPreferableBackend(cv::dnn::DNN_BACKEND_CUDA);
        net.setPreferableTarget(cv::dnn::DNN_TARGET_CUDA);
    }
    else
    {
        std::cout << "\nRunning on CPU" << std::endl;
        net.setPreferableBackend(cv::dnn::DNN_BACKEND_OPENCV);
        net.setPreferableTarget(cv::dnn::DNN_TARGET_CPU);
    }
}

cv::Mat Inference::formatToSquare(const cv::Mat &source)
{
    int col = source.cols;
    int row = source.rows;
    int _max = MAX(col, row);
    cv::Mat result = cv::Mat::zeros(_max, _max, CV_8UC3);
    source.copyTo(result(cv::Rect(0, 0, col, row)));
    return result;
}

4、效果图：